Genome-wide quantification of copy-number aberration impact on gene expression in ovarian high-grade serous carcinoma

Copy-number alterations (CNAs) are a hallmark of cancer and can regulate cancer cell states via altered gene expression values. Herein, we have developed a copy-number impact (CNI) analysis method that quantifies the degree to which a gene expression value is impacted by CNAs and leveraged this analysis at the pathway level. Our results show that a high CNA is not necessarily reflected at the gene expression level, and our method is capable of detecting genes and pathways whose activity is strongly influenced by CNAs. Furthermore, the CNI analysis enables unbiased categorization of CNA categories, such as deletions and amplifications. We identified six CNI-driven pathways associated with poor treatment response in ovarian high-grade serous carcinoma (HGSC), which we found to be the most CNA-driven cancer across 14 cancer types. The key driver in most of these pathways was amplified wild-type KRAS, which we validated functionally using CRISPR modulation. Our results suggest that wild-type KRAS amplification is a driver of chemotherapy resistance in HGSC and may serve as a potential treatment target.

Circulating tumor DNA-based copy-number profiles enable monitoring treatment effects during therapy in high-grade serous carcinoma

Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for detecting and profiling longitudinal genomics changes in cancer. While copy-number alterations (CNAs) play a major role in cancers, treatment effect monitoring using copy-number profiles has received limited attention as compared to mutations. A major reason for this is the insensitivity of CNA analysis for the real-life tumor-fraction ctDNA samples. We performed copy-number analysis on 152 plasma samples obtained from 29 patients with high-grade serous ovarian cancer (HGSC) using a sequencing panel targeting over 500 genes. Twenty-one patients had temporally matched tissue and plasma sample pairs, which enabled assessing concordance with tissues sequenced with the same panel or whole-genome sequencing and to evaluate sensitivity. Our approach could detect concordant CNA profiles in most plasma samples with as low as 5% tumor content and highly amplified regions in samples with ∼1% of tumor content. Longitudinal profiles showed changes in the CNA profiles in seven out of 11 patients with high tumor-content plasma samples at relapse. These changes included focal acquired or lost copy-numbers, even though most of the genome remained stable. Two patients displayed major copy-number profile changes during therapy. Our analysis revealed ctDNA-detectable subclonal selection resulting from both surgical operations and chemotherapy. Overall, longitudinal ctDNA data showed acquired and diminished CNAs at relapse when compared to pre-treatment samples. These results highlight the importance of genomic profiling during treatment as well as underline the usability of ctDNA.

DNA methylation patterns suggest the involvement of DNMT3B and TET1 in osteosarcoma development

DNA methylation may be involved in the development of osteosarcomas. Osteosarcomas commonly arise during the bone growth and remodeling in puberty, making it plausible to infer the involvement of epigenetic alterations in their development. As a highly studied epigenetic mechanism, we investigated DNA methylation and related genetic variants in 28 primary osteosarcomas aiming to identify deregulated driver alterations. Methylation and genomic data were obtained using the Illumina HM450K beadchips and the TruSight One sequencing panel, respectively. Aberrant DNA methylation was spread throughout the osteosarcomas genomes. We identified 3146 differentially methylated CpGs comparing osteosarcomas and bone tissue samples, with high methylation heterogeneity, global hypomethylation and focal hypermethylation at CpG islands. Differentially methylated regions (DMR) were detected in 585 loci (319 hypomethylated and 266 hypermethylated), mapped to the promoter regions of 350 genes. These DMR genes were enriched for biological processes related to skeletal system morphogenesis, proliferation, inflammatory response, and signal transduction. Both methylation and expression data were validated in independent groups of cases. Six tumor suppressor genes harbored deletions or promoter hypermethylation (DLEC1, GJB2, HIC1, MIR149, PAX6, and WNT5A), and four oncogenes presented gains or hypomethylation (ASPSCR1, NOTCH4, PRDM16, and RUNX3). Our analysis also revealed hypomethylation at 6p22, a region that contains several histone genes. Copy-number changes in DNMT3B (gain) and TET1 (loss), as well as overexpression of DNMT3B in osteosarcomas provide a possible explanation for the observed phenotype of CpG island hypermethylation. While the detected open-sea hypomethylation likely contributes to the well-known osteosarcoma genomic instability, enriched CpG island hypermethylation suggests an underlying mechanism possibly driven by overexpression of DNMT3B likely resulting in silencing of tumor suppressors and DNA repair genes.

