Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer.

Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

Pathogenic Germline Variants in 10,389 Adult Cancers.

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.

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer.

We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer.

Invasive lobular carcinoma (ILC) is the second most prevalent histologic subtype of invasive breast cancer. Here, we comprehensively profiled 817 breast tumors, including 127 ILC, 490 ductal (IDC), and 88 mixed IDC/ILC. Besides E-cadherin loss, the best known ILC genetic hallmark, we identified mutations targeting PTEN, TBX3, and FOXA1 as ILC enriched features. PTEN loss associated with increased AKT phosphorylation, which was highest in ILC among all breast cancer subtypes. Spatially clustered FOXA1 mutations correlated with increased FOXA1 expression and activity. Conversely, GATA3 mutations and high expression characterized luminal A IDC, suggesting differential modulation of ER activity in ILC and IDC. Proliferation and immune-related signatures determined three ILC transcriptional subtypes associated with survival differences. Mixed IDC/ILC cases were molecularly classified as ILC-like and IDC-like revealing no true hybrid features. This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options.

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

Cancer aneuploidies are shaped primarily by effects on tumour fitness.

Aneuploidies-whole-chromosome or whole-arm imbalances-are the most prevalent alteration in cancer genomes1,2. However, it is still debated whether their prevalence is due to selection or ease of generation as passenger events1,2. Here we developed a method, BISCUT, that identifies loci subject to fitness advantages or disadvantages by interrogating length distributions of telomere- or centromere-bounded copy-number events. These loci were significantly enriched for known cancer driver genes, including genes not detected through analysis of focal copy-number events, and were often lineage specific. BISCUT identified the helicase-encoding gene WRN as a haploinsufficient tumour-suppressor gene on chromosome 8p, which is supported by several lines of evidence. We also formally quantified the role of selection and mechanical biases in driving aneuploidy, finding that rates of arm-level copy-number alterations are most highly correlated with their effects on cellular fitness1,2. These results provide insight into the driving forces behind aneuploidy and its contribution to tumorigenesis.

The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Velcrin-induced selective cleavage of tRNALeu(TAA) by SLFN12 causes cancer cell death.

Velcrin compounds kill cancer cells expressing high levels of phosphodiesterase 3A (PDE3A) and Schlafen family member 12 (SLFN12) by inducing complex formation between these two proteins, but the mechanism of cancer cell killing by the PDE3A-SLFN12 complex is not fully understood. Here, we report that the physiological substrate of SLFN12 RNase is tRNALeu(TAA). SLFN12 selectively digests tRNALeu(TAA), and velcrin treatment promotes the cleavage of tRNALeu(TAA) by inducing PDE3A-SLFN12 complex formation in vitro. We found that distinct sequences in the variable loop and acceptor stem of tRNALeu(TAA) are required for substrate digestion. Velcrin treatment of sensitive cells results in downregulation of tRNALeu(TAA), ribosome pausing at Leu-TTA codons and global inhibition of protein synthesis. Velcrin-induced cleavage of tRNALeu(TAA) by SLFN12 and the concomitant global inhibition of protein synthesis thus define a new mechanism of apoptosis initiation.

Tangent normalization for somatic copy-number inference in cancer genome analysis.

MOTIVATION: Somatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas, the Broad Institute Genome Characterization Center developed the Tangent normalization method to generate copy-number profiles using data from single-nucleotide polymorphism (SNP) arrays and whole-exome sequencing (WES) technologies for over 10 000 pairs of tumors and matched normal samples. Here, we describe the Tangent method, which uses a unique linear combination of normal samples as a reference for each tumor sample, to subtract systematic errors that vary across samples. We also describe a modification of Tangent, called Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available. RESULTS: Tangent normalization substantially increases signal-to-noise ratios (SNRs) compared to conventional normalization methods in both SNP array and WES analyses. Tangent and Pseudo-Tangent normalizations improve the SNR by reducing noise with minimal effect on signal and exceed the contribution of other steps in the analysis such as choice of segmentation algorithm. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data. AVAILABILITY AND IMPLEMENTATION: Tangent is available at https://github.com/broadinstitute/tangent and as a Docker image (https://hub.docker.com/r/broadinstitute/tangent). Tangent is also the normalization method for the copy-number pipeline in Genome Analysis Toolkit 4 (GATK4). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Loss of heterozygosity does not occur in BRCA1/2 mutant pediatric solid and central nervous system tumors.

