Brian McConeghy

Androgen receptor gene aberrations in circulating cell-free DNA: biomarkers of therapeutic resistance in castration-resistant prostate cancer

Purpose: Although novel agents targeting the androgen–androgen receptor (AR) axis have altered the treatment paradigm of metastatic castration-resistant prostate cancer (mCRPC), development of therapeutic resistance is inevitable. In this study, we examined whether AR gene aberrations detectable in circulating cell-free DNA (cfDNA) are associated with resistance to abiraterone acetate and enzalutamide in mCRPC patients. Experimental Design: Plasma was collected from 62 mCRPC patients ceasing abiraterone acetate (n = 29), enzalutamide (n = 19), or other agents (n = 14) due to disease progression. DNA was extracted and subjected to array comparative genomic hybridization (aCGH) for chromosome copy number analysis, and Roche 454 targeted next-generation sequencing of exon 8 in the AR. Results: On aCGH, AR amplification was significantly more common in patients progressing on enzalutamide than on abiraterone or other agents (53% vs. 17% vs. 21%, P = 0.02, χ2). Missense AR exon 8 mutations were detected in 11 of 62 patients (18%), including the first reported case of an F876L mutation in an enzalutamide-resistant patient and H874Y and T877A mutations in 7 abiraterone-resistant patients. In patients switched onto enzalutamide after cfDNA collection (n = 39), an AR gene aberration (copy number increase and/or an exon 8 mutation) in pretreatment cfDNA was associated with adverse outcomes, including lower rates of PSA decline ≥ 30% (P = 0.013, χ2) and shorter time to radiographic/clinical progression (P = 0.010, Cox proportional hazards regression). Conclusions: AR gene aberrations in cfDNA are associated with resistance to enzalutamide and abiraterone in mCRPC. Our data illustrate that genomic analysis of cfDNA is a minimally invasive method for interrogating mechanisms of therapeutic resistance in mCRPC. Clin Cancer Res; 21(10); 2315–24. ©2015 AACR.

Genomic Alterations in Cell-Free DNA and Enzalutamide Resistance in Castration-Resistant Prostate Cancer

Importance The molecular landscape underpinning response to the androgen receptor (AR) antagonist enzalutamide in patients with metastatic castration-resistant prostate cancer (mCRPC) is undefined. Consequently, there is an urgent need for practical biomarkers to guide therapy selection and elucidate resistance. Although tissue biopsies are impractical to perform routinely in the majority of patients with mCRPC, the analysis of plasma cell-free DNA (cfDNA) has recently emerged as a minimally invasive method to explore tumor characteristics. Objective To reveal genomic characteristics from cfDNA associated with clinical outcomes during enzalutamide treatment. Design, Setting, and Participants Plasma samples were obtained from August 4, 2013, to July 31, 2015, at a single academic institution (British Columbia Cancer Agency) from 65 patients with mCRPC. We collected temporal plasma samples (at baseline, 12 weeks, end of treatment) for circulating cfDNA and performed array comparative genomic hybridization copy number profiling and deep AR gene sequencing. Samples collected at end of treatment were also subjected to targeted sequencing of 19 prostate cancer-associated genes. Exposure Enzalutamide, 160 mg, daily orally. Main Outcomes and Measures Prostate-specific antigen response rate (decline ≥50% from baseline confirmed ≥3 weeks later). Radiographic (as per Prostate Cancer Working Group 2 Criteria) and/or clinical progression (defined as worsening disease-related symptoms necessitating a change in anticancer therapy and/or deterioration in Eastern Cooperative Group performance status ≥2 levels). Results The 65 patients had a median (interquartile range) age of 74 (68-79) years. Prostate-specific antigen response rate to enzalutamide treatment was 38% (25 of 65), while median clinical/radiographic progression-free survival was 3.5 (95% CI, 2.1-5.0) months. Cell-free DNA was isolated from 122 of 125 plasma samples, and targeted sequencing was successful in 119 of 122. AR mutations and/or copy number alterations were robustly detected in 48% (31 of 65) and 60% (18 of 30) of baseline and progression samples, respectively. Detection of AR amplification, heavily mutated AR (≥2 mutations), and RB1 loss were associated with worse progression-free survival, with hazard ratios of 2.92 (95% CI, 1.59-5.37), 3.94 (95% CI, 1.46-10.64), and 4.46 (95% CI, 2.28-8.74), respectively. AR mutations exhibited clonal selection during treatment, including an increase in glucocorticoid-sensitive AR L702H and promiscuous AR T878A in patients with prior abiraterone treatment. At the time of progression, cfDNA sequencing revealed mutations or copy number changes in all patients tested, including clinically actionable alterations in DNA damage repair genes and PI3K pathway genes, and a high frequency (4 of 14) of activating CTNNB1 mutations. Conclusions and Relevance Clinically informative genomic profiling of cfDNA was feasible in nearly all patients with mCRPC and can provide important insights into enzalutamide response and resistance.

