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Prostate cancer is one of the most heritable cancers. Hundreds of germline polymorphisms have been linked to prostate cancer diagnosis and prognosis. Polygenic risk scores can predict genetic risk of a prostate cancer diagnosis. Although these scores inform the probability of developing a tumor, it remains unknown how germline risk influences the tumor molecular evolution. We cultivated a cohort of 1250 localized European-descent patients with germline and somatic DNA profiling. Men of European descent with higher genetic risk were diagnosed earlier and had less genomic instability and fewer driver genes mutated. Higher genetic risk was associated with better outcome. These data imply a polygenic “two-hit” model where germline risk reduces the number of somatic alterations required for tumorigenesis. These findings support further clinical studies of polygenic risk scores as inexpensive and minimally invasive adjuncts to standard risk stratification. Further studies are required to interrogate generalizability to more ancestrally and clinically diverse populations.

Extrachromosomal DNA (ecDNA) is a common mode of oncogene amplification but is challenging to analyze. Here, we adapt CRISPR-CATCH, in vitro CRISPR-Cas9 treatment and pulsed field gel electrophoresis of agarose-entrapped genomic DNA, previously developed for bacterial chromosome segments, to isolate megabase-sized human ecDNAs. We demonstrate strong enrichment of ecDNA molecules containing EGFR, FGFR2 and MYC from human cancer cells and NRAS ecDNA from human metastatic melanoma with acquired therapeutic resistance. Targeted enrichment of ecDNA versus chromosomal DNA enabled phasing of genetic variants, identified the presence of an EGFRvIII mutation exclusively on ecDNAs and supported an excision model of ecDNA genesis in a glioblastoma model. CRISPR-CATCH followed by nanopore sequencing enabled single-molecule ecDNA methylation profiling and revealed hypomethylation of the EGFR promoter on ecDNAs. We distinguished heterogeneous ecDNA species within the same sample by size and sequence with base-pair resolution and discovered functionally specialized ecDNAs that amplify select enhancers or oncogene-coding sequences.

The earliest events during human tumour initiation, although poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult preneoplasia by biallelic inactivation of TP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over 2 years. TP53 loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single-cell sequencing of TP53-deficient gastric organoids similarly indicates progression towards malignant transcriptional programmes. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programmes repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference and a marked degree of phenotypic convergence in premalignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and show evolutionary constraints and barriers to malignant transformation, with implications for earlier detection and interception of aggressive, genome-instable tumours.

