FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3096713247> ?p ?o ?g. }

• W3096713247 endingPage "817.e4" @default.
• W3096713247 startingPage "803" @default.
• W3096713247 abstract "•Genomics of 823 RCC tumors, including 134 sarcomatoid tumors, reveals 7 subtypes•Subtype specific angiogenesis, immune, metabolic, stromal, and cell-cycle profiles•Differential prevalence of PBRM1, KDM5C, CDKN2A/2B, and TP53 alterations in subsets•Differential outcomes to VEGF blockade alone or in combination with anti-PD-L1 Integrated multi-omics evaluation of 823 tumors from advanced renal cell carcinoma (RCC) patients identifies molecular subsets associated with differential clinical outcomes to angiogenesis blockade alone or with a checkpoint inhibitor. Unsupervised transcriptomic analysis reveals seven molecular subsets with distinct angiogenesis, immune, cell-cycle, metabolism, and stromal programs. While sunitinib and atezolizumab + bevacizumab are effective in subsets with high angiogenesis, atezolizumab + bevacizumab improves clinical benefit in tumors with high T-effector and/or cell-cycle transcription. Somatic mutations in PBRM1 and KDM5C associate with high angiogenesis and AMPK/fatty acid oxidation gene expression, while CDKN2A/B and TP53 alterations associate with increased cell-cycle and anabolic metabolism. Sarcomatoid tumors exhibit lower prevalence of PBRM1 mutations and angiogenesis markers, frequent CDKN2A/B alterations, and increased PD-L1 expression. These findings can be applied to molecularly stratify patients, explain improved outcomes of sarcomatoid tumors to checkpoint blockade versus antiangiogenics alone, and develop personalized therapies in RCC and other indications. Integrated multi-omics evaluation of 823 tumors from advanced renal cell carcinoma (RCC) patients identifies molecular subsets associated with differential clinical outcomes to angiogenesis blockade alone or with a checkpoint inhibitor. Unsupervised transcriptomic analysis reveals seven molecular subsets with distinct angiogenesis, immune, cell-cycle, metabolism, and stromal programs. While sunitinib and atezolizumab + bevacizumab are effective in subsets with high angiogenesis, atezolizumab + bevacizumab improves clinical benefit in tumors with high T-effector and/or cell-cycle transcription. Somatic mutations in PBRM1 and KDM5C associate with high angiogenesis and AMPK/fatty acid oxidation gene expression, while CDKN2A/B and TP53 alterations associate with increased cell-cycle and anabolic metabolism. Sarcomatoid tumors exhibit lower prevalence of PBRM1 mutations and angiogenesis markers, frequent CDKN2A/B alterations, and increased PD-L1 expression. These findings can be applied to molecularly stratify patients, explain improved outcomes of sarcomatoid tumors to checkpoint blockade versus antiangiogenics alone, and develop personalized therapies in RCC and other indications. Renal cell carcinoma (RCC) was diagnosed in more than 400,000 people and associated with approximately 175,000 deaths worldwide in 2018 (Bray et al., 2018Bray F. Ferlay J. Soerjomataram I. Siegel R.L. Torre L.A. Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin. 2018; 68: 394-424Crossref PubMed Scopus (36644) Google Scholar; Siegel et al., 2018Siegel R.L. Miller K.D. Jemal A. Cancer Statistics, 2018.CA Cancer J. Clin. 2018; 68: 7-30Crossref PubMed Scopus (5371) Google Scholar). Approximately 25% of patients present with metastatic disease at initial diagnosis (Dabestani et al., 2016Dabestani S. Thorstenson A. Lindblad P. Harmenberg U. Ljungberg B. Lundstam S. Renal cell carcinoma recurrences and metastases in primary non-metastatic patients: a population-based study.World J. Urol. 2016; 34: 1081-1086Crossref PubMed Scopus (108) Google Scholar). Clear cell carcinoma (ccRCC) is the most common histologic subtype (75%) in RCC (Choueiri and Motzer, 2017Choueiri T.K. Motzer R.J. Systemic therapy for metastatic renal-cell carcinoma.N. Engl. J. Med. 2017; 376: 354-366Crossref PubMed Scopus (533) Google Scholar). About 20% of tumors from patients with advanced RCC contain sarcomatoid elements. RCC tumors that include a sarcomatoid component are highly aggressive and lead to rapid metastasis and poor clinical prognosis (Lebacle et al., 2019Lebacle C. Pooli A. Bessede T. Irani J. Pantuck A.J. Drakaki A. Epidemiology, biology and treatment of sarcomatoid RCC: current state of the art.World J. Urol. 2019; 37: 115-123Crossref PubMed Scopus (14) Google Scholar; Mouallem et al., 2018Mouallem N.E. Smith S.C. Paul A.K. Sarcomatoid renal cell carcinoma: biology and treatment advances.Urol. Oncol. 