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Purpose: Prostate cancer is the second leading cause of male cancer deaths. Castration-resistant prostate cancer (CRPC) is a lethal stage of the disease that emerges when endocrine therapies are no longer effective at suppressing activity of the androgen receptor (AR) transcription factor. The purpose of this study was to identify genomic mechanisms that contribute to the development and progression of CRPC. Experimental Design: We used whole-genome and targeted DNA-sequencing approaches to identify mechanisms underlying CRPC in an aggregate cohort of 272 prostate cancer patients. We analyzed structural rearrangements at the genome-wide level and carried out a detailed structural rearrangement analysis of the AR locus. We used genome engineering to perform experimental modeling of AR gene rearrangements and long-read RNA sequencing to analyze effects on expression of AR and truncated AR variants (AR-V). Results: AR was among the most frequently rearranged genes in CRPC tumors. AR gene rearrangements promoted expression of diverse AR-V species. AR gene rearrangements occurring in the context of AR amplification correlated with AR overexpression. Cell lines with experimentally derived AR gene rearrangements displayed high expression of tumor-specific AR-Vs and were resistant to endocrine therapies, including the AR antagonist enzalutamide. Conclusions: AR gene rearrangements are an important mechanism of resistance to endocrine therapies in CRPC.

Case report of a 57-year-old male who underwent insertion of an inflatable penile prosthesis due to erectile dysfunction, secondary to poorly controlled Type 2 diabetes and Peyronie’s disease. The surgical procedure was uneventful and there were no immediate post-operative complications. During a routine follow-up, the patient described problems with the deflation of the implant and severe lower back and leg pain. Diagnostic MRI scans revealed reservoir migration, impingement of the obturator nerve and oedema in the adductor muscle group. The reservoir was initially repositioned, and later on removed due to ongoing symptoms.

Solid electrolyte interphase (SEI) formation is critical to the long-term performance of Li-ion battery anodes, however probing the decomposition processes occurring at the buried electrode-electrolyte interface is a significant challenge. We demonstrate here for the first time, the use of operando soft X-ray Absorption spectroscopy in total electron yield (TEY) mode to resolve the chemical evolution of the SEI during electrochemical formation in a Li-ion cell. Interface-sensitive O, F, and Si K-edge spectra, acquired as a function of potential, reveal when key reactions occur on high capacity amorphous Si anodes cycled in conventional carbonate-based electrolytes (LP30) with and with- out fluoroethylene carbonate (FEC) as an additive. Density functional theory (DFT) calculations provide insight into the species observed and the origins of their spectral features. LiF is observed as the dominant F-containing SEI product, forming at ∼0.6 V for LP30 and at a higher potential of ∼1.0 V for LP30+FEC. The formation of organic SEI components is detected at lower potentials, with the emergence of a distinct peak related to -(C=O)O- containing species. This sequential formation of inorganic and organic components is implicated in layering of the SEI. Operando TEY-XAS offers new insights into the formation mechanisms of electrode-electrolyte interphases and their stability for a wide variety of electrode materials and electrolyte formulations.

Inherited genetic variation profoundly influences cancer risk and outcome. While the impact of germline single nucleotide polymorphisms has been well-studied in several cancer types, the effects of germline structural variants (gSVs) on cancer biology and clinical outcomes is largely unknown. From our cohort of 300 men with localized, intermediate risk prostate cancer, we identified 6,003 gSVs present in at least 3% of patients; 48 were associated with recurrent somatic alterations or clinical outcome. Of these, approximately 50% were associated with expression of nearby genes or intersected with exons or regulatory regions. Using external cohorts, we validated three gSVs that were strongly associated with poor clinical outcomes, including an inversion at chr14q24.1 present in ~20% of patients. Notably, a strong synergistic effect on outcome was observed in patients with somatic TP53 alterations or high genomic instability, defining a new aggressive prostate cancer subtype with chr14INV as a novel, recurrent biomarker. Citation Format: Nicholas K. Wang, Alexandre Rouette, Kathleen E. Houlahan, Takafumi N. Yamaguchi, Julie Livingstone, Chol-Hee Jung, Peter Georgeson, Michael Fraser, Yu-Jia Shiah, Cindy Q. Yao, Vincent Huang, Natalie S. Fox, Natalie Kurganovs, Katayoon Kasaian, Veronica Y. Sabelnykova, Jay Jayalath, Kenneth Weke, Helen Zhu, Theodorus van der Kwast, Tony Papenfuss, Housheng H. He, Niall M. Corcoran, Robert G. Bristow, Alexandre R. Zlotta, Christopher Hovens, Paul C. Boutros. Germline structural variants shape prostate cancer clinical and molecular evolution. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4305.