Background. DCIS consists of a molecularly heterogeneous group of premalignant lesions, with variable risk of invasive progression. Understanding biomarkers for invasive progression could help individualize treatment recommendations based upon tumor biology. As part of the NCI Human Tumor Atlas Network (HTAN), we conducted comprehensive genomic analyses on two large DCIS case-control cohorts. Methods. We performed smart3-seq and low-pass whole genome sequencing on two independent, retrospective, longitudinally sampled DCIS case-control cohorts. TBCRC 038 was a multicenter cohort diagnosed with DCIS between 1998 and 2016 at one of the Translational Breast Cancer Research sites; the RAHBT (Resource of Archival Human Breast Tissue) cohort included women identified through the St. Louis Breast Tissue Repository, and the Women’s Health Repository diagnosed between 1997 and 2001. We studied the spectrum of molecular changes present and sought genomic predictors of subsequent ipsilateral breast events (iBEs: DCIS recurrence or invasive progression) in both DCIS epithelium and stroma in formalin fixed paraffin embedded tissue. We generated de novo tumor and stroma-centric subtypes for DCIS that represents fundamental transcriptomic organization. Copy number analysis was performed using low-pass DNA sequencing. Non-negative matrix factorization (NMF) was applied to the RNA expression of all coding genes to identify clusters. A negative-binomial regression model was used to identify differentially expressed genes. Results. We analyzed 677 DCIS samples from 481 patients with 7.1 years median follow-up. In TBCRC samples, we identified three clusters via NMF in TBCRC referred to as ER low, quiescent, and ER high. The ER-low cluster had significantly higher levels of ERBB2 and lower levels of ESR1 compared to quiescent and ER-high clusters. Quiescent cluster lesions were less proliferative and less metabolically active than ER high and ER low subtypes. These findings were replicated in the RAHBT cohort. Focusing on the stromal component of DCIS from laser capture microdissection in RAHBT samples, we identified four distinct DCIS-associated stromal clusters. A “normal-like” stromal cluster with ECM organization and PI3K-AKT signaling; a “collagen-rich” stromal cluster; a “desmoplastic” stromal cluster with high fibroblast and total myeloid abundance, mostly associated with macrophages and myeloid dendritic cells (mDC); and an “immune-dense” stromal cluster. Further, we compared differentially expressed genes in patients with or without subsequent iBEs within 5 years of diagnosis. Hypothesizing that the resulting 812 DE genes (DESeq2) represent multiple routes to subsequent iBEs, we leveraged NMF to identify paths to progression. In both TBCRC and RAHBT cohorts, poor outcome groups exhibited increased ER, MYC signaling, and oxidative phosphorylation, supporting that these pathways are important for DCIS recurrence and progression. Conclusion. Comprehensive genomic profiling in two independent DCIS cohorts with longitudinal outcomes shows distinct DCIS stromal expression patterns and immune cell composition. RNA expression profiles reveal underlying tumor biology that is associated with later iBEs in both cohorts. These studies provide new insight into DCIS biology and will guide the design of diagnostic strategies to prevent invasive progression. Citation Format: Siri H Strand, Belén Rivero-Gutiérrez, Kathleen E Houlahan, Jose A Seoane, Lorraine M King, Tyler Risom, Lunden Simpson, Sujay Vennam, Aziz Khan, Timothy Hardman, Bryan E Harmon, Fergus J Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F McAuliffe, Julie Nangia, Joanna Lee, Jennifer Tseng, Anna Maria Storniolo, Alastair Thompson, Gaorav Gupta, Robyn Burns, Deborah J Veis, Katherine DeSchryver, Chunfang Zhu, Magdalena Matusiak, Jason Wang, Shirley X Zhu, Jen Tappenden, Daisy Yi Ding, Dadong Zhang, Jingqin Luo, Shu Jiang, Sushama Varma, Cody Straub, Sucheta Srivastava, Christina Curtis, Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo Maley, Jeffrey R Marks, Graham A Colditz, E Shelley Hwang, Robert B West. The Breast PreCancer Atlas DCIS genomic signatures define biology and correlate with clinical outcomes: An analysis of TBCRC 038 and RAHBT cohorts [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS4-07.

Cancer represents a broad spectrum of molecularly and morphologically diverse diseases. Individuals with the same clinical diagnosis can have tumors with drastically different molecular profiles and clinical response to treatment. It remains unclear when these differences arise during disease course and why some tumors are addicted to one oncogenic pathway over another. Somatic genomic aberrations occur within the context of an individual’s germline genome, which can vary across millions of polymorphic sites. An open question is whether germline differences influence somatic tumor evolution. Interrogating 3,855 breast cancer lesions, spanning pre-invasive to metastatic disease, we demonstrate that germline variants in highly expressed and amplified genes influence somatic evolution by modulating immunoediting at early stages of tumor development. Specifically, we show that the burden of germline-derived epitopes in recurrently amplified genes selects against somatic gene amplification in breast cancer. For example, individuals with a high burden of germline-derived epitopes inERBB2,encoding human epidermal growth factor receptor 2 (HER2), are significantly less likely to develop HER2-positive breast cancer compared to other subtypes. The same holds true for recurrent amplicons that define four subgroups of ER-positive breast cancers at high risk of distant relapse. High epitope burden in these recurrently amplified regions is associated with decreased likelihood of developing high risk ER-positive cancer. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an “immune cold” phenotype. These data show the germline genome plays a previously unappreciated role in dictating somatic evolution. Exploiting germline-mediated immunoediting may inform the development of biomarkers that refine risk stratification within breast cancer subtypes.

