Somatic Mutations In Cancer Research Articles

Identification of Candidate mRNA Genes and Their Potential MicroRNA Targets in Lung Cancer Induced by Smoking Tobacco.

Smoking is considered the single highest risk factor for lung cancer and has been suggested to be associated with accelerated somatic mutations in respiratory mucosa that lead to the development of lung cancer. MicroRNAs serve as modulators in smoking-induced mRNA gene expression changes in the human airway epithelium and are linked to the development of lung cancer. The thermodynamics in the microRNA (miRNA)-mRNA interactions may be affected in tobacco smokers, consequently, leading to phenotypic variations in lung cancer patients. Therefore, this study aimed to investigate the impact of smoking tobacco on somatic mutations in mRNA genes and assess their potential impact on miRNA-mRNA interactions in lung cancers. The clinically significant pathogenic variants in mRNA genes in the dataset in lung cancer cases linked to smoking tobacco (n = 330) were obtained from the Cancer Atlas database (TCGA, http://cancergenome.nih.gov/) and used to assess the potential role of tobacco consumption in driving the genetic alterations in proto-oncogenes associated with lung cancer. The analysis of the miRNA interaction with the top five altered mRNA proto-oncogenes in lung cancer cases due to tobacco consumption was performed using the target prediction function in the miRDP program (Database version 5.2.3.1, https://mirdb.org/). We identified the top five mRNA proto-oncogenes enriched with simple somatic mutations (SSM) in lung cancer were TP53, EGFR, KRAS, FAT4, and KMT2D. Interestingly, we observed the highest incidence of SSM in the Tumor Protein p53 (TP53) gene at 63.64%. Similarly, the SSM incidence was 23.94% in the Epidermal Growth Factor Receptor (EGFR), 22.12% in the Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), 18.48% in the FAT Atypical Cadherin 4 (FAT4), and 14.24% in the Lysine (K)-Specific Methyltransferase 2D (KMT2D) genes. Subsequently, we used a bioinformatics approach to assess the effect of miRNA-mRNA interactions in lung cancer among the top five SSM-enriched mRNA proto-oncogenes. Among the top 20 identified and selected miRNAs, we observed 18 unique microRNAs that bind specifically to TP53, KRAS, and FAT4 genes and 17 and 19 microRNAs that exclusively bind with the EGFR and KMT2D genes, respectively. Our study found that the top five SSM-enriched mRNA proto-oncogenes in lung cancers among tobacco smokers were TP53, EGFR, KRAS, FAT4, and KMT2D. Further, our results provide an important insight into the involvement of the intricate network of mRNA-miRNA interactions in the development of lung cancer.

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply—but do not prove—that mutations exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, there are no current methods to quantify the strength of selection incorporating pairwise and higher-order epistatic effects on selection within the trajectory of likely cancer genotoypes. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling this continuous-time Markov chain model with a deconvolution approach provides estimates of underlying mutation rates and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 565 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53 LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.

Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes.

Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.

CPXM1 correlates to poor prognosis and immune cell infiltration in gastric cancer

BackgroundGastric cancer (GC) is the fourth most common cause of cancer-related death and the fifth most frequent malignant cancer, especially advanced GC. Carboxypeptidase X member 1 (CPXM1) is an epigenetic factor involved in many physiological processes, including osteoclast differentiation and adipogenesis. Several studies have shown the association of CPXM1 with multiple tumors; however, the mechanism of CPXM1 involvement in the progression of GC is yet to be characterized. MethodCPXM1 expression data were obtained from the Tumor Immune Estimation Resource. The Cancer Genome Atlas and the Gene Expression Omnibus databases were used to obtain patient-matched clinicopathological information, and the Kaplan–Meier plot database was utilized for the prognosis analysis of GC patients. The Catalog of Somatic Mutations in Cancer and cBioportal databases were adopted to study CPXM1 mutations in tumors. Next, we utilized the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis for mechanism research. Furthermore, we performed tumor microenvironment and immune infiltration analysis based on CPXM1. Finally, we predicted sensitivity to several targeted drugs in GC patients based on CPXM1.CPXM1 is upregulated in GC and is correlated with poor prognosis, gender, and tumor stage in GC patients. Gene enrichment analysis suggested that CPXM1 may regulate the occurrence and progression of GC via the PI3K–AKT and TGF-β pathway. Moreover, CPXM1 expression results in an increase in the proportion of immune and stromal cells. Additionally, the proportion of plasma cells was inversely related to the expression of CPXM1, whereas macrophage M2 expression was proportionate to CPXM1 expression. Finally, six small-molecule drugs that showed notable variations in IC50 between two groups were screened. ConclusionThese results suggested that CPXM1 regulates the progression of GC and may represent a novel target for the detection and treatment of GC.

