bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022‒01‒23
thirteen papers selected by
Sergio Marchini
Humanitas Research

  1. Cancers (Basel). 2022 Jan 14. pii: 416. [Epub ahead of print]14(2):
      The phenotypically informed histotype classification remains the mainstay of ovarian carcinoma subclassification. Histotypes of ovarian epithelial neoplasms have evolved with each edition of the WHO Classification of Female Genital Tumours. The current fifth edition (2020) lists five principal histotypes: high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), mucinous carcinoma (MC), endometrioid carcinoma (EC) and clear cell carcinoma (CCC). Since histotypes arise from different cells of origin, cell lineage-specific diagnostic immunohistochemical markers and histotype-specific oncogenic alterations can confirm the morphological diagnosis. A four-marker immunohistochemical panel (WT1/p53/napsin A/PR) can distinguish the five principal histotypes with high accuracy, and additional immunohistochemical markers can be used depending on the diagnostic considerations. Histotypes are further stratified into molecular subtypes and assessed with predictive biomarker tests. HGSCs have recently been subclassified based on mechanisms of chromosomal instability, mRNA expression profiles or individual candidate biomarkers. ECs are composed of the same molecular subtypes (POLE-mutated/mismatch repair-deficient/no specific molecular profile/p53-abnormal) with the same prognostic stratification as their endometrial counterparts. Although methylation analyses and gene expression and sequencing showed at least two clusters, the molecular subtypes of CCCs remain largely elusive to date. Mutational and immunohistochemical data on LGSC have suggested five molecular subtypes with prognostic differences. While our understanding of the molecular composition of ovarian carcinomas has significantly advanced and continues to evolve, the need for treatment options suitable for these alterations is becoming more obvious. Further preclinical studies using histotype-defined and molecular subtype-characterized model systems are needed to expand the therapeutic spectrum for women diagnosed with ovarian carcinomas.
    Keywords:  histotype; immunohistochemistry; molecular subtype; ovarian cancer; subclassification
  2. BMC Cancer. 2022 Jan 20. 22(1): 85
      BACKGROUND: Circulating cell-free DNA (cfDNA) in the plasma of cancer patients contains cell-free tumour DNA (ctDNA) derived from tumour cells and it has been widely recognized as a non-invasive source of tumour DNA for diagnosis and prognosis of cancer. Molecular profiling of ctDNA is often performed using targeted sequencing or low-coverage whole genome sequencing (WGS) to identify tumour specific somatic mutations or somatic copy number aberrations (sCNAs). However, these approaches cannot efficiently detect all tumour-derived genomic changes in ctDNA.METHODS: We performed WGS analysis of cfDNA from 4 breast cancer patients and 2 patients with benign tumours. We sequenced matched germline DNA for all 6 patients and tumour samples from the breast cancer patients. All samples were sequenced on Illumina HiSeqXTen sequencing platform and achieved approximately 30x, 60x and 100x coverage on germline, tumour and plasma DNA samples, respectively.
    RESULTS: The mutational burden of the plasma samples (1.44 somatic mutations/Mb of genome) was higher than the matched tumour samples. However, 90% of high confidence somatic cfDNA variants were not detected in matched tumour samples and were found to comprise two background plasma mutational signatures. In contrast, cfDNA from the di-nucleosome fraction (300 bp-350 bp) had much higher proportion (30%) of variants shared with tumour. Despite high coverage sequencing we were unable to detect sCNAs in plasma samples.
    CONCLUSIONS: Deep sequencing analysis of plasma samples revealed higher fraction of unique somatic mutations in plasma samples, which were not detected in matched tumour samples. Sequencing of di-nucleosome bound cfDNA fragments may increase recovery of tumour mutations from plasma.
    Keywords:  Cell-free DNA; Cell-free tumour DNA; Mutational signatures; Somatic mutations
  3. Brief Bioinform. 2022 Jan 17. pii: bbab554. [Epub ahead of print]
      Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social and environmental factors. DNA methylation patterns reflect such multivariate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with multivariate sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing regions mapped to genes enriched for brain glial functions. Comethyl is expected to be useful in uncovering the multivariate nature of health disparities for a variety of common disorders. Comethyl is available at with complete documentation and example analyses.
