Genomic Advances in Kidney Cancer


The incidence of renal cell carcinoma (RCC) continues to rise among all racial and ethnic groups in the United States, with more than 65,000 new cases reported annually. One in three of these individuals is diagnosed with metastatic disease. While the most common type of RCC — clear cell renal cell carcinoma (ccRCC) — accounts for slightly more than half of all renal cortical tumors, it’s responsible for 90 percent of those that metastasize. (1)

Considerable progress has been made in the treatment of metastatic RCC over the past decade, with the development and incorporation into clinical care of agents that block the vascular endothelial growth factor (VEGF) pathway (2) or the mammalian target of rapamycin (mTOR) pathway. (3)

However, while these agents extend life, complete responses remain elusive.

Now preliminary studies are showing promise for immunogenic therapies — such as agents that target programmed death ligand 1 (PD-L1) — to improve outcomes in individuals with metastatic disease. (4) Additionally, recent advances in our understanding of the genetic makeup of ccRCC have opened up several new avenues for investigation.

At MSK, we expect to extend these findings by conducting extensive genetic analysis of more than 100 renal tumors using our institution’s next-generation sequencing platform, MSK-IMPACT™.

Mutation Discovery: Moving Beyond VHL

In the late 1980s, ccRCC was discovered to be a consequence of biallelic loss of the von Hippel–Lindau tumor suppressor gene (VHL), which occurs through loss of the majority of the short arm of chromosome 3 with concomitant mutation or promoter methylation of the other VHL allele. (5) (6)

Recent large-scale multiplatform sequencing projects have identified driver genes in ccRCC beyond VHL: PBRM1, SETD2, BAP1, and KDM5C. (7) (8) (9) These genes are interesting and relevant for multiple reasons. They all belong to the newly recognized cancer gene category of epigenetic regulators, defined as genes that affect the expression and function of other genes without changing their DNA sequence — in the case of these four genes, by acting as histone modifiers and chromatin remodelers.

In addition, these genes are among the most common mutations across the genome for ccRCC (PBRM1 ~35-60%, SETD2 12-20%, BAP1 ~10-20%, KDM5C ~5-10%). (7) (9) The genes PBRM1, SETD2, and BAP1 map to the frequently lost 3p21 locus, in close vicinity to VHL. (10)

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Genetic Alterations and Prognosis

Our lab and others have recently reported that mutations of these epigenetic regulators are associated with advanced stage, grade, and invasiveness of tumors. We also discovered that BAP1 and SETD2 mutations are associated with lower rates of cancer-specific survival. (11) (12) (13)

The strong link between these chromatin-modulating genes and tumor behavior suggests that they may be valuable biomarkers, as predictors of disease behavior and possibly treatment response, and that detection of their mutation status may therefore aid in clinical decision-making.

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Challenges of Intra-tumor Heterogeneity

As reported by Gerlinger and colleagues, (14) (15) intra-tumor genetic heterogeneity is an obstacle to the development of tumor biomarkers and appropriate selection of precision therapies.

Using whole-exome sequencing and chromosome aberration analysis of multiple spatially distinct sites within primary tumors and metastatic lesions, the authors elegantly demonstrated intra-tumor genetic heterogeneity and constructed phylogenetic trees showing the evolutionary relationship between different genetic variants within single tumors and across lesions in patients with metastatic ccRCC.

In a 2014 paper in Cancer Medicine, we reported that a minimum of three spatially distinct biopsies are necessary to detect mutations in PBRM1, SETD2, BAP1, and/or KDM5C with 90 percent certainty. (16) We sequenced five RCC-associated genes (VHL, PBRM1, SETD2, BAP1, and KDM5C) in multiple spatially distinct biopsies within 14 renal tumors, revealing regional genetic variation in RCC.