Utilization of tumor-only sequencing has expanded in pediatric cancer patients, which can lead to identification of pathogenic variants in genes that may be germline and/or have uncertain relevance to the tumor in question, such as the homologous recombination (HR) pathway genes BRCA1/2. We identified patients with pathogenic BRCA1/2 mutations from somatic tumor sequencing, and performed additional germline sequencing to assess for the presence of loss of heterozygosity (LOH). Of seven patients identified, four (57.1%) mutations were found in the germline and none had associated LOH. Our data suggest that BRCA1/2 mutations identified in this context are likely incidental findings.

Quantification of aneuploidy in targeted sequencing data using ASCETS.

SUMMARY: The expansion of targeted panel sequencing efforts has created opportunities for large-scale genomic analysis, but tools for copy-number quantification on panel data are lacking. We introduce ASCETS, a method for the efficient quantitation of arm and chromosome-level copy-number changes from targeted sequencing data. AVAILABILITY AND IMPLEMENTATION: ASCETS is implemented in R and is freely available to non-commercial users on GitHub: https://github.com/beroukhim-lab/ascets, along with detailed documentation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mechanistic insights into cancer cell killing through interaction of phosphodiesterase 3A and schlafen family member 12.

Cytotoxic molecules can kill cancer cells by disrupting critical cellular processes or by inducing novel activities. 6-(4-(Diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one (DNMDP) is a small molecule that kills cancer cells by generation of novel activity. DNMDP induces complex formation between phosphodiesterase 3A (PDE3A) and schlafen family member 12 (SLFN12) and specifically kills cancer cells expressing elevated levels of these two proteins. Here, we examined the characteristics and covariates of the cancer cell response to DNMDP. On average, the sensitivity of human cancer cell lines to DNMDP is correlated with PDE3A expression levels. However, DNMDP could also bind the related protein, PDE3B, and PDE3B supported DNMDP sensitivity in the absence of PDE3A expression. Although inhibition of PDE3A catalytic activity did not account for DNMDP sensitivity, we found that expression of the catalytic domain of PDE3A in cancer cells lacking PDE3A is sufficient to confer sensitivity to DNMDP, and substitutions in the PDE3A active site abolish compound binding. Moreover, a genome-wide CRISPR screen identified the aryl hydrocarbon receptor-interacting protein (AIP), a co-chaperone protein, as required for response to DNMDP. We determined that AIP is also required for PDE3A-SLFN12 complex formation. Our results provide mechanistic insights into how DNMDP induces PDE3A-SLFN12 complex formation, thereby killing cancer cells with high levels of PDE3A and SLFN12 expression.

BRCA1 ensures genome integrity by eliminating estrogen-induced pathological topoisomerase II-DNA complexes.

Women having BRCA1 germ-line mutations develop cancer in breast and ovary, estrogen-regulated tissues, with high penetrance. Binding of estrogens to the estrogen receptor (ER) transiently induces DNA double-strand breaks (DSBs) by topoisomerase II (TOP2) and controls gene transcription. TOP2 resolves catenated DNA by transiently generating DSBs, TOP2-cleavage complexes (TOP2ccs), where TOP2 covalently binds to 5' ends of DSBs. TOP2 frequently fails to complete its catalysis, leading to formation of pathological TOP2ccs. We have previously shown that the endonucleolytic activity of MRE11 plays a key role in removing 5' TOP2 adducts in G1 phase. We show here that BRCA1 promotes MRE11-mediated removal of TOP2 adducts in G1 phase. We disrupted the BRCA1 gene in 53BP1-deficient ER-positive breast cancer and B cells. The loss of BRCA1 caused marked increases of pathological TOP2ccs in G1 phase following exposure to etoposide, which generates pathological TOP2ccs. We conclude that BRCA1 promotes the removal of TOP2 adducts from DSB ends for subsequent nonhomologous end joining. BRCA1-deficient cells showed a decrease in etoposide-induced MRE11 foci in G1 phase, suggesting that BRCA1 repairs pathological TOP2ccs by promoting the recruitment of MRE11 to TOP2cc sites. BRCA1 depletion also leads to the increase of unrepaired DSBs upon estrogen treatment both in vitro in G1-arrested breast cancer cells and in vivo in epithelial cells of mouse mammary glands. BRCA1 thus plays a critical role in removing pathological TOP2ccs induced by estrogens as well as etoposide. We propose that BRCA1 suppresses tumorigenesis by removing estrogen-induced pathological TOP2ccs throughout the cell cycle.