Investigation of cell signalings and therapeutic targets in PTPRK-RSPO3 fusion-positive colorectal cancer.

INTRODUCTION: Colorectal cancer (CRC) is one of the most deadly and common diseases in the world, accounting for over 881,000 casualties in 2018. The PTPRK-RSPO3 (P:R) fusion is a structural variation in CRC and well known for its ability to activate WNT signaling and tumorigenesis. However, till now, therapeutic targets and actionable drugs are limited in this subtype of cancer. MATERIALS AND
METHOD: The purpose of this study is to identify key genes and cancer-related pathways specific for P:R fusion-positive CRC. In addition, we also inferred the actionable drugs in bioinformatics analysis using the Cancer Genome Atlas (TCGA) data.
RESULTS: 2,505 genes were altered in RNA expression specific for P:R fusion-positive CRC. By pathway analysis based on the altered genes, ten major cancer-related signaling pathways (Apoptosis, Direct p53, EGFR, ErbB, JAK-STAT, tyrosine kinases, Pathways in Cancer, SCF-KIT, VEGFR, and WNT-related Pathway) were significantly altered in P:R fusion-positive CRC. Among these pathways, the most altered cancer genes (ALK, ACSL3, AXIN, MYC, TP53, GNAQ, ACVR2A, and FAS) specific for P:R fusion and involved in multiple cancer pathways were considered to have a key role in P:R fusion-positive CRC. Based on the drug-target network analysis, crizotinib, alectinib, lorlatinib, brigatinib, ceritinib, erdafitinib, infigratinib and pemigatinib were selected as putative therapeutic candidates, since they were already used in routine clinical practice in other cancer types and target genes of the drugs were involved in multiple cancer-pathways.

Introduction

Colorectal cancer (CRC) is the third most fatal and fourth most diagnosed cancer worldwide, according to 2018 global cancer data released by the IARC. Approximately 2 million new cases were recorded in year 2018 alone, resulting in approximately 1 million fatalities [1-3]. With the development of NGS technology, simultaneous detection of various mutations in colorectal cancer has become possible, including SNV, INDEL, CNV, fusion, and MSI [4-7]. The reason that the detection of these mutations is important is that it can be used as a target therapy for gene mutations, for example, EGFR-inhibitor for KRAS wildtype CRC and immune checkpoint inhibitors for MSI-high solid tumor [8].

The efforts to develop targeted drugs for treating colorectal cancer are increasing, however, the candidates of target drugs other signaling pathways besides EGFR and mismatch Repair are limited. WNT signaling is known as a major pathway in colorectal cancer and mostly is activated by mutation of the APC gene, which plays an important role in the pathogenesis of colorectal cancer [9-13]. Recently, PTPRK-RSPO3 (P:R) fusion also contributes to the activation of WNT signaling and causes colorectal cancer, and this mutation is mutually exclusive with the APC mutation and is recognized as another important mutation contributing to the development of colorectal cancer [14-17]. Recent studies have reported that LGK974 and RSPO3 antibodies may be beneficial at in vitro and in vivo levels, however, the development of targeted therapeutics for colorectal cancer patients with P:R fusions is still in its infancy [18, 19].

Herein, we systematically inferred drug candidates in P:R fusion colorectal cancer. First, we extracted RSPO3 expression correlated genes and selected oncogenic cell signal pathways containing those genes. Then, we constructed a drug-target network in P:R fusion colorectal cancer using the drug-target database, and finally, we prioritized a suitable therapeutic agent.

Materials and methods

Sample collection and quality control

The Broad GDAC Firehouse website (https://gdac.broadinstitute.org) provided gene level 3 (RSEM) mRNA expression with normalized read count values of the Cancer Genome Atlas (TCGA) colorectal cancer (CRC). The above-mentioned website provided information on the samples’ MAF files, TNM stages, and molecular subtypes among other clinical characteristics.

Case-control selection and selection of genes affected by PTPRK- RSPO3 fusion

We found seven samples with PTPRK-RSPO3 fusion using the TCGA fusion gene data portal (The Jackson Laboratory, https://www.tumorfusions.org), which were cross-checked with elevated RSPO3 expression levels. Of the 433 GDAC data downloaded, 53 normal and 1 metastasis data were omitted for the analysis of the remaining 379 data. For control sample selection, 50 samples were randomly selected among the samples with low RSPO3 expression (less than the median value of RSPO3 RNA expression, N = 186). To obtain R-values of 20,531 genes in correlation with RSPO3 in RNA expression, Pearson correlation-tests were performed in seven PTPRK-RSPO3 fusion-positive cases and 50 controls. Then, above tests were repeated 100 times. Based on the median of absolute R values from 100 tests, 20,531 genes were sorted in decreasing order. Using the median of absolute R values, mostly affected 2,505 genes were selected by correlation cut-off (R > 0.2). The R cut-off value, 0.2, were selected based on the 100,000 permutation tests. For each permutation test, randomly ordered expression values of a randomly selected gene were tested using Pearson correlation test with the expression values of reference gene (RSPO3) in 7 cases and 50 controls. After 100,000 tests, falsely selected genes correlated with RSPO3 are assumed to be 0.37% when the cut-off value for R was 0.2.

Pathway analysis via ConsensusPathDB (CPDB)

The aforementioned R-value data were used to perform over-representation analysis (ORA) using ConsensusPathDB (CPDB, http://cpdb.molgen.mpg.de/CPDB) on the 2,505 genes (including RSPO3). According to BioCarta [http://www.biocarta.com/], 177 biological pathways were combined from the following sources: INOH [20], KEGG [21], NetPath [22], PID [23], Reactome [24] and Wikipathways [25]. Analyzing the ontological features and the proportion of duplicated genes, the pathways enriched with chosen 2,505 genes (q-value < 0.05) were collapsed into 10 cancer-related pathways, having 848 genes as components.