Recurrent Somatic Copy Number Alterations and Their Association with Oncogene Expression Levels in High-Grade Ovarian Serous Carcinoma.

Somatic copy number alterations (SCNAs) are frequently observed in high-grade ovarian serous carcinoma (HGOSC). However, their impact on gene expression levels has not been systematically assessed. In this study, we explored the relationship between recurrent SCNA and gene expression using The Cancer Genome Atlas Pan Cancer dataset (OSC, TCGA, PanCancer Atlas) to identify cancer-related genes in HGOSC. We then investigated any association between highly correlated cancer genes and clinicopathological parameters, including age of diagnosis, disease stage, overall survival (OS), and progression-free survival (PFS). A total of 772 genes with recurrent SCNAs were observed. SCNA and mRNA expression levels were highly correlated for 274 genes; 24 genes were classified as a Tier 1 gene in the Cancer Gene Census in the Catalogue of Somatic Mutations in Cancer (CGC-COSMIC). Of these, 11 Tier 1 genes had highly correlated SCNA and mRNA expression levels: TBL1XR1, PIK3CA, UBR5, EIF3E, RAD21, EXT1, RECQL4, KRAS, PRKACA, BRD4, and TPM4. There was no association between gene amplification and disease stage or PFS. EIF3E, RAD21, and EXT1 were more frequently amplified in younger patients, specifically those under the age of 55 years. Patients with tumors carrying PRKACA, BRD4, or TPM4 amplification were associated with a significantly shorter OS. RECQL4 amplification was more frequent in younger patients, and tumors with this amplification were associated with a significantly better OS.

Suppression of KRAS and CBL Mutations and Hematological Improvement By Lenzilumab and Azacitidine Treatment in Proliferative Chronic Myelomonocytic Leukemia

