CRAVAT: cancer-related analysis of variants toolkit

Identification of high-risk non-synonymous SNPs (nsSNPs) in DNAH1 and DNAH17 genes associated with male infertility: a bioinformatics analysis

Male infertility is a significant reproductive issue affecting a considerable number of couples worldwide. While there are various causes of male infertility, genetic factors play a crucial role in its development. We focused on identifying and analyzing the high-risk nsSNPs in DNAH1 and DNAH17 genes, which encode proteins involved in sperm motility. A total of 20 nsSNPs for DNAH1 and 10 nsSNPs for DNAH17 were analyzed using various bioinformatics tools including SIFT, PolyPhen-2, CADD, PhD-SNPg, VEST-4, and MutPred2. As a result, V1287G, L2071R, R2356W, R3169C, R3229C, E3284K, R4096L, R4133C, and A4174T in DNAH1 gene and C1803Y, C1829Y, R1903C, and L3595P in DNAH17 gene were identified as high-risk nsSNPs. These nsSNPs were predicted to decrease protein stability, and almost all were found in highly conserved amino acid positions. Additionally, 4 nsSNPs were observed to alter post-translational modification status. Furthermore, the interaction network analysis revealed that DNAH1 and DNAH17 interact with DNAH2, DNAH3, DNAH5, DNAH7, DNAH8, DNAI2, DNAL1, CFAP70, DNAI3, DNAI4, ODAD1, and DNAI7, demonstrating the importance of DNAH1 and DNAH17 proteins in the overall functioning of the sperm motility machinery. Taken together, these findings revealed the detrimental effects of identified high-risk nsSNPs on protein structure and function and highlighted their potential relevance to male infertility. Further studies are warranted to validate these findings and to elucidate the underlying mechanisms.

Catalogue of Somatic Mutations in Cancer Database and Structural Modeling Analysis of CYP2D6 Mutations in Human Cancers

Single nucleotide polymorphisms (SNPs) in cytochrome P450 (CYP450) enzymes alter the metabolism of a variety of drugs. Numerous medications, including chemotherapies, are metabolized by CYP450 enzymes, making the expression of this suite of enzymes in tumor cells relevant to prescription regimens for patients with cancer. We analyzed the characteristics of mutations of the cytochrome P450 2D6 (CYP2D6) enzymes in cancer patients obtained from the Catalogue of Somatic Mutations in Cancer (COSMIC), including mutation type, age of the patient, tissue type, and histology. Mutations were analyzed through the Cancer-Related Analysis of Variants Toolkit (CRAVAT) software along with cancer-specific high-throughput annotation of somatic mutations (CHASMplus) and variant effect scoring tool (VEST4) algorithms to determine the likelihood of being a driver and/or pathogenic mutation. For mutations with significant CHASMplus and VEST4 scores, structural analysis of each corresponding mutant protein was performed. The effect of each mutation was evaluated for its impact on the overall protein stability and ligand binding using Foldit Standalone and SwissDock, respectively. Structural analysis revealed that several missense mutations in CYP2D6 resulted in altered stability after energy minimization. Three missense mutations of CYP2D6 significantly altered docking stability, and those located on alpha helices near the docking site had a more significant impact than those not found in secondary protein structures. In conclusion, we have identified a series of mutations to CYP2D6 enzymes with possible relevance to cancer pathologies. SIGNIFICANCE STATEMENT: CYP2D6 is responsible for the metabolism of many anticancer drugs. This study identified and characterized a series of mutations in the CYP2D6 enzyme that occurred in tumors. We found it likely that many of these mutations would alter enzyme function, leading to changes in drug metabolism in the tumor. We provide a basis for predicting the likelihood of a patient carrying these mutations to identify patients who may benefit from a precision medicine approach to drug selection and dosing.

Blood-based molecular and cellular biomarkers of early response to neoadjuvant PD-1 blockade in patients with non-small cell lung cancer

BACKGROUND

Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response.

METHODS

Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model.

RESULTS

The gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines.

CONCLUSION

This study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients.

Exploring the Role of Clustered Mutations in Carcinogenesis and Their Potential Clinical Implications in Cancer

Abnormal cell proliferation and growth leading to cancer primarily result from cumulative genome mutations. Single gene mutations alone do not fully explain cancer onset and progression; instead, clustered mutations-simultaneous occurrences of multiple mutations-are considered to be pivotal in cancer development and advancement. These mutations can affect different genes and pathways, resulting in cells undergoing malignant transformation with multiple functional abnormalities. Clustered mutations influence cancer growth rates, metastatic potential, and drug treatment sensitivity. This summary highlights the various types and characteristics of clustered mutations to understand their associations with carcinogenesis and discusses their potential clinical significance in cancer. As a unique mutation type, clustered mutations may involve genomic instability, DNA repair mechanism defects, and environmental exposures, potentially correlating with responsiveness to immunotherapy. Understanding the characteristics and underlying processes of clustered mutations enhances our comprehension of carcinogenesis and cancer progression, providing new diagnostic and therapeutic approaches for cancer.

Genome-wide analyses reveals an association between invasive urothelial carcinoma in the Shetland sheepdog and NIPAL1

Naturally occurring canine invasive urinary carcinoma (iUC) closely resembles human muscle invasive bladder cancer in terms of histopathology, metastases, response to therapy, and low survival rate. The heterogeneous nature of the disease has led to the association of large numbers of risk loci in humans, however most are of small effect. There exists a need for new and accurate animal models of invasive bladder cancer. In dogs, distinct breeds show markedly different rates of iUC, thus presenting an opportunity to identify additional risk factors and overcome the locus heterogeneity encountered in human mapping studies. In the association study presented here, inclusive of 100 Shetland sheepdogs and 58 dogs of other breeds, we identify a homozygous protein altering point mutation within the NIPAL1 gene which increases risk by eight-fold (OR = 8.42, CI = 3.12-22.71), accounting for nearly 30% of iUC risk in the Shetland sheepdog. Inclusion of six additional loci accounts for most of the disease risk in the breed and explains nearly 75% of the phenotypes in this study. When combined with sequence data from tumors, we show that variation in the MAPK signaling pathway is an overarching cause of iUC susceptibility in dogs.

