Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with Doxorubicin as well as etoposide.

Despite the availability of various treatment protocols, response to therapy in patients with Acute Myeloid Leukemia (AML) remains largely unpredictable. Transcriptomic profiling studies have thus far revealed the presence of molecular subtypes of AML that are not accounted for by standard clinical parameters or by routinely used biomarkers. Such molecular subtypes of AML are predicted to vary in response to chemotherapy or targeted therapy. The Renin-Angiotensin System (RAS) is an important group of proteins that play a critical role in regulating blood pressure, vascular resistance and fluid/electrolyte balance. RAS pathway genes are also known to be present locally in tissues such as the bone marrow, where they play an important role in leukemic hematopoiesis. In this study, we asked if the RAS genes could be utilized to predict drug responses in patients with AML. We show that the combined in silico analysis of up to five RAS genes can reliably predict sensitivity to Doxorubicin as well as Etoposide in AML. The same genes could also predict sensitivity to Doxorubicin when tested in vitro. Additionally, gene set enrichment analysis revealed enrichment of TNF-alpha and type-I IFN response genes among sensitive, and TGF-beta and fibronectin related genes in resistant cancer cells. However, this does not seem to reflect an epithelial to mesenchymal transition per se. We also identified that RAS genes can stratify patients with AML into subtypes with distinct prognosis. Together, our results demonstrate that genes present in RAS are biomarkers for drug sensitivity and the prognostication of AML.

Introduction

Leukemia, lymphoma and multiple myeloma are the three main types of highly heterogeneous hematological malignancies that are derived from myeloid and lymphoid cell lineages [1]. Acute myeloid leukemia (AML) is characterized by abnormal expansion of immature myeloid cells and their accumulation in the bone marrow and blood, interfering with normal cellular growth [2]. AML is a highly aggressive cancer with poor prognosis. It is also the most common type of acute leukemia in adults. Treatment strategies and success rates vary depending on many factors, including the subtype of AML, prognostic factors, age and general health status of the patient [3]. Standard treatment regimens based on patient stratification include the combination of chemotherapeutics such as Cytarabine, Daunorubicin and Etoposide with or without radiotherapy. However, high heterogeneity of clinical outcomes in AML patients suggests that current classifications fail to distinguish patient subgroups sufficiently [4].

A not so well studied protein network in the context of AML is the Renin-Angiotensin System (RAS). RAS is composed of several gene products which play a critical role in regulating blood pressure, renal vascular resistance and the fluid/electrolyte balance [5, 6]. The idea of a local RAS operating independent of the circulating RAS was brought into light by demonstrating localized RAS elements in organs other than liver (angiotensinogen), kidney (renin) and lung (ACE). Localized RAS elements were found in many organs such as the brain, blood vessels and heart [7, 8]. It is predicted that locally produced angiotensins have important homeostatic functions and may contribute to local tissue dysfunction and diseases [8]. The presence of local RAS specific to the hematopoietic bone marrow microenvironment was reported for the first time in 1996 [9]. Major RAS molecules have been identified in the bone marrow microenvironment, such as renin, angiotensinogen, angiotensin receptors and angiotensin converting enzymes (ACEs) [10]. Locally active bone marrow RAS affects important stages of physiological and pathological blood cell production through autocrine, paracrine and intracrine pathways [11, 12]. Local bone marrow RAS peptides control the development of hematopoietic niche, myelopoiesis, erythropoiesis, thrombopoiesis and other cellular lineages [13-19]. Local RAS is also active in the primitive embryonic hematopoiesis phase [20-23]. The presence of renin, ACE, angiotensin II (Ang-II) and angiotensinogen in leukemic blast cells has been demonstrated, and local bone marrow RAS has been shown to play a role in the development of neoplastic malignant blood cells [24-26].

Establishing a role for genes involved in the development and biology of cancers, as prognostic and chemotherapeutic markers, is one of the most effective and successful approach used in the classification of malignancies. Thus, here we aimed to define AML subgroups based on expression of RAS genes. We also aimed to test if the resulting tumor subtypes differ in their responses to drugs and to demonstrate distinct prognostic profiles.

Materials and methods

In silico

Datasets

Cancer Genome Project (CGP) gene expression data (E-MTAB-783) [27, 28] was downloaded from ArrayExpress website (https://www.ebi.ac.uk/arrayexpress/), and drug screening data [29] was downloaded from the CGP database. Microarray dataset GSE12417 [30], corresponding to AML patients, was downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. The training cohort of 163 patients in GSE12417 [30] was used for our analyses since this was the same microarray platform as the one used for CGP.

Data normalization and variance analysis

CGP gene expression data was normalized by the RMA method using the BRB-Array Tools software [31].

In order to choose the RAS genes that would be used in real-time PCR for validation studies, we first aimed to choose the most variable genes that would likely give detectable fold differences in vitro by PCR. Analysis of variance was performed using all 39 probesets corresponding to the 25 genes in the RAS and genes with at least 0.8 of variance. Above these thresholds, the mean expression was at least 5.5 and the log fold difference between min to max was above 3 for all probesets. Thus, nine probesets corresponding to eight genes (CTSG, CPA3, AGT, ANPEP, IGF2R (two probesets), RNPEP, ATP6AP2 and CTSA) were selected to be used in further analyses (S1 Table).

IC50 calculation methods

In order to calculate drug response parameters such as IC50, EC50, activity area and Amax, the growth rate of the cells were depicted as a function of drug concentration by being modeled with non-linear logistic regression as explained in De Lean et. al [32], which is also reported in NIH/NCGC assay guidelines [33]. While the non-linear logistic regression function used to model data is used widely for cytotoxicity calculations, here for the first time we used six different versions of this function and selected the one with the lowest standard error rate among all for the calculation of cytotoxicity values. We name this approach the 6-model (6M).

Thus, six different models were derived from the following non-linear logistic regression function: where Y is the percent growth of the cells, X is the arithmetic drug concentration, a is the percent growth of the cells when the cells are not treated with the drug, d is the percent growth of the cells for infinite dose, i.e. a dose for which there is no additional effect when increased, c is the dose corresponding to percent growth exactly between a and d, and b is the Hill slope factor that is used to define the steepness of the curve fitted.

The following are the conditions required for the generation of 6-models:

3-Parameter model: Curves were fitted without using Hill slope factor b.

3-Parameter Top 100 model: Curves were fitted without using Hill slope factor b and with a = 100.

3-Parameter Bottom 0 model: Curves were fitted without using Hill slope factor b and with d = 0.

4-Parameter model: Formula is used as it is.

4-Parameter Top 100 model: Curves were fitted with a = 100.

4-Parameter Bottom 0 model: Curves were fitted with d = 0.

Six different drug response parameters are calculated out of the fitted curves as follows:

IC50: Value of X when Ŷ = 50%

IC90: Value of X when Ŷ = 90%

IC95: Value of X when Ŷ = 95%

EC50: Value of X when Ŷ = a+d

Amax: a − d

Activity Area: ΣŶX, (sum of Ŷs for each 0.01 increment of X fitted), where Ŷ is the predicted value of Y by the curve fitted.

With the 6M approach we recalculated IC50 values that were also included in the raw CGP data for the 17 AML cell lines treated with four drugs (ATRA, Cytarabine, Etoposide and Doxorubicin) using an in-house R script “SixModelIC50 V3.r” (https://github.com/muratisbilen/6-Model_IC50_CalculationV3.git).

These drugs were selected as we obtained AML chemotherapy treatment protocols from the Department of Hematology, Hacettepe University and compiled a list for all drugs in these protocols. Among these only ATRA, Cytarabine, Etoposide and Doxorubicin were present in the CGP database.

We referred to the recalculated IC50 data as 6M IC50 and performed a Pearson r correlation analysis between CGP IC50s and recalculated 6M IC50s to test the compatibility.

In addition, IC50 values were calculated using the 6M approach on the data obtained from in vitro analysis in which nine AML cell lines were treated with Doxorubicin and Etoposide.

Linear regression analyses

We performed correlation analysis between expression values of the eight genes and drug data (both CGP IC50 data and 6M IC50 data) individually. To identify if multiple genes can be used to better identify the relationship between gene expression and drug sensitivity data, linear regression analyses were performed using the Minitab 17 software (https://www.minitab.com). Seventeen AML cell lines from the CGP database were either randomly divided into two groups, the discovery group (12 cell lines) and the test group (five cell lines), or chosen manually so that the sensitivity range of cells in both groups spanned as large variance as possible. To generate a linear regression model for each drug (ATRA, Cytarabine, Etoposide, Doxorubicin), IC50s of the discovery cell line group obtained either from CGP or recalculated as 6M IC50, and expression of the eight RAS genes which were selected from variance analysis, were used as predictors. As a measure of the response variable variation explained by each linear regression model, we used the adjusted (adj.) R2 values. To test consistency of the linear regression models generated with the eight genes, we replicated the random divison of groups ten times and reported the average of the adjusted R2.

Furthermore, to identify a minimal gene list for the prediction of chemosensitivity, the discovery group was used to fit a model explaining the drug response using “best subsets” function of the software, which runs all possible regression models with one variable, two variables and so on, based on a list of predictors, enabling the user to choose a smaller set of predictors that can explain the response. The subset with the highest R2 (adj.) was selected as the best model. Regression formula of the best models (y = ±a + [n1 × x1] ± [n2 × x2] ± [n3 × x3] ± [n4 × x4]…) were applied for the test group of each drug. In the regression formula y (predicted IC50 values) were calculated where a and n are the constant values, x: gene expression values of the 12 cell lines in the discovery group. Also, the goodness of fit measure Sy.x were computed by Graphpad. Sy.x is a standard deviation of the residuals that here has been used to describe the difference in standard deviations of CGP IC50 and 6M IC50 versus predicted IC50s. It is a goodness-of-fit measure used to show how well our predicted IC50s fit with CGP and 6M IC50 values. All the correlations were calculated with Graphpad software as Pearson’s r and p values.

Hierarchical clustering analysis

Cluster 3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm) [34] and Java Treeview (http://jtreeview.sourceforge.net/) [35] software were used for hierarchical clustering analysis with mean standardized gene expression values for each dataset. Hierarchical clustering was performed by clustering both genes and arrays using Euclidian distance as similarity metric and complete linkage as clustering method.

Gene sets enrichment analysis—GSEA

Gene set enrichment analysis was performed using the GSEA guideline (https://www.gsea-msigdb.org/gsea/index.jsp) [36].

Briefly, dataset E-MTAB-783 [27, 28] has 22277 probesets IDs and these were collapsed into 13321 genes. For genes with more than one probeset, one with the highest expression was selected. “C5_all Gene ontology v6.1 database” was used for the analysis which has gene sets that contain genes annotated by the same GO term. We used default filtering criteria in GSEA for gene set sizes, which includes genesets with sizes between 15–500. After applying this filter, analysis was performed for 4081 gene sets.

