bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022‒02‒06
seventeen papers selected by
Sergio Marchini
Humanitas Research

  1. Front Oncol. 2021 ;11 798173
      Ovarian clear cell carcinoma (OCCC) is aggressive and drug-resistant. The prevalence of homologous recombination repair (HRR) gene mutations and homologous recombination deficiency (HRD) remains largely unknown. It is also not clear whether the commonly used molecular-based classification for endometrial carcinoma (EC) is potentially applicable in OCCC. In this study, surgically resected samples were collected from 44 patients with OCCC. Genomic alterations were determined using next-generation sequencing. HRD was estimated by genomic instability. Of 44 patients with OCCC, two (4.5%) harbored likely pathogenic mutations in HRR genes. Notably, no pathogenic or likely pathogenic mutations were found in BRCA1/2. A total of 24 variants of uncertain significance (VUS) in HRR-related genes occurred in 18 (40.9%) patients. HRD was observed in only one case (2.3%). In addition, TP53 mutation and microsatellite instability-high (MSI-H) were identified in three patients (6.8%) and in one patient (2.3%), respectively. TP53 mutation was significantly associated with disease-free survival and overall survival. No POLE mutations were found. In conclusion, our results revealed a very low prevalence of HRR gene mutations and HRD in OCCC. Moreover, TP53 mutations and MSI-H are uncommon, while POLE mutations are extremely rare in OCCC. Our findings indicate that the evaluation of HRR gene mutations, HRD status, POLE mutations, and MSI-H may have limited clinical significance for OCCC treatment and prognostic stratification.
    Keywords:  homologous recombination deficiency; homologous recombination repair gene; molecular classification; next-generation sequencing; ovarian clear cell carcinoma
  2. Nucleic Acids Res. 2022 Jan 31. pii: gkac030. [Epub ahead of print]
      Epigenome-wide association studies often detect many differentially methylated sites, and many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated that CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here, we present MethReg, an R/Bioconductor package that analyzes matched DNA methylation and gene expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize and annotate CpG sites with high regulatory potential. At these CpG sites, TF-target gene associations are often only present in a subset of samples with high (or low) methylation levels, so they can be missed by analyses that use all samples. Using colorectal cancer and Alzheimer's disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.
  3. Bioinformatics. 2022 Jan 31. pii: btac037. [Epub ahead of print]
      BACKGROUND: Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighbouring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis.RESULTS: We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project.
    CONCLUSION: The GenomeTornadoPlot R-package provides a novel method to both visualize and score cancer-relevant CNVs. Exploratory or hypothesis-driven analyses can be performed on the basis of the integrated PCAWG data or in combination with data provided by the user.
    AVAILABILITY: GenomeTornadoPlot is written in R script and released via github: <>. The package is under the license of GPL-3.0.
  4. Nat Cancer. 2020 Mar;1(3): 276-290
      Techniques for analyzing circulating tumor DNA (ctDNA) to detect, characterize and monitor cancer have matured rapidly. An increasing body of clinical evidence is demonstrating the capabilities of this technology as a diagnostic test. The full potential of ctDNA liquid biopsy in the diagnosis, characterization and management of solid and hematological malignancies will be uncovered through interventional clinical trials evaluating clinical utility. In this Review, we discuss the current landscape of ctDNA liquid-biopsy applications across the cancer continuum and highlight opportunities for clinical investigation.
  5. Nat Cancer. 2021 Mar;2(3): 245-246
  6. J Transl Med. 2022 Feb 02. 20(1): 65
      BACKGROUND: Revealing the impacts of endogenous and exogenous mutagenesis processes is essential for understanding the etiology of somatic genomic alterations and designing precise prognostication and treatment strategies for cancer. DNA repair deficiency is one of the main sources of endogenous mutagenesis and is increasingly recognized as a target for cancer therapeutics. The role and prevalence of mechanisms that underly different forms of DNA repair deficiencies and their interactions remain to be elucidated in gynecological malignancies.METHODS: We analyzed 1231 exomes and 268 whole-genomes from three major gynecological malignancies including uterine corpus endometrial carcinoma (UCEC) as well as ovarian and cervical cancers. We also analyzed data from 134 related cell lines. We extracted and compared de novo and refitted mutational signature profiles using complementary and confirmatory approaches and performed interaction analysis to detect co-occurring and mutually exclusive signatures.