We then constructed evolutionary trees for each tumor and demonstrated that assessment of prognostic genetic mutations from a single-site needle biopsy may not reflect the broader tumor landscape:

Phylogenetic trees of three tumors displaying intra-tumor genetic heterogeneity

Phylogenetic trees of three tumors displaying intra-tumor genetic heterogeneity (grade, stage, and size noted at top; shared mutations shown in blue and non-shared mutations in red; tumor regions designated R1-R4; dashed lines indicate two distinct mutations detected in the same biopsy core)

Importantly, regional variability in a tumor’s genetic landscape is likely a key cause of treatment failure and drug resistance. If so, the development of strategies to address intra-tumor genetic heterogeneity is necessary to overcome drug resistance and devise lasting cures for this cancer.

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Biomarkers and the Road Ahead

An important implication of these studies is that single-site biopsies, even when multiple samples are obtained, may lead to false-negative biomarker assessments.

Clinically, the presence or absence of a prognostic gene mutation may impact the decision to forgo observation and perform surgical resection or ablative therapy for a small renal mass. Additionally, as we enter a new era of molecular pathway–based therapeutics, detection of gene mutations plays a role not only in risk stratification but also in selection of appropriate targeted therapies.

We expect the extensive prospective genetic analysis we are conducting — assaying more than 340 oncogenes and tumor suppressors via MSK-IMPACT — to help answer the following key clinical questions:

  • Do small renal masses exhibit the same variable intra-tumor genomic profiles as their advanced-stage counterparts?
  • Can multiregional sampling of a tumor provide prognostic and therapeutic information that improves patient outcomes?
  • Can combined multiregional sequencing offer accurate, cost-effective risk stratification?
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  1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012 Jan-Feb;62(1):10-29.
  2. Motzer RJ, Rini BI, Bukowski RM, et al. Sunitinib in patients with metastatic renal cell carcinoma. JAMA. 2006 Jun;295(21):2516-24.
  3. Motzer RJ, Escudier B, Oudard S, et al. Efficacy of everolimus in advanced renal cell carcinoma: a double-blind, randomised, placebo-controlled phase III trial. Lancet. 2008 Aug; 372(9637):449-56.
  4. Brahmer JR, Tykodi SS, Chow LQ, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med. 2012 Jun;366(26):2455-65.
  5. Zbar B, Brauch H, Talmadge C, Linehan M. Loss of alleles of loci on the short arm of chromosome 3 in renal cell carcinoma. Nature. 1987 Jun-Jul;327(6124):721-4.
  6. Herman JG, Latif F, Weng Y, et al. Silencing of the VHL tumor-suppressor gene by DNA methylation in renal carcinoma. Proc Natl Acad Sci USA. 1994 Oct;91(21):9700-4.
  7. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013 Jul;499(7456):43-9.
  8. Guo G, Gui Y, Gao S, et al. Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma. Nat Genet. 2012 Dec;44(1):17-9.
  9. Sato Y, Yoshizato T, Shiraishi Y, et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet. 2013 Aug;45(8):860-7.
  10. Hakimi AA, Pham CG, Hsieh JJ. A clear picture of renal cell carcinoma. Nat Genet. 2013 Aug;45(8):849-50.
  11. Hakimi AA, Chen YB, Wren J, et al. Clinical and pathologic impact of select chromatin-modulating tumor suppressors in clear cell renal cell carcinoma. Eur Urol. 2013 May;63(5):848-54.
  12. Hakimi AA, Ostrovnaya I, Reva B, et al. Adverse outcomes in clear cell renal cell carcinoma with mutations of 3p21 epigenetic regulators BAP1 and SETD2: a report by MSKCC and the KIRC TCGA research network. Clin Cancer Res. 2013 Jun;19(12):3259-67.
  13. Kapur P, Peña Llopis, Christie A, et al. Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. Lancet Oncol. 2013 Feb;14(2):159-67.
  14. Gerlinger M, Horswell S, Larkin J, et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet. 2014 Mar;46(3):225-33.
  15. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012 Mar;366(10):883-92.
  16. Sankin A, Hakimi AA, Mikkilineni N, et al. The impact of genetic heterogeneity on biomarker development in kidney cancer assessed by multiregional sampling. Cancer Medicine. 2014 Dec;3(6):1485-92.