Identifying cooperating cancer driver genes in individual patients through hypergraph random walk

OBJECTIVE

Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies.

METHODS

Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients.

RESULTS

The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments.

CONCLUSION

We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.

A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity

Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.

Whole-exome sequencing reveals novel cancer genes and actionable targets in biliary tract cancers in primary sclerosing cholangitis

BACKGROUND

People with primary sclerosing cholangitis (PSC) have a 20% lifetime risk of biliary tract cancer (BTC). Using whole-exome sequencing, we characterized genomic alterations in tissue samples from BTC with underlying PSC.

METHODS

We extracted DNA from formalin-fixed, paraffin-embedded tumor and paired nontumor tissue from 52 resection or biopsy specimens from patients with PSC and BTC and performed whole-exome sequencing. Following copy number analysis, variant calling, and filtering, putative PSC-BTC-associated genes were assessed by pathway analyses and annotated to targeted cancer therapies.

RESULTS

We identified 53 candidate cancer genes with a total of 123 nonsynonymous alterations passing filtering thresholds in 2 or more samples. Of the identified genes, 19% had not previously been implicated in BTC, including CNGA3, KRT28, and EFCAB5. Another subset comprised genes previously implicated in hepato-pancreato-biliary cancer, such as ARID2, ELF3, and PTPRD. Finally, we identified a subset of genes implicated in a wide range of cancers such as the tumor suppressor genes TP53, CDKN2A, SMAD4, and RNF43 and the oncogenes KRAS, ERBB2, and BRAF. Focal copy number variations were found in 51.9% of the samples. Alterations in potential actionable genes, including ERBB2, MDM2, and FGFR3 were identified and alterations in the RTK/RAS (p = 0.036), TP53 (p = 0.04), and PI3K (p = 0.043) pathways were significantly associated with reduced overall survival.

CONCLUSIONS

In this exome-wide characterization of PSC-associated BTC, we delineated both PSC-specific and universal cancer genes. Our findings provide opportunities for a better understanding of the development of BTC in PSC and could be used as a platform to develop personalized treatment approaches.

Impact of bevacizumab on clinical outcomes and its comparison with standard chemotherapy in metastatic colorectal cancer patients: a systematic review and meta-analysis

Background

Advances in targeted therapies have expanded the treatment options for colorectal cancer (CRC), allowing for more tailored and effective approaches to managing the disease. In targeted therapy, Bevacizumab is a commonly prescribed anti-VEGF monoclonal antibody that has a direct anti-vascular impact in cancer patients. Vascular Endothelial Growth Factors (VEGFs), especially VEGF-A, are significant agents in promoting tumour angiogenesis.

Objective

To assess the impact of adding Bevacizumab to chemotherapy on progression-free survival (PFS) and overall survival (OS) in patients with metastatic colorectal cancer.

Methodology

Comprehensive searches have been performed on electronic databases such as PubMed, and Google Scholar using the following terms: colorectal cancer, adenocarcinoma, Bevacizumab, chemotherapy, and monoclonal antibody.

Results

In the meta-analysis, 16 out of the 24 included studies were analysed. In the final analysis, incorporating Bevacizumab with chеmothеrapy demonstrated favourable outcomes for OS with a hazard ratio (HR = 0.689,95%CI: 0.51-0.83, I² = 39%, p <0.01) and for PFS with a hazard ratio (HR = 0.77 95% CI: 0.60-0.96, I² = 54%, p < 0.01). The subgroup analysis of PFS, categorised by study dеsign (prospеctivе vs rеtrospеctivе), reveals that the Hazard Ratio (HR = 0.82, 95% CI: 0.62-0.97, I² = 21%, p < 0.01) and for OS with a hazard ratio (HR = 0.73, 95% CI: 0.52-0.86, I² = 17%, p < 0.01).

Conclusion

Our findings indicate that combining Bevacizumab with chemotherapy enhances clinical outcomes and results in a significant increase in PFS and OS in patients with metastatic colorectal cancer. Positive outcomes are demonstrated by a substantial 23% increase in PFS and 31% increase in OS in patients with metastatic colorectal cancer who undergo Bevacizumab in conjunction with chemotherapy.

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers

Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.

A pan-sarcoma landscape of telomeric content shows that alterations in RAD51B and GID4 are associated with higher telomeric content

Tumor cells need to activate a telomere maintenance mechanism, enabling limitless replication. The bulk of evidence supports that sarcomas predominantly use alternative lengthening of telomeres (ALT) mechanism, commonly associated with alterations in ATRX and DAXX. In our dataset, only 12.3% of sarcomas harbored alterations in these genes. Thus, we checked for the presence of other genomic determinants of high telomeric content in sarcomas. Our dataset consisted of 13555 sarcoma samples, sequenced as a part of routine clinical care on the FoundationOne®Heme platform. We observed a median telomeric content of 622.3 telomeric reads per GC-matched million reads (TRPM) across all samples. In agreement with previous studies, telomeric content was significantly higher in ATRX altered and POT1 altered sarcomas. We further observed that sarcomas with alterations in RAD51B or GID4 were enriched in samples with high telomeric content, specifically within uterus leiomyosarcoma for RAD51B and soft tissue sarcoma (not otherwise specified, nos) for GID4, Furthermore, RAD51B and POT1 alterations were mutually exclusive with ATRX and DAXX alterations, suggestive of functional redundancy. Our results propose a role played by RAD51B and GID4 in telomere elongation in sarcomas and open research opportunities for agents aimed at targeting this critical pathway in tumorigenesis.

