Genomic landscape and evolution of metastatic chromophobe renal cell carcinoma.

Chromophobe renal cell carcinoma (chRCC) typically shows ~7 chromosome losses (1, 2, 6, 10, 13, 17, and 21) and ~31 exonic somatic mutations, yet carries ~5%-10% metastatic incidence. Since extensive chromosomal losses can generate proteotoxic stress and compromise cellular proliferation, it is intriguing how chRCC, a tumor with extensive chromosome losses and a low number of somatic mutations, can develop lethal metastases. Genomic features distinguishing metastatic from nonmetastatic chRCC are unknown. An integrated approach, including whole-genome sequencing (WGS), targeted ultradeep cancer gene sequencing, and chromosome analyses (FACETS, OncoScan, and FISH), was performed on 79 chRCC patients including 38 metastatic (M-chRCC) cases. We demonstrate that TP53 mutations (58%), PTEN mutations (24%), and imbalanced chromosome duplication (ICD, duplication of ≥ 3 chromosomes) (25%) were enriched in M-chRCC. Reconstruction of the subclonal composition of paired primary-metastatic chRCC tumors supports the role of TP53, PTEN, and ICD in metastatic evolution. Finally, the presence of these 3 genomic features in primary tumors of both The Cancer Genome Atlas kidney chromophobe (KICH) (n = 64) and M-chRCC (n = 35) cohorts was associated with worse survival. In summary, our study provides genomic insights into the metastatic progression of chRCC and identifies TP53 mutations, PTEN mutations, and ICD as high-risk features.

The SWI/SNF Protein PBRM1 Restrains VHL-Loss-Driven Clear Cell Renal Cell Carcinoma.

PBRM1 is the second most commonly mutated gene after VHL in clear cell renal cell carcinoma (ccRCC). However, the biological consequences of PBRM1 mutations for kidney tumorigenesis are unknown. Here, we find that kidney-specific deletion of Vhl and Pbrm1, but not either gene alone, results in bilateral, multifocal, transplantable clear cell kidney cancers. PBRM1 loss amplified the transcriptional outputs of HIF1 and STAT3 incurred by Vhl deficiency. Analysis of mouse and human ccRCC revealed convergence on mTOR activation, representing the third driver event after genetic inactivation of VHL and PBRM1. Our study reports a physiological preclinical ccRCC mouse model that recapitulates somatic mutations in human ccRCC and provides mechanistic and therapeutic insights into PBRM1 mutated subtypes of human ccRCC.

Genomic Biomarkers of a Randomized Trial Comparing First-line Everolimus and Sunitinib in Patients with Metastatic Renal Cell Carcinoma.

BACKGROUND: Metastatic renal cell carcinoma (RCC) patients are commonly treated with vascular endothelial growth factor (VEGF) inhibitors or mammalian target of rapamycin inhibitors. Correlations between somatic mutations and first-line targeted therapy outcomes have not been reported on a randomized trial. OBJECTIVE: To evaluate the relationship between tumor mutations and treatment outcomes in RECORD-3, a randomized trial comparing first-line everolimus (mTOR inhibitor) followed by sunitinib (VEGF inhibitor) at progression with the opposite sequence in 471 metastatic RCC patients. DESIGN, SETTING, AND PARTICIPANTS: Targeted sequencing of 341 cancer genes at ∼540× coverage was performed on available tumor samples from 258 patients; 220 with clear cell histology (ccRCC). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Associations between somatic mutations and median first-line progression free survival (PFS1L) and overall survival were determined in metastatic ccRCC using Cox proportional hazards models and log-rank tests. RESULTS AND LIMITATIONS: Prevalent mutations (≥ 10%) were VHL (75%), PBRM1 (46%), SETD2 (30%), BAP1 (19%), KDM5C (15%), and PTEN (12%). With first-line everolimus, PBRM1 and BAP1 mutations were associated with longer (median [95% confidence interval {CI}] 12.8 [8.1, 18.4] vs 5.5 [3.1, 8.4] mo) and shorter (median [95% CI] 4.9 [2.9, 8.1] vs 10.5 [7.3, 12.9] mo) PFS1L, respectively. With first-line sunitinib, KDM5C mutations were associated with longer PFS1L (median [95% CI] of 20.6 [12.4, 27.3] vs 8.3 [7.8, 11.0] mo). Molecular subgroups of metastatic ccRCC based on PBRM1, BAP1, and KDM5C mutations could have predictive values for patients treated with VEGF or mTOR inhibitors. Most tumor DNA was obtained from primary nephrectomy samples (94%), which could impact correlation statistics. CONCLUSIONS: PBRM1, BAP1, and KDM5C mutations impact outcomes of targeted therapies in metastatic ccRCC patients. PATIENT SUMMARY: Large-scale genomic kidney cancer studies reported novel mutations and heterogeneous features among individual tumors, which could contribute to varied clinical outcomes. We demonstrated correlations between somatic mutations and treatment outcomes in clear cell renal cell carcinoma, supporting the value of genomic classification in prospective studies.

Molecular analysis of aggressive renal cell carcinoma with unclassified histology reveals distinct subsets.