Changes in antioxidant status and DNA repair capacity are corroborated with molecular alterations in malignant thyroid tissue of patients with papillary thyroid cancer

Introduction: Papillary thyroid cancer (PTC) accounts for approximately 80% of all thyroid cancer cases. The mechanism of PTC tumourigenesis is not fully understood, but oxidative imbalance is thought to play a role. To gain further insight, this study evaluated antioxidant status, DNA repair capacity and genetic alterations in individuals diagnosed with benign thyroid lesion in one lobe (BTG) and PTC lesion in another. Methods: Individuals with coexisting BTG and PTC lesions in their thyroid lobes were included in this study. Reactive oxygen species (ROS) level, ABTS radical scavenging activity, ferric reducing antioxidant capacity, glutathione peroxidase and superoxide dismutase activities were measured in the thyroid tissue lysate. The expression of selected genes and proteins associated with oxidative stress defence and DNA repair were analysed through quantitative real-time PCR and Western blotting. Molecular alterations in genomic DNA were analysed through whole-exome sequencing and the potentially pathogenic driver genes filtered through Cancer-Related Analysis of Variants Toolkit (CRAVAT) analysis were subjected to pathway enrichment analysis using Metascape. Results: Significantly higher ROS level was detected in the PTC compared to the BTG lesions. The PTC lesions had significantly higher expression of GPX1, SOD2 and OGG1 but significantly lower expression of CAT and PRDX1 genes than the BTG lesions. Pathway enrichment analysis identified "regulation of MAPK cascade," "positive regulation of ERK1 and ERK2 cascade" and "negative regulation of reactive oxygen species metabolic process" to be significantly enriched in the PTC lesions only. Four pathogenic genetic variants were identified in the PTC lesions; BRAF V600E, MAP2K7-rs2145142862, BCR-rs372013175 and CD24 NM_001291737.1:p.Gln23fs while MAP3K9 and G6PD were among 11 genes that were mutated in both BTG and PTC lesions. Conclusion: Our findings provided further insight into the connection between oxidative stress, DNA damage, and genetic changes associated with BTG-to-PTC transformation. The increased oxidative DNA damage due to the heightened ROS levels could have heralded the BTG-to-PTC transformation, potentially through mutations in the genes involved in the MAPK signalling pathway and stress-activated MAPK/JNK cascade. Further in-vitro functional analyses and studies involving a larger sample size would need to be carried out to validate the findings from this pilot study.

Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data

Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.

Oncogenic signaling pathways and hallmarks of cancer in Korean patients with acral melanoma

Acral melanoma (AM), a rare subtype of cutaneous melanoma, shows higher incidence in Asians, including Koreans, than in Caucasians. However, the genetic modification associated with AM in Koreans is not well known and has not been comprehensively investigated in terms of oncogenic signaling, and hallmarks of cancer. We performed whole-exome and RNA sequencing for Korean patients with AM and acquired the genetic alterations and gene expression profiles. KIT alterations (previously known to be recurrent alterations in AM) and CDK4/CCND1 copy number amplifications were identified in the patients. Genetic and transcriptomic alterations in patients with AM were functionally converge to the hallmarks of cancer and oncogenic pathways, including 'proliferative signal persistence', 'apoptotic resistance', and 'activation of invasion and metastasis', despite the heterogeneous somatic mutation profiles of Korean patients with AM. This study may provide a molecular understanding for therapeutic strategy for AM.

Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment

We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study.

We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC).

At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4-4.5) or long-term (median 29 months; range 23.5-41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders.

IMPLICATIONS

Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.

The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma

Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software.

Pan-cancer analysis of co-occurring mutations in RAD52 and the BRCA1-BRCA2-PALB2 axis in human cancers

In human cells homologous recombination (HR) is critical for repair of DNA double strand breaks (DSBs) and rescue of stalled or collapsed replication forks. HR is facilitated by RAD51 which is loaded onto DNA by either BRCA2-BRCA1-PALB2 or RAD52. In human culture cells, double-knockdowns of RAD52 and genes in the BRCA1-BRCA2-PALB2 axis are lethal. Mutations in BRCA2, BRCA1 or PALB2 significantly impairs error free HR as RAD51 loading relies on RAD52 which is not as proficient as BRCA2-BRCA1-PALB2. RAD52 also facilitates Single Strand Annealing (SSA) that produces intra-chromosomal deletions. Some RAD52 mutations that affect the SSA function or decrease RAD52 association with DNA can suppress certain BRCA2 associated phenotypes in breast cancers. In this report we did a pan-cancer analysis using data reported on the Catalogue of Somatic Mutations in Cancers (COSMIC) to identify double mutants between RAD52 and BRCA1, BRCA2 or PALB2 that occur in cancer cells. We find that co-occurring mutations are likely in certain cancer tissues but not others. However, all mutations occur in a heterozygous state. Further, using computational and machine learning tools we identified only a handful of pathogenic or driver mutations predicted to significantly affect the function of the proteins. This supports previous findings that co-inactivation of RAD52 with any members of the BRCA2-BRCA1-PALB2 axis is lethal. Molecular modeling also revealed that pathogenic RAD52 mutations co-occurring with mutations in BRCA2-BRCA1-PALB2 axis are either expected to attenuate its SSA function or its interaction with DNA. This study extends previous breast cancer findings to other cancer types and shows that co-occurring mutations likely destabilize HR by similar mechanisms as in breast cancers.

VIVID: a web application for variant interpretation and visualisation in multidimensional analyses

Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form of DNA variants. Ultimately, the goal of many of these studies is to associate genetic variants to phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range of approaches for encoding genotypic to phenotypic information in any organism or disease, from an individual or population, in three-dimensional (3D) space. It allows mutation mapping and annotation, calculation of interactions and conservation scores, prediction of harmful effects, analysis of diversity and selection, and 3D visualization of genotypic information encoded in Variant Call Format on AlphaFold2 protein models. VIVID enables the rapid assessment of genes of interest in the study of adaptive evolution and the genetic load, and it helps prioritizing targets for experimental validation. We demonstrate the utility of VIVID by exploring the evolutionary genetics of the parasitic protist Plasmodium falciparum, revealing geographic variation in the signature of balancing selection in potential targets of functional antibodies.

Comparison of the mutational profiles of neuroendocrine breast tumours, invasive ductal carcinomas and pancreatic neuroendocrine carcinomas

The pathophysiology and the optimal treatment of breast neuroendocrine tumours (NETs) are unknown. We compared the mutational profiles of breast NETs (n = 53) with those of 724 publicly available invasive ductal carcinoma (IDC) and 98 pancreatic NET (PNET) cases. The only significantly different pathogenetic or unknown variant rate between breast NETs and IDCs was detected in the TP53 (11.3% in breast NETs and 41% in IDCs, adjusted p value 0.027) and ADCK2 (9.4% in breast NETs vs. 0.28% in IDCs, adjusted p value 0.045) genes. Between breast NETs and PNETs, different pathogenetic or unknown variant frequencies were detected in 30 genes. For example, MEN1 was mutated in only 6% of breast NETs and 37% in PNETs (adjusted p value 0.00050), and GATA3 pathogenetic or unknown variants were only found in 17.0% of breast NETs and 0% in PNETs (adjusted p value 0.0010). The most commonly affected oncogenic pathways in the breast NET cases were PI3K/Akt/mTOR, NOTCH and RTK-RAS pathways. Breast NETs had typically clock-like mutational signatures and signatures associated with defective DNA mismatch repair in their mutational landscape. Our results suggest that the breast NET mutational profile more closely resembles that of IDCs than that of PNETs. These results also revealed several potentially druggable targets, such as MMRd, in breast NETs. In conclusion, breast NETs are indeed a separate breast cancer entity, but their optimal treatment remains to be elucidated.