Ductal carcinoma in situ; Tumor microenvironment; Whole genome sequencing Carcinoma ductal in situ; Microambiente tumoral; Secuenciación del genoma completo Carcinoma ductal in situ; Microambient tumoral; Seqüenciació del genoma complet Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. This publication is part of the HTAN (Human Tumor Atlas Network) Consortium paper package. A list of HTAN members is available at humantumoratlas.org/htan-authors/. R01 CA185138-01 (E.S.H.); U2C CA-17-035 Pre-Cancer Atlas (PCA) Research Centers (E.S.H., R.B.W., C.M., K.P., G.A.C., K.O.); UO1 CA214183 (J.R.M.); DOD BC132057 (E.S.H., C.M.); BCRF 19-074 (E.S.H.); BCRF 19-028 (G.A.C.); PRECISION CRUK Grand Challenge (E.S.H.); R01CA193694 (R.B.W., G.A.C.), BCRF PPI-18-006 (R.B.W.). AEI RYC2019- 026576-I, "LaCaixa" Foundation LCF/PR/PR17/51120011 (J.A.S.). S.H.S. was supported by the Lundbeck Foundation (R288-2018-35) and the Danish Cancer Society (R229-A13616). K.E.H. was supported by a CIHR Banting Postdoctoral Fellowship. TBCRC 038 was conducted by the TBCRC, which receives major funding support from The Breast Cancer Research Foundation and Susan G. Komen. Some results in this paper are based upon data generated by the TCGA Research Network.

Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline–somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation. Significance: Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline–somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711

A person’s germline genome strongly influences their risk of developing cancer. Yet the molecular mechanisms linking the host genome to the specific somatic molecular phenotypes of individual cancers are largely unknown. We quantified the relationships between germline polymorphisms and somatic mutational features in prostate cancer. Across 1,991 prostate tumors, we identified 23 co-occurring germline and somatic events in close 2D or 3D spatial genomic proximity, affecting 10 cancer driver genes. These driver quantitative trait loci (dQTLs) overlap active regulatory regions, and shape the tumor epigenome, transcriptome and proteome. Some dQTLs are active in multiple cancer types, and information content analyses imply hundreds of undiscovered dQTLs. Specific dQTLs explain at least 16.7% ancestry-biases in rates ofTMPRSS2-ERGgene fusions and 67.3% of ancestry-biases in rates ofFOXA1point mutations. These data reveal extensive influences of common germline variation on somatic mutational landscapes.

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.

Prostate cancer is the second-most diagnosed cancer and the second leading cause of cancer death in American men. Early detection is common, but is followed by the more challenging task of prognosing a highly variable clinical course. Current clinical risk-assessment strategies such as serum abundance of prostate specific antigen (PSA), tumor size & extent, and tumor grade based on biopsy are highly imprecise: over a third of patients are over-treated. An improved method of risk stratification may lie in hereditary factors. Prostate cancer is one of the most strongly inherited (h2 = 57%), with accumulating evidence associating rare variants, common variants, and genetic ancestry to clinical outcomes. We have performed germline sequencing on blood from thousands of patients diagnosed with localized prostate cancer and with extensive follow-up data. We quantify the interactions of rare and common variants, and demonstrate that germline features provide insights into patient outcomes and optimal management strategies. Citation Format: Nicole Zeltser, Kathleen E. Houlahan, Sarah M. Al-Hiyari, Stefan E. Eng, Yash Patel, Takafumi N. Yamaguchi, Shu Tao, Rong Rong Huang, Robert E. Reiter, Huihui Ye, Adam S. Kinnaird, Paul C. Boutros. Evaluating biomarker potential of germline genomic factors for predicting clinical outcomes in prostate cancer [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 3315.

The earliest events during human tumor initiation, while poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult pre-neoplasia by bi-allelically inactivatingTP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over two years.TP53loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single cell sequencing ofTP53deficient gastric organoids similarly indicates progression towards malignant transcriptional programs. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programs repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference, and a striking degree of phenotypic convergence in pre-malignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and reveal evolutionary constraints and barriers to malignant transformation with implications for earlier detection and interception of aggressive, genome instable tumors.

