The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms

Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.

OGA mutant aberrantly hydrolyzes O-GlcNAc modification from PDLIM7 to modulate p53 and cytoskeleton in promoting cancer cell malignancy

O-GlcNAcase (OGA) is the only human enzyme that catalyzes the hydrolysis (deglycosylation) of O-linked beta-N-acetylglucosaminylation (O-GlcNAcylation) from numerous protein substrates. OGA has broad implications in many challenging diseases including cancer. However, its role in cell malignancy remains mostly unclear. Here, we report that a cancer-derived point mutation on the OGA's noncatalytic stalk domain aberrantly modulates OGA interactome and substrate deglycosylation toward a specific set of proteins. Interestingly, our quantitative proteomic studies uncovered that the OGA stalk domain mutant preferentially deglycosylated protein substrates with +2 proline in the sequence relative to the O-GlcNAcylation site. One of the most dysregulated substrates is PDZ and LIM domain protein 7 (PDLIM7), which is associated with the tumor suppressor p53. We found that the aberrantly deglycosylated PDLIM7 suppressed p53 gene expression and accelerated p53 protein degradation by promoting the complex formation with E3 ubiquitin ligase MDM2. Moreover, deglycosylated PDLIM7 significantly up-regulated the actin-rich membrane protrusions on the cell surface, augmenting the cancer cell motility and aggressiveness. These findings revealed an important but previously unappreciated role of OGA's stalk domain in protein substrate recognition and functional modulation during malignant cell progression.

Activation of mitogen-activated protein kinase signaling and development of papillary thyroid carcinoma in thyroid-stimulating hormone receptor D633H knockin mice

Objective

Nonautoimmune hyperthyroidism (NAH) is rare and occurs due to a constitutively activating thyroid stimulating hormone receptor (TSHR) mutation. In contrast to other thyroid nodules, no further evaluation for malignancy is recommended for hot thyroid nodules. In the first model for NAH in mice nearly all homozygous mice had developed papillary thyroid cancer by 12 months of age.

Methods

To further evaluate these mice, whole exome sequencing and phosphoproteome analysis were employed in a further generation of mice to identify any other mutations potentially responsible and to identify the pathways involved in thyroid carcinoma development.

Results

Only three genes (Nrg1, Rrs1, Rasal2) were mutated in all mice examined, none of which were known primary drivers of papillary thyroid cancer development. Wild-type and homozygous TSHR D633H knockin mice showed distinct phosphoproteome profiles with an enrichment of altered phosphosites found in ERK/mitogen-activated protein kinase (MAPK) signaling. Most importantly, phosphosites with known downstream effects included BRAF p.S766, which forms an inhibitory site: a decrease of phosphorylation at this site suggests an increase in MEK/ERK pathway activation. The decreased phosphorylation at BRAF p.S766 would suggest decreased AMP-activated protein kinase (AMPK) signaling, which is supported by the decreased phosphorylation of STIM1 p.S257, a downstream AMPK target.

Conclusion

The modified phosphoproteome profile of the homozygous mice in combination with human literature suggests a potential signaling pathway from constitutive TSHR signaling and cAMP activation to the activation of ERK/MAPK signaling. This is the first time that a specific mechanism has been identified for a possible involvement of TSH signaling in thyroid carcinoma development.

Tumour mutations in long noncoding RNAs enhance cell fitness

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.

Somatic mosaicism in inborn errors of immunity: Current knowledge, challenges, and future perspectives

Inborn errors of immunity (IEI) are a diverse group of monogenic disorders of the immune system due to germline variants in genes important for the immune response. Over the past decade there has been increasing recognition that acquired somatic variants present in a subset of cells can also lead to immune disorders or 'phenocopies' of IEI. Discovery of somatic mosaicism causing IEI has largely arisen from investigation of seemingly sporadic cases of IEI with predominant symptoms of autoinflammation and/or autoimmunity in which germline disease-causing variants are not detected. Disease-causing somatic mosaicism has been identified in genes that also cause germline IEI, such as FAS, and in genes without significant corresponding germline disease, such as UBA1 and TLR8. There are challenges in detecting low-level somatic variants, and it is likely that the extent of the somatic mosaicism causing IEI is largely uncharted. Here we review the field of somatic mosaicism leading to IEI and discuss challenges and methods for somatic variant detection, including diagnostic approaches for molecular diagnoses of patients.

Cancer-derived mutation in the OGA stalk domain promotes cell malignancy through dysregulating PDLIM7 and p53

O-GlcNAcase (OGA) is the sole enzyme that hydrolyzes O-GlcNAcylation from thousands of proteins and is dysregulated in many diseases including cancer. However, the substrate recognition and pathogenic mechanisms of OGA remain largely unknown. Here we report the first discovery of a cancer-derived point mutation on the OGA's non-catalytic stalk domain that aberrantly regulated a small set of OGA-protein interactions and O-GlcNAc hydrolysis in critical cellular processes. We uncovered a novel cancer-promoting mechanism in which the OGA mutant preferentially hydrolyzed the O-GlcNAcylation from modified PDLIM7 and promoted cell malignancy by down-regulating p53 tumor suppressor in different types of cells through transcription inhibition and MDM2-mediated ubiquitination. Our study revealed the OGA deglycosylated PDLIM7 as a novel regulator of p53-MDM2 pathway, offered the first set of direct evidence on OGA substrate recognition beyond its catalytic site, and illuminated new directions to interrogate OGA's precise role without perturbing global O-GlcNAc homeostasis for biomedical applications.

Transmicron: accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens

Transposon screens are powerful in vivo assays used to identify loci driving carcinogenesis. These loci are identified as Common Insertion Sites (CISs), i.e. regions with more transposon insertions than expected by chance. However, the identification of CISs is affected by biases in the insertion behaviour of transposon systems. Here, we introduce Transmicron, a novel method that differs from previous methods by (i) modelling neutral insertion rates based on chromatin accessibility, transcriptional activity and sequence context and (ii) estimating oncogenic selection for each genomic region using Poisson regression to model insertion counts while controlling for neutral insertion rates. To assess the benefits of our approach, we generated a dataset applying two different transposon systems under comparable conditions. Benchmarking for enrichment of known cancer genes showed improved performance of Transmicron against state-of-the-art methods. Modelling neutral insertion rates allowed for better control of false positives and stronger agreement of the results between transposon systems. Moreover, using Poisson regression to consider intra-sample and inter-sample information proved beneficial in small and moderately-sized datasets. Transmicron is open-source and freely available. Overall, this study contributes to the understanding of transposon biology and introduces a novel approach to use this knowledge for discovering cancer driver genes.