    RESULTS: We found an inverse relationship between homologous recombination deficiency (HRd) and mismatch repair deficiency (MMRd). Moreover, APOBEC co-occurred with HRd but was mutually exclusive with MMRd. UCEC tumors were dominated by MMRd, yet a subset of them manifested the HRd and APOBEC signatures. Conversely, ovarian tumors were dominated by HRd, while a subset represented MMRd and APOBEC. In contrast to both, cervical tumors were dominated by APOBEC with a small subsets showing the POLE, HRd, and MMRd signatures. Although the type, prevalence, and heterogeneity of mutational signatures varied across the tumor types, the patterns of co-occurrence and exclusivity were consistently observed in all. Notably, mutational signatures in gynecological tumor cell lines reflected those detected in primary tumors.
    CONCLUSIONS: Taken together, these analyses indicate that application of mutation signature analysis not only advances our understanding of mutational processes and their interactions, but also it has the potential to stratify patients that could benefit from treatments available for tumors harboring distinct mutational signatures and to improve clinical decision-making for gynecological malignancies.
    Keywords:  APOBEC; Cervical cancer; DNA damage and repair; Gynecological cancers; HR; MMR; Mutational Signature; NHEJ; Ovarian cancer; POLE; Tumor mutational burden; Uterine corpus endometrial carcinoma
  7. J Exp Clin Cancer Res. 2022 Feb 04. 41(1): 50
      BACKGROUND: High-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance. In BRCA1 (Breast Cancer gene 1) - or BRCA2-wild type (BRCAwt) HGSOC patients, resistance and progressive disease occur earlier and more often than in mutated BRCA. Identification of biomarkers helpful in predicting response to first-line chemotherapy is a challenge to improve BRCAwt HGSOC management.METHODS: To identify a gene signature that can predict response to first-line chemotherapy, pre-treatment tumor biopsies from a restricted cohort of BRCAwt HGSOC patients were profiled by RNA sequencing (RNA-Seq) technology. Patients were sub-grouped according to platinum-free interval (PFI), into sensitive (PFI > 12 months) and resistant (PFI < 6 months). The gene panel identified by RNA-seq analysis was then tested by high-throughput quantitative real-time PCR (HT RT-qPCR) in a validation cohort, and statistical/bioinformatic methods were used to identify eligible markers and to explore the relevant pathway/gene network enrichments of the identified gene set. Finally, a panel of primary HGSOC cell lines was exploited to uncover cell-autonomous mechanisms of resistance.
    RESULTS: RNA-seq identified a 42-gene panel discriminating sensitive and resistant BRCAwt HGSOC patients and pathway analysis pointed to the immune system as a possible driver of chemotherapy response. From the extended cohort analysis of the 42 DEGs (differentially expressed genes), a statistical approach combined with the random forest classifier model generated a ten-gene signature predictive of response to first-line chemotherapy. The ten-gene signature included: CKB (Creatine kinase B), CTNNBL1 (Catenin, beta like 1), GNG11 (G protein subunit gamma 11), IGFBP7 (Insulin-like growth factor-binding protein 7), PLCG2 (Phospholipase C, gamma 2), RNF24 (Ring finger protein 24), SLC15A3 (Solute carrier family 15 member 3), TSPAN31 (Tetraspanin 31), TTI1 (TELO2 interacting protein 1) and UQCC1 (Ubiquinol-cytochrome c reductase complex assembly factor). Cytotoxicity assays, combined with gene-expression analysis in primary HGSOC cell lines, allowed to define CTNNBL1, RNF24, and TTI1 as cell-autonomous contributors to tumor resistance.
    CONCLUSIONS: Using machine-learning techniques we have identified a gene signature that could predict response to first-line chemotherapy in BRCAwt HGSOC patients, providing a useful tool towards personalized treatment modalities.
    Keywords:  Bioinformatics; Biomarkers; Drug-resistance; HGSOC; Patient stratification; Primary ovarian cancer cells; Random forest classifier model; Transcriptomic
  8. Nat Cancer. 2020 Sep;1(9): 864-872
      Understanding the mechanisms underlying tumorigenesis requires comprehensive annotation of the cancer genome. The majority of the human genome consists of noncoding regions, harboring functional elements that regulate the expression of protein-coding genes, including proto-oncogenes or tumor-suppressor genes. Technologies such as whole-genome and long-read sequencing provide powerful means to identify alterations in the noncoding genome of cancer cells, paving the way for the study of their functional relevance. Recent analyses of noncoding alterations have revealed additional mechanisms underlying tumor development, progression and response to therapies, and uncovered new targets for drug development. Here we highlight recent findings about noncoding alterations, review established and emerging methods and models to study these alterations, and discuss the outlook for noncoding alterations as therapeutic targets.
