Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: Implications for translational research.

The original algorithm that classified triple-negative breast cancer (TNBC) into six subtypes has recently been revised. The revised algorithm (TNBCtype-IM) classifies TNBC into five subtypes and a modifier based on immunological (IM) signatures. The molecular signature may differ between cancer cells in vitro and their respective tumor xenografts. We identified cell lines with concordant molecular subtypes regardless of classification algorithm or analysis of cells in vitro or in vivo, to establish a panel of clinically relevant molecularly stable TNBC models for translational research. Gene expression data were used to classify TNBC cell lines using the original and the revised algorithms. Tumor xenografts were established from 17 cell lines and subjected to gene expression profiling with the original 2188-gene algorithm TNBCtype and the revised 101-gene algorithm TNBCtype-IM. A total of six cell lines (SUM149PT (BL2), HCC1806 (BL2), SUM149PT (BL2), BT549 (M), MDA-MB-453 (LAR), and HCC2157 (BL1)) maintained their subtype classification between in vitro and tumor xenograft analyses across both algorithms. For TNBC molecular classification-guided translational research, we recommend using these TNBC cell lines with stable molecular subtypes.

Introduction

The unfavorable prognosis of triple-negative breast cancer (TNBC) stems in part from a lack of effective targeted therapies and heterogeneity in clinical response to standard chemotherapy [1]. There is an urgent unmet need to identify features of TNBC that can predict response to current standard cytotoxic treatment and facilitate the development of new targeted therapies for this disease.

Towards filling this need, significant efforts have been made to define the molecular heterogeneity of TNBC and to correlate these molecular signatures with clinical outcome and therapeutic effectiveness. In a meta-analysis published in 2009, Lehmann et al. performed cluster expression analysis on 21 breast cancer datasets containing 587 TNBC cases and identified a set of 2,188 genes that could classify TNBC into six molecular subtypes displaying unique characteristics: basal-like 1 (BL1) and basal-like 2 (BL2), characterized by the presence of cell cycle and DNA damage response genes; immunomodulatory (IM); mesenchymal (M); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR) [2]. The classification algorithm based on these 2,188 genes is referred to as TNBCtype.

Later, Lehman et al., recognizing the variety in the histological features of the tumor specimens used to identify the TNBCtype set of genes and aiming to determine whether stromal elements contributed to the molecular classification of any subtype, refined this TNBC classification algorithm [3]. Using histopathological quantification and laser-capture microdissection, they determined that the IM and MSL subtypes were the result of infiltrating lymphocytes and tumor-associated stromal cells, respectively. Thus, the authors concluded that IM status should be determined independently of subtype, which led to removal of the IM and MSL subtypes and left a revised classification with 4 subtypes, BL1, BL2, M, and LAR [3], referred to as the TNBCtype-4 classification.

To overcome obstacles inherent to the original TNBCtype and TNBCtype-4 algorithms and thereby facilitate clinical adoption of TNBC subtyping and improve reproducibility, Ring et al. built a new 101-gene algorithm, TNBCtype-IM (Insight Genetics), using the same gene expression datasets used to develop the original TNBCtype algorithm (Table 1) [4]. Samples classified as IM using an independent model were removed, and shrunken centroids were used to define a minimal gene set for five subtypes: BL1, BL2, LAR, M, and MSL. The subtypes assigned by TNBCtype-IM matched the subtypes assigned by TNBCtype in 87% of cases in a set of seven TNBC cohorts and in 88% of cases in an independent cohort [4]. Evaluation of molecular subtype by principal component analysis revealed that IM could be recognized by the TNBCtype algorithm as a feature distinct from the intrinsic TNBC subtypes of BL1, BL2, LAR, and M; thus, the TNBCtype-IM algorithm determines IM status in addition to each subtype (for example, BL1/IM-positive or BL1/IM-negative) [5].

Table 1

Comparison of TNBC molecular subtypes assigned by TNBCtype and TNBCtype-IM algorithms.

TNBCtype subtype	Corresponding TNBCtype-IM subtype
Basal-like 1	Basal-like 1
Basal-like 2	Basal-like 2
Immunomodulatory	No corresponding subtype; IM is a potential modifier for each molecular subtype
Mesenchymal	Mesenchymal
Mesenchymal stem-like	Mesenchymal stem-like
Luminal androgen receptor	Luminal androgen receptor
Unstable	Unstable

In preclinical and translational research, cell lines and xenograft models are frequently used to identify the biological and cellular properties of distinct breast cancer subtypes. Heterogeneity in molecular background between cell lines thought to belong to the same subtype can influence therapeutic response; this raises the question of which particular cell line should be chosen for study [6]. Many investigators have used TNBCtype to select cell lines for investigational research; however, as mentioned above, the original classification has been revised to include five subtypes and the IM modifier. We speculated that TNBC cell lines with concordant molecular subtypes per TNBCtype and TNBCtype-IM are the most representative of their molecular subtypes and should be preferred models for translational research. The purpose of the study reported here was to identify xenograft-transplantable TNBC cell lines that maintained their molecular definition between the two algorithms and between in vitro and in vivo analyses, so as to identify the most appropriate models for preclinical study.