In this issue of Cancer Cell, Bagaev et al. discover conserved relationships between immune and stroma activity that are prognostic and predictive of response to immunotherapy across cancer types. The authors develop a visualization tool, akin to a tumor personality test, to integrate genomic and microenvironmental profiling and guide therapeutic decision-making.

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.

Multiparametric magnetic resonance imaging (mpMRI) has transformed the management of localized prostate cancer by improving identification of clinically significant disease at diagnosis. Approximately 20% of primary prostate tumors are invisible to mpMRI, and we hypothesize that this invisibility reflects fundamental molecular properties of the tumor. We therefore profiled the genomes and transcriptomes of 40 International Society of Urological Pathology grade 2 tumors: 20 mpMRI-invisible (Prostate Imaging-Reporting and Data System [PI-RADS] v2

SUMMARYDuctal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.HIGHLIGHTS⍰ Development of a new classifier for DCIS recurrence or progression⍰ Outcome associated pathways identified across multiple data types and compartments⍰ Four stroma-specific signatures identified⍰ CNAs characterize DCIS subgroups associated with high risk invasive cancers

Our knowledge of prostate cancer (PCa) genomics mainly reflects European (EUR) and Asian (ASN) populations. Our understanding of the influence of Middle Eastern (ME) and African (AFR) ancestry on the mutational profiles of prostate cancer is limited. To characterize genomic differences between ME, EUR, ASN, and AFR ancestry, fluorescent in situ hybridization (FISH) studies for NKX3-1 deletion and MYC amplification were carried out on 42 tumors arising in individuals of ME ancestry. These were supplemented by analysis of genome-wide copy number profiles of 401 tumors of all ancestries. FISH results of NKX3-1 and MYC were assessed in the ME cohort and compared to other ancestries. Gene level copy number aberrations (CNAs) for each sample were statistically compared between ancestry groups. NKX3-1 deletions by FISH were observed in 17/42 (17.5%) prostate tumors arising in men of ME ancestry, while MYC amplifications were only observed in 1/42 (2.3%). Using CNAs called from arrays, the incidence of NKX3-1 deletions was significantly lower in ME vs. other ancestries (20% vs. 52%, p = 2.3 × 10−3). Across the genome, tumors arising in men of ME ancestry had fewer CNAs than those in men of other ancestries (p = 0.014). Additionally, the somatic amplification of 21 specific genes was more frequent in tumors arising in men of ME vs. EUR ancestry (two-sided proportion test, Q &lt, 0.05). Those included amplifications in the glutathione S-transferase family on chromosome 1 (GSTM1, GSTM2, GSTM5) and the IQ motif-containing family on chromosome 3 (IQCF1, IQCF2, IQCF13, IQCF4, IQCF5, IQCF6). Larger studies investigating ME populations are warranted to confirm these observations.

Prostate cancer (PCa) risk-associated SNPs are enriched in noncoding cis-regulatory elements (rCREs), yet their modi operandi and clinical impact remain elusive. Here, we perform CRISPRi screens of 260 rCREs in PCa cell lines. We find that rCREs harboring high risk SNPs are more essential for cell proliferation and H3K27ac occupancy is a strong indicator of essentiality. We also show that cell-line-specific essential rCREs are enriched in the 8q24.21 region, with the rs11986220-containing rCRE regulating MYC and PVT1 expression, cell proliferation and tumorigenesis in a cell-line-specific manner, depending on DNA methylation-orchestrated occupancy of a CTCF binding site in between this rCRE and the MYC promoter. We demonstrate that CTCF deposition at this site as measured by DNA methylation level is highly variable in prostate specimens, and observe the MYC eQTL in the 8q24.21 locus in individuals with low CTCF binding. Together our findings highlight a causal mechanism synergistically driven by a risk SNP and DNA methylation-mediated 3D genome architecture, advocating for the integration of genetics and epigenetics in assessing risks conferred by genetic predispositions., Prostate cancer risk-associated SNPs are enriched in noncoding CREs. Here the authors perform CRISPRi screens of CREs in prostate cancer cell lines to describe a causal mechanism synergistically driven by a risk SNP and DNA methylation-mediated 3D genome architecture.