Inferring and prioritizing actionable drugs

The “Clinical Evidence Summaries” data was downloaded from the Clinical Interpretations of Variants in Cancer (CIViC) website (https://civic.genome.wustl.edu/releases) on July 1, 2021, and the “Actionable Variants” data was accessed and downloaded from the Precision Oncology Knowledge Base (OncoKB) website (http://oncokb.org/) on July 1, 2021. RSPO3-crrelated genes were annotated using 673 CIViC variations (181 genes) with predicted treatment effectiveness and 148 OncoKB actionable variants (53 genes). Then drug-target relationships were prioritized based on the scenario that properly working cancer drugs are generally inhibitors for activated oncogenes or activators for down-regulated tumor suppressor genes.

Statistical analysis and data visualization

All statistical analyses, including the Pearson correlation-tests, were performed using the open software R version 3.4.4. Complexheatmap, a R package, was used to visualize an RNA expression heatmap. KEGG mapper (https://www.genome.jp/kegg/mapper.html) was used to display target genes associated to WNT signaling pathway. The comprehensive network between targetable drugs and therapeutic agents was analyzed and illustrated using Cytoscape 3.5.3. In this study, statistical significance was determined as a p-value of 0.05 and false detection rate (FDR) as a q-value of 0.2 in over-representation analysis.

Results

Clinico-pathological characteristics

The clinicopathological characteristics in this study were described in Table 1. A total 379 tumor solid samples, and of the 372 samples, excluding the 7 fusion-positive samples, 186 samples expressing less than 50% RSPO3 mRNA expression were selected as control.

Table 1

Clinicopathological characteristics of PTPRK-RSPO3 fusion-positive and fusion-negative cases in TCGA colorectal cancer.

	*Fusion	**Control	***p values
	(N = 7)	(N = 186)
Age	46~76	31~90	NS
Sex			1
• Male	3/5 (60.0%)	68/121 (56.2%)
• Female	2/5 (40.0%)	53/121 (43.8%)
Vital status			NS
• Alive	5/5 (100.0%)	101/121 (83.8%)
• Dead	0/5 (0.0%)	20/121 (16.5%)
Stage			NS
• Stage I	0/4 (0.0%)	20/116 (17.2%)
• Stage II	2/4 (50.0%)	48/116 (41.4%)
• Stage III	2/4 (50.0%)	33/116 (28.4%)
• Stage IV	0/4 (0.0%)	15/116 (13.0%)
Microsatellite instability			NS
• MSI-high	1/5 (20.0%)	18/121 (14.5%)
• MSI-low	0/5 (0.0%)	19/121 (15.9%)
• MSS	4/5 (80.0%)	84/121 (69.4%)
Histological type			NS
• Adenocarcinoma	3/4 (75.0%)	113/120 (88.0%)
• Mucinous adenocarcinoma	1/4 (25.0%)	7/120 (12.0%)
Mutation profile
• TP53 mutation	3/7	113/186	NS
	(42.9%)	(60.8%)
• KRAS mutation	2/7	80/186	NS
	(28.6%)	(19.4%)
• PIK3CA mutation	2/7	50/186	NS
	(28.6%)	(43.0%)
• PTEN mutation	1/7	15/186	NS
	(14.3%)	(8.0%)
• BRAF mutation	2/7	23/186	NS
	(28.6%)	(12.7%)

* Samples harboring PTPRK-RSPO3 fusion

** Control group was extracted from samples demonstrating the lower median of RSPO3 mRNA expressions.

***p-value was calculated between fusion positive samples and controls using moonBook R package.

Abbreviations: AD, adenocarcinoma; NS, not significant; NA, not available

There showed no definite statistical significance of histological type, age, sex, vital status and TNM stage between fusion-positive cases and controls. Notably, no other mutation driver was identified in P:R fusion-positive patients, showing mutual exclusiveness. However, one case of microsatellite instability-high (MSI-H) was identified in these P:R fusion positive patients, implying the possibility of the co-occurrence of two oncogenic aberrations.

Key genes and pathways altered in PTPRK-RSPO3 fusion-positive colorectal cancer

By the correlations test and permutation tests (See Methods), 2,505 genes were passed the Pearson correlation-test with an R-value greater than the cut-off value. Eighteen genes including PPP1R12B, NPY, VIP, C2orf72, IQGAP2, SYT2, ADCYAP1, ZNF385D, SCIN, MAGEE2, SDCBP2, AHCYL2, C6orf105, ZNF229, BTNL8, SLC7A14, GPR88, and ASTN1 showed good correlation (R > 0.5) with RSPO3 in RNA expression (S1 Table).

In pathway analysis, ten different pathways were shown to be statistically significant: apoptosis-related pathway, direct p53-related pathway, EGFR-related pathway, ErbB-related pathway, JAK-STAT-related pathway, JAK-STAT-related pathway, tyrosine kinases-related pathway, pathways in cancer, SCF-KIT-related pathway, VEGFR-related pathway, and WNT-related pathway. Of these pathways, the P:R fusion-positive cases in comparison to the P:R fusion-negative control demonstrated 848 significantly over- or under-expressed RNA expressions of 848 genes (Figs 1–2 and S1–S2).

Fig 1

Gene expression heatmap of 7 cancer-related pathways enriched with genes that were correlated to RSPO3 in RNA expression.

