Predictive Biomarkers for Molecularly Targeted Therapies in Renal Cell Carcinoma

A significant challenge in oncology is the identification of predictive biomarkers of therapeutic benefit. The article in this issue by Fay et al (page 820) illustrates this issue in renal cell carcinoma (RCC). The challenge encompasses all RCC therapies, including immunotherapies (interleukin-2 and nivolumab) and molecularly targeted therapies, which comprise angiogenesis inhibitors (bevacizumab, sunitinib, sorafenib, pazopanib, axitinib, and, with a characteristic target spectrum, the recently approved drugs, lenvatinib and cabozantinib) and inhibitors of mTOR complex 1 (mTORC1), specifically the sirolimus analogues temsirolimus and everolimus. Response rates (almost always partial) to targeted therapies in the first or second line range between 10% and 40%, and primary resistance may be seen in a somewhat lower but still significant percentage of patients. Whether a patient will or will not benefit from a particular therapy cannot be predicted.

An understanding of the molecular mechanism of drug action, the target, and corresponding pathway has assisted in pinpointing potential predictive biomarkers for mTORC1 inhibitors. Sirolimus analogues are highly specific—they form a complex with FK506-binding proteins (ie, FKBP12, also called FKBP1A) and bind to mTOR in a gatekeeper domain (FKBP12-rapamycin–binding [FRB] domain).1 When bound to mTOR, they restrict substrate access and inhibit mTORC1 function. mTORC1 is activated by Ras homolog enriched in brain (RHEB), a small GTPase, which in turn is regulated by a GTPase-activating protein complex made up of tuberous sclerosis (TSC) 1 and 2 proteins and TBC1D7. Function-altering mutations acquired during tumor development in the corresponding genes, MTOR,2 RHEB,3 TSC1,4 and TSC2,57 likely reveal a heightened dependency on mTORC1 and may identify patients more likely to benefit from mTORC1 inhibitors.4,8,9 Mutations in genes encoding signaling nodes further upstream, such as phosphatase and tensin homolog (PTEN) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), may also confer dependency on mTORC1.9

For patients with mutations in mTORC1 pathway genes, the clinical benefit of mTORC1 inhibitors may be significant. Time to progression typically decreases with each subsequent line of therapy, and in these patients, mTORC1 inhibitors may increase progression-free survival (PFS) by more than 2-fold compared with previous lines of therapy.8 However, the frequency of mTORC1 pathway mutations is low (5%–20% in aggregate), and 20% of the patients with mutant tumors may not benefit significantly from mTORC1 inhibitor therapy (possibly due to nonfunction-altering and subclonal mutations). Further, mutations are only detected in approximately 50% of patients with substantial benefit from mTORC1 inhibitors.9 Whether immunohistochemistry (IHC) for markers of mTORC1 activation10 would improve the sensitivity and specificity afforded by mutation detection is unclear. Nevertheless, I speculate that the prospective identification of patients with function-altering somatic mutations in proximal mTORC1 regulators would significantly enrich for patients likely to benefit from mTORC1 inhibitors.

Estimates from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) suggest that fewer than 40% of patients receive an mTORC1 inhibitor in first- or second-line therapy.11 Moreover, this number is likely to decrease further given results of recent phase III trials showing superiority of nivolumab and cabozantinib compared with everolimus.12,13 Thus, the identification of patients likely to benefit from mTORC1 inhibitor therapy may become even more critical.

In contrast to mTORC1 inhibitors, less is understood about the mechanism of action of angiogenesis inhibitors (except for bevacizumab). Specifically, the relative contribution of vascular endothelial growth factor (VEGF) receptor 2 versus other kinases is poorly understood, and as catalytic inhibitors, these small molecules have pleiotropic effects. To identify determinants of sensitivity to angiogenesis inhibitors, Fay et al evaluated 27 patients with extreme response to first-line sunitinib or pazopanib (including 7 from The Cancer Genome Atlas [TCGA]). The series included 13 patients with an outstanding response (partial or complete response for ≥3 years) and 14 patients with rapid progression (within the first 3 months). Notwithstanding patients from TCGA, formalin-fixed paraffin-embedded (FFPE) samples (largely from archival nephrectomy specimens) were subjected to whole-exome sequencing. The authors found polybromo 1 (PBRM1) mutations enriched in tumors from the responding group (7 of 13 vs 1 of 14; unadjusted P=.01).