Integrative pan-cancer analysis reveals the importance of PAQR family in lung cancer

BACKGROUND

The progestin and adipoQ receptors (PAQRs) family contains 11 genes involved in the regulation of metabolism and cancer development. However, a comprehensive understanding of the role of PAQRs in cancer remains largely scarce, and the associations between their expression levels and immune signatures also need to be researched.

METHODS

Here, we applied pan-cancer analysis to explore the associations between PAQRs expression and survival, tumor microenvironment (TME), and drug sensitivity from the UCSC Xena and CellMiner databases. Besides, we further studied the expression, survival and somatic mutations of PAQRs in lung cancer (LC) from TCGA database.

RESULTS

The results showed that PAQRs had significant heterogeneity with some upregulation and some downregulation in most tumors. Specifically, compared with PAQR3/5/6/9 and MMD2, ADIPOR1/2, PAQR4/7/8 and MMD had higher levels of average expression in all tumor types. PAQRs expression was greatly correlated with survival, immune subtypes, TME, and drug sensitivity. Furthermore, this research concentrated on analyzing the relationship of PAQRs expression with LC prognosis, and proved that ADIPOR2, PAQR4/9 and MMD were independent prognostic factors for LC patients. Finally, based on somatic mutation data, the genetic mutations in LC patients were majorly missense mutations, and TP53 and TTN had the top two highest mutation frequencies.

CONCLUSION

Collectively, PAQRs may serve as robust biomarkers to predict the prognosis and guide immunotherapy of tumors, especially LC, which enables novel ways for improving cancer treatment.

Unravelling the nexus: Towards a unified model of development, ageing, and cancer

This work presents a comprehensive model that aims to unify our understanding of embryogenesis, ageing, and cancer. While there have been previous attempts to construct models separately for two of these phenomena (such as embryogenesis and cancer, ageing and cancer), models encompassing all three are relatively scarce, if not entirely absent. The model's most notable feature is the presence of driver cells throughout the body, which may correspond to Spemann's organisers. These driver cells play a vital role in propelling development as they dynamically emerge from non-driver cells and inhabit specialised niches. Remarkably, this continuous process persists throughout an organism's entire lifespan, signifying that development unfolds from conception to the end of life. Driver cells orchestrate change events through the induction of distinctive epigenetic patterns of gene activation. Events occurring at young age drive development, are subject to high evolutionary pressure and hence carefully optimised. Events occurring after reproduction age are subject to decreasing evolutionary pressure: for this reason, such events are "pseudorandom" -deterministic but erratic. Some of these events lead to age-related benign conditions, such as gray hair. Some lead to serious age-related diseases, such as diabetes and Alzheimer's disease. Furthermore, some of these events might perturb epigenetically key pathways involved in driver activation and formation, leading to cancer. In our model, this driver cell-based mechanism represents the backbone of multicellular biology: understanding and correcting its functioning may give the chance to solve a wide range of conditions at once.

Landscape of MicroRNA Regulatory Network Architecture and Functional Rerouting in Cancer

Somatic mutations are a major source of cancer development, and many driver mutations have been identified in protein coding regions. However, the function of mutations located in miRNA and their target binding sites throughout the human genome remains largely unknown. Here, we built detailed cancer-specific miRNA regulatory networks across 30 cancer types to systematically analyze the effect of mutations in miRNAs and their target sites in 3' untranslated region (3' UTR), coding sequence (CDS), and 5' UTR regions. A total of 3,518,261 mutations from 9,819 samples were mapped to miRNA-gene interactions (mGI). Mutations in miRNAs showed a mutually exclusive pattern with mutations in their target genes in almost all cancer types. A linear regression method identified 148 candidate driver mutations that can significantly perturb miRNA regulatory networks. Driver mutations in 3'UTRs played their roles by altering RNA binding energy and the expression of target genes. Finally, mutated driver gene targets in 3' UTRs were significantly downregulated in cancer and functioned as tumor suppressors during cancer progression, suggesting potential miRNA candidates with significant clinical implications. A user-friendly, open-access web portal (mGI-map) was developed to facilitate further use of this data resource. Together, these results will facilitate novel noncoding biomarker identification and therapeutic drug design targeting the miRNA regulatory networks.

SIGNIFICANCE

A detailed miRNA-gene interaction map reveals extensive miRNA-mediated gene regulatory networks with mutation-induced perturbations across multiple cancers, serving as a resource for noncoding biomarker discovery and drug development.

A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma

Immune checkpoint inhibitor (ICI) therapy has become a powerful clinical strategy for treating melanoma. The relationship between somatic mutations and the clinical benefits of immunotherapy has been widely recognized. However, the gene-based predictive biomarkers are less stable due to the heterogeneity of cancer at the individual gene level. Recent studies have suggested that the accumulation of gene mutations in biological pathways may activate antitumor immune responses. Herein, a novel pathway mutation signature (PMS) was constructed to predict the survival and efficacy of ICI therapy. In a dataset of melanoma patients treated with anti-CTLA-4, we mapped the mutated genes into the pathways and then identified seven significant mutation pathways associated with survival and immunotherapy response, which were used to construct the PMS model. According to the PMS model, the patients in the PMS-high group showed better overall survival (hazard ratio (HR) = 0.37; log-rank test, p < 0.0001) and progression-free survival (HR = 0.52; log-rank test, p = 0.014) than those in the PMS-low group. The PMS-high patients also showed a significantly higher objective response rate to anti-CTLA-4 therapy than the PMS-low patients (Fisher's exact test, p = 0.0055), and the predictive power of the PMS model was superior to that of TMB. Finally, the prognostic and predictive value of the PMS model was validated in two independent validation sets. Our study demonstrated that the PMS model can be considered a potential biomarker to predict the clinical outcomes and response to anti-CTLA-4 therapy in melanoma patients.

