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Metastatic and locally-advanced neuroendocrine neoplasms (aNEN) form clinically and genetically heterogeneous malignancies, characterized by distinct prognoses based upon primary tumor localization, functionality, grade, proliferation index and diverse outcomes to treatment. Here, we report the mutational landscape of 85 whole-genome sequenced aNEN. This landscape reveals distinct genomic subpopulations of aNEN based on primary localization and differentiation grade; we observe relatively high tumor mutational burdens (TMB) in neuroendocrine carcinoma (average 5.45 somatic mutations per megabase) with TP53 , KRAS , RB1 , CSMD3 , APC , CSMD1 , LRATD2 , TRRAP and MYC as major drivers versus an overall low TMB in neuroendocrine tumors (1.09). Furthermore, we observe distinct drivers which are enriched in somatic aberrations in pancreatic ( MEN1 , ATRX , DAXX , DMD and CREBBP ) and midgut-derived neuroendocrine tumors ( CDKN1B ). Finally, 49% of aNEN patients reveal potential therapeutic targets based upon actionable (and responsive) somatic aberrations within their genome; potentially directing improvements in aNEN treatment strategies. Metastatic and locally-advanced neuroendocrine neoplasms (aNEN) display heterogeneous clinical and genetic characteristics. Here, the authors investigate the mutational landscape of 85 aNEN by whole genome sequencing and identify distinct subpopulations, tumour mutational burden patterns, drivers and actionable somatic alterations.

In contrast to primary colorectal cancer (CRC) little is known about the genomic landscape of metastasized CRC. Here we present whole genome sequencing data of metastases of 429 CRC patients participating in the pan-cancer CPCT-02 study (NCT01855477). Unsupervised clustering using mutational signature patterns highlights three major patient groups characterized by signatures known from primary CRC, signatures associated with received prior treatments, and metastasis-specific signatures. Compared to primary CRC, we identify additional putative (non-coding) driver genes and increased frequencies in driver gene mutations. In addition, we identify specific genes preferentially affected by microsatellite instability. CRC-specific 1kb-10Mb deletions, enriched for common fragile sites, and LINC00672 mutations are associated with response to treatment in general, whereas FBXW7 mutations predict poor response specifically to EGFR-targeted treatment. In conclusion, the genomic landscape of mCRC shows defined changes compared to primary CRC, is affected by prior treatments and contains features with potential clinical relevance. Molecular landscapes of metastatic colorectal cancers (mCRC) have often been restricted to coding regions or low numbers of patients. Here the authors present a whole-genome landscape of 429 mCRC patients, revealing the mutational impact of prior therapies and potential actionable targets.

Background Testicular germ cell cancer (TGCC), being the most frequent malignancy in young Caucasian males, is initiated from an embryonic germ cell. This study determines intratumour heterogeneity to unravel tumour progression from initiation until metastasis. Methods In total, 42 purified samples of four treatment-resistant nonseminomatous (NS) TGCC were investigated, including the precursor germ cell neoplasia in situ (GCNIS) and metastatic specimens, using whole-genome and targeted sequencing. Their evolution was reconstructed. Results Intratumour molecular heterogeneity did not correspond to the supposed primary tumour histological evolution. Metastases after systemic treatment could be derived from cancer stem cells not identified in the primary cancer. GCNIS mostly lacked the molecular marks of the primary NS and comprised dominant clones that failed to progress. A BRCA-like mutational signature was observed without evidence for direct involvement of BRCA1 and BRCA2 genes. Conclusions Our data strongly support the hypothesis that NS is initiated by whole-genome duplication, followed by chromosome copy number alterations in the cancer stem cell population, and accumulation of low numbers of somatic mutations, even in therapy-resistant cases. These observations of heterogeneity at all stages of tumourigenesis should be considered when treating patients with GCNIS-only disease, or with clinically overt NS.

Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12 −/− and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12 −/− and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups. Detecting genomic abnormalities in metastatic castration-resistant prostate cancer (mCRPC) may impact clinical treatment. Here, the authors present whole-genome sequencing of metastatic biopsies from 197 mCRPC patients, highlighting the landscape of microsatellite stability, homologous repair deficiency, and other genomic subgroups.

Background Current normalization methods for RNA-sequencing data allow either for intersample comparison to identify differentially expressed (DE) genes or for intrasample comparison for the discovery and validation of gene signatures. Most studies on optimization of normalization methods typically use simulated data to validate methodologies. We describe a new method, GeTMM, which allows for both inter- and intrasample analyses with the same normalized data set. We used actual (i.e. not simulated) RNA-seq data from 263 colon cancers (no biological replicates) and used the same read count data to compare GeTMM with the most commonly used normalization methods (i.e. TMM (used by edgeR), RLE (used by DESeq2) and TPM) with respect to distributions, effect of RNA quality, subtype-classification, recurrence score, recall of DE genes and correlation to RT-qPCR data. Results We observed a clear benefit for GeTMM and TPM with regard to intrasample comparison while GeTMM performed similar to TMM and RLE normalized data in intersample comparisons. Regarding DE genes, recall was found comparable among the normalization methods, while GeTMM showed the lowest number of false-positive DE genes. Remarkably, we observed limited detrimental effects in samples with low RNA quality. Conclusions We show that GeTMM outperforms established methods with regard to intrasample comparison while performing equivalent with regard to intersample normalization using the same normalized data. These combined properties enhance the general usefulness of RNA-seq but also the comparability to the many array-based gene expression data in the public domain.

