Computational methods for detecting cancer hotspots

The Impact of Mutational Hotspots on Cancer Survival

BACKGROUND

Cofactors, biomarkers, and the mutational status of genes such as TP53, EGFR, IDH1/2, or PIK3CA have been used for patient stratification. However, many genes exhibit recurrent mutational positions known as hotspots, specifically linked to varying degrees of survival outcomes. Nevertheless, few hotspots have been analyzed (e.g., TP53 and EGFR). Thus, many other genes and hotspots remain unexplored.

METHODS

We systematically screened over 1400 hotspots across 33 TCGA cancer types. We compared the patients carrying a hotspot against (i) all cases, (ii) gene-mutated cases, (iii) other mutated hotspots, or (iv) specific hotspots. Due to the limited number of samples in hotspots and the inherent group imbalance, besides Cox models and the log-rank test, we employed VALORATE to estimate their association with survival precisely.

RESULTS

We screened 1469 hotspots in 6451 comparisons, where 314 were associated with survival. Many are discussed and linked to the current literature. Our findings demonstrate associations between known hotspots and survival while also revealing more potential hotspots. To enhance accessibility and promote further investigation, all the Kaplan-Meier curves, the log-rank tests, Cox statistics, and VALORATE-estimated null distributions are accessible on our website.

CONCLUSIONS

Our analysis revealed both known and putatively novel hotspots associated with survival, which can be used as biomarkers. Our web resource is a valuable tool for cancer research.

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we collected the data for experimentally known driver mutations in 22 different cancer types and classified them into six categories: breast cancer (BRCA), acute myeloid leukaemia (LAML), endometrial carcinoma (EC), stomach cancer (STAD), skin cancer (SKCM), and other cancer types which contains 5747 disease prone and 5514 neutral sites in 516 proteins. The analysis of amino acid distribution along mutant sites revealed that the motifs AAA and LR are preferred in disease-prone sites whereas QPP and QF are dominant in neutral sites. Further, we developed a method using deep neural networks to predict disease-prone sites with amino acid sequence-based features such as physicochemical properties, secondary structure, tri-peptide motifs and conservation scores. We obtained an average AUC of 0.97 in five cancer types BRCA, LAML, EC, STAD and SKCM in a test dataset and 0.72 in all other cancer types together. Our method showed excellent performance for identifying cancer-specific mutations with an average sensitivity, specificity, and accuracy of 96.56 %, 97.39 %, and 97.64 %, respectively. We developed a web server for identifying cancer-prone sites, and it is available at https://web.iitm.ac.in/bioinfo2/MutBLESS/index.html. We suggest that our method can serve as an effective method to identify disease-prone sites and assist to develop therapeutic strategies.

HotSPOT: A Computational Tool to Design Targeted Sequencing Panels to Assess Early Photocarcinogenesis

Mutations found in skin are acquired in specific patterns, clustering around mutation-prone genomic locations. The most mutation-prone genomic areas, mutation hotspots, first induce the growth of small cell clones in healthy skin. Mutations accumulate over time, and clones with driver mutations may give rise to skin cancer. Early mutation accumulation is a crucial first step in photocarcinogenesis. Therefore, a sufficient understanding of the process may help predict disease onset and identify avenues for skin cancer prevention. Early epidermal mutation profiles are typically established using high-depth targeted next-generation sequencing. However, there is currently a lack of tools for designing custom panels to capture mutation-enriched genomic regions efficiently. To address this issue, we created a computational algorithm that implements a pseudo-exhaustive approach to identify the best genomic areas to target. We benchmarked the current algorithm in three independent mutation datasets of human epidermal samples. Compared to the sequencing panel designs originally used in these publications, the mutation capture efficacy (number of mutations/base pairs sequenced) of our designed panel improved 9.6-12.1-fold. Mutation burden in the chronically sun-exposed and intermittently sun-exposed normal epidermis was measured within genomic regions identified by hotSPOT based on cutaneous squamous cell carcinoma (cSCC) mutation patterns. We found a significant increase in mutation capture efficacy and mutation burden in cSCC hotspots in chronically sun-exposed vs. intermittently sun-exposed epidermis (p < 0.0001). Our results show that our hotSPOT web application provides a publicly available resource for researchers to design custom panels, enabling efficient detection of somatic mutations in clinically normal tissues and other similar targeted sequencing studies. Moreover, hotSPOT also enables the comparison of mutation burden between normal tissues and cancer.