BACKGROUND: Germline variants explain more than a third of prostate cancer (PrCa) risk, but very few associations have been identified between heritable factors and clinical progression.OBJECTIVE: To find rare germline variants that predict time to biochemical recurrence (BCR) after radical treatment in men with PrCa and understand the genetic factors associated with such progression.DESIGN, SETTING, AND PARTICIPANTS: Whole-genome sequencing data from blood DNA were analysed for 850 PrCa patients with radical treatment from the Pan Prostate Cancer Group (PPCG) consortium from the UK, Canada, Germany, Australia, and France. Findings were validated using 383 patients from The Cancer Genome Atlas (TCGA) dataset.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A total of 15,822 rare (MAF RESULTS AND LIMITATIONS: Optimal Cox regression multifactor models showed that rare predicted-deleterious germline variants in "Hallmark" gene sets were consistently associated with altered time to BCR. Three gene sets had a statistically significant association with risk-elevated outcome when modelling all samples: PI3K/AKT/mTOR, Inflammatory response, and KRAS signalling (up). PI3K/AKT/mTOR and KRAS signalling (up) were also associated among patients with higher-grade cancer, as were Pancreas-beta cells, TNFA signalling via NKFB, and Hypoxia, the latter of which was validated in the independent TCGA dataset.CONCLUSIONS: We demonstrate for the first time that rare deleterious coding germline variants robustly associate with time to BCR after radical treatment, including cohort-independent validation. Our findings suggest that germline testing at diagnosis could aid clinical decisions by stratifying patients for differential clinical management.PATIENT SUMMARY: Prostate cancer patients with particular genetic mutations have a higher chance of relapsing after initial radical treatment, potentially providing opportunities to identify patients who might need additional treatments earlier.

Significance Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. Aim To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Universite de Montreal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Quebec-Universite Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (-2 % ), +0 % (-3 % ), +2 % (-2 % ), +4 (+3)], the AUC was improved in both testing sets. Conclusions Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

Purpose We present an analysis of various operational metrics for a novel compact proton therapy system, including clinical case mix, subsystems utilization, and quality assurance trends in beam delivery parameters over a period of 5 years. Materials and Methods Patient-specific data from a total of 850 patients (25,567 fractions) have been collected and analyzed. The patient mix include a variety of simple, intermediate, and complex cases. Beam-specific delivery parameters for a total of 3585 beams were analyzed. In-room imaging system usage for off-line adaptive purpose is reported. We also report key machine performances metrics based on routine quality assurance in addition to uptime. Results Our analysis shows that system subcomponents including gantry and patient positioning system have maintained a tight mechanical tolerance over the 5-year period. Various beam parameters were all within acceptable tolerances with no clear trends. Utilization frequency histograms of gantry and patient positioning system show that only a small fraction of all available angles was used for patient deliveries with cardinal angels as the most usable. Similarly, beam-specific metrics, such as range, modulation, and air gaps, were clustered unevenly over the available range indicating that this compact system was more than capable to treat the complex variety of tumors of our patient mix. Conclusion Our data show that this compact system is versatile, robust, and capable of delivering complex treatments like a large full-gantry system. Utilization data show that a fraction of all subcomponents range of angular motion has been used. Compilation of beam-specific metrics, such as range and modulation, show uneven distributions with specific clustering over the entire usable range. Our findings could be used to further optimize the performance and cost-effectiveness of future compact proton systems.

Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor’s epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays. Genetic variants in the germ line modulate DNA methylation in tumors and contribute to the aggressiveness of prostate cancer.

