Novel RB1-Loss Transcriptomic Signature Is Associated with Poor Clinical Outcomes Across Cancer Types William S

Published OnlineFirst April 22, 2019; DOI: 10.1158/1078-0432.CCR-19-0404

Precision Medicine and Imaging Clinical Cancer Research Novel RB1-Loss Transcriptomic Signature Is Associated with Poor Clinical Outcomes across Cancer Types William S. Chen1,2, Mohammed Alshalalfa1,3, Shuang G. Zhao4, Yang Liu5, Brandon A. Mahal3, David A. Quigley1, Ting Wei1, Elai Davicioni5, Timothy R. Rebbeck6, Philip W. Kantoff7, Christopher A. Maher8,9,10, Karen E. Knudsen11, Eric J. Small1,12, Paul L. Nguyen3, and Felix Y. Feng1,13

Abstract

Purpose: Rb-pathway disruption is of great clinical interest, was associated with promoter hypermethylation (P ¼ 0.008) as it has been shown to predict outcomes in multiple cancers. and gene body hypomethylation (P ¼ 0.002), suggesting RBS We sought to develop a transcriptomic signature for detecting could detect epigenetic gene silencing. TCGA Pan-Cancer biallelic RB1 loss (RBS) that could be used to assess the clinical clinical analyses revealed that high RBS was associated with implications of RB1 loss on a pan-cancer scale. short progression-free (P < 0.00001), overall (P ¼ 0.0004), and Experimental Design: We utilized data from the Cancer disease-specific(P < 0.00001) survival. On multivariable anal- Cell Line Encyclopedia (N ¼ 995) to develop the first pan- yses, high RBS was predictive of shorter progression-free cancer transcriptomic signature for predicting biallelic RB1 survival in TCGA Pan-Cancer (P ¼ 0.03) and of shorter overall loss (RBS). Model accuracy was validated using The Cancer survival in mCRPC (P ¼ 0.004) independently of the number Genome Atlas (TCGA) Pan-Cancer dataset (N ¼ 11,007). RBS of DNA alterations in RB1. was then used to assess the clinical relevance of biallelic RB1 Conclusions: Our study provides the first validated tool to loss in TCGA Pan-Cancer and in an additional metastatic assess RB1 biallelic loss across cancer types based on gene castration-resistant prostate cancer (mCRPC) cohort. expression. RBS can be useful for analyzing datasets with or Results: RBS outperformed the leading existing signature without DNA-sequencing results to investigate the emerging for detecting RB1 biallelic loss across all cancer types in TCGA prognostic and treatment implications of Rb-pathway Pan-Cancer (AUC, 0.89 vs. 0.66). High RBS (RB1 biallelic loss) disruption.

1Helen Diller Family Comprehensive Cancer Center, University of California, San Introduction 2 Francisco, San Francisco, California. Yale School of Medicine, New Haven, RB1 is a tumor suppressor that has been implicated in the Connecticut. 3Dana-Farber Cancer Institute and Brigham and Women's Hospital, 4 pathogenesis of numerous cancer types. In addition to causing Boston, Massachusetts. Department of Radiation Oncology, University of RB1 Michigan, Ann Arbor, Michigan. 5GenomeDx Biosciences, Vancouver, British pediatric retinoblastoma, alterations have been shown to Columbia, Canada. 6Department of Epidemiology, Harvard T.H. Chan School of play a major role in the progression of osteosarcoma (1), lym- Public Health, Boston, Massachusetts. 7Department of Medicine, Memorial Sloan phoma (2), and breast (3–5), lung (6, 7), and prostate (8, 9) Kettering Cancer Center, New York, New York. 8McDonnell Genome Institute, malignancies. Moreover, recent studies have highlighted RB1 loss 9 Washington University in St. Louis, St. Louis, Missouri. Department of Internal as an important clinical prognostic factor in specific cancer types. Medicine, Washington University in St. Louis, St. Louis, Missouri. 10Department of For example, RB1 loss has been shown to be associated with poor Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri. 11Departments of Cancer Biology and Medical Oncology, Thomas Jefferson overall survival (OS) in osteosarcoma (1), glioblastoma (10), and University, Philadelphia, Pennsylvania. 12Department of Medicine, University of lung cancers (11) and has been shown to predict resistance or California, San Francisco, San Francisco, California. 13Departments of Radiation sensitivity to various small-cell lung cancer (7), pancreatic can- Oncology and Urology, University of California, San Francisco, San Francisco, cer (12), and breast cancer therapies (3, 13). California. In order to study the clinical implications of Rb-pathway Note: Supplementary data for this article are available at Clinical Cancer disruption, one must first be able to confidently assess RB1 status. Research Online (http://clincancerres.aacrjournals.org/). Next-generation DNA-sequencing approaches are well suited for W.S. Chen and M. Alshalalfa contributed equally to this article. identifying mutations, copy-number alterations, and structural P.L. Nguyen and F.Y. Feng contributed equally to this article. variants. However, there is often uncertainty as to whether a DNA alteration truly inactivates the affected allele. Moreover, other Corresponding Author: Felix Y. Feng, University of California, San Francisco, San mechanisms of gene inactivation exist that may not be captured Francisco, CA 94143. Phone: 415-885-7627; Fax: 415-353-9883; E-mail: [email protected] by DNA-sequencing techniques (e.g., epigenetic, posttranscrip- tional, or posttranslational modifications). An alternative Clin Cancer Res 2019;XX:XX–XX approach to assessing gene inactivation is to examine the sequelae doi: 10.1158/1078-0432.CCR-19-0404 of genomic alterations by assessing the resulting expression of 2019 American Association for Cancer Research. related, downstream genes.