Mutation analyses

Mutation data of AML cell lines was downloaded from Genomics of Drug sensitivity in Cancer database (https://www.cancerrxgene.org/downloads/bulk_download) [37]. 14 out of the 17 AML cell lines used in our analyses were available. Seven genes which were mutated in at least three AML cell lines were analyzed further.

In vitro

Cell lines and cytotoxicity experiments

HEL92.1.7 (2111706), and QIMR-WIL (86030601) cell lines were purchased from Sigma Aldrich (St. Louis, Mo., USA), KASUMI-3 (CRL-2725), GDM-1 (CRL-2627) and CESS (TIB-190) cell lines were purchased from ATCC (Virginia, USA) and P31/FUJ (JCRB0091), NOMO-1 (IFO50474), KASUMI-1 (JCRB1003) and SKM-1 (JCRB0118) cell lines were purchased from JCRB Cell Bank (Osaka, Japan). Cell lines were authenticated by manufacturers, all cell lines were morphologically checked by microscope and routine mycoplasma testing was performed by PCR. HEL92.1.7, GDM-1, CESS, P31/FUJ and NOMO-1 were cultured and maintained in RPMI-1640 medium (Sigma-Aldrich, R0883 (St. Louis, Mo., USA)) supplemented with 10% fetal bovine serum (FBS) (Sigma-Aldrich, F6178 (St. Louis, Mo., USA)), 1% penicillin-streptomycin (Sigma-Aldrich, 11074440001 (St. Louis, Mo., USA)), and 1% 200 mM L-glutamine (Sigma-Aldrich, G7513 (St. Louis, Mo., USA)). KASUMI-1, SKM-1 and KASUMI-3were cultured in RPMI-1640 medium but with 20% FBS. QIMR-WIL was cultured in DMEM medium (Sigma-Aldrich, D6546 (St. Louis, Mo., USA)) but with 10% FBS, 1% penicillin-streptomycin, and 1% 200 mM L-glutamine. All cell lines were cultured at 5% CO2 and 37 °C in a humidified incubator.

Doxorubicin (D1515) and Etoposide (E1383) were purchased from Sigma-Aldrich (St. Louis, Mo., USA) and were dissolved in DMSO. Cell viability was measured using CellTiter-Glo reagent (G7572, Promega, Fitchburg, Wisconsin, USA). 7000 cells/well in 90 μl medium were plated in each well of a 96-well plate. Cells were treated with six different concentrations of Doxorubicin or Etoposide separately (20, 10, 2, 1, 0.2, 0.1 μM). After 72 hours of drug treatment, cells were treated with CellTiter-Glo reagent and the luminescence signal was then recorded with a microplate luminometer (Turner Designs, CA, USA). All drug treatment experiments were repeated three times. Growth percentages were calculated for each drug and cell line, and cytotoxicity values were calculated using the 6M approach.

qRT-PCR

AGT, ANPEP, ATP6AP2, CPA3, CTSA and IGF2R genes’ expression was quantified using SYBR ™ Green master mix (Bio-Rad, #1725150, (USA)). PCR reactions were run under cycling conditions according to manufacturer’s instructions. GAPDH was used as a reference gene in all reactions. qRT-PCR relative gene expression data was calculated using ddCT method [38].

Using qRT-PCR relative gene expression data, predicted IC50 values were calculated with the formulas generated by linear regression analyses of in silico data using qRT-PCR based expression values as predictors. Primers used in this study are shown in Table 1. GAPDH was used as endogenous control.

Table 1

Primer sequences are for selected genes.

Gene Name	Forward Primer	Reverse Primer
AGT	GGCCAGCAGCAGATAACAACC	AACTGGGAGGTGCATTTGTGC
ANPEP	CGTTCTCTCTGCCTGTGAGC	AGGCCGTTCATTGTCCATCG
ATP6AP2	GATCCTTGTTGACGCTCTGC	CTTGCTGGGTTCTTCGCTTG
CPA3	TGCCCTCTGTTTGGAATAAGCC	GCTGGGTCCAAACTTCACTTGG
CTSA	CTCTACCGAAGCATGAACTCCC	TACTTCACTAACCAGGGCCG
IGF2R_probe1	CTCCCACCCAGTGAGAAACG	TCGTCATGGAAGGACACCAG
IGF2R_probe2	GGTGTTCTTATTCTGGCGGC	CAAACAAGCCAGCCAAACCG
GAPDH	GGAGCGAGATCCCTCCAAAAT	GGCTGTTGTCATACTTCTCATGG

Clinical data validation

Log Rank with Multiple Cutoffs (LRMC) and survival analysis

In regression analysis, four formulas were generated for Doxorubicin and Etoposide using both CGP and 6M IC50 data. IGF2R, CTSA, ATP6AP2 are common in three of the four formulas except for 6M IC50 data for Etoposide. Therefore, these genes were chosen to test relationships with clinical outcome.

Clinical data were obtained from the training cohort of the GSE12417 [30] dataset (AML Cooperating Group 1999). In the AMLCG 1999 cohort, patients were treated with TAD: Thioguanine, Cytarabine and Daunorubicin, or HAM: Cytarabine, Mitoxantrone protocols followed by the TAD protocol. We used an in-house R script (https://github.com/muratisbilen/LRMC.git) (Log Rank Multiple Cutoff, LRMC) by which log-rank test-based p-values associated with hazard ratio (HR) could be obtained using all possible cutoff values representing each sample in a given dataset and best cutoff is selected as in [39, 40]. Using this approach, we selected best cutoffs for IGF2R, ATP6AP2 and CTSA genes to be used for clinical correlation studies and Kaplan-Meier plots.

Patients with gene expression lower than cutoff, for each gene individually, were labeled as ‘Low’ (low expression) and higher than cutoff were labelled as ‘High’ (high expression). Univariate cox regression analyses were performed and Kaplan-Meier graphs were drawn using SPSS Statistics 19 (IBM, Chicago, IL, USA).

Additionally, the expression of all these three genes (IGF2R, CTSA and ATP6AP2) was evaluated together as good and bad prognostic groups. Patients were grouped as “Good” if they have high expression levels of IGF2R and CTSA and low expression levels of ATP6AP2 defined by expression value cutoffs in previous analysis. Rest of the patients were grouped as “Bad”. Then Kaplan Meier survival analysis was performed for these groups.

Results

Discovery of RAS drug sensitivity biomarker genes

The RAS consists of the 25 genes, corresponding to 39 probesets in Affymetrix HG-U133A, a microarray platforms used in the Cancer Genome Project (CGP) [27, 28]. For the 17 AML cell lines, both drug cytotoxicity and gene expression data are available in the CGP database [27, 28]. We focused only on genes which showed high variation in expression for further validation and therefore, selected nine probesets (eight genes) as described in the methods section (S1 Table). We recalculated IC50 values using the 6M approach applied to raw CGP cytotoxicity data (see Materials and methods). Using Pearson correlation we observed strong correlations between CGP IC50 and 6M IC50 for drugs widely used in AML (Cytarabine, Etoposide, Doxorubicin) but not for ATRA (S2 Table). To identify biomarkers of chemosensitivity, we calculated Pearson correlation between gene expression and IC50 values obtained from CGP and generated by 6M approach. We thus identified six gene/drug cytotoxicity correlations which were significant with CGP IC50 values, and seven significant correlations with 6M IC50 values. Four gene/drug associations were common to both analyses (S3 Table). Linear regression analysis was then performed to test whether the combined expression analyses of genes could correlate better with drug sensitivity data or not. Thus, we generated discovery and test groups. Each group include a wide range of cell line IC50 values as possible. Linear regression models for drug sensitivity prediction were generated for the discovery group (12 cell lines) using expression data of highly variant eight genes and IC50 values obtained from CGP and 6M IC50 of four drugs in Minitab 17. Then, obtained results tested with the validation group (five cell lines). The models generated with combined expression analyses of the eight genes resulted in high R2 (adj) values for Etoposide, Doxorubicin and Cytarabine but no model could be generated for ATRA (S4 Table). As independent datasets with drug sensitivity data for these compounds do not exist, we utilized a cross-validation method to test the robustness of the proposed models by generating the discovery and test groups 10 times, with 12 and five cell lines, respectively. The average of 10 R2 values generated from discovery groups was calculated for both CGP and 6M IC50s. Our results showed that the 10 random models of sensitivity to Doxorubicin had an average R2 above 85% for both CGP and 6M IC50s, but R2 decreased slightly for models of sensitivity to Etoposide while R2 values highly decreased for models of sensitivity to Cytarabine (S4 Table) when compared to those generated for cell lines that were manually selected. We therefore, focused on Doxorubicin and Etoposide for further analyses.

We then aimed to identify the minimal number of genes that needed to be included in combinations into the models that would give the highest correlation using the ‘best subsets function’ of Minitab. The software selected three genes/probesets for Doxorubicin when either CGP and 6M IC50 values were used and, four and five genes/probeset combinations for Etoposide using CGP and 6M IC50 values, respectively; all together corresponding to a total of six genes (AGT, ANPEP, ATP6AP2, CPA3, CTSA and IGF2R (two probesets)) (S5 Table), when the analysis was performed with the discovery group. Applying the resulting models to the test group showed the reliability of all models. As shown in Fig 1, the goodness of fit measures (R sq. and Sy.x) were 0.9 and 0.21 for Doxorubicin as modeled using 6M IC50 data and 0.89 and 0.34 when we used CGP IC50 values. Similarly, for Etoposide, these two measures were 0.78 and 0.34 for 6M IC50 and 0.77 and 0.57 for CGP IC50 values.

Fig 1

Reliability of Doxorubicin and Etoposide sensitivity predictions in linear regression models generated using the 12 AML cells.

Linear regression models were generated using the discovery group and applied to the test group to predict sensitivity values. Reliability of sensitivity predictions was measured with goodness of fit test for Doxorubicin 6M IC50 (A) resulting 0.9 R sq. and 0.21 Sy.x Doxorubicin CGP IC50 resulting (B) 0.89 R sq. and 0.34 Sy.x Etoposide 6M IC50 resulting (C) 0.78 R sq. and 0.34 Sy.x Etoposide CGP IC50 resulting (D) 0.77 R sq. and 0.57 Sy.x with 90, 95, and 99% confidence intervals. Black dots represent cell lines used for discovery group, and red dots for the test/validation group.

In vitro validation of biomarker genes

We next asked if the linear regression models generated in silico could predict in vitro cytotoxicity. For this purpose, we determined gene expression values by qRT-PCR for the six RAS genes (AGT, ANPEP, ATP6AP2, CPA3, CTSA and IGF2R (two probesets)) and used these to predict in vitro IC50 values obtained for Etoposide and Doxorubicin calculated with 6M approach for nine AML cell lines (see Materials and methods section). Correlation analysis showed that in silico and in vitro gene expression data were highly concordant except for CTSA (r: >0.7 and p-value <0.05) (S6 Table). We applied normalized gene expression values obtained in vitro to the in silico generated linear regression models (using four regression formulas) (S7 Table). Thus, utilized linear regression formulas with qRT-PCR gene expression data showed a good correlation with in silico data for Doxorubicin but not Etoposide (Table 2).