2018; 36: 265-271Crossref PubMed Scopus (26) Google Scholar). Inactivation of the VHL gene function by deletion of chromosome 3p, mutation, and/or promoter methylation is a predominant feature of ccRCC (Cancer Genome Atlas Research, 2013Cancer Genome Atlas Research N. Comprehensive molecular characterization of clear cell renal cell carcinoma.Nature. 2013; 499: 43-49Crossref PubMed Scopus (2010) Google Scholar; Gnarra et al., 1994Gnarra J.R. Tory K. Weng Y. Schmidt L. Wei M.H. Li H. Latif F. Liu S. Chen F. Duh F.M. et al.Mutations of the VHL tumour suppressor gene in renal carcinoma.Nat. Genet. 1994; 7: 85-90Crossref PubMed Scopus (1451) Google Scholar; Linehan et al., 1995Linehan W.M. Lerman M.I. Zbar B. Identification of the von Hippel-Lindau (VHL) gene. Its role in renal cancer.JAMA. 1995; 273: 564-570Crossref PubMed Scopus (283) Google Scholar) and leads to abnormal accumulation of hypoxia-inducible factors (HIF) and activation of the angiogenesis program (Kaelin, 2007Kaelin Jr., W.G. The von Hippel-Lindau tumor suppressor protein and clear cell renal carcinoma.Clin. Cancer Res. 2007; 13: 680s-684sCrossref PubMed Scopus (258) Google Scholar; Majmundar et al., 2010Majmundar A.J. Wong W.J. Simon M.C. Hypoxia-inducible factors and the response to hypoxic stress.Mol. Cell. 2010; 40: 294-309Abstract Full Text Full Text PDF PubMed Scopus (1405) Google Scholar; Semenza, 2013Semenza G.L. HIF-1 mediates metabolic responses to intratumoral hypoxia and oncogenic mutations.J. Clin. Invest. 2013; 123: 3664-3671Crossref PubMed Scopus (741) Google Scholar). However, VHL loss in itself is insufficient for tumorigenesis, and additional genomic aberrations, such as mutations in 3p-associated genes PBRM1, SETD2, and BAP1; loss of CDKN2A and CDKN2B genes via focal or arm-level deletion of the 9p21 locus; and alterations in KDM5C, TP53, MTOR, or PTEN have been implicated in disease progression and degree of aggressiveness (Cancer Genome Atlas Research, 2013Cancer Genome Atlas Research N. Comprehensive molecular characterization of clear cell renal cell carcinoma.Nature. 2013; 499: 43-49Crossref PubMed Scopus (2010) Google Scholar; Chen et al., 2016Chen F. Zhang Y. Senbabaoglu Y. Ciriello G. Yang L. Reznik E. Shuch B. Micevic G. De Velasco G. Shinbrot E. et al.Multilevel genomics-based taxonomy of renal cell carcinoma.Cell Rep. 2016; 14: 2476-2489Abstract Full Text Full Text PDF PubMed Scopus (206) Google Scholar; Kapur et al., 2013Kapur P. Pena-Llopis S. Christie A. Zhrebker L. Pavia-Jimenez A. Rathmell W.K. Xie X.J. Brugarolas J. Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation.Lancet Oncol. 2013; 14: 159-167Abstract Full Text Full Text PDF PubMed Scopus (294) Google Scholar; Ricketts et al., 2018Ricketts C.J. De Cubas A.A. Fan H. Smith C.C. Lang M. Reznik E. Bowlby R. Gibb E.A. Akbani R. Beroukhim R. et al.The cancer genome atlas comprehensive molecular characterization of renal cell carcinoma.Cell Rep. 2018; 23: 3698Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar; Voss et al., 2018Voss M.H. Reising A. Cheng Y. Patel P. Marker M. Kuo F. Chan T.A. Choueiri T.K. Hsieh J.J. Hakimi A.A. et al.Genomically annotated risk model for advanced renal-cell carcinoma: a retrospective cohort study.Lancet Oncol. 2018; 19: 1688-1698Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar). ccRCC is also characterized as a highly inflamed tumor type, with one of the highest immune infiltration scores in pan-cancer analysis and high expression of immune checkpoints, such as PD-L1 and CTLA-4 (Rooney et al., 2015Rooney M.S. Shukla S.A. Wu C.J. Getz G. Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity.Cell. 2015; 160: 48-61Abstract Full Text Full Text PDF PubMed Scopus (1539) Google Scholar; Senbabaoglu et al., 2016Senbabaoglu Y. Gejman R.S. Winer A.G. Liu M. Van Allen E.M. de Velasco G. Miao D. Ostrovnaya I. Drill E. Luna A. et al.Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures.Genome Biol. 2016; 17: 231Crossref PubMed Scopus (318) Google Scholar). Given the distinct but variable hyper-vascularity, immune cell infiltration, and PD-L1 expression in ccRCC, inhibitors of the vascular endothelial growth factor (VEGF) pathway and PD-(L)1 axis as monotherapy or in combination have resulted in significant improvement in clinical outcomes in patients with advanced RCC (Choueiri and Motzer, 2017Choueiri T.K. Motzer R.J. Systemic therapy for metastatic renal-cell carcinoma.N. Engl. J. Med. 2017; 376: 354-366Crossref PubMed Scopus (533) Google Scholar; Motzer et al., 2018Motzer R.J. Tannir N.M. McDermott D.F. Aren Frontera O. Melichar B. Choueiri T.K. Plimack E.R. Barthelemy P. Porta C. George S. et al.Nivolumab plus ipilimumab versus sunitinib in advanced renal-cell carcinoma.N. Engl. J. Med. 2018; 378: 1277-1290Crossref PubMed Scopus (1812) Google Scholar, Motzer et al., 2019Motzer R.J. Penkov K. Haanen J. Rini B. Albiges L. Campbell M.T. Venugopal B. Kollmannsberger C. Negrier S. Uemura M. et al.Avelumab plus axitinib versus sunitinib for advanced renal-cell carcinoma.N. Engl. J. Med. 2019; 380: 1103-1115Crossref PubMed Scopus (903) Google Scholar; Rini et al., 2009Rini B.I. Campbell S.C. Escudier B. Renal cell carcinoma.Lancet. 2009; 373: 1119-1132Abstract Full Text Full Text PDF PubMed Scopus (1068) Google Scholar, Rini et al., 2019aRini B.I. Plimack E.R. Stus V. Gafanov R. Hawkins R. Nosov D. Pouliot F. Alekseev B. Soulieres D. Melichar B. et al.Pembrolizumab plus axitinib versus sunitinib for advanced renal-cell carcinoma.N. Engl. J. Med. 2019; 380: 1116-1127Crossref PubMed Scopus (1096) Google Scholar, Rini et al., 2019cRini B.I. Powles T. Atkins M.B. Escudier B. McDermott D.F. Suarez C. Bracarda S. Stadler W.M. Donskov F. Lee J.L. et al.Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial.Lancet. 2019; 393: 2404-2415Abstract Full Text Full Text PDF PubMed Scopus (399) Google Scholar). However, not all patients respond and these treatments can produce significant toxicities. Thus, a better understanding of the molecular basis of clinical heterogeneity in patients with advanced RCC is needed to inform treatment selection strategies and delineate resistance mechanisms. Here, we report integrated multi-omics analyses leading to identification of robust molecular subtypes in 823 tumors from patients with advanced RCC, including 134 tumors with sarcomatoid features, from a randomized, global phase III trial (IMmotion151). These molecular subgroups associate with differential clinical outcomes to the combination of an anti-angiogenesis agent (bevacizumab, anti-VEGF) and a checkpoint inhibitor (CPI; atezolizumab or anti-PD-L1) versus a VEGF receptor tyrosine kinase inhibitor (sunitinib). The biological and clinical insights gained from this study may inform biomarker strategies for personalized treatment and guide future therapeutic development in RCC and other cancers. The study design and primary clinical findings from IMmotion151 were reported previously (Rini et al., 2019cRini B.I. Powles T. Atkins M.B. Escudier B. McDermott D.F. Suarez C. Bracarda S. Stadler W.M. Donskov F. Lee J.L. et al.Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial.Lancet. 2019; 393: 2404-2415Abstract Full Text Full Text PDF PubMed Scopus (399) Google Scholar). Here, we report integrated RNA sequencing (RNA-seq) and targeted somatic variant analysis using pre-treatment tumor samples from this study. Baseline tumors from 823/915 (90%) patients were available for biomarker evaluation (Tables S1 and S2; STAR Methods). This subset comprised 625 primary and 198 metastatic tumors, all of which were collected no longer than 2 years before enrollment in the study. Of these, 688 tumors were of clear cell histology without a sarcomatoid component, 110 tumors were of clear cell histology with any sarcomatoid component, 1 tumor was of clear cell histology with unknown sarcomatoid component, and 24 tumors were of non-clear cell histology with any sarcomatoid component. In these exploratory