Integrated genome and transcriptome sequencing identifies a novel form of hybrid and aggressive prostate cancer

Next‐generation sequencing is making sequence‐based molecular pathology and personalized oncology viable. We selected an individual initially diagnosed with conventional but aggressive prostate adenocarcinoma and sequenced the genome and transcriptome from primary and metastatic tissues collected prior to hormone therapy. The histology‐pathology and copy number profiles were remarkably homogeneous, yet it was possible to propose the quadrant of the prostate tumour that likely seeded the metastatic diaspora. Despite a homogeneous cell type, our transcriptome analysis revealed signatures of both luminal and neuroendocrine cell types. Remarkably, the repertoire of expressed but apparently private gene fusions, including C15orf21:MYC, recapitulated this biology. We hypothesize that the amplification and over‐expression of the stem cell gene MSI2 may have contributed to the stable hybrid cellular identity. This hybrid luminal‐neuroendocrine tumour appears to represent a novel and highly aggressive case of prostate cancer with unique biological features and, conceivably, a propensity for rapid progression to castrate‐resistance. Overall, this work highlights the importance of integrated analyses of genome, exome and transcriptome sequences for basic tumour biology, sequence‐based molecular pathology and personalized oncology. Copyright

Poly-Gene Fusion Transcripts and Chromothripsis in Prostate Cancer

Complex genome rearrangements are frequently observed in cancer but their impact on tumor molecular biology is largely unknown. Recent studies have identified a new phenomenon involving the simultaneous generation of tens to hundreds of genomic rearrangements, called chromothripsis. To understand the molecular consequences of these events, we sequenced the genomes and transcriptomes of two prostate tumors exhibiting evidence of chromothripsis. We identified several complex fusion transcripts, each containing sequence from three different genes, originating from different parts of the genome. One such poly‐gene fusion transcript appeared to be expressed from a chain of small genomic fragments. Furthermore, we detected poly‐gene fusion transcripts in the prostate cancer cell line LNCaP, suggesting they may represent a common phenomenon. Finally in one tumor with chromothripsis, we identified multiple mutations in the p53 signaling pathway, expanding on recent work associating aberrant DNA damage response mechanisms with chromothripsis. Overall, our data show that chromothripsis can manifest as massively rearranged transcriptomes. The implication that multigenic changes can give rise to poly‐gene fusion transcripts is potentially of great significance to cancer genetics.

Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer

BackgroundGenomic analyses of hundreds of prostate tumors have defined a diverse landscape of mutations and genome rearrangements, but the transcriptomic effect of this complexity is less well understood, particularly at the individual tumor level. We selected a cohort of 25 high-risk prostate tumors, representing the lethal phenotype, and applied deep RNA-sequencing and matched whole genome sequencing, followed by detailed molecular characterization.ResultsTen tumors were exposed to neo-adjuvant hormone therapy and expressed marked evidence of therapy response in all except one extreme case, which demonstrated early resistance via apparent neuroendocrine transdifferentiation. We observe high inter-tumor heterogeneity, including unique sets of outlier transcripts in each tumor. Interestingly, outlier expression converged on druggable cellular pathways associated with cell cycle progression, translational control or immune regulation, suggesting distinct contemporary pathway affinity and a mechanism of tumor stratification. We characterize hundreds of novel fusion transcripts, including a high frequency of ETS fusions associated with complex genome rearrangements and the disruption of tumor suppressors. Remarkably, several tumors express unique but potentially-oncogenic non-ETS fusions, which may contribute to the phenotype of individual tumors, and have significance for disease progression. Finally, one ETS-negative tumor has a striking tandem duplication genotype which appears to be highly aggressive and present at low recurrence in ETS-negative prostate cancer, suggestive of a novel molecular subtype.ConclusionsThe multitude of rare genomic and transcriptomic events detected in a high-risk tumor cohort offer novel opportunities for personalized oncology and their convergence on key pathways and functions has broad implications for precision medicine.

SiNVICT: Ultra-Sensitive Detection of Single Nucleotide Variants and Indels in Circulating Tumour DNA.