A total of 256 genes associated with Apoptosis, Direct p53, EGFR, ErbB, SCF-KIT, VEGFR, WNT signaling showed significant differences in expression between RSPO3 fusion-positive colorectal samples and the control samples (see details in Methods). The RNA expression was transformed to z-score. The x-axis represents the sample, and the y-axis represents the RNA expression.

Fig 2

Over- and under-expressed genes are highlighted in WNT signaling pathway.

The KEGG pathway map for the human WNT signaling pathway (hsa 04310) was illustrated using the KEGG Mapper; genes correlated with RSPO3 expression are colored in pink.

Among the 848 genes, 36 genes were annotated as cancer genes using the cancer gene census database offered by the CatalogueOfSomaticMutationsInCancer DB (COSMIC, https://cancer.sanger.ac.uk). Of these, ten genes from highest R-values were as follows: ALK, ACSL3, AXIN2, PTPRK, CDX2, MYC, TP53, GNAQ, ACVR2A, and FAS. RSPO3-correlated cancer genes involved in more than four pathways were as follows; JUN was involved the most in 9 of 10 pathways, APC, 6 pathways, AXIN2, 5 pathways, FGFR2, 5 pathways, JAK2, 7 pathways, MDM2, 5 pathways, MYC, 9 pathways, RAC1, 8 pathways, and lastly, TP53, 7 pathways. In addition, 4 genes were associated with 4 pathways, 4 genes in 3 pathways and 7 genes in 2 pathways (S3 Fig). Amongst these genes, those with a correlation R-value greater than 0.3 were ALK(R = 0.44), ACSL3(R = 0.43), AXIN(R = -0.38), MYC (R = -0.34), TP53 (R = -0.33), GNAQ (R = 0.31), ACVR2A (R = 0.31), and FAS (R = 0.31).

Identification of therapeutic targets and inferring repurposed drug candidates

By matching the 848 genes included in the 10 pathways associated with P:R fusion-positive colorectal cancer using the CiVIC database and OncoKB, we were able to infer 673 and 262 drugs to have actionable target potential.

In the CIViC database, following 19 genes among 848 genes were related with actionable drugs: ALK, FGFR2, TP53, HIF1A, EPAS1, KRAS, CEBPA, NOTCH1, STK11, JAK2, PGR, RAD50, PIK3R1, CDKN1B, NQO1, NT5E, MAP2K1, GNAQ, and PTEN. ALK was identified to be a class A-type drug (Proven/consensus association in human medicine) which can be targeted using crizotinib, alectinib and ceritinib. In other class-type (B, C, D, and E) gene-drug association, additional thirteen drugs were found, considering the scenario for inhibitors for activated oncogenes or activators for down-regulated tumor suppressor genes (Fig 3A).

Fig 3

Inferred drug-target network in PTPRK-RSPO3 fusion-positive colorectal cancer.

Drug-target relation was obtained based on CIViC and OncoKB databases: White boxes, drugs; circles, underlined white boxes, substitute drugs; genes; red circles, genes that are over-expressed in fusion-positive cancer; blue circles, genes that are under-expressed in fusion-positive cancer. The red lines are prioritized drug-target relationships based on the scenario that properly working cancer drugs are generally inhibitors for activated oncogenes or activators for down-regulated tumor suppressor genes.

When using the OncoKB database, 4 genes (KRAS, FGFR2, ALK, and JAK2) were identified and they were all included in the inferred results using CIViC database. Level 1 drugs (FDA-recognized biomarker predictive of response to an FDA-approved drug) for target genes are as follows: lorlatinib, brigatinib, crizotinib, ceritinib, alectinib for ALK; erdafitinib, infigratinib, pemigatinib for FGFR2; sotorasib for KRAS. These cancer drugs appeared to be inhibitors for activated oncogenes (Fig 3B).

Of 19 druggable genes, ten were involved in the multiple pathway: PIK3R1 for 8 cancer-related pathways (Apoptosis, EGFR, ErbB related Pathway, JAK-STAT, Pathways in Cancer, SCF-KIT, Tyrosine kinases, VEGFR related pathway); KRAS for 6 cancer-related pathways (EGFR, ErbB related Pathway, Pathways in Cancer, SCF-KIT, Tyrosine kinases, VEGFR related pathway); JAK2 for 5 cancer-related pathways (JAK-STAT pathway, Pathways in Cancer, SCF-KIT, Tyrosine kinases, VEGFR related pathway); TP53 for 5 cancer-related pathways (Apoptosis, Direct p53, ErbB related Pathway, Pathways in Cancer, WNT-related Pathway); MAP2K1 for 5 cancer-related pathways (EGFR, ErbB related Pathway, JAK-STAT, Pathways in Cancer, VEGFR related pathway); FGFR2 for 3 cancer-related pathways (Tyrosine kinases, pathways In cancer, VEGFR related pathway); ALK for 2 cancer-related pathways (VEGFR related pathway and pathways in cancer); HIF1A for 2 cancer-related pathways (VEGFR related pathway and pathways in cancer); CDKN1B for 2 cancer-related pathways (ErbB related pathway and Pathways in Cancer); PTEN for 2 cancer-related pathways (Direct p53 and Pathways in Cancer).

Discussion

In this study, drug candidates were identified in P:R fusion colorectal cancer as follows. First, genes correlated with RSPO3 RNA expression were extracted, and oncogenic cell signaling pathways including these genes were selected. We then used the drug target database to build a drug target network in P:R fusion colorectal cancer, and prioritize suitable therapeutics (Fig 4). As a result, this study is expected to provide an opportunity to try a wider range of therapeutics in colorectal cancer, where EGFR inhibitors and ICI are limitedly used as targeted therapeutics [8].

