High Concordance of Different Assays in the Determination of Homologous Recombination Deficiency-Associated Genomic Instability in Ovarian Cancer.

PURPOSE: Poly(ADP-ribose) polymerase inhibitors (PARPi) have shown promising clinical results in the treatment of ovarian cancer. Analysis of biomarker subgroups consistently revealed higher benefits for patients with homologous recombination deficiency (HRD). The test that is most often used for the detection of HRD in clinical studies is the Myriad myChoice assay. However, other assays can also be used to assess biomarkers, which are indicative of HRD, genomic instability (GI), and BRCA1/2 mutation status. Many of these assays have high potential to be broadly applied in clinical routine diagnostics in a time-effective decentralized manner. Here, we compare the performance of a multitude of alternative assays in comparison with Myriad myChoice in high-grade serous ovarian cancer (HGSOC). METHODS: DNA from HGSOC samples was extracted from formalin-fixed paraffin-embedded tissue blocks of cases previously run with the Myriad myChoice assay, and GI was measured by multiple molecular assays (CytoSNP, AmoyDx, Illumina TSO500 HRD, OncoScan, NOGGO GISv1, QIAseq HRD Panel and whole genome sequencing), applying different bioinformatics algorithms. RESULTS: Application of different assays to assess GI, including Myriad myChoice, revealed high concordance of the generated scores ranging from very substantial to nearly perfect fit, depending on the assay and bioinformatics pipelines applied. Interlaboratory comparison of assays also showed high concordance of GI scores. CONCLUSION: Assays for GI assessment not only show a high concordance with each other but also in correlation with Myriad myChoice. Thus, almost all of the assays included here can be used effectively to assess HRD-associated GI in the clinical setting. This is important as PARPi treatment on the basis of these tests is compliant with European Medicines Agency approvals, which are methodologically not test-bound.

Low microsatellite instability: A distinct instability type in gastric cancer?

PURPOSE: We recently showed that low microsatellite instability (MSI-L) is associated with a good response to platinum/5-fluorouracil (5-FU) neoadjuvant chemotherapy (CTx) in gastric cancer. The purpose of this study was to characterize the instability pattern and to investigate an association of MSI-L tumors with mutations in genes of DNA repair pathways and with total tumor mutation burden (TMB). METHODS: MSI patterns were compared between 67 MSI high (-H) and 35 MSI-L tumors. Whole-exome sequencing was performed in 34 microsatellite stable (MSS) and 20 MSI-L tumors after or without neoadjuvant CTx. RESULTS: Of the 35 MSI-L tumors, 33 tumors had instability at a dinucleotide repeat marker. In the homologous recombination (HR) pathway, 10 of the 34 (29%) MSS and 10 of the 20 (50%) MSI-L tumors showed variants (p = 0.154). In the DNA damage tolerance pathway, 6 of the 34 (18%) MSS and 7 of the 20 (35%) MSI-L tumors had variants (p = 0.194). The HR deficiency score was similar in both tumor groups. TMB was significantly higher in MSI-L compared to MSS tumors after CTx (p = 0.046). In the MSS and MSI-L tumors without CTx no difference was observed (p = 1.00). CONCLUSION: MSI-L due to instability at dinucleotide repeat markers was associated with increased TMB after neoadjuvant CTx treatment, indicating sensitivity to platinum/5-FU CTx. If confirmed in further studies, this could contribute to refined chemotherapeutic options including immune-based strategies for GC patients with MSI-L tumors.

An integrated cellular and molecular model of gastric neuroendocrine cancer evolution highlights therapeutic targets.

Gastric neuroendocrine carcinomas (G-NEC) are aggressive malignancies with poorly understood biology and a lack of disease models. Here, we use genome sequencing to characterize the genomic landscapes of human G-NEC and its histologic variants. We identify global and subtype-specific alterations and expose hitherto unappreciated gains of MYC family members in a large part of cases. Genetic engineering and lineage tracing in mice delineate a model of G-NEC evolution, which defines MYC as a critical driver and positions the cancer cell of origin to the neuroendocrine compartment. MYC-driven tumors have pronounced metastatic competence and display defined signaling addictions, as revealed by large-scale genetic and pharmacologic screening of cell lines and organoid resources. We create global maps of G-NEC dependencies, highlight critical vulnerabilities, and validate therapeutic targets, including candidates for clinical drug repurposing. Our study gives comprehensive insights into G-NEC biology.

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.

Chromatyping: Reconstructing Nucleosome Profiles from NOMe Sequencing Data.

Measuring nucleosome positioning in cells is crucial for the analysis of epigenetic gene regulation. Reconstruction of nucleosome profiles of individual cells or subpopulations of cells remains challenging because most genome-wide assays measure nucleosome positioning and DNA accessibility for thousands of cells using bulk sequencing. In this study we use characteristics of the NOMe (nucleosome occupancy and methylation)-sequencing assay to derive a new approach, called ChromaClique, for deconvolution of different nucleosome profiles (chromatypes) from cell subpopulations of one NOMe-seq measurement. ChromaClique uses a maximal clique enumeration algorithm on a newly defined NOMe read graph that is able to group reads according to their nucleosome profiles. We show that the edge probabilities of that graph can be efficiently computed using hidden Markov models. We demonstrate using simulated data that ChromaClique is more accurate than a related method and scales favorably, allowing genome-wide analyses of chromatypes in cell subpopulations.