Prostate cancer is a heterogeneous disease whose progression is linked to genome instability. However the impact of this instability on the three-dimensional chromatin organization and how this drives progression is unclear. Using primary benign and tumour tissue, we find a high concordance in the higher-order three-dimensional genome organization across normal and prostate cancer cells. This concordance argues for constraints to the topology of prostate tumour genomes. Nonetheless, we identify changes to focal chromatin interactions and show how structural variants can induce these changes to guide cis-regulatory element hijacking. Such events result in opposing differential expression on genes found at antipodes of rearrangements. Collectively, our results argue that cis-regulatory element hijacking from structural variant-induced altered focal chromatin interactions overshadows higher-order topological changes in the development of primary prostate cancer.

Prostate cancer shows remarkable clinical heterogeneity, which manifests in spatial and clonal genomic diversity. By contrast, the transcriptomic heterogeneity of prostate tumours is poorly understood. Here we have profiled the transcriptomes of 36,424 single cells from 13 prostate tumours and identified the epithelial cells underlying disease aggressiveness. The tumour microenvironment (TME) showed activation of multiple progression-associated transcriptomic programs. Notably, we observed promiscuous KLK3 expression and validated the ability of cancer cells in altering T-cell transcriptomes. Profiling of a primary tumour and two matched lymph nodes provided evidence that KLK3 ectopic expression is associated with micrometastases. Close cell-cell communication exists among cells. We identified an endothelial subset harbouring active communication (activated endothelial cells, aECs) with tumour cells. Together with sequencing of an additional 11 samples, we showed that aECs are enriched in castration-resistant prostate cancer and promote cancer cell invasion. Finally, we created a user-friendly web interface for users to explore the sequenced data.

Purpose The National Comprehensive Cancer Network (NCCN) has recently endorsed the stratification of intermediate-risk prostate cancer (IR-PCa) into favorable and unfavorable subgroups and recommend the addition of androgen deprivation therapy (ADT) to radiation therapy (RT) for unfavorable IR-PCa. Recently, more accurate prognostication was demonstrated by integrating a 22-feature genomic classifier (GC) to the NCCN stratification system. Here, we test the utility of the GC to better identify patients with IR-PCa who are sufficiently treated by RT alone. Methods and Materials We identified a novel cohort comprising 121 patients with IR-PCa treated with dose-escalated image guided RT (78 Gy in 39 fractions) without ADT. GC scores were derived from tumors sampled in diagnostic biopsies. Multivariable analyses, including both NCCN subclassification and GC scores, were performed for biochemical failure (prostate-specific antigen nadir + 2 ng/mL) and metastasis occurrence. Results By NCCN subclassification, 33 (27.3%) and 87 (71.9%) of men were classified as having favorable and unfavorable IR-PCa, respectively (1 case unclassifiable). GC scores were high in 3 favorable IR-PCa and low in 60 unfavorable IR-PCa. Higher GC scores, but not NCCN risk subgroups, were associated with biochemical relapse (hazard ratio, 1.36; 95% confidence interval [CI], 1.09-1.71] per 10% increase; P = .007) and metastasis (hazard ratio, 2.05; 95% CI, 1.24-4.24; P = .004). GC predicted biochemical failure at 5 years (area under the curve, 0.78; 95% CI, 0.59-0.91), and the combinatorial NCCN + GC model significantly outperformed the NCCN alone model for predicting early-onset metastasis (area under the curve for 5-year metastasis of 0.89 vs 0.86 [GC alone] vs 0.54 [NCCN alone]). Conclusions We demonstrated the accuracy of the GC for predicting disease recurrence in IR-PCa treated with dose-escalated image guided RT alone. Our findings highlight the need to evaluate this GC in a prospective clinical trial investigating the role of ADT-RT in clinicogenomic-defined IR-PCa subgroups.

Background The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. Methods We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40). Results We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort). Conclusions These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.

