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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.

To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single-cell chromatin accessibility profiles and single-cell transcriptomes from 1,000 to 10,000 cells per sample for 48 polyps, 27 normal tissues and 6 CRCs collected from patients with or without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from homeostasis to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T cells and a subtype of pre-cancer-associated fibroblasts. In the cancerous state, we observe T cell exhaustion, RUNX1-regulated cancer-associated fibroblasts and increasing accessibility associated with HNF4A motifs in epithelia. DNA methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.

Window of opportunity trials (WOT) are increasingly common in oncology research. In WOT participants receive a drug between diagnosis and anti-cancer treatment, usually for the purpose of investigating that drugs effect on cancer biology. This qualitative study aimed to understand patient perspectives on WOT.We recruited adults diagnosed with early-stage breast cancer awaiting definitive therapy at a single-academic medical center to participate in semi-structured interviews. Thematic and content analyses were performed to identify attitudes and factors that would influence decisions about WOT participation.We interviewed 25 women diagnosed with early-stage breast cancer. The most common positive attitudes toward trial participation were a desire to contribute to research and a hope for personal benefit, while the most common concerns were the potential for side effects and how they might impact fitness for planned treatment. Participants indicated family would be an important normative factor in decision-making and, during the COVID-19 pandemic, deemed the absence of family members during clinic visits a barrier to enrollment. Factors that could hinder participation included delay in standard treatment and the requirement for additional visits or procedures. Ultimately, most interviewees stated they would participate in a WOT if offered (N = 17/25).In this qualitative study, interviewees weighed altruism and hypothetical personal benefit against the possibility of side effect from a WOT. In-person family presence during trial discussion, challenging during COVID-19, was important for many. Our results may inform trial design and communication approaches in future window of opportunity efforts.

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.

Phenotypes in acinar cell-of-origin Trp53 missense mutation mouse cohorts

Analysis of pancreatic cancer development from adult mouse acinar cells

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Supplementary Data from Metabolic Profiling Reveals a Dependency of Human Metastatic Breast Cancer on Mitochondrial Serine and One-Carbon Unit Metabolism

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Mutations in ARID1A rank among the most common molecular aberrations in human cancer. However, oncogenic consequences of ARID1A mutation in human cells remain poorly defined due to lack of forward genetic models. Here, CRISPR/Cas9-mediated ARID1A knockout (KO) in primary TP53−/− human gastric organoids induced morphologic dysplasia, tumorigenicity, and mucinous differentiation. Genetic WNT/β-catenin activation rescued mucinous differentiation, but not hyperproliferation, suggesting alternative pathways of ARID1A KO-mediated transformation. ARID1A mutation induced transcriptional regulatory modules characteristic of microsatellite instability and Epstein–Barr virus–associated subtype human gastric cancer, including FOXM1-associated mitotic genes and BIRC5/survivin. Convergently, high-throughput compound screening indicated selective vulnerability of ARID1A-deficient organoids to inhibition of BIRC5/survivin, functionally implicating this pathway as an essential mediator of ARID1A KO-dependent early-stage gastric tumorigenesis. Overall, we define distinct pathways downstream of oncogenic ARID1A mutation, with nonessential WNT-inhibited mucinous differentiation in parallel with essential transcriptional FOXM1/BIRC5-stimulated proliferation, illustrating the general utility of organoid-based forward genetic cancer analysis in human cells.Significance:We establish the first human forward genetic modeling of a commonly mutated tumor suppressor gene, ARID1A. Our study integrates diverse modalities including CRISPR/Cas9 genome editing, organoid culture, systems biology, and small-molecule screening to derive novel insights into early transformation mechanisms of ARID1A-deficient gastric cancers.See related commentary by Zafra and Dow, p. 1327.This article is highlighted in the In This Issue feature, p. 1307

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Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with a 5-year survival rate of approximately 9%. An improved understanding of PDAC initiation and progression is paramount for discovering strategies to better detect and combat this disease. Although transcriptomic analyses have uncovered distinct molecular subtypes of human PDAC, the factors that influence subtype development remain unclear. Here, we interrogate the impact of cell of origin and different Trp53 alleles on tumor evolution, using a panel of tractable genetically engineered mouse models. Oncogenic KRAS expression, coupled with Trp53 deletion or point mutation, drives PDAC from both acinar and ductal cells. Gene-expression analysis reveals further that ductal cell–derived and acinar cell–derived tumor signatures are enriched in basal-like and classical subtypes of human PDAC, respectively. These findings highlight cell of origin as one factor that influences PDAC molecular subtypes and provide insight into the fundamental impact that the very earliest events in carcinogenesis can have on cancer evolution.Significance:Although human PDAC has been classified into different molecular subtypes, the etiology of these distinct subtypes remains unclear. Using mouse genetics, we reveal that cell of origin is an important determinant of PDAC molecular subtype. Deciphering the biology underlying pancreatic cancer subtypes may reveal meaningful distinctions that could improve clinical intervention.This article is highlighted in the In This Issue feature, p. 521

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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.