analyses, we evaluated biomarker associations with objective response (OR) and progression-free survival (PFS), as these clinical outcomes capture the immediate effect of therapeutic intervention and are less affected than overall survival (OS) by subsequent treatments. We previously reported associations between Angiogenesis and T-effector gene expression signatures and clinical outcome to treatment with atezolizumab + bevacizumab or sunitinib in the randomized phase II trial IMmotion150 (McDermott et al., 2018McDermott D.F. Huseni M.A. Atkins M.B. Motzer R.J. Rini B.I. Escudier B. Fong L. Joseph R.W. Pal S.K. Reeves J.A. et al.Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma.Nat. Med. 2018; 24: 749-757Crossref PubMed Scopus (466) Google Scholar). We evaluated the association of these signatures with clinical outcomes in IMmotion151 by pre-determining transcriptional cutoffs for both signatures in IMmotion150 and retrospectively applying them in IMmotion151 to define high- and low-expression patient subsets (Figure S1A; STAR Methods). Supporting observations in IMmotion150, high expression of the Angiogenesis signature was associated with improved PFS in the sunitinib treatment arm (hazard ratio [HR] = 0.59; 95% confidence interval [CI]: 0.47, 0.75; Figure S1B). When compared across treatment arms, no difference in PFS was observed in the Angiogenesishigh or T-effectorlow tumors. Atezolizumab + bevacizumab improved PFS versus sunitinib in T-effectorhigh (HR = 0.76; 95% CI: 0.59–0.99) and in Angiogenesislow (HR = 0.68; 95% CI: 0.52–0.88) tumors (Figure S1C). These findings underscore the relevance of immune and angiogenesis biology as reproducible biomarkers of differential clinical outcomes to checkpoint and angiogenesis blockade in independent advanced RCC cohorts. To expand our understanding of the biology of RCC, we next leveraged this large IMmotion151 RNA-seq dataset to further identify and refine transcriptionally defined subgroups of patients in an unbiased manner by utilizing non-negative matrix factorization (NMF). NMF is an unsupervised clustering algorithm that iteratively selects the most robust clustering pattern within a given dataset (Brunet et al., 2004Brunet J.P. Tamayo P. Golub T.R. Mesirov J.P. Metagenes and molecular pattern discovery using matrix factorization.Proc. Natl. Acad. Sci. U S A. 2004; 101: 4164-4169Crossref PubMed Scopus (1126) Google Scholar). Here, NMF identified seven clusters of patients based on the top 10% (3,074) most variable genes in the IMmotion151 cohort (Figures 1A and S2A). To understand the main biological features driving these clusters, we compared them individually to all others using quantitative set analysis for gene expression (QuSAGE) (Yaari et al., 2013Yaari G. Bolen C.R. Thakar J. Kleinstein S.H. Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations.Nucleic Acids Res. 2013; 41: e170Crossref PubMed Scopus (91) Google Scholar), leveraging hallmark gene sets from the Molecular Signatures Database (MSigDb) (Liberzon et al., 2015Liberzon A. Birger C. Thorvaldsdottir H. Ghandi M. Mesirov J.P. Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection.Cell Syst. 2015; 1: 417-425Abstract Full Text Full Text PDF PubMed Scopus (2332) Google Scholar) combined with the previously described Angiogenesis, T-effector, and Myeloid Inflammation signatures (McDermott et al., 2018McDermott D.F. Huseni M.A. Atkins M.B. Motzer R.J. Rini B.I. Escudier B. Fong L. Joseph R.W. Pal S.K. Reeves J.A. et al.Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma.Nat. Med. 2018; 24: 749-757Crossref PubMed Scopus (466) Google Scholar) (Figure 1B). We complemented this analysis with differential gene expression (DGE) analysis, again contrasting each cluster to all others, and conducting pathway enrichment analysis using gene sets from the Reactome database (Fabregat et al., 2018Fabregat A. Jupe S. Matthews L. Sidiropoulos K. Gillespie M. Garapati P. Haw R. Jassal B. Korninger F. May B. et al.The reactome pathway knowledgebase.Nucleic Acids Res. 2018; 46: D649-D655Crossref PubMed Scopus (1192) Google Scholar) (Table S3). To summarize these pathway-level analyses and further refine discriminatory