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UCSC Xena Browser to annotate RB1 copy number (CN) and Translational Relevance mutation calls (18). Cell lines with GISTC score < -0.8 were RB1 loss is a recurrent genomic alteration that has been annotated as deep (two-copy) deletion (CN-2), and cell lines shown to predict response to various treatments including with GISTC score between -0.8 and -0.4 were annotated as shallow radiotherapy, platinum-based chemotherapy, and CDK4/6 (single-copy) deletion (CN-1). The remaining cell lines were inhibitors in multiple cancer types. Leveraging the transcrip- annotated as two-copy intact (CN-0). To build an mRNA classifier tomic and DNA-sequencing data of over 11,000 cancer cell to predict RB1 functional loss, we defined the tumor cell lines with lines and clinical tumor samples, we identified a novel pan- predicted biallelic loss (i.e., deep deletion, shallow deletion with cancer transcriptomic signature for identifying RB1 loss (RBS). additional DNA mutation, or 2þ DNA mutations) as the RB1-loss RBS is more accurate than existing transcriptomic signatures in group and remaining cell lines as the RB1-intact group. To identify detecting RB1 loss and can be used alongside DNA sequencing differentially expressed genes between the two groups, we used to identify Rb-loss tumors more comprehensively. Using RBS, the Wilcoxon Mann–Whitney test with an adjusted P value we found that RB1 loss was associated with impaired survival threshold of P < 1 10 10. across cancer types, supporting the notion that RB1 loss We then used a nearest shrunken centroid approach (PAM; constitutes a biologically and clinically distinct subgroup of ref. 19) to generate our gene signature based on the expression cancers. Our novel transcriptomic signature can be used to pattern of the genes selected as described above. We trained the further investigate the clinical implications of RB1 loss and model by applying PAM to CCLE expression data, using posterior may be coupled with treatment response data to help develop class probabilities for RB1-loss class predictions. The model was personalized cancer treatment regimens. trained using 10-fold cross validation to optimize the PAM shrinkage parameter. RBS was then validated on the TCGA Pan-Cancer RNA-sequencing (RNA-seq) expression dataset of 11,007 tumor samples span- RB1 There exist a few gene sets (genes theorized to be collec- ning 33 cancer types, downloaded from UCSC Xena Browser RB1 tively indicative of status) and two gene signatures (combi- using the Synapse platform (syn4976369). RB1 copy-number natorial expression pattern of the genes in a gene set) for predict- calls and mutation data for these samples were obtained from RB1 ing loss (14, 15). However, they all share the key limitation UCSC Xena Browser, and the same GISTIC2.0 copy-number that they consist largely of cell-cycle genes (whose expression is thresholds and mutation criteria as used in the CCLE training set fi RB1 not speci cto loss). Moreover, because these gene sets and were applied to the validation set. Final RB1-loss annotations signatures were primarily developed using breast cancer data, were defined based on the number of RB1 DNA alterations their generalizability to different cancer types has not been val- observed: 2 alterations (deep deletion, shallow deletion with one fi RB1 idated. Our rst aim was to develop a novel pan-cancer mutation, or 2þ mutations), 1 alteration (shallow deletion with biallelic loss gene signature (RBS) that outperformed existing no mutations or one mutation with no deletion), 0 alteration (no RB1 RB1 -loss signatures and accurately predicted biallelic loss deletions or mutations). Model accuracy was assessed based on across cancer types. area under the ROC curve (AUC), benchmarked against the After generating and validating RBS, we then sought to use it to leading existing RB1-loss signature (14). assess RB1 loss as a prognostic factor across all major cancer types using The Cancer Genome Atlas (TCGA) Pan-Cancer database (N ¼ 11,007). Because RB1 loss was known to be clinically RBS pathway enrichment analysis important in metastatic prostate cancer (not included in the TCGA The EnrichR web tool was used to identify genomic pathways fi Pan-Cancer dataset), we examined the prognostic significance of enriched in the RBS gene set. Candidate gene sets were de ned as RBS in an independent metastatic castration-resistant prostate all pathways in the KEGG, Reactome, WikiPathways, and Bio- fi cancer (mCRPC) cohort. Carta databases. Pathways were considered signi cantly enriched if their adjusted P values were less than a predetermined significance level of 0.05. Materials and Methods Variable definitions Differential expression analysis of RB1 loss due to two or more We defined "RB1 loss" in our article as predicted biallelic loss of RB1 mutations RB1. For the purposes of training and testing our RB1-loss clas- Differential expression analysis between CN-0 tumors with no sifier (RBS), ground-truth labels of RB1 status for each tumor were mutations and CN-0 tumors with two or more mutations was assigned based on the number of DNA alterations (i.e., nonsilent performed to identify genes that were differentially expressed in exonic mutations, copy-number loss, and inactivating structural tumors with 2þ RB1 mutations. Given that there were far fewer variants) observed in RB1. For these ground-truth labels, RB1 loss tumors with 2þ mutations than there were with no mutations, we was defined as presence of at least two DNA alterations in RB1. randomly subsampled a set of CN-0 tumors with no mutations equal in size to the subset of tumors with 2þ mutations. We then RB1-loss gene signature (RBS) development and validation performed a differential expression analysis between the tumors using the CCLE and TCGA Pan-Cancer datasets with 2þ mutations and the tumors with no mutations using the Taking an unbiased approach to selecting genes indicative of Wilcoxon Mann–Whitney test with an adjusted P-value threshold RB1 loss, we leveraged microarray log2-normalized RPKM gene of P < 0.001. For statistical robustness, we performed a boot- expression data of 951 pan-cancer cell lines from the Cancer Cell strapped analysis with 1,000 different subsamples. Genes were Line Encyclopedia (CCLE; ref. 16). We extracted GISTC2.0 (17) considered significantly differentially expressed if, in >95% of all and whole-exome sequencing (WES)–based mutation calls from comparisons, they demonstrated the same directionality of over-