Identification of new telomere- and telomerase-associated autoantigens in systemic sclerosis

PURPOSE

In up to 20% of patients with systemic sclerosis (SSc) no known autoantibody specificity can be identified. Recently discovered autoantigens, such as telomeric repeat binding factor 1 (TERF1), as well as established autoantigens, like RuvBL1/2, are associated with telomere and telomerase biology. We aimed to identify new telomere- and telomerase-associated autoantigens in patients with SSc without known autoantibody specificity.

METHODS

Unlabelled protein immunoprecipitation combined with gel-free liquid chromatography-tandem mass spectrometry (IP-MS) was performed with sera of 106 patients with SSc from two tertiary referral centres that had a nuclear pattern on HEp-2 indirect immunofluorescence without previously identified autoantibody. Telomere- or telomerase-associated proteins or protein complexes precipitated by individual sera were identified. Candidate autoantigens were confirmed through immunoprecipitation-western blot (IP-WB). A custom Luminex xMAP assay for 5 proteins was evaluated with sera from persons with SSc (n = 467), other systemic autoimmune rheumatic diseases (n = 923), non-rheumatic disease controls (n = 187) and healthy controls (n = 199).

RESULTS

Eight telomere- and telomerase-associated autoantigens were identified in a total of 11 index patients, including the THO complex (n = 3, all with interstitial lung disease and two with cardiac involvement), telomeric repeat-binding factor 2 (TERF2, n = 1), homeobox-containing protein 1 (HMBOX1, n = 2), regulator of chromosome condensation 1 (RCC1, n = 1), nucleolar and coiled-body phosphoprotein 1 (NOLC1, n = 1), dyskerin (DKC1, n = 1), probable 28S rRNA (cytosine(4447)-C(5))-methyltransferase (NOP2, n = 1) and nuclear valosin-containing protein-like (NVL, n = 2). A Luminex xMAP assay for THO complex subunit 1 (THOC1), TERF2, NOLC1, NOP2 and NVL revealed high reactivity in all index patients, but also in other patients with SSc and disease controls. However, the reactivity by xMAP assay in these other patients was not confirmed by IP-WB.

CONCLUSION

IP-MS revealed key telomere- and telomerase-associated proteins and protein complexes as autoantigens in patients with SSc.

Neutrophil Transcriptional Deregulation by the Periodontal Pathogen Fusobacterium nucleatum in Gastric Cancer: A Bioinformatic Study

Background

Infection with the periodontal pathogen Fusobacterium nucleatum (F. nucleatum) has been associated with gastric cancer. The present study is aimed at uncovering the putative biological mechanisms underlying effects of F. nucleatum–mediated neutrophil transcriptional deregulation in gastric cancer.

Materials and Methods

A gene expression dataset pertaining to F. nucleatum-infected human neutrophils was utilized to identify differentially expressed genes (DEGs) using the GEO2R tool. Candidate genes associated with gastric cancer were sourced from the “Candidate Cancer Gene Database” (CCGD). Overlapping genes among these were identified as link genes. Functional profiling of the link genes was performed using “g:Profiler” tool to identify enriched Gene Ontology (GO) terms, pathways, miRNAs, transcription factors, and human phenotype ontology terms. Protein-protein interaction (PPI) network was constructed for the link genes using the “STRING” tool, hub nodes were identified as key candidate genes, and functionally enriched terms were determined.

Results

The gene expression dataset GEO20151 was downloaded, and 589 DEGs were identified through differential analysis. 886 candidate gastric cancer genes were identified in the CGGD database. Among these, 36 overlapping genes were identified as the link genes. Enriched GO terms included molecular function “enzyme building,” biological process “protein folding,'” cellular components related to membrane-bound organelles, transcription factors ER71 and Sp1, miRNAs miR580 and miR155, and several human phenotype ontology terms including squamous epithelium of esophagus. The PPI network contained 36 nodes and 53 edges, where the top nodes included PH4 and CANX, and functional terms related to intracellular membrane trafficking were enriched.

Conclusion

F nucleatum-induced neutrophil transcriptional activation may be implicated in gastric cancer via several candidate genes including DNAJB1, EHD1, IER2, CANX, and PH4B. Functional analysis revealed membrane-bound organelle dysfunction, intracellular trafficking, transcription factors ER71 and Sp1, and miRNAs miR580 and miR155 as other candidate mechanisms, which should be investigated in experimental studies.

Identifying Lethal Dependencies with HUGE Predictive Power

Recent functional genomic screens—such as CRISPR-Cas9 or RNAi screening—have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem is related to a large number of gene knockouts limiting the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the “HUb effect in Genetic Essentiality” (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens—Project Score, CERES score and DEMETER score—identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. Using acute myeloid leukemia, breast cancer, lung adenocarcinoma and colon adenocarcinoma as disease models, we validate that our predictions are enriched in a recent harmonized knowledge base of clinical interpretations of somatic genomic variants in cancer (AUROC > 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer. All the graphs showing lethal dependencies for the 19 tumor types analyzed can be visualized in an interactive tool.

Evaluation of cfDNA as an early detection assay for dense tissue breast cancer

A cell-free DNA (cfDNA) assay would be a promising approach to early cancer diagnosis, especially for patients with dense tissues. Consistent cfDNA signatures have been observed for many carcinogens. Recently, investigations of cfDNA as a reliable early detection bioassay have presented a powerful opportunity for detecting dense tissue screening complications early. We performed a prospective study to evaluate the potential of characterizing cfDNA as a central element in the early detection of dense tissue breast cancer (BC). Plasma samples were collected from 32 consenting subjects with dense tissue and positive mammograms, 20 with positive biopsies and 12 with negative biopsies. After screening and before biopsy, cfDNA was extracted, and whole-genome next-generation sequencing (NGS) was performed on all samples. Copy number alteration (CNA) and single nucleotide polymorphism (SNP)/insertion/deletion (Indel) analyses were performed to characterize cfDNA. In the positive-positive subjects (cases), a total of 5 CNAs overlapped with 5 previously reported BC-related oncogenes (KSR2, MAP2K4, MSI2, CANT1 and MSI2). In addition, 1 SNP was detected in KMT2C, a BC oncogene, and 9 others were detected in or near 10 genes (SERAC1, DAGLB, MACF1, NVL, FBXW4, FANK1, KCTD4, CAVIN1; ATP6V0A1 and ZBTB20-AS1) previously associated with non-BC cancers. For the positive–negative subjects (screening), 3 CNAs were detected in BC genes (ACVR2A, CUL3 and PIK3R1), and 5 SNPs were identified in 6 non-BC cancer genes (SNIP1, TBC1D10B, PANK1, PRKCA and RUNX2; SUPT3H). This study presents evidence of the potential of using cfDNA somatic variants as dense tissue BC biomarkers from a noninvasive liquid bioassay for early cancer detection.