Table 2

Pearson’s correlation analysis between in vitro 6M IC50 values and predicted IC50 values from CGP / 6M IC50 linear regression formulas.

Applied formulas	Pearson’s r	p-value
Etoposide (CGP)	0.1271	0.7446
Etoposide (6M IC50)	-0.0579	0.8825
Doxorubicin (CGP)	0.7107	0.0319
Doxorubicin (6M IC50)	0.6925	0.0387

Predicted IC50 values obtained from linear regression formulas generated with 6M IC50 and CGP IC50 values showed high correlation with in vitro IC50s obtained from cytotoxicity experiments for Doxorubicin but not for Etoposide. For prediction of IC50s, normalized qRT-PCR gene expression values were used in the linear regression formulas.

Biological features of drug sensitive and resistant cells

Cell lines sensitive to Etoposide and Doxorubicin were almost identical (S1 Fig). To determine molecular mechanisms underlying differential response to Etoposide and Doxorubicin, we performed gene set enrichment analyses (GSEA) with sensitive and resistant subgroups for Gene Ontology (GO) gene sets. Several gene sets were significantly enriched among sensitive and resistant cell lines (FDR q-value<0.25). Gene sets enriched in sensitive cells with a FDR q-value of lower than 0.25 included TNF-receptor interacting, and response to type I IFN stimulus; while gene sets such as regulation of TGF-beta production and FN-binding were enriched in resistant cells suggesting a mesenchymal phenotype (S2 Fig and S8 Table).

To determine if the differentially expressed genes could be reflecting Epithelial-Mesenchymal Transition (EMT), we compared E-Cadherin (epithelial marker) and Vimentin (mesenchymal marker) expression using t-test, between sensitive and resistant cell groups. EMT is the process that epithelial cells lose the apical-basal polarity and cell adhesion, and transform to invasive mesenchymal cells [41]. It is known to play an important role in biological and pathological processes such as cancer progression, metastasis and drug resistance [42-44]. In our analysis, E-cadherin and Vimentin expression were not significantly different between sensitive and resistant groups defined in S1 Fig (p>0.1) (S3 Fig).

Then, in order to understand whether the mutational profile is involved in sensitivity to Doxorubicin, we analyzed mutational data of sensitive, intermediate and resistant groups of AML cell lines (see Materials and methods). Although we have a small sample size, especially in the resistant group (n = 2), we observed that both of the resistant cell lines are NRAS and P53 mutant, whereas all of the sensitive cell lines (n = 5) were wild type for these genes. However, these results need to be validated in larger sample sizes to be conclusive (S9 Table).

RAS genes are prognostic biomarkers for AML

We then asked if the RAS gene expression could help prognosticate AML patients. For this purpose, we utilized the training set within the GSE12417 [30] dataset, including 163 samples of bone marrow or peripheral blood mononuclear cells from adult patients with untreated acute myeloid leukemia. Patients in this cohort were also-treated with TAD protocol which contains Daunorubicin, which is also used as the starting material for semi-synthetic manufacturing of Doxorubicin. We found that high expression of genes IGF2R and CTSA were both associated with better overall survival, while the opposite was true for ATP6AP2 when patients were classified in either “High” or “Low” groups based upon LRMC cutoffs for each gene separately (see Materials and methods section) (Fig 2). We then stratified patients into “Good” and “Bad” prognosis groups using the best cutoff values obtained for these three genes as explained in the method section. As shown in Fig 3, it was revealed that there was a striking difference in overall survival in the groups that were predicted as "Good" and "Bad". The "Good" group showed better survival than the "Bad" group. Since the patients were all treated with Daunorubicin, these data suggest that the expression pattern of these genes was able to identify patients which are responders of this therapy.

Fig 2

Log Rank Multiple Cutoff (LRMC) plots and Kaplan Meier curves for dataset GSE12417.

(A) LRMCs of IGF2R (Probeset: 201392_s_at), CTSA (Probeset: 200661_at) and ATP6AP2 (Probeset: 201444_s_at). Graphic shows log rank based p values in the y axis for the “high” and “low” expression groups generated by all possible expression based cutoffs shown on the x axis (for details see Materials and methods). HRs above one and below one are shown with red and blue colors for specific cutoffs, respectively. Vertical dotted lines show 25th, 50th and 75th percentiles and horizontal dotted line shows significance cutoff 0.05 (-log10(p) = 1.301). From LRMC graphs, we selected cutoffs 7.077, 11.247 and 11.773 for IGF2R, CTSA and ATP6AP2 respectively which are highlighted in figure with red circle. Patients were divided into high and low groups based on these cutoffs. (B) Kaplan Meier plots for patients classified in high and low expression based on LRMC cutoffs. Patients classifed in high expression group of IGF2R and CTSA showed better overall survival when compared with low expression group and high expression group of ATP6AP2 showed worse survival when compared with low expression group. For all survival plots, overall survival time is shown in days for 163 AML patients. Table at the bottom shows number of patients in each group, median survival for each group and Log rank p value for Kaplan Meier analysis.

Fig 3

Combined classification using IGF2R, CTSA and ATP6AP2 expression.

Patients were grouped as “Good” if they have high expression levels of IGF2R and CTSA and low expression levels of ATP6AP2 defined by expression value cutoffs in Fig 2. Rest of the patients were grouped as “Bad”. Kaplan Meier plot shows “Good” group showed better survival when compared with “Bad” group as expected. Table at the bottom shows number of patients in each group, median survival for each group and Log rank p value for Kaplan Meier analysis.

Discussion

RAS’ local presence in the marrow affects the most important stages of physiological and pathological blood cell proliferation, and also has important roles in the development of blood cancers. It has been shown that RAS plays important roles in drug resistance to chemotherapeutic agent in addition to angiogenesis, invasion and proliferation [9, 24, 45–47]. Inevitably, most of these processes are interdependent. Most of the increased metastasis and invasion occurs due to an active RAS results in angiogenesis [45, 48, 49]. AT1R upregulation in ovarian cancer and increased expression of AT1R and ACE in prostate cancer, and AGTR1 in breast cancer; localized RAS presence in gastric cancer and its correlation with tumor spread and progression; demonstrate strong associations of RAS with various cancers. Irregularity of RAS components in cancer is strongly associated with increased angiogenesis and metastasis, and these parameters are associated with poor prognosis [50-54].

Gene expression profiling has revealed various AML subtypes related to diagnosis, therapy response and prognosis [55, 56]. Although gene expression profiling has not yet been integrated into clinical practice, this is expected to happen in near future.

In our study, we focused on RAS genes and identified their association with Doxorubicin and Etoposide sensitivity. We also show that RAS genes can be used to stratify AML patients into groups with distinct prognoses. Similar to our findings, low expression of IGF2R in non-small cell liver cancer has been associated with poor prognosis and high expression in bladder cancer has been associated with good prognosis [57, 58]. Although high CTSA expression was associated with poorer outcome in breast ductal carcinoma in situ, it was also found to suppress invasion and metastasis of colorectal cancer, suggesting tissue-specific differential roles [59, 60]. Recent studies linked ATP6AP2 up-regulation to the progression of glioma and colorectal cancer, due to its roles in aberrant activation of the Wnt/beta-catenin signalling pathway [61, 62]. ATP6AP2 was also shown to be a key component of the pro-angiogenic/proliferative arm of the RAS, which plays a role in the growth and spread of endometrial cancer [63]. Compared to the presence in the lysosome, it is found more in the cell membrane. Thus, it is clear that in this way it induces TGF-beta pathway activation. IGF2R is located in the membrane of organelles and is responsible for the transport to lysosome, and its intracellular functions have not yet been clearly identified. CTSA is a protease found in the lysosome. The fact that these three genes function together in the lysosome suggests that lysosomal functions can contribute to cell sensitivity. ATP6AP2 gene was found to cause disruption of V-ATPase formation and defects in the lysosomal glycosylation and autophagy [64]. Supportively, Doxorubicin has been reported to cause autophagy induced cell death in AML cells [65, 66].

GSEA revealed that sensitive cells were correlated with TNF-receptor interacting and response to type I IFN gene sets and resistant cells were correlated with regulation of TGF-beta production and FN-binding gene sets in AML, suggesting a mesenchymal phenotype.

A good and reliable subgrouping which can predict Doxorubicin sensitivity in AML was performed with the ATP6AP2, IGF2R, and CTSA gene combination. For those analyses, we utilized a Daunorubicin treated cohort, which is used as the starting material for semi-synthetic manufacturing of Doxorubicin. Therefore, the combination of these genes which can predict the sensitivity of Doxorubicin in AML patients may, therefore, be confirmed ex vivo.

The mutational analyses performed in this study had a small sample size with only two resistant cells. Therefore more conclusive results would be reached when this type of analysis is performed with larger sample sizes, or when mutational profiling is performed in patients treated with Doxorubicin, which may shed light on future studies.

Conclusions

As a result, we identified IGF2R, CTSA and ATP6AP2 gene biomarkers, which can subgroup AML patients into distinct good and bad prognostic groups. ATP6AP2 was associated to resistance and IGF2R and CTSA were associated to sensitivity for Doxorubicin in silico and in vitro. In future studies, it is important to investigate whether these genes can be used for personalized treatment and improve the effectiveness of treatments.

Hierarchical clustering of AML cell lines by sensitivity profiles for Doxorubicin and Etoposide.

The analysis reveals sensitive (six cell lines-green), intermediate (eight cell lines-orange) and resistant (three cell lines-red) subgroups for the 17 AML cell lines. Sensitivity to Doxorubicin and Etoposide is highly concordant in three subgroups. Green indicate low expression, orange indicate intermediate expression and red indicates high expression.

(TIF)

Click here for additional data file.

Comparative analysis of differentially enriched gene sets among drug sensitive and resistant cell lines.

(A) Plots showing gene sets enriched in sensitive cells, including genes interacting with TNF-receptor and genes affected in response to type I IFN stimulus. (B) Plots showing gene sets enriched in resistant cell lines, including genes having role in regulation of TGF-B production and genes interacting selectively and non-covalently with Fibronectin.

(TIF)

Click here for additional data file.

Expression levels of E-cadherin and Vimentin genes in AML cell lines.

RMA normalized gene expression values of CGP microarray data (y-axis) were used to determine EMT status of sensitive and resistant AML cell lines (x-axis) defined in S1 Fig. VIM: Vimentin (black bars), CDH1: E-cadherin (white bars). n.s. (not significant).

(TIF)

Click here for additional data file.

Expression variance of RAS genes in AML cell lines.

RMA normalized gene expression values of 25 RAS genes were used to analyze variance, standard deviation (SD), mean and min-max difference among cell lines. Gene names is shown along with probe set ID.

(XLSX)

Click here for additional data file.

Pearson correlation analysis between CGP IC50s and 6M IC50s.

IC50 values recalculated according to 6M approach using CGP raw cytotoxicity measurements were used to calculate Pearson correlation analysis with CGP IC50 values. Strong correlations are observed for all drugs except for ATRA.

(PDF)

Click here for additional data file.