The regulation role and diagnostic value of fibrinogen-like protein 1 revealed by pan-cancer analysis

Although the role of fibrinogen-like protein 1 (FGL1) in tumorigenesis is well known, a pan-cancer analysis of FGL1 lacks. We used bioinformatics techniques to analyze cancer data from publicly available datasets from The Cancer Genome Atlas, UALCAN, TIMER, Gene Expression Profiling Interactive Analysis, cBioPortal, Search Tool for the Retrieval of Interacting Genes, and DAVID. FGL1 expression was significantly regulated in various common tumors than in normal tissues; it was increased in lung adenocarcinoma and decreased in colon adenocarcinoma. Cox regression analysis demonstrated that the upregulation of FGL1 expression was correlated with poor overall survival (OS) and disease-free survival (DFS) in stomach adenocarcinoma, brain low-grade glioma, cervical squamous cell carcinoma, and endocervical adenocarcinoma. Decreased FGL1 methylation levels were observed in majority of tumor types. FGL1 expression was significantly associated with the levels of immune cell subtypes and immune checkpoint genes. Deep deletion was the most common genetic mutation in FGL1 that led to frame-shift mutations, which was closely associated with poor progression-free interval, disease-specific survival, and OS in patients with FGL1 mutations. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that FGL1-related genes participate in diverse pathways. Ubiquitin-mediated proteolysis is significantly correlated to the function of FGL1, which was identified for the first time in the present study. This pan-cancer study provides a deep understanding of the functions of FGL1 in progression of many tumors and demonstrates that FGL1 may be a potential biomarker for the diagnosis, prognosis, and immune infiltration in cancer.

Knockdown of TMEM132A restrains the malignant phenotype of gastric cancer cells via inhibiting Wnt signaling

Transmembrane protein 132 A (TMEM132A) has been recently reported to be a novel regulator of the Wnt signaling pathway, which is a cancer-associated cascade. However, the role of TMEM132A in cancer is not well characterized. Here, we used bioinformatics analysis to analyze the differential expression of TMEM132A in gastric cancer (GC) tissues and determine its diagnostic and prognostic value. Results showed that TMEM132A expression was upregulated in GC tissues. TMEM132A was also found to have diagnostic and prognostic roles in patient with GC. Furthermore, as evaluated by in vitro assays, knockdown of TMEM132A restrained cell proliferation, migration, and invasion of GC cells, while overexpression of TMEM132A exerted opposite effects. However, the effects of TMEM132A silencing and overexpression on GC cells were reversed by treatment with LiCl and ICG-001 (the Wnt signaling activator and inhibitor), respectively. In addition, in vivo assays showed that knockdown of TMEM132A suppressed GC tumorigenesis. Hence, our results provide new insights into the oncogenic role of TMEM132A in regulating GC cell proliferation, migration, and invasion, as well as its prognostic and therapeutic roles in patients with GC. These data highlight the diagnostic, prognostic, and therapeutic potential of TMEM132A in GC.

Identification of Warning Transition Points from Hepatitis B to Hepatocellular Carcinoma Based on Mutation Accumulation for the Early Diagnosis and Potential Drug Treatment of HBV-HCC

The accumulation of multiple genetic mutations is essential during the occurrence and development of hepatocellular carcinoma induced by hepatitis B (HBV-HCC), but understanding their cooperative effects and identifying the warning transition point from hepatitis B to HCC are challenges. In the genomic analysis of somatic mutations of the patient with HBV-HCC in a patient-specific protein-protein interaction (ps-PPI) network, we find mutation influence can propagate along the ps-PPI network. Therefore, in the article, we got the mutation cluster as a new research unit using the Random Walks with Restarts algorithm that is used to describe the efficient boundary of mutation influences. The connection of mutation cluster leads to dysregulation of signaling pathways corresponding to HCC, while dysregulated signaling pathways accumulate gradually and experience a process from quantitative to qualitative changes including a critical mutation cluster called transition point (TP) from hepatitis B to HCC. Moreover, two subtypes of HCC patients with different prognosis and their corresponding biological and clinical characteristics were identified according to TP. The poor prognosis HCC subtype was associated with significant metabolic pathway dysregulation and lower immune cell infiltration, while we also identified several preventive drugs to block the transformation of hepatitis B to hepatocellular carcinoma. The network-level study integrated multiomics data not only showed the sequence of multiple somatic mutations and their cooperative effect but also identified the warning transition point in HCC tumorigenesis for each patient. Our study provides new insight into exploring the cooperative molecular mechanism of chronic inflammatory malignancy in the liver and lays the foundation for the development of new approaches for early prediction and diagnosis of hepatocellular carcinoma and personalized targeted therapy.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.

OMEN: Network-based Driver Gene Identification using Mutual Exclusivity

MOTIVATION

Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed.

RESULTS

We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark data set derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways.

AVAILABILITY

The source code is freely available for download at www.github.com/DriesVanDaele/OMEN The data set is archived at https://doi.org/10.5281/zenodo.6419097 and the code at https://doi.org/10.5281/zenodo.6419764.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell profiling-guided combination therapy of c-Fos and histone deacetylase inhibitors in diffuse large B-cell lymphoma

Background

Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of non‐Hodgkin lymphoma. Histone deacetylase inhibitors (HDACis) have been widely applied in multiple tumours, but the expected efficacy was not observed in DLBCL. Therefore, this study is aimed to explore superior HDACis and optimise a relative combinational therapeutic strategy.