Motivation: Successful development and application of precision oncology approaches require robust elucidation of the genomic landscape of a patient’s cancer and, ideally, the ability to monitor therapy-induced genomic changes in the tumour in an inexpensive and minimally invasive manner. Thanks to recent advances in sequencing technologies, ‘liquid biopsy’, the sampling of patient’s bodily fluids such as blood and urine, is considered as one of the most promising approaches to achieve this goal. In many cancer patients, and especially those with advanced metastatic disease, deep sequencing of circulating cell free DNA (cfDNA) obtained from patient’s blood yields a mixture of reads originating from the normal DNA and from multiple tumour subclones—called circulating tumour DNA or ctDNA. The ctDNA/cfDNA ratio as well as the proportion of ctDNA originating from specific tumour subclones depend on multiple factors, making comprehensive detection of mutations difficult, especially at early stages of cancer. Furthermore, sensitive and accurate detection of single nucleotide variants (SNVs) and indels from cfDNA is constrained by several factors such as the sequencing errors and PCR artifacts, and mapping errors related to repeat regions within the genome. In this article, we introduce SiNVICT, a computational method that increases the sensitivity and specificity of SNV and indel detection at very low variant allele frequencies. SiNVICT has the capability to handle multiple sequencing platforms with different error properties; it minimizes false positives resulting from mapping errors and other technology specific artifacts including strand bias and low base quality at read ends. SiNVICT also has the capability to perform time-series analysis, where samples from a patient sequenced at multiple time points are jointly examined to report locations of interest where there is a possibility that certain clones were wiped out by some treatment while some subclones gained selective advantage. Results: We tested SiNVICT on simulated data as well as prostate cancer cell lines and cfDNA obtained from castration-resistant prostate cancer patients. On both simulated and biological data, SiNVICT was able to detect SNVs and indels with variant allele percentages as low as 0.5%. The lowest amounts of total DNA used for the biological data where SNVs and indels could be detected with very high sensitivity were 2.5 ng on the Ion Torrent platform and 10 ng on Illumina. With increased sequencing and mapping accuracy, SiNVICT might be utilized in clinical settings, making it possible to track the progress of point mutations and indels that are associated with resistance to cancer therapies and provide patients personalized treatment. We also compared SiNVICT with other popular SNV callers such as MuTect, VarScan2 and Freebayes. Our results show that SiNVICT performs better than these tools in most cases and allows further data exploration such as time-series analysis on cfDNA sequencing data. Availability and Implementation: SiNVICT is available at: https://sfu-compbio.github.io/sinvict Supplementary information: Supplementary data are available at Bioinformatics online. Contact: cenk@sfu.ca

Systematic Identification and Characterization of RNA Editing in Prostate Tumors

RNA editing modifies the sequence of primary transcripts, potentially resulting in profound effects to RNA structure and protein-coding sequence. Recent analyses of RNA sequence data are beginning to provide insights into the distribution of RNA editing across the entire transcriptome, but there are few published matched whole genome and transcriptome sequence datasets, and designing accurate bioinformatics methodology has proven highly challenging. To further characterize the RNA editome, we analyzed 16 paired DNA-RNA sequence libraries from prostate tumor specimens, employing a comprehensive strategy to rescue low coverage sites and minimize false positives. We identified over a hundred thousand putative RNA editing events, a third of which were recurrent in two or more samples, and systematically characterized their type and distribution across the genome. Within genes the majority of events affect non-coding regions such as introns and untranslated regions (UTRs), but 546 genes had RNA editing events predicted to result in deleterious amino acid alterations. Finally, we report a potential association between RNA editing of microRNA binding sites within 3′ UTRs and increased transcript expression. These results provide a systematic characterization of the landscape of RNA editing in low coverage sequence data from prostate tumor specimens. We demonstrate further evidence for RNA editing as an important regulatory mechanism and suggest that the RNA editome should be further studied in cancer.

BAP1 Loss Predicts Therapeutic Vulnerability in Malignant Peritoneal Mesothelioma