BACKGROUND: Polygenic risk scores (PRSs) have been demonstrated to identify women of European, Asian, and Latino ancestry at elevated risk of developing breast cancer (BC). We evaluated the performance of existing PRSs trained in European ancestry populations among women of African ancestry. METHODS: We assembled genotype data for women of African ancestry, including 9241 case subjects and 10 193 control subjects. We evaluated associations of 179- and 313-variant PRSs with overall and subtype-specific BC risk. PRS discriminatory accuracy was assessed using area under the receiver operating characteristic curve. We also evaluated a recalibrated PRS, replacing the index variant with variants in each region that better captured risk in women of African ancestry and estimated lifetime absolute risk of BC in African Americans by PRS category. RESULTS: For overall BC, the odds ratio per SD of the 313-variant PRS (PRS(313)) was 1.27 (95% confidence interval [CI] = 1.23 to 1.31), with an area under the receiver operating characteristic curve of 0.571 (95% CI = 0.562 to 0.579). Compared with women with average risk (40th-60th PRS percentile), women in the top decile of PRS(313) had a 1.54-fold increased risk (95% CI = 1.38-fold to 1.72-fold). By age 85 years, the absolute risk of overall BC was 19.6% for African American women in the top 1% of PRS(313) and 6.7% for those in the lowest 1%. The recalibrated PRS did not improve BC risk prediction. CONCLUSION: The PRSs stratify BC risk in women of African ancestry, with attenuated performance compared with that reported in European, Asian, and Latina populations. Future work is needed to improve BC risk stratification for women of African ancestry.

Background: Our knowledge of prostate cancer (PCa) genomics mainly reflects European (EUR) and Asian (ASN) populations. Our understanding of the influence of Middle Eastern (ME) and African (AFR) ancestry on the mutational profiles of prostate cancer is limited. Design, setting and participants: To characterize genomic differences between ME, EUR, ASN and AFR ancestry, fluorescent in situ hybridization (FISH) studies for NKX3-1 deletions and MYC amplification was carried out on 42 tumors arising in individuals of ME ancestry. These were supplemented by analysis of copy number profiles of 401 tumors of all ancestries. Outcome measurement and statistical analysis: FISH results of NKX3-1 and MYC were assessed in the ME cohort and compared to other ancestries. Gene level copy number aberrations (CNAs) for each sample were statistically compared between ancestry groups. Results and Limitation: NKX3-1 deletions by FISH were observed in 17/42 (17.5%) prostate tumors arising in men of ME ancestry, while MYC amplifications were only observed in 1/42 (0.23%). Using CNAs called from arrays, the incidence of NKX3-1 deletions was significantly lower in ME vs. other ancestries (20% vs. 52%; p = 2.3 x 10-3). No differences in MYC amplifications was noted between the two cohorts using array-based CNAs. Across the genome, tumors arising in men of ME ancestry had fewer CNAs than those of other ancestries (p = 0.014). Additionally, the somatic amplification of 21 specific genes was more frequent in tumors arising in men of ME vs. EUR ancestry (Proportions test; Q < 0.05). Those included amplifications in the glutathione S-transferase family on chromosome 1 (GSTM1, GSTM2, GSTM5) and the IQ Motif Containing family on chromosome 3 (IQCF1, IQCF2, IQCF13, IQCF4, IQCF5, IQCF6). Conclusion: Significant differences in genetic background exists between different ancestries. The lower number of somatic CNAs observed in prostate cancer arising in men of ME ancestry may explain the relatively good prognosis in this population. Larger studies are warranted.