Discovery of pan-cancer related genes via integrative network analysis

Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method's potential for application in identifying driver gene candidates for further biological experimental verification.

Tracking Clonal Evolution of Multiple Myeloma Using Targeted Next-Generation DNA Sequencing

Clonal evolution drives treatment failure in multiple myeloma (MM). Here, we used a custom 372-gene panel to track genetic changes occurring during MM progression at different stages of the disease. A tumor-only targeted next-generation DNA sequencing was performed on 69 samples sequentially collected from 30 MM patients. The MAPK/ERK pathway was mostly affected with KRAS mutated in 47% of patients. Acquisition and loss of mutations were observed in 63% and 37% of patients, respectively. Four different patterns of mutation evolution were found: branching-, mutation acquisition-, mutation loss- and a stable mutational pathway. Better response to anti-myeloma therapy was more frequently observed in patients who followed the mutation loss-compared to the mutation acquisition pathway. More than two-thirds of patients had druggable genes mutated (including cases of heavily pre-treated disease). Only 7% of patients had a stable copy number variants profile. Consequently, a redistribution in stages according to R-ISS between the first and paired samples (R-ISS″) was seen. The higher the R-ISS″, the higher the risk of MM progression and death. We provided new insights into the genetics of MM evolution, especially in heavily pre-treated patients. Additionally, we confirmed that redefining R-ISS at MM relapse is of high clinical value.

An isogenic cell line panel for sequence-based screening of targeted anticancer drugs

We describe the creation of an isogenic cell line panel representing common cancer pathways, with features optimized for high-throughput screening. More than 1,800 cell lines from three normal human cell lines were generated using CRISPR technologies. Surprisingly, most of these lines did not result in complete gene inactivation despite integration of sgRNA at the desired genomic site. A subset of the lines harbored biallelic disruptions of the targeted tumor suppressor gene, yielding a final panel of 100 well-characterized lines covering 19 frequently lost cancer pathways. This panel included genetic markers optimized for sequence-based ratiometric assays for drug-based screening assays. To illustrate the potential utility of this panel, we developed a high-throughput screen that identified Wee1 inhibitor MK-1775 as a selective growth inhibitor of cells with inactivation of TP53. These cell lines and screening approach should prove useful for researchers studying a variety of cellular and biochemical phenomena.

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Creation of an isogenic cell line panel representing the loss of 19 cancer pathways

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HTS confirmed MK-1775 as a selective inhibitor of cells with loss of TP53

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These cell lines are useful for studying a variety of cellular biochemical phenomena

Evidence for reduced BRCA2 functional activity in Homo sapiens after divergence from the chimpanzee-human last common ancestor

We performed a comparative analysis of human and 12 non-human primates to identify sequence variations in known cancer genes. We identified 395 human-specific fixed non-silent substitutions that emerged during evolution of human. Using bioinformatics analyses for functional consequences, we identified a number of substitutions that are predicted to alter protein function; one of these mutations is located at the most evolutionarily conserved domain of human BRCA2.

Genome Nexus: A Comprehensive Resource for the Annotation and Interpretation of Genomic Variants in Cancer

PURPOSE

Interpretation of genomic variants in tumor samples still presents a challenge in research and the clinical setting. A major issue is that information for variant interpretation is fragmented across disparate databases, and aggregation of information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one-stop shop for variant annotation with a user-friendly interface for cancer researchers and clinicians.

METHODS

Genome Nexus (1) aggregates variant information from sources that are relevant to cancer research and clinical applications, (2) allows high-performance programmatic access to the aggregated data via a unified application programming interface, (3) provides a reference page for individual cancer variants, (4) provides user-friendly tools for annotating variants in patients, and (5) is freely available under an open source license and can be installed in a private cloud or local environment and integrated with local institutional resources.

RESULTS

Genome Nexus is available at https://www.genomenexus.org. It displays annotations from more than a dozen resources including those that provide variant effect information (variant effect predictor), protein sequence annotation (Uniprot, Pfam, and dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, and SIFT), population prevalences (gnomAD, dbSNP, and ExAC), cancer population prevalences (Cancer hotspots and SignalDB), and clinical actionability (OncoKB, CIViC, and ClinVar). We describe several use cases that demonstrate the utility of Genome Nexus to clinicians, researchers, and bioinformaticians. We cover single-variant annotation, cohort analysis, and programmatic use of the application programming interface. Genome Nexus is unique in providing a user-friendly interface specific to cancer that allows high-performance annotation of any variant including unknown ones.

CONCLUSION

Interpretation of cancer genomic variants is improved tremendously by having an integrated resource for annotations. Genome Nexus is freely available under an open source license.

Identification of driver genes based on gene mutational effects and network centrality

BACKGROUND

As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments.

RESULTS

To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality.

CONCLUSIONS

Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

A Genome-Wide Profiling of Glioma Patients with an IDH1 Mutation Using the Catalogue of Somatic Mutations in Cancer Database

Simple Summary

Glioma patients that present a somatic mutation in the isocitrate dehydrogenase 1 (IDH1) gene have a significantly better prognosis and overall survival than patients with the wild-type genotype. An IDH1 mutation is hypothesized to occur early during cellular transformation and leads to further genetic instability. A genome-wide profiling of glioma patients in the Catalogue of Somatic Mutations in Cancer (COSMIC) database was performed to classify the genetic differences in IDH1-mutant versus IDH1-wildtype patients. This classification will aid in a better understanding of how this specific mutation influences the genetic make-up of glioma and the resulting prognosis. Key differences in co-mutation and gene expression levels were identified that correlate with an improved prognosis.

Abstract

Gliomas are differentiated into two major disease subtypes, astrocytoma or oligodendroglioma, which are then characterized as either IDH (isocitrate dehydrogenase)-wild type or IDH-mutant due to the dramatic differences in prognosis and overall survival. Here, we investigated the genetic background of IDH1-mutant gliomas using the Catalogue of Somatic Mutations in Cancer (COSMIC) database. In astrocytoma patients, we found that IDH1 is often co-mutated with TP53, ATRX, AMBRA1, PREX1, and NOTCH1, but not CHEK2, EGFR, PTEN, or the zinc finger transcription factor ZNF429. The majority of the mutations observed in these genes were further confirmed to be either drivers or pathogenic by the Cancer-Related Analysis of Variants Toolkit (CRAVAT). Gene expression analysis showed down-regulation of DRG2 and MSN expression, both of which promote cell proliferation and invasion. There was also significant over-expression of genes such as NDRG3 and KCNB1 in IDH1-mutant astrocytoma patients. We conclude that IDH1-mutant glioma is characterized by significant genetic changes that could contribute to a better prognosis in glioma patients.

