Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing

Elucidation of clinical implications Arising from circadian rhythm and insights into the tumor immune landscape in breast cancer

Background

Circadian rhythm is an internal timing system generated by circadian-related genes (CRGs). Disruption in this rhythm has been associated with a heightened risk of breast cancer (BC) and regulation of the immune microenvironment of tumors. This study aimed to investigate the clinical significance of CRGs in BC and the immune microenvironment.

Methods

CRGs were identified using the GeneCards and MSigDB databases. Through unsupervised clustering, we identified two circadian-related subtypes in patients with BC. We constructed a prognostic model and nomogram for circadian-related risk scores using LASSO and Cox regression analyses. Using multi-omics analysis, the mutation profile and immunological microenvironment of tumors were investigated, and the immunotherapy response in different groups of patients was predicted based on their risk strata.

Results

The two circadian-related subtypes of BC that were identified differed significantly in their prognoses, clinical characteristics, and tumor immune microenvironments. Subsequently, we constructed a circadian-related risk score (CRRS) model containing eight signatures (SIAH2, EZR, GSN, TAGLN2, PRDX1, MCM4, EIF4EBP1, and CD248) and a nomogram. High-risk individuals had a greater burden of tumor mutations, richer immune cell infiltration, and higher expression of immune checkpoint genes, than low-risk individuals, indicating a "hot tumor" immune phenotype and a more favorable treatment outcome.

Conclusions

Two circadian-related subtypes of BC were identified and used to establish a CRRS prognostic model and nomogram. These will be valuable in providing guidance for forecasting prognosis and developing personalized treatment plans for BC.

Pan-cancer analyses reveal IGSF10 as an immunological and prognostic biomarker

Background: IGSF10 is a member of the immunoglobulin superfamily. Over the previous decade, growing proof has validated definitive correlations between individuals of the immunoglobulin superfamily and human diseases. However, the function of IGSF10 in pan-cancer stays unclear. We aimed to analyze the immunological and prognostic value of IGSF10 in pan-cancer. Methods: We utilized a vary of bioinformatic ways to inspect the function of IGSF10 in pan-cancer, including its correlation with prognosis, immune cell infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), DNA methyltransferases, genetic alteration, drug sensitivity, etc. Results: We noticed low expression of IGSF10 in most cancer types. IGSF10 expression in tumor samples correlates with prognosis in most cancers. In most cancer types, IGSF10 expression was strongly related to immune cells infiltration, immune checkpoints, immune modulators, TMB, MSI, MMR, and DNA methyltransferases, among others. Functional enrichment analyses indicated that IGSF10 expression was involved in lymphocyte differentiation, cell molecules adhesion, etc. Furthermore, low IGSF10 expression could increase the drug sensitivity of many drugs. Conclusion: IGSF10 could serve as a novel prognostic marker and attainable immunotherapy target for several malignancies.

SmMIP-tools: a computational toolset for processing and analysis of single-molecule molecular inversion probes-derived data

MOTIVATION

Single-molecule molecular inversion probes (smMIPs) provide an exceptionally cost-effective and modular approach for routine or large-cohort next-generation sequencing. However, processing the derived raw data to generate highly accurate variants calls remains challenging.

RESULTS

We introduce SmMIP-tools, a comprehensive computational method that promotes the detection of single nucleotide variants and short insertions and deletions from smMIP-based sequencing. Our approach delivered near-perfect performance when benchmarked against a set of known mutations in controlled experiments involving DNA dilutions and outperformed other commonly used computational methods for mutation detection. Comparison against clinically approved diagnostic testing of leukaemia patients demonstrated the ability to detect both previously reported variants and a set of pathogenic mutations that did not pass detection by clinical testing. Collectively, our results indicate that increased performance can be achieved when tailoring data processing and analysis to its related technology. The feasibility of using our method in research and clinical settings to benefit from low-cost smMIP technology is demonstrated.

AVAILABILITY AND IMPLEMENTATION

The source code for SmMIP-tools, its manual and additional scripts aimed to foster large-scale data processing and analysis are all available on github (https://github.com/abelson-lab/smMIP-tools). Raw sequencing data generated in this study have been submitted to the European Genome-Phenome Archive (EGA; https://ega-archive.org) and can be accessed under accession number EGAS00001005359.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA

The primary objective of the FDA-led Sequencing and Quality Control Phase 2 (SEQC2) project is to develop standard analysis protocols and quality control metrics for use in DNA testing to enhance scientific research and precision medicine. This study reports a targeted next-generation sequencing (NGS) method that will enable more accurate detection of actionable mutations in circulating tumor DNA (ctDNA) clinical specimens. To accomplish this, a synthetic internal standard spike-in was designed for each actionable mutation target, suitable for use in NGS following hybrid capture enrichment and unique molecular index (UMI) or non-UMI library preparation. When mixed with contrived ctDNA reference samples, internal standards enabled calculation of technical error rate, limit of blank, and limit of detection for each variant at each nucleotide position in each sample. True-positive mutations with variant allele fraction too low for detection by current practice were detected with this method, thereby increasing sensitivity.

Technical and biological constraints on ctDNA-based genotyping

Circulating tumor DNA (ctDNA) enables real-time genomic profiling of cancer without the need for tissue biopsy. ctDNA-based technology is seeing rapid uptake in clinical practice due to the potential to inform patient management from diagnosis to advanced disease. In metastatic disease, ctDNA can identify somatic mutations, copy-number variants (CNVs), and structural rearrangements that are predictive of therapy response. However, the ctDNA fraction (ctDNA%) is unpredictable and confounds variant detection strategies, undermining confidence in liquid biopsy results. Assay design also influences which types of genomic alterations are identifiable. Here, we describe the relationships between ctDNA%, methodology, and sensitivity-specificity for major classes of genomic alterations in prostate cancer. We provide recommendations to navigate the technical complexities that constrain the detection of clinically relevant genomic alterations in ctDNA.