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versus underexpression and had adjusted P values below the ed RB1 loss and cell lines that had intact RB1. Note that 951 of the predetermined significance level of 0.001. 995 total cell lines had both copy-number and microarray expression data available. Of these 951, 126 were identified as having Promoter and gene body methylation analysis biallelic RB1 loss (99 harbored two-copy deletions, 23 harbored To assess the utility of RBS in detecting gene silencing due to single-copy deletions with an additional mutation, and 11 har- methylation, we downloaded TCGA Pan-Cancer methylation bored 2þ mutations) and 797 were identified as RB1 intact. Our RB1 data for 49 methylation probes from the UCSC Xena Browser. unbiased approach to defining an RB1-loss gene set using CCLE fi fi fi We rst ltered out probes that were previously identi ed to be of data identified a final set of 186 genes that were indicative of RB1 low quality (20). We then computed Spearman correlation coef- loss (Supplementary Table S1A). Of note, only 7 of the 186 genes fi cients between RBS score and Illumina DNA methylation 450K overlapped with genes in the existing RB1-loss signature (14). RB1 array beta values for each methylation probe. To test whether To assess the potential utility of our 186-gene signature for the correlations between RBS score and methylation probe values predicting RB1 loss, we first performed t-SNE dimensionality fi RB1 were signi cant in the promoter and gene body regions, we reduction on the CCLE training data (N ¼ 951). Visualization generated a null model by computing the correlation between of the t-SNE embedding revealed that cell lines with 2þ DNA RBS score and methylation in the promoter and gene body regions alterations in RB1 tended to map to similar parts of the embed- of 20 other random tumor suppressors not known to be related to ding, suggesting that these cell lines had similar 186-gene expres- RB1 N ¼ . For this analysis, a large set of tumor suppressors ( 1,217) sion profiles (Fig. 1A). This finding supported the hypothesis that was downloaded from the Tumor Suppressor Gene Database (21) the 186 genes were useful in differentiating between RB1-loss and RB1 and those not located on the same chromosome as (i.e., not RB1-intact samples. on chromosome 13) and not included in RBS were used as The expression values of the 186 genes nominated as described fi candidate genes for the null model. Spearman correlation coef - above were then used in a supervised learning approach (PAM) to cients computed between RBS and each methylation probe in the compute an RBS score for predicting RB1 loss. The model was fi promoter region of a gene (de ned as 1.5kb of the transcription trained using the gene expression profiles of CCLE cell lines with start site; ref. 22) were then modeled as a distribution. The known RB1 status (i.e., RB1-loss vs. RB1-intact). The model RB1 distribution of correlations between RBS and promoter identified 144 genes whose expression values were most predic- methylation probes was compared with the distribution of cor- tive of RB1 status—these genes were used to compute the final RBS RB1 relations between RBS and non- promoter methylation score (Fig. 1B; Supplementary Table S1B). RB1 and CCND1 were – probes using the Kolmogorov Smirnov test with a two-sided among the genes expressed at relatively low levels in RB1-loss fi signi cance level of 0.05. Analogous analyses were performed samples, whereas CDKN2A was among the genes expressed at fi for the gene body region, where gene body was de ned as the relatively high levels in RB1-loss samples. This was consistent with region 1.5 kb downstream of the transcription start site to the prior studies which found a high ratio of CDKN2A to CCND1 transcription terminator. Transcription start sites and terminators expression to be associated with RB1 loss in multiple cancer fi were de ned using the "biomaRt" R package (23). types (27, 28). Because we noticed that prior RB1 gene sets and Characterizing the prognostic value of RB1 loss across cancer gene signatures largely consisted of cell proliferation genes, we types assessed the association between RBS and a previously published Clinical outcomes data [progression-free survival (PFS), OS, cell proliferation activity score (29). Although a previously pub- RB1 and disease-specific survival (DSS)] were obtained from the TCGA lished -loss signature (14) was found to be strongly correlated r ¼ Pan-Cancer Clinical Data Resource (24). All patients with avail- with the cell proliferation score ( 0.93), we found that RBS was r ¼ able log -normalized RPKM RNA-seq data and clinical outcomes only weakly correlated with the cell proliferation score ( 0.03). 2 fi data were included in survival analyses. Microarray expression These ndings suggested that RBS was not a surrogate marker for cell proliferation and was potentially more specifictoRB1 loss data were log2-normalized and scaled prior to RBS analysis. Data for the mCRPC cohort were obtained from a previously published than existing signatures. Moreover, EnrichR pathway enrichment study (25). This cohort consisted of 101 patients with deep whole- analysis revealed that RBS was enriched for genes not only in the –CDK4/6 – genome sequencing, whole-transcriptome RNA-seq, and clinical cyclin D and cell-cycle related pathways but also in the DNA damage response and TP53 signaling pathways (Supple- outcomes data available. The mCRPC RNA-seq data were log2- normalized FPKM values. The clinical endpoint examined was mentary Table S2). Altogether, these results were consistent with RB1 OS, with time of study entry defined as date of mCRPC diagnosis. recent literature that suggests may play a role in processes The threshold of RBS score used to assign binary RB1-impaired other than cell-cycle control (30). versus RB1-intact status in both cohorts was determined by using To validate RBS as an accurate model for predicting biallelic RB1 the Youden index (computed using the "OptimalCutpoints" R loss, we used the TCGA Pan-Cancer atlas expression dataset package; ref. 26) to select a threshold that maximized prediction containing RNA expression data for 11,007 tumors spanning 33 accuracy in the CCLE training dataset. Cox proportional hazard cancer types with known mutation and copy-number annota- models were used to model time-to-event data. All survival tions. Note that 698 of these samples were annotated as having RB1 analyses were performed using R version 3.5.0. two or more DNA alterations [559 had deep deletion (CN-2), 89 had shallow deletion with mutation (CN-1/mut), and 50 had two or more mutations with no deletions], 1,514 as having one Results RB1 alteration [1,332 with shallow deletion and no mutation RB1-loss gene signature development and validation using (CN-1/no-mut), and 182 with one mutation and no deletions CCLE and TCGA Pan-Cancer data (CN-0/mut)], and 7,727 as having no RB1 DNA alterations. RBS To define our RB1-loss gene set, we identified genes that were achieved an AUC of 0.89 for predicting RB1 biallelic loss in this differentially expressed between CCLE cell lines that demonstrat- validation set—far superior to an AUC of 0.66 achieved by