Methods

The antitumour effects of the drug were evaluated by Cell Counting Kit‐8 (CCK‐8) assay and apoptosis analysis. Single‐cell RNA sequencing (scRNA‐Seq) was used to analyse the intratumoural heterogeneity of DLBCL cells. Whole‐exome sequencing and RNA sequencing were performed to analyse the genetic and transcriptional features. Western blotting, qRT–PCR, protein array, immunohistochemistry, and chromatin immunoprecipitation assays were applied to explore the involved pathways. The antitumour effects of the compounds were assessed using subcutaneous xenograft tumour models.

Results

LAQ824 was screened and confirmed to kill DLBCL cells effectively. Using scRNA‐Seq, we characterised the heterogeneity of DLBCL cells under different drug pressures, and c‐Fos was identified as a critical factor in the survival of residual tumour cells. Moreover, we demonstrated that combinatorial treatment with LAQ824 and a c‐Fos inhibitor more potently inhibited tumour cells both in vitro and in vivo.

Conclusion

Altogether, we found an HDACi, LAQ824, with high efficacy in DLBCL and provided a promising HDACi‐based combination therapy strategy.

Real-world PM2.5 exposure induces pathological injury and DNA damage associated with miRNAs and DNA methylation alteration in rat lungs

Fine particulate matter (PM_2.5) has been demonstrated to threaten public health and increase lung cancer risk. DNA damage is involved in the pathogenesis of lung cancer. However, the mechanisms of epigenetic modification of lung DNA damage are still unclear. This study developed a real-world air PM_2.5 inhalation system and exposed rats for 1 and 2 months, respectively, and investigated rat lungs pathological changes, inflammation, oxidative stress, and DNA damage effects. OGG1 and MTH1 expression was measured, along with their DNA methylation status and related miRNAs expression. The results showed that PM_2.5 exposure led to pathological injury, influenced levels of inflammatory cytokines and oxidative stress factors in rat lungs. Of note, 2-month PM_2.5 exposure aggravated pathological injury. Besides, PM_2.5 significantly elevated OGG1 expression and suppressed MTH1 expression, which was correlated to oxidative stress and partially mediated by reducing OGG1 DNA methylation status and increasing miRNAs expression related to MTH1 in DNA damage with increases of γ-H2AX, 8-OHdG and GADD153. PM_2.5 also activated c-fos and c-jun levels and inactivated PTEN levels in rat lungs. These suggested that epigenetic modification was probably a potential mechanism by which PM_2.5-induced genotoxicity in rat lungs.

A pan-cancer landscape of telomeric content shows that RAD21 and HGF alterations are associated with longer telomeres

Background

Cancer cells can proliferate indefinitely through telomere maintenance mechanisms. These mechanisms include telomerase-dependent elongation, mediated by TERT activation, and alternative lengthening of telomeres (ALT), linked to loss of ATRX or DAXX.

Methods

We analyzed the telomeric content of 89,959 tumor samples within the Foundation Medicine dataset and investigated the genomic determinants of high telomeric content, linking them to clinical outcomes, when available.

Results

Telomeric content varied widely by disease type with leiomyosarcoma having the highest and Merkel cell carcinoma having the lowest telomeric content. In agreement with previous studies, telomeric content was significantly higher in samples with alterations in TERC, ATRX, and DAXX. We further identified that amplifications in two genes, RAD21 and HGF, were enriched in samples with high telomeric content, which was confirmed using the PCAWG/ICGC dataset. We identified the minimal amplified region associated with high telomeric content for RAD21 (8q23.1–8q24.12), which excludes MYC, and for HGF (7q21.11). Our results demonstrated that RAD21 and HGF exerted an additive telomere lengthening effect on samples with existing alterations in canonical genes previously associated with telomere elongation. Furthermore, patients with breast cancer who harbor RAD21 alterations had poor median overall survival and trended towards higher levels of Ki-67 staining.

Conclusions

This study highlights the importance of the role played by RAD21 (8q23.1–8q24.12) and HGF (7q21.11) in the lengthening of telomeres, supporting unlimited replication in tumors. These findings open avenues for work aimed at targeting this crucial pathway in tumorigenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01029-7.

Identifying Actionable Variants Using Capture-Based Targeted Sequencing in 563 Patients With Non-Small Cell Lung Carcinoma

Although the NSCLC diagnostic standards recommend the detection of driver gene mutation, comprehensive genomic profiling has not been used widely in clinical practice. As to the different mutation spectrum characteristics between populations, the research based on Chinese NSCLC cohort is very important for clinical practice. Therefore, we collected 563 surgical specimens from patients with non-small cell lung carcinoma and applied capture-based sequencing using eight-gene panel. We identified 556 variants, with 416 potentially actionable variants in 54.88% (309/563) patients. These single nucleotide variants, insertions and deletions were most commonly found in EGFR (55%), followed by ERBB2 (12%), KRAS (11%), PIK3CA (9%), MET (8%), BRAF (7%), DDR2 (2%), NRAS (0.3%). By using ten protein function prediction algorithms, we also identified 30 novel potentially pathogenic variants. Ninety-eight patients harbored EFGR exon 21 p.L858R mutation and the catalytic domain of the protein tyrosine kinase (PTKc) in EGFR is largely mutated. In addition, there were nine frequent pathogenic variants found in five or more patients. This data provides the potential molecular basis for directing the treatment of lung cancer.

SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types

Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.

