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Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2–6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co‐occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well‐studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS ‐mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/ . SYNOPSIS Cancer Rule‐Set Optimization (CRSO) identifies sets of combinations of two or more alterations predicted to cooperatively drive tumor formation in individual patients. Application to 19 cancers nominates novel multi‐gene biomarkers. CRSO is a stochastic, multi‐objective algorithm that leverages tumor‐specific, alteration‐specific passenger probabilities. Combinations prioritized by CRSO are interpretable and testable and could translate into improved clinical stratification or novel therapeutic strategies. CRSO identifies known driver combinations not detected by other approaches and nominates novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies are identified in NRAS ‐mutant melanomas that may be therapeutically relevant. Graphical Abstract Cancer Rule‐Set Optimization (CRSO) identifies sets of combinations of two or more alterations predicted to cooperatively drive tumor formation in individual patients. Application to 19 cancers nominates novel multi‐gene biomarkers.

The improvement of treatment for patients with ‘driver‐gene‐negative’ lung adenocarcinoma (LUAD) remains a critical problem to be solved. We aimed to explore the role of methylation of N6 adenosine (m6A)‐related long noncoding RNA (lncRNA) in stratifying ‘driver‐gene‐negative’ LUAD risk. Patients negative for mutations in EGFR, KRAS, BRAF, HER2, MET, ALK, RET, and ROS1 were identified as ‘driver‐gene‐negative’ cases. RNA sequencing was performed in 46 paired tumors and adjacent normal tissues from patients with ‘driver‐gene‐negative’ LUAD. Twenty‐three m6A regulators and relevant lncRNAs were identified using Pearson's correlation analysis. K‐means cluster analysis was used to stratify patients, and a prognostic nomogram was developed. The CIBERSORT and pRRophetic algorithms were employed to quantify the immune microenvironment and chemosensitivity. We identified two clusters highly consistent with the prognosis based on their unique expression profiles for 46 m6AlncRNAs. A risk model constructed from nine m6A lncRNAs could stratify patients into high‐ and low‐risk groups with promising predictive power (C‐index = 0.824), and the risk score was an independent prognostic factor. The clusters and risk models were closely related to immune characteristics and chemosensitivity. Additional pan‐cancer analysis using the nine m6AlncRNAs showed that the expression of DIO3 opposite strand upstream RNA (DIO3OS) is closely related to the immune/stromal score and tumor stemness in a variety of cancers. Our results show that m6AlncRNAs are a reliable prognostic tool and can aid treatment decision‐making in ‘driver‐gene‐negative’ LUAD. DIO3OS is associated with the development of various cancers and has potential clinical applications. Based on RNA sequencing results for patients with ‘driver‐gene‐negative’ lung adenocarcinoma (LUAD), m6A‐related lncRNAs were found and experimentally verified to have good predictive efficacy for the prognosis and immune characteristics of patients with ‘driver‐gene‐negative’ LUAD. DIO3OS as a key lncRNA was found to affect tumor cell stemness in a variety of cancers.

Background Abnormal DNA methylation is one of the most general epigenetic modifications in hepatocellular carcinoma (HCC). Recent research showed that DNA methylation was a prognostic indicator of all‐cause HCC and nonviral HCC. However, whether DNA methylation‐driver genes could be used for predicting survival, the probability of hepatitis‐positive HCC remains unclear. Methods In this study, DNA methylation‐driver genes (MDGs) were screened by a joint analysis of methylome and transcriptome data of 142 hepatitis‐positive HCC patients. Subsequently, a prognostic risk score and nomogram were constructed. Finally, correlation analyses between the risk score and signaling pathways and immunity were conducted by GSVA and CIBERSORT. Results Through random forest screening and Cox progression analysis, 10 prognostic methylation‐driver genes (AC008271.1, C11orf53, CASP8, F2RL2, GBP5, LUCAT1, RP11‐114B7.6, RP11‐149I23.3, RP11‐383 J24.1, and SLC35G2) were screened out. As a result, a prognostic risk score signature was constructed. The independent value of the risk score for prognosis prediction were addressed in the TCGA‐HCC and the China‐HCC cohorts. Next, clinicopathological features were analyzed and HBV status and histological grade were screened to construct a nomogram together with the risk score. The prognostic efficiency of the nomogram was validated by the calibration curves and the concordance index (C index: 0.829, 95% confidence interval: 0.794–0.864), while its clinical application ability was confirmed by decision curve analysis (DCA). At last, the relationship between the risk score and signaling pathways, as well as the correlations between immune cells were elucidated preliminary. Conclusions Taken together, our study explored a novel DNA methylation‐driver gene risk score signature and an efficient nomogram for long‐term survival prediction of hepatitis‐positive HCC patients. DNA methylation‐driver genes (MDGs) were screened by a joint analysis of methylome and transcriptome data of 142 hepatitis‐positive HCC patients. A prognostic risk score and nomogram were constructed based on these MDGs and clinicopathological features.