Development of a Sensitive Digital Droplet PCR Screening Assay for the Detection of GPR126 Non-Coding Mutations in Bladder Cancer Urine Liquid Biopsies

Recent whole-genome sequencing studies identified two novel recurrent mutations in the enhancer region of GPR126 in urothelial bladder cancer (UBC) tumor samples. This mutational hotspot is the second most common after the TERT promoter in UBC. The aim of the study was to develop a digital droplet PCR screening assay for the simultaneous detection of GPR126 mutations in a single tube. Its performance combined with TERT promoter mutation analysis was evaluated in urine of healthy volunteers (n = 50) and patients with cystitis (n = 22) and UBC (n = 70). The developed assay was validated using DNA constructs carrying the studied variants. None of the mutations were detected in control and cystitis group samples. GPR126 mutations were observed in the urine of 25/70 UBC patients (area under the ROC curve (AUC) of 0.679; mutant allele fraction (MAF) of 21.61 [8.30-44.52] %); TERT mutations-in 40/70 (AUC of 0.786; MAF = 28.29 [19.03-38.08] %); ≥1 mutation-in 47/70 (AUC of 0.836)). The simultaneous presence of GPR126 and TERT mutations was observed in 18/70 cases, with no difference in MAFs for the paired samples (31.96 [14.78-47.49] % vs. 27.13 [17.00-37.62] %, p = 0.349, respectively). The combined analysis of these common non-coding mutations in urine allows the sensitive and non-invasive detection of UBC.

Multivariate analysis in data science for the geospatial distribution of the breast cancer mortality rate in Colombia

This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it's found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Elucidation of the conformational dynamics and assembly of Argonaute−RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Argonaute (AGO) proteins, the core of RNA-induced silencing complex, are guided by microRNAs (miRNAs) to recognize target RNA for repression. The miRNA–target RNA recognition forms initially through pairing at the seed region while the additional supplementary pairing can enhance target recognition and compensate for seed mismatch. The extension of miRNA lengths can strengthen the target affinity when pairing both in the seed and supplementary regions. However, the mechanism underlying the effect of the supplementary pairing on the conformational dynamics and the assembly of AGO–RNA complex remains poorly understood. To address this, we performed large-scale molecular dynamics simulations of AGO–RNA complexes with different pairing patterns and miRNA lengths. The results reveal that the additional supplementary pairing can not only strengthen the interaction between miRNA and target RNA, but also induce the increased plasticity of the PAZ domain and enhance the domain connectivity among the PAZ, PIWI, N domains of the AGO protein. The strong community network between these domains tightens the mouth of the supplementary chamber of AGO protein, which prevents the escape of target RNA from the complex and shields it from solvent water attack. Importantly, the inner stronger matching pairs between the miRNA and target RNA can compensate for weaker mismatches at the edge of supplementary region. These findings provide guidance for the design of miRNA mimics and anti-miRNAs for both clinical and experimental use and open the way for further engineering of AGO proteins as a new tool in the field of gene regulation.

In Silico Analysis of Dengue Virus Serotype 2 Mutations Detected at the Intrahost Level in Patients with Different Clinical Outcomes

Intrahost genetic diversity is thought to facilitate arbovirus adaptation to changing environments and hosts, and it may also be linked to viral pathogenesis. Intending to shed light on the viral determinants for severe dengue pathogenesis, we previously analyzed the DENV-2 intrahost genetic diversity in 68 patients clinically classified as dengue fever (n = 31), dengue with warning signs (n = 19), and severe dengue (n = 18), performing viral whole-genome deep sequencing from clinical samples with an amplicon-free approach. From it, we identified a set of 141 relevant mutations distributed throughout the viral genome that deserved further attention. Therefore, we employed molecular modeling to recreate three-dimensional models of the viral proteins and secondary RNA structures to map the mutations and assess their potential effects. Results showed that, in general lines, disruptive variants were identified primarily among dengue fever cases. In contrast, potential immune-escape variants were associated mainly with warning signs and severe cases, in line with the latter's longer intrahost evolution times. Furthermore, several mutations were located on protein-surface regions, with no associated function. They could represent sites of further investigation, as the interaction of viral and host proteins is critical for both host immunomodulation and virus hijacking of the cellular machinery. The present analysis provides new information about the implications of the intrahost genetic diversity of DENV-2, contributing to the knowledge about the viral factors possibly involved in its pathogenesis within the human host. Strengthening our results with functional studies could allow many of these variants to be considered in the design of therapeutic or prophylactic compounds and the improvement of diagnostic assays. IMPORTANCE Previous evidence showed that intrahost genetic diversity in arboviruses may be linked to viral pathogenesis and that one or a few amino acid replacements within a single protein are enough to modify a biological feature of an RNA virus. To assess dengue virus serotype 2 determinants potentially involved in pathogenesis, we previously analyzed the intrahost genetic diversity of the virus in patients with different clinical outcomes and identified a set of 141 mutations that deserved further study. Thus, through a molecular modeling approach, we showed that disruptive variants were identified primarily among cases with mild dengue fever, while potential immune-escape variants were mainly associated with cases of greater severity. We believe that some of the variants pointed out in this study were attractive enough to be potentially considered in future intelligent designs of therapeutic or prophylactic compounds or the improvement of diagnostic tools. The present analysis provides new information about DENV-2 viral factors possibly involved in its pathogenesis within the human host.