Materials and methods

Cell lines and cell culture conditions

Seventeen human TNBC cell lines were used in this study as described in Table 2. The cell lines were purchased from American Type Culture Collection with the exception of SUM159PT and SUM149PT, which were purchased from Asterand Bioscience, and HCC3153, from The University of Texas Southwestern Medical Center. Cell lines were grown in a humidified sterile incubator at 37°C in an atmosphere of 5% CO2. The MDAMB231, MDAMB468, MDAMB453, BT549, MDAMB157, DU4475, MDAMB436, and BT20 cell lines were maintained in DMEM/F12 supplemented with fetal bovine serum (10%) and penicillin/streptomycin (100 U/mL). The HCC1187, HCC1806, HCC70, HCC1937, and HCC3153 cell lines were maintained in RPMI1640 supplemented with fetal bovine serum (10%) and penicillin/streptomycin (100 U/mL). The SUM159PT, SUM149PT, and SUM185PE cell lines were maintained in F12 medium supplemented with insulin (5 μg/mL) and hydrocortisone (1 μg/mL). Cell lines were validated using a short-tandem-repeat method based on a primer extension to detect single-base derivations by the Characterized Cell Line Core Facility at The University of Texas MD Anderson Cancer Center.

Table 2

Molecular subtypes of 17 TNBC cell lines and xenografts derived from the same cell lines according to classification with the TNBCtype-IM algorithm.

Cell Line	Subtype per TNBCtype cell line data	Subtype per TNBCtype-IM cell line data	Subtype per TNBCtype Xenograft model	Subtype per TNBCtype-IM Xenograft model
Concordant Stable Subtypes
HCC70	BL2 (0.24)	BL2 (0.27)1	BL2 (0.36)	BL2 (0.38)
SUM149PT	BL2 (0.3)	BL2 (0.21)2	BL2 (0.40)	BL2 (0.37)
HCC1806	BL2 (0.22)	BL2 (0.26)	BL2 (0.42)	BL2 (0.49)
BT549	M (0.21)	M (0.15)	M (0.40)	M (0.41)
MDAMB453	LAR (0.53)	LAR (0.4)	LAR (0.37)	LAR (0.38)
HCC2157	BL1 (0.66)	BL1 (0.4)	BL1 (0.68)	BL1 (0.33)
Discordant or Unstable Subtypes
SUM185PE	LAR (0.39)	UNS	UNS	LAR (0.32)
BT20	UNS	BL2 (0.18)	LAR (0.36)	LAR (0.32)
MDAMB157	MSL (0.25)	LAR (0.12)	BL2 (0.21)	LAR (0.17)
SUM159PT	MSL (0.14)	BL2 (0.18)3	BL2 (0.47)	BL2 (0.54)
MDAMB468	BL1 (0.19)	BL2 (0.2)4	UNS	BL2 (0.24)5
MDAMB231	MSL (0.12)	BL2 (0.24)	UNS	BL2 (0.25)
HCC1187	IM (0.22)	BL2 (0.17)	BL2 (0.32)	BL2 (0.17)6
DU4475	IM (0.17)	BL1 (0.14)	UNS	UNS
MDAMB436	MSL (0.13)	UNS	LAR (0.33)	LAR (0.39)
HCC1937	BL1 (0.28)	BL2 (0.37)	BL1 (0.37)	BL2 (0.34)7
HCC3153	BL1 (0.24)	BL1 (0.37)	UNS	M (0.45)

The values in parentheses are correlation values.

1 Dual subtype of BL1 (0.25)

2 Dual subtype of M (0.17)

3 Dual subtype of LAR (0.16)

4 Dual subtype of BL1 (0.13)

5 Dual subtype of M (0.23)

6 Dual subtype of BL1 (0.14)

7 Dual subtype of M (0.19)

Establishment of xenograft tumors

Tumor xenografts from 17 TNBC cell lines (Table 2) were analyzed in this study. All animal experiments were approved by the Institutional Animal Care and Use Committee (protocol 1305-RN01) of MD Anderson Cancer Center. Human xenograft tumors were established in 4- to 6-week-old immunocompromised mice (Nod-SCID-Gamma) that were bred in-house (Department of Experimental Radiation Oncology, MD Anderson Cancer Center) and housed in pathogen-free conditions within the MD Anderson Research Animal Support Facility. Mice were treated in accordance with NIH guidelines and received standard chow and water ad libitum. Individual tumor xenografts were established in anesthetized mice by implanting 5 × 106 TNBC cells, re-suspended in a 50:50 Matrigel:PBS solution, into the fourth inguinal mammary gland. Established tumors were monitored three times weekly by caliper measurements, and mice were euthanized by CO2 asphyxiation when tumors reached 750 mm3. Excised tumors were fixed in 10% buffered formalin and embedded in paraffin. Tumor blocks were sectioned (5 μm thick) and mounted onto poly-L-lysine glass slides. Five slides for each tumor were prepared histologically, whole-scraped, and processed collectively using QIAGEN’s RNeasy FFPE Kit (Hilden, Germany) according to the manufacturer’s recommendations.