Using SuperDendrix, Park et al. examine associations between genetic dependencies in 769 cancer cell lines. They report 127 genetic dependencies explained by combinations of mutually exclusive somatic mutations congregating into a few oncogenic pathways across cancer subtypes. These present a small number of prominent and highly specific genetic vulnerabilities in cancer.

Graphical abstract

A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.

A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis

High-throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity-coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers.

CDPath: Cooperative Driver Pathways Discovery Using Integer Linear Programming and Markov Clustering

Discovering driver pathways is an essential task to understand the pathogenesis of cancer and to design precise treatments for cancer patients. Increasing evidences have been indicating that multiple pathways often function cooperatively in carcinogenesis. In this study, we propose an approach called CDPath to discover cooperative driver pathways. CDPath first uses Integer Linear Programming to explore driver core modules from mutation profiles by enforcing co-occurrence and functional interaction relations between modules, and by maximizing the mutual exclusivity and coverage within modules. Next, to enforce cooperation of pathways and help the follow-up exact cooperative driver pathways discovery, it performs Markov clustering on pathway-pathway interaction network to cluster pathways. After that, it identifies pathways in different modules but in the same clusters as cooperative driver pathways. We apply CDPath on two TCGA datasets: breast cancer (BRCA) and endometrial cancer (UCEC). The results show that CDPath can identify known (i.e., TP53) and potential driver genes (i.e., SPTBN2). In addition, the identified cooperative driver pathways are related with the target cancer, and they are involved with carcinogenesis and several key biological processes. CDPath can uncover more potential biological associations between pathways (over 100 percent) and more cooperative driver pathways (over 200 percent) than competitive approaches. The demo codes of CDPath are available at http://mlda.swu.edu.cn/codes.php?name=CDPath.

Concordance Analysis of ALK Gene Fusion Detection Methods in Patients with Non-Small-Cell Lung Cancer from Chile, Brazil, and Peru

About 4% to 7% of the non-small-cell lung cancer patients have anaplastic lymphoma kinase (ALK) rearrangements, and specific targeted therapies improve patients' outcomes significantly. ALK gene fusions are detected by immunohistochemistry or fluorescent in situ hybridization as gold standards in South America. Next-generation sequencing-based assays are a reliable alternative, able to perform simultaneous detection of multiple events from a single sample. We analyzed 4240 non-small-cell lung cancer samples collected in 37 hospitals from Chile, Brazil, and Peru, where ALK rearrangements were determined as part of their standard of care (SofC) using either immunohistochemistry or fluorescent in situ hybridization. A subset of 1450 samples was sequenced with the Oncomine Focus Assay (OFA), and the concordance with the SofC tests was measured. An orthogonal analysis was performed using a real-time quantitative PCR echinoderm microtubule-associated protein-like 4-ALK fusion detection kit. ALK fusion prevalence is similar for Chile (3.67%; N = 2142), Brazil (4.05%; N = 1013), and Peru (4.59%; N = 675). Although a comparison between OFA and SofC assays showed similar sensitivity, OFA had significantly higher specificity and higher positive predictive value, which opens new opportunities for a more specific determination of ALK gene rearrangements.

Transgenic mouse models of breast cancer

Transgenic breast cancer mouse models are critical tools for preclinical studies of human breast cancer. Genetic editing of the murine mammary gland allows for modeling of abnormal genetic events frequently found in human breast cancers. Genetically engineered mouse models (GEMMs) of breast cancer employ tissue-specific genetic manipulation for tumorigenic induction within the mammary tissue. Under the transcriptional control of mammary-specific promoters, transgenic mouse models can simulate spontaneous mammary tumorigenesis by expressing one or more putative oncogenes, such as MYC, HRAS, and PIK3CA. Alternatively, the Cre-Lox system allows for tissue-specific deletion of tumor suppressors, such as p53, Rb1, and Brca1, or specific knock-in of putative oncogenes. Thus, GEMMs can be designed to implement one or more genetic events to induce mammary tumorigenesis. Features of GEMMs, such as age of transgene expression, breeding quality, tumor latency, histopathological characteristics, and propensity for local and distant metastasis, are variable and strain-dependent. This review aims to summarize currently available transgenic breast cancer mouse models that undergo spontaneous mammary tumorigenesis upon genetic manipulation, their varying characteristics, and their individual genetic manipulations that model aberrant signaling events observed in human breast cancers.

Identifying collateral and synthetic lethal vulnerabilities within the DNA-damage response

BACKGROUND

A pair of genes is defined as synthetically lethal if defects on both cause the death of the cell but a defect in only one of the two is compatible with cell viability. Ideally, if A and B are two synthetic lethal genes, inhibiting B should kill cancer cells with a defect on A, and should have no effects on normal cells. Thus, synthetic lethality can be exploited for highly selective cancer therapies, which need to exploit differences between normal and cancer cells.

RESULTS

In this paper, we present a new method for predicting synthetic lethal (SL) gene pairs. As neighbouring genes in the genome have highly correlated profiles of copy number variations (CNAs), our method clusters proximal genes with a similar CNA profile, then predicts mutually exclusive group pairs, and finally identifies the SL gene pairs within each group pairs. For mutual-exclusion testing we use a graph-based method which takes into account the mutation frequencies of different subjects and genes. We use two different methods for selecting the pair of SL genes; the first is based on the gene essentiality measured in various conditions by means of the "Gene Activity Ranking Profile" GARP score; the second leverages the annotations of gene to biological pathways.