Introduction: Mutations in RAS-pathway genes ( NRAS, KRAS, CBL) are amongst the most common somatic mutations in cancer and historically resistant to most therapies. Targeted therapies that impact the RAS-pathway constitute a major unmet need in oncology. The proliferative form of chronic myelomonocytic leukemia (CMML) is commonly associated with RAS-pathway mutations and has a high propensity to develop into acute myeloid leukemia. In pre-clinical models, Lenzilumab (LENZ; Humanigen, Inc., Short Hills, NJ), a proprietary Humaneered ® first-in-class monoclonal antibody with best-in-class off-rate and affinity that neutralizes GM-CSF, resulted in a reduction of colony numbers and viability of CMML cells, with the greatest sensitivity in cells possessing RAS-pathway ( NRAS/ KRAS/ CBL) mutations, suggesting benefit in the targeted treatment of CMML. The PREcision Approach to CHronic Myelomonocytic Leukemia (PREACH-M) trial assesses the efficacy of LENZ, in addition to azacytidine (AZA), in CMML subjects with RAS-pathway mutations and high dose sodium ascorbate (ASC) and AZA in CMML subjects without RAS-pathway mutations. Interim data from 11 subjects with RAS-pathway ( NRAS/ KRAS/ CBL) mutations in PREACH-M receiving LENZ/AZA demonstrate reductions in circulating GM-CSF and CRP with 8 subjects achieving complete response or optimal marrow response. This report describes improvements in variant allele frequencies (VAF) for RAS-pathway mutations and hematologic improvements associated with LENZ/AZA treatment in CMML. Methods: PREACH-M is a Phase 2/3 non-randomized, open-label trial in 72 subjects, aged at least 18 years, with newly diagnosed CMML based on the WHO 2016 criteria; and RAS-pathway mutations at a variant allele frequency ≥3%. Subjects received 24 cycles (every 28 days) of AZA (SC; 75 mg/m 2 for 7 days) and LENZ (IV; 552 mg; d1 & d15 of cycle 1 and d1 only for all subsequent cycles). Subjects without RAS-pathway mutations received the same AZA regimen and sodium ascorbate (IV; 30g for 7 days (15g for 1st dose only; 30g thereafter if no evidence of tumor lysis syndrome); PO; 1.1g on all other days). VAF was determined from bone marrow mononuclear cells using a 41-myeloid panel using Illumina Hi-Seq with a depth of 1000x performed on bone marrow aspirates obtained at baseline and Day 1 of treatment cycles 4, 7, and 12. Results: As of July 2023, 11 subjects completed at least 3 months of LENZ/AZA treatments and follow-up sequencing data were available for 10 subjects. Five of 10 subjects showed >10% decrease in VAF for at least one mutation detected at baseline, including 5 decreased alleles in the RAS-pathway ( KRAS, CBL) out of 22 total VAF mutation responses. CBL mutations showed the largest change with a decrease in >50% VAF in three subjects. Decreases in RAS-pathway clones by LENZ/AZA were durable at 6 months. No decreases in VAF for any mutations were observed in 2 evaluable subjects following ASC/AZA treatment. Following 3 months LENZ/AZA treatment, blood monocyte count improved from a baseline of 11.0 ± 6.0 x 10 9/L to 2.0 ± 1.0 x 10 9/L (p=0.030). Blast differential improved from 10.5 ± 5.0% to 4.0 ± 3.5% (p=0.038). Platelet count increased from 90.0 ± 50.0 x 10 9/L to 150.0 ± 60.0 x 10 9/L (p=0.010). Statistical improvements remained durable for at least 6 (n=6) and 12 (n=3) months. Hemoglobin concentration increased 100.0 ± 22.0 g/L to 115.0 ± 10.0 g/L at 3 months (p=NS, n=11) and progressively increased to 122.0 ± 10.0 g/L at 12 months (p=0.024, n=3). Spleen length decreased from 15.0 ± 4.0 cm to 13.0 ± 4.0 cm (p=0.03) and decreased further to 11.0 ± 2.0 cm in subjects who received 12 months of treatment (n=3). No evidence of disease progression or relapse was observed in any subject. Conclusion: In 11 evaluable subjects with proliferative CMML and RAS-pathway mutations, GM-CSF neutralization with LENZ in addition to AZA standard of care, resulted in significant decreases in the proportion of KRAS and CBL mutant leukemic cells, accompanied by clinically significant hematologic improvements and a reduction in splenomegaly. Lenzilumab may have efficacy in preventing outgrowth of RAS-pathway mutations, specifically KRAS and CBL, in the context of CMML and other myeloid malignancies.

COSMIC:a curated database of somatic variants and clinical data for cancer.

The Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic, is an expert-curated knowledgebase providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. The catalogue is accessed and used by thousands of cancer researchers and clinicians daily, allowing them to quickly access information from an immense pool of data curated from over 29 thousand scientific publications and large studies. Within the last 4 years, COSMIC has substantially expanded its utility by adding new resources: the Mutational Signatures catalogue, the Cancer Mutation Census, and Actionability. To improve data accessibility and interoperability, somatic variants have received stable genomic identifiers that are associated with their genomic coordinates in GRCh37 and GRCh38, and new export files with reduced data redundancy have been made available for download.

Homology Modeling and Structure Based Drug Design for Human Gastric Cancer

Abstract: This research article delves into the multifaceted world of structural biology and bioinformatics, exploring a diverse array of tools and resources employed to decipher the intricacies of proteins and their implications in health and disease. The paper navigates through various computational tools and databases used for sequence analysis, structural analysis, mutation site detection, and molecular docking, shedding light on their individual contributions in unraveling the secrets of proteins. COBALT serves as the initial compass, guiding researchers in the alignment of protein sequences, identifying conserved domains, and elucidating structural features in proteins. The exploration continues, with a focus on tools such as RasMol, PyMol, and Pharmit. This research article explores a range of bioinformatics tools and databases crucial for elucidating the complex world of proteins and their involvement in diseases like gastric cancer. It begins by highlighting the pivotal role of the Catalogue of Somatic Mutations in Cancer (COSMIC) in mutation site detection, providing a comprehensive repository of somatic mutations in cancer. The Kyoto Encyclopedia of Genes and Genomes (KEGG) complements this with a wealth of biological data and pathways. Shifting the focus to protein stability and dynamics, the article introduces DynaMUT, a computational tool employing molecular dynamics simulations to predict mutation impacts on protein behavior. Lastly, SwissDock takes the stage for drug discovery, facilitating the prediction and analysis of interactions between small molecules and target proteins. Collectively, these tools and databases offer an interdisciplinary lens through which researchers unravel protein intricacies and their relevance in diseases, exemplifying the indispensable role of structural biology and bioinformatics in advancing biological understanding.