Localized prostate tumors show remarkably diverse clinical courses, with some being cured by local therapy alone, while others rapidly relapse and have a lethal course despite precision surgery or radiotherapy. Many genomic biomarkers have been developed to predict this clinical behavior, but these are confounded by the extreme spatial heterogeneity of prostate tumors: most are multifocal and harbor multiple subclonal populations. To quantify the influence of spatial heterogeneity on genomic prognostic biomarkers, we developed a case-control high-risk cohort (n = 42) using a prospective registry, risk matched by clinicopathologic prognostic indices. Half of the cohort had early biochemical recurrence (BCR; ie, ≤18 mo), while half remained without evidence of disease for at least 48 mo after radical prostatectomy. We then genomically profiled multiple tumor foci per patient, analyzing 119 total specimens. These data allowed us to validate three published genomic prognostic biomarkers and quantify their sensitivity to tumor spatial heterogeneity. Remarkably, all three biomarkers robustly predicted early BCR, and all three were robust to spatiogenomic variability. These data suggest that DNA-based genomic biomarkers can overcome intratumoral heterogeneity: single biopsies may be sufficient to estimate the risk of early BCR after radical treatment in patients with high-risk disease. Patient summary We investigated whether heterogeneity between tumor regions within the prostate affects the accuracy of DNA-based biomarkers predicting early relapse after prostatectomy. We observed persistent accuracy in predicting disease relapse, suggesting that spatial heterogeneity may not hinder biomarker performance.

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

PURPOSE: Prostate cancer is the second leading cause of male cancer deaths. Castration-resistant prostate cancer (CRPC) is a lethal stage of the disease that emerges when endocrine therapies are no longer effective at suppressing activity of the androgen receptor (AR) transcription factor. The purpose of this study was to identify genomic mechanisms that contribute to development and progression of CRPC. EXPERIMENTAL DESIGN: We used whole genome and targeted DNA sequencing approaches to identify mechanisms underlying CRPC in an aggregate cohort of 272 prostate cancer patients. We analyzed structural rearrangements at the genome-wide level and carried out a detailed structural rearrangement analysis of the AR locus. We used genome engineering to perform experimental modeling of AR gene rearrangements and long-read RNA-sequencing to analyze effects on expression of AR and truncated AR variants (AR-Vs). RESULTS: AR was among the most frequently rearranged genes in CRPC tumors. AR gene rearrangements promoted expression of diverse AR-V species. AR gene rearrangements occurring in the context of AR amplification correlated with AR overexpression. Cell lines with experimentally-derived AR gene rearrangements displayed high expression of tumor-specific AR-Vs and were resistant to endocrine therapies, including the AR antagonist enzalutamide. CONCLUSIONS: AR gene rearrangements are an important mechanism of resistance to endocrine therapies in CRPC.

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.

A key challenge in the management of localized prostate cancer is the identification of men with a high likelihood of progression to an advanced, incurable stage. Patients who harbour germline BRCA2 mutations have worse clinical outcomes than noncarriers when treated with surgery or radiotherapy. Insights from different disciplines have improved our understanding of why patients with BRCA2-mutant tumours have a high likelihood of failing on conventional management after diagnosis. Treatment-naive BRCA2-mutant tumours are defined by aggressive clinical and molecular features early in the disease course, and the genomic landscape of these BRCA2-mutant tumours is characterized by a unique molecular profile and higher genomic instability than noncarrier tumours. Moreover, BRCA2-mutant tumours commonly show the concurrent presence of the intraductal carcinoma of the prostate (IDCP) pathology, a poor prognostic indicator. Subclonal analyses have revealed that IDCP and invasive adenocarcinoma in BRCA2-mutant tumours can arise from the same ancestral clone, implying that a temporal evolutionary trajectory exists. Finally, functional studies have shown that BRCA2-mutant tumours can harbour a subpopulation of cancer cells that can tolerate castration de novo, enabling the tumour to evade androgen deprivation therapy. Importantly, future challenges remain regarding how to best model the biology underpinning this aggressive phenotype and translate these findings to improve clinical outcomes. In the localized prostate cancer setting, germline BRCA2 mutations confer aggressive clinicopathological features and poor outcomes. This Review discusses the clinical influence of BRCA2 mutations in localized disease, highlighting biological insights into disease aggressiveness from genomic studies and preclinical models.

Melanocortin analogues, such as melanotan, are illegally used for artificial tanning. They have also been suggested as possible therapeutic agents in the treatment of erectile dysfunction. This case study presents a patient attending the accident and emergency department, in a tertiary urology centre, with acute priapism after abdominal subcutaneous injection of melanotan. The priapism was diagnosed as ‘low-flow’ and managed with cavernosal aspiration, irrigation and subsequent intracavernosal injection of phenylephrine. The patient avoided requiring surgical shunting but had not yet recovered erectile function at 4-week follow-up. Acute priapism is an unreported side effect of melanocortin analogue use and this case report presents a patient managed without surgical intervention. Future therapeutic application of these agents will need to take this potential life altering complication into consideration.