Comprehensive Genetic Analysis of DGAT2 Mutations and Gene Expression Patterns in Human Cancers

DGAT2 is a transmembrane protein encoded by the DGAT2 gene that functions in lipid metabolism, triacylglycerol synthesis, and lipid droplet regulation. Cancer cells exhibit altered lipid metabolism and mutations in DGAT2 may contribute to this state. Using data from the Catalogue of Somatic Mutations in Cancer (COSMIC), we analyzed all cancer genetic DGAT2 alterations, including mutations, copy number variations and gene expression. We find that several DGAT2 mutations fall within the catalytic site of the enzyme. Using the Variant Effect Scoring Tool (VEST), we identify multiple mutations with a high likelihood of contributing to cellular transformation. We also found that D222V is a mutation hotspot neighboring a previously discovered Y223H mutation that causes Axonal Charcot-Marie-Tooth disease. Remarkably, Y223H has not been detected in cancers, suggesting that it is inhibitory to cancer progression. We also identify several single nucleotide polymorphisms (SNP) with high VEST scores, indicating that certain alleles in human populations have a pathogenic predisposition. Most mutations do not correlate with a change in gene expression, nor is gene expression dependent on high allele copy number. However, we did identify eight alleles with high expression levels, suggesting that at least in certain cases, the excess DGAT2 gene product is not inhibitory to cellular proliferation. This work uncovers unknown functions of DGAT2 in cancers and suggests that its role may be more complex than previously appreciated.

Myopia in African Americans Is Significantly Linked to Chromosome 7p15.2-14.2

Purpose

The purpose of this study was to perform genetic linkage analysis and association analysis on exome genotyping from highly aggregated African American families with nonpathogenic myopia. African Americans are a particularly understudied population with respect to myopia.

Methods

One hundred six African American families from the Philadelphia area with a family history of myopia were genotyped using an Illumina ExomePlus array and merged with previous microsatellite data. Myopia was initially measured in mean spherical equivalent (MSE) and converted to a binary phenotype where individuals were identified as affected, unaffected, or unknown. Parametric linkage analysis was performed on both individual variants (single-nucleotide polymorphisms [SNPs] and microsatellites) as well as gene-based markers. Family-based association analysis and transmission disequilibrium test (TDT) analysis modified for rare variants was also performed.

Results

Genetic linkage analysis identified 2 genomewide significant variants at 7p15.2 and 7p14.2 (in the intergenic region between MIR148A and NFE2L3 and in the noncoding RNA LOC401324) and 2 genomewide significant genes (CRHR2 and AVL9) both at 7p14.3. No genomewide results were found in the association analyses.

Conclusions

This study identified a significant linkage peak in African American families for myopia at 7p15.2 to 7p14.2, the first potential risk locus for myopia in African Americans. Interesting candidate genes are located in the region, including PDE1C, which is highly expressed in the eyes, and known to be involved in retinal development. Further identification of the causal variants at this linkage peak will help elucidate the genetics of myopia in this understudied population.

OncoGEMINI: software for investigating tumor variants from multiple biopsies with integrated cancer annotations

Background

DNA sequencing has unveiled extensive tumor heterogeneity in several different cancer types, with many exhibiting diverse subclonal populations. Identifying and tracing mutations throughout the expansion and progression of a tumor represents a significant challenge. Furthermore, prioritizing the subset of such mutations most likely to contribute to tumor evolution or that could serve as potential therapeutic targets represents an ongoing problem.

Results

Here, we describe OncoGEMINI, a new tool designed for exploring the complex patterns and trajectory of somatic and inherited variation observed in heterogeneous tumors biopsied over the course of treatment. This is accomplished by creating a searchable database of variants that includes tumor sampling time points and allows for filtering methods that reflect specific changes in variant allele frequencies over time. Additionally, by incorporating existing annotations and resources that facilitate the interpretation of cancer mutations (e.g., CIViC, DGIdb), OncoGEMINI enables rapid searches for, and potential identification of, mutations that may be driving subclonal evolution.

Conclusions

By combining relevant genomic annotations alongside specific filtering tools, OncoGEMINI provides powerful and customizable approaches that enable the quick identification of individual tumor variants that meet specified criteria. It can be applied to a wide range of tumor-derived sequence data, but is especially designed for studies with multiple samples, including longitudinal datasets. It is available under an MIT license at github.com/fakedrtom/oncogemini.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00854-6.

In silico screening and analysis of nonsynonymous SNPs in human CYP1A2 to assess possible associations with pathogenicity and cancer susceptibility

Cytochrome P450 1A2 (CYP1A2) is one of the main hepatic CYPs involved in metabolism of carcinogens and clinically used drugs. Nonsynonymous single nucleotide polymorphisms (nsSNPs) of this enzyme could affect cancer susceptibility and drug efficiency. Hence, identification of human CYP1A2 pathogenic nsSNPs could be of great importance in personalized medicine and pharmacogenetics. Here, 176 nsSNPs of human CYP1A2 were evaluated using a variety of computational tools, of which 18 nsSNPs were found to be associated with pathogenicity. Further analysis suggested possible association of 9 nsSNPs (G73R, G73W, R108Q, R108W, E168K, E346K, R431W, F432S and R456H) with the risk of hepatocellular carcinoma. Molecular dynamics simulations revealed higher overall flexibility, decreased intramolecular hydrogen bonds and lower content of regular secondary structures for both cancer driver variants G73W and F432S when compared to the wild-type structure. In case of F432S, loss of the conserved hydrogen bond between Arg137 and heme propionate oxygen may affect heme stability and the observed significant rise in fluctuation of the CD loop could modify CYP1A2 interactions with its redox partners. Together, these findings propose CYP1A2 as a possible candidate for hepatocellular carcinoma and provide structural insights into how cancer driver nsSNPs could affect protein structure, heme stability and interaction network.

Current cancer driver variant predictors learn to recognize driver genes instead of functional variants

BACKGROUND

Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task.

RESULTS

In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions.

CONCLUSIONS

To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.