Pearson correlation analysis between CGP gene expression data and CGP / 6M IC50s for 17 AML cell lines.

Eight genes expression data (nine probesets) was used to calculate correlation (as R2 coefficient of determination) with IC50 values of four drugs (ATRA, Cytarabine, Etoposide, Doxorubicin) from CGP database along with recalculated data with 6M approach. Highlighted values with green and red indicate significant correlation in negative and positive manner respectively.

(PDF)

Click here for additional data file.

Linear regression analysis between expression values of nine probesets and CGP / 6M IC50s for the discovery group (12 cell lines) and also for the ten times randomly divided different discovery groups (12 cell lines).

Adjusted R2 values were calculated in Minitab 17 with eight genes (nine probesets) for four drugs using CGP gene expression data and CGP / 6M IC50 values of the 12 AML cell lines. High correlations are observed with Etoposide and Doxorubicin (bold, for Etoposide R2 > 90%, for Doxorubicin R2 > 80%). Averages of adjusted R2 values of ten randomly divided groups were calculated in Minitab 17 with eight genes (nine probesets) for four drugs using CGP gene expression data and CGP / 6M IC50 values of the 12 AML cell lines. High correlation is observed with Doxorubicin and fine with Etoposide (bold, for Doxorubicin R2 > 85%, for Etoposide R2 > 60%). Asterisk represents the analysis in which Minitab could not perform linear regression analysis.

(PDF)

Click here for additional data file.

Generation of linear regression models using CGP gene expression data and CGP / 6M IC50 data of the discovery group (12 AML cell lines) for drug sensitivity predictions.

(A) Individual genes and gene combinations were used to generate linear regression models using IC50 values of Doxorubicin and Etoposide from CGP and 6M IC50. Highest correlation is observed in IGF2R/ATP6AP2/CTSA combination with Doxorubicin CGP and 6M IC50 values. And, highest correlation is observed in IGF2R/ATP6AP2/CTSA/CPA3 combination with Etoposide CGP and in ANPEP/ATP6AP2/CTSA/CPA3/AGT combination with Etoposide 6M IC50 values. (B) Regression formulas for gene panels with highest correlations.

(PDF)

Click here for additional data file.

Relative expression values of ATP6AP2, IGF2R (two probesets), CTSA, CPA3, AGT and ANPEP genes in nine AML cell lines and Pearson’s correlation analysis for six genes between CGP gene expression data and in vitro qRT-PCR gene expression data of nine AML cell lines.

(A) Expressions of all genes was normalized to GAPDH expression. (B) ATP6AP2, IGF2R (two probesets), CPA3, AGT, and ANPEP gene expression data obtained from CGP in silico and in vitro qRT-PCR expression data from nine cell lines show significant correlations with in vitro qRT-PCR expression data with the exception of CTSA.

(PDF)

Click here for additional data file.

In vitro and predicted IC50s (from CGP and 6M IC50 linear regression formulas) of Doxorubicin and Etoposide for nine AML cell lines.

In vitro IC50 values were obtained from cell viability measurements of the cell lines that are treated with six different concentrations of Doxorubicin and Etoposide separately (20, 10, 2, 1, 0.2, 0.1 μM). Predicted IC50s were calculated using the four formulas generated (with CGP / 6M IC50s) in the linear regression analysis with the normalized gene expression data obtained from qRT-PCR.

(PDF)

Click here for additional data file.

Gene sets enriched in (A) sensitive cell lines, and (B) resistant cell lines.

(PDF)

Click here for additional data file.

Mutational data of sensitive, intermediate and resistant groups of AML cell lines.

Seven genes which are mutated in at least three AML cell lines were included to examine the relationship between mutational status and drug sensitivity. Red: mutations that cause change in aminoacid sequence, grey: unkown status of aminoacid change, change at the DNA level; blue: wild type.

(PDF)

Click here for additional data file.

(XLSX)

Click here for additional data file.

7 May 2020

PONE-D-20-09590

Renin Angiotensin System Genes are Biomarkers for Personalized Treatment of Acute Myeloid Leukemia with Doxorubicin or Etoposide

PLOS ONE

Dear Dr. Turk,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process by both Reviewers.

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2. Please provide additional information about each of the cell lines used in this work, including any quality control testing procedures (authentication, characterisation, and mycoplasma testing). For more information, please see " ext-link-type="uri" xlink:type="simple">http://journals.plos.org/plosone/s/submission-guidelines#loc-cell-lines."

3. Please provide the source, product number and any lot numbers of the doxorubicin and etoposide obtained for your study.”

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Turk and colleagues in their research article entitled “Renin Angiotensin System Genes are Biomarkers for Personalized Treatment of Acute Myeloid Leukemia with Doxorubicin or Etoposide” perform a series of analyses with the aim to to verify if RAS genes can be good predictors of the sensitivity of two chemoterapeutics. Their bioinformatic approach identifies a series of genes that have been, in this research, tested with in vitro experiment. Additionally, applying again a computational approach, the authors stratify a cohort of patients previously sequenced on the basis of the previously mentioned genes.

Although this research article is a good piece of work, I think that, in its current state, it is not suitable for publication but it can be potentially interesting if some modifications will be done to the analyses and to the manuscript.

The main concern here is about the methodology implied in the first computational section.

I please recommend to specifically indicate, particularly in the method section, if the workflow-analyses performed have been either applied in previous researches or are reported here for the first time. One example is in the “IC50 Calculation Methods” section: the six different models seem introduced by the authors for the first time while, in the result section (line 207) it is referred to them as the “NCBI proposed 6-model approach”, is it the same? Can the author add a reference to this?

Finally I suggest to be more consistent and clear with the numbers/genes along the text.

The major points are listed here.

In the “Data normalization and variance analysis” is there a reason why “the genes whose variance was above 0.8 SD of the mean” were chosen? Additionally this number is not the same of the results in which is reported “which showed high variation in expression and therefore, selected 9 probesets (8 genes) with standard deviation values above 0.9” (line 205), I would suggest to add a reference or better explain this method. I was wondering why the author did not consider to calculate and consider adjusted p-value for the genes selected.

In “linear regression analyses” section the authors need to better clarify the steps they followed during this methodology, I suggest either to insert some references or clarify the steps. Please also clarify if in this case all the genes or only the 8 were used.

Moreover, I wonder if the Pearson’s correlation was always applied on normal distribution of data, if this is not the case I would suggest a Spearman correlation test.

In the results section the authors refer to 6M data which have not been explained before in the method section, these data likely are deriving from the raw CGP after applying the six model approach, I would suggest to the authors to add this information in the method section.

I suggest to replicate the random division of the groups and test if the results are consistent with the one obtained here. Moreover in the method section there is no mention of such a random division, please, add it. If there is a reason why the division was not replicated, please, mention it.

In vitro experiment the genes used are six, and the primers reported in the table 1 are for seven genes. Please clarify this and explain the reason why the authors did not consider all the eight genes from the in silico workflow. Along the text it is not clear if the sub-groups of genes belong to the initial eight. Please refromulate the text in order to give a better explanation of these numbers and other numbers of genes.

My suggestion is to either reorganize the figures or change the captions: in Fig.3 there is no explanation of the three panels (A, B and C) and neither of the colors. Moreover, beside the Kaplan-Meier curves there are other 3 plots which are not explained. The same for Fig. 4.

Minor points:

- Line 79, when E-MTAB-783 is indicated, please cite the two research articles that contributed to produce these data:

1) Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, Liu Q. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012 Mar;483(7391):570-5.

2) Venkova L, Aliper A, Suntsova M, Kholodenko R, Shepelin D, Borisov N, Malakhova G, Vasilov R, Roumiantsev S, Zhavoronkov A, Buzdin A. Combinatorial high-throughput experimental and bioinformatic approach identifies molecular pathways linked with the sensitivity to anticancer target drugs. Oncotarget. 2015 Sep 29;6(29):27227.

The 1) is already present in the manuscript as ref number 32.

-Line 89, please insert the article “the” when referring to the 17 AML and to the 25 genes that are taken by CGP and are indicated in the results section. Moreover, consider to add this info also on the methods.

-Lines 121, please insert the website of Minitab 17

-Line123-124 if the authors are referring to the same eight genes that have been mentioned in the MM Normalization section I would suggest to point it out.

-Lines138 Please cite the reference or website for Cluster3.0 and Java Treeview software.

- The link at line 144 does not work, please indicate the number of pathways and the number of genes that were present in the C5_all Gene ontology database, and which version of the database was used.

-Line 188 if the script is available provide it as supplementary information or in a github repository

-Line 217 the authors are referring to 4 drugs and 8 genes, are these numbers and data the same that have been identified in the previously mentioned analyses? Why did the authors perform linear regression analyses at this step? Please report this information in this section of the manuscript and if the genes/drugs are the ones already mentioned add the definite article “the”.

-Lines 245-247 please refer to which correlation analysis the authors are referring to. Moreover, PLOS does not accept references to “data not shown.”

- Line 248 please indicate why only four formulas were applied and change 4 in four.

-Lines291-292 when the authors refer to “We then stratified patients using the best cut-off values obtained for these 3 genes” please add, “as explained in the method section”.

-Line 292 please substitute 3 with three

-Figure 1 A) and B) are not indicated. Define the Sy.x parameter, is it the value for the residuals? Please add this information also in the methods.

-Line562 please reformulate “ve resistant”

-Uniform the numbers, below 10 the number should be indicated as word.

Reviewer #2: Seyhan Turk and co-worker in their work demonstrate that expression of Renin-Angiotensin System (RAS)-related genes predict drug responses (Doxorubicin and Etoposide) in AML patients. Moreover, authors show that identified RAS genes expression stratify AML patients into different subtypes with distinct prognosis. Overall, presented data support use of RAS gene expression analysis as novel tool for AML drug-sensitivity and disease prognostication. Unfortunatley, altough an elegant set of in-silico approaches have been employed, the lack of experimental analyses with appropriate functional in-vitro and in-vivo represents the main drawback of the entire work. In detail:

Major points

• 17 AML cell lines included in CGP database have been chosen for in-silico analysis. In parallel, 9 AML cell lines have been testd for in vitro studies. Are those the same cells included in short list for in-silico analysis? Furthermore, did you see any differences based on their specific genetic background (mutational analysis)?

• Importantly, GEP analysis have been performed on genes, among those of RAS system, with higher expression variability. Why did you reject those with less variation for your analysis?

• Could you please detail the NCBI proposed 6-model used approach?

• To make in vitro date more consostent, could be useful including gene expression analysis as well as IC50 values for all tested AML cell lines.

• Data showed in Table 2 are not clear. Could you please describe it better?

• The prognostic role of 3-gene expression signature need deeper analysis. Why did you analyze only 3 genes? What about other RAS genes? Did you perform a cumulative analysis of RAS-related genes?

• As per Authors own admission, the major study limitation is lack of mutational data analysis. Indeed, it’s worth to be investigated AML patiens subclasses including those carryng poor prognostic mutations such as FLT3. To this aim a detailed description of used AML cell lines could help (i.e. carryng FLT3-ITD or WT, NPM1 etc.)