    Keywords:  DNA methylation; autism spectrum disorder; epigenetics; epigenome; systems biology; weighted gene correlation network analysis; whole-genome bisulfite sequencing
  4. Cancers (Basel). 2022 Jan 14. pii: 404. [Epub ahead of print]14(2):
      Ovarian cancer is the eighth global leading cause of cancer-related death among women. The most common form is the high-grade serous ovarian carcinoma (HGSOC). No further improvements in the 5-year overall survival have been seen over the last 40 years since the adoption of platinum- and taxane-based chemotherapy. Hence, a better understanding of the mechanisms governing this aggressive phenotype would help identify better therapeutic strategies. Recent research linked onset, progression, and response to treatment with dysregulated components of the tumor microenvironment (TME) in many types of cancer. In this study, using bioinformatic approaches, we identified a 19-gene TME-related HGSOC prognostic genetic panel (19 prognostic genes (PLXNB2, HMCN2, NDNF, NTN1, TGFBI, CHAD, CLEC5A, PLXNA1, CST9, LOXL4, MMP17, PI3, PRSS1, SERPINA10, TLL1, CBLN2, IL26, NRG4, and WNT9A) by assessing the RNA sequencing data of 342 tumors available in the TCGA database. Using machine learning, we found that specific patterns of infiltrating immune cells characterized each risk group. Furthermore, we demonstrated the predictive potential of our risk score across different platforms and its improved prognostic performance compared with other gene panels.
    Keywords:  TCGA; bioinformatics; extracellular matrix; high-grade serous ovarian carcinoma; machine learning; ovarian cancer; precision medicine; prognostic biomarker
  5. Nature. 2022 Jan;601(7893): 297
    Keywords:  Cancer; Genomics; Health care
  6. Small Methods. 2022 Jan 22. e2101251
      DNA methylation is associated with transcriptional repression, genomic imprinting, stem cell differentiation, embryonic development, and inflammation. Aberrant DNA methylation can indicate disease states, including cancer and neurological disorders. Therefore, the prevalence and location of 5-methylcytosine in the human genome is a topic of interest. Whole-genome bisulfite sequencing (WGBS) is a high-throughput method for analyzing DNA methylation. This technique involves library preparation, alignment, and quality control. Advancements in epigenetic technology have led to an increase in DNA methylation studies. This review compares the detailed experimental methodology of WGBS using accessible and up-to-date analysis tools. Practical codes for WGBS data processing are included as a general guide to assist progress in DNA methylation studies through a comprehensive case study.
    Keywords:  DNA methylation; alignment algorithm comparison; library preparation methods; methylation data analysis pipeline; whole genome bisulfite sequencing
  7. Eur J Med Chem. 2022 Jan 12. pii: S0223-5234(22)00011-3. [Epub ahead of print]230 114109
      DDR (DNA damage response) defects in cells drive tumor formation by promoting DNA mutations, which also provides cancer-specific vulnerabilities that can be targeted by synthetic lethality-based therapies. Until now, PARP inhibitors like olaparib are the first successful case of utilizing synthetic lethality-based therapy to treat cancers with DNA-repairing deficiency (e.g. BRCA1 or BRCA2 mutation), which has fueled the search for more targetable components in the DDR signaling pathway by exploiting synthetic lethality, including but not limited to DNA-PK, ATR, ATM, CHK1, and WEE1. After years of efforts, numerous DDR kinase inhibitors have been discovered. Some of them are being investigated in clinical trials and have shown promising results for cancer therapy. In this review, we summarize the latest advancement in the development of DDR kinase inhibitors including those in preclinical stages and clinical trials, the crystal structures of DDR enzymes, and binding modes of inhibitors with target proteins. The biological functions involving different genes and proteins (ATR, DNA-PK, ATM, PARP, CHK1, and WEE1) are also elucidated.
