Background & Aims Colorectal cancers (CRCs) with microsatellite instability (MSI) and a mismatch repair (MMR) immunohistochemical deficit without hypermethylation of the MLH1 promoter are likely to ...be caused by Lynch syndrome. Some patients with these cancers have not been found to have pathogenic germline mutations and are considered to have Lynch-like syndrome (LLS). The aim of this study was to determine the risk of cancer in families of patients with LLS. Methods We studied a population-based cohort of 1705 consecutive patients, performing MSI tests and immunohistochemical analyses of MMR proteins. Patients were diagnosed with Lynch syndrome when they were found to have pathogenic germline mutations. Patients with MSI and loss of MSH2 and/or MSH6 expression, isolated loss of PMS2 or loss of MLH1 without MLH1 promoter hypermethylation, and no pathogenic mutation were considered to have LLS. The clinical characteristics of patients and the age- and sex-adjusted standardized incidence ratios (SIRs) of cancer in families were compared between groups. Results The incidence of CRC was significantly lower in families of patients with LLS than in families with confirmed cases of Lynch syndrome (SIR for Lynch syndrome, 6.04; 95% confidence interval CI, 3.58–9.54; SIR for LLS, 2.12; 95% CI, 1.16–3.56; P < .001). However, the incidence of CRC was higher in families of patients with LLS than in families with sporadic CRC (SIR for sporadic CRC, 0.48; 95% CI, 0.27–0.79; P < .001). Conclusions The risk of cancer in families with LLS is lower that of families with Lynch syndrome but higher than that of families with sporadic CRC. These results confirm the need for special screening and surveillance strategies for these patients and their relatives.
Mutational signatures have been proved as a valuable pattern in somatic genomics, mainly regarding cancer, with a potential application as a biomarker in clinical practice. Up to now, several ...bioinformatic packages to address this topic have been developed in different languages/platforms. MutationalPatterns has arisen as the most efficient tool for the comparison with the signatures currently reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. However, the analysis of mutational signatures is nowadays restricted to a small community of bioinformatic experts.
In this work we present Mutational Signatures in Cancer (MuSiCa), a new web tool based on MutationalPatterns and built using the Shiny framework in R language. By means of a simple interface suited to non-specialized researchers, it provides a comprehensive analysis of the somatic mutational status of the supplied cancer samples. It permits characterizing the profile and burden of mutations, as well as quantifying COSMIC-reported mutational signatures. It also allows classifying samples according to the above signature contributions.
MuSiCa is a helpful web application to characterize mutational signatures in cancer samples. It is accessible online at http://bioinfo.ciberehd.org/GPtoCRC/en/tools.html and source code is freely available at https://github.com/marcos-diazg/musica .
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
While clinical practice guidelines for hereditary diffuse gastric cancer are well established, there is no consensus on the approach for familial intestinal gastric cancer (FIGC). In ...low‐incidence gastric cancer (GC) areas such as the United States or most European countries, there are no evidence‐based recommendations on endoscopic assessment in FIGC families. We aim to describe the yield of GC surveillance in these families, and to identify epidemiological risk factors for the development of GC and its precursor lesions. This is a multicenter observational study involving nine tertiary Spanish hospitals, in which all individuals fulfilling FIGC criteria who underwent endoscopic surveillance were included between 1991 and 2020. Forty‐one healthy individuals of 31 families were recruited. The median number of upper gastrointestinal endoscopies per individual was 3 (interquartile range, IQR, 1‐4). The median interval time between tests was 2 years (IQR 1.5‐2.5), and the median follow‐up was 9 years (IQR 3‐14.5). In 18 (43.9%) subjects, a precursor lesion of GC was found during follow‐up, and in 2 (4.9%), an early GC was identified, in which curative treatment was offered.
Helicobacter pylori
(
Hp
) infection proved to be independently associated with an increased risk of developing precursor lesions or GC, adjusted by age, gender and follow‐up, with an Odds Ratio of 6.443 (1.36‐30.6,
P
value .019). We present the first outcomes that support endoscopic surveillance with biopsies and detection of
Hp
in FIGC families, although the periodicity has yet to be defined.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Somatic copy number alterations (CNAs) are a hallmark of cancer, but their role in tumorigenesis and clinical relevance remain largely unclear. Here, we developed CNApp, a web-based tool that allows ...a comprehensive exploration of CNAs by using purity-corrected segmented data from multiple genomic platforms. CNApp generates genome-wide profiles, computes CNA scores for broad, focal and global CNA burdens, and uses machine learning-based predictions to classify samples. We applied CNApp to the TCGA pan-cancer dataset of 10,635 genomes showing that CNAs classify cancer types according to their tissue-of-origin, and that each cancer type shows specific ranges of broad and focal CNA scores. Moreover, CNApp reproduces recurrent CNAs in hepatocellular carcinoma and predicts colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and discrete genomic imbalances. In summary, CNApp facilitates CNA-driven research by providing a unique framework to identify relevant clinical implications. CNApp is hosted at https://tools.idibaps.org/CNApp/.