Minor

• Please pospone figures legend at the end of manuscript right after refernces

• The gene set enrichment analysis revealed findinds that are not supported by experimental data. Overall these data are somehow confusing because are not conclusive at all. In my opinion it’s better including these data as supplementary results to make conclusion more focused

• In the Materials and methods the first sentence of paraghraph is quite misleading. Additionally, please include reference for CGP database.

**********

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Reviewer #1: No

Reviewer #2: Yes: Michele Cea

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Submitted filename: PLOS one revision.docx

Click here for additional data file.

21 Jun 2020

Dear Editor,

We would like to thank the Editorial Board and the Referees for all of the important contributions, which will improve the paper.

We have carefully reviewed the comments and revised the manuscript accordingly. Below please find the answers to the Editor’s and Reviewer’s comments.

Yours faithfully,

Journal Requirements:

Comment 1.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response 1.

PLOS ONE's style requirements fully checked and revisions were done.

Comment 2.

2. Please provide additional information about each of the cell lines used in this work, including any quality control testing procedures (authentication, characterization, and mycoplasma testing). For more information, please see http://journals.plos.org/plosone/s/submission-guidelines#loc-cell-lines."

Response 2.

Additional information is added for the used cell lines (see In vitro section in Materials and Methods).

Comment 3.

3. Please provide the source, product number and any lot numbers of the doxorubicin and etoposide obtained for your study.”

Response 3.

Drug information has been added to the manuscript.

Comment 4.

4. Please note that PLOS does not permit references to “data not shown.” Authors should provide the relevant data within the manuscript, the Supporting Information files, or in a public repository. If the data are not a core part of the research study being presented, we ask that authors remove any references to these data.

Response 4.

The “data not shown” was removed and the data was added as “S8 Table”.

Comments to the Author

Reviewer #1:

Comment 1.

The main concern here is about the methodology implied in the first computational section.

I please recommend to specifically indicate, particularly in the method section, if the workflow-analyses performed have been either applied in previous researches or are reported here for the first time. One example is in the “IC50 Calculation Methods” section: the six different models seem introduced by the authors for the first time while, in the result section (line 207) it is referred to them as the “NCBI proposed 6-model approach”, is it the same? Can the author add a reference to this? Finally, I suggest to be more consistent and clearer with the numbers/genes along the text.

Response 1.

Here, we report the 6-model (6M) approach for the first time which depends on a non-linear logistic regression function explained in NIH/NCGC assay guidelines. We derived six different versions of this function and select the one with the lowest error rate among all for the calculation of cytotoxicity values. References were added and the “IC50 Calculation Methods” section has been re-written. No inconsistency could be found with the numbers/genes given throughout the entire paper.

The major points are listed here.

Comment 2.

In the “Data normalization and variance analysis” is there a reason why “the genes whose variance was above 0.8 SD of the mean” were chosen? Additionally, this number is not the same of the results in which is reported “which showed high variation in expression and therefore, selected 9 probesets (8 genes) with standard deviation values above 0.9” (line 205), I would suggest to add a reference or better explain this method. I was wondering why the author did not consider to calculate and consider adjusted p-value for the genes selected.

Response 2.

In order to choose the genes which will be used in Real-time PCR for validations, we aimed to choose the most variable genes which could give detectable fold differences in vitro. The variance value of 0.8 was chosen arbitrarily. For these analyses, variance and standard deviation are the same values; we decided to use “variance”. The “Data normalization and variance analysis” section has been expanded and detailed. Since, we are here trying to choose the most variant genes, we did not calculate and consider adjusted p-value for the genes selected.

Comment 3.

In “linear regression analyses” section the authors need to better clarify the steps they followed during this methodology, I suggest either to insert some references or clarify the steps. Please also clarify if in this case all the genes or only the 8 were used.

Moreover, I wonder if the Pearson’s correlation was always applied on normal distribution of data, if this is not the case I would suggest a Spearman correlation test.

Response 3.

The linear regression analyses section has been re-written. Linear regression analysis was performed with the highly variant eight RAS genes and minimal gene combinations which are now explained more clearly.

We performed Pearson r correlation analysis throughout the paper as we consistently obtained better p values with it, as compared to Spearman’s test.

Comment 4.

In the results section the authors refer to 6M data which have not been explained before in the method section, these data likely are deriving from the raw CGP after applying the six model approach, I would suggest to the authors to add this information in the method section.

Response 4.

With 6M approach we recalculated IC50 values from raw CGP data for 17 AML cell lines treated with four drugs (ATRA, Cytarabine, Etoposide and Doxorubicin) using an in-house R script. We refer to this data as 6M IC50s.

Additionally, we treated 9 AML cell lines with Doxorubicin and Etoposide in vitro and their IC50 values were calculated using 6M approach, as well. We refer to this data as in vitro IC50s.

These are explained in the methods section.

Comment 5.

I suggest to replicate the random division of the groups and test if the results are consistent with the one obtained here. Moreover, in the method section there is no mention of such a random division, please, add it. If there is a reason why the division was not replicated, please, mention it.

Response 5.

In response to this comment we divided 17 cells randomly 10 times and generated 10 different discovery and test groups consisting of 12 cell lines and 5 cell lines, respectively. Linear regression models were generated in the 10 discovery groups separately. The 10 random models of sensitivity to Doxorubicin still have an average R2 above 85% for both CGP and 6M IC50, but R2 decreased slightly for models of sensitivity to Etoposide. Average R2 values now given in “S5 Table”. We added the 10 times random division to the method section and also mentioned it in the results section. However, the reason we performed our analyses without random divisions was because we wanted both the discovery and test cohorts to contain cells that spanned as large a sensitivity interval as possible. We therefore, present in this revised version results from both analyses.

Comment 6.

In vitro experiment the genes used are six, and the primers reported in the table 1 are for seven genes. Please clarify this and explain the reason why the authors did not consider all the eight genes from the in silico workflow. Along the text it is not clear if the sub-groups of genes belong to the initial eight. Please reformulate the text in order to give a better explanation of these numbers and other numbers of genes.

Response 6.

GAPDH was used as endogenous reference control. That’s why the primers are seven in the Table 1.

We used six genes because in our linear regression analyses, highest correlation is observed in IGF2R/ATP6AP2/CTSA combination with Doxorubicin (both CGP and 6-model) and highest correlation is observed in IGF2R/ATP6AP2/CTSA/CPA3 combination with Etoposide (CGP) and ANPEP/ATP6AP2/CTSA/CPA3/AGT combination with Etoposide (6-model). Prediction model contains totally six genes for Doxorubicin and Etoposide. The section has been re-written for the sake of clarity.

Comment 7.

My suggestion is to either reorganize the figures or change the captions: in Fig.3 there is no explanation of the three panels (A, B and C) and neither of the colors. Moreover, beside the Kaplan-Meier curves there are other 3 plots which are not explained. The same for Fig. 4.

Response 7.

Figures and their legends were re-organized.

Minor points:

Comment 8.

- Line 79, when E-MTAB-783 is indicated, please cite the two research articles that contributed to produce these data:

1) Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, Liu Q. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012 Mar;483(7391):570-5.

2) Venkova L, Aliper A, Suntsova M, Kholodenko R, Shepelin D, Borisov N, Malakhova G, Vasilov R, Roumiantsev S, Zhavoronkov A, Buzdin A. Combinatorial high-throughput experimental and bioinformatic approach identifies molecular pathways linked with the sensitivity to anticancer target drugs. Oncotarget. 2015 Sep 29;6(29):27227.

The 1) is already present in the manuscript as ref number 32.

Response 8.

Citations were added and the text was reorganized.

Comment 9.

-Line 89, please insert the article “the” when referring to the 17 AML and to the 25 genes that are taken by CGP and are indicated in the results section. Moreover, consider to add this info also on the methods.

Response 9.

In the method and results sections article "the" were inserted. Source of AML cell lines, CGP, was indicated.

Comment 10.

-Lines 121, please insert the website of Minitab 17

Response 10.

The website was added.

Comment 11.

-Line123-124 if the authors are referring to the same eight genes that have been mentioned in the MM Normalization section, I would suggest to point it out.

Response 11.

These are the same genes, which is now indicated.

Comment 12.

-Lines138 Please cite the reference or website for Cluster3.0 and Java Treeview software.

Response 12.

The websites for software were added.

Comment 13.

- The link at line 144 does not work, please indicate the number of pathways and the number of genes that were present in the C5_all Gene ontology database, and which version of the database was used.

Response 13.

The reference was added.

Dataset E-MTAB-783 has 22277 probesets IDs and these were collapsed into 13321 genes. For genes with more than one probeset, one with the highest expression was selected. “C5_all Gene ontology v6.1 database” was used for the analysis which has gene sets that contain genes annotated by the same GO term. Default filtering criteria in GSEA for gene sets is that it should have minimally 15 genes and maximally 500 genes. After applying this filter, analysis was performed for 4081 gene sets. This information was added to the method section, as well.

Comment 14.

-Line 188 if the script is available provide it as supplementary information or in a github repository.

Response 14.

The R script was provided as supplementary.

Comment 15.

-Line 217 the authors are referring to 4 drugs and 8 genes, are these numbers and data the same that have been identified in the previously mentioned analyses? Why did the authors perform linear regression analyses at this step? Please report this information in this section of the manuscript and if the genes/drugs are the ones already mentioned add the definite article “the”.

Response 15.

Yes, four drugs and eight genes are the same in the previously mentioned analyses. To examine the correlation of drug sensitivity with combined expression profile of the eight genes, linear regression analyses were performed. “Linear regression analyses” section was reviewed. Article “the” was added when needed.

Comment 16.

-Lines 245-247 please refer to which correlation analysis the authors are referring to. Moreover, PLOS does not accept references to “data not shown.”

Response 16.

This correlation analysis is referring to test the compatibility of in silico and in vitro gene expression data with each other. “data not shown” removed from the text and data now given in “S8 Table”.

Comment 17.

- Line 248 please indicate why only four formulas were applied and change 4 in four.

Response 17.

We applied four formulas since there are four linear regression models for Doxorubicin (CGP and 6M IC50s) and Etoposide (CGP and 6M IC50s) with minimal gene lists, we used these four formulas to predict IC50 values for both drugs in test groups.

“4” was changed to “four”.

Comment 18.

-Lines291-292 when the authors refer to “We then stratified patients using the best cut-off values obtained for these 3 genes” please add, “as explained in the method section”.

Response 18.

The text was rewritten.

Comment 19.

-Line 292 please substitute 3 with three

Response 19.

The number was indicated as “three”.

Comment 20.

-Figure 1 A) and B) are not indicated. Define the Sy.x parameter, is it the value for the residuals? Please add this information also in the methods.

Response 20.

A), B), C) and D) were indicated in the Figure 1.

Sy.x is a standard deviation of the residuals. In our linear regression analyses the residual standard deviation used to describe the difference in standard deviations of CGP and 6M IC50s versus predicted IC50s. It is a goodness-of-fit measure used to show how well our predicted IC50s fit with the CGP and 6M IC50s. It is also mentioned in the methods section.