transcriptomic profiles, we derived simplified signatures of representative genes associated with Cell Cycle, Stroma, the Complement Cascade, small nucleolar RNAs (snoRNAs), and metabolism-related pathways, including Fatty Acid Oxidation (FAO)/AMP-activated protein kinase (AMPK) signaling, Fatty Acid Synthesis (FAS)/pentose phosphate, and biological oxidation pathways that complemented our initial T-effector, Angiogenesis, and Myeloid Inflammation signatures (STAR Methods). These transcriptional programs were summarized across patient clusters both at the gene (Figure 1C) and signature levels (Figure 1D, Figure S2B). In addition, we applied xCell (Aran et al., 2017Aran D. Hu Z. Butte A.J. xCell: digitally portraying the tissue cellular heterogeneity landscape.Genome Biol. 2017; 18: 220Crossref PubMed Scopus (671) Google Scholar) to infer relative frequency of immune and stromal cell types across the tumor transcriptomes (Figure S2C). Patient tumors in NMF-derived clusters 1 (n = 98, 12%) and 2 (n = 245, 30%) were primarily characterized as highly angiogenic, with enrichment of vascular and VEGF pathway-related genes (Figures 1B–1D) as well as inferred endothelial cell presence (Figure S2C). These clusters also exhibited high expression of transforming growth factor β, WNT, hedgehog, and NOTCH signaling modules (Figure 1B). Cluster 1 differentiated from cluster 2 by higher stroma-specific expression (Figures 1C, 1D, and S2C), exemplified by high degree of fibroblast-derived gene expression (Figure S2C), and increased expression of collagens and activated stroma-associated genes (FAP, FN1, POSTN, and MMP2). Cluster 2 also showed moderate T-effector gene signature expression, low cell-cycle-associated genes, and higher expression of genes associated with catabolic metabolism, including those in FAO (CPT2, PPARA, and CPT1A) and AMPK (PRKAA2, PDK2, and PRKAB1) pathways. We thus labeled cluster 1 as Angiogenic/Stromal and cluster 2 as Angiogenic. Tumors in cluster 3 (n = 156, 19%) were characterized by relatively lower expression of both angiogenesis and immune genes and moderate expression of cell-cycle genes. These tumors showed increased expression of genes associated with the complement cascade (C3, C1S, and C1R), which has been associated with poor prognosis in the ccRCC The Cancer Gene Atlas cohort (Roumenina et al., 2019Roumenina L.T. Daugan M.V. Petitprez F. Sautes-Fridman C. Fridman W.H. Context-dependent roles of complement in cancer.Nat. Rev. Cancer. 2019; 19: 698-715Crossref PubMed Scopus (63) Google Scholar), as well as genes associated with the cytochrome P450 family, which is involved in omega oxidation. We labeled this cluster as the Complement/Ω-oxidation cluster. Tumors in clusters 4 (n = 116, 14%), 5 (n = 74, 9%), and 6 (n = 106, 13%) were characterized by enrichment of cell-cycle transcriptional programs (G2M, E2F targets, MYC targets), and lower expression of angiogenesis-related genes. We observed mutual exclusion between the Angiogenesis signature enriched in clusters 1 and 2 and the Cell Cycle signature (including the cyclin-dependent kinases CDK2, CDK4, and CDK6) enriched in clusters 4, 5, and 6 (Figures 1C and 1D), which was confirmed by correlation analysis (R = −0.50, p < 0.001; Figure S2E). Clusters 4, 5, and 6 also exhibited an anabolic metabolism transcriptomic profile, with higher expression of genes associated with FAS (FASN, PARP1, and ACACA) and the pentose phosphate pathway (TKT, TALDO1, and PGD), which may be related to the proliferative nature of these tumors. Tumors in cluster 4 were additionally characterized as highly immunogenic, exhibiting strong enrichment in T-effector, JAK/STAT, and interferon-α and -ɣ gene expression modules (Figures 1B and 1C). These tumors also showed the highest expression of PD-L1 by immunohistochemistry (IHC) (Figure 1E) and highest infiltration of both adaptive and innate immune cell subsets, including CD8+, CD4+, and regulatory T cells, B cells, macrophages, and dendritic cells (Figure S2C). In contrast, while tumors in clusters 5 and 6 showed enrichment of the Myeloid Inflammation gene signature and innate immune cell presence as inferred from xCell, they exhibited