  9. Methods Mol Biol. 2022 ;2458 47-62
      Bisulfite sequencing is the "gold-standard" technique for DNA methylation analysis. By combining bisulfite sequencing with high-throughput, next-generation sequencing technology, we can document methylation from many thousands of individual reads (equivalent to alleles or "cells"), for multiple target regions and from many samples simultaneously. Here, we describe a next-generation bisulfite-sequencing assay for targeted DNA methylation analysis which offers scope for the simultaneous interrogation of multiple genomic loci across numerous samples.
    Keywords:  Analysis; Bisulfite conversion; DNA methylation; Next-generation sequencing
  10. Transl Cancer Res. 2021 Mar;10(3): 1537-1548
      Background: This study aimed to investigate prognostic genes in ovarian cancer (OC) and to explore their potential underlying biological mechanisms through a comprehensive bioinformatics analysis.Methods: Common differentially expressed genes (DEGs) in 3 OC datasets from the Gene Expression Omnibus (GEO) (GSE26712, GSE18520, and GSE14407) were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by Metascape. The protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database. The prognostic value of DEGs were determined using the Kaplan-Meier plotter. The ONCOMINE and Human Protein Atlas databases were used to verify the expression levels of prognostic genes in OC. Genomic analysis of prognostic genes were also investigated by cBio Cancer Genomics Portal (cBioPortal) database, UCSC Xena browser and UALCAN. Gene set enrichment analysis (GSEA) was used to predict the possible pathways and biological processes of the prognostic genes.
    Results: Integration of the 3 datasets have found 879 common DEGs. A high expression of structural maintenance of chromosomes protein 4 (SMC4) was revealed in the Kaplan-Meier plotter analysis to be meaningful for the prognosis of OC and was verified at both the mRNA and protein levels. The results from cBioPortal showed that SMC4 alterations accounted for 7 to 18% of genetic alterations in OC, and the majority alterations were copy number amplifications. Finally, the GSEA results showed that samples with SMC4 overexpression were mainly enriched in the cell cycle, spliceosome, ubiquitin mediated proteolysis, and adherens junctions.
    Conclusions: High SMC4 expression is linked with a poor prognosis in patients with OC and might serve as a prognostic biomarker for the disease.
    Keywords:  Ovarian cancer (OC); bioinformatics analysis; prognostic genes; structural maintenance of chromosomes protein 4 (SMC4)
  11. Methods Mol Biol. 2022 ;2394 433-451
      The use of next-generation sequencing (NGS) to profile genomic variation of individual cancer species is revolutionizing the practice of clinical oncology. In liquid biopsy of cancer, sequencing of circulating-free DNA (cfDNA) is gradually applied to all stages of cancer diagnosis and treatment, serving as complement or replacement of tissue biopsies. However, analysis of cfDNA obtained from blood draws still faces technical obstacles due in part to an excess of wild-type DNA originating from normal tissues and hematopoietic cells. The resulting low-level mutation abundance often falls below routine NGS detection sensitivity and limits reliable mutation identification that meets clinical sensitivity and specificity standards. Despite sample preparation advances that reduce sequencing error rates via use of unique molecular identifiers (molecular barcodes) and error-suppression algorithms, excessive amounts of sequencing are still required to detect mutations at allelic frequency levels below 1%. This requirement reduces throughput and increases cost.In this chapter, we describe a sensitive multiplex mutation detection method that enriches mutation-containing DNA during sample preparation, prior to sequencing, thereby increasing signal-to-noise ratios and providing low-level mutation detection without excessive sequencing depth. We couple targeted next-generation sequencing with wild-type DNA removal using Nuclease-assisted Minor-allele Enrichment using Probe Overlap, NaME-PrO, a recently developed method to eliminate wild-type sequences from multiple targets simultaneously. A step by step guide to library preparation and data analysis are provided as well as some precautions during the sample handling.