Subtyping of TNBC cell lines and xenografts

Normalized data from the GSE-10890 and E-TABM-157 publicly available gene data sets were used to classify the 17 TNBC cell lines using the original 2,188-gene algorithm (TNBCtype) and RPKM expression data provided by the Cancer Cell Line Encyclopedia (DepMap Public 19Q3), or GSE-10890 in the case of HCC3153, for the 101-gene TNBCtype-IM algorithm [2,4,7]. We were not able to use E-TABM-157 gene data for analysis with the modified 101-gene TNBCtype-IM algorithm because of a significant number (greater than 10%) of missing genes.

Gene expression profiles were created for the TNBC xenograft tumors by using exome capture-based RNA sequencing on RNA samples derived from respective tumors. Briefly, exome-enriched cDNA libraries were constructed using TruSeq RNA Exome (Illumina, San Diego, CA) according to the manufacturer’s recommendations. Libraries were loaded on a NextSeq 500 sequencing system (Illumina, San Diego, CA) with a high-output v3 150 cycle reagent kit, with a mean of 25 million reads per sample. Base call files from each sequencing run were converted to fastq format using bcl2fastq conversion software (Illumina, San Diego, CA) and aligned to the Ensembl GRCh37 Homo sapiens reference using STAR (Spliced Transcripts Alignment to a Reference, v.020201). Transcript assembly and expression analysis were performed on each sample with cufflinks v. 2.2.1, resulting in fragments per kilobase million (FPKM) values for each transcript in the genes of interest, which were summed into one FPKM value for each gene [8,9]. The resulting FPKM data for each sample were compiled into a comma-separated values file and analyzed using the original TNBCtype and TNBCtype-IM algorithms to establish the subtype and determine whether IM features were present.

Gene expression profiles from the cell lines were correlated to the centroids for each of the TNBC subtypes defined in each algorithm using Spearman’s test, because we have observed gene expression profiles to change in a monotonic relationship but not necessarily in a linear relationship between the various subtypes, and this is often best correlated using Spearman’s test. Cell lines were assigned to the TNBC subtype with the highest correlation. Data were log base 2 transformed (either from FPKM or Affy), and then each gene was centered across the batch that was being analyzed. Correlation values were then determined by using Spearman’s rank order method against a centroid value set for each subtype. The cutoff for each subtype was 0.1 (for consistency between TNBCtype and TNBCtype-IM), and all model coefficients and cutoffs were determined using the 14 discovery data sets used in the original Lehmann et al. analysis [2] and were not altered afterwards [4]. If multiple subtypes exceeded a correlation value of 0.1, the z-score method was performed for significance between the subtypes above the cutoff. If there was not a significant difference, multiple subtypes were reported ranked by their level of correlation. If one subtype surpassed the mathematically determined cutoff value, the cell line was assigned to that subtype. If more than one subtype surpassed the cutoff value with a significant difference between the two, the cell line was assigned to the predominant subtype. If more than one subtype surpassed the cutoff value with no significant difference between the two, the cell line was considered to have a dual subtype. For this study, subtype determinations were based on the highest correlation value to establish concordance between the two subtyping algorithms. Finally, if no subtype surpassed the cutoff value, the cell line was classified as unstable (UNS).

Results

TNBC cell lines display partial concordance between TNBCtype and TNBCtype-IM subtyping in vitro

Of the 17 in vitro TNBC cell lines evaluated, 41% (7/17) were classified similarly by the TNBCtype and TNBCtype-IM algorithms (HCC70, SUM149PT, HCC1806, BT549, MDAMB453, HCC2157, and HCC3153) (Table 2). The subtype on TNBCtype-IM was the same as the subtype on TNBCtype for 50% (1/2) of the cell lines classified as LAR by TNBCtype, 50% (2/4) of the cell lines classified as BL1 by TNBCtype, 100% (1/1) of the cell lines classified as M by TNBCtype, 100% (3/3) of the cell lines classified as BL2 by TNBCtype, and 0% (0/4) of the cell lines classified as MSL by TNBCtype. Two cell lines were classified as IM by TNBCtype but were not classified as IM by TNBCtype-IM, which suggests that this modifier requires a microenvironment with the presence of stromal cell infiltrates, which are lacking within in vitro culture. These findings seemed to support the hypothesis from Lehman et al. [3] that IM and MSL are not subtypes of TNBC but have additional underlying biology. Further, we tried to classify each cell line according to the subtype with the highest correlation, but four cell lines (HCC70, SUM149PT, SUM159PT, and MDAMB468) were classified as having dual subtypes on the basis of analysis of in vitro cell lines using the TNBCtype-IM algorithm. For comparisons between subtyping algorithms, we listed the subtype with the highest correlation in Table 2, but we also indicated the dual subtypes in footnotes to the table.