These results are consistent with a retrospective analysis of patients enrolled in RECORD-3 (a large randomized phase II trial comparing sunitinib and everolimus sequences), which found that PBRM1 mutations were associated with longer PFS to first-line sunitinib and also everolimus.14 However, one caveat is that PBRM1 mutations could be prognostic. Indeed, overall survival was also improved in patients with PBRM1-mutant tumors in the study by Fay et al. In addition, at least in the localized setting, patients with tumors deficient for PBRM1 exhibit improved RCC-specific and overall survival compared with those with other genotypes.15,16

In addition, Fay et al report an association between TP53 mutations and the resistant group (4 of 14 vs 0 of 13; unadjusted P=.09). However, patients with TP53-mutant tumors also had a significantly worse overall survival compared with those with tumors wild-type for TP53. Thus, whether TP53 and PBRM1 are indeed predictive or simply prognostic remains to be determined.

The identification of predictive biomarkers of responsiveness to angiogenesis inhibitors has been particularly challenging, because these drugs do not target the cancer cell directly but rather target the vasculature.17 Tumor angiogenesis likely involves paracrine effects from tumor cells on endothelial cells, and experimental systems suitable to dissect the crosstalk have been lacking. Recently, we showed that primary cultures from RCC activate mitogenic signaling in endothelial cells in coculture experiments.18 Endothelial cell activation could be blocked by sunitinib. However, primary cultures derived from patients with sunitinib-resistant tumors retained the ability to induce mitogenic signaling in endothelial cells despite sunitinib. This in vitro system appears therefore to reproduce the situation in patients.

Interestingly, dovitinib, an inhibitor of angiogenesis that also targets fibroblast growth factor receptors, blocked endothelial cell activation by sunitinib-resistant RCCs.18 Dovitinib was not better than sorafenib in a phase III trial, however.19 One potential explanation for these results is the differential effect of dovitinib on endothelial cells and fibroblasts. Although dovitinib effectively inhibits endothelial cell activation by RCC cells, it fails to consistently inhibit fibroblasts, a cell type that may contribute to tumorigenesis.18 Thus, failure to inhibit other cell types may explain the modest activity of dovitinib against RCC. Like dovitinib, lenvatinib (which was recently approved in combination with everolimus20) also targets fibroblast growth factor receptors, but how it would affect signaling by RCC is unknown.

Given the paracrine effects of the tumor on the vasculature, a potential avenue to identify biomarkers involves gene expression analyses. A study using gene expression arrays involving 98 clear cell RCCs identified several characteristic gene expression patterns in unsupervised analyses, and the groups identified differed in their responsiveness to sunitinib.21 However, these patterns failed to clearly separate responsive from resistant tumors, and the patterns were associated with different overall survival in patients. This raises the concern that they may be prognostic rather than predictive. Overall, these data suggest that sensitivity and resistance may not be associated with distinct biologic entities identifiable by unsupervised gene expression analyses. Rather, supervised analyses focusing on cytokines and growth factors may be required.

VEGF has been extensively evaluated as a candidate predictive biomarker of angiogenesis inhibitors. Most studies have focused, however, on circulating VEGF isoforms (or soluble forms of the receptors),22,23 which may be impacted by other factors besides the tumor. However, VEGF expression in tumors may more accurately reflect tumor angiogenesis, and in co-culture experiments, sunitinib sensitivity correlates with VEGF production.18 Notably, VEGF production may differ not only across tumors from different patients but also among metastases within the same patient. This is evidenced by an elegant, albeit small, study using 89Zr-bevacizumab PET.24 Different mechanisms of angiogenesis across metastases further complicates the identification of predictive biomarkers.