lower expression of T-effector gene signature and inferred T cell presence (Figure S2C). The expression of FAS/Pentose phosphate pathway-associated genes was highest in cluster 5. Moreover, cluster 5 included 15 tumors that contained TFE fusions (12 tumors with TFE3 fusions and 3 tumors with TFEB fusions; Figure S2F), which have been implicated in mTORC1 signaling, upregulation of cyclin proteins, dysregulation of metabolic pathways, and increased tumor aggressiveness (Brady et al., 2018Brady O.A. Jeong E. Martina J.A. Pirooznia M. Tunc I. Puertollano R. The transcription factors TFE3 and TFEB amplify p53 dependent transcriptional programs in response to DNA damage.eLife. 2018; 7https://doi.org/10.7554/eLife.40856Crossref Scopus (40) Google Scholar; Kauffman et al., 2014Kauffman E.C. Ricketts C.J. Rais-Bahrami S. Yang Y. Merino M.J. Bottaro D.P. Srinivasan R. Linehan W.M. Molecular genetics and cellular features of TFE3 and TFEB fusion kidney cancers.Nat. Rev. Urol. 2014; 11: 465-475Crossref PubMed Scopus (147) Google Scholar). Cluster 6 showed high expression of the epithelial-mesenchymal transition (EMT) transcriptional module and enrichment of collagen- and fibroblast-associated stromal genes. We termed cluster 4 as T-effector/Proliferative, cluster 5 as Proliferative, and cluster 6 as Stromal/Proliferative. Finally, cluster 7 (n = 28, 3%) was characterized by enrichment of expression of snoRNAs, especially C/D box snoRNAs (SNORDs). SNORDs have been implicated in alterations of epigenetic and translation programs and have been linked to carcinogenesis (Gong et al., 2017Gong J. Li Y. Liu C.J. Xiang Y. Li C. Ye Y. Zhang Z. Hawke D.H. Park P.K. Diao L. et al.A pan-cancer analysis of the expression and clinical relevance of small nucleolar RNAs in human cancer.Cell Rep. 2017; 21: 1968-1981Abstract Full Text Full Text PDF PubMed Scopus (95) Google Scholar). For example, SNORD66, which was upregulated in this cluster, has been reported to be associated with lung cancer tumorigenesis (Braicu et al., 2019Braicu C. Zimta A.A. Harangus A. Iurca I. Irimie A. Coza O. Berindan-Neagoe I. The function of non-coding RNAs in lung cancer tumorigenesis.Cancers (Basel). 2019; 11https://doi.org/10.3390/cancers11050605Crossref Scopus (49) Google Scholar). The precise role of the overexpressed SNORDs in RCC tumors remains to be characterized. We labeled this small cluster as the snoRNA cluster. Overall, molecular stratification of 823 RCC tumors identified seven groups of patients with biologically distinct transcriptomes. Given that the tumors in IMmotion151 included both primary and metastatic collections, we evaluated the prevalence of each across the seven NMF subsets. As shown in Figure S2D, metastatic tumors were distributed across all clusters, suggesting that our transcriptional stratification scheme is not primarily driven by the primary or metastatic origin of tumors. To validate these molecular subgroups in an independent cohort, we trained a random forest classifier (STAR Methods) from the RNA-seq data in IMmotion151 and predicted the NMF class of tumors from patients in the IMmotion150 randomized phase II trial. The observed distribution of the NMF clusters and the transcriptional expression profile of these clusters in IMmotion150 were highly concordant with those in IMmotion151 (Figures S3A and S3B), confirming the robustness of these molecular subtypes. The Memorial Sloan Kettering Cancer Center (MSKCC) and the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) models are frequently applied in advanced RCC for patient prognostication (Heng et al., 2009Heng D.Y. Xie W. Regan M.M. Warren M.A. Golshayan A.R. Sahi C. Eigl B.J. Ruether J.D. Cheng T. North S. et al.Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: results from a large, multicenter study.J. Clin. Oncol. 2009; 27: 5794-5799Crossref PubMed Scopus (1345) Google Scholar; Motzer et al., 1999Motzer R.J. Mazumdar M. Bacik J. Berg W. Amsterdam A. Ferrara J. Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma.J. Clin. Oncol. 