Malignant Peritoneal Mesothelioma (PeM) is a rare but frequently fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no effective targeted therapies for PeM yet exist. In the search for novel therapeutic target candidates in PeM, we performed a comprehensive integrative multi-omics analysis of 19 treatment-naive PeM tumors. The analysis identified PeM tumors with BAP1 loss to form a distinct molecular subtype characterized by distinct expression patterns of genes involved in chromatin remodeling, DNA repair pathway, and immune checkpoint receptor activation. This PeM subtype could potentially benefit from immune checkpoint, PARP, or HDAC inhibition therapies. Our findings uncover BAP1 as a trackable prognostic and predictive biomarker, and refine PeM disease classification. This integrated molecular characterization provides a comprehensive foundation for developing PeM precision medicine.Background Malignant Peritoneal Mesothelioma (PeM) is a rare and fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no effective targeted therapies for PeM exist. In the search for novel therapeutic targets for PeM, we performed a comprehensive integrative multi-omics analysis of 19 treatment-naïve PeM. Although, BAP1 loss of function is known to be a key driver event in PeM, its downstream significance has not been investigated in this type of tumor. Furthermore, molecular subtypes of PeM has not been well defined. Results Using our recently developed cancer driver gene prioritization algorithm, HIT’nDRIVE, we identified PeM with BAP1 loss to form a distinct molecular subtype characterized by distinct gene expression patterns of chromatin remodeling, DNA repair pathways, and immune checkpoint receptor activation. We demonstrate that this subtype is correlated with an inflammatory tumor microenvironment and thus is a candidate for immune checkpoint blockade therapies. Conclusions Our findings reveal BAP1 to be a trackable prognostic and predictive biomarker for PeM immunotherapy that refines PeM disease classification. BAP1 stratification may improve drug response rates in ongoing phase-I and II clinical trials exploring the use of immune checkpoint blockade therapies in PeM in which BAP1 status is not considered. This integrated molecular characterization provides a comprehensive foundation for improved management of a subset of PeM patients. Significance Our first-in-field multi-omics analysis of PeM tumors identified BAP1 loss as a distinct molecular subtype and a candidate for immune checkpoint blockade therapies. This is significant because almost half of PeM cases are now candidates for these therapies. BAP1 status is not currently taken into account in the ongoing phase-I and II clinical trials exploring the use of immune checkpoint blockade therapies in PeM. Moreover, this is the first study to demonstrate evidence of inflammatory tumor microenvironment in PeM. Our findings identify BAP1 as a tractable prognostic and predictive biomarker for immunotherapy that refines PeM disease stratification and may improve drug response rates.Abstract Background Malignant Peritoneal Mesothelioma (PeM) is a rare and fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no effective targeted therapies for PeM exist. Some immune checkpoint inhibitor studies of mesothelioma have found positivity to be associated with a worse prognosis. Methods To search for novel therapeutic targets for PeM, we performed a comprehensive integrative multi-omics analysis of the genome, transcriptome, and proteome of 19 treatment-naive PeM, and in particular we examined BAP1 mutation and copy-number status and its relationship to immune checkpoint inhibitor activation. Results We found that PeM could be divided into tumors with an inflammatory tumor microenvironment and those without, and that this distinction correlated with haploinsufficiency of BAP1. To further investigate the role of BAP1, we used our recently developed cancer driver gene prioritization algorithm, HIT’nDRIVE, and observed that PeM with BAP1 haploinsufficiency form a distinct molecular subtype characterized by distinct gene expression patterns of chromatin remodeling, DNA repair pathways, and immune checkpoint receptor activation. We demonstrate that this subtype is correlated with an inflammatory tumor microenvironment and thus is a candidate for immune checkpoint blockade therapies. Conclusions Our findings reveal BAP1 to be a potential, easily trackable prognostic and predictive biomarker for PeM immunotherapy that refines PeM disease classification. BAP1 stratification may improve drug response rates in ongoing phase-I and II clinical trials exploring the use of immune checkpoint blockade therapies in PeM in which BAP1 status is not considered. This integrated molecular characterization provides a comprehensive foundation for improved management of a subset of PeM patients.

Alignment-free Clustering of UMI Tagged DNA Molecules

MOTIVATION Next-Generation Sequencing has led to the availability of massive genomic datasets whose processing raises many challenges, including the handling of sequencing errors. This is especially pertinent in cancer genomics, e.g. for detecting low allele frequency variations from circulating tumor DNA. Barcode tagging of DNA molecules with unique molecular identifiers (UMI) attempts to mitigate sequencing errors; UMI tagged molecules are polymerase chain reaction (PCR) amplified, and the PCR copies of UMI tagged molecules are sequenced independently. However, the PCR and sequencing steps can generate errors in the sequenced reads that can be located in the barcode and/or the DNA sequence. Analyzing UMI tagged sequencing data requires an initial clustering step, with the aim of grouping reads sequenced from PCR duplicates of the same UMI tagged molecule into a single cluster, and the size of the current datasets requires this clustering process to be resource-efficient. RESULTS We introduce Calib, a computational tool that clusters paired-end reads from UMI tagged sequencing experiments generated by substitution-error-dominant sequencing platforms such as Illumina. Calib clusters are defined as connected components of a graph whose edges are defined in terms of both barcode similarity and read sequence similarity. The graph is constructed efficiently using locality sensitive hashing and MinHashing techniques. Calibs default clustering parameters are optimized empirically, for different UMI and read lengths, using a simulation module that is packaged with Calib. Compared to other tools, Calib has the best accuracy on simulated data, while maintaining reasonable runtime and memory footprint. On a real dataset, Calib runs with far less resources than alignment-based methods, and its clusters reduce the number of tentative false positive in downstream variation calling. AVAILABILITY AND IMPLEMENTATION Calib is implemented in C++ and its simulation module is implemented in Python. Calib is available at https://github.com/vpc-ccg/calib. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.