Complete genome sequence and bioinformatics analysis of nine Egyptian females with clinical information from different geographic regions in Egypt

Egyptians are at a crossroad between Africa and Eurasia, providing useful genomic resources for analyzing both genetic and environmental factors for future personalized medicine. Two personal Egyptian whole genomes have been published previously by us and here nine female whole genome sequences with clinical information have been added to expand the genomic resource of Egyptian personal genomes. Here we report the analysis of whole genomes of nine Egyptian females from different regions using Illumina short-read sequencers. At 30x sequencing coverage, we identified 12 SNPs that were shared in most of the subjects associated with obesity which are concordant with their clinical diagnosis. Also, we found mtDNA mutation A4282G is common in all the samples and this is associated with chronic progressive external ophthalmoplegia (CPEO). Haplogroup and Admixture analyses revealed that most Egyptian samples are close to the other north Mediterranean, Middle Eastern, and European, respectively, possibly reflecting the into-Africa influx of human migration. In conclusion, we present whole-genome sequences of nine Egyptian females with personal clinical information that cover the diverse regions of Egypt. Although limited in sample size, the whole genomes data provides possible geno-phenotype candidate markers that are relevant to the region's diseases.

Comprehensive Analysis of MEN1 Mutations and Their Role in Cancer

Simple Summary

Cancers are characterized by accumulation of genetic mutations in key cell cycle regulators that alter or disable the function of these genes. Such mutations can be inherited or arise spontaneously during the life of the individual. The MEN1 gene prevents uncontrolled cell division and it is considered a tumor suppressor. Inherited MEN1 mutations are associated with certain parathyroid and pancreatic syndromes while spontaneous mutations have been detected in cancer cells. We investigated whether inherited mutations appear in cancer cells which would suggest that patients with parathyroid and pancreatic syndromes have a predisposition to develop cancer. We find a weak correlation between the spectrum of inherited mutations and those appearing spontaneously. Thus, inherited MEN1 mutations may not be a good predictor of tumorigenesis.

Abstract

MENIN is a scaffold protein encoded by the MEN1 gene that functions in multiple biological processes, including cell proliferation, migration, gene expression, and DNA damage repair. MEN1 is a tumor suppressor gene, and mutations that disrupts MEN1 function are common to many tumor types. Mutations within MEN1 may also be inherited (germline). Many of these inherited mutations are associated with a number of pathogenic syndromes of the parathyroid and pancreas, and some also predispose patients to hyperplasia. In this study, we cataloged the reported germline mutations from the ClinVar database and compared them with the somatic mutations detected in cancers from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. We then used statistical software to determine the probability of mutations being pathogenic or driver. Our data show that many confirmed germline mutations do not appear in tumor samples. Thus, most mutations that disable MEN1 function in tumors are somatic in nature. Furthermore, of the germline mutations that do appear in tumors, only a fraction has the potential to be pathogenic or driver mutations.

RAS internal tandem duplication disrupts GTPase-activating protein (GAP) binding to activate oncogenic signaling

The oncogene RAS is one of the most widely studied proteins in cancer biology, and mutant active RAS is a driver in many types of solid tumors and hematological malignancies. Yet the biological effects of different RAS mutations and the tissue-specific clinical implications are complex and nuanced. Here, we identified an internal tandem duplication (ITD) in the switch II domain of NRAS from a patient with extremely aggressive colorectal carcinoma. Results of whole-exome DNA sequencing of primary and metastatic tumors indicated that this mutation was present in all analyzed metastases and excluded the presence of any other clear oncogenic driver mutations. Biochemical analysis revealed increased interaction of the RAS ITD with Raf proto-oncogene Ser/Thr kinase (RAF), leading to increased phosphorylation of downstream MAPK/ERK kinase (MEK)/extracellular signal-regulated kinase (ERK). The ITD prevented interaction with neurofibromin 1 (NF1)-GTPase-activating protein (GAP), providing a mechanism for sustained activity of the RAS ITD protein. We present the first crystal structures of NRAS and KRAS ITD at 1.65-1.75 Å resolution, respectively, providing insight into the physical interactions of this class of RAS variants with its regulatory and effector proteins. Our in-depth bedside-to-bench analysis uncovers the molecular mechanism underlying a case of highly aggressive colorectal cancer and illustrates the importance of robust biochemical and biophysical approaches in the implementation of individualized medicine.

Differences between Well-Differentiated Neuroendocrine Tumors and Ductal Adenocarcinomas of the Pancreas Assessed by Multi-Omics Profiling

Most pancreatic neuroendocrine tumors (PNETs) are indolent, while pancreatic ductal adenocarcinomas (PDACs) are particularly aggressive. To elucidate the basis for this difference and to establish the biomarkers, by using the deep sequencing, we analyzed somatic variants across coding regions of 409 cancer genes and measured mRNA/miRNA expression in nine PNETs, eight PDACs, and four intestinal neuroendocrine tumors (INETs). There were 153 unique somatic variants considered pathogenic or likely pathogenic, found in 50, 57, and 24 genes in PDACs, PNETs, and INETs, respectively. Ten and 11 genes contained a pathogenic mutation in at least one sample of all tumor types and in PDACs and PNETs, respectively, while 28, 34, and 11 genes were found to be mutated exclusively in PDACs, PNETs, and INETs, respectively. The mRNA and miRNA transcriptomes of PDACs and NETs were distinct: from 54 to 1659 differentially expressed mRNAs and from 117 to 250 differentially expressed miRNAs exhibited high discrimination ability and resulted in models with an area under the receiver operating characteristics curve (AUC-ROC) >0.9 for both miRNA and mRNA. Given the miRNAs high stability, we proposed exploring that class of RNA as new pancreatic tumor biomarkers.

funtrp: identifying protein positions for variation driven functional tuning

Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. An in-depth understanding of sequence changes is also fundamental for synthetic protein design and stability assessments. However, the variant effect predictor performance gain observed in recent years has not kept up with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene and protein features for modeling variant effects, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the variation observable in vivo is arguably weaker in its impact, thus requiring evaluation at a higher level of resolution. Here, we describe functionNeutral/Toggle/Rheostatpredictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types can improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

Comprehensive assessment of computational algorithms in predicting cancer driver mutations

BACKGROUND

The initiation and subsequent evolution of cancer are largely driven by a relatively small number of somatic mutations with critical functional impacts, so-called driver mutations. Identifying driver mutations in a patient's tumor cells is a central task in the era of precision cancer medicine. Over the decade, many computational algorithms have been developed to predict the effects of missense single-nucleotide variants, and they are frequently employed to prioritize mutation candidates. These algorithms employ diverse molecular features to build predictive models, and while some algorithms are cancer-specific, others are not. However, the relative performance of these algorithms has not been rigorously assessed.