Comment 21.

-Line562 please reformulate “ve resistant”

Response 21.

The text was reformulated.

Comment 22.

-Uniform the numbers, below 10 the number should be indicated as word.

Response 22.

Below 10 the number are indicated as word.

Reviewer #2:

Major points

Comment 1.

• 17 AML cell lines included in CGP database have been chosen for in-silico analysis. In parallel, 9 AML cell lines have been testd for in vitro studies. Are those the same cells included in short list for in-silico analysis? Furthermore, did you see any differences based on their specific genetic background (mutational analysis)?

Response 1.

Yes, they are same cell lines. 17 cell lines used in CGP data are listed in S1 Table. These are CTV-1, HL-60, GDM-1, HEL92.1.7, KASUMI-1, KMOE-2, K052, ML-2, MONO-MAC-6, NKM-1, NOMO-1, P31-FUJ, THP-1, QIMR-WIL, CMK, CESS, OCI-AML2.

Cell lines studied in vitro experiments are shown in the method section “Cell lines and cytotoxicity experiments”. These are HEL92.1.7, KASUMI-3, GDM-1, QIMR-WIL, CESS, P31/FUJ, NOMO-1, KASUMI-1 and SKM-1.

Seven cell lines used for in silico analyses were also used for in vitro experiments (HEL92.1.7, KASUMI-1, GDM-1, QIMR-WIL, CESS, P31/FUJ, NOMO-1), as stated in the manuscript.

We performed a comprehensive mutational analysis in order to see if any mutational pattern overlaps with the sensitivity profile, and added the results in the manuscript. Also, nine AML cell lines were WT for NPM1 and FLT mutations.

Comment 2.

• Importantly, GEP analysis have been performed on genes, among those of RAS system, with higher expression variability. Why did you reject those with less variation for your analysis?

Response 2.

In order to choose the genes that will be used further in Real-time PCR for validations, we first aimed to choose the most variable genes which are also highly likely to give detectable fold differences via PCR.

Comment 3.

• Could you please detail the NCBI proposed 6-model used approach?

Response 3.

It is detailed in the “IC50 Calculation methods” section.

NCBI methodology or "NIH/NCGC-proposed methodology" suggests a calculation methodology similar to 6-model approach. The function used to model the data is widely being used for cytotoxicity calculations. We derived different versions of this function, which is partly suggested by NIH/NCGC, and select the one with the lowest error rate among all for the calculation of cytotoxicity values.

Comment 4.

• To make in vitro date more consistent, could be useful including gene expression analysis as well as IC50 values for all tested AML cell lines.

Response 4.

To make more consistent in vitro cytotoxicity IC50s, predicted IC50 and QPCR relative gene expression values are now given in “S7 Table” and “S9 Table”.

Comment 5.

• Data showed in Table 2 are not clear. Could you please describe it better?

Response 5.

The data has been described in more detail.

Comment 6.

• The prognostic role of 3-gene expression signature need deeper analysis. Why did you analyze only 3 genes? What about other RAS genes? Did you perform a cumulative analysis of RAS-related genes?

Response 6.

First, we started analyzing the 25 RAS genes all together. After applying a variance cut-off, this number decreased to eight genes. After linear regression analysis, these eight genes decreased to six genes (four regression formulas contain totally six genes for Etoposide and Doxorubicin and for two different IC50s (CGP and 6M)). But three genes (IGF2R, CTSA, ATP6AP2) were common for all regression formulas except for one “Etoposide 6M”. Therefore, we analyzed that three genes’ biomarker potential.

Even for Doxorubicin, only these three genes come out in the regression formulas with both CGP and 6M IC50s. Only one additional gene (CPA3) comes out in the regression formula for Etoposide CGP IC50s. Analyses of six genes was necessary to produce predicted IC50s from regression formulas.

Comment 7.

• As per Authors own admission, the major study limitation is lack of mutational data analysis. Indeed, it’s worth to be investigated AML patiens subclasses including those carryng poor prognostic mutations such as FLT3. To this aim a detailed description of used AML cell lines could help (i.e. carryng FLT3-ITD or WT, NPM1 etc.)

Response 7.

Since there is no mutational data in the patient dataset we could not perform mutational analyses with clinical data. However, we evaluated the mutational profile of 14 out of 17 AML cell lines used in in silico analyses. Seven genes (TP53, RBMX, NRAS, ANKRD36C, TNS1, TTN and ASXL1) which are mutated in at least three cell lines were included in mutational analysis and our results are given in “S4 Fig”.

For FLT3 and NPM1 gene, none of the used in vitro AML cell lines were mutants.

Minor points

Comment 8.

• Please pospone figures legend at the end of manuscript right after refernces

Response 8.

PLOS Journal requirements state the following: Place figure captions in the manuscript text in read order, immediately following the paragraph where the figure is first cited.

Comment 9.

• The gene set enrichment analysis revealed findinds that are not supported by experimental data. Overall these data are somehow confusing because are not conclusive at all. In my opinion it’s better including these data as supplementary results to make conclusion more focused.

Response 9.

The gene set enrichment analysis results have been changed as supplementary “S2 Fig” and “S10 Table”.

Comment 10.

• In the Materials and methods the first sentence of paraghraph is quite misleading. Additionally, please include reference for CGP database.

Response 10.

Paragraph was re-written. The CGP database references were added.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

14 Jul 2020

PONE-D-20-09590R1

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with doxorubicin as well as etoposide

PLOS ONE

Dear Dr. Turk,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process, particularly by Reviewer #1.

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

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Reviewer #1: Partly

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I appreciate the revision of the methods and the extension of the non-linear regression model section, now the methods are sufficiently explained and more clear. However I have noticed that the new version of the manuscript has some parts that need to be reformulated, many typos and inconsistencies along the text.

Additionally it is missing the explanation of the statistics applied in some parts of the manuscript and my concern here is whether or not these tests were rigorously applied.

Finally, I suggest a revision of the Supplementary Materials provided and that the R scripts file will be provided.

For these reasons I am still not considering the manuscript suitable for publication in its current form but I suggest the following points to be addressed to be taken in consideration for publication.

Line 82 the authors should insert the references in the correct location, if the reference number 30 is referring to the GSE12417 dataset it should be inserted right after it. As this, there are other similar cases along the text, therefore, I please invite the authors to check this in the manuscript.

Line 91, please remove “were chosen arbitrarily”, if any other study used this parameter please cite it.

Line 99-100 “explained in De Lean et. al, which is also explained” there is a repetition of the word explain, please substitute with “reported”.

Line129 please, insert here that the IC50 values were included in the CGP. If I am not wrong the authors mentioned this already at line 243.

Line 130-131 Please provide the R script and refer to it as supplementary material.

Line 131 “We refer to this data as 6M IC50”, please be consistent with this nomenclature along the text, sometimes it is called “6M IC50” others “IC50 6M” other only “6M”

Line 135 “9 AML cell lines were treated with” the number 9 needs to be written in words. Additionally I suggest to reformulate or clarify the meaning of the sentence “IC50 values were calculated using the 6M approach in vitro.”, did the authors mean that the values were calculated using the 6M approach on the data obtained from the in vitro analysis?

Line 138 8 needs to be written as a word.

The correlations to which the authors are referring here is the one shown in S3 Table, how was this correlation calculated? If it was with graphpad I would suggest inserting a sentence at the end of this section saying that all the correlations were calculated with Graphpad software. Moreover, Pearson correlation should be used when both the variables are normally distributed; in the response to the reviewers the authors mentioned that they got better results with this method but I was wondering if the two variables were tested for normal distribution or not.

Line 151 please delete “for 10 models” at the end of the sentence, it is redundant.

Line 161 please substitute “that is used to describe” with “that here has been used to describe”.

Line 173-174 please correct “Gene set enrichment analyses was” with “Gene set enrichment analysis was”

Line 223 please correct “Clinical data was” with “Clinical data were”

Line 226 please, when the R script is mentioned in the text, refer to the supplementary material in which it is contained.

Lines 228-229 These lines need a reformulation. First, you should put as references the two studies (PMID: 31949498, PMID: 28607584), second, please change “ 'Low' = low expression” with “‘Low’ (low expression)” and “‘High = high expression’” with “‘High’ (high expression)” and finally, substitute “our previous studies” with “as in refX and refY”.

Line 242 I ask the authors to rewrite the citations when CGP database is mentioned.

Line248 the authors mention: “We observed strong correlations between IC50 values obtained from CGP”, as said above, this correlations need to be clarified in the MM section.

Line254 The sentence “We then asked whether combined expression analyses of

genes could correlate better with drug sensitivity data.” should be linked with the next paragraph.

Line257-262 please re-arrange these sentences because they are not clear. The explanation of the workflow used has some typos and english grammar errors. Moreover, some parts are already mentioned in the MM section and should be removed.

Line 264 please, remove “RAS genes” it is a redundancy; if it is not, I please ask the authors to explain why it is mentioned here.

TableS4 and S5 should be merged into the same file.

The name of the columns should be revised, precisely: on the top of the column referring to CGP please indicate CGP and on the top of the column referring to 6M IC50 indicate 6M IC50. I also suggest to name the sheets of the .xlsx table according to the table.

The two above mentioned indications are applicable also to the other S Tables.

Line277 6M IC50 needs to be indicated accordingly along the text.

TableS6, the columns referring to each group need to be marked.

Figure 1: The names in the title need to be consistent with the content of the text, therefore 6M needs to be substituted with 6M IC50.

Line278 I would suggest to report also here the name of the six genes that have been selected.

Line294 a comma between “ANPEP ATP6AP2” is missing.

I suggest the authors either merge table S7 and S8 or put them in different sheets of the same file.

S1 Fig: I suggest that the legend of this figure will also include the meaning of the colors. Maybe a scale of colors should be provided in the figure.

Line320-322 EMT acronym is not explained along the text and there is no mention of the statistical test involved when the authors say: “there were no significant differences between the two groups”. The authors need to mention the test performed (Wilcoxon or t-test according to the distribution of the data) and/or report it in the mm section.

S4 Fig needs to be included as a table or a different figure should be provided.

Line 334-335 I was wondering how the authors investigated the up-regulation of the three genes. Additionally, as previously mentioned from the reviewer 2, it is still not clear to me the choice of these three genes; if it is related to the fact that these genes were the one common for all regression formulas I suggest to mention it in the text and/or mm section.

Line332 I recommend to add again the reference for GSE12417 when it is mentioned.

Line334 Doxorubicin needs to be indicated with the first letter in uppercase.

Line 337 please add in the parenthesis together with Fig2 “see Materials and Methods section”.

Line 339-341 these lines need to be reformulated.

Line352 the word “cut-off” needs to be consistent along the text and the numbers need to be rounded.

Since many S tables are really small I suggest to insert them in a unique pdf file and leave as excel only those that do not fit on a pdf page.

The script file is missing, it should be provided in a .zip file with all the codes.