    Keywords:  DNA damage Response; Small molecule inhibitors; Synthetic lethality
  8. Cancers (Basel). 2022 Jan 16. pii: 436. [Epub ahead of print]14(2):
      With more than 70 different histological sarcoma subtypes, accurate classification can be challenging. Although characteristic genetic events can largely facilitate pathological assessment, large-scale molecular profiling generally is not part of regular diagnostic workflows for sarcoma patients. We hypothesized that whole genome sequencing (WGS) optimizes clinical care of sarcoma patients by detection of diagnostic and actionable genomic characteristics, and of underlying hereditary conditions. WGS of tumor and germline DNA was incorporated in the diagnostic work-up of 83 patients with a (presumed) sarcomas in a tertiary referral center. Clinical follow-up data were collected prospectively to assess impact of WGS on clinical decision making. In 12/83 patients (14%), the genomic profile led to revision of cancer diagnosis, with change of treatment plan in eight. All twelve patients had undergone multiple tissue retrieval procedures and immunohistopathological assessments by regional and expert pathologists prior to WGS analysis. Actionable biomarkers with therapeutic potential were identified for 30/83 patients. Pathogenic germline variants were present in seven patients. In conclusion, unbiased genomic characterization with WGS identifies genomic biomarkers with direct clinical implications for sarcoma patients. Given the diagnostic complexity and high unmet need for new treatment opportunities in sarcoma patients, WGS can be an important extension of the diagnostic arsenal of pathologists.
    Keywords:  advanced sarcoma; broad molecular profiling; diagnostic biomarkers; precision oncology; whole genome sequencing
  9. Am J Transl Res. 2021 ;13(12): 13625-13639
      Methylcytosine (m5C) is an important posttranscriptional RNA methylation modification. Studies have reported that aberrant RNA methylation can regulate tumorigenesis and development, indicating the importance of exploring the distribution and biological functions of m5C modification in human high-grade serous ovarian cancer (HGSOC) lncRNAs. In the current study, we identified 2,050 dysregulated m5C peaks, 1,767 of which were significantly upregulated, while 283 were significantly downregulated by performing methylated RNA immunoprecipitation sequencing on 3 pairs of human HGSOC tissues and paired normal tissues. GO enrichment analysis showed that genes altered by the m5C peak played a key role in phylogeny, protein metabolism, and gene mismatch repair. KEGG pathway analysis revealed that these genes were enriched in some important pathways in cancer regulation, such as the PI3K-Akt signalling pathway, transcriptional dysregulation in cancer, and mismatch repair pathways. In addition, through joint analysis of MeRIP-seq and RNA-seq data, we identified 1671 differentially methylated m5C peaks and synchronous differentially expressed genes. These genes play a key role in cell growth or maintenance, RNA metabolism and material transport. We analyzed expression of the m5C modification regulatory gene collagen type IV alpha 3 chain (COL4A3) in 80 HGSOC tissue samples by immunohistochemistry and found that high expression of COL4A3 was significantly correlated with CA125 level (P=0.016), lymph node metastasis (P<0.001), degree of interstitial invasion (P<0.001) and FIGO staging (P<0.001) and indicated a poorer prognosis. Our results revealed the critical role of m5C methylation of lncRNAs in HGSOC, and provided a reference for the prognostic stratification and treatment strategy of HGSOC.
    Keywords:  5-methylcytosine; MeRIP sequencing; high-grade serous cancer; lncRNA; prognosis
  10. Cancers (Basel). 2022 Jan 11. pii: 349. [Epub ahead of print]14(2):
      DNA methylation is an essential epigenetic mark. Alterations of normal DNA methylation are a defining feature of cancer. Here, we review experimental and bioinformatic approaches to showcase the breadth and depth of information that this epigenetic mark provides for cancer research. First, we describe classical approaches for interrogating bulk DNA from cell populations as well as more recently developed approaches for single cells and multi-Omics. Second, we focus on the computational analysis from primary data processing to the identification of unique methylation signatures. Additionally, we discuss challenges such as sparse data and cellular heterogeneity.
