Synthetic lethality-an interaction between two genetic events through which the co-occurrence of these two genetic events leads to cell death, but each event alone does not-can be exploited for cancer therapeutics(1). DNA repair processes represent attractive synthetic lethal targets, because many cancers exhibit an impairment of a DNA repair pathway, which can lead to dependence on specific repair proteins(2). The success of poly(ADP-ribose) polymerase 1 (PARP-1) inhibitors in cancers with deficiencies in homologous recombination highlights the potential of this approach(3). Hypothesizing that other DNA repair defects would give rise to synthetic lethal relationships, we queried dependencies in cancers with microsatellite instability (MSI), which results from deficient DNA mismatch repair. Here we analysed data from large-scale silencing screens using CRISPR-Cas9-mediated knockout and RNA interference, and found that the RecQ DNA helicase WRN was selectively essential in MSI models in vitro and in vivo, yet dispensable in models of cancers that are microsatellite stable. Depletion of WRN induced double-stranded DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models required the helicase activity of WRN, but not its exonuclease activity. These findings show that WRN is a synthetic lethal vulnerability and promising drug target for MSI cancers.

H3K27M mutations resulting in epigenetic dysfunction are frequently observed in diffuse intrinsic pontine glioma (DIPGs), an incurable pediatric cancer. We conduct a CRISPR screen revealing that knockout of KDM1A encoding lysine-specific demethylase 1 (LSD1) sensitizes DIPG cells to histone deacetylase (HDAC) inhibitors. Consistently, Corin, a bifunctional inhibitor of HDACs and LSD1, potently inhibits DIPG growth in vitro and in xenografts. Mechanistically, Corin increases H3K27me3 levels suppressed by H3K27M histones, and simultaneously increases HDAC-targeted H3K27ac and LSD1-targeted H3K4me1 at differentiation-associated genes. Corin treatment induces cell death, cell-cycle arrest, and a cellular differentiation phenotype and drives transcriptional changes correlating with increased survival time in DIPG patients. These data suggest a strategy for treating DIPG by simultaneously inhibiting LSD1 and HDACs.

Cancer research relies on model systems, which reflect the biology of actual human tumours to only a certain extent. One important feature of human cancer is its intra-tumour genomic heterogeneity and instability. However, the extent of such genomic instability in cancer models has received limited attention in research. Here, we review the state of knowledge of genomic instability of cancer models and discuss its biological origins and implications for basic research and for cancer precision medicine. We discuss strategies to cope with such genomic evolution and evaluate both the perils and the emerging opportunities associated with it.

Glioblastoma (GBM) is the most common primary adult brain tumor. Despite extensive efforts, the median survival for GBM patients is approximately 14 months. GBM therapy could benefit greatly from patient-specific targeted therapies that maximize treatment efficacy. Here we report a platform termed SynergySeq to identify drug combinations for the treatment of GBM by integrating information from The Cancer Genome Atlas (TCGA) and the Library of Integrated Network-Based Cellular Signatures (LINCS). We identify differentially expressed genes in GBM samples and devise a consensus gene expression signature for each compound using LINCS L1000 transcriptional profiling data. The SynergySeq platform computes disease discordance and drug concordance to identify combinations of FDA-approved drugs that induce a synergistic response in GBM. Collectively, our studies demonstrate that combining disease-specific gene expression signatures with LINCS small molecule perturbagen-response signatures can identify preclinical combinations for GBM, which can potentially be tested in humans.