Fig 4

Overall design of this study.

Transcriptome data for colorectal cancer (CRC) was attained from the Broad GDAC Firehose database. Following the RNA expression analysis of a total of 20,531 genes, 2,505 genes correlated with RSPO3 expression were selected. (R-value > 0.2, see Methods). Over-representation analysis of the 2,505 genes showed significant relation to 10 major cancer-related pathways (Apoptosis Related Pathway, Direct p53 Related Pathway, EGFR Related Pathway, ErbB Related Pathway, JAK-STAT Related Pathway, JAK-STAT Related Pathway, Tyrosine Kinases Related Pathway, Pathways in Cancer, SCF-KIT Related Pathway, VEGFR Related Pathway, WNT Related Pathway). Potential targets and repurposed drugs were inferred by analyzing target-drug associations via literature reviews and network analysis using the differentially expressed gene list and target-drug databases.

Previous studies that systematically explore gene biomarkers with bioinformatics analysis in colorectal cancer have focused on discovering prognosis-related biomarkers using differentially expressed genes (DEGs) analysis and machine learning techniques [26, 27]. To our best knowledge, our study differs from previous studies in two respects. First, the purpose of this study is to discover novel targets and therapeutics related to original mutations by analyzing downstream pathways and genes affected by target mutations that cannot be directly targeted. Second, our study is based on a structural variation (P:R fusion by DNA structural variation) that is a driver mutation in colorectal cancer. As consequence, almost all genes correlated with P:R fusion are downstream-level genes affected by fusion. In this aspect, our study is different from other studies, and, for example, it is not clear whether COL11A1 is a primary driver or is affected by other drivers in the study by Ritwik et al [27].

The WNT signaling pathway is an important mediator in tissue homeostasis and recovery while it acts an important role in tumor-development of colorectal cancer [18]. Both in vitro experiments in human-colon cancer cell line HT-29 and in vivo experiments in CRISPR-based xenograft mice provided the evidence that RSPO3 fusion gene was involved in the initiation and development of CRC via activating WNT signaling [14, 19]. This means that human CRC is a sensitive tumor for WNT-targeted treatment, suggesting that RSPO3 fusion gene can be an effective therapeutic target.

As a result of our analysis, it is interesting that ALK up-regulated in P:R fusion-positive CRC has the following three characteristics. First, the correlation between ALK RNA expression and RSPO3 RNA expression in P:R fusion-positive CRC was the highest among COSMIC common oncogenes (R = 0.44). Second, ALK was a gene involved in multiple cancer pathways. Finally, ALK inhibitors are FDA-approved therapeutics that perform well in other carcinomas (e.g. lung cancer) [28, 29]. Taken together, in-silico analysis showed that ALK inhibitors were highly likely to act in P:R fusion positivity [30].

Despite the limited number of samples, the clinical characteristics of P:R-positive and P:R-negative patients were found to be similar. This indicates that even if the clinical properties are similar, the molecular properties may be different, which may require treatment to target the molecular properties. One interesting point is that P:R fusions can also be found in MSI-H. In this case, further clinical evaluation is needed to determine if there is a synergistic effect between ICI and the targeted therapy we propose.

In summary, we were able to present key indicators and clinically viable therapeutics for P:R fusion-positive CRC. Our findings will serve as a steppingstone for future research in the development of precision medicine targeting colorectal cancer.

Gene expression heatmap of cancer-related pathways enriched with genes correlated to RSPO3 in RNA expression.

(PDF)

Click here for additional data file.

The KEGG pathway maps for the human ERBB signaling pathway and pathways in cancer using the KEGG Mapper; genes correlated with RSPO3 expression are colored in pink.

(PDF)

Click here for additional data file.

Putative target genes involved in multiple pathways of PTPRK-RSPO3 fusion-positive cancer.

(PDF)

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Inferred drug-target network in PTPRK-RSPO3 fusion-positive colorectal cancer based on VICC database.

(PDF)

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2,505 genes correlated with RSPO3 (R >0.2).

(XLSX)

Click here for additional data file.

Putative target genes and actionable drugs involved in ten major cancer-related pathways.

(XLSX)

Click here for additional data file.

16 May 2022

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Additional Editor Comments (if provided):

Oncogenesis events in CRC is indeed multifaceted and considering the impact of P:R fusion-positive CRC authors need to present a comparative view with the recently published papers citing the association of multiple signaling pathways in the course of CRC pathophysiology. Authors may follow and cite DOI: 10.3389/fgene.2021.608313 as well as relevant literature to improve the manuscript. Fig. 1 could be omitted and a scheme may be added at the last.

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Reviewer #1: Jeong HJ et al. presented key indicators and clinically viable therapeutics for P:R fusion-positive CRC as well as inferred the actionable drugs in bioinformatics analysis using the Cancer Genome Atlas (TCGA) data. Through drug-target network analysis, several putative therapeutic candidates were shown effective as they were applied and involved in multiple cancer-pathways.

The manuscript is generally well written and structured. However, in my opinion the data have some shortcomings in regards to some analyses, explanation, and logical sense.

Comments:

Line 82: the following sentences are not clear. 372 tumor samples with the barcode 01A were chosen, with other types of tumor samples, 11A (Normal) or 06A (Metastasized), being excluded. What do these numbers and barcodes referred to?

- How was the permutation test calculated?

As stated by the authors, the median of absolute R values, mostly affected 2,505 genes were selected by correlation cut-off (R > 0.2). What is the criteria to set a cut-off of R > 0.2?