Identification of six cell lines that display similar results in cell lines and animal models

Concordance was observed between the in vitro cell line subtype and the in vivo xenograft subtype in six of the 17 tumor xenograft models tested (HCC70, SUM149PT, HCC1806, BT549, MDAMB453 and HCC2157) (Table 2). To confirm the reproducibility of our results we reclassified each of these six cell lines using the TNBCtype-IM algorithm and cell line data from 5 different sources and found that they were consistently classified to the same subtype (Table 3)(S1 Table). Similar to the in vitro assessment, no positive IM modifier or MSL subtype was detected in any of the xenograft tumor samples analyzed.

Table 3

Subtypes of 6 molecularly stable TNBC cell lines and xenografts derived from multiple sources maintains subtype according to classification with the TNBCtype-IM algorithm.

Cell Line	Subtype per TNBCtype-IM from GSE15361	Subtype per TNBCtype-IM from GSE10890	Subtype per TNBCtype-IM from CCLE	Subtype per TNBCtype-IM Xenograft model	Subtype per TNBCtype Lehman, 2011
HCC70	BL2 (0.22)	BL2 (0.26)	BL2 (0.27)	BL2 (0.38)	BL2 (0.24)
SUM149PT	BL2 (0.14)	*	BL2 (0.21)	BL2 (0.37)	BL2 (0.30)
HCC1806	*	BL2 (0.42)	BL2 (0.26)	BL2 (0.49)	BL2 (0.22)
BT549	M (0.18)	M (0.11)	M (0.15)	M (0.41)	M (0.21)
MDAMB453	LAR (0.30)	LAR (0.48)	LAR (0.40)	LAR (0.38)	LAR (0.53)
HCC2157	BL1 (0.44)	*	BL1 (0.40)	BL1 (0.33)	BL1 (0.66)

*Indicates cell line not represented in dataset.

Discussion

This is the first study to clearly define molecularly stable cell lines to represent the BL1, BL2, LAR, and M TNBC subtypes. We found that of the cell lines examined, HCC70 (BL2), SUM149PT (BL2), HCC1806 (BL2), BT549 (M), MDAMB453 (LAR) and HCC2157(BL1) were stable across both algorithms, between the in vitro and in vivo xenograft models and were consistently classified to the same subtype using multiple datasets, which demonstrates reproducibility (Table 3). Therefore, we consider these cell lines the most suitable representatives of their respective subtypes.

Previous work focusing on breast cancer cell line characterization has shown the difficulty of clearly determining molecular subtypes. A study that determined ER, PR, and HER2 expression in human breast cancer cell lines using immunohistochemical and immunocytochemistry assays did not find complete concordance between molecular subtypes determined using the two methods [10]. Another study determined protein expression using immunoblot analyses to characterize breast cancer cell lines, including 18 TNBC cell lines. Within each subtype, a significant level of genetic heterogeneity was found. Profiled pathway activation status was examined to determine activated pathways resulting from these mutational combinations, which provided better insight into the molecular classification of these cell lines [6].

Our study utilized two distinct gene-based algorithms to molecularly characterize TNBC cell lines in vitro and validated the results using in vivo animal tumor models derived from these cell lines, which we believe can give insight into which are the most molecularly stable and representative of their respective subtype. We tried to classify each cell line according to the subtype with the highest correlation, but four cell lines had confounding subtypes, most likely indicating dual subtypes that may express gene ontologies of more than one type. The molecular expression of more than one subtype can may call into question the suitability of these cell lines for research that may rely on the TNBCtype molecular classification. However, cell lines with dual subtypes may serve as models for studying the influence of external factors on the evolution of tumor subtype.

It is important to note that the IM subtype was not found in any of our specimens using the refined TNBCtype-IM algorithm, confirming the findings of Lehman et al. that IM should be considered an indicator of the presence of tumor-associated lymphocytes and determined independently of the subtype [3]. In fact, TNBCtype-IM was used in a recently published case report in which a patient eligible for immunotherapy tested negative for PD-L1 by immunohistochemistry but positive for IM by TNBCtype-IM. The patient had received exhaustive chemotherapy and experienced a complete radiologic response after four cycles of pembrolizumab [5]. Similar to what was observed in our analyses of cells in vitro, we were not able to identify positive IM status in the gene expression profiles of TNBC xenograft tumors. Since IM status has been shown to be dependent on the presence of tumor-infiltrating lymphocytes [3], it would be of interest to determine whether IM status could be observed in “humanized mice” bearing TNBC xenografts or freshly derived patient-derived xenograft models with human tumor-infiltrating immune cells.