RESULTS

We construct five complementary benchmark datasets: mutation clustering patterns in the protein 3D structures, literature annotation based on OncoKB, TP53 mutations based on their effects on target-gene transactivation, effects of cancer mutations on tumor formation in xenograft experiments, and functional annotation based on in vitro cell viability assays we developed including a new dataset of ~ 200 mutations. We evaluate the performance of 33 algorithms and found that CHASM, CTAT-cancer, DEOGEN2, and PrimateAI show consistently better performance than the other algorithms. Moreover, cancer-specific algorithms show much better performance than those designed for a general purpose.

CONCLUSIONS

Our study is a comprehensive assessment of the performance of different algorithms in predicting cancer driver mutations and provides deep insights into the best practice of computationally prioritizing cancer mutation candidates for end-users and for the future development of new algorithms.

Exome sequencing identifies de novo splicing variant in XRCC6 in sporadic case of autism

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with heterogeneity in presentation, genetic etiology, and clinical outcome. Although numerous ASD susceptibility genes have been described, they only account for a small fraction of the estimated heritability, supporting the need to identify more risk variants. This study reports the whole exome sequencing for 24 simplex families with sporadic cases of ASD. These families were selected following a rigorous family history study designed to exclude families with any history of neurodevelopmental or psychiatric disease. Fifteen rare, de novo variants, including fourteen missense variants and one splicing variant, in thirteen families were identified. We describe a splicing variant in XRCC6 which was predicted to destroy the 5' splice site in intron 9 and introduce a premature stop codon. We observed intron 9 retention in XRCC6 transcripts and reduced XRCC6 expression in the proband. Reduced XRCC6 activity and function may be relevant to ASD etiology due to XRCC6's role in nonhomologous DNA repair and interactions of the C-terminal SAP domain with DEAF1, a nuclear transcriptional regulator that is important during embryonic development.

Molecular characterization of lung adenocarcinoma from Korean patients using next generation sequencing

The treatment of Lung adenocarcinoma (LUAD) could benefit from the incorporation of precision medicine. This study was to identify cancer-related genetic alterations by next generation sequencing (NGS) in resected LUAD samples from Korean patients and to determine their associations with clinical features. A total of 201 tumors and their matched peripheral blood samples were analyzed using targeted sequencing via the Illumina HiSeq 2500 platform of 242 genes with a median depth of coverage greater than 500X. One hundred ninety-two tumors were amenable to data analysis. EGFR was the most frequently mutated gene, occurring in 106 (55%) patients, followed by TP53 (n = 67, 35%) and KRAS (n = 11, 6%). EGFR mutations were strongly increased in patients that were female and never-smokers. Smokers had a significantly higher tumor mutational burden (TMB) than never-smokers (average 4.84 non-synonymous mutations/megabase [mt/Mb] vs. 2.84 mt/Mb, p = 0.019). Somatic mutations of APC, CTNNB1, and AMER1 in the WNT signaling pathway were highly associated with shortened disease-free survival (DFS) compared to others (median DFS of 89 vs. 27 months, p = 0.018). Patients with low TMB, annotated as less than 2 mt/Mb, had longer DFS than those with high TMB (p = 0.041). A higher frequency of EGFR mutations and a lower of KRAS mutations were observed in Korean LUAD patients. Profiles of 242 genes mapped in this study were compared with whole exome sequencing genetic profiles generated in The Cancer Genome Atlas Lung Adenocarcinoma. NGS-based diagnostics can provide clinically relevant information such as mutations or TMB from readily available formalin-fixed paraffin-embedded tissue.

Mutation Profiling of Premalignant Colorectal Neoplasia

Accumulation of allelic variants in genes that regulate cellular proliferation, differentiation, and apoptosis may result in expansion of the aberrant intestinal epithelium, generating adenomas. Herein, we compared the mutation profiles of conventional colorectal adenomas (CNADs) across stages of progression towards early carcinoma. DNA was isolated from 17 invasive adenocarcinomas (ACs) and 58 large CNADs, including 19 with low-grade dysplasia (LGD), 21 with LGD adjacent to areas of high-grade dysplasia and/or carcinoma (LGD-H), and 28 with high-grade dysplasia (HGD). Ion AmpliSeq Comprehensive Cancer Panel libraries were prepared and sequenced on the Ion Proton. We identified 956 unique allelic variants; of these, 499 were considered nonsynonymous variants. Eleven genes (APC, KRAS, SYNE1, NOTCH4, BLNK, FBXW7, GNAS, KMT2D, TAF1L, TCF7L2, and TP53) were mutated in at least 15% of all samples. Out of frequently mutated genes, TP53 and BCL2 had a consistent trend in mutation prevalence towards malignancy, while two other genes (HNF1A and FBXW7) exhibited the opposite trend. HGD adenomas had significantly higher mutation rates than LGD adenomas, while LGD-H adenomas exhibited mutation frequencies similar to those of LGD adenomas. A significant increase in copy number variant frequency was observed from LGD through HGD to malignant samples. The profiling of advanced CNADs demonstrated variations in mutation patterns among colorectal premalignancies. Only limited numbers of genes were repeatedly mutated while the majority were altered in single cases. Most genetic alterations in adenomas can be considered early contributors to colorectal carcinogenesis.

Multi-Omics Characterization of the Spontaneous Mesenchymal-Epithelial Transition in the PMC42 Breast Cancer Cell Lines

Epithelial-mesenchymal plasticity (EMP), encompassing epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET), are considered critical events for cancer metastasis. We investigated chromosomal heterogeneity and chromosomal instability (CIN) profiles of two sister PMC42 breast cancer (BC) cell lines to assess the relationship between their karyotypes and EMP phenotypic plasticity. Karyotyping by GTG banding and exome sequencing were aligned with SWATH quantitative proteomics and existing RNA-sequencing data from the two PMC42 cell lines; the mesenchymal, parental PMC42-ET cell line and the spontaneously epithelially shifted PMC42-LA daughter cell line. These morphologically distinct PMC42 cell lines were also compared with five other BC cell lines (MDA-MB-231, SUM-159, T47D, MCF-7 and MDA-MB-468) for their expression of EMP and cell surface markers, and stemness and metabolic profiles. The findings suggest that the epithelially shifted cell line has a significantly altered ploidy of chromosomes 3 and 13, which is reflected in their transcriptomic and proteomic expression profiles. Loss of the TGFβR2 gene from chromosome 3 in the epithelial daughter cell line inhibits its EMT induction by TGF-β stimulus. Thus, integrative 'omics' characterization established that the PMC42 system is a relevant MET model and provides insights into the regulation of phenotypic plasticity in breast cancer.