In summary, despite more than a decade of molecularly targeted therapies in RCC, clinically actionable predictive biomarkers remain unavailable. Progress has been hampered by biological complexity (and tumor heterogeneity) as well as drugs targeting the stroma with poorly understood mechanisms of action. The development of experimental approaches suitable to dissect the crosstalk between tumor and stroma, and a deeper understanding of the biology, may pave the way to identification of candidate predictive biomarkers.

References

  • 1.

    Yang H, Rudge DG, Koos JD. mTOR kinase structure, mechanism and regulation. Nature 2013;497:217223.

  • 2.

    Sato T, Nakashima A, Guo L. Single amino-acid changes that confer constitutive activation of mTOR are discovered in human cancer. Oncogene 2010;29:27462752.

    • Search Google Scholar
    • Export Citation
  • 3.

    Ghosh AP, Marshall CB, Coric T. Point mutations of the mTOR-RHEB pathway in renal cell carcinoma. Oncotarget 2015;6:1789517910.

  • 4.

    Kucejova B, Pena-Llopis S, Yamasaki T. Interplay between pVHL and mTORC1 pathways in clear-cell renal cell carcinoma. Mol Cancer Res 2011;9:12551265.

    • Search Google Scholar
    • Export Citation
  • 5.

    Creighton CJ, Morgan M, Gunaratne PH. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013;499:4349.

  • 6.

    Sato Y, Yoshizato T, Shiraishi Y. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet 2013;45:860867.

  • 7.

    Scelo G, Riazalhosseini Y, Greger L. Variation in genomic landscape of clear cell renal cell carcinoma across Europe. Nat Commun 2014;5:5135.

  • 8.

    Voss MH, Hakimi AA, Pham CG. Tumor genetic analyses of patients with metastatic renal cell carcinoma and extended benefit from mTOR inhibitor therapy. Clin Cancer Res 2014;20:19551964.

    • Search Google Scholar
    • Export Citation
  • 9.

    Kwiatkowski DJ, Choueiri TK, Fay AP. Mutations in TSC1, TSC2, and MTOR are associated with response to rapalogs in patients with metastatic renal cell carcinoma. Clin Cancer Res 2016;22:24452452.

    • Search Google Scholar
    • Export Citation
  • 10.

    Cho D, Signoretti S, Dabora S. Potential histologic and molecular predictors of response to temsirolimus in patients with advanced renal cell carcinoma. Clin Genitourin Cancer 2007;5:379385.

    • Search Google Scholar
    • Export Citation
  • 11.

    Ko JJ, Xie W, Kroeger N. The International Metastatic Renal Cell Carcinoma Database Consortium model as a prognostic tool in patients with metastatic renal cell carcinoma previously treated with first-line targeted therapy: a population-based study. Lancet Oncol 2015;16:293300.

    • Search Google Scholar
    • Export Citation
  • 12.

    Motzer RJ, Escudier B, McDermott DF. Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 2015;373:1803w1813.

  • 13.

    Choueiri TK, Escudier B, Powles T. Cabozantinib versus everolimus in advanced renal-cell carcinoma. N Engl J Med 2015;373:18141823.

  • 14.

    Hsieh J, Chen DY, Wang P. Identification of efficacy biomarkers in a large metastatic renal cell carcinoma (mRCC) cohort through next generation sequencing (NGS): results from RECORD-3 [abstract]. J Clin Oncol 2015;33(Suppl):Abstract 4509.

    • Search Google Scholar
    • Export Citation
  • 15.

    Kapur P, Pena-Llopis S, Christie A. Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. Lancet Oncol 2013;14:159167.

    • Search Google Scholar
    • Export Citation
  • 16.

    Joseph RW, Kapur P, Serie DJ. Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression. J Urol 2016;195:180187.

  • 17.

    Huang D, Ding Y, Li Y. Sunitinib acts primarily on tumor endothelium rather than tumor cells to inhibit the growth of renal cell carcinoma. Cancer Res 2010;70:10531062.