Isolation of a Human Betaretrovirus from Patients with Primary Biliary Cholangitis

A human betaretrovirus (HBRV) has been linked with the autoimmune liver disease, primary biliary cholangitis (PBC), and various cancers, including breast cancer and lymphoma. HBRV is closely related to the mouse mammary tumor virus, and represents the only exogenous betaretrovirus characterized in humans to date. Evidence of infection in patients with PBC has been demonstrated through the identification of proviral integration sites in lymphoid tissue, the major reservoir of infection, as well as biliary epithelium, which is the site of the disease process. Accordingly, we tested the hypothesis that patients with PBC harbor a transmissible betaretrovirus by co-cultivation of PBC patients’ lymph node homogenates with the HS578T breast cancer line. Because of the low level of HBRV replication, betaretrovirus producing cells were subcloned to optimize viral isolation and production. Evidence of infection was provided by electron microscopy, RT-PCR, in situ hybridization, cloning of the HBRV proviral genome and demonstration of more than 3400 integration sites. Further evidence of viral transmissibility was demonstrated by infection of biliary epithelial cells. While HBRV did not show a preference for integration proximal to specific genomic features, analyses of common insertion sites revealed evidence of integration proximal to cancer associated genes. These studies demonstrate the isolation of HBRV with features similar to mouse mammary tumor virus and confirm that patients with PBC display evidence of a transmissible viral infection.

Stage-differentiated ensemble modeling of DNA methylation landscapes uncovers salient biomarkers and prognostic signatures in colorectal cancer progression

Background

Aberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, contributing to cancer etiology. A gap in our understanding concerns the epigenomics of stagewise cancer progression. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC).

Methods

The methylation β-matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using an ensemble of approaches involving stage-differentiated modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2), and then filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by matching with clinical data and performing Kaplan–Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis.

Results

We found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. The stage-differentiated models yielded the following consensus salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were significantly hypermethylated in the promoter regions, indicating down-regulation of expression and implying a putative CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1 survived all the analytical filters, and represents a novel early-stage epigenetic biomarker / target.

Conclusions

We have designed and executed a workflow for stage-differentiated epigenomic analysis of colorectal cancer progression, and identified several stage-salient diagnostic biomarkers, and an early-stage prognostic biomarker panel. The study has led to the discovery of an alternative CIMP-like signature in colorectal cancer, reinforcing the role of CIMP drivers in tumor pathophysiology.

Mutational characteristics of young and elderly gastric cancer: a comparative study

BACKGROUND

Young gastric cancer (YGC) has been indicated as having a worse prognosis than in elderly gastric cancer (EGC). It has been reported that YGC and EGC patients show different genomic profiles; however, there has been no comparative study conducted to reveal their mutational characteristics.

METHODS

Firstly, we divided and analyzed the mutational landscape and 50 cancer-related genes characters of YGC (n=18) and EGC (n=18) patients from The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD). A total of 8 gastric cancer samples including 4 YGC and 4 EGC patients were collected to detect 50 cancer-related genes by multiplex polymerase chain reaction (PCR) next generation sequencing. The R/maftools package was used to describe the mutational characteristics.

RESULTS

Our results showed that the EGC group harbored more mutations than the YGC group. In 50 cancer-related genes in our cohort, the YGC group tended to be different from the EGC group using multiplex PCR next generation sequencing. In the YGC group, candidate mutations were identified within the following genes: IDH2, PDGFRA, KRAS, FLT3, FGFR2, and FGFR3. The YGC group showed less tumor mutational burden (TMB) level then EGC.

CONCLUSIONS

The YGC group tended to be more sensitive to molecularly targeted therapy because of it having more somatic mutations in 50 cancer-related genes using targeted next-generation sequencing.

Discovering the key genes and important DNA methylation regions in breast cancer

BACKGROUND

Breast cancer is the malignant tumor with the highest incidence in women. DNA methylation has an important effect on breast cancer, but the effect of abnormal DNA methylation on gene expression in breast cancer is still unclear. Therefore, it is very important to find therapeutic targets related to DNA methylation.

RESULTS

In this work, we calculated the DNA methylation distribution and gene expression level in cancer and para-cancerous tissues for breast cancer samples. We found that DNA methylation in key regions is closely related to gene expression by analyzing the relationship between the distribution characteristics of DNA methylation in different regions and the change of gene expression level. Finally, the 18 key genes (17 tumor suppressor genes and 1 oncogene) related to prognosis were confirmed by the survival analysis of clinical data. Some important DNA methylation regions in these genes that result in breast cancer were found.

CONCLUSIONS

We believe that 17 TSGs and 1 oncogene may be breast cancer biomarkers regulated by DNA methylation in key regions. These results will help to explore DNA methylation biomarkers as potential therapeutic targets for breast cancer.

Somatic Mutational Profile of High-Grade Serous Ovarian Carcinoma and Triple-Negative Breast Carcinoma in Young and Elderly Patients: Similarities and Divergences

Background: Triple-negative breast cancer (TNBC) and High-Grade Serous Ovarian Cancer (HGSOC) are aggressive malignancies that share similarities; however, different ages of onset may reflect distinct tumor behaviors. Thus, our aim was to compare somatic mutations in potential driver genes in 109 TNBC and 81 HGSOC from young (Y ≤ 40 years) and elderly (E ≥ 75 years) patients. Methods: Open access mutational data (WGS or WES) were collected for TNBC and HGSOC patients. Potential driver genes were those that were present in the Cancer Gene Census—CGC, the Candidate Cancer Gene Database—CCGD, or OncoKB and those that were considered pathogenic in variant effect prediction tools. Results: Mutational signature 3 (homologous repair defects) was the only gene that was represented in all four subgroups. The median number of mutated CGCs per sample was similar in HGSOC (Y:3 vs. E:4), but it was higher in elderly TNBC than it was in young TNBC (Y:3 vs. E:6). At least 90% of the samples from TNBC and HGSOC from Y and E patients presented at least one known affected TSG. Besides TP53, which was mutated in 67–83% of the samples, the affected TSG in TP53 wild-type samples were NF1 (yHGSOC and yTNBC), PHF6 (eHGSOC and yTNBC), PTEN, PIK3R1 and ZHFX3 (yTNBC), KMT2C, ARID1B, TBX3, and ATM (eTNBC). A few samples only presented one affected oncogene (but no TSG): KRAS and TSHR in eHGSOC and RAC1 and PREX2 (a regulator of RAC1) in yTNBC. At least ⅔ of the tumors presented mutated oncogenes associated with tumor suppressor genes; the Ras and/or PIK3CA signaling pathways were altered in 15% HGSOC and 20–35% TNBC (Y vs. E); DNA repair genes were mutated in 19–33% of the HGSOC tumors but were more frequently mutated in E-TNBC (56%). However, in HGSOC, 9.5% and 3.3% of the young and elderly patients, respectively, did not present any tumors with an affected CGC nor did 4.65% and none of the young and elderly TNBC patients. Conclusion: Most HGSOC and TNBC from young and elderly patients present an affected TSG, mainly TP53, as well as mutational signature 3; however, a few tumors only present an affected oncogene or no affected cancer-causing genes.

Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10^-4, C = 0.84, logrank-p = 3.8x10^-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10⁻⁴ (C = 0.88, logrank-p = 1.44x10^-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

Universal and cross-cancer prognostic biomarkers for predicting survival risk of cancer patients from expression profile of apoptotic pathway genes

Numerous cancer-specific prognostic models have been developed in the past, wherein one model is applicable for only one type of cancer. In this study, an attempt has been made to identify universal or multi-cancer prognostic biomarkers and develop models for predicting survival risk across different types of cancer patients. In order to accomplish this, we gauged the prognostic role of mRNA expression of 165 apoptosis-related genes across 33 cancers in the context of patient survival. Firstly, we identified specific prognostic biomarker genes for 30 cancers. The cancer-specific prognostic models achieved a minimum Hazard Ratio, HR_SKCM = 1.99 and maximum HR_THCA = 41.59. Secondly, a comprehensive analysis was performed to identify universal biomarkers across many cancers. Our best prognostic model consisted of 11 genes (TOP2A, ISG20, CD44, LEF1, CASP2, PSEN1, PTK2, SATB1, SLC20A1, EREG, and CD2) and stratified risk groups across 27 cancers (HR_OV = 1.53-HR_UVM = 11.74). The model was validated on eight independent cancer cohorts and exhibited a comparable performance. Further, we clustered cancer-types on the basis of shared survival related apoptosis genes. This approach proved helpful in development of cross-cancer prognostic models. To show its efficacy, a prognostic model consisting of 15 genes was thereby developed for LGG-KIRC pair (HR_KIRC = 3.27, HR_LGG = 4.23). Additionally, we predicted potential therapeutic candidates for LGG-KIRC high risk patients.

Interpretation of cancer mutations using a multiscale map of protein systems

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

DNA methylation is required to maintain both DNA replication timing precision and 3D genome organization integrity

DNA replication timing and three-dimensional (3D) genome organization are associated with distinct epigenome patterns across large domains. However, whether alterations in the epigenome, in particular cancer-related DNA hypomethylation, affects higher-order levels of genome architecture is still unclear. Here, using Repli-Seq, single-cell Repli-Seq, and Hi-C, we show that genome-wide methylation loss is associated with both concordant loss of replication timing precision and deregulation of 3D genome organization. Notably, we find distinct disruption in 3D genome compartmentalization, striking gains in cell-to-cell replication timing heterogeneity and loss of allelic replication timing in cancer hypomethylation models, potentially through the gene deregulation of DNA replication and genome organization pathways. Finally, we identify ectopic H3K4me3-H3K9me3 domains from across large hypomethylated domains, where late replication is maintained, which we purport serves to protect against catastrophic genome reorganization and aberrant gene transcription. Our results highlight a potential role for the methylome in the maintenance of 3D genome regulation.

Framing the potential of public frameshift peptides as immunotherapy targets in colon cancer

Approximately 15% of Colon Cancers are Microsatellite Instable (MSI). Frameshift Peptides (FPs) formed in MSI Colon Cancer are potential targets for immunotherapeutic strategies. Here we comprehensively characterize the mutational landscape of 71 MSI Colon Cancer patients from the cancer genome atlas (TCGA). We confirm that the mutations in MSI Colon Cancers are frequently frameshift deletions (23% in MSI; 1% in microsatellite stable), We find that these mutations cluster at specific locations in the genome which are mutated in up to 41% of the patients. We filter these for an adequate variant allele frequency, a sufficient mean mRNA level and the formation of a Super Neo Open Reading Frame (SNORF). Finally, we check the influence of Nonsense Mediated Decay (MMD) by comparing RNA and DNA sequencing results. Thereby we identify a set of 20 NMD-escaping Public FPs (PFPs) that cover over 90% of MSI Colon, 62.2% of MSI Endometrial and 58.8% of MSI Stomach cancer patients and 3 out of 4 Lynch patients in the TCGA-COAD. This underlines the potential for PFP directed immunotherapy, both in a therapeutic and a prophylactic setting in multiple types of MSI cancers.

Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer

INTRODUCTION

Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs.

OBJECTIVES

The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature.

METHODS

This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes.

RESULTS

Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10^-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10^-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10^-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86.

CONCLUSION

We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.

Recent trends in biodegradable polyester nanomaterials for cancer therapy

Biodegradable polyester nanomaterials-based drug delivery vehicles (DDVs) have been largely used in most of the cancer treatments due to its high biological performance and wider applications. In several previous studies, various biodegradable and biocompatible polyester backbones were used which are poly(lactic acid) (PLA), poly(ε-caprolactone) (PCL), poly(propylene fumarate) (PPF), poly(lactic-co-glycolic acid) (PLGA), poly(propylene carbonate) (PPC), polyhydroxyalkanoates (PHA), and poly(butylene succinate) (PBS). These polyesters were fabricated into therapeutic nanoparticles that carry drug molecules to the target site during the cancer disease treatment. In this review, we elaborately discussed the chemical synthesis of different synthetic polyesters and their use as nanodrug carriers (NCs) in cancer treatment. Further, we highlighted in brief the recent developments of metal-free semi-aromatic polyester nanomaterials along with its role as cancer drug delivery vehicles.

Identification of cancer driver genes using Sleeping Beauty transposon mutagenesis

Cancer genome sequencing studies have identified driver genes for a variety of different cancers and helped to understand the genetic landscape of human cancer. It is still challenging, however, to identify cancer driver genes with confidence simply from genetic data alone. In vivo forward genetic screens using Sleeping Beauty (SB) transposon mutagenesis provides another powerful genetic tool for identifying candidate cancer driver genes in wild-type and sensitized mouse tumors. By comparing cancer driver genes identified in human and mouse tumors, cancer driver genes can be identified with additional confidence based upon comparative oncogenomics. This review describes how SB mutagenesis works in mice and focuses on studies that have identified cancer driver genes in the mouse gastrointestinal tract.

Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs

Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance.

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data, to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms the competing methods and can identify new associations. Furthermore, it captures the underlying biology predictive of drug response. Through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problem.

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.