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Figure 1. A, t-SNE embedding of CCLE cell lines colored by number of DNA alterations in RB1. Embedding was constructed based on expression levels of the 186 genes found to be differentially expressed between RB1-impaired and RB1-intact cell lines. B, Heatmap visualizing expression values of 186 genes (rows) in 951 CCLE cell lines (columns). Cell lines are ordered from left to right in terms of increasing RBS score, where high RBS score denotes impaired RB1. Orange represents high expression, and blue represents low expression.

applying the leading existing RB1-loss signature (14) to the same RBS was highly accurate at identifying RB1 loss due to deep dataset (Fig. 2A and B). RBS also outperformed a predictive model deletion and due to shallow deletion with additional mutation, based solely on the ratio of CDKN2A to CCND1 expression (AUC which comprised the large majority of RB1-loss tumors. However, ¼ 0.72), which was previously reported to be associated with RB1 RBS was less effective at detecting the few RB1-loss tumors with loss. Genes including CAMK2N2, CDKN2A, and GPR137C were 2þ RB1 mutations, suggesting that these tumors may have a positively correlated with RBS score (i.e., high expression in RB1 distinct gene expression proﬁle. To investigate this further, we loss), whereas genes including MED4 and RB1 were most nega- performed a bootstrapped differential expression analysis to tively correlated with RBS score (Fig. 2C). identify genes over- and underexpressed in CN-0 tumors with

Figure 2. Boxplots showing accuracy of (A) RBS in predicting biallelic RB1 loss compared with (B) the leading existing model. C, Heatmap of TCGA Pan-Cancer data showing mRNA expression proﬁles of 186 genes (rows) in 11,007 patients (columns). Patients are ordered from left to right in terms of increasing RBS score, where high RBS score denotes impaired RB1. Orange represents high expression, and blue represents low expression.

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Figure 3. High RBS (impaired RB1)is(A) positively correlated with methylation of CpGs in the RB1 promoter region and (B) negatively correlated with methylation of CpGs in the RB1 gene body. Given prior reports of promoter hypermethylation and gene body hypomethylation being associated with gene inactivation, these results suggest RBS may detect tumors with impaired RB1 due to methylation-based gene silencing.

two or more RB1 mutations compared to tumors with no RB1 RBS can be useful for capturing the effects of gene inactivation mutations (Materials and Methods). We identified 448 genes due to epigenetic modification significantly overexpressed and 245 genes significantly under- To assess the utility of RBS in capturing the effects of expressed in the tumors with two or more RB1 mutations epigenetic events on gene expression, we examined the corre- (Supplementary Table S3). Of these, 16 overexpressed genes lation between RBS score and the methylation scores of 39 (including CCNE2 and CDKN2A) and 3 underexpressed genes methylation probes in the Pan-Cancer cohort (Fig. 3). To test (most notably RB1) were also found in RBS. In addition, several whether the pattern of correlation between RBS and methyla- known regulators or effectors of RB1 such as CCNE1, CDK2, tion probe values was significant in the RB1 promoter and gene EZH2, HOXB7,andselectE2F-family genes were not in RBS body regions, we compared our results with the correlation but were differentially expressed in the tumors with two or betweenRBSscoreandmethylationinthepromoterandgene more mutations in RB1 (30–34). Altogether, these findings body regions of 20 other random tumor suppressors unrelated suggested that there are some transcriptomic similarities but to RB1 (Supplementary Table S4). We found that the positive also notable differences between RB1 loss due to deletion and correlation between RBS score and RB1 promoter methylation due to biallelic RB1 mutations. and negative correlation between RBS score and RB1 gene body

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methylation were significant (P ¼ 0.0077 and P ¼ 0.0016, predictive of short PFS [median PFS, 36 vs. 56 months; HR, 1.3; respectively). The directionality of correlation was also consis- 95% confidence interval (CI), 1.18–1.44; P < 0.0001; Fig. 4A], tent with existing literature, which suggests that promoter short DSS (median DSS, 88 vs. 219 months; HR, 1.34; 95% CI, methylation is associated with decreased gene expression and 1.17–1.55; P < 0.0001; Fig. 4B), and short OS (median OS, 70 vs. gene body methylation is associated with increased gene 94 months; HR, 1.23; 95%, CI, 1.09–1.38; P ¼ 0.0004; Fig. 4C). In expression in tumor suppressors (22). These findings supported a multivariable survival model including both RBS and cancer the hypothesis that RBS could detect the downstream effects of type, high RBS was found to be independently prognostic of short RB1 loss due to multiple etiologies, including those (such as PFS (HR, 1.12; 95% CI, 1.02–1.26; P ¼ 0.04). These findings methylation) that may not be captured using DNA-sequencing supported the hypothesis that RB1 loss is clinically important techniques. across cancer types and may indicate more advanced or aggressive disease in general. RBS highlights RB1 loss as a recurrent genomic event and We additionally assessed the prognostic significance of a DNA- prognostic factor across cancer types sequencing–based definition of RB1 loss, namely, having at least After assessing the accuracy of RBS for predicting RB1 loss, we two DNA alterations in RB1. We found that similarly to high RBS, sought to use RBS to investigate the prognostic significance of RB1 presence of 2þ DNA alterations in RB1 was associated with short loss across cancer types. For this analysis, we included patients in OS, PFS, and DFS compared with presence of 0 or 1 DNA the TCGA Pan-Cancer dataset with available clinical follow-up. alterations in RB1 (Fig. 4D–F). These findings suggested that our High RBS was defined as scores above a threshold of 0.6, deter- definition of "RB1 loss" as predicted biallelic loss of RB1 was mined based on the Youden Index approach applied to the clinically meaningful. CCLE training dataset. Of note, we found that the majority of cancer types had an RB1 2-hit prevalence of greater than 5%, RBS is predictive of poor clinical outcomes independently of suggesting that RB1 loss was common and potentially important the number of DNA alterations in RB1 in many cancer types. In our pooled analysis of all patient samples In our methylation analysis, we showed that RBS could poten- across cancer types, we found that RB1 loss defined using RBS was tially be used to detect RB1 loss through mechanisms that could

