Systematic discovery of mutation-directed neo-protein-protein interactions in cancer.

Comprehensive sequencing of patient tumors reveals genomic mutations across tumor types that enable tumorigenesis and progression. A subset of oncogenic driver mutations results in neomorphic activity where the mutant protein mediates functions not engaged by the parental molecule. Here, we identify prevalent variant-enabled neomorph-protein-protein interactions (neoPPI) with a quantitative high-throughput differential screening (qHT-dS) platform. The coupling of highly sensitive BRET biosensors with miniaturized coexpression in an ultra-HTS format allows large-scale monitoring of the interactions of wild-type and mutant variant counterparts with a library of cancer-associated proteins in live cells. The screening of 17,792 interactions with 2,172,864 data points revealed a landscape of gain of interactions encompassing both oncogenic and tumor suppressor mutations. For example, the recurrent BRAF V600E lesion mediates KEAP1 neoPPI, rewiring a BRAFV600E/KEAP1 signaling axis and creating collateral vulnerability to NQO1 substrates, offering a combination therapeutic strategy. Thus, cancer genomic alterations can create neo-interactions, informing variant-directed therapeutic approaches for precision medicine.

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Evolutionary Pressure against MHC Class II Binding Cancer Mutations.

The anti-cancer immune response against mutated peptides of potential immunological relevance (neoantigens) is primarily attributed to MHC-I-restricted cytotoxic CD8+ T cell responses. MHC-II-restricted CD4+ T cells also drive anti-tumor responses, but their relation to neoantigen selection and tumor evolution has not been systematically studied. Modeling the potential of an individual's MHC-II genotype to present 1,018 driver mutations in 5,942 tumors, we demonstrate that the MHC-II genotype constrains the mutational landscape during tumorigenesis in a manner complementary to MHC-I. Mutations poorly bound to MHC-II are positively selected during tumorigenesis, even more than mutations poorly bound to MHC-I. This emphasizes the importance of CD4+ T cells in anti-tumor immunity. In addition, we observed less inter-patient variation in mutation presentation for MHC-II than for MHC-I. These differences were reflected by age at diagnosis, which was correlated with presentation by MHC-I only. Collectively, our results emphasize the central role of MHC-II presentation in tumor evolution.

Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks.

A comprehensive catalog of cancer driver mutations is essential for understanding tumorigenesis and developing therapies. Exome-sequencing studies have mapped many protein-coding drivers, yet few non-coding drivers are known because genome-wide discovery is challenging. We developed a driver discovery method, ActiveDriverWGS, and analyzed 120,788 cis-regulatory modules (CRMs) across 1,844 whole tumor genomes from the ICGC-TCGA PCAWG project. We found 30 CRMs with enriched SNVs and indels (FDR < 0.05). These frequently mutated regulatory elements (FMREs) were ubiquitously active in human tissues, showed long-range chromatin interactions and mRNA abundance associations with target genes, and were enriched in motif-rewiring mutations and structural variants. Genomic deletion of one FMRE in human cells caused proliferative deficiencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating observations in FMRE-mutated tumors. Pathway analysis revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored landscape of infrequent driver mutations in the non-coding genome.

Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways.

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.

Unified views on variant impact across many diseases.

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Somatic Mosaicism in Biology and Disease.

Contrary to earlier beliefs, every cell in the individual is genetically different due to somatic mutations. Consequently, tissues become a mixture of cells with distinct genomes, a phenomenon termed somatic mosaicism. Recent advances in genome sequencing technology have unveiled possible causes of mutations and how they shape the unique mutational landscape of the tissues. Moreover, the analysis of sequencing data in combination with clinical information has revealed the impacts of somatic mosaicism on disease processes. In this review, we discuss somatic mosaicism in various tissues and its clinical implications for human disease.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight.

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

An evolution-based machine learning to identify cancer type-specific driver mutations.

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Molecular profile of driver genes in lung adenocarcinomas of Brazilian patients who have never smoked: implications for targeted therapies.