Background Recent large-scale cancer sequencing studies have discovered many novel cancer driver genes (CDGs) in human cancers. Some studies also suggest that CDG mutations contribute to cancer-associated epigenomic and transcriptomic alterations across many cancer types. Here we aim to improve our understanding of the connections between CDG mutations and altered cancer cell epigenomes and transcriptomes on pan-cancer level and how these connections contribute to the known association between epigenome and transcriptome. Method Using multi-omics data including somatic mutation, DNA methylation, and gene expression data of 20 cancer types from The Cancer Genome Atlas (TCGA) project, we conducted a pan-cancer analysis to identify CDGs, when mutated, have strong associations with genome-wide methylation or expression changes across cancer types, which we refer as methylation driver genes (MDGs) or expression driver genes (EDGs), respectively. Results We identified 32 MDGs, among which, eight are known chromatin modification or remodeling genes. Many of the remaining 24 MDGs are connected to chromatin regulators through either regulating their transcription or physically interacting with them as potential co-factors. We identified 29 EDGs, 26 of which are also MDGs. Further investigation on target genes’ promoters methylation and expression alteration patterns of these 26 overlapping driver genes shows that hyper-methylation of target genes’ promoters are significantly associated with down-regulation of the same target genes and hypo-methylation of target genes’ promoters are significantly associated with up-regulation of the same target genes. Conclusion This finding suggests a pivotal role for genetically driven changes in chromatin remodeling in shaping DNA methylation and gene expression patterns during tumor development.

With the rapid development of lung cancer molecular detection and precision therapy, targeted therapy has covered the entire process of diagnosis and treatment of nonsmall cell lung cancer patients. Overall mortality from lung cancer has decreased significantly over the past 20 years, especially since the introduction of targeted drugs in 2013. In 2022, targeted therapy for lung cancer has developed rapidly. The optimization of treatment modes and the exploration of new target drugs such as antibody‐drug conjugates will broaden the selection range of nonsmall cell lung cancer patients with positive driver genes. This article reviews the latest advances in targeted therapy for driver gene‐positive lung cancer in 2022. The two most frequent alterations in nonsmall cell lung cancer patients were epidermal growth factor receptor and KRAS mutations, accounting for 10%–40% and 25%–32%, respectively. Although the incidence rate is relatively low, other gene mutations including anaplastic lymphoma kinase fusion, MET ex14+, ROS1 fusion, KRAS mutation, and so forth, are also crucial to the survival of patients, and numerous significant pharmacological advances have been produced so far.

Hepatocellular carcinoma (HCC) is one of the deadliest cancers worldwide and the only cancer with an increasing incidence in the United States. Recent advances in sequencing technology have enabled detailed profiling of liver cancer genomes and revealed extensive inter- and intra-tumor heterogeneity, making it difficult to identify driver genes for HCC. To identify HCC driver genes, we performed transposon mutagenesis screens in a mouse HBV model of HCC and discovered many candidate cancer genes (SB/HBV-CCGs). Here, we show that one of these genes, RNF125 is a potent anti-proliferative tumor suppressor gene in HCC. RNF125 is one of nine CCGs whose expression was >3-fold downregulated in human HCC. Depletion of RNF125 in immortalized mouse liver cells led to tumor formation in transplanted mice and accelerated growth of human liver cancer cell lines, while its overexpression inhibited their growth, demonstrating the tumor-suppressive function of RNF125 in mouse and human liver. Whole-transcriptome analysis revealed that RNF125 transcriptionally suppresses multiple genes involved in cell proliferation and/or liver regeneration, including Egfr, Met, and Il6r. Blocking Egfr or Met pathway expression inhibited the increased cell proliferation observed in RNF125 knockdown cells. In HCC patients, low expression levels of RNF125 were correlated with poor prognosis demonstrating an important role for RNF125 in HCC. Collectively, our results identify RNF125 as a novel anti-proliferative tumor suppressor in HCC.