Figure 4. Differences in (A) PFS, (B) DSS, and (C) OS between high-RBS (RNA-seq proﬁle consistent with impaired RB1) and low-RBS cancers. Differences in (D) PFS, (E) DSS, and (F) OS between patients with 0, 1, and 2 DNA alterations in RB1.

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not be detected by DNA sequencing. In addition, it is known that OS independently of the number of DNA alterations present. not all DNA mutations and copy-number loss events in a gene First, we examined the degree of concordance between RB1 status have the same effect on the affected allele (i.e., resulting protein as defined based on number of DNA alterations observed and as may still be partly or completely functional). Because RBS mea- defined based on RBS score. We found that although RBS score sures the downstream effects of DNA and non-DNA alterations at was strongly related to the number of DNA alterations observed the gene expression level, we hypothesized that RBS may offer (AUC ¼ 0.90), not all tumors with high RBS score harbored 2þ information on Rb-pathway disruption that is independent of DNA alterations and vice versa (Fig. 5A). By expanding the DNA- DNA-sequencing results. sequencing–based definition of RB1-loss (2þ RB1 DNA altera- To explore this hypothesis, we assessed the prognostic sig- tions) to include tumors with fewer than 2 DNA alterations in RB1 nificance of high RBS for predicting survival in the TCGA Pan- but with high RBS, one could recover 50% more tumors with RB1- Cancer cohort independently of number of observed DNA impaired status (Supplementary Fig. S1B). Next, we examined the alterations. For these analyses, we focused on PFS as our clinical prognostic significance of high RBS in the mCRPC cohort. We endpoint of interest due to a prior study that found that PFS found that RB1 loss as defined by high RBS was predictive of short was generally the most accurate endpoint collected across all OS in mCRPC (median OS, 15.0 vs. 42.0 months; HR, 2.93; 95% cancer types in the TCGA Pan-Cancer dataset (24). On multi- CI, 1.47–5.83; P ¼ 0.001; Fig. 5B). Finally, to assess whether the variable analysis adjusting for number of DNA alterations in RNA-seq (high RBS) and DNA-sequencing (number of DNA RB1, high RBS was independently predictive of short PFS (HR, alterations in RB1) results were independently predictive of 1.14; 95% CI, 1.02–1.29; P ¼ 0.03). This suggested that RBS survival outcomes, we performed a multivariable analysis includ- may help distinguish patients with a more pronounced RB1- ing both the RNA-seq and DNA-sequencing definitions of pre- impaired clinical phenotype from those with a less-pronounced dicted RB1 loss. We found that both the RNA-seq and DNA- phenotype independently of the number of DNA alterations sequencing definitions were independently predictive of short OS observed in the gene. Moreover, using a criterion of high RBS or (P ¼ 0.0036 and P ¼ 0.046, respectively), suggesting that both 2þ DNA alterations in RB1 to select RB1-impaired patients RNA-seq and DNA sequencing offered unique information on resulted in a 73% increase in group size as compared with using RB1 status that could be used to detect a clinical phenotype of the criteria of just 2þ DNA alterations (Supplementary Fig. RB1-impaired, clinically aggressive mCRPC. S1A). Thus, RBS may be useful for identifying a more comprehensive group of patients with Rb-pathway disruption than can be recovered using DNA sequencing alone. Discussion To explore this concept further, we examined a previously In order to assess the clinical implications of RB1 loss across published cohort of patients with mCRPC (25)—the lethal sub- cancer types, we developed a pan-cancer RB1-loss signature (RBS) type of prostate cancer not represented in the TCGA Pan-Cancer that predicted biallelic loss of RB1 based on gene expression data. cohort. RB1 loss (as defined based on detected DNA alterations in We found that RBS was highly accurate at predicting RB1 loss RB1) has been shown to be associated with short survival in across cancer types compared with existing RB1 gene signatures. mCRPC (35). Interrogating the mCRPC cohort of 101 patients Moreover, RBS was able to capture RB1 inactivation due to both with both whole-genome sequencing and RNA-seq data available, DNA and epigenetic changes. Using pan-cancer (N ¼ 10,486) and we aimed to assess whether high RBS might be predictive of short metastatic prostate cancer (N ¼ 101) cohorts, we demonstrated