In vivo functional screening for systems-level integrative cancer genomics

With the genetic portraits of all major human malignancies now available, we next face the challenge of characterizing the function of mutated genes, their downstream targets, interactions and molecular networks. Moreover, poorly understood at the functional level are also non-mutated but dysregulated genomes, epigenomes or transcriptomes. Breakthroughs in manipulative mouse genetics offer new opportunities to probe the interplay of molecules, cells and systemic signals underlying disease pathogenesis in higher organisms. Herein, we review functional screening strategies in mice using genetic perturbation and chemical mutagenesis. We outline the spectrum of genetic tools that exist, such as transposons, CRISPR and RNAi and describe discoveries emerging from their use. Genome-wide or targeted screens are being used to uncover genomic and regulatory landscapes in oncogenesis, metastasis or drug resistance. Versatile screening systems support experimentation in diverse genetic and spatio-temporal settings to integrate molecular, cellular or environmental context-dependencies. We also review the combination of in vivo screening and barcoding strategies to study genetic interactions and quantitative cancer dynamics during tumour evolution. These scalable functional genomics approaches are transforming our ability to interrogate complex biological systems.

Somatic mutation profiling in BRCA-negative breast and ovarian cancer patients by multigene panel sequencing

Targeted therapeutic agents such as poly (ADP-ribose) polymerases (PARP) inhibitors have emerged in treating cancers associated with germline BRCA mutations. Recently studies demonstrated the effectiveness of PARP inhibitors in treating patients with somatic BRCA mutations. Somatic mutations in 122 Chinese breast or ovarian cancer patients without BRCA, PTEN and TP53 mutations were screened using multigene sequencing panel. The five most frequent pathogenic or likely pathogenic mutated genes identified in breast cancer patients were PIK3CA (28.6%), TP53 (16.9%), MAP3K1 (14.3%), GATA3 (14.3%) and PTEN (5.2%). The five most frequently mutated genes identified in ovarian patients were TP53 (52.9%), KRAS (23.5%) and PIK3CA (11.8%), BRCA1 (5.9%) and RB1 (5.9%). Somatic PIK3CA and TP53 mutations were common events in both germline BRCA-negative breast and ovarian cancer patients. In contrast, somatic screening of BRCA mutations in BRCA-negative breast cancer patients has limited value. The results highlight the benefit of somatic testing to guide future research directions on other targeted therapies for breast and ovarian malignancies.

Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer

MOTIVATION

Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer.

METHODS

CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predict additional essential genes on broader scale. Finally, the expression status and potential roles of these genes were explored based on TCGA portal and regulatory network analysis.

RESULTS

We collected 926 samples from 10 CRISPR-based screening studies involving 33 different types of cancer to identify cancer-essential genes, which consists of 799 protein-coding genes (PCGs) and 97 long non-coding RNAs (lncRNAs). Then, we constructed a 'bi-colored' network with both PCGs and lncRNAs and applied it to predict additional essential genes including 495 PCGs and 280 lncRNAs on a broader scale using hypergeometrics test and RWR. After obtaining all essential genes, we further investigated their potential roles in cancer and found that essential genes have higher and more stable expression levels, and are associated with multiple cancer-associated biological processes and survival time. The regulatory network analysis detected two intriguing modules of essential genes participating in the regulation of cell cycle and ribosome biogenesis in cancer.

AVAILABILITY AND IMPLEMENTATION

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SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data

Prognostic biomarkers dedicating to treat cancer are very difficult to identify. Although high-throughput sequencing technology allows us to mine prognostic biomarkers much deeper by analyzing omics data, there is lack of effective methods to comprehensively utilize multi-omics data. In this work, we integrated multi-omics data [DNA methylation (DM), gene expression (GE), somatic copy number alternation, and microRNA expression (ME)] and proposed a method to rank genes by desiring a “Score.” Applying the method, cancer-specific prognostic biomarkers for 13 cancers were obtained. The prognostic powers of the biomarkers were further assessed by C-indexes (ranged from 0.76 to 0.96). Moreover, by comparing the 13 survival-related gene lists, seven genes (SLK, API5, BTBD2, PTAR1, VPS37A, EIF2B1, and ZRANB1) were found to be associated with prognosis in a variety of cancers. In particular, SLK was more likely to be cancer-related due to its high missense mutation rate and associated with cell adhesion. Furthermore, after network analysis, EPRS, HNRNPA2B1, BPTF, LRRK1, and PUM1 were demonstrated to have a broad correlation with cancers. In summary, our method has a better integration of multi-omics data that can be extended to the researches of other diseases. And the prognostic biomarkers had a better prognostic power than previous methods. Our results could provide a reference for translational medicine researchers and clinicians.

Biosystem Analysis of the Hypoxia Inducible Domain Family Member 2A: Implications in Cancer Biology

The expression of HIGD2A is dependent on oxygen levels, glucose concentration, and cell cycle progression. This gene encodes for protein HIG2A, found in mitochondria and the nucleus, promoting cell survival in hypoxic conditions. The genomic location of HIGD2A is in chromosome 5q35.2, where several chromosomal abnormalities are related to numerous cancers. The analysis of high definition expression profiles of HIGD2A suggests a role for HIG2A in cancer biology. Accordingly, the research objective was to perform a molecular biosystem analysis of HIGD2A aiming to discover HIG2A implications in cancer biology. For this purpose, public databases such as SWISS-MODEL protein structure homology-modelling server, Catalogue of Somatic Mutations in Cancer (COSMIC), Gene Expression Omnibus (GEO), MethHC: a database of DNA methylation and gene expression in human cancer, and microRNA-target interactions database (miRTarBase) were accessed. We also evaluated, by using Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR), the expression of Higd2a gene in healthy bone marrow-liver-spleen tissues of mice after quercetin (50 mg/kg) treatment. Thus, among the structural features of HIG2A protein that may participate in HIG2A translocation to the nucleus are an importin α-dependent nuclear localization signal (NLS), a motif of DNA binding residues and a probable SUMOylating residue. HIGD2A gene is not implicated in cancer via mutation. In addition, DNA methylation and mRNA expression of HIGD2A gene present significant alterations in several cancers; HIGD2A gene showed significant higher expression in Diffuse Large B-cell Lymphoma (DLBCL). Hypoxic tissues characterize the “bone marrow-liver-spleen” DLBCL type. The relative quantification, by using qRT-PCR, showed that Higd2a expression is higher in bone marrow than in the liver or spleen. In addition, it was observed that quercetin modulated the expression of Higd2a gene in mice. As an assembly factor of mitochondrial respirasomes, HIG2A might be unexpectedly involved in the change of cellular energetics happening in cancer. As a result, it is worth continuing to explore the role of HIGD2A in cancer biology.