1999; 17: 2530-2540Crossref PubMed Google Scholar). These models utilize clinical and laboratory parameters to stratify patients into favorable-, intermediate-, and poor-risk categories. However, the molecular features of tumors associated with these risk categories are incompletely understood. We evaluated the distribution of our NMF molecular clusters across MSKCC and IMDC risk categories, and observed enrichment of the Angiogenic/Stromal (no. 1) and Angiogenic (no. 2) clusters in the favorable risk groups in both classifications. Conversely, the T-effector/Proliferative (no. 4), Proliferative (no. 5), and Stromal/Proliferative (no. 6) clusters were enriched in the poor-risk groups (Figure 2A). We subsequently evaluated clinical outcomes to atezolizumab + bevacizumab and sunitinib treatment in each cluster. Patients in the Angiogenic/Stromal (no. 1) and Angiogenic (no. 2) clusters demonstrated longer PFS in both treatment arms, suggesting better outcome regardless of treatment, while those in the Stromal/Proliferative cluster (no. 6) had relatively shorter PFS (atezolizumab + bevacizumab mPFS: 6.8 months; sunitinib mPFS: 5.2 months), suggesting poor prognostic association of proliferative/stromal biology with clinical outcomes (Figure 2B). When evaluated across treatment arms, no apparent difference in clinical outcomes was observed between atezolizumab + bevacizumab and sunitinib arms in the Angiogenic/Stromal (no. 1), Angiogenic (no. 2), and Complement/Ω-oxidation (no. 3) clusters (Figures 2C and 2D). Atezolizumab + bevacizumab demonstrated improved OR rate ([ORR]; 52.0% versus 19.4%, p < 0.001) and PFS (HR = 0.52; 95% CI: 0.33–0.82) versus sunitinib (Figures 2C and 2D) in the T-effector/Proliferative cluster (no. 4), confirming the contribution of pre-existing intratumoral adaptive immune presence in determining benefit to immunotherapy containing regimens. In addition, atezolizumab + bevacizumab showed improved ORR (26.2% versus 3.1%, p < 0.001; Figure 2C) and PFS (HR = 0.47; 95% CI: 0.27–0.82; Figure 2D) in the Proliferative cluster (no. 5), including in tumors that harbored TFE fusions (Figure S2G), implicating the relevance of PD-L1 blockade in this low angiogenesis, but high proliferative subgroup. Atezolizumab + bevacizumab also showed improved PFS (HR = 0.1; 95% CI: 0.01–0.77) in the snoRNA cluster (no. 7); however, the biological basis of this effect in this small cluster of patients remains to be elucidated. We subsequently compared the HRs obtained above using a univariate Cox proportional hazard model that only tests treatment arm in each NMF subgroup against a model that included treatment arm, PD-L1 IHC, and MSKCC clinical risk score. These multivariate analyses confirmed that the differential clinical benefit observed in these NMF clusters is independent of PD-L1 expression and MSKCC prognostic risk (Table S4). Finally, we also evaluated differentially expressed genes between responders (complete or partial OR) and non-responders (progressive disease) within and across treatment arms. In sunitinib-treated patients, linear modeling complemented with MSigDb hallmark gene set enrichment analysis revealed higher expression of genes associated with the VEGF pathway in tumors from responders and higher expression of cell-cycle-associated pathways in tumors from non-responders (Figures S4A and S4B). Comparison of gene expression in responders with non-responders treated with atezolizumab + bevacizumab did not identify any significantly differentially expressed genes (false discovery rate [FDR] < 0.05). Within responders across treatment arms, genes associated with proliferation and immune pathways were enriched in patients responding to atezolizumab + bevacizumab, while genes associated with VEGF signaling (hypoxia) were enriched in patients responding to sunitinib (Figures S4C and S4D). No differentially expressed genes (FDR < 0.05) were observed in non-responders treated with atezolizumab + bevacizumab versus su" @default.
• W3096713247 created "2020-11-09" @default.
• W3096713247 creator A5002295684 @default.
• W3096713247 creator A5007052233 @default.
• W3096713247 creator A5010583172 @default.
• W3096713247 creator A5011890417 @default.
• W3096713247 creator A5012800133 @default.
• W3096713247 creator A5025529463 @default.
• W3096713247 creator A5026388611 @default.