CONCLUSIONS

This method is unique among current SL prediction approaches, it reduces false-positive SL predictions compared to previous methods, and it allows establishing explicit collateral lethality relationship of gene pairs within mutually exclusive group pairs.

The origins and genetic interactions of KRAS mutations are allele- and tissue-specific

Mutational activation of KRAS promotes the initiation and progression of cancers, especially in the colorectum, pancreas, lung, and blood plasma, with varying prevalence of specific activating missense mutations. Although epidemiological studies connect specific alleles to clinical outcomes, the mechanisms underlying the distinct clinical characteristics of mutant KRAS alleles are unclear. Here, we analyze 13,492 samples from these four tumor types to examine allele- and tissue-specific genetic properties associated with oncogenic KRAS mutations. The prevalence of known mutagenic mechanisms partially explains the observed spectrum of KRAS activating mutations. However, there are substantial differences between the observed and predicted frequencies for many alleles, suggesting that biological selection underlies the tissue-specific frequencies of mutant alleles. Consistent with experimental studies that have identified distinct signaling properties associated with each mutant form of KRAS, our genetic analysis reveals that each KRAS allele is associated with a distinct tissue-specific comutation network. Moreover, we identify tissue-specific genetic dependencies associated with specific mutant KRAS alleles. Overall, this analysis demonstrates that the genetic interactions of oncogenic KRAS mutations are allele- and tissue-specific, underscoring the complexity that drives their clinical consequences.

A greedy approach for mutual exclusivity analysis in cancer study

The main challenge in cancer genomics is to distinguish the driver genes from passenger or neutral genes. Cancer genomes exhibit extensive mutational heterogeneity that no two genomes contain exactly the same somatic mutations. Such mutual exclusivity (ME) of mutations has been observed in cancer data and is associated with functional pathways. Analysis of ME patterns may provide useful clues to driver genes or pathways and may suggest novel understandings of cancer progression. In this article, we consider a probabilistic, generative model of ME, and propose a powerful and greedy algorithm to select the mutual exclusivity gene sets. The greedy method includes a pre-selection procedure and a stepwise forward algorithm which can significantly reduce computation time. Power calculations suggest that the new method is efficient and powerful for one ME set or multiple ME sets with overlapping genes. We illustrate this approach by analysis of the whole-exome sequencing data of cancer types from TCGA.

Mutational Signatures Driven by Epigenetic Determinants Enable the Stratification of Patients with Gastric Cancer for Therapeutic Intervention

DNA mismatch repair deficiency (dMMR) is associated with the microsatellite instability (MSI) phenotype and leads to increased mutation load, which in turn may impact anti-tumor immune responses and treatment effectiveness. Various mutational signatures directly linked to dMMR have been described for primary cancers. To investigate which mutational signatures are associated with prognosis in gastric cancer, we performed a de novo extraction of mutational signatures in a cohort of 787 patients. We detected three dMMR-related signatures, one of which clearly discriminates tumors with MLH1 gene silencing caused by promoter hypermethylation (area under the curve = 98%). We then demonstrated that samples with the highest exposure of this signature share features related to better prognosis, encompassing clinical and molecular aspects and altered immune infiltrate composition. Overall, the assessment of the prognostic value and of the impact of modifications in MMR-related genes on shaping specific dMMR mutational signatures provides evidence that classification based on mutational signature exposure enables prognosis stratification.

DriveWays: a method for identifying possibly overlapping driver pathways in cancer

The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWay’s output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.

A double-stranded DNA catalyzed strand displacement system for detection of small genetic variations

A double-stranded DNA catalyzed strand displacement system (dsCSD) was established for the detection of small genetic variations, which showed greatly enhanced specificity compared to the conventional single-stranded DNA catalyzed strand displacement (ssCSD) system. The system achieved limits of detection (LODs) of 0.05% and 0.1% for synthesized DNA samples and clinical gene samples, respectively.

Analysis of candidate genes expected to be essential for melanoma surviving

Introduction

Cancers may be treated by selective targeting of the genes vital for their survival. A number of attempts have led to discovery of several genes essential for surviving of tumor cells of different types. In this work, we tried to analyze genes that were previously predicted to be essential for melanoma surviving. Here we present the results of transient siRNA-mediated knockdown of the four of such genes, namely, UNC45A, STK11IP, RHPN2 and ZNFX1, in melanoma cell line A375, then assayed the cells for their viability, proliferation and ability to migrate in vitro. In our study, the knockdown of the genes predicted as essential for melanoma survival does not lead to statistically significant changes in cell viability. On the other hand, for each of the studied genes, mobility assays showed that the knockdown of each of the target genes accelerates the speed of cells migrating. Possible explanation for such counterintuitive results may include insufficiency of the predicting computational models or the necessity of a multiplex knockdown of the genes.

Aims

To examine the hypothesis of essentiality of hypomutated genes for melanoma surviving we have performed knockdown of several genes in melanoma cell line and analyzed cell viability and their ability to migrate.

Methods

Knockdown was performed by siRNAs transfected by Metafectene PRO. The levels of mRNAs before and after knockdown were evaluated by RT-qPCR analysis. Cell viability and proliferation were assessed by MTT assay. Cell migration was assessed by wound healing assay.

Results

The knockdown of the genes predicted as essential for melanoma survival does not lead to statistically significant changes in cell viability. On the other hand, for each of the studied genes, mobility assays showed that the knockdown of each of the target genes accelerates the speed of cells migrating.

Conclusion

Our results do not confirm initial hypothesis that the genes predicted essential for melanoma survival as a matter of fact support the survival of melanoma cells.

