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Metastasis is the main cause of death for patients with breast cancer. Many studies have characterized the genomic landscape of breast cancer during its early stages. However, there is evidence that genomic alterations are acquired during the evolution of cancers from their early to late stages, and that the genomic landscape of early cancers is not representative of that of lethal cancers 1 – 7 . Here we investigated the landscape of somatic alterations in 617 metastatic breast cancers. Nine driver genes ( TP53 , ESR1 , GATA3 , KMT2C , NCOR1 , AKT1 , NF1 , RIC8A and RB1 ) were more frequently mutated in metastatic breast cancers that expressed hormone receptors (oestrogen and/or progesterone receptors; HR + ) but did not have high levels of HER2 (HER2 − ; n  = 381), when compared to early breast cancers from The Cancer Genome Atlas. In addition, 18 amplicons were more frequently observed in HR + /HER2 − metastatic breast cancers. These cancers showed an increase in mutational signatures S2, S3, S10, S13 and S17. Among the gene alterations that were enriched in HR + /HER2 − metastatic breast cancers, mutations in TP53 , RB1 and NF1 , together with S10, S13 and S17, were associated with poor outcome. Metastatic triple-negative breast cancers showed an increase in the frequency of somatic biallelic loss-of-function mutations in genes related to homologous recombination DNA repair, compared to early triple-negative breast cancers (7% versus 2%). Finally, metastatic breast cancers showed an increase in mutational burden and clonal diversity compared to early breast cancers. Thus, the genomic landscape of metastatic breast cancer is enriched in clinically relevant genomic alterations and is more complex than that of early breast cancer. The identification of genomic alterations associated with poor outcome will allow earlier and better selection of patients who require the use of treatments that are still in clinical trials. The genetic complexity observed in advanced breast cancer suggests that such treatments should be introduced as early as possible in the disease course. Patient data from six clinical trials are used to compare the genomic landscapes of breast cancer metastases with those of primary tumours, revealing an increase in mutational burden and clonal diversity.

Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated HCCs. Here we present an integrated proteogenomic analysis of HCCs across clinical stages and etiologies. Pathways related to cell cycle, transcriptional and translational control, signaling transduction, and metabolism are dysregulated and differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. We describe candidate copy number-driven driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β-catenin, AKT/mTOR and Notch pathways, cell cycle and DNA damage regulation. The targetable aurora kinase A and CDKs are upregulated. CTNNB1 and TP53 mutations are associated with altered protein phosphorylation related to actin filament organization and lipid metabolism, respectively. Integrative proteogenomic clusters show that HCC constitutes heterogeneous subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our study provides a comprehensive overview of the proteomic and phophoproteomic landscapes of HCCs, revealing the major pathways altered in the (phospho)proteome. Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated tumours and lacked information about the phosphoproteome. Here, the authors present a comprehensive HCC proteogenomics and phosphoproteomics study in patient samples from multiple etiologies and stages.

The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is the development of progressive resistance to platinum-based chemotherapy. The objective of this study was to determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and the emergence of subclonal tumour populations in HGSOC was associated with the development of resistant disease. Evolutionary inference and phylogenetic quantification of heterogeneity was performed using the MEDICC algorithm on high-resolution whole genome copy number profiles and selected genome-wide sequencing of 135 spatially and temporally separated samples from 14 patients with HGSOC who received platinum-based chemotherapy. Samples were obtained from the clinical CTCR-OV03/04 studies, and patients were enrolled between 20 July 2007 and 22 October 2009. Median follow-up of the cohort was 31 mo (interquartile range 22-46 mo), censored after 26 October 2013. Outcome measures were overall survival (OS) and progression-free survival (PFS). There were marked differences in the degree of clonal expansion (CE) between patients (median 0.74, interquartile range 0.66-1.15), and dichotimization by median CE showed worse survival in CE-high cases (PFS 12.7 versus 10.1 mo, p = 0.009; OS 42.6 versus 23.5 mo, p = 0.003). Bootstrap analysis with resampling showed that the 95% confidence intervals for the hazard ratios for PFS and OS in the CE-high group were greater than 1.0. These data support a relationship between heterogeneity and survival but do not precisely determine its effect size. Relapsed tissue was available for two patients in the CE-high group, and phylogenetic analysis showed that the prevalent clonal population at clinical recurrence arose from early divergence events. A subclonal population marked by a NF1 deletion showed a progressive increase in tumour allele fraction during chemotherapy. This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. Subclonal tumour populations are present in pre-treatment biopsies in HGSOC and can undergo expansion during chemotherapy, causing clinical relapse.

Therapy resistance and metastatic processes in prostate cancer (PCa) remain undefined, due to lack of experimental models that mimic different disease stages. We describe an androgen-dependent PCa patient-derived xenograft (PDX) model from treatment-naïve, soft tissue metastasis (PNPCa). RNA and whole-exome sequencing of the PDX tissue and organoids confirmed transcriptomic and genomic similarity to primary tumor. PNPCa harbors BRCA2 and CHD1 somatic mutations, shows an SPOP/FOXA1 -like transcriptomic signature and microsatellite instability, which occurs in 3% of advanced PCa and has never been modeled in vivo. Comparison of the treatment-naïve PNPCa with additional metastatic PDXs (BM18, LAPC9), in a medium-throughput organoid screen of FDA-approved compounds, revealed differential drug sensitivities. Multikinase inhibitors (ponatinib, sunitinib, sorafenib) were broadly effective on all PDX- and patient-derived organoids from advanced cases with acquired resistance to standard-of-care compounds. This proof-of-principle study may provide a preclinical tool to screen drug responses to standard-of-care and newly identified, repurposed compounds. To date, patients still succumb to cancer, due to tumors not responding to therapy or ultimately acquiring resistance. Here the authors show that by exploiting patient derived organoids and a treatment-naïve patient derived xenograft, patient therapy can be personalized.