Transposon Insertion Mutagenesis in Mice for Modeling Human Cancers: Critical Insights Gained and New Opportunities

Transposon mutagenesis has been used to model many types of human cancer in mice, leading to the discovery of novel cancer genes and insights into the mechanism of tumorigenesis. For this review, we identified over twenty types of human cancer that have been modeled in the mouse using Sleeping Beauty and piggyBac transposon insertion mutagenesis. We examine several specific biological insights that have been gained and describe opportunities for continued research. Specifically, we review studies with a focus on understanding metastasis, therapy resistance, and tumor cell of origin. Additionally, we propose further uses of transposon-based models to identify rarely mutated driver genes across many cancers, understand additional mechanisms of drug resistance and metastasis, and define personalized therapies for cancer patients with obesity as a comorbidity.

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis.

Co-occurrence and Mutual Exclusivity Analysis of DNA Methylation Reveals Distinct Subtypes in Multiple Cancers

Co-occurrence and mutual exclusivity (COME) of DNA methylation refer to two or more genes that tend to be positively or negatively correlated in DNA methylation among different samples. Although COME of gene mutations in pan-cancer have been well explored, little is known about the COME of DNA methylation in pan-cancer. Here, we systematically explored the COME of DNA methylation profile in diverse human cancer. A total of 5,128,332 COME events were identified in 14 main cancers types in The Cancer Genome Atlas (TCGA). We also identified functional epigenetic modules of the zinc finger gene family in six cancer types by integrating the gene expression and DNA methylation data and the frequently occurred COME network. Interestingly, most of the genes in those functional epigenetic modules are epigenetically repressed. Strikingly, those frequently occurred COME events could be used to classify the patients into several subtypes with significant different clinical outcomes in six cancers as well as pan-cancer (p-value ≤ = 0.05). Moreover, we observed significant associations between different COME subtypes and clinical features (e.g., age, gender, histological type, neoplasm histologic grade, and pathologic stage) in distinct cancers. Taken together, we identified millions of COME events of DNA methylation in pan-cancer and detected functional epigenetic COME events that could separate tumor patients into different subtypes, which may benefit the diagnosis and prognosis of pan-cancer.

Quantifying gene selection in cancer through protein functional alteration bias

Compiling the catalogue of genes actively involved in cancer is an ongoing endeavor, with profound implications to the understanding and treatment of the disease. An abundance of computational methods have been developed to screening the genome for candidate driver genes based on genomic data of somatic mutations in tumors. Existing methods make many implicit and explicit assumptions about the distribution of random mutations. We present FABRIC, a new framework for quantifying the selection of genes in cancer by assessing the effects of de-novo somatic mutations on protein-coding genes. Using a machine-learning model, we quantified the functional effects of ∼3M somatic mutations extracted from over 10 000 human cancerous samples, and compared them against the effects of all possible single-nucleotide mutations in the coding human genome. We detected 593 protein-coding genes showing statistically significant bias towards harmful mutations. These genes, discovered without any prior knowledge, show an overwhelming overlap with known cancer genes, but also include many overlooked genes. FABRIC is designed to avoid false discoveries by comparing each gene to its own background model using rigorous statistics, making minimal assumptions about the distribution of random somatic mutations. The framework is an open-source project with a simple command-line interface.

Discovering master regulators in hepatocellular carcinoma: one novel MR, SEC14L2 inhibits cancer cells

Identification of master regulator (MR) genes offers a relatively rapid and efficient way to characterize disease-specific molecular programs. Since strong consensus regarding commonly altered MRs in hepatocellular carcinoma (HCC) is lacking, we generated a compendium of HCC datasets from 21 studies and identified a comprehensive signature consisting of 483 genes commonly deregulated in HCC. We then used reverse engineering of transcriptional networks to identify the MRs that underpin the development and progression of HCC. After cross-validation in different HCC datasets, systematic assessment using patient-derived data confirmed prognostic predictive capacities for most HCC MRs and their corresponding regulons. Our HCC signature covered well-established liver cancer hallmarks, and network analyses revealed coordinated interaction between several MRs. One novel MR, SEC14L2, exerted an anti-proliferative effect in HCC cells and strongly suppressed tumor growth in a mouse model. This study advances our knowledge of transcriptional MRs potentially involved in HCC development and progression that may be targeted by specific interventions.

Role of ion channels in gastrointestinal cancer

In their seminal papers Hanahan and Weinberg described oncogenic processes a normal cell undergoes to be transformed into a cancer cell. The functions of ion channels in the gastrointestinal (GI) tract influence a variety of cellular processes, many of which overlap with these hallmarks of cancer. In this review we focus on the roles of the calcium (Ca2+), sodium (Na+), potassium (K+), chloride (Cl-) and zinc (Zn2+) transporters in GI cancer, with a special emphasis on the roles of the KCNQ1 K+ channel and CFTR Cl- channel in colorectal cancer (CRC). Ca2+ is a ubiquitous second messenger, serving as a signaling molecule for a variety of cellular processes such as control of the cell cycle, apoptosis, and migration. Various members of the TRP superfamily, including TRPM8, TRPM7, TRPM6 and TRPM2, have been implicated in GI cancers, especially through overexpression in pancreatic adenocarcinomas and down-regulation in colon cancer. Voltage-gated sodium channels (VGSCs) are classically associated with the initiation and conduction of action potentials in electrically excitable cells such as neurons and muscle cells. The VGSC NaV1.5 is abundantly expressed in human colorectal CRC cell lines as well as being highly expressed in primary CRC samples. Studies have demonstrated that conductance through NaV1.5 contributes significantly to CRC cell invasiveness and cancer progression. Zn2+ transporters of the ZIP/SLC39A and ZnT/SLC30A families are dysregulated in all major GI organ cancers, in particular, ZIP4 up-regulation in pancreatic cancer (PC). More than 70 K+ channel genes, clustered in four families, are found expressed in the GI tract, where they regulate a range of cellular processes, including gastrin secretion in the stomach and anion secretion and fluid balance in the intestinal tract. Several distinct types of K+ channels are found dysregulated in the GI tract. Notable are hERG1 upregulation in PC, gastric cancer (GC) and CRC, leading to enhanced cancer angiogenesis and invasion, and KCNQ1 down-regulation in CRC, where KCNQ1 expression is associated with enhanced disease-free survival in stage II, III, and IV disease. Cl- channels are critical for a range of cellular and tissue processes in the GI tract, especially fluid balance in the colon. Most notable is CFTR, whose deficiency leads to mucus blockage, microbial dysbiosis and inflammation in the intestinal tract. CFTR is a tumor suppressor in several GI cancers. Cystic fibrosis patients are at a significant risk for CRC and low levels of CFTR expression are associated with poor overall disease-free survival in sporadic CRC. Two other classes of chloride channels that are dysregulated in GI cancers are the chloride intracellular channels (CLIC1, 3 & 4) and the chloride channel accessory proteins (CLCA1,2,4). CLIC1 & 4 are upregulated in PC, GC, gallbladder cancer, and CRC, while the CLCA proteins have been reported to be down-regulated in CRC. In summary, it is clear, from the diverse influences of ion channels, that their aberrant expression and/or activity can contribute to malignant transformation and tumor progression. Further, because ion channels are often localized to the plasma membrane and subject to multiple layers of regulation, they represent promising clinical targets for therapeutic intervention including the repurposing of current drugs.