- What does median in Table S1 imply?

Line 128: As stated, 7 patients demonstrated presence of the fusion mutation, whereas the remaining 416 patients were negative for the fusion based on the clinicpathological characteristics. These cases are not fully presented in the Table 1. The characteristics of PTPRK-RSPO3 fusion-positive and fusion-negative cases in TCGA colorectal cancer is not clear in Table 1. Moreover, what does median in Table S1 imply?

All the supplementary Tables and figures should be renamed as indicated in Sup section including tittle of each and applied methods for data extraction.

As Table 1 shows, none of the clinicopathological characteristics of selected cases is significant. Eighteen genes showed good correlation (R > 0.5) with RSPO3 in RNA expression. Is that correlation defines any significant RNA expression?

Line 153: How come the ten different pathways were shown to be statistically significant? How was the significant means elaborated?

Line 163: the following sentence “ten genes from highest R-values were as follows” is not clear. What is the implication of highest R-values?

Fig 4 represents Drug-target relation obtained based on Civic and OncoKB databases. What was the intention to select these two databases? The VICC meta-database has recently been reported the most extensive source of information, featuring 92% of variants with a drug association (https://dx.doi.org/10.21873%2Fcgp.20250). Is there any explanation?

Line 184: 4 genes (KRAS, FGFR2, ALK, and JAK2) were identified and they were all included in the inferred results using CIViC database. How the authors link the gene presence with their RNA expression involved in several pathways as well as disease conditions?

Line 191: Of 19 druggable genes, five were involved in the multiple pathways. Have these genes as well as their related pathways experimentally reported?

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Reviewer #1: Yes: Meysam Sarshar

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29 Jun 2022

Response to Reviewer Comments

We deeply appreciate the reviewer Meysam Sarshar’s attention to our paper. Below, we address each of the reviewer's comments point by point. We revised our manuscript using MS Word and highlighted the changes with yellow color.

Point 1: Line 82: the following sentences are not clear. 372 tumor samples with the barcode 01A were chosen, with other types of tumor samples, 11A (Normal) or 06A (Metastasized), being excluded. What do these numbers and barcodes referred to?

Response 1: Thank you for your comment. For better understanding of the TCGA data for readers, the following sentence has been modified as follows (Line: 78-79):

“Of the 433 GDAC data downloaded, 53 normal and 1 metastasis data were omitted, and the remaining 379 data were analyzed.”

The barcode contains information concerning the institution, patient, sample, and method of analysis. For instance, the two digits from 01A, 06A, 11A barcodes reveal information on the type of patient sample whether it be normal or tumor, and the alphabet indicates the vial containing the patient sample (https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes). Although the majority of the TCGA data were tumor samples including 01-09, a portion of them, 10-19 were normal samples. Therefore, it was necessary to select and assess samples according to the purpose of analysis. We extracted primary solid tumor samples, which have barcodes 01, used for this study.

Point 2: How was the permutation test calculated? As stated by the authors, the median of absolute R values, mostly affected 2,505 genes were selected by correlation cut-off (R > 0.2). What is the criteria to set a cut-off of R > 0.2?

Response 2: Thank you for your detailed comment that improving our study. Of the 57 samples, we selected two genes and their RNA expression values were randomly ordered respectively, and Pearson correlation coefficient R value was obtained between two genes. This process was repeated 100,000 times. The resulting frequency of an R value higher than 0.2 by chance was 378 out of 100,000 times. Among the 2,505 genes that showed a correlation with RSPO3 of an R value of 0.2 or higher, approximately 0.378%, that is 20,531 gene multiplied by 0.00378 were estimated to yield 78 false positives. Hence, of the 2,505 genes, 2,427 genes may have a correlation with RSPO3.

Point 3: What does median in Table S1 imply? (Moreover, what does median in Table S1 imply?)

Response 3: Thank you for your comment. For instance, should KRAS mutation-positive samples occupy a majority of the 50 samples that were selected as control, there is a high likelihood for a large number of KRAS signature to be generated. In order to minimize the bias from undesired signature accumulation, 50 samples from the 186 samples expressing low RSPO3 expression were randomly selected as the control. Pearson R values were calculated between reference gene (RSPO3) and the other genes. Above process was repeated 100 times and the median R values were calculated in each gene.

Point 4: Line 128: As stated, 7 patients demonstrated presence of the fusion mutation, whereas the remaining 416 patients were negative for the fusion based on the clinicopathological characteristics. These cases are not fully presented in the Table 1. The characteristics of PTPRK-RSPO3 fusion-positive and fusion-negative cases in TCGA colorectal cancer is not clear in Table 1.

Response 4: Thank you for valuable comment. According to your comment, table 1 has been revised (Line: 138-140). The number of fusion and control samples were more clearly described in our study. The TCGA colorectal RNA samples used were extracted from the GDAC firehose. Of them, 379 were tumor solid samples, and of the 372 samples, excluding the 7 fusion-positive samples, 186 samples expressing less than 50% RSPO3 mRNA expression were selected as control (fusion-positive). The clinicopathological analysis was performed according to the criteria above and reflected in Table 1 and its pertinent text.

We modified the sentences on line 123-126 as follows:

“The clinicopathological characteristics in this study were described in Table 1. A total 379 tumor solid samples, and of the 372 samples, excluding the 7 fusion-positive samples, 186 samples expressing less than 50% RSPO3 mRNA expression were selected as control.”

Point 5: All the supplementary Tables and figures should be renamed as indicated in Sup section including tittle of each and applied methods for data extraction.