Centuries of genome instability and evolution in soft-shell clam, Mya arenaria, bivalve transmissible neoplasia

Transmissible cancers are infectious parasitic clones that metastasize to new hosts, living past the death of the founder animal in which the cancer initiated. We investigated the evolutionary history of a cancer lineage that has spread though the soft-shell clam (Mya arenaria) population by assembling a chromosome-scale soft-shell clam reference genome and characterizing somatic mutations in transmissible cancer. We observe high mutation density, widespread copy-number gain, structural rearrangement, loss of heterozygosity, variable telomere lengths, mitochondrial genome expansion and transposable element activity, all indicative of an unstable cancer genome. We also discover a previously unreported mutational signature associated with overexpression of an error-prone polymerase and use this to estimate the lineage to be >200 years old. Our study reveals the ability for an invertebrate cancer lineage to survive for centuries while its genome continues to structurally mutate, likely contributing to the evolution of this lineage as a parasitic cancer.

Characterization of DNA Damage Response-Associated Somatic Mutations in Borderline Resectable and Locally Advanced Pancreatic Cancer

The role of radiation for pancreatic cancer remains controversial, with recent studies showing conflicting results, highlighting the need to develop biomarkers of radiation response. Despite its potential utility in predicting radiosensitivity, the landscape of somatic mutations in borderline resectable pancreatic cancer (BRPC) and locally advanced pancreatic cancer (LAPC), as related to DNA damage response (DDR), has not been well characterized. This study aimed to characterize the frequency of such mutations in a cohort of patients with BRPC/LAPC treated with neoadjuvant chemotherapy and stereotactic body radiotherapy (SBRT). Mutational data was collected from patients with BRPC/LAPC treated at a single institution with neoadjuvant chemotherapy and SBRT, followed by surgical resection from 2016-2021. Chemotherapy consisted of modified FOLFIRINOX or gemcitabine/nab-paclitaxel, and patients were treated with SBRT in 33 Gy in 5 fractions. Genomic data was obtained from either endoscopic biopsy or surgical specimens, and next-generation sequencing was performed either in-house with a Solid Tumor Panel or with FoundationOne CDx. Specific emphasis was placed on the characterization of double-strand DNA break (DSB) repair genes, as this is the type of tumor cell damage traditionally induced by radiation therapy. Genes associated with the two main pathways of DSB repair, non-homologous end joining (NHEJ) and homologous repair (HR), were analyzed. Specific HR pathway mutations assessed were BLM, BRCA1/2, MRE11, NBN, PALB2, RAD50, RAD51B-D, and RAD54L, while PRKDC mutations were assessed for the NHEJ pathway. Mutations in ATM, an important initiator of DDR pathways, were also analyzed. Additionally, the frequency of mutations in TP53, CDKN2A and SMAD4 in patients with concomitant KRAS mutations was assessed. Eighty-five patients were included in the study. Five (5.9%) patients had mutations in the NHEJ pathway of the PRKDC gene. Twenty (23.5%) patients had mutations in the HR pathway, including BRCA2 (10/85; 11.8%), PALB2 (5/85; 5.9%), BRCA1 (3/85; 3.5%), and RAD50 (1/85; 1.2%). Six (7.1%) patients had mutations in ATM. No patients were found to have mutations in BLM, RAD51B-D, RAD54L, or NBN. Amongst patients with KRAS mutations (72/85), concomitant mutations were observed in TP53 (47/85; 55.3%), CDKN2A (16/85; 18.8%), and SMAD4 (9/85; 10.6%). Herein, we characterized the frequency of somatic mutations associated with DSB repair genes in patients with BRPC/LAPC. Data analysis on outcomes related to radiation response in patients with mutations in DDR pathways is ongoing, but will likely also benefit from multi-institutional efforts to increase the power to answer this question.