INTRODUCTION: Lung cancer in never-smoker (LCINS) patients accounts for 20% of lung cancer cases, and its biology remains poorly understood, particularly in genetically admixed populations. We elucidated the molecular profile of driver genes in Brazilian LCINS. METHODS: The mutational and gene fusion status of 119 lung adenocarcinomas from self-reported never-smoker patients, was assessed using targeted sequencing (NGS), nCounter, and immunohistochemistry. A panel of 46 ancestry-informative markers determined patients' genetic ancestry. RESULTS: The most frequently mutated gene was EGFR (49.6%), followed by TP53 (39.5%), ALK (12.6%), ERBB2 (7.6%), KRAS (5.9%), PIK3CA (1.7%), and less than 1% alterations in RET, NTRK1, MET∆ex14, PDGFRA, and BRAF. Except for TP53 and PIK3CA, all other alterations were mutually exclusive. Genetic ancestry analysis revealed a predominance of European (71.1%), and a higher African ancestry was associated with TP53 mutations. CONCLUSION: Brazilian LCINS exhibited a similar molecular profile to other populations, except the increased ALK and TP53 alterations. Importantly, 73% of these patients have actionable alterations that are suitable for targeted treatments.

Machine learning methods for prediction of cancer driver genes: a survey paper.

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Prognostic impact of nectin-like molecule-5 (CD155) expression in non-small cell lung cancer.

BACKGROUND: CD155 is a transmembrane protein that inhibits antitumor immune response and represents a predictor of worse prognosis in non-small-cell lung cancer (NSCLC). However, it remains unexplored its association with clinical characteristics and genomic status of Latin American patients. This study characterizes the CD155 expression and its clinical implications in this population. METHODS: Tissue biopsies from 86 patients with locally-advanced or metastatic NSCLC were assessed for CD155 protein expression, ALK rearrangements and EGFR mutations. Cutoff values for high CD155 expression (CD155high) were determined from receiver operating characteristic (ROC) curves according to 2-year survival. It was evaluated its association with clinicopathological features, median progression-free survival (mPFS) and overall survival (mOS). RESULTS: the cutoff score for CD155high was 155 in the entire cohort and in patients without oncogenic alterations, and it was 110 in patients with oncogenic alterations. Eighty-four patients (97.7%) were CD155 positive, of which fifty-six (65.0%) had CD155high. EGFR L858R mutation related to lower CD155 IHC score than exon 19 deletion. Individuals with CD155high showed a shorter mOS (13.0 vs. 30.8 months; HR: 1.96 [95% CI, 1.15-3.35]; p = 0.014). Patients without oncogenic alterations having a CD155high displayed shorter mPFS (1.6 vs. 6.4 months, HR: 2.09 [95% CI, 1.06-4.20]; p = 0.034) and mOS (2.9 vs. 23.1 months; HR: 1.27 [95% CI, 1.07- 4.42]; p = 0.032). Patients with oncogenic alterations having CD155high only showed a trend to shorter mOS (26.3 vs. 52.0 months; HR: 2.39 [95% CI, 0.98-5.83]; p = 0.058). CONCLUSION: CD155high is a predictor of worse outcomes in patients with advanced NSCLC, predominantly among those without oncogenic alterations. CD155 could be a potential biomarker and a molecular target in patients with poor responses to current therapies.

Tertiary Lymphoid Structures Gene Signature Predicts Prognosis and Immune Infiltration Analysis in Head and Neck Squamous Cell Carcinoma.

Objectives: This study aims to assess the prognostic implications of gene signature of the tertiary lymphoid structures (TLSs) in head and neck squamous cell carcinoma (HNSCC) and scrutinize the influence of TLS on immune infiltration. Methods: Patients with HNSCC from the Cancer Genome Atlas were categorized into high/low TLS signature groups based on the predetermined TLS signature threshold. The association of the TLS signature with the immune microenvironment, driver gene mutation status, and tumor mutational load was systematically analyzed. Validation was conducted using independent datasets (GSE41613 and GSE102349). Results: Patients with a high TLS signature score exhibited better prognosis compared to those with a low TLS signature score. The group with a high TLS signature score had significantly higher immune cell subpopulations compared to the group with a low TLS signature score. Moreover, the major immune cell subpopulations and immune circulation characteristics in the tumor immune microenvironment were positively correlated with the TLS signature. Mutational differences in driver genes were observed between the TLS signature high/low groups, primarily in the cell cycle and NRF2 signaling pathways. Patients with TP53 mutations and high TLS signature scores demonstrated a better prognosis compared to those with TP53 wild-type. In the independent cohort, the relationship between TLS signatures and patient prognosis and immune infiltration was also confirmed. Additionally, immune-related biological processes and signaling pathways were activated with elevated TLS signature. Conclusion: High TLS signature is a promising independent prognostic factor for HNSCC patients. Immunological analysis indicated a correlation between TLS and immune cell infiltration in HNSCC. These findings provide a theoretical basis for future applications of TLS signature in HNSCC prognosis and immunotherapy.

Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer genome atlases reveal that kinase domains, which consist of 300 amino acids, can harbor numerous (150 to 200) single-point mutations across different patients in the same disease. This preponderance of mutations-some activating, some silent-in a known target protein make clinical decisions for enrolling patients in drug trials challenging since the relevance of the target and its drug sensitivity often depend on the mutational status in a given patient. We show through computational studies using molecular dynamics (MD) as well as enhanced sampling simulations that the experimentally determined activation status of a mutated kinase can be predicted effectively by identifying a hydrogen bonding fingerprint in the activation loop and the αC-helix regions, despite the fact that mutations in cancer patients occur throughout the kinase domain. In our study, we find that the predictive power of MD is superior to a purely data-driven machine learning model involving biochemical features that we implemented, even though MD utilized far fewer features (in fact, just one) in an unsupervised setting. Moreover, the MD results provide key insights into convergent mechanisms of activation, primarily involving differential stabilization of a hydrogen bond network that engages residues of the activation loop and αC-helix in the active-like conformation (in >70% of the mutations studied, regardless of the location of the mutation).

[Uveal Melanoma: Molecular and Genetic Mechanisms of Development and Therapeutic Approaches].

Uveal melanoma (UM) is a neuroectodermal tumor that results from malignant transformation of melanocytes in the eye uvea, including the iris, the ciliary body, and the choroid. UM accounts for 5% of all melanoma cases and is extremely aggressive with half of the UM patients developing metastases within the first 1-2 years after tumor development. Molecular mechanisms of UM carcinogenesis are poorly understood, but are known to differ from those of skin melanoma. Activating mutations of the GNAQ and GNA11 genes, which code for the large G protein subunits Gq and G11, respectively, are found in 90% of UM patients. The Gaq/PKC/MAPK signaling pathway is a main signaling cascade that leads to the transformation of melanocytes of the uveal tract, and major regulators of the cascade provide targets for the development of drugs. Metastatic UM (MUM) is most often associated with mutations of BAP1, EIF1AX, GNA11, GNAQ, and SF3B1. A combination of a commercial expression test panel of 15 genes and a mutation panel of 7 genes, supplemented with data on the size of the primary tumor, is highly efficient in predicting the risk of metastasis. The risk of metastasis determines the choice of therapy and the patient follow-up regimen. However, no systemic therapy for MUM has been developed to date. New drugs undergoing clinical trials are mostly targeted drugs designed to inhibit the protein products of mutant genes or immunotherapeutic agents designed to stimulate the immune response against specific antigens. In addition to these approaches, potential therapeutic targets of epigenetic regulation of UM development are considered in the review.

[Clonal evolution in myeloproliferative neoplasms].

Recent advances in sequencing technologies have clarified the driver gene landscape in Philadelphia chromosomenegative (Ph-) myeloproliferative neoplasms (MPNs) and progressed understanding of MPN pathogenesis. Beyond mutations in the main three drivers of MPN, namely JAK2, MPL and CALR, somatic mutations in the epigenetic regulators and RNA splicing factors have been identified and their association with transformation to myelofibrosis and acute myeloid leukemia have been determined. Clonal expansion of hematopoietic cells with driver mutations (clonal hematopoiesis) has been detected in healthy individuals, especially in elderly people. In MPN patients, however, initial driver mutations such as those in JAK2 and DNMT3A have been shown to be acquired in utero or during childhood. In this review, I will summarize the recent findings about clonal evolution in MPN and the role of driver mutations.

Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology.

Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in cancer. Elucidating the functional pathways altered by loss-of-function (LOF) or gain-of-function (GOF) mutations will be crucial for prioritizing cancer-causing variants and their resultant therapeutic liabilities. In this review, we highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also summarize advances in experimental and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of mutations. Together, systematic investigations of the function of GOF mutations will provide an important missing piece for cancer biology and precision therapy.

Cellular and Genetic Background of Osteosarcoma.

Osteosarcoma describes a tumor of mesenchymal origin with an annual incidence rate of four to five people per million. Even though chemotherapy treatment has shown success in non-metastatic osteosarcoma, metastatic disease still has a low survival rate of 20%. A targeted therapy approach is limited due to high heterogeneity of tumors, and different underlying mutations. In this review, we will summarize new advances obtained by new technologies, such as next generation sequencing and single-cell sequencing. These new techniques have enabled better assessment of cell populations within osteosarcoma, as well as an understanding of the molecular pathogenesis. We also discuss the presence and properties of osteosarcoma stem cells-the cell population within the tumor that is responsible for metastasis, recurrence, and drug resistance.