Many published studies have adopted TNBCtype molecular subtyping for cell line selection for research. However, some cells lines have different molecular classifications in the in vitro and in vivo settings. Our results suggest that data interpretation and experimental planning should be interpreted with caution. Our data do not suggest that cell lines that demonstrate different molecular subtypes in the in vitro and in vivo settings are not suitable for experiments, but rather suggest that if the premise of the research is based on TNBCtype molecular classification, cell lines should be used that are molecularly stable regardless of the experimental condition.

Conclusions

We identified several TNBC cell lines that have concordant molecular subtypes according to TNBCtype and TNBCtype-IM and between cell lines and xenografts. We believe that such cell lines are the most molecularly stable and the most representative of their respective subtype. In our study, those cell lines were HCC70 (BL2), SUM149PT (BL2), HCC1806 (BL2), BT549 (M), MDAMB453 (LAR) and HCC2157(BL1). Therefore, for drug development studies based on TNBCtype molecular subtyping, we recommend using these cell lines.

Correlation values from each subtype of the 6 molecularly stable TNBC cell lines and xenografts derived from multiple sources according to classification with the TNBCtype-IM algorithm.

(DOCX)

Click here for additional data file.

21 Sep 2019

PONE-D-19-21744

Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: implications for translational research

PLOS ONE

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The reviewers felt that this manuscript would be important in providing a framework for investigators to select in vitro and in vivo models of TNBC. Please address the reviewer comments, which focused on a lack of sufficient details in the classification strategy and which publicly available data were used for analysis and rationale for value cut-offs. In addition, please comment on how reproducible the classification calls are using independent public data sets. There were also several clarifications or improvements needed related to data presented in the Figures and data tables (Reviewers 1-2). Finally, a statistics reviewer raised questions related to correlation and cut-off values.

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Reviewer #1: PONE-D-19-21744

The manuscript by Fernandez and colleagues relays important information for the molecular classification of TNBC subtypes in cell line models. There are a number of points that should be clarified and/or expanded prior to publication.

1. The methods indicate that publicly available gene expression data were used to classify the 28 TNBC cell lines. The authors should explicitly state which publically available data. Two references were cited. How was the data from the different articles analyzed? Analysis of this data should be more thoroughly explained in the methods section. Additionally, could the use of publicly available data have an impact on discordant results between cell line and xenograft results? For discordant results did the authors repeat RNA sequencing from cell lines grown in their own laboratory and compare to their RNA sequencing analysis from the xenograft studies presented here?

2. Figure 1: could the authors please indicate which cell lines are represented in Figure 1. Also, please include the data for each cell line in a supplemental file.

3. In Table 1 it would be helpful to annotate which cell lines were classified as dual subtypes.

4. The authors should present the analysis using TNBCtype from the xenograft data in addition to the TNBCtype-IM (Table 3).

5. Page 9, line 177 indicates that 6 of 17 tumor xenograft models tested were concordant, but in Table 3 HCC70 cell line and xenograft data also appears to be concordant for BL2.

6. Please clarify, were xenografts only established from 17/28 because 11 of the cell lines did not grow in vivo? Also clarify, was xenograft tumor data from an n=1 for each cell line?

Reviewer #2: Identification of triple negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: implications of translational research.

Fernandez et al.

Manuscript: PONE-D-19-21744

The study by Fernandez et al. investigates the classification of triple negative breast cancer cell lines grown in vitro and as orthotopic xenograft transplant models in vivo. The goal is to define the TNBC subtypes of these cell lines using the original TNBCtype and modified TNBCtype –IM algorithms. The impact of the proposed studies lies in defining the molecular subtypes in order to provide the scientific community a framework by which to select these lines, either in vitro or in vivo, to model TNBC subtypes for mechanistic and/or preclinical studies. While the current study does address this goal, I have several moderate and minor concerns that I have outlined below.

Major Concerns:

1. The classification strategy is not well described. The authors have not defined the correlation cut-off used to define the subgroup classification (line 154-157) nor have they described the rationale for the selection of that value. Likewise, the authors state (line 153-154): “If more than one subtype surpassed the cut-off value with no significant difference between the two...” It was not clear how the authors determined whether there was a significant difference. In general, the methods should be more clearly written.

2. The authors should provide a table of correlation coefficients for each cell lines and each subgroup. While the authors have made the calls for each cell line, it would allow the reader to gauge the strength of the correlation if these data were provided.