Many primary tumor sub-regions have low levels of molecular oxygen, termed hypoxia. Hypoxic tumors are at elevated risk for local failure and distant metastasis, but the molecular hallmarks of tumor hypoxia remain poorly defined. To fill this gap, we quantified hypoxia in 8,006 tumors across 19 tumor types. In ten tumor types, hypoxia was associated with elevated genomic instability. In all 19 tumor types, hypoxic tumors exhibited characteristic driver mutation signatures. We observed widespread hypoxia-associated dysregulation of miRNAs across cancers and functionally validated miR-133a-3p as a hypoxia-modulated miRNA. In localized prostate cancer, hypoxia was associated with elevated rates of chromothripsis, allelic loss of PTEN and shorter telomeres. These associations are particularly enriched in polyclonal tumors, representing a constellation of features resembling tumor nimbosus – an aggressive cellular phenotype. Overall, this work establishes that tumor hypoxia may drive aggressive molecular features across cancers and shape the clinical trajectory of individual tumors. Citation Format: Vinayak Bhandari, Christianne Hoey, Lydia Liu, Emilie Lalonde, Jessica Ray, Julie Livingstone, Robert Lesurf, Yu-Jia Shiah, Tina Vujcic, Xiaoyong Huang, Shadrielle M.G. Espiritu, Lawrence E. Heisler, Fouad Yousif, Vincent Huang, Takafumi N. Yamaguchi, Cindy Q. Yao, Veronica Y. Sabelnykova, Michael Fraser, Melvin L.K. Chua, Theodorus van der Kwast, Stanley K. Liu, Paul C. Boutros, Robert G. Bristow. Molecular landmarks of tumor hypoxia across cancer types [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr IA-018.

BACKGROUND Intraductal carcinoma (IDC) and cribriform architecture (CA) represent unfavorable subpathologies in localized prostate cancer. We recently showed that IDC shares a clonal ancestry with the adjacent glandular adenocarcinoma. OBJECTIVE We investigated for the co-occurrence of "aggression" factors, genomic instability and hypoxia, and performed gene expression profiling of these tumors. DESIGN, SETTING, AND PARTICIPANTS A total of 1325 men were treated for localized prostate cancer from four academic institutions (University Health Network, CHU de Québec-Université Laval, Memorial Sloan Kettering Cancer Center [MSKCC], and Erasmus Medical Center). Pathological specimens were centrally reviewed. Gene copy number and expression, and intraprostatic oxygenation were assessed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS IDC/CA was separately assessed for biochemical relapse risk in the Canadian and MSKCC cohorts. Both cohorts were pooled for analyses on metastasis. RESULTS AND LIMITATION Presence of IDC/CA independently predicted for increased risks of biochemical relapse (HRCanadian 2.17, p3-fold higher (p

Copyright management organizations’ licence schemes were established as market recovery mechanisms to address a failure in the market for permissions to copy works. With network media convergence, consumers now expect instant access to all content in any media, for any use and purpose. They generally see copyright as a barrier to access and an obstacle to creativity and freedom of expression. Many consumers use peerto-peer file-sharing networks, piracy is normalized, and publishers are losing their social licence to be in business. Possible responses by publishers include continued advocacy for copyright, new proprietary and centrally managed licensing solutions, setting standard minimum shares of royalties for authors, and advocacy for reasonable responsibility and liability among online intermediaries. Publishers and rights management organisations should adopt open interoperable metadata standards for content and rights transactions. Automated copyright registration would facilitate new business models. A virtual, distributed, actionable rights registry should record digital rights management information and action rights exchanges with direct lawful access to content. Copyright is the legal expression of a human right and it is a pillar of freedom of expression. It is essential to authors’ and publishers’ independence. Independent and autonomous authors and publishers are indispensable to democratic society.