Figure 5. A, Boxplot showing distribution of RBS scores for mCRPC tumors stratiﬁed by number of DNA alterations in RB1. AUC represents accuracy of RBS in predicting presence of 2þ DNA alterations in RB1. B, Difference in OS between Rb-loss (high-RBS) and Rb-intact (low-RBS) patients.

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that high RBS was predictive of poor clinical outcomes indepen- response to various cancer therapies including radiothera- dently of the number of DNA alterations in RB1. py (3, 41), platinum-based chemotherapy (3, 7), and CDK4/ There are several possible explanations as to why RBS was much 6 inhibitors (13, 15) in breast, prostate, and small-cell lung more accurate than the leading existing RB1 signature at predict- cancers. RBS may be useful for detecting differential response to ing biallelic loss of RB1 (AUC of 0.89 vs. 0.66). For one, RBS was specific cancer therapies for an even broader range of therapies the only RB1-loss signature that was designed to be applied across and cancer types than has been already studied. Third, RBS is cancer types. Because RBS was trained on CCLE cell-line data specifictoRB1-loss and not strongly correlated with cell pro- derived from many different primary tissue types, it was well- liferation scores (in contrast to existing RB1-loss signatures). suited to assess RB1 loss in the TCGA Pan-Cancer validation set, Altogether, our study along with others suggest RB1 may have which also included patient samples from many different disease important functions aside from regulating cell proliferation, sites. Moreover, in contrast to existing RB1-loss signatures, which such as DNA damage repair (41–43). Additional studies are included genes largely or exclusively based on prior annotations, needed to assess this in greater detail. Fourth, our transcrip- the RBS gene set was selected in an unbiased, unsupervised tomic signature may be used to identify RB1-impaired tumors manner. Our approach nominated genes from the set of all that may not be detected using standard DNA-sequencing– existing genes that were most differentially expressed in our based definitions of predicted RB1 loss. The results of our pan-cancer, RB1-loss training set samples. A final methodological multivariable analyses on two independent cohorts suggest strength of RBS was that it was trained on a very large dataset (N ¼ that both RNA-seq and DNA-sequencing results may be useful 995) including many samples with known RB1 loss (N ¼ 133) to identify a more complete set of RB1-impaired patients. that could be collectively used to represent a distinct RB1-loss Our approach to developing an RB1-loss signature is general- expression pattern. izable to studying a wide range of genomic alterations and may It is important to note that that the "accuracy" of our model for serve as a paradigm for generating expression-based gene signa- AUC analyses was defined as concordance between (RBS-based) tures in an unbiased manner. Because RBS is an expression-based RB1-loss calls and DNA-sequencing–based variant calls (i.e., signature, it is complementary to and potentially more holistic mutation, copy-number, and structural variant data when avail- than DNA-sequencing–based approaches, which may fail to able). This was because DNA-sequencing results are commonly capture the full spectrum of genomic events that can result in a used to predict gene functional status and were the only data specific gene expression profile or phenotype. Given the plethora available for comparison. However, DNA-sequencing calls do not of studies highlighting RB1 loss as a driver event in a number of capture certain forms of gene inactivation such as DNA methyl- cancer types, the potential clinical implications, and the increas- ation of the RB1 promoter. Although RBS demonstrated high ing availability of gene expression data for both retrospective and concordance with DNA-sequencing calls in our pan-cancer and prospective cohorts, RBS