Prognostic significance of TP53 and PIK3CA mutations analyzed by next-generation sequencing in breast cancer.

Breast cancer is one of the most prevalent malignant tumors affecting women globally. It is a heterogeneous disease characterized by mutations in several genes. Several gene panels have been applied to assess the risk of breast cancer and determine the appropriate treatment. As a powerful tool, Next-generation sequencing (NGS) has been widely utilized in cancer research due to its advantages, including high speed, high throughput, and high accuracy. In this study, we aim to analyze the correlation between somatic mutations in breast cancer, analyzed using NGS, and the prognosis of patients. Between May 2018 and May 2019, a total of 313 patients with breast cancer underwent surgical treatment, which included total mastectomy and breast-conserving surgery. Among these patients, 265 were diagnosed with invasive ductal carcinoma. In this study, we analyzed the NGS results, clinicopathological characteristics, and their correlation with prognosis. Using a gene panel, we examined 143 somatic mutations in solid cancers. Notably, the study population included patients who had received neoadjuvant chemotherapy. The mean age of the patients was 53.1 (±10.28) years, and the median follow-up time was 48 months (range, 8-54). Among the 265 patients, 68 had received prior systemic therapy. Of these, 203 underwent breast-conserving surgery, and 62 underwent a mastectomy. Various somatic mutations were observed in NGS, with the most frequent mutation being PIK3CA mutations, which accounted for 44% of all mutations. TP53 mutations were the second most frequent, and ERBB2 mutations were the third most frequent. TP53 mutations were associated with poor disease-free survival (P = .027), while PIK3CA mutations were associated with better disease-free survival (P = .035) than PIK3CA wild-type. In our study, we identified various somatic mutations in breast cancer. Particularly, we found that TP53 and PIK3CA mutations are potentially associated with the prognosis of breast cancer. These findings suggest that the presence of specific mutations may have implications for predicting the prognosis of breast cancer. Further research and validation are needed to gain a deeper understanding of the role of these mutations and their mechanisms in prognosis prediction.

Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches.

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38-0.83) and quantitatively (Kendall tau-b = 0.18-0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9-24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution.

Identification of secretory pathway-related genes based on Random Forest algorithm to predict the prognosis and immunotherapy response of hepatocellular carcinoma.

The dysfunction of secretory pathways may represent biomarkers or therapeutic targets of cancer. The hepatocellular carcinoma (HCC) phenotype was studied in relation to the genes in the secretory pathway and to screen for a combination of genes that may be a viable therapeutic target for HCC and connected to the pathophysiological features of the tumor. Using the HCC information from The Cancer Genome Atlas, somatic mutation and prognostic association analysis were performed on the secretory pathway genes. Based on prognostic genes in the secretory pathway, the samples were consensus clustered, and a Random Forest model was built. The clinical characteristics, tumor mutation burden, functional status and potential responses to immunotherapy and tumor suppressor medications of various subtypes and risk groups were discussed. Of the 84 genes for secretory pathway, 32 were prognostic genes related to HCC, which divided HCC into two categories: C1 and C2. By comparing the two types of HCC samples, it was found that the survival outcome of C1 was inferior, with stronger adaptive and innate immunity, but less sensitive to immunotherapy than C2. The constructed prognostic signature included seven of the 32 prognostic genes in the secretory pathway, which showed significant correlation with the prognosis, somatic mutation, biological pathway status, potential response to immunotherapy and sensitivity of 72 tumor suppressor drugs from different HCC cohorts, and had a feasible prognostic effect for 31 types of cancer and immunotherapy cohorts. In this study, HCC was divided into two molecular subtypes according to prognostic genes in the secretory pathway, and seven of them were combined into one signature, which produced significant results in evaluating the prognosis of different HCC cohorts, pan-cancer cohorts and immunotherapy cohorts, and had potential guiding significance for prophylactic immunotherapy in patients with HCC.