Long noncoding RNAs (lncRNAs) represent an attractive class of candidates to mediate cancer risk. Through integrative analysis of the lncRNA transcriptome with genomic data and SNP data from prostate cancer genome-wide association studies (GWAS), we identified 45 candidate lncRNAs associated with risk to prostate cancer. We further evaluated the mechanism underlying the top hit, PCAT1, and found that a risk-associated variant at rs7463708 increases binding of ONECUT2, a novel androgen receptor (AR)-interacting transcription factor, at a distal enhancer that loops to the PCAT1 promoter, resulting in upregulation of PCAT1 upon prolonged androgen treatment. In addition, PCAT1 interacts with AR and LSD1 and is required for their recruitment to the enhancers of GNMT and DHCR24, two androgen late-response genes implicated in prostate cancer development and progression. PCAT1 promotes prostate cancer cell proliferation and tumor growth in vitro and in vivo. These findings suggest that modulating lncRNA expression is an important mechanism for risk-associated SNPs in promoting prostate transformation.

Background: Penile cancer is a rare disease, with approximately 600 cases diagnosed every year in the UK. In this study, we assessed the impact of social deprivation on penile cancer, concentrating on incidence, disease factors, surgical treatment and mortality within our ‘Supranetwork’ population. Methods: All cases of penile cancer in the West of Scotland were identified from the uro-oncology multidisciplinary team meetings over a 10-year period covering January 2008 to December 2017. Patients underwent treatment within the remit of a centralised service, and social deprivation was determined using the Scottish Index of Multiple Deprivation (SIMD), which is the Scottish government’s official tool to identify areas of multiple deprivations. Results: A total of 278 patients were identified, with an age range of 27–97 years ( M=64 years). The incidence of penile cancer in SIMD category 1 (most deprived) is 7.2/100,000 population at risk compared to 2.8/100,000 population at risk in SIMD category 5 (least deprived). Histologically, a higher proportion of aggressive grade 3 cancers (45% vs. 16%, p=0.03) and more advanced N2/N3 nodal disease (63% vs. 33%, p=0.04) was found in SIMD category 1 compared to SIMD category 5, suggesting higher incidence of delayed presentation with more advanced and aggressive disease in the most deprived populations. Conclusions: The level of social deprivation shows a significant association with penile cancer incidence, tumour grade and stage at time of diagnosis, with a resulting disproportionate impact on morbidity and mortality in the most deprived cohort of patients. Public awareness and efforts to increase earlier diagnoses of penile cancer in these ‘hard to reach’ men should be an important step in improving overall outcomes from penile cancer.

Over the last decade, advancements in massively-parallel DNA sequencing and computational biology have allowed for unprecedented insights into the fundamental mutational processes that underlie virtually every major cancer type. Two major cancer genomics consortia-The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC)-have produced rich databases of mutational, pathological, and clinical data that can be mined through web-based portals, allowing for correlative studies and testing of novel hypotheses on well-powered patient cohorts.In this chapter, we will review the impact of these technological developments on the understanding of molecular subtypes that promote prostate cancer initiation, progression, metastasis, and clinical aggression. In particular, we will focus on molecular subtypes that define clinically-relevant patient cohorts and assess how a better understanding of how these subtypes-in both somatic and germline genomes-may influence the clinical course for individual men diagnosed with prostate cancer.

The cancer transcriptome is remarkably complex, including low-abundance transcripts, many not polyadenylated. To fully characterize the transcriptome of localized prostate cancer, we performed ultra-deep total RNA-seq on 144 tumors with rich clinical annotation. This revealed a linear transcriptomic subtype associated with the aggressive intraductal carcinoma sub-histology and a fusion profile that differentiates localized from metastatic disease. Analysis of back-splicing events showed widespread RNA circularization, with the average tumor expressing 7,232 circular RNAs (circRNAs). The degree of circRNA production was correlated to disease progression in multiple patient cohorts. Loss-of-function screening identified 11.3% of highly abundant circRNAs as essential for cell proliferation; for ∼90% of these, their parental linear transcripts were not essential. Individual circRNAs can have distinct functions, with circCSNK1G3 promoting cell growth by interacting with miR-181. These data advocate for adoption of ultra-deep RNA-seq without poly-A selection to interrogate both linear and circular transcriptomes.