SBCDDB: Sleeping Beauty Cancer Driver Database for gene discovery in mouse models of human cancers

Large-scale oncogenomic studies have identified few frequently mutated cancer drivers and hundreds of infrequently mutated drivers. Defining the biological context for rare driving events is fundamentally important to increasing our understanding of the druggable pathways in cancer. Sleeping Beauty (SB) insertional mutagenesis is a powerful gene discovery tool used to model human cancers in mice. Our lab and others have published a number of studies that identify cancer drivers from these models using various statistical and computational approaches. Here, we have integrated SB data from primary tumor models into an analysis and reporting framework, the Sleeping Beauty Cancer Driver DataBase (SBCDDB, http://sbcddb.moffitt.org), which identifies drivers in individual tumors or tumor populations. Unique to this effort, the SBCDDB utilizes a single, scalable, statistical analysis method that enables data to be grouped by different biological properties. This allows for SB drivers to be evaluated (and re-evaluated) under different contexts. The SBCDDB provides visual representations highlighting the spatial attributes of transposon mutagenesis and couples this functionality with analysis of gene sets, enabling users to interrogate relationships between drivers. The SBCDDB is a powerful resource for comparative oncogenomic analyses with human cancer genomics datasets for driver prioritization.

Impact of chronically alternating light-dark cycles on circadian clock mediated expression of cancer (glioma)-related genes in the brain

Disruption of the circadian rhythm is a risk factor for cancer, while glioma is a leading contributor to mortality worldwide. Substantial efforts are being undertaken to decrypt underlying molecular pathways. Our understanding of the mechanisms through which disrupted circadian rhythm induces glioma development and progression is incomplete. We, therefore, examined changes in the expression of glioma-related genes in the mouse brain after chronic jetlag (CJL) exposure. A total of 22 candidate tumor suppressor (n= 14) and oncogenes (n= 8) were identified and analyzed for their interaction with clock genes. Both the control and CJL groups were investigated for the expression of candidate genes in the nucleus accumbens, hippocampus, prefrontal cortex, hypothalamus, and striatum of wild type, Bmal1-/- and Cry1/2 double knockout male mice. We found significant variations in the expression of candidate tumor suppressor and oncogenes in the brain tissues after CJL treatment in the wild type, Bmal1-/- and Cry1/2 double knockout mice. In response to CJL treatment, some of the genes were regulated in the wild type, Bmal1-/- and Cry1/2 similarly. However, the expression of some of the genes indicated their association with the functional clock. Overall, our result suggests a link between CJL and gliomas risk at least partially dependent on the circadian clock. However, further studies are needed to investigate the molecular mechanism associated with CJL and gliomas.

PLAGL2 and POFUT1 are regulated by an evolutionarily conserved bidirectional promoter and are collaboratively involved in colorectal cancer by maintaining stemness

Background

Our previous study revealed that PLAGL2 or POFUT1 can promote tumorigenesis and maintain significant positive correlations in colorectal cancer (CRC). However, the mechanism leading to the co-expression and the underlying functional and biological implications remain unclear.

Methods

Clinical tumor tissues and TCGA dataset were utilized to analyze the co-expression of PLAGL2 and POFUT1. Luciferase reporter assays, specially made bidirectional promoter vectors and ectopic expression of 3’UTR were employed to study the mechanisms of co-expression. In vitro and in vivo assays were performed to further confirm the oncogenic function of both. The sphere formation assay, immunofluorescence, Western blot and qRT-PCR were performed to investigate the effect of both genes in colorectal cancer stem cells (CSCs).

Findings

PLAGL2 and POFUT1 maintained co-expression in CRC (r = 0.91, p < .0001). An evolutionarily conserved bidirectional promoter, rather than post-transcriptional regulation by competing endogenous RNAs, caused the co-expression of PLAGL2 and POFUT1 in CRC. The bidirectional gene pair PLAGL2/POFUT1 was subverted in CRC and acted synergistically to promote colorectal tumorigenesis by maintaining stemness of colorectal cancer stem cells through the Wnt and Notch pathways. Finally, PLAGL2 and POFUT1 share transcription factor binding sites, and introducing mutations into promoter regions with shared transcription regulatory elements led to a decrease in the PLAGL2/POFUT1 promoter activity in both directions.

Interpretation

Our team identified for the first time a bidirectional promoter pair oncogene, PLAGL2-POFUT1, in CRC. The two genes synergistically promote the progression of CRC and affect the characteristics of CSCs, which can offer promising intervention targets for clinicians and researchers.

Fund

National Nature Science Foundation of China, the Hunan province projects of Postgraduate Independent Exploration and Innovation of Central South University.

A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model

To-date, most proteomic studies aimed at discovering tissue-based cancer biomarkers have compared the quantity of selected proteins between case and control groups. However, proteins generally function in association with other proteins to form modules localized in particular subcellular compartments in specialized cell types and tissues. Sub-cellular mislocalization of proteins has in fact been detected as a key feature in a variety of cancer cells. Here, we describe a strategy for tissue-biomarker detection based on a mitochondrial fold enrichment (mtFE) score, which is sensitive to protein abundance changes as well as changes in subcellular distribution between mitochondria and cytosol. The mtFE score integrates protein abundance data from total cellular lysates and mitochondria-enriched fractions, and provides novel information for the classification of cancer samples that is not necessarily apparent from conventional abundance measurements alone. We apply this new strategy to a panel of wild-type and mutant mice with a liver-specific gene deletion of Liver receptor homolog 1 (Lrh-1^hep−/−), with both lines containing control individuals as well as individuals with liver cancer induced by diethylnitrosamine (DEN). Lrh-1 gene deletion attenuates cancer cell metabolism in hepatocytes through mitochondrial glutamine processing. We show that proteome changes based on mtFE scores outperform protein abundance measurements in discriminating DEN-induced liver cancer from healthy liver tissue, and are uniquely robust against genetic perturbation. We validate the capacity of selected proteins with informative mtFE scores to indicate hepatic malignant changes in two independent mouse models of hepatocellular carcinoma (HCC), thus demonstrating the robustness of this new approach to biomarker research. Overall, the method provides a novel, sensitive approach to cancer biomarker discovery that considers contextual information of tested proteins.