Methods for Real-Time Quantitative RT-PCR, Assessment of medal accuracy, Gene expression, copy number and survival analysis

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1–S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1–S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1–S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NFκB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NFκB and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NFκB signaling. Cancer Res; 77(7); 1575–85. ©2017 AACR.

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Legend Figure S1: Schematic of the experimental framework Legend Figure S2: Predicted phenotypic value and assessment of predictive power of the model. Legend Figure S3: Taget knock-down. Legend Figure S4: Treatment with individual siRNAs against TRIB1 leads to G1-S arrest. Legend Figure S5: RNA levels of cell cycle genes. Legend Figure S6: TRIB1 knockdown results in inhibition of NFκB-responsive promoter activity: comparison of individual siRNA duplexes. Legend Figure S7: TRIB1 resistant to siRNA (TRIB1-siR) rescues the phenotype induced by TRIB1-siRNA. Legend Figure S8: Downregulation of NFkB target genes after treatment with TRIB1 siRNA. Legend Figure S9: TRIB1 knock down leads to inhibition of NFκB pathway. Legend Figure S10: TRIB1 siRNA promotes TRAIL-induced apoptosis through activation of caspase-8 cleavage. Legend Figure S11: TRIB1 knockdown results in decrease of YY1gene expression. Legend Figure S12: Kaplan-Meier plots Legend Figure S13: Hazard Ratios for BCCS distribution. Legend Figure S14: Hazard Ratios for OS distribution.

PDF file - 63K, Figure S6. Inferred parameters G and a for ZNF454 and SLC5A7. For the transit amplifying stages parameter G and the apoptosis rate a, we report agreement of multiple molecular clocks. Figure S7. Heterogeneity induced by different gland splitting algorithms. In our model we implemented the gland fission mechanism as the equal division of the cell population into the two daughter cells. Although this is the most commonly advocated method of gland splitting, an alternative scenario is to consider a single founding cell for the new gland. We predict that this would substantially increase the level of heterogeneity in the glands, due to the increased number of cell divisions required by the founding CSC to grow the new gland. We do not consider this alternative process in our analysis. Figure S8. Prior distributions used in SCAI. We used non-informative priors for our analysis. However, some parameter values are less likely to yield a tumor. For example, a high apoptotic rate may completely extinguish the CSC population when this is small enough. For this reason, the symmetric division rate and apoptosis rate are not taken to be uniform. The tumor age is a secondary parameter that results from the combination of all the other parameters. Figure S9. Validation of the SCAI framework with an 8 CpG-long synthetic dataset. Using synthetic dataset with a molecular clock made by 8 CpGs produces very similar results to a synthetic dataset with 16 CpGs-long clocks. Figure S10. Patient-specific inference results for a single copy of IRX2. Inference results considering a haploid version of the IRX2 locus in each cell (e.g. due to copy number loss), are very similar to the results obtained by considering 2 copies of IRX2 per cell. This confirms the robustness of our analysis to copy number alterations. Moreover copy number changes of the IRX2 locus in CRC are reported in an extremely limited number of cases (~0.3%). Figure S11. Expected variation in summary statistics for different sequencing depths. The simulation of different sampling depths from the same gland reveals a considerable difference in summary statistics, particularly for the number of patterns, singletons, Kolmogorov distance and Shannon index.

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Glioblastoma, the most common and aggressive adult brain tumor, is characterized by extreme phenotypic diversity and treatment failure. Through fluorescence-guided resection, we identified fluorescent tissue in the sub-ependymal zone (SEZ) of patients with glioblastoma. Histologic analysis and genomic characterization revealed that the SEZ harbors malignant cells with tumor-initiating capacity, analogous to cells isolated from the fluorescent tumor mass (T). We observed resistance to supramaximal chemotherapy doses along with differential patterns of drug response between T and SEZ in the same tumor. Our results reveal novel insights into glioblastoma growth dynamics, with implications for understanding and limiting treatment resistance. Cancer Res; 75(1); 194–202. ©2014 AACR.