Response 5: Thank you for your comment. We revised the supplementary section on line 263-275 as follows:

S1 Fig: Gene expression heatmap of cancer-related pathways enriched with genes correlated to RSPO3 in RNA expression.

S2 Fig: The KEGG pathway maps for the human ERBB signaling pathway and pathways in cancer using the KEGG Mapper; genes correlated with RSPO3 expression are colored in pink.

S3 Fig: Putative target genes involved in multiple pathways of PTPRK-RSPO3 fusion-positive cancer.

S4 Fig: Inferred drug-target network in PTPRK-RSPO3 fusion-positive colorectal cancer based on VICC database.

S1 Table: 2,505 genes correlated with RSPO3 (R >0.2).

S2 Table: Putative target genes and actionable drugs involved in ten major cancer-related pathways.

Point 6: As Table 1 shows, none of the clinicopathological characteristics of selected cases is significant. Eighteen genes showed good correlation (R > 0.5) with RSPO3 in RNA expression. Is that correlation defines any significant RNA expression?

Response 6: Thank you for your comment. Since an overexpression of RSPO3 mRNA level is observed in PTPRK-RSPO3 fusion samples (PMID: 32631391 Figure 2B), we assumed that downstream genes affected by upregulated RSPO3 expression with fusion would have high R values in expression level by the Pearson correlation test. Due to the limited number (n=7) of RSPO3 fusion samples, it was not ideal to infer a statistically significant difference from clinical variable.

Point 7: Line 153: How come the ten different pathways were shown to be statistically significant? How was the significant means elaborated?

Response 7: Thank you for your comment. In summary, a P-value is calculated using the hypergeometric distribution. The P-value reflects the significance of the observed overlap between the input gene list and the module's members when compared to random expectations. A low P-value indicates that more of the module's members are present in the input list than would be expected by chance. The p-values are corrected for multiple testing using the false discovery rate method.

Detailed explanation: PMID: 18940869

Point 8: Line 163: the following sentence “ten genes from highest R-values were as follows” is not clear. What is the implication of highest R-values?

Response 8: Thank you for your comment. Since mRNA overexpression of RSPO3 occurs by PTPRK-RSPO3 fusion (PMID: 32631391 Figure 2B), it was determined that genes affected by expression downstream by upregulated RSPO3 would have high R values in the Pearson correlation test. The reason for selecting the 10 highest R-value genes is to find the most likely downstream pathway or target in the absence of a direct target therapy for RSPO3. Genes with the highest R values were extracted with the purpose of identifying therapeutic targets from the downstream pathway of RSPO3.

Point 9: Fig 4 represents Drug-target relation obtained based on Civic and OncoKB databases. What was the intention to select these two databases? The VICC meta-database has recently been reported the most extensive source of information, featuring 92% of variants with a drug association (https://dx.doi.org/10.21873%2Fcgp.20250). Is there any explanation?

Response 9: Thank you for your comment. The data provided by VICC meta-database is in the form of a JSON file. However, since the JSON files were broken and not unified in ordinary format, we had to manually parse every five databases (jax, brca, cgi, molecularmatch, pmkb) except CIViC and OncoKB. Then, we were only able to extract information about genes and drugs from jax, brca and molecularmatch databases. However, advanced information such as clinical significance, evidence direction and evidence type were limited to parse. For this reason, the analyzed results using VICC database were not included in the main figure but were included in the supplementary figures. We added explanation about VICC figures in our manuscript (Line: 270, 295).

Point 10: Line 184: 4 genes (KRAS, FGFR2, ALK, and JAK2) were identified and they were all included in the inferred results using CIViC database. How the authors link the gene presence with their RNA expression involved in several pathways as well as disease conditions?

Response 10: Thank you for your comment. Generally, there are three classes for activation signaling including hotspot mutation, amplification, and overexpression. For this study, RNA sequence-based overexpression was considered an activating signal. The drug database provides information on the relationship between the activating signaling (three classes mentioned above) and available drugs. For example, MET activating mutations are including amplification, over-expression, and activating point mutations and the three class of mutations are mostly sharing target-drug sensitivity (capmatinib, tepotinib). So, the three types of mutations were considered as showing similar target-drug sensitivity in silico level. Although the sensitivity of the drugs may differ according to the various types of signal activation, the purpose of this study is to enroll as many drugs with high potential as possible. It would be ideal for these hypotheses to be validated with further additional experimentations. However, the scope of this study does not encompass validation experiments and will take into consideration for future studies.

Point 11: Line 191: Of 19 druggable genes, five were involved in the multiple pathways. Have these genes as well as their related pathways experimentally reported?

Response 11: Thank you for your comment. While reviewing the content, five additional druggable genes involved multiple pathways were found and added to the manuscript (Line:217-230). The Consensus Path Database (CPDB, http://cpdb.molgen.mpg.de/CPDB) we utilized is a database combined by integrating 177 biological pathways based on experimentally reported studies: INOH (PMID: 2212066), KEGG (PMID: 27899662), NetPath (PMID: 20067622), PID (PMID: 18832364), Reactome (PMID: 26656494), and Wikipathways (PMID: 26481357). We attached a table of CPDB sources and 10 druggable genes involved in multiple pathways on below:

Gene Source Pathway

PIK3R1 KEGG Apoptosis

PIK3R1 KEGG VEGFR Related Pathway

PIK3R1 KEGG ErbB Related Pathway

PIK3R1 KEGG JAK-STAT Pathway

PIK3R1 KEGG EGFR

PIK3R1 KEGG Tyrosine Kinases

PIK3R1 KEGG Pathways In Cancer

PIK3R1 KEGG SCF-KIT

PIK3R1 Reactome Apoptosis

PIK3R1 Reactome VEGFR Related Pathway

PIK3R1 Reactome ErbB Related Pathway

PIK3R1 Reactome JAK-STAT Pathway

PIK3R1 Reactome EGFR

PIK3R1 Reactome Tyrosine Kinases

PIK3R1 Reactome Pathways In Cancer

PIK3R1 Reactome SCF-KIT

PIK3R1 Wikipathways Apoptosis

PIK3R1 Wikipathways VEGFR Related Pathway

PIK3R1 Wikipathways ErbB Related Pathway

PIK3R1 Wikipathways JAK-STAT Pathway

PIK3R1 Wikipathways EGFR

PIK3R1 Wikipathways Tyrosine Kinases

PIK3R1 Wikipathways Pathways In Cancer

PIK3R1 Wikipathways SCF-KIT

PIK3R1 PharmGKB Apoptosis

PIK3R1 PharmGKB VEGFR Related Pathway

PIK3R1 PharmGKB ErbB Related Pathway

PIK3R1 PharmGKB JAK-STAT Pathway

PIK3R1 PharmGKB EGFR

PIK3R1 PharmGKB Tyrosine Kinases

PIK3R1 PharmGKB Pathways In Cancer

PIK3R1 PharmGKB SCF-KIT

PIK3R1 PID Apoptosis

PIK3R1 PID VEGFR Related Pathway

PIK3R1 PID ErbB Related Pathway

PIK3R1 PID JAK-STAT Pathway

PIK3R1 PID EGFR

PIK3R1 PID Tyrosine Kinases

PIK3R1 PID Pathways In Cancer

PIK3R1 PID SCF-KIT

PIK3R1 INOH Apoptosis

PIK3R1 INOH VEGFR Related Pathway

PIK3R1 INOH ErbB Related Pathway

PIK3R1 INOH JAK-STAT Pathway

PIK3R1 INOH EGFR

PIK3R1 INOH Tyrosine Kinases

PIK3R1 INOH Pathways In Cancer

PIK3R1 INOH SCF-KIT

KRAS Reactome Pathways In Cancer

KRAS Reactome Tyrosine Kinases

KRAS Reactome ErbB Related Pathway

KRAS Reactome SCF-KIT

KRAS Reactome VEGFR Related Pathway

KRAS Reactome EGFR

KRAS KEGG Pathways In Cancer

KRAS KEGG Tyrosine Kinases

KRAS KEGG ErbB Related Pathway

KRAS KEGG SCF-KIT

KRAS KEGG VEGFR Related Pathway

KRAS KEGG EGFR

KRAS Wikipathways Pathways In Cancer

KRAS Wikipathways Tyrosine Kinases

KRAS Wikipathways ErbB Related Pathway

KRAS Wikipathways SCF-KIT

KRAS Wikipathways VEGFR Related Pathway

KRAS Wikipathways EGFR

KRAS PharmGKB Pathways In Cancer

KRAS PharmGKB Tyrosine Kinases

KRAS PharmGKB ErbB Related Pathway

KRAS PharmGKB SCF-KIT

KRAS PharmGKB VEGFR Related Pathway

KRAS PharmGKB EGFR

JAK2 KEGG SCF-KIT

JAK2 KEGG Tyrosine Kinases

JAK2 KEGG Pathways In Cancer

JAK2 KEGG VEGFR Related Pathway

JAK2 KEGG JAK-STAT Pathway

JAK2 Reactome SCF-KIT

JAK2 Reactome Tyrosine Kinases

JAK2 Reactome Pathways In Cancer

JAK2 Reactome VEGFR Related Pathway

JAK2 Reactome JAK-STAT Pathway

JAK2 INOH SCF-KIT

JAK2 INOH Tyrosine Kinases

JAK2 INOH Pathways In Cancer

JAK2 INOH VEGFR Related Pathway

JAK2 INOH JAK-STAT Pathway

JAK2 Wikipathways SCF-KIT

JAK2 Wikipathways Tyrosine Kinases

JAK2 Wikipathways Pathways In Cancer

JAK2 Wikipathways VEGFR Related Pathway

JAK2 Wikipathways JAK-STAT Pathway

TP53 KEGG Direct p53 effectors

TP53 KEGG ErbB Related Pathway

TP53 KEGG Wnt Related Pathway

TP53 KEGG Apoptosis

TP53 KEGG Pathways In Cancer

TP53 Wikipathways Direct p53 effectors

TP53 Wikipathways ErbB Related Pathway

TP53 Wikipathways Wnt Related Pathway

TP53 Wikipathways Apoptosis

TP53 Wikipathways Pathways In Cancer

TP53 PID Direct p53 effectors

TP53 PID ErbB Related Pathway

TP53 PID Wnt Related Pathway

TP53 PID Apoptosis

TP53 PID Pathways In Cancer

MAP2K1 Wikipathways JAK-STAT Pathway

MAP2K1 Wikipathways VEGFR Related Pathway

MAP2K1 Wikipathways Pathways In Cancer

MAP2K1 Wikipathways EGFR

MAP2K1 Wikipathways ErbB Related Pathway

MAP2K1 KEGG JAK-STAT Pathway

MAP2K1 KEGG VEGFR Related Pathway

MAP2K1 KEGG Pathways In Cancer

MAP2K1 KEGG EGFR

MAP2K1 KEGG ErbB Related Pathway

MAP2K1 PharmGKB JAK-STAT Pathway

MAP2K1 PharmGKB VEGFR Related Pathway

MAP2K1 PharmGKB Pathways In Cancer

MAP2K1 PharmGKB EGFR

MAP2K1 PharmGKB ErbB Related Pathway

MAP2K1 INOH JAK-STAT Pathway

MAP2K1 INOH VEGFR Related Pathway

MAP2K1 INOH Pathways In Cancer

MAP2K1 INOH EGFR

MAP2K1 INOH ErbB Related Pathway

FGFR2 KEGG Pathways In Cancer

FGFR2 KEGG VEGFR Related Pathway

FGFR2 KEGG Tyrosine Kinases

FGFR2 Reactome Pathways In Cancer

FGFR2 Reactome VEGFR Related Pathway

FGFR2 Reactome Tyrosine Kinases

FGFR2 PharmGKB Pathways In Cancer

FGFR2 PharmGKB VEGFR Related Pathway

FGFR2 PharmGKB Tyrosine Kinases

FGFR2 INOH Pathways In Cancer

FGFR2 INOH VEGFR Related Pathway

FGFR2 INOH Tyrosine Kinases

ALK KEGG VEGFR Related Pathway

ALK KEGG Pathways In Cancer

ALK INOH VEGFR Related Pathway

ALK INOH Pathways In Cancer

HIF1A PID VEGFR Related Pathway

HIF1A PID Pathways In Cancer