    • Search Google Scholar
    • Export Citation
  • 18.

    Tran TA, Leong HS, Pavia-Jimenez A. Fibroblast growth factor receptor-dependent and -independent paracrine signaling by sunitinib-resistant renal cell carcinoma. Mol Cell Biol 2016;36:18361855.

    • Search Google Scholar
    • Export Citation
  • 19.

    Schmidinger M. Third-line dovitinib in metastatic renal cell carcinoma. Lancet Oncol 2014;15:245246.

  • 20.

    Motzer RJ, Hutson TE, Ren M. Independent assessment of lenvatinib plus everolimus in patients with metastatic renal cell carcinoma. Lancet Oncol 2016;17:e45.

    • Search Google Scholar
    • Export Citation
  • 21.

    Beuselinck B, Job S, Becht E. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 2015;21:13291339.

    • Search Google Scholar
    • Export Citation
  • 22.

    Maroto P, Rini B. Molecular biomarkers in advanced renal cell carcinoma. Clin Cancer Res 2014;20:20602071.

  • 23.

    Funakoshi T, Lee CH, Hsieh JJ. A systematic review of predictive and prognostic biomarkers for VEGF-targeted therapy in renal cell carcinoma. Cancer Treat Rev 2014;40:533547.

    • Search Google Scholar
    • Export Citation
  • 24.

    Oosting SF, Brouwers AH, van Es SC. 89Zr-bevacizumab PET visualizes heterogeneous tracer accumulation in tumor lesions of renal cell carcinoma patients and differential effects of antiangiogenic treatment. J Nuclear Med 2015;56:6369.

    • Search Google Scholar
    • Export Citation

If the inline PDF is not rendering correctly, you can download the PDF file here.

James Brugarolas, MD, PhD, a Virginia Murchison Linthicum Endowed Scholar in Medical Research, is the leader of the Kidney Cancer Program at UT Southwestern Medical Center, and a tenured Associate Professor in the Department of Internal Medicine, Division of Hematology-Oncology. He received a medical degree from the University of Navarra (Spain), and a PhD from the Massachusetts Institute of Technology. He completed an internship and residency in internal medicine at Duke University Medical Center and a medical oncology fellowship at a combined program of the Massachusetts General Hospital, Brigham and Women's Hospital, and the Dana-Farber Cancer Institute. Dr. Brugarolas' clinical practice is devoted to the care of patients with renal cell carcinoma (RCC) and his research spans the spectrum from basic, to translational and clinical. He is a member of the American Society for Clinical Investigation and has served in multiple committees, including the integration and vision-setting panel for congressionally-directed medical research programs in cancer.

The ideas and viewpoints expressed in this commentary are those of the author and do not necessarily represent any policy, position, or program of NCCN.

  • 1.

    Yang H, Rudge DG, Koos JD. mTOR kinase structure, mechanism and regulation. Nature 2013;497:217223.

  • 2.

    Sato T, Nakashima A, Guo L. Single amino-acid changes that confer constitutive activation of mTOR are discovered in human cancer. Oncogene 2010;29:27462752.

    • Search Google Scholar
    • Export Citation
  • 3.

    Ghosh AP, Marshall CB, Coric T. Point mutations of the mTOR-RHEB pathway in renal cell carcinoma. Oncotarget 2015;6:1789517910.

  • 4.

    Kucejova B, Pena-Llopis S, Yamasaki T. Interplay between pVHL and mTORC1 pathways in clear-cell renal cell carcinoma. Mol Cancer Res 2011;9:12551265.

    • Search Google Scholar
    • Export Citation
  • 5.

    Creighton CJ, Morgan M, Gunaratne PH. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013;499:4349.

  • 6.

    Sato Y, Yoshizato T, Shiraishi Y. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet 2013;45:860867.

  • 7.

    Scelo G, Riazalhosseini Y, Greger L. Variation in genomic landscape of clear cell renal cell carcinoma across Europe. Nat Commun 2014;5:5135.