3. It is not clear how reproducible these classification calls are. The authors should use an independent dataset (i.e. CCLE RNAseq data) to examine subtype classification for these cell lines. This would provide additional confidence that subtype calls are not dependent on culturing methods, specific growth conditions or technical or experimental variables. This is particularly relevant as subtype calls for 8/28 or 28.6% of the cell lines analyzed in the current study and the original Lehman JCI paper are not concordant using the TNBCtype calls (Table 2 of this study vs. Table 3 in Lehman 2011 JCI paper).

Minor Concerns:

1. Figure 1 is not very clear. It also appears to be lacking a legend as well as y-axis labels.

2. Table 2 and 3 seem somewhat redundant and the data could be merged into a single table.

3. Reference 2 appear to be merged with Reference 1 in the References Cited section.

Reviewer #3: A) Spearman's correlation test was used to determine sub-type categories. A score of .195 was presented in Figure 1 as the cutoff selected; however, that score is considered "weak correlation" at best. Could the author address this as well as why Spearman's was selected over other tests available?

B) The number of available samples in each subcategory seems rather now. In MSL category only two of the available four was correctly selected.

**********

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27 Dec 2019

Reviewers' comments:

Reviewer #1: PONE-D-19-21744

The manuscript by Fernandez and colleagues relays important information for the molecular classification of TNBC subtypes in cell line models. There are a number of points that should be clarified and/or expanded prior to publication.

1. The methods indicate that publicly available gene expression data were used to classify the 28 TNBC cell lines. The authors should explicitly state which publically available data. Two references were cited. How was the data from the different articles analyzed? Analysis of this data should be more thoroughly explained in the methods section. Additionally, could the use of publicly available data have an impact on discordant results between cell line and xenograft results? For discordant results did the authors repeat RNA sequencing from cell lines grown in their own laboratory and compare to their RNA sequencing analysis from the xenograft studies presented here?

We appreciate the reviewer’s comment. Normalized data from the CCLE (RNAseq), GSE-10890, and E-TABM-157 publicly available gene data sets were used to classify the 17 TNBC cell lines using both the original 2,188-gene algorithm (TNBCtype) and the 101-gene TNBCtype-IM algorithm. We added a reference for this data sets: Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes Cancer Cell. 2006; 10(6): 515–527, where the methods are explained.

In terms of discordance, it was our intention to find TNBC cell lines that would remain stable with regard to molecular subtype regardless of the algorithm or type of model (in vitro, in vivo) used. Please also see our responses below to reviewer 2.

2. Figure 1: could the authors please indicate which cell lines are represented in Figure 1. Also, please include the data for each cell line in a supplemental file.

To avoid confusion, we decided to eliminate Figure 1from the manuscript because it does not represent any particular cell line data. Also, the correlation values for each cell line have been added to Table 2.

3. In Table 1 it would be helpful to annotate which cell lines were classified as dual subtypes.

We thank the reviewer for this comment. No cell line data results are described in Table 1. However, we have annotated dual subtypes in Table 2 and added correlation values.

4. The authors should present the analysis using TNBCtype from the xenograft data in addition to the TNBCtype-IM (Table 3).

We agree with the reviewer. We have added the results of TNBCtype analysis from the xenograft data to table 2 to further demonstrate molecular subtype stability.

5. Page 9, line 177 indicates that 6 of 17 tumor xenograft models tested were concordant, but in Table 3 HCC70 cell line and xenograft data also appears to be concordant for BL2.

We agree with the reviewer and have updated our discussion of the results in Table 2 accordingly.

6. Please clarify, were xenografts only established from 17/28 because 11 of the cell lines did not grow in vivo? Also clarify, was xenograft tumor data from an n=1 for each cell line?

Only 17 xenografts were established because those are the animal models we had available in our research laboratory. To reduce confusion, we have eliminated all mentions of the 11 cell lines that were not used for xenograft tumor generation. The data from these cell lines (formerly in Table 2) are largely redundant with the xenograft table and data did not meet the bar set forth by this manuscript of demonstrating subtype stability. Various changes have been made in the manuscript as a result of our decision to not mention the 11 cell lines for which xenografts were not established.

Reviewer #2: Identification of triple negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: implications of translational research.

Fernandez et al.

Manuscript: PONE-D-19-21744

The study by Fernandez et al. investigates the classification of triple negative breast cancer cell lines grown in vitro and as orthotopic xenograft transplant models in vivo. The goal is to define the TNBC subtypes of these cell lines using the original TNBCtype and modified TNBCtype –IM algorithms. The impact of the proposed studies lies in defining the molecular subtypes in order to provide the scientific community a framework by which to select these lines, either in vitro or in vivo, to model TNBC subtypes for mechanistic and/or preclinical studies. While the current study does address this goal, I have several moderate and minor concerns that I have outlined below.