Supplementary experimental procedures: this file contains an expanded materials and methods section on 5-ALA administration and sample collection, cell lines propagation, immunofluorescence and in vivo assays, genomic and molecular clock analysis, fluorescence in situ hybridization, drug concentration used in the proliferation assay and MGMT promoter methylation analysis. Supplementary Figure S1: this figure illustrates the identification of the sub-ependymal zone (SEZ) in two GB patients included in this study. Supplementary Figure S2: this figure shows the sampling and morphology of the SEZ tissue. Supplementary Figure S3: this figure summarizes the results of real time analysis for markers of glial, precursor, stem cells and proliferation in paired T and SEZ from three GB patients. Supplementary Figure S4: this figure shows an example of common copy number aberration breakpoints between the SEZ and the corresponding T in each of the GB patients included in this study. Supplementary Figure S5: this figure summarizes the fluorescence in situ hybridization (FISH) results for EGFR, MET and PTEN in three GB patients. Supplementary Figure S6: this figure shows the copy number aberration breakpoints between T and SEZ and the corresponding cell lines in patient sp14. Supplementary Figure S7: this figure shows the results of growth curve analysis of tumor-initiating cells (TICs) derived from matched T and SEZ of five GBs. Supplementary Figure S8: this figure summarizes the results of clonogenic index between three paired T and SEZ cells and multipotency of SEZ cells. Supplementary Figure S9: this figure shows the immunofluorescence results for stem cell markers on T and SEZ cells in two GBs. Supplementary Figure S10: this figure summarizes the results of cumulative Kaplan-Meier survival analysis from five additional GBs. Supplementary Figure S11: this figure shows an analysis of the in vivo properties of TICs isolated from the SEZ of three GBs. Supplementary Figure S12: this figure shows the results of the cell proliferation assay of 7 paired TICs using 50μM of Temozolomide (TMZ). Supplementary Figure S13: this figure summarizes the cell proliferation data of additional 20 TICs treated with TMZ for dose escalation analysis. Supplementary Figure S14: this figure shows the results of cell proliferation assay of additional 4 paired TICs treated with TMZ, Cisplatin and Cediranib. Supplementary Figure S15: this figure shows a gel image of MGMT promoter status analysis for T and SEZ of the analyzed 7 paired TICs. Supplementary Figure S16: this figure summarizes the immunofluorescence results for vascular endothelial growth factor receptor 2 (Vegfr2) on T and SEZ cells from three GBs. Supplementary Figure S17: this figure summarizes the cell proliferation data of three control lines. Supplementary Figure S18: this figure illustrates a model of residual disease in human GB and summarizes the cardinal features of T and SEZ. Supplementary Figure S19: this figure shows the results of growth curve analysis of two SEZ in absence of fluorescence. Supplementary Table S1: this table summarizes the results of the histological features of T and SEZ tissues. Supplementary Table S2: this table provides a summary of the samples used for the genomic analysis and of the clinical information available for the eleven GB patients included in this study. Supplementary Table S3: this table shows the gene ontology (GO) terms whose genes are differentially expressed between SEZ and T. Supplementary Table S4: this table provides a summary of the results of MGMT methylation analysis by pyrosequencing in sixteen GBs. Supplementary Table S5: this table summarizes the overall survival of the eight GB patients whose TICs were used for the cell proliferation assay and includes the MGMT methylation by pyrosequencing. Supplementary Table S6: this table shows the gene expression data of PDGF and VEGF receptors in T and SEZ.

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.

Nonsteroidal anti-inflammatory drugs (NSAID) are commonly used and prescribed across the globe. A defining characteristic of NSAIDs is their poor water solubility; therefore, in a practical setting this translates directly to poor systemic absorption in the body1. The ability of a drug to effectively enter and carry out microscopic processes within cells depends on its ability to transverse the lipid membrane surrounding cells. The metric used to evaluate this capacity is called the partition coefficient, or the logP. The logP is the logarithm of concentration of a molecule between octanol and water partitions, where membrane permeability can be evaluated from this value as octanol is comparable to the cell membrane2. A logP above >0 indicates a preferential solubility in lipids, a high logP is correlated with poor solubility and absorption3.It is recommended that a drug must be overall lipophilic but still maintain aqueous solubility4. Naproxen is an NSAID that is used to treat inflammation found in many common illnesses. Naproxen has a logP value of 3.3; however a logP value

Models to study metastatic disease in rare cancers are needed to advance preclinical therapeutics and to gain insight into disease biology, especially for highly aggressive cancers with a propensity for metastatic spread. Osteosarcoma is a rare cancer with a complex genomic landscape in which outcomes for patients with metastatic disease are poor. As osteosarcoma genomes are highly heterogeneous, a large panel of models is needed to fully elucidate key aspects of disease biology and to recapitulate clinically-relevant phenotypes. We describe the development and characterization of osteosarcoma patient-derived xenografts (PDXs) and a panel of PDX-derived cell lines. Matched patient samples, PDXs, and PDX-derived cell lines were comprehensively evaluated using whole genome sequencing and RNA sequencing. PDXs and PDX-derived cell lines largely maintained the expression profiles of the patient from which they were derived despite the emergence of whole-genome duplication (WGD) in a subset of cell lines. These cell line models were heterogeneous in their metastatic capacity and their tissue tropism as observed in both intravenous and orthotopic models. As proof-of-concept study, we used one of these models to test the preclinical effectiveness of a CDK inhibitor on the growth of metastatic tumors in an orthotopic amputation model. Single-agent dinaciclib was effective at dramatically reducing the metastatic burden in this model.Graphical Abstract

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.