Advanced prostate cancer patients can present with both widely metastatic or oligometastatic disease. Accumulating clinical evidence suggests that patients with oligometastatic disease have improved clinical responses from metastasis-directed therapy. This suggests that tumours that give rise to the oligometastatic state are distinct biologically and genetically from those that induce widely metastatic lesions. Detailed genomic analysis of the oligometastatic state will identify the molecular events that distinguish localised from metastatic disease, defining the molecular signatures of curability. The GAP6 consortium is well poised to address this question. PATIENT SUMMARY: In this report, we have reviewed the evidence that prostate cancer patients with only a small number of distant tumour deposits have cancers that are driven by genetic and biological changes, which are distinct from those tumours that readily spread to many distant sites. So far, the evidence is not clear cut; however, in-depth studies to answer this question are underway.

Many primary-tumor subregions have low levels of molecular oxygen, termed hypoxia. Hypoxic tumors are at elevated risk for local failure and distant metastasis, but the molecular hallmarks of tumor hypoxia remain poorly defined. To fill this gap, we quantified hypoxia in 8,006 tumors across 19 tumor types. In ten tumor types, hypoxia was associated with elevated genomic instability. In all 19 tumor types, hypoxic tumors exhibited characteristic driver-mutation signatures. We observed widespread hypoxia-associated dysregulation of microRNAs (miRNAs) across cancers and functionally validated miR-133a-3p as a hypoxia-modulated miRNA. In localized prostate cancer, hypoxia was associated with elevated rates of chromothripsis, allelic loss of PTEN and shorter telomeres. These associations are particularly enriched in polyclonal tumors, representing a constellation of features resembling tumor nimbosus, an aggressive cellular phenotype. Overall, this work establishes that tumor hypoxia may drive aggressive molecular features across cancers and shape the clinical trajectory of individual tumors.

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials. Postprint

Background Prostate cancer (PC), initially diagnosed on biopsies by pathologists, is the most frequent cancer in North American men. However, better tools are needed for pathologists to diagnose intraductal carcinoma of the prostate (IDC), an aggressive histopathological variant of PC for which therapeutic options are now available. Indeed, no technique or biomarker is clinically available to support the diagnosis of IDC. Raman spectroscopy (RS) provides a global molecular characterisation of the tissue by analysing how photons interact with the molecules present in the tissue. Indeed, we and other groups previously used RS to detect cancer from multiple organ types, machine learning classification models being employed to process the complex Raman data. Methods We used Raman micro-spectroscopy (RµS) to detect IDC on tissues from 483 first-line radical prostatectomies from three Canadian institutions. Following a rapid, standardized and low-cost protocol, we acquired an average of 7 Raman spectra per patient and generated classification models using machine learning technology. Importantly, models were trained with data from one institution before independent testing on the data from the other two institutions. Results The three institutions included 272, 76 and 135 patients. Median age at diagnosis ranged from 61-62 years-old, with median pre-operative PSA ranging from 6.6-7.4 µg/L. Most patient had ≤3 + 4 Gleason score (60-80% of the specimens) and pT3-stage incidence was 31-55%. IDC was identified in 6-18% of the patients of each cohort. Overall, we acquired an average of 7 Raman spectra per patient. In the training cohort (N = 272), RµS identified IDC with a sensitivity of 95%, a specificity of 94% and an accuracy of 94%. Results from the testing cohort were in a similar range, with sensitivities of 88 and 92%, specificities of 83 and 91% and accuracies of 85 and 91%. Conclusions As clinically available biomarkers of IDC have reported sensitivities/specificities of ∼80%, we here identified IDC with accuracies ≥85%. Since our classification model was trained on a cohort and independently tested on the other two, these are likely to be close to real life experience making clinical implementation a realistic outcome. Legal entity responsible for the study The authors. Funding Centre de Recherche du Centre Hospitalier de l’Universite de Montreal (CRCHUM, continuum program) IVADO, Institut de valorisation des donnees. Disclosure All authors have declared no conflicts of interest.