Major Concerns:

1. The classification strategy is not well described. The authors have not defined the correlation cut-off used to define the subgroup classification (line 154-157) nor have they described the rationale for the selection of that value. Likewise, the authors state (line 153-154): “If more than one subtype surpassed the cut-off value with no significant difference between the two...” It was not clear how the authors determined whether there was a significant difference. In general, the methods should be more clearly written.

We appreciate these comments. To address them, we have added the following to our methods section:

Data was log base 2 transformed (either from RPKM or Affy), followed by centering of each gene across the batch that was being analyzed. Correlation values were then determined by using Spearman’s rank order method against a centroid value set for each subtype. The cutoff for each subtype was 0.1 (for consistency between TNBCtype and TNBCtype-IM), all model coefficients and cutoffs were determined using the 14 discovery data sets, as in the original Lehmann et al. analysis [2], and were not altered afterwards [4]. If multiple subtypes exceed a correlation value of 0.1, the z-score method was performed for significance between the subtypes above the cutoff. If there was not a significant difference, then multiple subtypes were reported ranked by their level of correlation. (lines 154 to 163)

To better compare TNBCtype and TNBCtype-IM, we applied the correlation cutoff of 0.1 to TNBCtype-IM to match TNBCtype. This resulted in two xenograft results changing. Please see Table 2.

2. The authors should provide a table of correlation coefficients for each cell lines and each subgroup. While the authors have made the calls for each cell line, it would allow the reader to gauge the strength of the correlation if these data were provided.

We agree with the reviewer and have added these details to Table 2.

3. It is not clear how reproducible these classification calls are. The authors should use an independent dataset (i.e. CCLE RNAseq data) to examine subtype classification for these cell lines. This would provide additional confidence that subtype calls are not dependent on culturing methods, specific growth conditions or technical or experimental variables. This is particularly relevant as subtype calls for 8/28 or 28.6% of the cell lines analyzed in the current study and the original Lehman JCI paper are not concordant using the TNBCtype calls (Table 2 of this study vs. Table 3 in Lehman 2011 JCI paper).

We agree with the reviewer. We have re-analyzed all cell line data using the CCLE RNAseq data have generated the in vitro TNBCtype-IM subtypes (updated Table 2). The xenograft samples were analyzed using RNAseq, so this is a more appropriate comparison. In addition, we were not able to use the E-TABM-157 dataset used by Lehman due to several missing genes from the modified 101-gene algorithm (TNBCtype-IM). However, owing to availability, we used GSE-10890 to determine the subtype call for HCC3153 (see lines 130 to 135 and lines 181 to 195)

We do expect discordant calls between the two algorithms for the following reasons:

1. As shown in the Ring paper, using a large dataset, we expect approximately 87% concordance between the two algorithms.

2. TNBCtype-IM allows for a dual subtype call with the IM subtype removed and is included as a modifier for each subtype.

3. Using data previously reported, with histopathological quantification and laser-capture microdissection, it was determined that the IM and MSL subtypes as reported in the original TNBCtype algorithm were likely due to tumor-infiltrating lymphocytes and tumor-associated stromal cells, respectively. Therefore, we are more confident in the calls of TNBCtype-IM, which has been modified and does not bias cohorts towards finding samples of these two subtypes.

Minor Concerns:

1. Figure 1 is not very clear. It also appears to be lacking a legend as well as y-axis labels.

We agree with this comment. We have eliminated this figure and added details to the Methods section.

2. Table 2 and 3 seem somewhat redundant and the data could be merged into a single table.

We agree with the reviewer and have made this modification. See lines 34 to 42.

3. Reference 2 appear to be merged with Reference 1 in the References Cited section.

Thank you for the comment. We have corrected this error.

Reviewer #3: A) Spearman's correlation test was used to determine sub-type categories. A score of .195 was presented in Figure 1 as the cutoff selected; however, that score is considered "weak correlation" at best. Could the author address this as well as why Spearman's was selected over other tests available?

The correlation value cutoffs selected have been determined empirically. We have observed gene expression profiles to change in a monotonic but not necessarily linear relationship between the various subtypes, which is often best correlated using Spearman’s test as compared to Pearson’s test. We have added this information to the Methods section. (lines 150 to 153)

B) The number of available samples in each subcategory seems rather now. In MSL category only two of the available four was correctly selected.

Using data previously reported, with histopathological quantification and laser-capture microdissection, it was determined that the IM and MSL subtypes as reported in the original TNBCtype algorithm were likely due to infiltrating lymphocytes and tumor-associated stromal cells, respectively. It is because of these data, in combination with the new centering method, that it is possible the MSL subtype was overrepresented with the original TNBCtype algorithm and is more accurately represented using TNBCtype-IM.

Submitted filename: Response to Reviewers PONE-D-19-21744.pdf

Click here for additional data file.