Network pharmacology and molecular docking study-based approach to explore mechanism of benzimidazole-based anthelmintics for the treatment of lung cancer

Emerging studies have reported the potential anticancer activity of benzimidazole-based anthelmintics (BBA) against lung cancer (LC). However, mechanism underlying the anticancer activity of BBA is unclear. Therefore, in the current study, network pharmacology and molecular docking-based approach were used to explore the potential molecular mechanism for the treatment of LC. The potential targets for BBA were obtained from multiple databases including SwissTargetPrediction, Drug Bank, Therapeutic Target Database, and Comparative Toxicogenomics Database while LC targets were collected from DisGeNet gene discovery platform, Integrated Genomic Database of NSCLC, Catalogue of Somatic Mutations in Cancer and Online Mendelian Inheritance in Man database. Protein-protein interaction (PPI) diagram of common targets was constructed using STRING online platform. Topological analysis was performed using Cytoscape and gene enrichment analysis was conducted using FunRich software. Highest degree targets were then confirmed using molecular docking and molecular dynamics simulations. The BBA were prioritized according to their S scores, with ricobendazole ranking highest followed by flubendazole, fenbendazole, mebendazole, triclabendazole, albendazole, oxibendazole, parbendazole, thiabendazole and oxfendazole. The potential targets of BBA identified using topological analysis and molecular docking were found to be CCND1 (cyclin D1), EGFR (Epidermal Growth Factor Receptor), ERBB2 (Erb-B2 Receptor Tyrosine Kinase 2/CD340), PTGS2 (Prostaglandin-endoperoxide synthase 2), and SRC (Proto-oncogene tyrosine-protein kinase). Furthermore, molecular dynamics confirmed that CCND1 and EGFR are the potential targets of ricobendazole for the treatment of LC. BBA can be further explored as a therapeutic strategy for the treatment of lung cancer under in vitro and in vivo studies. Communicated by Ramaswamy H. Sarma

Global impact of somatic structural variation on the cancer proteome

Both proteome and transcriptome data can help assess the relevance of non-coding somatic mutations in cancer. Here, we combine mass spectrometry-based proteomics data with whole genome sequencing data across 1307 human tumors spanning various tissues to determine the extent somatic structural variant (SV) breakpoint patterns impact protein expression of nearby genes. We find that about 25% of the hundreds of genes with SV-associated cis-regulatory alterations at the mRNA level are similarly associated at the protein level. SVs associated with enhancer hijacking, retrotransposon translocation, altered DNA methylation, or fusion transcripts are implicated in protein over-expression. SVs combined with altered protein levels considerably extend the numbers of patients with tumors somatically altered for critical pathways. We catalog both SV breakpoint patterns involving patient survival and genes with nearby SV breakpoints associated with increased cell dependency in cancer cell lines. Pan-cancer proteogenomics identifies targetable non-coding alterations, by virtue of the associated deregulated genes.

F-box DNA Helicase 1 (FBH1) Contributes to the Destabilization of DNA Damage Repair Machinery in Human Cancers.

Homologous recombination (HR) is the major mechanism of rescue of stalled replication forks or repair of DNA double-strand breaks (DSBs) during S phase or mitosis. In human cells, HR is facilitated by the BRCA2-BRCA1-PALB2 module, which loads the RAD51 recombinase onto a resected single-stranded DNA end to initiate repair. Although the process is essential for error-free repair, unrestrained HR can cause chromosomal rearrangements and genome instability. F-box DNA Helicase 1 (FBH1) antagonizes the role of BRCA2-BRCA1-PALB2 to restrict hyper-recombination and prevent genome instability. Here, we analyzed reported FBH1 mutations in cancer cells using the Catalogue of Somatic Mutations in Cancers (COSMIC) to understand how they interact with the BRCA2-BRCA1-PALB2. Consistent with previous results from yeast, we find that FBH1 mutations co-occur with BRCA2 mutations and to some degree BRCA1 and PALB2. We also describe some co-occurring mutations with RAD52, the accessory RAD51 loader and facilitator of single-strand annealing, which is independent of RAD51. In silico modeling was used to investigate the role of key FBH1 mutations on protein function, and a Q650K mutation was found to destabilize the protein structure. Taken together, this work highlights how mutations in several DNA damage repair genes contribute to cellular transformation and immortalization.

Higher 13-Gene-Estimated TMB Detected from Plasma ctDNA is Associated with Worse Outcome for T-Cell Lymphoma Patients.