Analysis, identification and visualization of subgroups in genomics

Motivation

Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients’ samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches.

Results

Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar (‘analysis and visualization of alteration data’) that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection.

Availability

AVAtar is available at: https://github.com/sysbio-bioinf/avatar

Contact

hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules

MOTIVATION

Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.

RESULTS

We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Modelling cancer progression using Mutual Hazard Networks

MOTIVATION

Cancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurrence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap.

RESULTS

Here we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data. MHN model events by their spontaneous rate of fixation and by multiplicative effects they exert on the rates of successive events. MHN compared favourably to acyclic models in cross-validated model fit on four datasets tested. In application to the glioblastoma dataset from The Cancer Genome Atlas, MHN proposed a novel interaction in line with consecutive biopsies: IDH1 mutations are early events that promote subsequent fixation of TP53 mutations.

AVAILABILITY AND IMPLEMENTATION

Implementation and data are available at https://github.com/RudiSchill/MHN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Oncogenic ERBB2 aberrations and KRAS mutations cooperate to promote pancreatic ductal adenocarcinoma progression

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with few therapeutic options, representing one of the great challenges in oncology. Activating KRAS mutation, occurring in >90% PDACs, is present in pancreatic intraepithelial neoplasia lesions, the precursor ductal lesions of PDAC, indicating additional genetic alterations contribute to the pathogenesis of PDAC. PDAC sequencing projects identify recurrent genomic ERBB2 alterations, mutations and amplifications, in 8.5% of PDAC patients, ranking as the top hit among the 100 receptor tyrosine kinases-encoding genes. Introduction of the ERBB2 mutations encoding protein variants S310F, S423R, R678Q, Q679L, E717D, L755S, V777L and V842I into human pancreatic epithelial cells causes oncogenic transformation, increasing ERBB2 signaling, anchorage-independent cell growth and tumor xenograft growth in nude mice, demonstrating that they are activating mutations. Interestingly, in many PDACs, mutations in ERBB2 and KRAS occur together. ERBB2 activating mutants facilitate KRAS-driven oncogenic properties. Introduction of ERBB2 mutations into KRAS-mutant PDAC cells activates ERBB2 signaling, promotes tumor growth and attenuates KRAS dependency. In contrast, a CRISPR-mediated knockout (KO) of ERBB2 in ERBB2-amplified PDAC cells inhibits ERBB2 signaling, colony formation, anchorage-independent growth and tumor xenograft formation. Finally, oncogenic ERBB2 aberrations can be abrogated by treatment with small-molecule inhibitors. ERBB2 and KRAS inhibition cooperate to suppress PDAC cell growth in vitro and to promote tumor regression in nude mice, providing a rationale for testing an anti-ERBB2 drug in combination with a KRAS inhibitor in ERBB2-mutant PDAC patients that are currently untreatable.

Genomic characteristics in Chinese non-small cell lung cancer patients and its value in prediction of postoperative prognosis

Background

The genomic profile of non-small cell lung cancer (NSCLC) in Asians is distinct from that of Caucasians, but comprehensive genetic profiling reports have been limited for Asian patients. We aimed to elucidate genomic characteristics of Chinese NSCLC patients and develop potential model including genomic characteristics to predict postoperative prognosis.

Methods

Resected tumor samples from 511 patients with stage I–IV lung cancer were subjected to targeted sequencing using a panel of 295 cancer-related genes. Based on the molecular profiles and clinical features, we established nomogram models with predictors consisting of integrated clinical and genomic characteristics to provide post-operative risk stratification.

Results

Compared to the TCGA population (mainly Caucasians), there was a significantly higher frequency of EGFR (53.7% vs. 14.4%) and NOTCH3 (8.4% vs. 1.3%) mutations and less mutated KRAS (11.0% vs. 32.6%), KEAP1 (4.4% vs. 17.4%) and LRP1B (16.3% vs. 29.6%) in Chinese lung adenocarcinomas (LUAD). Distinct patterns of mutually exclusive and co-occurring mutations were identified between LUAD and lung squamous cell carcinoma (LUSC), indicating the unique histology-specific tumorigenesis mechanism of each subtype. We observed alterations in pathways correlated with clinical characteristics. Additionally, we constructed nomogram model with predictors consisting of clinical and genomic characteristics, which were more accurate than models with clinical characteristics or TNM staging only both in stage I–IIIA patients and T1-2N0M0 sub-cohort.

Conclusions

This study revealed Chinese NSCLC patients have unique genomic profile. Furthermore, the nomogram model combining clinical features with genomic characteristics could improve risk stratification in early-stage NSCLC.

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual's life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency

BACKGROUND

Cancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers. While many frequently-mutated cancer driver genes have already been identified and are being utilized for diagnostic, prognostic, or therapeutic purposes, identifying driver genes that harbor mutations occurring with low frequency in human cancers is an ongoing endeavor. Typically, mutations that do not confer growth advantage to tumors - passenger mutations - dominate the mutation landscape of tumor cell genome, making identification of low-frequency driver mutations a challenge. The leading approach for discovering new putative driver genes involves analyzing patterns of mutations in large cohorts of patients and using statistical methods to discriminate driver from passenger mutations.

RESULTS

We propose a novel cancer driver gene detection method, QuaDMutNetEx. QuaDMutNetEx discovers cancer drivers with low mutation frequency by giving preference to genes encoding proteins that are connected in human protein-protein interaction networks, and that at the same time show low deviation from the mutual exclusivity pattern that characterizes driver mutations occurring in the same pathway or functional gene group across a cohort of cancer samples.