Analysis of copy number alterations in bladder cancer stem cells revealed a prognostic role of LRP1B

Purpose

Bladder cancer is the most common malignancy of the urinary tract and one of the most prevalent cancers worldwide. It represents a spectrum of diseases, from recurrent non-invasive tumors (NMIBCs) managed chronically, to muscle infiltrating and advanced-stage disease (MIBC) that requires multimodal and invasive treatment. Multiple studies have underlined the complexity of bladder tumors genome, highlighting many specific genetic lesions and genome-wide occurrences of copy-number alterations (CNAs). In this study, we analyzed CNAs of selected genes in our cohorts of cancer stem cells (CSCs) and in The Cancer Genome Atlas (TCGA-BLCA) cohort with the aim to correlate their frequency with patients’ prognosis.

Methods

CNAs have been verified on our array-CGH data previously reported on 19 bladder cancer biopsies (10 NMIBCs and 9 MIBCs) and 16 matched isolated CSC cultures. In addition, CNAs data have been consulted on the TCGA database, to search correlations with patients’ follow-up. Finally, mRNA expression levels of LRP1B in TGCA cohort were obtained from The Human Protein Atlas.

Results

We firstly identified CNAs differentially represented between TGCA data and CSCs derived from NMIBCs and MIBCs, and we correlated the presence of these CNAs with patients’ follow-up. LRP1B loss was significantly increased in CSCs and linked to short-term poor prognosis, both at genomic and transcriptomic level, confirming its pivotal role in bladder cancer tumorigenesis.

Conclusion

Our study allowed us to identify potential "predictive" prognostic CNAs for bladder cancer, implementing knowledge for the ultimate goal of personalized medicine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-022-04093-1.

Aneuploidy: An Opportunity Within Single-Cell RNA Sequencing Analysis

Single-cell sequencing data has transformed the understanding of biological heterogeneity. While many flavors of single-cell sequencing have been developed, single-cell RNA sequencing (scRNA-seq) is currently the most prolific form in published literature. Bioinformatic analysis of differential biology within the population of cells studied relies on inferences and grouping of cells due to the spotty nature of data within individual cell scRNA-seq gene counts. One biologically relevant variable is readily inferred from scRNA-seq gene count tables regardless of individual gene representation within single cells: aneuploidy. Since hundreds of genes are present on chromosome arms, high-quality inferences of aneuploidy can be made from scRNA-seq datasets. This viewpoint summarizes how utilization of these bioinformatic pipelines can benefit scRNA-seq studies, particularly in oncology wherein aneuploidy is both rampant and a hallmark of the studied disease. Awareness and use of these analytical pipelines will improve each field's ability to understand the studied diseases. Authors are encouraged to attempt these aneuploid analyses when reporting scRNA-seq data, much like copy-number variants are commonly reported in bulk genome sequencing data.

Somatic DNA Copy-Number Alterations Detection for Esophageal Adenocarcinoma Using Digital Polymerase Chain Reaction

Somatic copy-number alterations are commonly found in cancer and play key roles in activating oncogenes and deactivating tumor suppressor genes. Digital polymerase chain reaction is an effective way to detect the changes in copy number. In esophageal adenocarcinoma, detection of somatic copy-number alterations could predict the prognosis of patients as well as the response to therapy. This chapter will review the methods involved in digital polymerase chain reaction for the research or potential clinical applications in esophageal adenocarcinoma.