Early-onset colorectal cancer (CRC) represents a clinically distinct form of CRC that is often associated with a poor prognosis. Methylation levels of genomic repeats such as LINE-1 elements have ...been recognized as independent factors for increased cancer-related mortality. The methylation status of LINE-1 elements in early-onset CRC has not been analyzed previously.
We analyzed 343 CRC tissues and 32 normal colonic mucosa samples, including 2 independent cohorts of CRC diagnosed ≤ 50 years old (n=188), a group of sporadic CRC >50 years (MSS n=89; MSI n=46), and a group of Lynch syndrome CRCs (n=20). Tumor mismatch repair protein expression, microsatellite instability status, LINE-1 and MLH1 methylation, somatic BRAF V600E mutation, and germline MUTYH mutations were evaluated.
Mean LINE-1 methylation levels (± SD) in the five study groups were early-onset CRC, 56.6% (8.6); sporadic MSI, 67.1% (5.5); sporadic MSS, 65.1% (6.3); Lynch syndrome, 66.3% (4.5) and normal mucosa, 76.5% (1.5). Early-onset CRC had significantly lower LINE-1 methylation than any other group (p<0.0001). Compared to patients with <65% LINE-1 methylation in tumors, those with ≥ 65% LINE-1 methylation had significantly better overall survival (p=0.026, log rank test).
LINE-1 hypomethylation constitutes a potentially important feature of early-onset CRC, and suggests a distinct molecular subtype. Further studies are needed to assess the potential of LINE-1 methylation status as a prognostic biomarker for young people with CRC.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Colorectal cancer (CRC) is a complex disorder for which the majority of the underlying germline predisposition factors remain still unidentified. Here, we combined whole‐exome sequencing (WES) and ...linkage analysis in families with multiple relatives affected by CRC to identify candidate genes harboring rare variants with potential high‐penetrance effects. Forty‐seven affected subjects from 18 extended CRC families underwent WES. Genome‐wide linkage analysis was performed under linear and exponential models. Suggestive linkage peaks were identified on chromosomes 1q22–q24.2 (maxSNP = rs2134095; LODlinear = 2.38, LODexp = 2.196), 7q31.2–q34 (maxSNP = rs6953296; LODlinear = 2.197, LODexp = 2.149) and 10q21.2–q23.1 (maxSNP = rs1904589; LODlinear = 1.445, LODexp = 2.195). These linkage signals were replicated in 10 independent sets of random markers from each of these regions. To assess the contribution of rare variants predicted to be pathogenic, we performed a family‐based segregation test with 89 rare variants predicted to be deleterious from 78 genes under the linkage intervals. This analysis showed significant segregation of rare variants with CRC in 18 genes (weighted p‐value > 0.0028). Protein network analysis and functional evaluation were used to suggest a plausible candidate gene for germline CRC predisposition. Etiologic rare variants implicated in cancer germline predisposition may be identified by combining traditional linkage with WES data. This approach can be used with already available NGS data from families with several sequenced members to further identify candidate genes involved germline predisposition to disease. This approach resulted in one candidate gene associated with increased risk of CRC but needs evidence from further studies.
What's new?
Inherited genetic factors are thought to account for more than one‐third of colorectal cancer (CRC) cases. Most predisposing genetic factors, however, remain unidentified. Here, genome‐wide linkage analysis using whole‐exome sequencing (WES) data was performed in families with marked CRC aggregation. The combined linkage‐sequencing approach identified possible linkage peaks on chromosomes 1q22‐q24.2, 7q31.2‐q34, and 10q21.2‐q23.1. Analyses of potentially pathogenic variants revealed significant segregation of rare variants in 18 genes, while functional analyses identified a plausible candidate gene for germline CRC predisposition. The findings underscore the utility of linkage analysis employing WES for the discovery of candidate genes for disease predisposition.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
PDF