Purpose Cell-free DNA (cfDNA) offers the potential for minimally invasive genome-wide profiling of tumor alterations without tumor biopsy and may be associated with patient prognosis. Triple-negative breast cancer (TNBC) is characterized by few mutations but extensive somatic copy number alterations (SCNAs), yet little is known regarding SCNAs in metastatic TNBC. We sought to evaluate SCNAs in metastatic TNBC exclusively via cfDNA and determine if cfDNA tumor fraction is associated with overall survival in metastatic TNBC. Patients and Methods In this retrospective cohort study, we identified 164 patients with biopsy-proven metastatic TNBC at a single tertiary care institution who received prior chemotherapy in the (neo)adjuvant or metastatic setting. We performed low-coverage genome-wide sequencing of cfDNA from plasma. Results Without prior knowledge of tumor mutations, we determined tumor fraction of cfDNA for 96.3% of patients and SCNAs for 63.9% of patients. Copy number profiles and percent genome altered were remarkably similar between metastatic and primary TNBCs. Certain SCNAs were more frequent in metastatic TNBCs relative to paired primary tumors and primary TNBCs in publicly available data sets The Cancer Genome Atlas and METABRIC, including chromosomal gains in drivers NOTCH2, AKT2, and AKT3. Prespecified cfDNA tumor fraction threshold of >/= 10% was associated with significantly worse metastatic survival (median, 6.4 v 15.9 months) and remained significant independent of clinicopathologic factors (hazard ratio, 2.14; 95% CI, 1.4 to 3.8; P .001). Conclusion We present the largest genomic characterization of metastatic TNBC to our knowledge, exclusively from cfDNA. Evaluation of cfDNA tumor fraction was feasible for nearly all patients, and tumor fraction >/= 10% is associated with significantly worse survival in this large metastatic TNBC cohort. Specific SCNAs are enriched and prognostic in metastatic TNBC, with implications for metastasis, resistance, and novel therapeutic approaches.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

In the version of this article originally published, some text above the "Tri-nucleotide sequence motifs" label in Fig. 2a appeared incorrectly. The text was garbled and should have appeared as nucleotide codes.Additionally, the labels on the bars in Fig. 2c were not italicized in the original publication. These are gene symbols, and they should have been italicized.The colored labels above the graphs in Fig. 4b were also erroneously not italicized. These labels represent gene names and loci, and they should have been italicized.

Diffuse large B cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is a clinically and genetically heterogeneous disease that is further classified into transcriptionally defined activated B cell (ABC) and germinal center B cell (GCB) subtypes. We carried out a comprehensive genetic analysis of 304 primary DLBCLs and identified low-frequency alterations, captured recurrent mutations, somatic copy number alterations, and structural variants, and defined coordinate signatures in patients with available outcome data. We integrated these genetic drivers using consensus clustering and identified five robust DLBCL subsets, including a previously unrecognized group of low-risk ABC-DLBCLs of extrafollicular/marginal zone origin; two distinct subsets of GCB-DLBCLs with different outcomes and targetable alterations; and an ABC/GCB-independent group with biallelic inactivation of TP53, CDKN2A loss, and associated genomic instability. The genetic features of the newly characterized subsets, their mutational signatures, and the temporal ordering of identified alterations provide new insights into DLBCL pathogenesis. The coordinate genetic signatures also predict outcome independent of the clinical International Prognostic Index and suggest new combination treatment strategies. More broadly, our results provide a roadmap for an actionable DLBCL classification.

Human cancer cell lines are the workhorse of cancer research. Although cell lines are known to evolve in culture, the extent of the resultant genetic and transcriptional heterogeneity and its functional consequences remain understudied. Here we use genomic analyses of 106 human cell lines grown in two laboratories to show extensive clonal diversity. Further comprehensive genomic characterization of 27 strains of the common breast cancer cell line MCF7 uncovered rapid genetic diversification. Similar results were obtained with multiple strains of 13 additional cell lines. Notably, genetic changes were associated with differential activation of gene expression programs and marked differences in cell morphology and proliferation. Barcoding experiments showed that cell line evolution occurs as a result of positive clonal selection that is highly sensitive to culture conditions. Analyses of single-cell-derived clones demonstrated that continuous instability quickly translates into heterogeneity of the cell line. When the 27 MCF7 strains were tested against 321 anti-cancer compounds, we uncovered considerably different drug responses: at least 75% of compounds that strongly inhibited some strains were completely inactive in others. This study documents the extent, origins and consequences of genetic variation within cell lines, and provides a framework for researchers to measure such variation in efforts to support maximally reproducible cancer research.