BackgroundPlatform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. In disciplines that rely heavily on high-throughput data generation, such as genomics, reducing the impact of false positive and false negative rates in results is a top priority. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile. To determine how to create subsets of predictions for validation that maximize inference of global error profiles, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates.ResultsTo evaluate these selection strategies, we obtained 261 sets of somatic mutation calls from a single-nucleotide variant caller benchmarking challenge where 21 teams competed on whole-genome sequencing datasets of three computationally-simulated tumours. By using synthetic data, we had complete ground truth of the tumours’ mutations and, therefore, we were able to accurately determine how estimates from the selected subset of verification candidates compared to the complete prediction set. We found that selection strategy performance depends on several verification study characteristics. In particular the verification budget of the experiment (i.e. how many candidates can be selected) is shown to influence estimates.ConclusionsThe Valection framework is flexible, allowing for the implementation of additional selection algorithms in the future. Its applicability extends to any discipline that relies on experimental verification and will benefit from the optimization of verification candidate selection.

BACKGROUND: Clinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa. OBJECTIVE: To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa). DESIGN, SETTING, AND PARTICIPANTS: To develop the signature, whole-genome and whole- transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures. RESULTS AND LIMITATIONS: A close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures. CONCLUSIONS: Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors. PATIENT SUMMARY: Our stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Further-more, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors.

Summary Background Clinical prognostic groupings for localised prostate cancers are imprecise, with 30–50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors. Methods We used DNA-based indices alone or in combination with intra-prostatic hypoxia measurements to develop four prognostic indices in 126 low-risk to intermediate-risk patients (Toronto cohort) who will receive image-guided radiotherapy. We validated these indices in two independent cohorts of 154 (Memorial Sloan Kettering Cancer Center cohort [MSKCC] cohort) and 117 (Cambridge cohort) radical prostatectomy specimens from low-risk to high-risk patients. We applied unsupervised and supervised machine learning techniques to the copy-number profiles of 126 pre-image-guided radiotherapy diagnostic biopsies to develop prognostic signatures. Our primary endpoint was the development of a set of prognostic measures capable of stratifying patients for risk of biochemical relapse 5 years after primary treatment. Findings Biochemical relapse was associated with indices of tumour hypoxia, genomic instability, and genomic subtypes based on multivariate analyses. We identified four genomic subtypes for prostate cancer, which had different 5-year biochemical relapse-free survival. Genomic instability is prognostic for relapse in both image-guided radiotherapy (multivariate analysis hazard ratio [HR] 4·5 [95% CI 2·1–9·8]; p=0·00013; area under the receiver operator curve [AUC] 0·70 [95% CI 0·65–0·76]) and radical prostatectomy (4·0 [1·6–9·7]; p=0·0024; AUC 0·57 [0·52–0·61]) patients with prostate cancer, and its effect is magnified by intratumoral hypoxia (3·8 [1·2–12]; p=0·019; AUC 0·67 [0·61–0·73]). A novel 100-loci DNA signature accurately classified treatment outcome in the MSKCC low-risk to intermediate-risk cohort (multivariate analysis HR 6·1 [95% CI 2·0–19]; p=0·0015; AUC 0·74 [95% CI 0·65–0·83]). In the independent MSKCC and Cambridge cohorts, this signature identified low-risk to high-risk patients who were most likely to fail treatment within 18 months (combined cohorts multivariate analysis HR 2·9 [95% CI 1·4–6·0]; p=0·0039; AUC 0·68 [95% CI 0·63–0·73]), and was better at predicting biochemical relapse than 23 previously published RNA signatures. Interpretation This is the first study of cancer outcome to integrate DNA-based and microenvironment-based failure indices to predict patient outcome. Patients exhibiting these aggressive features after biopsy should be entered into treatment intensification trials. Funding Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.

Thousands of noncoding somatic single-nucleotide variants (SNVs) of unknown function are reported in tumors. Partitioning the genome according to cistromes reveals the enrichment of somatic SNVs in prostate tumors as opposed to adjacent normal tissue cistromes of master transcription regulators, including AR, FOXA1, and HOXB13. This parallels enrichment of prostate cancer genetic predispositions over these transcription regulators' tumor cistromes, exemplified at the 8q24 locus harboring both risk variants and somatic SNVs in cis-regulatory elements upregulating MYC expression. However, Massively Parallel Reporter Assays reveal that few SNVs can alter the transactivation potential of individual cis-regulatory elements. Instead, similar to inherited risk variants, SNVs accumulate in cistromes of master transcription regulators required for prostate cancer development.