CONCLUSIONS

Evaluation of QuaDMutNetEx on four different tumor sample datasets show that the proposed method finds biologically-connected sets of low-frequency driver genes, including many genes that are not found if the network connectivity information is not considered. Improved quality and interpretability of the discovered putative driver gene sets compared to existing methods shows that QuaDMutNetEx is a valuable new tool for detecting driver genes. QuaDMutNetEx is available for download from https://github.com/bokhariy/QuaDMutNetExunder the GNU GPLv3 license.

Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways

Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene-pathway and gene-miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.

Oncogenic Roles of GOLPH3 in the Physiopathology of Cancer

Golgi phosphoprotein 3 (GOLPH3), a Phosphatidylinositol 4-Phosphate [PI(4)P] effector at the Golgi, is required for Golgi ribbon structure maintenance, vesicle trafficking and Golgi glycosylation. GOLPH3 has been validated as an oncoprotein through combining integrative genomics with clinopathological and functional analyses. It is frequently amplified in several solid tumor types including melanoma, lung cancer, breast cancer, glioma, and colorectal cancer. Overexpression of GOLPH3 correlates with poor prognosis in multiple tumor types including 52% of breast cancers and 41% to 53% of glioblastoma. Roles of GOLPH3 in tumorigenesis may correlate with several cellular activities including: (i) regulating Golgi-to-plasma membrane trafficking and contributing to malignant secretory phenotypes; (ii) controlling the internalization and recycling of key signaling molecules or increasing the glycosylation of cancer relevant glycoproteins; and (iii) influencing the DNA damage response and maintenance of genomic stability. Here we summarize current knowledge on the oncogenic pathways involving GOLPH3 in human cancer, GOLPH3 influence on tumor metabolism and surrounding stroma, and its possible role in tumor metastasis formation.

S100a4 upregulation in Pik3caH1047R;Trp53R270H;MMTV-Cre-driven mammary tumors promotes metastasis

Background

PIK3CA mutations are frequent in human breast cancer. Pik3caH1047R mutant expression in mouse mammary gland promotes tumorigenesis. TP53 mutations co-occur with PIK3CA mutations in human breast cancers. We previously generated a conditionally activatable Pik3caH1047R;MMTV-Cre mouse model and found a few malignant sarcomatoid (spindle cell) carcinomas that had acquired spontaneous dominant-negative Trp53 mutations.

Methods

A Pik3caH1047R;Trp53R270H;MMTV-Cre double mutant mouse breast cancer model was generated. Tumors were characterized by histology, marker analysis, transcriptional profiling, single-cell RNA-seq, and bioinformatics. Cell lines were developed from mutant tumors and used to identify and confirm genes involved in metastasis.

Results

We found Pik3caH1047R and Trp53R270H cooperate in driving oncogenesis in mammary glands leading to a shorter latency than either alone. Double mutant mice develop multiple histologically distinct mammary tumors, including adenocarcinoma and sarcomatoid (spindle cell) carcinoma. We found some tumors to be invasive and a few metastasized to the lung and/or the lymph node. Single-cell RNA-seq analysis of the tumors identified epithelial, stromal, myeloid, and T cell groups. Expression analysis of the metastatic tumors identified S100a4 as a top candidate gene associated with metastasis. Metastatic tumors contained a much higher percentage of epithelial–mesenchymal transition (EMT)-signature positive and S100a4-expressing cells. CRISPR/CAS9-mediated knockout of S100a4 in a metastatic tumor-derived cell line disrupted its metastatic potential indicating a role for S100a4 in metastasis.

Conclusions

Pik3caH1047R;Trp53R270H;MMTV-Cre mouse provides a preclinical model to mimic a subtype of human breast cancers that carry both PIK3CA and TP53 mutations. It also allows for understanding the cooperation between the two mutant genes in tumorigenesis. Our model also provides a system to study metastasis and develop therapeutic strategies for PIK3CA/TP53 double-positive cancers. S100a4 found involved in metastasis in this model can be a potential diagnostic and therapeutic target.

A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines

Biomarker discovery involves identifying genetic abnormalities within a tumor. However, one of the main challenges in defining such therapeutic targets is accounting for the molecular heterogeneity of cancer. By integrating somatic mutation and gene expression data from hundreds of heterogeneous cell lines from the Cancer Cell Line Encyclopedia (CCLE), we identify sequences of genetic events that may help explain common patterns of oncogenesis across 22 tumor types, and evaluate the general effect of late-stage mutations on drug sensitivity and resistance mechanisms. Through gene enrichment analysis, we find several cancer-specific and immune pathways that are significantly enriched in each of our three proposed phases of cancer progression. By further analyzing the drug activity area associated with compounds that target the BRAF oncogene, a known predictor of drug sensitivity for several compounds used in cancer treatment, we verify that the acquisition of new driver mutations interferes with the targeted drug mechanism, meaning that cells without late-stage mutations generally respond better to drugs.

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM's results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.

The Evolution of Melanoma - Moving beyond Binary Models of Genetic Progression

To date, over 1000 melanocytic neoplasms, spanning all stages of tumorigenesis, have been sequenced, offering detailed views into their -omic landscapes. This has coincided with advances in genetic engineering technologies that allow molecular biologists to edit the human genome with extreme precision and new mouse models to simulate disease progression. In this review, we describe how these technologies are being harnessed to provide insights into the evolution of melanoma at an unprecedented resolution, revealing that prior models of melanoma evolution, in which pathways are turned 'on' or 'off' in a binary fashion during the run-up to melanoma, are oversimplified.