  • 8.

    Voss MH, Hakimi AA, Pham CG. Tumor genetic analyses of patients with metastatic renal cell carcinoma and extended benefit from mTOR inhibitor therapy. Clin Cancer Res 2014;20:19551964.

    • Search Google Scholar
    • Export Citation
  • 9.

    Kwiatkowski DJ, Choueiri TK, Fay AP. Mutations in TSC1, TSC2, and MTOR are associated with response to rapalogs in patients with metastatic renal cell carcinoma. Clin Cancer Res 2016;22:24452452.

    • Search Google Scholar
    • Export Citation
  • 10.

    Cho D, Signoretti S, Dabora S. Potential histologic and molecular predictors of response to temsirolimus in patients with advanced renal cell carcinoma. Clin Genitourin Cancer 2007;5:379385.

    • Search Google Scholar
    • Export Citation
  • 11.

    Ko JJ, Xie W, Kroeger N. The International Metastatic Renal Cell Carcinoma Database Consortium model as a prognostic tool in patients with metastatic renal cell carcinoma previously treated with first-line targeted therapy: a population-based study. Lancet Oncol 2015;16:293300.

    • Search Google Scholar
    • Export Citation
  • 12.

    Motzer RJ, Escudier B, McDermott DF. Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 2015;373:1803w1813.

  • 13.

    Choueiri TK, Escudier B, Powles T. Cabozantinib versus everolimus in advanced renal-cell carcinoma. N Engl J Med 2015;373:18141823.

  • 14.

    Hsieh J, Chen DY, Wang P. Identification of efficacy biomarkers in a large metastatic renal cell carcinoma (mRCC) cohort through next generation sequencing (NGS): results from RECORD-3 [abstract]. J Clin Oncol 2015;33(Suppl):Abstract 4509.

    • Search Google Scholar
    • Export Citation
  • 15.

    Kapur P, Pena-Llopis S, Christie A. Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. Lancet Oncol 2013;14:159167.

    • Search Google Scholar
    • Export Citation
  • 16.

    Joseph RW, Kapur P, Serie DJ. Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression. J Urol 2016;195:180187.

  • 17.

    Huang D, Ding Y, Li Y. Sunitinib acts primarily on tumor endothelium rather than tumor cells to inhibit the growth of renal cell carcinoma. Cancer Res 2010;70:10531062.

    • Search Google Scholar
    • Export Citation
  • 18.

    Tran TA, Leong HS, Pavia-Jimenez A. Fibroblast growth factor receptor-dependent and -independent paracrine signaling by sunitinib-resistant renal cell carcinoma. Mol Cell Biol 2016;36:18361855.

    • Search Google Scholar
    • Export Citation
  • 19.

    Schmidinger M. Third-line dovitinib in metastatic renal cell carcinoma. Lancet Oncol 2014;15:245246.

  • 20.

    Motzer RJ, Hutson TE, Ren M. Independent assessment of lenvatinib plus everolimus in patients with metastatic renal cell carcinoma. Lancet Oncol 2016;17:e45.

    • Search Google Scholar
    • Export Citation
  • 21.

    Beuselinck B, Job S, Becht E. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 2015;21:13291339.

    • Search Google Scholar
    • Export Citation
  • 22.

    Maroto P, Rini B. Molecular biomarkers in advanced renal cell carcinoma. Clin Cancer Res 2014;20:20602071.

  • 23.

    Funakoshi T, Lee CH, Hsieh JJ. A systematic review of predictive and prognostic biomarkers for VEGF-targeted therapy in renal cell carcinoma. Cancer Treat Rev 2014;40:533547.

    • Search Google Scholar
    • Export Citation
  • 24.

    Oosting SF, Brouwers AH, van Es SC. 89Zr-bevacizumab PET visualizes heterogeneous tracer accumulation in tumor lesions of renal cell carcinoma patients and differential effects of antiangiogenic treatment. J Nuclear Med 2015;56:6369.

    • Search Google Scholar
    • Export Citation
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