    Keywords:  DNA methylation; cancer; computational analysis; methods; software
  11. Pathol Res Pract. 2022 Jan 10. pii: S0344-0338(22)00003-6. [Epub ahead of print]230 153760
      Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.
    Keywords:  Clinical oncology; Next-generation sequencing (NGS); Personalized medicine (PM); Pharmacogenomics
  12. J Ovarian Res. 2022 Jan 20. 15(1): 12
      BACKGROUND: The five-year overall survival (OS) of advanced-stage ovarian cancer remains nearly 25-35%, although several treatment strategies have evolved to get better outcomes. A considerable amount of heterogeneity and complexity has been seen in ovarian cancer. This study aimed to establish gene signatures that can be used in better prognosis through risk prediction outcome for the survival of ovarian cancer patients. Different studies' heterogeneity into a single platform is presented to explore the penetrating genes for poor or better survival. The integrative analysis of multiple data sets was done to determine the genes that influence poor or better survival. A total of 6 independent data sets was considered. The Cox Proportional Hazard model was used to obtain significant genes that had an impact on ovarian cancer patients. The gene signatures were prepared by splitting the over-expressed and under-expressed genes parallelly by the variable selection technique. The data visualisation techniques were prepared to predict the overall survival, and it could support the therapeutic regime.RESULTS: We preferred to select 20 genes in each data set as upregulated and downregulated. Irrespective of the selection of multiple genes, not even a single gene was found common among data sets for the survival of ovarian cancer patients. However, the same analytical approach adopted. The chord plot was presented to make a comprehensive understanding of the outcome.
    CONCLUSIONS: This study helps us to understand the results obtained from different studies. It shows the impact of the heterogeneity from one study to another. It shows the requirement of integrated studies to make a holistic view of the gene signature for ovarian cancer survival.
    Keywords:  Clinical prediction; Data visualization; Gene signature; Integrative analysis; Ovarian cancer
  13. Diagn Pathol. 2022 Jan 20. 17(1): 12
      BACKGROUND: The aim of this study was to evaluate the clinicopathological factors and prognosis of mucinous carcinoma (MC) with infiltrative invasion, MC with expansile invasion, and high-grade serous carcinoma (HGSC).METHODS: Cases of MC and HGSC between 1984 and 2019 were identified. The clinicopathological factors and prognosis of MC with infiltrative invasion or expansile invasion and HGSC were retrospectively compared. Although our present study included cases in our previous studies, we extended observational period when analysis was performed. Accordingly, our study added increased cases and survival analysis was newly conducted.
    RESULTS: After pathological review, 27 cases of MC with infiltrative invasion, 25 cases of MC with expansile invasion, and 219 cases of HGSC were included. MC had a better prognosis in terms of progression-free survival (PFS, p < 0.01) and overall survival (OS, p < 0.01) than HGSC for all International Federation of Gynecology and Obstetrics (FIGO) stages; however, multivariate analysis did not show statistical differences in PFS and OS. There were no statistically significant differences in PFS and OS for all FIGO stages between MC with infiltrative invasion and HGSC. However, in cases with FIGO stages II to IV, MC with infiltrative invasion had worse PFS (p < 0.01) and OS (p < 0.01) than HGSC. In univariate analysis, MC with infiltrative invasion was a worse prognostic factor for PFS (hazard ratio [HR] 2.83, p < 0.01) and OS (HR 3.83, p < 0.01) than HGSC. Compared with HGSC, MC with expansile invasion had better PFS (p < 0.01) and OS (p < 0.01). Multivariate analysis demonstrated that MC with expansile invasion was a better prognostic factor for PFS (HR 0.17, p < 0.01) and OS (HR 0.18, p = 0.03) than HGSC.
    CONCLUSIONS: Compared to the prognosis of HGSC, that of MC was different according to the invasive pattern and FIGO stage. Therefore, future study may be needed to consider this association.
    Keywords:  2020 World Health Organization; Expansile invasion; Infiltrative invasion; Ovarian high-grade serous carcinoma; Ovarian mucinous carcinoma