Tumor heterogeneity and acquired drug resistance in FGFR2-fusion-positive cholangiocarcinoma through rapid research autopsy

Cholangiocarcinoma is a highly aggressive and lethal malignancy, with limited treatment options available. Recently, FGFR inhibitors have been developed and utilized in FGFR-mutant cholangiocarcinoma; however, resistance often develops and the genomic determinants of resistance are not fully characterized. We completed whole-exome sequencing (WES) of 11 unique tumor samples obtained from a rapid research autopsy on a patient with FGFR-fusion-positive cholangiocarcinoma who initially responded to the pan-FGFR inhibitor, INCB054828. In vitro studies were carried out to characterize the novel FGFR alteration and secondary FGFR2 mutation identified. Multisite WES and analysis of tumor heterogeneity through subclonal inference identified four genetically distinct cancer cell populations, two of which were only observed after treatment. Additionally, WES revealed an FGFR2 N549H mutation hypothesized to confer resistance to the FGFR inhibitor INCB054828 in a single tumor sample. This hypothesis was corroborated with in vitro cell-based studies in which cells expressing FGFR2-CLIP1 fusion were sensitive to INCB054828 (IC50 value of 10.16 nM), whereas cells with the addition of the N549H mutation were resistant to INCB054828 (IC50 value of 1527.57 nM). Furthermore, the FGFR2 N549H secondary mutation displayed cross-resistance to other selective FGFR inhibitors, but remained sensitive to the nonselective inhibitor, ponatinib. Rapid research autopsy has the potential to provide unprecedented insights into the clonal evolution of cancer throughout the course of the disease. In this study, we demonstrate the emergence of a drug resistance mutation and characterize the evolution of tumor subclones within a cholangiocarcinoma disease course.

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models.

ONCOGENE PANEL SEQUENCING ANALYSIS IDENTIFIES CANDIDATE ACTIONABLE GENES IN ADVANCED WELL-DIFFERENTIATED GASTROENTEROPANCREATIC NEUROENDOCRINE TUMORS

Objective: To report the rate of candidate actionable somatic mutations in patients with locally advanced and metastatic gastro-enteropancreatic (GEP) neuroendocrine tumors (NET) and of other genetic alterations that may be associated with tumorigenesis. Methods: A phase II mutation targeted therapy trial was conducted in patients with advanced well-differentiated G1/G2 GEP-NET. Mutations found in the mTOR pathway-associated genes led to treatment with the mTOR inhibitor everolimus, and were defined as actionable. Tumor deoxyribonucleic acid (DNA) from GEP-NET were sequenced and compared with germline DNA, using the OncoVAR-NET assay, designed for hybrid capture sequencing of 500 tumor suppressor genes and oncogenes. Somatic variants were called and copy-number (CN) variant analysis was performed. Results: Thirty patients (14 small-intestine, 8 pancreatic, 3 unknown primary NET, and 5 of other primary sites) harbored 37 lesions (4 patients had DNA of multiple lesions sequenced). Only 2 patients with sporadic NET (n = 26) had an actionable mutation leading to treatment with everolimus. Driver somatic mutations were detected in 18 of 30 patients (21/37 lesions sequenced). In the remaining samples without a driver mutation, CN alterations were found in 11/16 tumors (10/12 patients), including CN loss of chromosome (Chr) 18 (P<.05), CN gain of Chr 5, and loss of Chr 13. CN losses in Chr 18 were more common in patients without driver mutations detected. Pronounced genetic heterogeneity was detected in patients with multiple lesions sequenced. Conclusion: Genome-wide DNA sequencing may identify candidate actionable genes and lead to the identification of novel target genes for advanced well-differentiated GEP-NET. Abbreviations: Chr = chromosome; CN = copy number; DNA = deoxyribonucleic acid; FDA = Food and Drug Administration; GEP = gastro-enteropancreatic; MEN-1 = multiple endocrine neoplasia syndrome type 1; mTOR = mammalian target of rapamycin; NET = neuroendocrine tumor; PFS = progression-free survival; PNET = pancreatic neuroendocrine tumors; SINET = small-intestine neuroendocrine tumor.

The molecular landscape of the University of Michigan laryngeal squamous cell carcinoma cell line panel

BACKGROUND

Laryngeal squamous cell carcinomas (LSCCs) have a high risk of recurrence and poor prognosis. Patient-derived cancer cell lines remain important preclinical models for advancement of new therapeutic strategies, and comprehensive characterization of these models is vital in the precision medicine era.

METHODS

We performed exome and transcriptome sequencing as well as copy number analysis of a panel of LSCC-derived cell lines that were established at the University of Michigan and are used in laboratories worldwide.

RESULTS

We observed a complex array of alterations consistent with those reported in The Cancer Genome Atlas head and neck squamous cell carcinoma project, including aberrations in PIK3CA, EGFR, CDKN2A, TP53, and NOTCH family and FAT1 genes. A detailed analysis of FAT family genes and associated pathways showed disruptions to these genes in most cell lines.

CONCLUSIONS

The molecular profiles we have generated indicate that as a whole, this panel recapitulates the molecular diversity observed in patients and will serve as useful guides in selecting cell lines for preclinical modeling.

Finding driver mutations in cancer: Elucidating the role of background mutational processes

Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes. This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend. Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction. Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods.

Identifying Driver Interfaces Enriched for Somatic Missense Mutations in Tumors

Human cancers often harbor large numbers of somatic mutations. However, only a small proportion of these mutations are expected to contribute to tumor growth and progression. Therefore, determining causal driver mutations and the genes they target is becoming an important challenge in cancer genomics. Here we describe an approach for mapping somatic mutations onto 3D structures of human proteins in complex to identify "driver interfaces." Our strategy relies on identifying protein-interaction interfaces that are unexpectedly biased toward nonsynonymous mutations, which suggests that these interfaces are subject to positive selection during tumorigenesis, implicating the interacting proteins as candidate drivers.

A review study: Computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases

Non-Synonymous Single-Nucleotide Variants (nsSNVs) and mutations can create a diversity effect on proteins as changing genotype and phenotype, which interrupts its stability. The alterations in the protein stability may cause diseases like cancer. Discovering of nsSNVs and mutations can be a useful tool for diagnosing the disease at a beginning stage. Many studies introduced the various predicting singular and consensus tools that based on different Machine Learning Techniques (MLTs) using diverse datasets. Therefore, we introduce the current comprehensive review of the most popular and recent unique tools that predict pathogenic variations and Meta-tool that merge some of them for enhancing their predictive power. Also, we scanned the several types computational techniques in the state-of-the-art and methods for predicting the effect both of coding and noncoding variants. We then displayed, the protein stability predictors. We offer the details of the most common benchmark database for variations including the main predictive features used by the different methods. Finally, we address the most common fundamental criteria for performance assessment of predictive tools. This review is targeted at bioinformaticians attentive in the characterization of regulatory variants, geneticists, molecular biologists attentive in understanding more about the nature and effective role of such variants from a functional point of views, and clinicians who may hope to learn about variants in human associated with a specific disease and find out what to do next to uncover how they impact on the underlying mechanisms.