Ductal carcinoma in situ (DCIS) is the most commonly diagnosed precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes to identify predictors of subsequent ipsilateral breast events. We analyzed 677 DCIS samples from 481 patients with 7.1 years median follow-up from two independent cohorts. We made observations on DNA, RNA, and protein expression, and generated a de novo clustering scheme for DCIS that represents the fundamental transcriptomic organization at this early stage of breast neoplasia. Distinct DCIS stromal expression patterns and immune cell compositions were identified. We also found RNA expression patterns that correlate with later events in both cohorts. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

Transcriptional dysregulation is a hallmark of prostate cancer (PCa). We mapped the RNA polymerase II-associated (RNA Pol II-associated) chromatin interactions in normal prostate cells and PCa cells. We discovered thousands of enhancer-promoter, enhancer-enhancer, as well as promoter-promoter chromatin interactions. These transcriptional hubs operate within the framework set by structural proteins - CTCF and cohesins - and are regulated by the cooperative action of master transcription factors, such as the androgen receptor (AR) and FOXA1. By combining analyses from metastatic castration-resistant PCa (mCRPC) specimens, we show that AR locus amplification contributes to the transcriptional upregulation of the AR gene by increasing the total number of chromatin interaction modules comprising the AR gene and its distal enhancer. We deconvoluted the transcription control modules of several PCa genes, notably the biomarker KLK3, lineage-restricted genes (KRT8, KRT18, HOXB13, FOXA1, ZBTB16), the drug target EZH2, and the oncogene MYC. By integrating clinical PCa data, we defined a germline-somatic interplay between the PCa risk allele rs684232 and the somatically acquired TMPRSS2-ERG gene fusion in the transcriptional regulation of multiple target genes - VPS53, FAM57A, and GEMIN4. Our studies implicate changes in genome organization as a critical determinant of aberrant transcriptional regulation in PCa.

Although DNA methylation is a key regulator of gene expression, the comprehensive methylation landscape of metastatic cancer has never been defined. Through whole-genome bisulfite sequencing paired with deep whole-genome and transcriptome sequencing of 100 castration-resistant prostate metastases, we discovered alterations affecting driver genes that were detectable only with integrated whole-genome approaches. Notably, we observed that 22% of tumors exhibited a novel epigenomic subtype associated with hypermethylation and somatic mutations in TET2, DNMT3B, IDH1 and BRAF. We also identified intergenic regions where methylation is associated with RNA expression of the oncogenic driver genes AR, MYC and ERG. Finally, we showed that differential methylation during progression preferentially occurs at somatic mutational hotspots and putative regulatory regions. This study is a large integrated study of whole-genome, whole-methylome and whole-transcriptome sequencing in metastatic cancer that provides a comprehensive overview of the important regulatory role of methylation in metastatic castration-resistant prostate cancer.

The potent MYC oncoprotein is deregulated in many human cancers, including breast carcinoma, and is associated with aggressive disease. To understand the mechanisms and vulnerabilities of MYC-driven breast cancer, we have generated an in vivo model that mimics human disease in response to MYC deregulation. MCF10A cells ectopically expressing a common breast cancer mutation in the phosphoinositide 3 kinase pathway (PIK3CAH1047R) led to the development of organised acinar structures in mice. Expressing both PIK3CAH1047R and deregulated MYC led to the development of invasive ductal carcinoma. Therefore, the deregulation of MYC expression in this setting creates a MYC-dependent normal-to-tumour switch that can be measured in vivo. These MYC-driven tumours exhibit classic hallmarks of human breast cancer at both the pathological and molecular level. Moreover, tumour growth is dependent upon sustained deregulated MYC expression, further demonstrating addiction to this potent oncogene and regulator of gene transcription. We therefore provide a MYC-dependent model of breast cancer, which can be used to assay in vivo tumour signalling pathways, proliferation and transformation from normal breast acini to invasive breast carcinoma. We anticipate that this novel MYC-driven transformation model will be a useful research tool to better understand the oncogenic function of MYC and for the identification of therapeutic vulnerabilities., Summary: We present a MYC-driven transformation model of breast cancer that recapitulates the disease in vivo and which can be used to identify MYC-dependent cancer vulnerabilities.

INTRODUCTION AND OBJECTIVES:Multiparametric MRI (mpMRI) has transformed prostate cancer (PCa) management by improving identification of clinically significant disease. However, ∼20% of primary pros...

Background We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. Results This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. Conclusion BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general Electronic supplementary material The online version of this article (10.1186/s12859-019-2610-2) contains supplementary material, which is available to authorized users.

Background The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information. Results To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches. Conclusions The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon. Electronic supplementary material The online version of this article (10.1186/s13059-018-1539-5) contains supplementary material, which is available to authorized users.

Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely available for tumor specimens, the datasets upon which computational learning methods can be trained vary in coverage from sample to sample and from data type to data type. Methods that can 'connect the dots' to leverage more of the information provided by these studies could offer major advantages for maximizing predictive potential. We introduce a multi-view machinelearning strategy called PLATYPUS that builds 'views' from multiple data sources that are all used as features for predicting patient outcomes. We show that a learning strategy that finds agreement across the views on unlabeled data increases the performance of the learning methods over any single view. We illustrate the power of the approach by deriving signatures for drug sensitivity in a large cancer cell line database. Code and additional information are available from the PLATYPUS website https://sysbiowiki.soe.ucsc.edu/platypus.

Background: Platform-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. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile. Results: To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets. Conclusions: Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection.

The detection of somatic single nucleotide variants (SNVs) is critical in both research and clinical applications. Studies of human cancer typically use matched normal (reference) samples from a distant tissue to increase SNV prediction accuracy. This process both doubles sequencing costs and poses challenges when reference samples are not readily available, such as for many cell-lines. To address these challenges, we created S22S: an approach for the prediction of somatic mutations without need for matched reference tissue. S22S takes underlying sequence data, augments them with genomic background context and population frequency information, and classifies SNVs as somatic or non-somatic. We validated S22S using primary tumor/normal pairs from four tumor types, spanning two different sequencing technologies. S22S robustly identifies somatic SNVs, with the area under the precision recall curve reaching 0.97 in kidney clear cell carcinoma, comparable to the best tumor/normal analysis pipelines. S22S is freely available at http://labs.oicr.on.ca/Boutros-lab/software/s22s.

The c-MYC (MYC) oncoprotein is deregulated in over 50% of cancers, yet regulatory mechanisms controlling MYC remain unclear. To this end, we interrogated the MYC interactome using BioID mass spectrometry (MS) and identified PP1 (protein phosphatase 1) and its regulatory subunit PNUTS (protein phosphatase-1 nuclear-targeting subunit) as MYC interactors. We demonstrate that endogenous MYC and PNUTS interact across multiple cell types and that they co-occupy MYC target gene promoters. Inhibiting PP1 by RNAi or pharmacological inhibition results in MYC hyperphosphorylation at multiple serine and threonine residues, leading to a decrease in MYC protein levels due to proteasomal degradation through the canonical SCFFBXW7 pathway. MYC hyperphosphorylation can be rescued specifically with exogenous PP1, but not other phosphatases. Hyperphosphorylated MYC retained interaction with its transcriptional partner MAX, but binding to chromatin is significantly compromised. Our work demonstrates that PP1/PNUTS stabilizes chromatin-bound MYC in proliferating cells., Deregulated MYC activity is oncogenic and is deregulated in a large fraction of human cancers. Here the authors find that protein phosphatase 1 and its regulatory subunit PNUTS controls MYC stability and its interaction with chromatin.

The potent MYC oncoprotein is deregulated in many human cancers, including breast carcinoma, and is associated with aggressive disease. To understand the mechanisms and vulnerabilities of MYC-driven breast cancer, we have generated anin vivomodel that mimics human disease in response to MYC deregulation. MCF10A cells ectopically expressing a common breast cancer mutation in the PI3 kinase pathway (PIK3CAH1047R) lead to the development of organized acinar structures in mice. However, expressing both PIK3CAH1047Rand deregulated-MYC lead to the development of invasive ductal carcinoma, thus creating a model in which a MYC-dependent normal-to-tumour switch occursin vivo. These MYC-driven tumors exhibit classic hallmarks of human breast cancer at both the pathological and molecular levels. Moreover, tumour growth is dependent upon sustained deregulated MYC expression, further demonstrating addiction to this potent oncogene and regulator of gene transcription. We therefore provide a MYC-dependent human model of breast cancer which can be assayed forin vivotumour initiation, proliferation, and transformation from normal breast acini into invasive breast carcinoma. Taken together, we anticipate that this novel MYC-driven transformation model will be a useful research tool to both better understand MYC’s oncogenic function and identify therapeutic vulnerabilities.Conflict of interest statementThe authors declare no potential 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.

