Proteogenomics decodes the evolution of human ipsilateral breast cancer

Ipsilateral breast tumor recurrence (IBTR) is a clinically important event, where an isolated in-breast recurrence is a potentially curable event but associated with an increased risk of distant metastasis and breast cancer death. It remains unclear if IBTRs are associated with molecular changes that can be explored as a resource for precision medicine strategies. Here, we employed proteogenomics to analyze a cohort of 27 primary breast cancers and their matched IBTRs to define proteogenomic determinants of molecular tumor evolution. Our analyses revealed a relationship between hormonal receptors status and proliferation levels resulting in the gain of somatic mutations and copy number. This in turn re-programmed the transcriptome and proteome towards a highly replicating and genomically unstable IBTRs, possibly enhanced by APOBEC3B. In order to investigate the origins of IBTRs, a second analysis that included primaries with no recurrence pinpointed proliferation and immune infiltration as predictive of IBTR. In conclusion, our study shows that breast tumors evolve into different IBTRs depending on hormonal status and proliferation and that immune cell infiltration and Ki-67 are significantly elevated in primary tumors that develop IBTR. These results can serve as a starting point to explore markers to predict IBTR formation and stratify patients for adjuvant therapy.

Whole-genome Sequencing of Follicular Thyroid Carcinomas Reveal Recurrent Mutations in MicroRNA Processing Subunit DGCR8

BACKGROUND

The genomic and transcriptomic landscape of widely invasive follicular thyroid carcinomas (wiFTCs) and Hürthle cell carcinoma (HCC) are poorly characterized, and subsets of these tumors lack information on genetic driver events.

OBJECTIVE

The aim of this study was to bridge this gap.

METHODS

We performed whole-genome and RNA sequencing and subsequent bioinformatic analyses of 11 wiFTCs and 2 HCCs with a particularly poor prognosis, and matched normal tissue.

RESULTS

All wiFTCs exhibited one or several mutations in established thyroid cancer genes, including TERT (n = 4), NRAS (n = 3), HRAS, KRAS, AKT, PTEN, PIK3CA, MUTYH, TSHR, and MEN1 (n = 1 each). MutSig2CV analysis revealed recurrent somatic mutations in FAM72D (n = 3, in 2 wiFTCs and in a single HCC), TP53 (n = 3, in 2 wiFTCs and a single HCC), and EIF1AX (n = 3), with DGCR8 (n = 2) as borderline significant. The DGCR8 mutations were recurrent p.E518K missense alterations, known to cause familial multinodular goiter via disruption of microRNA (miRNA) processing. Expression analyses showed reduced DGCR8 messenger RNA expression in FTCs in general, and the 2 DGCR8 mutants displayed a distinct miRNA profile compared to DGCR8 wild-types. Copy number analyses revealed recurrent gains on chromosomes 4, 6, and 10, and fusion gene analyses revealed 27 high-quality events. Both HCCs displayed hyperploidy, which was fairly unusual in the FTC cohort. Based on the transcriptome data, tumors amassed in 2 principal clusters.

CONCLUSION

We describe the genomic and transcriptomic landscape in wiFTCs and HCCs and identify novel recurrent mutations and copy number alterations with possible driver properties and lay the foundation for future studies.

Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

Whole-genome sequencing (WGS) is a fundamental technology for research to advance precision medicine, but the limited availability of portable and user-friendly workflows for WGS analyses poses a major challenge for many research groups and hampers scientific progress. Here we present Sarek, an open-source workflow to detect germline variants and somatic mutations based on sequencing data from WGS, whole-exome sequencing (WES), or gene panels. Sarek features (i) easy installation, (ii) robust portability across different computer environments, (iii) comprehensive documentation, (iv) transparent and easy-to-read code, and (v) extensive quality metrics reporting. Sarek is implemented in the Nextflow workflow language and supports both Docker and Singularity containers as well as Conda environments, making it ideal for easy deployment on any POSIX-compatible computers and cloud compute environments. Sarek follows the GATK best-practice recommendations for read alignment and pre-processing, and includes a wide range of software for the identification and annotation of germline and somatic single-nucleotide variants, insertion and deletion variants, structural variants, tumour sample purity, and variations in ploidy and copy number. Sarek offers easy, efficient, and reproducible WGS analyses, and can readily be used both as a production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups. The Sarek source code, documentation and installation instructions are freely available at https://github.com/nf-core/sarek and at https://nf-co.re/sarek/.

Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

Whole-genome sequencing (WGS) is a fundamental technology for research to advance precision medicine, but the limited availability of portable and user-friendly workflows for WGS analyses poses a major challenge for many research groups and hampers scientific progress. Here we present Sarek, an open-source workflow to detect germline variants and somatic mutations based on sequencing data from WGS, whole-exome sequencing (WES), or gene panels. Sarek features (i) easy installation, (ii) robust portability across different computer environments, (iii) comprehensive documentation, (iv) transparent and easy-to-read code, and (v) extensive quality metrics reporting. Sarek is implemented in the Nextflow workflow language and supports both Docker and Singularity containers as well as Conda environments, making it ideal for easy deployment on any POSIX-compatible computers and cloud compute environments. Sarek follows the GATK best-practice recommendations for read alignment and pre-processing, and includes a wide range of software for the identification and annotation of germline and somatic single-nucleotide variants, insertion and deletion variants, structural variants, tumour sample purity, and variations in ploidy and copy number. Sarek offers easy, efficient, and reproducible WGS analyses, and can readily be used both as a production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups. The Sarek source code, documentation and installation instructions are freely available at https://github.com/nf-core/sarek and at https://nf-co.re/sarek/.

Clinical candidate and genistein analogue AXP107-11 has chemoenhancing functions in pancreatic adenocarcinoma through G protein-coupled estrogen receptor signaling

Despite advances in cancer therapeutics, pancreatic cancer remains difficult to treat and often develops resistance to chemotherapies. We have evaluated a bioavailable genistein analogue, AXP107-11 which has completed phase Ib clinical trial, as an approach to sensitize tumor cells to chemotherapy. Using organotypic cultures of 14 patient-derived xenografts (PDX) of pancreatic ductal adenocarcinoma, we found that addition of AXP107-11 indeed sensitized 57% of cases to gemcitabine treatment. Results were validated using PDX models in vivo. Further, RNA-Seq from responsive and unresponsive tumors proposed a 41-gene treatment-predictive signature. Functional and molecular assays were performed in cell lines and demonstrated that the effect was synergistic. Transcriptome analysis indicated activation of G-protein-coupled estrogen receptor (GPER1) as the main underlying mechanism of action, which was corroborated using GPER1-selective agonists and antagonists. GPER1 expression in pancreatic tumors was indicative of survival, and our study proposes that activation of GPER1 may constitute a new avenue for pancreatic cancer therapeutics.

Targeted sequencing of tonsillar and base of tongue cancer and human papillomavirus positive unknown primary of the head and neck reveals prognostic effects of mutated FGFR3

BACKGROUND

Human papillomavirus positive (HPV+) tonsillar cancer (TSCC), base of tongue cancer (BOTSCC) and unknown primary cancer of the head and neck (HNCUP) have better outcome than corresponding HPV- cancers. To find predictive markers for response to treatment, and correlations and differences in mutated oncogenes and suppressor genes between HPV+ TSCC/BOTSSCC and HPV+ HNCUP and HPV- TSCC/BOTSCC targeted next-generation sequencing was performed of frequently mutated regions in 50 cancer related genes.

PATIENTS AND METHODS

DNA from 348 TSCC/BOTSCC and 20 HNCUP from patients diagnosed 2000-2011, was sequenced by the Ion Proton sequencing platform using the Ion AmpliSeq Cancer Hotspot Panel v2 to identify frequently mutated regions in 50 cancer related genes. Ion Torrent Variant Caller software was used to call variants.

RESULTS

279 HPV+ TSCC/BOTSCC, 46 HPV- TSCC/BOTSCC and 19 HPV+ HNCUP samples qualified for further analysis. Mutations/tumor were fewer in HPV+ TSCC/BOTSCC and HNCUP, compared to HPV- tumors (0.92 vs. 1.32 vs. 1.68). Differences in mutation frequency of TP53 and PIK3CA were found between HPV+ TSCC/BOTSCC and HNCUP and HPV- TSCC/BOTSCC. In HPV+ TSCC/BOTSCC presence of FGFR3 mutations correlated to worse prognosis. Other correlations to survival within the groups were not disclosed.

CONCLUSIONS

In HPV+ TSCC/BOTSCC mutation of PIK3CA was most frequently observed, while TP53 mutations dominated in HPV- TSCC/BOTSCC. In HPV+ TSCC/BOTSCC and HNCUP, mutations/tumor were similar in frequency and fewer compared to that in HPV- TSCC/BOTSCC. Notably, FGFR3 mutations in HPV+ TSCC/BOTSCC indicated worse prognosis.

Patchwork: allele-specific copy number analysis of whole-genome sequenced tumor tissue

Whole-genome sequencing of tumor tissue has the potential to provide comprehensive characterization of genomic alterations in tumor samples. We present Patchwork, a new bioinformatic tool for allele-specific copy number analysis using whole-genome sequencing data. Patchwork can be used to determine the copy number of homologous sequences throughout the genome, even in aneuploid samples with moderate sequence coverage and tumor cell content. No prior knowledge of average ploidy or tumor cell content is required. Patchwork is freely available as an R package, installable via R-Forge (http://patchwork.r-forge.r-project.org/).