The mutational landscape of recurrent versus nonrecurrent human papillomavirus-related oropharyngeal cancer

BACKGROUND

Human papillomavirus-related (HPV-related) oropharyngeal squamous cell carcinomas (OPSCCs) have an excellent response rate to platinum-based chemoradiotherapy. Genomic differences between primary HPV-related OPSCCs that do or do not recur are unknown. Furthermore, it is unclear if HPV-related OPSCCs that recur share a genomic landscape with HPV-negative head and neck cancers (HNCs).

METHODS

We utilized whole exome sequencing to analyze somatic nucleotide (SNVs) and copy number variants (CNVs) among a unique set of 51 primary HPV-related OPSCCs, including 35 that did not recur and 16 that recurred. We evaluated 12 metachronous recurrent OPSCCs (7 with paired primary OPSCCs) and 33 primary HPV-unrelated oral cavity and OPSCCs.

RESULTS

KMT2D was the most frequently mutated gene among primary HPV-related OPSCCs (n = 51; 14%) and among metachronous recurrent OPSCCs (n = 12; 42%). Primary HPV-related OPSCCs that recurred shared a genomic landscape with primary HPV-related OPSCCs that did not recur. However, TSC2, BRIP1, NBN, and NFE2L2 mutations occurred in primary OPSCCs that recurred but not in those that did not recur. Moreover, primary HPV-related OPSCCs that recur harbor features of HPV-unrelated HNCs, notably including MAPK, JAK/STAT, and differentiation signaling pathway aberrations. Metachronous recurrent OPSCCs shared a genomic landscape with HPV-unrelated HNCs, including a high frequency of TP53, CASP8, FAT1, HLA-A, AJUBA, and NSD1 genomic alterations.

CONCLUSION

Overall, primary HPV-related OPSCCs that recur share a genomic landscape with nonrecurrent OPSCCs. Metachronous recurrent OPSCCs share genomic features with HPV-negative HNCs. These data aim to guide future deescalation endeavors and functional experiments.

FUNDING

This study is supported by the American Cancer Society (RSG TBG-123653), funding support for RAH (T32DC00018, Research Training in Otolaryngology, University of Washington), funds to EM from Seattle Translational Tumor Research (Fred Hutchinson Cancer Research Center), and center funds from the Fred Hutchinson Cancer Research Center to EM. UD is supported by the Department of Veterans Affairs, Biomedical Laboratory Research and Development (BLR&D), grant IO1-oo23456, and funds from the Pittsburgh Foundation and PNC Foundation.

CRIMEtoYHU: a new web tool to develop yeast-based functional assays for characterizing cancer-associated missense variants

Evaluation of the functional impact of cancer-associated missense variants is more difficult than for protein-truncating mutations and consequently standard guidelines for the interpretation of sequence variants have been recently proposed. A number of algorithms and software products were developed to predict the impact of cancer-associated missense mutations on protein structure and function. Importantly, direct assessment of the variants using high-throughput functional assays using simple genetic systems can help in speeding up the functional evaluation of newly identified cancer-associated variants. We developed the web tool CRIMEtoYHU (CTY) to help geneticists in the evaluation of the functional impact of cancer-associated missense variants. Humans and the yeast Saccharomyces cerevisiae share thousands of protein-coding genes although they have diverged for a billion years. Therefore, yeast humanization can be helpful in deciphering the functional consequences of human genetic variants found in cancer and give information on the pathogenicity of missense variants. To humanize specific positions within yeast genes, human and yeast genes have to share functional homology. If a mutation in a specific residue is associated with a particular phenotype in humans, a similar substitution in the yeast counterpart may reveal its effect at the organism level. CTY simultaneously finds yeast homologous genes, identifies the corresponding variants and determines the transferability of human variants to yeast counterparts by assigning a reliability score (RS) that may be predictive for the validity of a functional assay. CTY analyzes newly identified mutations or retrieves mutations reported in the COSMIC database, provides information about the functional conservation between yeast and human and shows the mutation distribution in human genes. CTY analyzes also newly found mutations and aborts when no yeast homologue is found. Then, on the basis of the protein domain localization and functional conservation between yeast and human, the selected variants are ranked by the RS. The RS is assigned by an algorithm that computes functional data, type of mutation, chemistry of amino acid substitution and the degree of mutation transferability between human and yeast protein. Mutations giving a positive RS are highly transferable to yeast and, therefore, yeast functional assays will be more predictable. To validate the web application, we have analyzed 8078 cancer-associated variants located in 31 genes that have a yeast homologue. More than 50% of variants are transferable to yeast. Incidentally, 88% of all transferable mutations have a reliability score >0. Moreover, we analyzed by CTY 72 functionally validated missense variants located in yeast genes at positions corresponding to the human cancer-associated variants. All these variants gave a positive RS. To further validate CTY, we analyzed 3949 protein variants (with positive RS) by the predictive algorithm PROVEAN. This analysis shows that yeast-based functional assays will be more predictable for the variants with positive RS. We believe that CTY could be an important resource for the cancer research community by providing information concerning the functional impact of specific mutations, as well as for the design of functional assays useful for decision support in precision medicine.

Somatic mutations in early onset luminal breast cancer

Breast cancer arising in very young patients may be biologically distinct; however, these tumors have been less well studied. We characterized a group of very young patients (≤ 35 years) for BRCA germline mutation and for somatic mutations in luminal (HER2 negative) breast cancer. Thirteen of 79 unselected very young patients were BRCA1/2 germline mutation carriers. Of the non-BRCA tumors, eight with luminal subtype (HER2 negative) were submitted for whole exome sequencing and integrated with 29 luminal samples from the COSMIC database or previous literature for analysis. We identified C to T single nucleotide variants (SNVs) as the most common base-change. A median of six candidate driver genes was mutated by SNVs in each sample and the most frequently mutated genes were PIK3CA, GATA3, TP53 and MAP2K4. Potential cancer drivers affected in the present non-BRCA tumors include GRHL2, PIK3AP1, CACNA1E, SEMA6D, SMURF2, RSBN1 and MTHFD2. Sixteen out of 37 luminal tumors (43%) harbored SNVs in DNA repair genes, such as ATR, BAP1, ERCC6, FANCD2, FANCL, MLH1, MUTYH, PALB2, POLD1, POLE, RAD9A, RAD51 and TP53, and 54% presented pathogenic mutations (frameshift or nonsense) in at least one gene involved in gene transcription. The differential biology of luminal early-age onset breast cancer needs a deeper genomic investigation.