Mutational signatures connect characteristic mutational patterns in the genome with biological or chemical processes that take place in cancers. Analysis of mutational signatures can help elucidate tumor evolution, prognosis, and therapeutic strategies. Although tools for extracting mutational signatures de novo have been extensively benchmarked, a similar effort is lacking for tools that fit known mutational signatures to a given catalog of mutations. We fill this gap by comprehensively evaluating twelve signature fitting tools on synthetic mutational catalogs with empirically driven signature weights corresponding to eight cancer types. On average, SigProfilerSingleSample and SigProfilerAssignment/MuSiCal perform best for small and large numbers of mutations per sample, respectively. We further show that ad hoc constraining the list of reference signatures is likely to produce inferior results. Evaluation of real mutational catalogs suggests that the activity of signatures that are absent in the reference catalog poses considerable problems to all evaluated tools. Various biological and chemical processes leave characteristic patterns, mutational signatures, in the genome. Here the authors assess tools for fitting known mutational signatures to sequenced samples (to determine the signature contributions to each individual sample), finding that they are all prone to underfitting due to the activity of unknown signatures.

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions ( ARID1A-TEAD1 and AXIN1-URI1 ) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets. Synthetic lethality can be used to identify potential drug targets in cancer based on simultaneous inactivation of two genes through genetic aberrations and gene silencing. Here, the authors develop a statistical framework to identify synthetic lethal pairs from large scale perturbation screens across multiple cancer types.

Collisions of the transcription and replication machineries on the same DNA strand can pose a significant threat to genomic stability. These collisions occur in part due to the formation of RNA-DNA hybrids termed R-loops, in which a newly transcribed RNA molecule hybridizes with the DNA template strand. This study investigated the role of RAD52, a known DNA repair factor, in preventing collisions by directing R-loop formation and resolution. We show that RAD52 deficiency increases R-loop accumulation, exacerbating collisions and resulting in elevated DNA damage. Furthermore, RAD52’s ability to interact with the transcription machinery, coupled with its capacity to facilitate R-loop dissolution, highlights its role in preventing collisions. Lastly, we provide evidence of an increased mutational burden from double-strand breaks at conserved R-loop sites in human tumor samples, which is increased in tumors with low RAD52 expression. In summary, this study underscores the importance of RAD52 in orchestrating the balance between replication and transcription processes to prevent collisions and maintain genome stability. Collisions of transcription and replication machineries on the same DNA strand threaten genomic stability. Here, the authors show that RAD52 prevents these collisions by regulating R-loop formation and resolution. RAD52 deficiency leads to increased R-loops, exacerbated collisions, DNA damage, and higher mutational burden in tumors.

Genetic variants of the interferon lambda ( IFNL ) gene locus are strongly associated with spontaneous and IFN treatment-induced clearance of hepatitis C virus (HCV) infections. Individuals with the ancestral IFNL4-dG allele are not able to clear HCV in the acute phase and have more than a 90% probability to develop chronic hepatitis C (CHC). Paradoxically, the IFNL4-dG allele encodes a fully functional IFNλ4 protein with antiviral activity against HCV. Here we describe an effect of IFNλ4 on HCV antigen presentation. Only minor amounts of IFNλ4 are secreted, because the protein is largely retained in the endoplasmic reticulum (ER) where it induces ER stress. Stressed cells are significantly weaker activators of HCV specific CD8 + T cells than unstressed cells. This is not due to reduced MHC I surface presentation or extracellular IFNλ4 effects, since T cell responses are restored by exogenous loading of MHC with HCV antigens. Rather, IFNλ4 induced ER stress impairs HCV antigen processing and/or loading onto the MHC I complex. Our results provide a potential explanation for the IFNλ4–HCV paradox. A genetic variant in the IFN-lambda 4 gene has been associated with poor hepatitis C virus prognosis but it is not clear how this functions. Here the authors show that IFN-lambda 4 promotes ER stress and inhibits presentation of HCV epitopes to CD8 + T cells.

Improved survival rates for prostate cancer through more effective therapies have also led to an increase in the diagnosis of metastases to infrequent locations such as the brain. Here we investigate the repertoire of somatic genetic alterations present in brain metastases from 51 patients with prostate cancer brain metastases (PCBM). We highlight the clonal evolution occurring in PCBM and demonstrate an increased mutational burden, concomitant with an enrichment of the homologous recombination deficiency mutational signature in PCBM compared to non-brain metastases. Focusing on known pathogenic alterations within homologous recombination repair genes, we find 10 patients (19.6%) fulfilling the inclusion criteria used in the PROfound clinical trial, which assessed the efficacy of PARP inhibitors (PARPi) in homologous recombination deficient prostate cancer. Eight (15.7%) patients show biallelic loss of one of the 15 genes included in the trial, while 5 patients (9.8%) harbor pathogenic alterations in BRCA1/2 specifically. Uncovering these molecular features of PCBM may have therapeutic implications, suggesting the need of clinical trial enrollment of PCBM patients when evaluating potential benefit from PARPi. The diagnosis of prostate cancer brain metastasis (PCBM) has increased. Here, the authors investigate the landscape of somatic genetic alterations in brain metastases in a PCBM cohort of 51 patients with non-synchronous matched primary samples available for 20 patients.