is an immediately useful tool that can be mCRPC-specific analyses (AUCs of 0.89 and 0.87, respectively), used to assess RB1 loss in a variety of settings. Our analyses and the the differences in RB1-loss assignments may not be due to error findings of others suggest that RB1 loss may be predictive not only but rather improved identification of RB1 gene inactivation. of survival but also of response to cytotoxic and targeted therapies. This study is not without limitations. We evaluated RBS as a RBS may be invaluable for investigating these relationships fur- potential tool to identify RB1 loss due to DNA-sequence altera- ther with the broader goal of developing personalized cancer tions and DNA methylation at the RB1 locus. However, still other treatment regimens. mechanisms of RB1 inactivation exist, such as CDK phosphorylation of the Rb protein (36, 37). It is unclear whether these Disclosure of Potential Conflicts of Interest mechanisms of RB1 inactivation result in a similar pattern of gene S.G. Zhao and E. Davicioni hold ownership interest (including patents) in expression and whether RBS can be used to identify these Rb- GenomeDx Biosciences. C.A. Maher holds ownership interest (including inactivated tumors. Future work may involve collecting and patents) in Illumina. K.E. Knudsen reports receiving commercial research integrating phosphoproteomic data with DNA-sequencing and grants from Celgene and CellCentric, speakers bureau honoraria from RB1 Celgene, and is a consultant/advisory board member for CellCentric. E.J. RNA-seq data to study these additional cases of tumors with Small holds ownership interest (including patents) in Harpoon Therapeutics gene inactivation. In addition, because our analysis was con- and Fortis Therapeutics, and is a consultant/advisory board member for ducted primarily using the CCLE and TCGA Pan-Cancer databases Janssen, Fortis Therapeutics, and Beigene. P.L. Nguyen reports receiving (which focus on primary cancers), an extension to metastatic commercial research grants from Janssen and Astellas, holds ownership cancers is needed. In particular, as RB1 loss and RB1 underexpres- interest (including patents) in Augmenix, and is a consultant/advisory board fi sion have been implicated as predictors of more advanced disease member for Augmenix, Boston Scienti c, Ferring, Bayer, Dendreon, Blue – fi Earth Diagnostics, Astellas, GenomeDx Biosciences, Nanobiotix, and Cota. in various cancers (38 40), future disease-speci c studies with a F.Y. Feng reports receiving commercial research grants from Zenith, is range of indolent and aggressive tumors may leverage RBS to a consultant/advisory board member for Bayer, Blue Earth Diagnostics, study RB1 loss in the context of disease progression. Celgene, Clovis, Janssen, EMD Serono, Sanofi, Dendreon, Ferring, and The data presented here offer several novel insights and con- Astellas, and reports receiving other remuneration from PFS Genomics. No tributions. First, our study is the first to examine the clinical potential conflicts of interest were disclosed by the other authors. implications of RB1 loss on a pan-cancer scale. We found that RB1 loss was associated with shorter PFS, OS, and DSS, Authors' Contributions highlighting the widespread clinical importance of the genomic Conception and design: W.S. Chen, M. Alshalalfa, S.G. Zhao, Y. Liu, B.A. Mahal, event. Second, our novel transcriptomic signature (RBS) is P.W. Kantoff, E.J. Small, P.L. Nguyen, F.Y. Feng highly accurate at predicting RB1 loss and can be used as a Development of methodology: W.S. Chen, M. Alshalalfa, S.G. Zhao, Y. Liu, B.A. Mahal, F.Y. Feng tool in future studies to shed new light on the biological and Acquisition of data (provided animals, acquired and managed patients, clinical impact of RB1 loss. This is especially relevant in light of provided facilities, etc.): W.S. Chen, M. Alshalalfa, Y. Liu, E. Davicioni, recent studies which suggest that RB1 loss may associated with E.J. Small

