bims-ovdlit 2022-01-23 papers

Issue of 2022–01–23
five papers selected by
Lara Paracchini, Humanitas Research

Nature. 2022 Jan 19.

Why mutation is not as random as we thought.

Shamini Bundell, Benjamin Thompson.

Keywords: Chemical biology; Evolution; Genetics

DOI: https://doi.org/10.1038/d41586-022-00142-2

Gynecol Oncol. 2022 Jan 18. pii: S0090-8258(22)00022-1. [Epub ahead of print]

Contraceptive use and the risk of ovarian cancer among women with a BRCA1 or BRCA2 mutation.

Hereditary Ovarian Cancer Clinical Study Group

Background BRCA1 and BRCA2 (BRCA) mutation carriers face a high lifetime risk of developing ovarian cancer. Oral contraceptives are protective in this population; however, the impact of other types of contraception (e.g. intrauterine devices, implants, injections) is unknown. We undertook a matched case-control study to evaluate the relationship between type of contraception and risk of ovarian cancer among women with BRCA mutations. Methods A total of 1733 matched pairs were included in this analysis. Women were matched according to year of birth, date of study entry, country of residence, BRCA mutation type and history of breast cancer. Detailed information on hormonal, reproductive and lifestyle exposures were collected from a routinely administered questionnaire. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) associated with each contraceptive exposure. Results Ever use of any contraceptive was significantly associated with reduced risk of ovarian cancer (OR = 0.62; 95% CI 0.52-0.75; P < 0.0001), which was driven by significant inverse associations with oral contraceptives (OR = 0.66; 95% CI 0.54-0.79; P < 0.0001) and contraceptive implants (OR = 0.30; 95% CI 0.12-0.73; P = 0.008). We observed a similar effect with use of injections (OR = 0.37; 95% CI 0.10-1.38; P = 0.14), but this did not achieve significance. No significant associations were observed between patterns of intrauterine device use and risk of ovarian cancer. Conclusions These findings support a protective effect of oral contraceptives and implants on risk of ovarian cancer among women with BRCA mutations. The possible protective effect of injections requires further evaluation.

Keywords: BRCA; Case-control; Contraception; Intrauterine device; Ovarian cancer

DOI: https://doi.org/10.1016/j.ygyno.2022.01.014

Mol Cancer. 2022 Jan 21. 21(1): 26

Deep cfDNA fragment end profiling enables cancer detection.

Yulia V Zhitnyuk, Anastasia P Koval, Aleksandr A Alferov, Yanina A Shtykova, Ilgar Z Mamedov, Nikolay E Kushlinskii, Dmitriy M Chudakov, Dmitry S Shcherbo.

Keywords: Colorectal cancer; Early cancer detection; Fragmentomics; Liquid biopsy; Renal cancer; cfDNA; cfDNA-FEP

DOI: https://doi.org/10.1186/s12943-021-01491-8

Cancers (Basel). 2022 Jan 14. pii: 404. [Epub ahead of print]14(2):

The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer.

Yuri Belotti, Elaine Hsuen Lim, Chwee Teck Lim.

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

DOI: https://doi.org/10.3390/cancers14020404

BMC Cancer. 2022 Jan 20. 22(1): 85

Whole genome deep sequencing analysis of cell-free DNA in samples with low tumour content.

Devika Ganesamoorthy, Alan James Robertson, Wenhan Chen, Michael B Hall, Minh Duc Cao, Kaltin Ferguson, Sunil R Lakhani, Katia Nones, Peter T Simpson, Lachlan J M Coin.

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

DOI: https://doi.org/10.1186/s12885-021-09160-1