HIF1A KEGG VEGFR Related Pathway

HIF1A KEGG Pathways In Cancer

CDKN1B KEGG Pathways In Cancer

CDKN1B KEGG ErbB Related Pathway

CDKN1B Wikipathways Pathways In Cancer

CDKN1B Wikipathways ErbB Related Pathway

PTEN KEGG Direct p53 effectors

PTEN KEGG Pathways In Cancer

PTEN PID Direct p53 effectors

PTEN PID Pathways In Cancer

PTEN Wikipathways Direct p53 effectors

PTEN Wikipathways Pathways In Cancer

Submitted filename: Response to Reviewers.docx

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4 Jul 2022

18 Jul 2022

July 18th, 2022

Editor, Plos One

Dear Editor

We deeply appreciate your attention to our paper. Below, we address each of the reviewer's comments point by point. We revised our manuscript using MS Word and highlighted the changes with yellow color.

Point 1: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response 1: Thank you for your comment. We corrected a miscited 20th reference from “Wilson RG, Smith AN, Bird CC. Immunohistochemical detection of abnormal cell proliferation in colonic mucosa of subjects with polyps. J Clin Pathol. 1990;43(9):744-747. doi:10.1136/jcp.43.9.744” to “Yamamoto S, Sakai N, Nakamura H, Fukagawa H, Fukuda K, Takagi T. INOH: ontology-based highly structured database of signal transduction pathways. Database (Oxford). 2011;2011: bar052. Published 2011 Nov 26. doi:10.1093/database/bar052”. We also checked and confirmed other references.

Point 2: Oncogenesis events in CRC is indeed multifaceted and considering the impact of P:R fusion-positive CRC authors need to present a comparative view with the recently published papers citing the association of multiple signaling pathways in the course of CRC pathophysiology. Authors may follow and cite DOI: 10.3389/fgene.2021.608313 as well as relevant literature to improve the manuscript.

Response 2: We appreciate your valuable comment. We reviewed the recent articles regarding integrated bioinformatics approach of colorectal cancer and presented a comparative view in the discussion section on line 241-252 as follows:

“Previous studies that systematically explore gene biomarkers with bioinformatics analysis in colorectal cancer have focused on discovering prognosis-related biomarkers using differentially expressed genes (DEGs) analysis and machine learning techniques. (26, 27). To our best knowledge, our study differs from previous studies in two respects. First, the purpose of this study is to discover novel targets and therapeutics related to original mutations by analyzing downstream pathways and genes affected by target mutations that cannot be directly targeted. Second, our study is based on a structural variation (P:R fusion by DNA structural variation) that is a driver mutation in colorectal cancer. As consequence, almost all genes correlated with P:R fusion are downstream-level genes affected by fusion. In this aspect, our study is different from other studies, and, for example, it is not clear whether COL11A1 is a primary driver or is affected by other drivers in the study by Ritwik et al (27).”

Point 3: Fig. 1 could be omitted, and a scheme may be added at the last.

Response 3: Thank you for your comment. We omitted Fig.1 and generated Fig.4 regarding a scheme of this study on line 228-238.

Additionally, we removed all the funding-related information in our manuscript and

would like to change the funding information in “Financial Disclosure” section as

follows:

“This study was supported by a VHS Medical Center Research Grant, Republic of

Korea (VHSMC22057), grant no 18-2018-023 from the SNUBH Research Fund, and The

National Research Foundation of Korea (NRF) grant funded by the Korea government

(MSIT) (No. 2022R1C1C1012986).”

Thank you again for your thoughtful consideration.

Best regards,

Sejoon Lee

Department of Pathology and Translational Medicine

Clinical Precision Medicine Center

Seoul National University Bundang Hospital

Tel:+82-31-787-8124

Submitted filename: Response to Reviewers.docx

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31 Aug 2022

Investigation of cell signalings and therapeutic targets in PTPRK-RSPO3 fusion-positive colorectal cancer.

PONE-D-22-06899R2

Dear Dr. Lee,

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Additional Editor Comments (optional):

Reviewers' comments:

8 Sep 2022

PONE-D-22-06899R2

Investigation of cell signalings and therapeutic targets in PTPRK-RSPO3 fusion-positive colorectal cancer.

Dear Dr. Lee:

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