    Keywords:  Circulating DNA; Liquid biopsy; Mutation enrichment; Sequencing sample preparation
  12. Nat Commun. 2022 Feb 01. 13(1): 448
      The vast majority of epithelial ovarian cancer arises from tissues that are embryologically derived from the Müllerian Duct. Here, we demonstrate that a DNA methylation signature in easy-to-access Müllerian Duct-derived cervical cells from women with and without ovarian cancer (i.e. referred to as the Women's risk IDentification for Ovarian Cancer index or WID-OC-index) is capable of identifying women with an ovarian cancer in the absence of tumour DNA with an AUC of 0.76 and women with an endometrial cancer with an AUC of 0.81. This and the observation that the cervical cell WID-OC-index mimics the epigenetic program of those cells at risk of becoming cancerous in BRCA1/2 germline mutation carriers (i.e. mammary epithelium, fallopian tube fimbriae, prostate) further suggest that the epigenetic misprogramming of cervical cells is an indicator for cancer predisposition. This concept has the potential to advance the field of risk-stratified cancer screening and prevention.
  13. Nature. 2022 Feb 01.
    Keywords:  Geography; History; Zoology
  14. Cancer Cell. 2022 Feb 03. pii: S1535-6108(22)00014-9. [Epub ahead of print]
      Cancers other than breast, colorectal, cervical, and lung do not have guideline-recommended screening. New multi-cancer early detection (MCED) tests-using a single blood sample-have been developed based on circulating cell-free DNA (cfDNA) or other analytes. In this commentary, we review the current evidence on these tests, provide several major considerations for new MCED tests, and outline how their evaluation will need to differ from that established for traditional single-cancer screening tests.
  15. Front Genet. 2021 ;12 759357
      DNA methylation age (DNAm age, epigenetic clock) is a novel and promising biomarker of aging. It is calculated from the methylation fraction of specific cytosine phosphate guanine sites (CpG sites) of genomic DNA. Several groups have proposed epigenetic clock algorithms and these differ mostly regarding the number and location of the CpG sites considered and the method used to assess the methylation status. Most epigenetic clocks are based on a large number of CpGs, e.g. as measured by DNAm microarrays. We have recently evaluated an epigenetic clock based on the methylation fraction of seven CpGs that were determined by methylation-sensitive single nucleotide primer extension (MS-SNuPE). This method is more cost-effective when compared to array-based technologies as only a few CpGs need to be examined. However, there is only little data on the correspondence in epigenetic age estimation using the 7-CpG clock and other algorithms. To bridge this gap, in this study we measured the 7-CpG DNAm age using two methods, via MS-SNuPE and via the MethylationEPIC array, in a sample of 1,058 participants of the Berlin Aging Study II (BASE-II), assessed as part of the GendAge study. On average, participants were 75.6 years old (SD: 3.7, age range: 64.9-90.0, 52.6% female). Agreement between methods was assessed by Bland-Altman plots. DNAm age was highly correlated between methods (Pearson's r = 0.9) and Bland-Altman plots showed a difference of 3.1 years. DNAm age by the 7-CpG formula was 71.2 years (SD: 6.9 years, SNuPE) and 68.1 years (SD: 6.4 years, EPIC array). The mean of difference in methylation fraction between methods for the seven individual CpG sites was between 0.7 and 13 percent. To allow direct conversion of DNAm age obtained from both methods we developed an adjustment formula with a randomly selected training set of 529 participants using linear regression. After conversion of the Illumina data in a second and independent validation set, the adjusted DNAm age was 71.44 years (SD: 6.1 years, n = 529). In summary, we found the results of DNAm clocks to be highly comparable. Furthermore, we developed an adjustment formula that allows for direct conversion of DNAm age estimates between methods and enables one singular clock to be used in studies that employ either the Illumina or the SNuPE method.
    Keywords:  BASE-II; Berlin Aging Study II; GendAge study; aging; biological age; epigenetic clock; methylation age; single-nucleotide primer extension assay
  16. Nat Cancer. 2020 Aug;1(8): 774-783
      The molecular characterization of tumors now informs clinical cancer care for many patients. This advent of molecular oncology has been driven by the expanding number of therapeutic biomarkers that can predict sensitivity to both approved agents and investigational agents. Beyond its role in driving clinical-trial enrollments and guiding therapy in individual patients, large-scale clinical genomics in oncology also represents a rapidly expanding research resource for translational scientific discovery. Here we review the progress, opportunities, and challenges of scientific and translational discovery from prospective clinical genomic screening programs now routinely conducted for patients with cancer.