The Four Core Genotypes (FCG) is a mouse model system used to disentangle the function of sex chromosomes and hormones. We report that a copy of a 3.2 MB region of the X chromosome has translocated to the Y Sry- chromosome and thus increased the expression of X-linked genes including the single-stranded RNA sensor and autoimmune disease mediator Tlr7 . This previously-unreported X-Y translocation complicates the interpretation of studies reliant on C57BL/6J FCG mice. Here the authors find a genetic alteration in the popular “Four Core Genotypes” mouse model that is used to distinguish sex-biased phenotypes caused by sex chromosomes and gonads. This alteration increases the expression of some X-linked genes, which might confound the interpretation of the model.

Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n  = 155) with matching whole-transcriptomics (WTS; n  = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models. Tumor mutational burden ( q  < 0.001), structural variants ( q  < 0.05), tandem duplications ( q  < 0.05) and deletions ( q  < 0.05) are enriched in poor responders, coupled with distinct transcriptomic expression profiles. Validating various classification models predicting treatment duration with ARSI on our internal and external mCRPC cohort reveals two best-performing models, based on the combination of prior treatment information with either the four combined enriched genomic markers or with overall transcriptomic profiles. In conclusion, predictive models combining genomic, transcriptomic, and clinical data can predict response to ARSI in mCRPC patients and, with additional optimization and prospective validation, could improve treatment guidance. Prostate cancer is known to have a variable response to androgen receptor signalling inhibitors. Here, the authors use machine learning to predict response to therapy from genomic, transcriptomic and clinical data.

Cell‐free circulating tumor DNA (ctDNA) has emerged as a promising biomarker for response evaluation in metastatic castration‐resistant prostate cancer (mCRPC). The current study evaluated the modified fast aneuploidy screening test‐sequencing system (mFast‐SeqS), a quick, tumor‐agnostic and affordable ctDNA assay that requires a small input of DNA, to generate a genome‐wide aneuploidy (GWA) score in mCRPC patients, and correlated this to matched metastatic tumor biopsies. In this prospective multicenter study, GWA scores were evaluated from blood samples of 196 mCRPC patients prior to treatment (baseline) with taxanes (docetaxel and cabazitaxel) and androgen receptor signaling inhibitors (ARSI; abiraterone and enzalutamide), and from 74 mCRPC patients at an early timepoint during treatment (early timepoint; median 21 days). Z‐scores per chromosome arm were tested for their association with tumor tissue genomic alterations. We found that a high tumor load in blood (GWAhigh) at baseline was associated with poor response to ARSI [HR: 2.63 (95% CI: 1.86–3.72) P < 0.001] but not to taxanes. Interestingly, GWAhigh score at the early timepoint was associated with poor response to both ARSIs [HR: 6.73 (95% CI: 2.60–17.42) P < 0.001] and taxanes [2.79 (95% CI: 1.34–5.78) P = 0.006]. A significant interaction in Cox proportional hazards analyses was seen when combining GWA status and type of treatment (at baseline P = 0.008; early timepoint P = 0.018). In summary, detection of ctDNA in blood by mFast‐SeqS is cheap, fast and feasible, and could be used at different timepoints as a potential predictor for outcome to ARSI and taxane treatment in mCRPC. mFast‐SeqS‐based genome‐wide aneuploidy scores are concordant with aneuploidy scores obtained by whole genome sequencing from tumor tissue and can predict response to ARSI treatment at baseline and, at an early time point, to ARSI and taxanes. This assay can be easily performed at low cost and requires little input of cfDNA.

Treatment-induced AR alterations, including AR alternative splice variants (AR-Vs), have been extensively linked to harboring roles in primary and acquired resistance to conventional and next-generation hormonal therapies in prostate cancer and therefore have gained momentum. Our aim was to uniformly determine recurrent AR-Vs in metastatic castration-resistant prostate cancer (mCRPC) using whole transcriptome sequencing in order to assess which AR-Vs might hold potential diagnostic or prognostic relevance in future research. This study reports that in addition to the promising AR-V7 as a biomarker, AR45 and AR-V3 were also seen as recurrent AR-Vs and that the presence of any AR-V could be associated with higher AR expression. With future research, these AR-Vs may therefore harbor similar or complementary roles to AR-V7 as predictive and prognostic biomarkers in mCRPC or as proxies for abundant AR expression.