• W3096713247 creator A5035213072 @default.
• W3096713247 creator A5041518689 @default.
• W3096713247 creator A5045259437 @default.
• W3096713247 creator A5045910697 @default.
• W3096713247 creator A5049672639 @default.
• W3096713247 creator A5049692454 @default.
• W3096713247 creator A5052526230 @default.
• W3096713247 creator A5054773232 @default.
• W3096713247 creator A5059063118 @default.
• W3096713247 creator A5064049215 @default.
• W3096713247 creator A5064969470 @default.
• W3096713247 creator A5067607476 @default.
• W3096713247 creator A5074024725 @default.
• W3096713247 creator A5087092015 @default.
• W3096713247 creator A5090168075 @default.
• W3096713247 date "2020-12-01" @default.
• W3096713247 modified "2023-10-17" @default.
• W3096713247 title "Molecular Subsets in Renal Cancer Determine Outcome to Checkpoint and Angiogenesis Blockade" @default.
• W3096713247 cites W1985458442 @default.
• W3096713247 cites W2002583936 @default.
• W3096713247 cites W2010160635 @default.
• W3096713247 cites W2022451432 @default.
• W3096713247 cites W2022914202 @default.
• W3096713247 cites W2028433016 @default.
• W3096713247 cites W2066604995 @default.
• W3096713247 cites W2092892924 @default.
• W3096713247 cites W2096441981 @default.
• W3096713247 cites W2099618765 @default.
• W3096713247 cites W2099752220 @default.
• W3096713247 cites W2110320497 @default.
• W3096713247 cites W2131444088 @default.
• W3096713247 cites W2131473358 @default.
• W3096713247 cites W2136787567 @default.
• W3096713247 cites W2143813821 @default.
• W3096713247 cites W2152874907 @default.
• W3096713247 cites W2160981405 @default.
• W3096713247 cites W2214074259 @default.
• W3096713247 cites W2264794294 @default.
• W3096713247 cites W2288944226 @default.
• W3096713247 cites W2335012284 @default.
• W3096713247 cites W2474869522 @default.
• W3096713247 cites W2551911481 @default.
• W3096713247 cites W2573357293 @default.
• W3096713247 cites W2602238141 @default.
• W3096713247 cites W2607364201 @default.
• W3096713247 cites W2614480329 @default.
• W3096713247 cites W2750328162 @default.
• W3096713247 cites W2766016994 @default.
• W3096713247 cites W2771031897 @default.
• W3096713247 cites W2781525129 @default.
• W3096713247 cites W2781873681 @default.
• W3096713247 cites W2789574985 @default.
• W3096713247 cites W2792937256 @default.
• W3096713247 cites W2796207353 @default.
• W3096713247 cites W2797282777 @default.
• W3096713247 cites W2797542577 @default.
• W3096713247 cites W2802645166 @default.
• W3096713247 cites W2805302875 @default.
• W3096713247 cites W2806746916 @default.
• W3096713247 cites W2806945293 @default.
• W3096713247 cites W2889646458 @default.
• W3096713247 cites W2890106399 @default.
• W3096713247 cites W2899539231 @default.
• W3096713247 cites W2909973609 @default.
• W3096713247 cites W2910857268 @default.
• W3096713247 cites W2911812451 @default.
• W3096713247 cites W2913745075 @default.
• W3096713247 cites W2942725870 @default.
• W3096713247 cites W2943887705 @default.
• W3096713247 cites W2947589787 @default.
• W3096713247 cites W2947664767 @default.
• W3096713247 cites W2952001873 @default.
• W3096713247 cites W2977671651 @default.
• W3096713247 cites W2977883209 @default.
• W3096713247 cites W2980759299 @default.
• W3096713247 cites W2982402173 @default.
• W3096713247 cites W2982426375 @default.
• W3096713247 cites W4233698560 @default.
• W3096713247 cites W4297119881 @default.
• W3096713247 doi "https://doi.org/10.1016/j.ccell.2020.10.011" @default.
• W3096713247 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8436590" @default.
• W3096713247 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33157048" @default.
• W3096713247 hasPublicationYear "2020" @default.
• W3096713247 type Work @default.
• W3096713247 sameAs 3096713247 @default.
• W3096713247 citedByCount "231" @default.
• W3096713247 countsByYear W30967132472018 @default.
• W3096713247 countsByYear W30967132472020 @default.
• W3096713247 countsByYear W30967132472021 @default.

• next