22 Jan 2020

PONE-D-19-21744R1

Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: implications for translational research

PLOS ONE

Dear Dr. Ueno,

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.

Please address Reviewer #2's concerns about rigor and reproducibility of the data sets, including concerns about correlation coefficients and variance that may be introduced by different growth conditions among laboratories. In particular, please address this comment: "..the investigators should demonstrate that the same cell line, grown in the same way (i.e. in vitro or in vivo) is consistently classified to the same subtype; there are multiple publicly available RNAseq datasets that can be used to complete these studies." It would also be useful to address some of the reviewers concerns and potential limitations of the study in the discussion, which Reviewer #2 found to be brief.

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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: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

**********

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Reviewer #2: Partly

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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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: No

Reviewer #3: Yes

**********

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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.

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Reviewer #2: Yes

Reviewer #3: 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: (No Response)

Reviewer #2: The revised manuscript is improved; however a few concerns remain. First, the level of rigor in the subtype calls is not clear. The authors should report the correlation coefficient values for each cell line or xenograft and each TNBC subtype. Given that most of the reported correlation coefficients are low, this would afford the reader additional insight into the strength of the calls and potential concerns with selecting any given model system for subsequent studies. Secondly, while the authors do demonstrate concordance in subtype calls when specific cell lines are grown in vitro or as an in vivo xenograft, it is unclear how reproducible the subtypes call is between multiple datasets. As I noted in my previous review, and in this review, the subtype correlation coffecicients are relatively weak. As such, the investigators should demonstrate that the same cell line, grown in the same way (i.e. in vitro or in vivo) is consistently classified to the same subtype; there are multiple publicly available RNAseq datasets that can be used to complete these studies. If this is not reproducible, there are concerns with the selection of these lines for future studies as growth conditions will undoubtedly vary between laboratories. Finally, the manuscript is relatively well written, but there are a number of awkwardly worded sections and the discussion is rather brief.

Reviewer #3: This reviewer believes the authors have completed all changes needed in the manuscript. The paper should be accepted.

**********

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Reviewer #2: No

Reviewer #3: 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 to be viewed.]

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9 Mar 2020

Dear Dr. Heber,

Thank you for the opportunity to revise our manuscript “Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: implications for translational research.” We have addressed the reviewers’ concerns, as indicated by our point-by-point responses to the comments in italics below.

Reviewers' comments:

Reviewer #2: The revised manuscript is improved; however a few concerns remain. First, the level of rigor in the subtype calls is not clear. The authors should report the correlation coefficient values for each cell line or xenograft and each TNBC subtype. Given that most of the reported correlation coefficients are low, this would afford the reader additional insight into the strength of the calls and potential concerns with selecting any given model system for subsequent studies. Secondly, while the authors do demonstrate concordance in subtype calls when specific cell lines are grown in vitro or as an in vivo xenograft, it is unclear how reproducible the subtypes call is between multiple datasets. As I noted in my previous review, and in this review, the subtype correlation coffecicients are relatively weak. As such, the investigators should demonstrate that the same cell line, grown in the same way (i.e. in vitro or in vivo) is consistently classified to the same subtype; there are multiple publicly available RNAseq datasets that can be used to complete these studies. If this is not reproducible, there are concerns with the selection of these lines for future studies as growth conditions will undoubtedly vary between laboratories. Finally, the manuscript is relatively well written, but there are a number of awkwardly worded sections and the discussion is rather brief.

We thank the reviewer for the suggestions. First, we are aware that the correlation coefficient values we are reporting are low so in order to offer the reader additional insight into the strength of each subtype call, we have added a supplementary table where we report correlation values from each

subtype of the 6 molecularly stable TNBC cell lines and xenografts derived from multiple sources according to classification with the TNBCtype-IM algorithm.

Secondly, to demonstrate the strength and reproducibility of our results despite the low correlation coefficient values, we reproduced the calls for the six specific concordant cell lines (SUM149PT, HCC1806, SUM149PT, BT549, MDA-MB-453, and HCC2157) using 5 different datasets (GSE15361, GSE10890, CCLE, Xenograft model, and Lehman, 2011). We found that all cell lines were consistently classified to the same subtype across all datasets. We have included this data in the results and discussion section, and we have also added the results and source of each dataset in table 3.

Sincerely,

Naoto T. Ueno, M.D., Ph.D., F.A.C.P.

Professor of Medicine

Submitted filename: Response to reviewers.pdf

Click here for additional data file.

6 Apr 2020

Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: implications for translational research

PONE-D-19-21744R2

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PLOS ONE

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Reviewer #2: All comments have been addressed

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**********

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Reviewer #2: Yes

**********

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Reviewer #2: Yes

**********

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21 Apr 2020

PONE-D-19-21744R2

Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: implications for translational research

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