Login to Access Video or Poster Abstract: MP39-01
Sources of Funding: Funded in part by the Sidney Kimmel Center for Prostate and Urologic Cancers and the National Cancer Institute Training Grant T32 CA082088 (BM MG)._x000D_ Clinical and Translational Science Center at Weill Cornell Medical Center UL1TR00457 (BM)

Introduction

Management of small renal masses (SRMs) may include active surveillance (AS) in selected patients. Clear cell renal cell carcinoma (ccRCC) is the most common histology among these SRMs. Current AS algorithms largely rely on growth parameters of the masses measured over time. We sought to identify genomic biomarkers that could potentially refine the management of SRMs, especially in patients being evaluated for AS.

Methods

From four databases, we identified 205 patients who had SRMs (<4 cm) at time of surgery and had sequencing performed on their primary tumors. We included patients from our institutional prospective database (n=25), and from three publicly available cohorts, The Cancer Genome Atlas (n=110), University of Tokyo (n=38), and The International Cancer Genome Consortium (n=32). We analyzed the frequency of recurrent somatic mutations among the entire cohort. Using Chi-Square analysis, the frequency of mutations that occurred in at least 5% of patients were compared in patients who had recurrence or died from their disease during follow-up with those who did not. Kaplan?Meier survival plots were generated for these frequently mutated genes. Analysis was adjusted for multiple testing using the false discovery rate.

Results

Median follow-up was 43.1 months among survivors. Mutations in VHL, PBRM1, SETD2, BAP1, KDM5C, and MTOR were present in more than 5% of tumors. Of the 205 patients, 25 (12.2%) had recurrence or died of their disease. Mutations in KDM5C were found to be significantly more common in those who had recurrence or died of their disease (24% vs. 4%; adjusted p=0.02). Survival analysis showed patients with KDM5C having statistically significant inferior cancer-specific survival (adjusted p=<0.01) and a trend for inferior survival in those with SETD2 mutations (adjusted p=0.11) (Figure 1).

Conclusions

We identified mutations in SRMs that are associated with recurrence and lethality. The strongest association was seen with KDM5C mutations. Use of these potential genomic biomarkers may improve risk stratification of patients with SRMs and for those who may be appropriate for AS. Prospective evaluation of these markers is needed.

Funding

Funded in part by the Sidney Kimmel Center for Prostate and Urologic Cancers and the National Cancer Institute Training Grant T32 CA082088 (BM MG)._x000D_ Clinical and Translational Science Center at Weill Cornell Medical Center UL1TR00457 (BM)