Renal cell carcinomas with unclassified histology (uRCC) constitute a significant portion of aggressive non-clear cell renal cell carcinomas that have no standard therapy. The oncogenic drivers in these tumours are unknown. Here we perform a molecular analysis of 62 high-grade primary uRCC, incorporating targeted cancer gene sequencing, RNA sequencing, single-nucleotide polymorphism array, fluorescence in situ hybridization, immunohistochemistry and cell-based assays. We identify recurrent somatic mutations in 29 genes, including NF2 (18%), SETD2 (18%), BAP1 (13%), KMT2C (10%) and MTOR (8%). Integrated analysis reveals a subset of 26% uRCC characterized by NF2 loss, dysregulated Hippo-YAP pathway and worse survival, whereas 21% uRCC with mutations of MTOR, TSC1, TSC2 or PTEN and hyperactive mTORC1 signalling are associated with better clinical outcome. FH deficiency (6%), chromatin/DNA damage regulator mutations (21%) and ALK translocation (2%) distinguish additional cases. Altogether, this study reveals distinct molecular subsets for 76% of our uRCC cohort, which could have diagnostic and therapeutic implications.

Phase II Trial and Correlative Genomic Analysis of Everolimus Plus Bevacizumab in Advanced Non-Clear Cell Renal Cell Carcinoma.

Purpose The decreased effectiveness of single-agent targeted therapies in advanced non-clear cell renal cell carcinoma (ncRCC) compared with clear cell renal cell carcinoma (RCC) supports the study of combination regimens. We evaluated the efficacy of everolimus plus bevacizumab in patients with metastatic ncRCC. Patients and Methods In this single-center phase II trial, treatment-naive patients received everolimus 10 mg oral once per day plus bevacizumab 10 mg/kg intravenously every 2 weeks. The primary end point was progression-free survival (PFS) at 6 months. Correlative analyses explored candidate tissue biomarkers through next-generation sequencing. Results Thirty-five patients were enrolled with the following histologic subtypes: chromophobe (n = 5), papillary (n = 5), and medullary (n = 2) RCC and unclassified RCC (uRCC, n = 23). The majority of patients had papillary growth as a major component (n = 14). For 34 evaluable patients, median PFS, overall survival, and objective response rate (ORR) were 11.0 months, 18.5 months, and 29%, respectively. PFS varied by histology ( P < .001), and ORR was higher in patients with significant papillary (seven of 18) or chromophobe (two of five) elements than for others (one of 11). Presence of papillary features were associated with benefit, including uRCC, where it correlated with ORR (43% v 11%), median PFS (12.9 v 1.9 months), and overall survival (28.2 v 9.3 months; P < .001). Several genetic alterations seemed to segregate by histology. In particular, somatic mutations in ARID1A were seen in five of 14 patients with papillary features but not in other RCC variants. All five patients achieved treatment benefit. Conclusion The study suggests efficacy for this combination in patients with ncRCC characterized by papillary features. Distinct mutational profiles among ncRCCs vary according to specific histology.

Taspase1 cleaves MLL1 to activate cyclin E for HER2/neu breast tumorigenesis.

Taspase1, a highly conserved threonine protease, cleaves nuclear transcriptional regulators mixed-lineage leukemia (MLL, MLL1), MLL2, TFIIA, and ALF to orchestrate a wide variety of biological processes. In vitro studies thus far demonstrated that Taspase1 plays important roles in the proliferation of various cancer cell lines, including HER2-positive breast cancer cells. To investigate the role of Taspase1 in breast tumorigenesis in vivo, we deleted Taspase1 from mouse mammary glands by generating MMTV-neu;MMTV-cre;Tasp1(F/-) mice. We demonstrate that initiation of MMTV-neu- but not MMTV-wnt-driven breast cancer is blocked in the absence of Taspase1. Importantly, Taspase1 loss alone neither impacts normal development nor pregnancy physiology of the mammary gland. In mammary glands Taspase1 deficiency abrogates MMTV-neu-induced cyclins E and A expression, thereby preventing tumorigenesis. The mechanisms were explored in HER2-positive breast cancer cell line BT474 and HER2-transformed MCF10A cells and validated using knockdown-resistant Taspase1. As Taspase1 was shown to cleave MLL which forms complexes with E2F transcription factors to regulate Cyclins E, A, and B expression in mouse embryonic fibroblasts (MEFs), we investigated whether the cleavage of MLL by Taspase1 constitutes an essential in vivo axis for HER2/neu-induced mammary tumorigenesis. To this end, we generated MMTV-neu;MLL(nc/nc) transgenic mice that carry homozygous non-cleavable MLL alleles. Remarkably, these mice are also protected from HER2/neu-driven breast tumorigenesis. Hence, MLL is the primary Taspase1 substrate whose cleavage is required for MMTV-neu-induced tumor formation. As Taspase1 plays critical roles in breast cancer pathology, it may serve as a therapeutic target for HER2-positive human breast cancer.

FBP1 Is an Interacting Partner of Menin.

Multiple endocrine neoplasia type 1 (MEN1) is a syndrome characterized by tumors in multiple endocrine tissues such as the parathyroid glands, the pituitary gland, and the enteropancreatic neuroendocrine tissues. MEN1 is usually caused by mutations in the MEN1 gene that codes for the protein menin. Menin interacts with proteins that regulate transcription, DNA repair and processing, and maintenance of cytoskeletal structure. We describe the identification of FBP1 as an interacting partner of menin in a large-scale pull-down assay that also immunoprecipitated RBBP5, ASH2, and LEDGF, which are members of complex proteins associated with SET1 (COMPASS), a protein complex that methylates histone H3. This interaction was confirmed by coimmunoprecipitation and Flag-pull-down assays. Furthermore, menin localized to the FUSE site on the MYC promoter, a site that is transactivated by FBP1. This investigation therefore places menin in a pathway that regulates MYC gene expression and has important implications for the biological function of menin.

Mapping functional transcription factor networks from gene expression data.

A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein-DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.