The section Acknowledgments is blank.

Reviewer #2: (No Response)

**********

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Reviewer #2: Yes: Michele Cea

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27 Aug 2020

Dear Editor,

We would like to thank the Editorial Board and the Reviewer for all the contributions. You can find all the responses and the necessary revisions based on the reviewer’s comments below.

Yours faithfully,

Dr. Seyhan TURK, PhD

Review Comments to the Author

Reviewer #1: I appreciate the revision of the methods and the extension of the non-linear regression model section, now the methods are sufficiently explained and more clear. However I have noticed that the new version of the manuscript has some parts that need to be reformulated, many typos and inconsistencies along the text.

Additionally it is missing the explanation of the statistics applied in some parts of the manuscript and my concern here is whether or not these tests were rigorously applied.

Finally, I suggest a revision of the Supplementary Materials provided and that the R scripts file will be provided.

For these reasons I am still not considering the manuscript suitable for publication in its current form but I suggest the following points to be addressed to be taken in consideration for publication.

Response:

Accordingly, all sections were reformulated. All typos and inconsistencies have been corrected along with the text. All necessary explanations of all applied statistics were added to the manuscript.

All tests were applied rigorously. All Supplementary Materials were reviewed and R script file was provided as a new Supplementary (S2 Table).

Comment 1:

Line 82 the authors should insert the references in the correct location, if the reference number 30 is referring to the GSE12417 dataset it should be inserted right after it. As this, there are other similar cases along the text, therefore, I please invite the authors to check this in the manuscript.

Response 1:

The locations of all references have been checked and necessary insertions were done.

Comment 2:

Line 91, please remove “were chosen arbitrarily”, if any other study used this parameter please cite it.

Response 2:

The “were chosen arbitrarily” were removed from the text.

Comment 3:

Line 99-100 “explained in De Lean et. al, which is also explained” there is a repetition of the word explain, please substitute with “reported”.

Response 3:

The “explained” was substituted with “reported”.

Comment 4:

Line129 please, insert here that the IC50 values were included in the CGP. If I am not wrong the authors mentioned this already at line 243.

Response 4:

Text was reviewed.

The “IC50 values that were also included in the raw CGP data” and

“We used two versions of CGP data, one original CGP data, second is recalculated 6M IC50 data by R script” were added to the text.

Comment 5:

Line 130-131 Please provide the R script and refer to it as supplementary material.

Response 5:

The R script was provided in the text and it was referred as a new “S2 Table”

Comment 6:

Line 131 “We refer to this data as 6M IC50”, please be consistent with this nomenclature along the text, sometimes it is called “6M IC50” others “IC50 6M” other only “6M”

Response 6:

“6M IC50” it was corrected in all necessary locations in the manuscript.

Comment 7:

Line 135 “9 AML cell lines were treated with” the number 9 needs to be written in words. Additionally I suggest to reformulate or clarify the meaning of the sentence “IC50 values were calculated using the 6M approach in vitro.”, did the authors mean that the values were calculated using the 6M approach on the data obtained from the in vitro analysis?

Response 7:

The number 9 was written in words.

The sentence reformulated as

“In addition, IC50 values were calculated using the 6M IC50 approach on the data obtained from in vitro analysis in which nine AML cell lines were treated with Doxorubicin and Etoposide” to clarify the meaning.

Comment 8:

Line 138 8 needs to be written as a word.

The correlations to which the authors are referring here is the one shown in S3 Table, how was this correlation calculated? If it was with graphpad I would suggest inserting a sentence at the end of this section saying that all the correlations were calculated with Graphpad software. Moreover, Pearson correlation should be used when both the variables are normally distributed; in the response to the reviewers the authors mentioned that they got better results with this method but I was wondering if the two variables were tested for normal distribution or not.

Response 8:

The number 8 was written in words.

At the end of the section “all the correlations were calculated with Graphpad software” were added.

Pearson's correlation analysis was applied only on normally distributed data. We observed that there was no evidence to reject normality in any variables (p>0.05) except for CPA3 gene (marked in red). The Supplementary data was updated so that an Excel file for the normality test results was added as "Correlation Analyses Results - Kolmogorov Smirnov (KS).

Comment 9:

Line 151 please delete “for 10 models” at the end of the sentence, it is redundant.

Response 9:

The “for 10 models” was removed from the text.

Comment 10:

Line 161 please substitute “that is used to describe” with “that here has been used to describe”.

Response 10:

The “that is used to describe” was substituted with “that here has been used to describe”.

Comment 11:

Line 173-174 please correct “Gene set enrichment analyses was” with “Gene set enrichment analysis was”

Response 11:

The “Gene set enrichment analyses was” was corrected with “Gene set enrichment analysis was”

Comment 12:

Line 223 please correct “Clinical data was” with “Clinical data were”

Response 12:

The “Clinical data was” was corrected with “Clinical data were”

Comment 13:

Line 226 please, when the R script is mentioned in the text, refer to the supplementary material in which it is contained.

Response 13:

It was referred to the “S2 Table” when the R script is mentioned in the text.

Comment 14:

Lines 228-229 These lines need a reformulation. First, you should put as references the two studies (PMID: 31949498, PMID: 28607584), second, please change “ 'Low' = low expression” with “‘Low’ (low expression)” and “‘High = high expression’” with “‘High’ (high expression)” and finally, substitute “our previous studies” with “as in refX and refY”.

Response 14:

The necessary references were added (for PMID: 31949498, PMID: 28607584).

The “'Low' = low expression” was changed to “‘Low’ (low expression)” and “‘High = high expression’” was changed to “‘High’ (high expression)”

And the “our previous studies” was changed to “as in [38,39].”

Comment 15:

Line 242 I ask the authors to rewrite the citations when CGP database is mentioned.

Response 15:

The citations were rewritten.

Comment 16:

Line248 the authors mention: “We observed strong correlations between IC50 values obtained from CGP”, as said above, this correlations need to be clarified in the MM section.

Response 16:

The section were clarified in the MM section.

The “We performed a Pearson r correlation analysis between CGP IC50s and 6M IC50s to test the compatibility and observed strong correlations between them for drugs widely used in AML (Cytarabine, Etoposide, Doxorubicin) but not for ATRA (S3 Table)” was added.

Comment 17:

Line254 The sentence “We then asked whether combined expression analyses of

genes could correlate better with drug sensitivity data.” should be linked with the next paragraph.

Response 17:

The sentences were linked together.

Comment 18:

Line257-262 please re-arrange these sentences because they are not clear. The explanation of the workflow used has some typos and english grammar errors. Moreover, some parts are already mentioned in the MM section and should be removed.

Response 18:

The section was rearranged.

Comment 19:

Line 264 please, remove “RAS genes” it is a redundancy; if it is not, I please ask the authors to explain why it is mentioned here.

Response 19:

The “RAS genes” were removed.

Comment 20:

TableS4 and S5 should be merged into the same file.

The name of the columns should be revised, precisely: on the top of the column referring to CGP please indicate CGP and on the top of the column referring to 6M IC50 indicate 6M IC50. I also suggest to name the sheets of the .xlsx table according to the table.

The two above mentioned indications are applicable also to the other S Tables.

Response 20:

The S4 Table and S5 Table were merged into the same file as a new “S5 Table”.

The name of the columns was revised. CGP and 6M IC50 were indicated on the top of the columns.

The sheets of the .xlsx tables were named according to the tables.

Other S tables were also reviewed.

Comment 21:

Line277 6M IC50 needs to be indicated accordingly along the text.

Response 21:

“6M IC50” was indicated accordingly along the manuscript.

Comment 22:

TableS6, the columns referring to each group need to be marked.

Response 22:

S6 Table was revised accordingly.

Comment 23:

Figure 1: The names in the title need to be consistent with the content of the text, therefore 6M needs to be substituted with 6M IC50.

Response 23:

The names in the titles of the Fig1 were renewed.

Comment 24:

Line278 I would suggest to report also here the name of the six genes that have been selected.

Response 24:

The gene names were also reported in the text.

Comment 25:

Line294 a comma between “ANPEP ATP6AP2” is missing.

Response 25:

The comma was added.

Comment 26:

I suggest the authors either merge table S7 and S8 or put them in different sheets of the same file.

Response 26:

Accordingly, Table S7 and Table S8 were merged as a new “S7 Table”.

Comment 27:

S1 Fig: I suggest that the legend of this figure will also include the meaning of the colors. Maybe a scale of colors should be provided in the figure.

Response 27:

A color scale was added to the S1 Fig. And the meanings of the colors were added to the legend.

Comment 28:

Line320-322 EMT acronym is not explained along the text and there is no mention of the statistical test involved when the authors say: “there were no significant differences between the two groups”. The authors need to mention the test performed (Wilcoxon or t-test according to the distribution of the data) and/or report it in the mm section.

Response 28:

The EMT acronym was explained in the text. The citations were added. We used t-test for statistical test. Accordingly, t-test was mentioned in the text.

Comment 29:

S4 Fig needs to be included as a table or a different figure should be provided.

Response 29:

S4 Fig was included as a new “S10 Table”.

Comment 30:

Line 334-335 I was wondering how the authors investigated the up-regulation of the three genes. Additionally, as previously mentioned from the reviewer 2, it is still not clear to me the choice of these three genes; if it is related to the fact that these genes were the one common for all regression formulas I suggest to mention it in the text and/or mm section.

Response 30:

Accordingly, the sentence was corrected as:

“We found that high expression of genes IGF2R and CTSA were both associated with better overall survival, while the opposite was true for ATP6AP2 when patients were classified in either “High” or “Low” groups based upon LRMC cutoffs for each gene separately (see Materials and Methods section) (Fig 2).”

The High expression and the opposite were determined using Log-rank with multiple cut-offs (LRMCs) algorithm as described in methods sections under the heading of clinical data validation. LRMC generates all possible cutoffs with their respective p values associated with Hazard ratios (Fig 2A). So for each gene, these cutoffs were generated and one cutoff with the smallest p-value was selected. Patients with expression values above this cutoff were labeled high and the rest were labeled as low. And for these groups, Kaplan Meier graphs were generated (Fig 2B). This explanation is also given in Fig 2 legend along with cutoff values as well.

The reason why these three genes are selected, as explained previously, is because they are common for both Doxorubicin and Etoposide except for Etoposide 6M IC50. As suggested by the reviewer the required explanation has been added to the “Clinical data validation-Log rank with multiple cutoffs (LRMC) and survival analysis” in the MM section.

Comment 31:

Line332 I recommend to add again the reference for GSE12417 when it is mentioned.

Response 31:

The reference for GSE12417 was added when needed along with the manuscript.

Comment 32:

Line334 Doxorubicin needs to be indicated with the first letter in uppercase.

Response 32:

The correction was done.

Comment 33:

Line 337 please add in the parenthesis together with Fig2 “see Materials and Methods section”.

Response 33:

The “see Materials and Methods section” was added.

Comment 34:

Line 339-341 these lines need to be reformulated.

Response 34:

The lines were reformulated.