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer. A pan-cancer analysis identifies hundreds of predisposing germline variants.

The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.

In some mammals, halogenated aromatic hydrocarbon (HAH) exposure causes wasting syndrome, defined as significant weight loss associated with lethal outcomes. The most potent HAH in causing wasting is 2,3,7,8-tetrachlorodibenzo-r-dioxin (TCDD), which exerts its toxic effects through the aryl hydrocarbon receptor (AHR). Since TCDD toxicity is thought to predominantly arise from dysregulation of AHR-transcribed genes, it was hypothesized that wasting syndrome is a result of to TCDD-induced dysregulation of genes involved in regulation of food-intake. As the hypothalamus is the central nervous systems' regulatory center for food-intake and energy balance. Therefore, mRNA abundances in hypothalamic tissue from two rat strains with widely differing sensitivities to TCDD-induced wasting syndrome: TCDD-sensitive Long–Evans rats and TCDD-resistant Han/Wistar rats, 23h after exposure to TCDD (100 mg/kg) or corn oil vehicle. TCDD exposure caused minimal transcriptional dysregulation in the hypothalamus, with only 6 genes significantlyaltered in Long–Evans rats and 15 genes in Han/Wistar rats. Two of the most dysregulated genes were Cyp1a1 and Nqo1, which are induced by TCDD across a wide range of tissues and are considered sensitive markers of TCDD exposure. The minimal response of the hypothalamic transcriptome to a lethal dose of TCDD at an early time-point suggests that the hypothalamus is not the predominant site of initial events leading to hypophagia and associated wasting. TCDD may affect feeding behaviour via events upstream or downstream of the hypothalamus, and further work is required to evaluate this at the level of individual hypothalamic nuclei and subregions. a 2014 The Authors. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

BackgroundThe phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries, however there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold-standards, extensive resource requirements and difficulties arising from the need to share personal genomic information.ResultsTo facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowd-sourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error-profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.ConclusionsThe synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available athttps://github.com/adamewing/bamsurgeon.

Background 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is the most potent congener of the dioxin class of environmental contaminants. Exposure to TCDD causes a wide range of toxic outcomes, ranging from chloracne to acute lethality. The severity of toxicity is highly dependent on the aryl hydrocarbon receptor (AHR). Binding of TCDD to the AHR leads to changes in transcription of numerous genes. Studies evaluating the transcriptional changes brought on by TCDD may provide valuable insight into the role of the AHR in human health and disease. We therefore compiled a collection of transcriptomic datasets that can be used to aid the scientific community in better understanding the transcriptional effects of ligand-activated AHR. Results Specifically, we have created a datasets package – TCDD.Transcriptomics – for the R statistical environment, consisting of 63 unique experiments comprising 377 samples, including various combinations of 3 species (human derived cell lines, mouse and rat), 4 tissue types (liver, kidney, white adipose tissue and hypothalamus) and a wide range of TCDD exposure times and doses. These datasets have been fully standardized using consistent preprocessing and annotation packages (available as of September 14, 2015). To demonstrate the utility of this R package, a subset of “AHR-core” genes were evaluated across the included datasets. Ahrr, Nqo1 and members of the Cyp family were significantly induced following exposure to TCDD across the studies as expected while Aldh3a1 was induced specifically in rat liver. Inmt was altered only in liver tissue and primarily by rat-AHR. Conclusions Analysis of the “AHR-core” genes demonstrates a continued need for studies surrounding the impact of AHR-activity on the transcriptome; genes believed to be consistently regulated by ligand-activated AHR show surprisingly little overlap across species and tissues. Until now, a comprehensive assessment of the transcriptome across these studies was challenging due to differences in array platforms, processing methods and annotation versions. We believe that this package, which is freely available for download (http://labs.oicr.on.ca/boutros-lab/tcdd-transcriptomics) will prove to be a highly beneficial resource to the scientific community evaluating the effects of TCDD exposure as well as the variety of functions of the AHR. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3446-z) contains supplementary material, which is available to authorized users.