OF8 Clin Cancer Res; 2019 Clinical Cancer Research

Pan-Cancer RB1-Loss Signature Associated with Poor Survival

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, Acknowledgments computational analysis): W.S. Chen, M. Alshalalfa, S.G. Zhao, Y. Liu, S.G. Zhao, B.A. Mahal, D.A. Quigley, E.J. Small, and F.Y. Feng are supported D.A. Quigley, E. Davicioni, T.R. Rebbeck, C.A. Maher, K.E. Knudsen, by the Prostate Cancer Foundation. P.L. Nguyen, F.Y. Feng Writing, review, and/or revision of the manuscript: W.S. Chen, M. Alshalalfa, The costs of publication of this article were defrayed in part by the payment of S.G. Zhao, Y. Liu, B.A. Mahal, D.A. Quigley, T. Wei, E. Davicioni, T.R. Rebbeck, page charges. This article must therefore be hereby marked advertisement in P.W. Kantoff, C.A. Maher, K.E. Knudsen, E.J. Small, P.L. Nguyen, F.Y. Feng accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): W.S. Chen, E. Davicioni, T.R. Rebbeck, F.Y. Feng Received January 31, 2019; revised March 27, 2019; accepted April 17, 2019; Study supervision: T.R. Rebbeck, E.J. Small, P.L. Nguyen, F.Y. Feng published ﬁrst April 22, 2019.

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OF10 Clin Cancer Res; 2019 Clinical Cancer Research

Novel RB1-Loss Transcriptomic Signature Is Associated with Poor Clinical Outcomes across Cancer Types

William S. Chen, Mohammed Alshalalfa, Shuang G. Zhao, et al.

Clin Cancer Res Published OnlineFirst April 22, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-19-0404

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