Piwi‐like RNA‐mediated gene silencing 1 (PIWIL1) has been identified as a novel extremely highly expressed cancer‐testis (CT) gene in lung adenocarcinoma. However, the exact function and mechanism of PIWIL1 in lung adenocarcinoma remains unclear. Herein, we sought to investigate the role of PIWIL1 in the occurrence and development of lung adenocarcinoma. We examined the expression pattern of PIWIL1 in The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples, and validated it by Real‐Time PCR (RT‐PCR) in additional 21 paired lung adenocarcinoma tissues and 16 normal tissues. Subsequently, we explored the biological function of PIWIL1 in A549 and H1299 cell lines by gain and loss‐of‐function analyses. Using TCGA lung adenocarcinoma data, we further performed coexpression and Gene Ontology (GO) analyses, and analyzed the association of DNA methylation levels in PIWIL1 promoter region with its expression. Finally, we evaluated its expression in different mutation status of significantly mutated genes (SMGs) in TCGA lung adenocarcinoma data. We observed that PIWIL1 was expressed in testis and lung adenocarcinoma but not in other normal tissues, and its high expression was associated with shortened survival of lung cancer patients. Overexpression of PIWIL1 could facilitate the proliferation, invasion and migration of lung adenocarcinoma cells and vice versa. GO analysis revealed that PIWIL1 upregulated genes were enriched in embryonic development, cell proliferation and regulation of transcription. Moreover, promoter DNA hypomethylation of PIWIL1 could contribute to its aberrant expression in tumors. Interestingly, PIWIL1 expression was significantly higher in patients without hepatocyte growth factor (HGF) or serine/threonine kinase 11 (STK11) mutation (P = 0.006 and 0.005, respectively). PIWIL1 is an epidriver gene in lung adenocarcinoma, indicating a potential target for further therapy. PIWIL1 is a novel extremely highly expressed cancer‐testis gene in lung adenocarcinoma. PIWIL1 could promote the ability of proliferation, invasion, and migration in lung adenocarcinoma. The expression status of PIWIL1 is regulated by methylation in promoter region.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. Several genome sequencing studies have provided comprehensive CRC genomic datasets. Likewise, in our previous study, we performed genome-wide Sleeping Beauty transposon-based mutagenesis screening in mice and provided comprehensive datasets of candidate CRC driver genes. However, functional validation for most candidate CRC driver genes, which were commonly identified from both human and mice, has not been performed. Here, we describe a platform for functionally validating CRC driver genes that utilizes CRISPR-Cas9 in mouse intestinal tumor organoids and human CRC-derived organoids in xenograft mouse models. We used genetically defined benign tumor-derived organoids carrying 2 frequent gene mutations (Apc and Kras mutations), which act in the early stage of CRC development, so that we could clearly evaluate the tumorigenic ability of the mutation in a single gene. These studies showed that Acvr1b, Acvr2a, and Arid2 could function as tumor suppressor genes (TSGs) in CRC and uncovered a role for Trp53 in tumor metastasis. We also showed that co-occurrent mutations in receptors for activin and transforming growth factor-β (TGF-β) synergistically promote tumorigenesis, and shed light on the role of activin receptors in CRC. This experimental system can also be applied to mouse intestinal organoids carrying other sensitizing mutations as well as organoids derived from other organs, which could further contribute to identification of novel cancer driver genes and new drug targets.

Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/ .

Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, that is, bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied to driver gene prediction methods. We used this framework to compare the performance of eight such methods. One of these methods, described here, incorporated a machine-learning–based ratiometric approach. We show that the driver genes predicted by each of the eight methods vary widely. Moreover, the P values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false-positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future.