Comment 35:

Line352 the word “cut-off” needs to be consistent along the text and the numbers need to be rounded.

Response 35:

All “cutoff” terms were made consistent and the numbers were rounded.

Comment 36:

Since many S tables are really small I suggest to insert them in a unique pdf file and leave as excel only those that do not fit on a pdf page.

Response 36:

Except for S1 Table, all Supplementary Tables were converted to PDF files.

Comment 37:

The script file is missing, it should be provided in a .zip file with all the codes.

Response 37:

The R Script file has only “one code” and it was provided in the new “S2 Table”.

Comment 38:

The section Acknowledgments is blank.

Response 38:

Accordingly, the title was deleted because this section is empty.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

11 Sep 2020

PONE-D-20-09590R2

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with doxorubicin as well as etoposide

PLOS ONE

Dear Dr. Turk,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process by Reviewer #1.

Please submit your revised manuscript by Oct 26 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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Francesco Bertolini, MD, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Turks and colleagues addressed most of the points raised during the last round of revision but one of the most important points, together with some typos/inconsistencies have not been corrected and/or introduced. In order to be accepted I recommend precisely covering all the points raised and to check for possible mistakes newly introduced.

Main point: the R script/scripts are still not included in the current version of the manuscript therefore I kindly ask to provide them in one of the following manners: either as file .R / .Rmd in a compressed file (.zip, gzip, tar.gz, ecc ecc) or as a link to a public repository. Currently the only file provided is a 1 x 1 table with the name of the script “SixModelIC50 V3.r” which does not include any code line.

Minor points to be addressed:

Line 86-87 It is not clear which dataset has been used for data normalization and variance analysis, the name “CGP microarray” combines the “CGP gene expression data” and the “microarray dataset GSE12417”. I please suggest, if the authors intend the “CGP gene expression data”, to use this name.

Line 103 in the new version of the manuscript it comes out that both the models and data have the same name “6M IC50”. This intent was not clear from the previous version, since there was a little bit of confusion in the names. Therefore, I suggest to use two different names for model and data (maybe using lowercase letters in one of the two or only 6M when referring to the model while 6M IC50 when referring to the data). Plase, be consistent along the text with this nomenclature when referring to one or to the other.

line 131-133: “. We referred to this data as 6M IC50. We used two versions of CGP data, one original CGP data, second is recalculated 6M IC50 data by this script.” Please, reformulate this sentence, it does not seem in the right place and it is not clear. I ask the authors to be consistent with the nomenclature and terms used in the other section.

138-139 the sentence “and observed strong correlations between them for drugs widely used in AML (Cytarabine, Etoposide, Doxorubicin) but not for ATRA (S3 Table).” needs to be moved in the result section. Plus there is a conflict on what it is mentioned in lines 254-256 of the results: “ Using t-test we observed strong correlations between CGP IC50 and 6M IC50, for drugs widely used in AML (Cytarabine, Etoposide, Doxorubicin) but not for ATRA (S3 Table).” The authors need to clarify if they used a t-test or a correlation Pearson test. The table is referring to the Pearson correlation.

Line 144: this is the first time the authors refer to the “CGP 6M IC50 data”, please be consistent with the nomenclature as mentioned in the previous paragraph and revision. If the name was only 6M IC50 it needs to be like this, otherwise if a new name is introduced, it needs to be specified before and the authors should explain it.

Line 232 It is quoted another R script that is not the same as the one used to calculate the 6M model but it is referred to as the same. I please ask the authors to correct this, and if it is available, to also provide this script together with the previous one. They can be put together in a compressed (.zip, gzip, tar.gz, ecc ecc) file.

Line 227-228 these new inserted lines need to be reformulated. “IGF2R, CTSA, ATP6AP2 were selected for clinical correlation studies is because they are common for all regression formulas except for Etoposide 6M IC50.” Likely “is because” is a typo, and this is the first time that the authors indicate “Etoposide 6M IC50”, what are they referring to?

Line 302 I kindly ask the authors to revise the use of the article “the” in this location. Have these cells been previously indicated in the text of the results? I suggest to remove the “the” and add (see Materials and Methods section) if the authors agree.

Lines 324-326 the authors should clarify how they “ tested E-Cadherin (epithelial marker) and Vimentin (mesenchymal marker) expression in silico”, if this analysis is referring to the S3 Fig, I please the authors to add at the end of this paragraph (S3 Fig). Moreover, the following paragraph (Lines 332-334) should not be separated if the authors are agreed. Finally the t-test is not shown in the S3 Fig and therefore the quote “(S3 Fig)” should be removed from line 334.

Fig 2 I please ask the authors to explain also the meaning of the red circle in the figure legend. Moreover, I think that the panel A legend needs to be more clear: I find it difficult to read it and it is not explicative of the figure.

Reviewer #2: Authors have addressed all my concerns thus making manuscript suitable for publication in its present form

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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12 Oct 2020

Review Comments to the Author

Reviewer #1: Turks and colleagues addressed most of the points raised during the last round of revision but one of the most important points, together with some typos/inconsistencies have not been corrected and/or introduced. In order to be accepted I recommend precisely covering all the points raised and to check for possible mistakes newly introduced.

Main point: the R script/scripts are still not included in the current version of the manuscript therefore I kindly ask to provide them in one of the following manners: either as file .R / .Rmd in a compressed file (.zip, gzip, tar.gz, ecc ecc) or as a link to a public repository. Currently the only file provided is a 1 x 1 table with the name of the script “SixModelIC50 V3.r” which does not include any code line.

Response:

R Scripts were included in the manuscript as a link to a public Github Repository. “S2 Table was removed”

Minor points to be addressed:

Comment 1.

Line 86-87 It is not clear which dataset has been used for data normalization and variance analysis, the name “CGP microarray” combines the “CGP gene expression data” and the “microarray dataset GSE12417”. I please suggest, if the authors intend the “CGP gene expression data”, to use this name.

Response 1.

“CGP microarray” was changed to “CGP gene expression data”.

Comment 2.

Line 103 in the new version of the manuscript it comes out that both the models and data have the same name “6M IC50”. This intent was not clear from the previous version, since there was a little bit of confusion in the names. Therefore, I suggest to use two different names for model and data (maybe using lowercase letters in one of the two or only 6M when referring to the model while 6M IC50 when referring to the data). Plase, be consistent along the text with this nomenclature when referring to one or to the other.

Response 2.

As suggested by the reviewer, we used “6M” when referring “model” and we used “6M IC50” when referring “data”.

Comment 3.

line 131-133: “. We referred to this data as 6M IC50. We used two versions of CGP data, one original CGP data, second is recalculated 6M IC50 data by this script.” Please, reformulate this sentence, it does not seem in the right place and it is not clear. I ask the authors to be consistent with the nomenclature and terms used in the other section.

Response 3.

We reformulated and relocated the line.

Comment 4.

138-139 the sentence “and observed strong correlations between them for drugs widely used in AML (Cytarabine, Etoposide, Doxorubicin) but not for ATRA (S3 Table).” needs to be moved in the result section. Plus there is a conflict on what it is mentioned in lines 254-256 of the results: “ Using t-test we observed strong correlations between CGP IC50 and 6M IC50, for drugs widely used in AML (Cytarabine, Etoposide, Doxorubicin) but not for ATRA (S3 Table).” The authors need to clarify if they used a t-test or a correlation Pearson test. The table is referring to the Pearson correlation.

Response 4.

We removed the “and observed strong correlations between them for drugs widely used in AML (Cytarabine, Etoposide, Doxorubicin) but not for ATRA (S3 Table)” from the Materials and Methods section.

“T-test” was changed to “Pearson correlation” in the Results section.

Comment 5.

Line 144: this is the first time the authors refer to the “CGP 6M IC50 data”, please be consistent with the nomenclature as mentioned in the previous paragraph and revision. If the name was only 6M IC50 it needs to be like this, otherwise if a new name is introduced, it needs to be specified before and the authors should explain it.

Response 5.

“CGP 6M IC50 data” was changed to “6M IC50 data”.

Comment 6.

Line 232 It is quoted another R script that is not the same as the one used to calculate the 6M model but it is referred to as the same. I please ask the authors to correct this, and if it is available, to also provide this script together with the previous one. They can be put together in a compressed (.zip, gzip, tar.gz, ecc ecc) file.

Comment 6.

R Scripts were included in the manuscript as a link to a public Github Repository. “S2 Table was removed”

Comment 7.

Line 227-228 these new inserted lines need to be reformulated. “IGF2R, CTSA, ATP6AP2 were selected for clinical correlation studies is because they are common for all regression formulas except for Etoposide 6M IC50.” Likely “is because” is a typo, and this is the first time that the authors indicate “Etoposide 6M IC50”, what are they referring to?

Response 7.

We reformulated the line.

Comment 8.

Line 302 I kindly ask the authors to revise the use of the article “the” in this location. Have these cells been previously indicated in the text of the results? I suggest to remove the “the” and add (see Materials and Methods section) if the authors agree.

Response 8.

We removed the “the” and added “(see Materials and Methods section)”.

Comment 9.

Lines 324-326 the authors should clarify how they “ tested E-Cadherin (epithelial marker) and Vimentin (mesenchymal marker) expression in silico”, if this analysis is referring to the S3 Fig, I please the authors to add at the end of this paragraph (S3 Fig). Moreover, the following paragraph (Lines 332-334) should not be separated if the authors are agreed. Finally the t-test is not shown in the S3 Fig and therefore the quote “(S3 Fig)” should be removed from line 334.

Response 9.

We compared E-Cadherin (epithelial marker) and Vimentin (mesenchymal marker) expression using t-test. We clarified it and showed t-test p value on the S3 Fig.

We added “(S3 Fig)” at the end of paragraph and combined with the next paragraph.

Comment 10.

Fig 2 I please ask the authors to explain also the meaning of the red circle in the figure legend. Moreover, I think that the panel A legend needs to be more clear: I find it difficult to read it and it is not explicative of the figure.

Response 10.

We simplified the legend, since we already have explanations for this method in methods section and we cited two previous studies. We also explained the meaning of red circle in the legend.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

4 Nov 2020

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with doxorubicin as well as etoposide

PONE-D-20-09590R3

Dear Dr. Turk,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Francesco Bertolini, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Turk and colleagues substantially improved the work, and I found that the manuscript in its current state can be suitable for publication in PLOSONE journal.

I only recommend that just a few very minor typos should be corrected before the final acceptance or the publication of this manuscript:

Line 179: the number "2227" needs to be indicate as 2,227.

Lines 268-270: please reformulate the sentence.

Line 648: the sentence is starting with “and”, I please the authors to correct this.

I please the authors be consistent with the name “CGP I50” when referring to it also in the Supp Tables (i.e: S3, S7 Tables).

Reviewer #2: The additional editing performed by Authors have made manuscript fully suitable for publication in PlosOne journal

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

6 Nov 2020

PONE-D-20-09590R3

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with doxorubicin as well as etoposide

Dear Dr. Turk:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Francesco Bertolini

Academic Editor

PLOS ONE