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Background: Cell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings., Methods: Whole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel's ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy., Results: The panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci., Conclusion: Machine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients.

Background: Pan-cancer studies of somatic copy number alterations (SCNAs) have demonstrated common SCNA patterns across cancer types, but despite demonstrable differences in aggressiveness of some cancers by race, pan-cancer SCNA variation by race has not been explored. This study investigated a) racial differences in SCNAs in both breast and prostate cancer, b) the degree to which they are shared across cancers, and c) the impact of these shared, race-differentiated SCNAs on cancer survival., Methods: Utilizing data from The Cancer Genome Atlas (TCGA), SCNAs were identified using GISTIC 2.0, and in each tumor type, differences in SCNA magnitude between African Americans (AA) and European Americans (EA) were tested using linear regression. Unsupervised hierarchical clustering of the copy number of genes residing in race-differentiated SCNAs shared between tumor types was used to identify SCNA-defined patient groups, and Cox proportional hazards regression was used to test for association between those groups and overall/progression-free survival (PFS)., Results: We identified SCNAs that differed by race in breast (n = 58 SCNAs; permutation p < 10 - 4 ) and prostate tumors (n = 78 SCNAs; permutation p = 0.006). Six race-differentiated SCNAs common to breast and prostate found at chromosomes 5q11.2-q14.1, 5q15-q21.1, 8q21.11-q21.13, 8q21.3-q24.3, 11q22.3, and 13q12.3-q21.3 had consistent differences by race across both tumor types, and all six were of higher magnitude in AAs, with the chromosome 8q regions being the only amplifications. Higher magnitude copy number differences in AAs were also identified at two of these race-differentiated SCNAs in two additional hormonally-driven tumor types: endometrial (8q21.3-q24.3 and 13q12.3-q21.3) and ovarian (13q12.3-q21.3) cancers. Race differentiated SCNA-defined patient groups were significantly associated with survival differences in both cancer types, and these groups also differentiated within triple negative breast cancers based on PFS. While the frequency of the SCNA-defined patient groups differed by race, their effects on survival did not., Conclusions: This study identified race-differentiated SCNAs shared by two related cancers. The association of SCNA-defined patient groups with survival demonstrates the clinical significance of combinations of these race-differentiated genomic aberrations, and the higher frequency of these alterations in AA relative to EA patients may explain racial disparities in risk of aggressive breast and prostate cancer.

Background: While programmed death 1 (PD-1) and programmed death-ligand 1 (PD-L1) checkpoint inhibitors have activity in a proportion of patients with advanced bladder cancer, strongly predictive and prognostic biomarkers are still lacking. In this study, we evaluated PD-L1 protein expression on circulating tumor cells (CTCs) isolated from patients with muscle invasive (MIBC) and metastatic (mBCa) bladder cancer and explore the prognostic value of CTC PD-L1 expression on clinical outcomes., Methods: Blood samples from 25 patients with MIBC or mBCa were collected at UCSF and shipped to Epic Sciences. All nucleated cells were subjected to immunofluorescent (IF) staining and CTC identification by fluorescent scanners using algorithmic analysis. Cytokeratin expressing (CK) + and (CK) - CTCs (CD45 - , intact nuclei, morphologically distinct from WBCs) were enumerated. A subset of patient samples underwent genetic characterization by fluorescence in situ hybridization (FISH) and copy number variation (CNV) analysis., Results: CTCs were detected in 20/25 (80 %) patients, inclusive of CK + CTCs (13/25, 52 %), CK - CTCs (14/25, 56 %), CK + CTC Clusters (6/25, 24 %), and apoptotic CTCs (13/25, 52 %). Seven of 25 (28 %) patients had PD-L1 + CTCs; 4 of these patients had exclusively CK - /CD45 - /PD-L1 + CTCs. A subset of CTCs were secondarily confirmed as bladder cancer via FISH and CNV analysis, which revealed marked genomic instability. Although this study was not powered to evaluate survival, exploratory analyses demonstrated that patients with high PD-L1 + /CD45 - CTC burden and low burden of apoptotic CTCs had worse overall survival., Conclusions: CTCs are detectable in both MIBC and mBCa patients. PD-L1 expression is demonstrated in both CK + and CK - CTCs in patients with mBCa, and genomic analysis of these cells supports their tumor origin. Here we demonstrate the ability to identify CTCs in patients with advanced bladder cancer through a minimally invasive process. This may have the potential to guide checkpoint inhibitor immune therapies that have been established to have activity, often with durable responses, in a proportion of these patients.

Background: Copy number variants (CNVs), including deletions, amplifications, and other rearrangements, are common in human and cancer genomes. Copy number data from array comparative genome hybridization (aCGH) and next-generation DNA sequencing is widely used to measure copy number variants. Comparison of copy number data from multiple individuals reveals recurrent variants. Typically, the interior of a recurrent CNV is examined for genes or other loci associated with a phenotype. However, in some cases, such as gene truncations and fusion genes, the target of variant lies at the boundary of the variant., Results: We introduce Neighborhood Breakpoint Conservation (NBC), an algorithm for identifying rearrangement breakpoints that are highly conserved at the same locus in multiple individuals. NBC detects recurrent breakpoints at varying levels of resolution, including breakpoints whose location is exactly conserved and breakpoints whose location varies within a gene. NBC also identifies pairs of recurrent breakpoints such as those that result from fusion genes. We apply NBC to aCGH data from 36 primary prostate tumors and identify 12 novel rearrangements, one of which is the well-known TMPRSS2-ERG fusion gene. We also apply NBC to 227 glioblastoma tumors and predict 93 novel rearrangements which we further classify as gene truncations, germline structural variants, and fusion genes. A number of these variants involve the protein phosphatase PTPN12 suggesting that deregulation of PTPN12, via a variety of rearrangements, is common in glioblastoma., Conclusions: We demonstrate that NBC is useful for detection of recurrent breakpoints resulting from copy number variants or other structural variants, and in particular identifies recurrent breakpoints that result in gene truncations or fusion genes. Software is available at http://http.//cs.brown.edu/people/braphael/software.html.