Exome sequencing of in situ tumor samples reveals that mutated genes can predict the prognosis of patients with T-cell lymphoma (TCL). However, how tumor mutation burden (TMB) derived from circulating tumor DNA (ctDNA) may stratify TCL patients remains unclear.The plasma ctDNA of 79 newly diagnosed TCL patients from the clinical center is used for targeted exome sequencing, and the exome data of 4035 TCL patients from the Catalogue of Somatic Mutations in Cancer (COSMIC) database is obtained for comparison analysis.TCL patients with higher TMB, as evaluated with a panel of 120 genes (panel-TMB120), are associated with poor prognosis. More importantly, COX regression analysis identifies a subset of 13 genes in panel-TMB120, including AP3B1 (Adaptor related protein complex 3 subunit beta 1), ATM (Ataxia-telangiectasia mutated), BCL6 (B cell lymphoma 6), BRAF (B-Raf proto-oncogene, serine/threonine kinase), CDKN2B (Cyclin dependent kinase inhibitor 2B), EPCAM (Epithelial cell adhesion molecule), FBXO11 (F-box protein 11), JAK1 (Janus kinase 1), MDM2 (Murine double minute 2), NF1 (Neurofibromin 1), STAT5B (Signal transducer and activator of transcription 5B), STAT6 (Signal transducer and activator of transcription 6), and TET2 (Tet methylcytosine dioxygenase 2), which are significantly associated with prognosis. Specifically, higher TMB values calculated with these 13 genes (panel-TMB13) are able to significantly predict unfavorable prognosis for these patients. Together, panel-TMB13 and the International Prognostic Index (IPI) are used for risk stratification.Panel-TMB13 is identified, which can predict poor prognosis for TCL patients carrying higher panel-TMB13 scores and suggest that panel-TMB13 may be a potential biomarker for supplement risk stratification of TCL patients.

Predator: Predicting the impact of cancer somatic mutations on protein-protein interactions.

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

Both cell autonomous and non-autonomous processes modulate the association between replication timing and mutation rate

Cancer somatic mutations are the product of multiple mutational and repair processes, some of which are tightly associated with DNA replication. Mutation rates (MR) are known to be higher in late replication timing (RT) regions, but different processes can affect this association. Systematic analysis of the mutational landscape of 2787 tumors from 32 tumor types revealed that approximately one third of the tumor samples show weak association between replication timing and mutation rate. Further analyses revealed that those samples have unique mutational signatures and are enriched with mutations in genes involved in DNA replication, DNA repair and chromatin structure. Surprisingly, analysis of differentially expressed genes between weak and strong RT-MR association groups revealed that tumors with weak association are enriched with genes associated with cell–cell communication and the immune system, suggesting a non-autonomous response to DNA damage.

Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.

Next-generation sequencing (NGS) panels for mature B-cell neoplasms (MBNs) are widely applied clinically but have yet to be routinely used in a manner that is suitable for subtype differential diagnosis. This study retrospectively investigated newly diagnosed cases of MBNs from our laboratory to investigate mutation landscapes in Chinese patients with MBNs and to combine mutational information and machine learning (ML) into clinical applications for MBNs, especially for subtype classification. Samples from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database were collected for ML model construction and cases from our laboratory were used for ML model validation. Five repeats of 10-fold cross-validation Random Forest algorithm was used for ML model construction. Mutation detection was performed by NGS and tumor cell size was confirmed by cell morphology and/or flow cytometry in our laboratory. Totally 849 newly diagnosed MBN cases from our laboratory were retrospectively identified and included in mutational landscape analyses. Patterns of gene mutations in a variety of MBN subtypes were found, important to investigate tumorigenesis in MBNs. A long list of novel mutations was revealed, valuable to both functional studies and clinical applications. By combining gene mutation information revealed by NGS and ML, we established ML models that provide valuable information for MBN subtype classification. In total, 8895 cases of 8 subtypes of MBNs in the COSMIC database were collected and utilized for ML model construction, and the models were validated on the 849 MBN cases from our laboratory. A series of ML models was constructed in this study, and the most efficient model, with an accuracy of 0.87, was based on integration of NGS testing and tumor cell sizes. The ML models were of great significance in the differential diagnosis of all cases and different MBN subtypes. Additionally, using NGS results to assist in subtype classification of MBNs by method of ML has positive clinical potential.