CAMIRADA: Cancer microRNA association discovery algorithm, a case study on breast cancer

In recent studies, non-coding protein RNAs have been identified as microRNA that can be used as biomarkers for early diagnosis and treatment of cancer, that decrease mortality in cancer. A microRNA may target hundreds or thousands of genes and a gene may regulate several microRNAs, so determining which microRNA is associated with which cancer is a big challenge. Many computational methods have been performed to detect micoRNAs association with cancer, but more effort is needed with higher accuracy. Increasing research has shown that relationship between microRNAs and TFs play a significant role in the diagnosis of cancer. Therefore, we developed a new computational framework (CAMIRADA) to identify cancer-related microRNAs based on the relationship between microRNAs and disease genes (DG) in the protein network, the functional relationships between microRNAs and Transcription Factors (TF) on the co-expression network, and the relationship between microRNAs and the Differential Expression Gene (DEG) on co-expression network. The CAMIRADA was applied to assess breast cancer data from two HMDD and miR2Disease databases. In this study, the AUC for the 65 microRNAs of the top of the list was 0.95, which was more accurate than the similar methods used to detect microRNAs associated with the cancer artery.

Insertional mutagenesis using the Sleeping Beauty transposon system identifies drivers of erythroleukemia in mice

Insertional mutagenesis is a powerful means of identifying cancer drivers in animal models. We used the Sleeping Beauty (SB) transposon/transposase system to identify activated oncogenes in hematologic cancers in wild-type mice and mice that express a stabilized cyclin E protein (termed cyclin ET74AT393A). Cyclin E governs cell division and is misregulated in human cancers. Cyclin ET74AT393A mice develop ineffective erythropoiesis that resembles early-stage human myelodysplastic syndrome, and we sought to identify oncogenes that might cooperate with cyclin E hyperactivity in leukemogenesis. SB activation in hematopoietic precursors caused T-cell leukemia/lymphomas (T-ALL) and pure red blood cell erythroleukemias (EL). Analysis of >12,000 SB integration sites revealed markedly different oncogene activations in EL and T-ALL: Notch1 and Ikaros were most common in T-ALL, whereas ETS transcription factors (Erg and Ets1) were targeted in most ELs. Cyclin E status did not impact leukemogenesis or oncogene activations. Whereas most SB insertions were lost during culture of EL cell lines, Erg insertions were retained, indicating Erg's key role in these neoplasms. Surprisingly, cyclin ET74AT393A conferred growth factor independence and altered Erg-dependent differentiation in EL cell lines. These studies provide new molecular insights into erythroid leukemia and suggest potential therapeutic targets for human leukemia.

Structure, function and disease relevance of Wnt inhibitory factor 1, a secreted protein controlling the Wnt and hedgehog pathways

Wnts and Hedgehogs (Hh) are large, lipid-modified extracellular morphogens that play key roles in embryonic development and stem cell proliferation of Metazoa. Both morphogens signal through heptahelical Frizzled-type receptors of the G-Protein Coupled Receptor family and there are several other similarities that suggest a common evolutionary origin of the Hh and Wnt pathways. There is evidence that the secreted protein, Wnt inhibitory factor 1 (WIF1) modulates the activity of both Wnts and Hhs and may thus contribute to the intertwining of these pathways. In this article, we review the structure, evolution, molecular interactions and functions of WIF1 with major emphasis on its role in carcinogenesis.

TranscriptAchilles: a genome-wide platform to predict isoform biomarkers of gene essentiality in cancer

BACKGROUND

Aberrant alternative splicing plays a key role in cancer development. In recent years, alternative splicing has been used as a prognosis biomarker, a therapy response biomarker, and even as a therapeutic target. Next-generation RNA sequencing has an unprecedented potential to measure the transcriptome. However, due to the complexity of dealing with isoforms, the scientific community has not sufficiently exploited this valuable resource in precision medicine.

FINDINGS

We present TranscriptAchilles, the first large-scale tool to predict transcript biomarkers associated with gene essentiality in cancer. This application integrates 412 loss-of-function RNA interference screens of >17,000 genes, together with their corresponding whole-transcriptome expression profiling. Using this tool, we have studied which are the cancer subtypes for which alternative splicing plays a significant role to state gene essentiality. In addition, we include a case study of renal cell carcinoma that shows the biological soundness of the results. The databases, the source code, and a guide to build the platform within a Docker container are available at GitLab. The application is also available online.

CONCLUSIONS

TranscriptAchilles provides a user-friendly web interface to identify transcript or gene biomarkers of gene essentiality, which could be used as a starting point for a drug development project. This approach opens a wide range of translational applications in cancer.

eQTL analysis from co-localization of 2739 GWAS loci detects associated genes across 14 human cancers

Genetic variants can predict other "linked" diseases because alterations in one or more genes in vivo may affect relevant phenotype properties. Our study systematically explored the pan-cancer common gene and cancer type-specific genes based on GWAS loci and TCGA data of multiple cancers. It was found that there were 17 SNPs were significantly associated with the expression of 18 genes. Associations between the 18 cis-regulatory genes and the pathologic stage of each cancer showed that MYL2 and PTGFR in HNSC, 4 genes (F8, SATB2, G6PD and UGT1A6) in KIRP, 3 genes (CHMP4C, MAP3K1 and MECP2) in LUAD were all strongly associated with cancer stage levels. Additionally, the survival association analysis showed that SATB2 was correlated with HNSC survival, and MPP1 was strongly associated with the survival of SARC. This study will shed light on the biological pathways involved in cancer-genetic associations, and has the potential to be applied to the predictions of the risk of cancers developing in healthy individuals.

Sleeping Beauty Screen Identifies RREB1 and Other Genetic Drivers in Human B-cell Lymphoma

Follicular lymphoma and diffuse large B-cell lymphoma (DLBCL) are the most common non-Hodgkin lymphomas distinguishable by unique mutations, chromosomal rearrangements, and gene expression patterns. Here, it is demonstrated that early B-cell progenitors express 2',3'-cyclic-nucleotide 3' phosphodiesterase (CNP) and that when targeted with Sleeping Beauty (SB) mutagenesis, Trp53R270H mutation or Pten loss gave rise to highly penetrant lymphoid diseases, predominantly follicular lymphoma and DLBCL. In efforts to identify the genetic drivers and signaling pathways that are functionally important in lymphomagenesis, SB transposon insertions were analyzed from splenomegaly specimens of SB-mutagenized mice (n = 23) and SB-mutagenized mice on a Trp53R270H background (n = 7) and identified 48 and 12 sites with statistically recurrent transposon insertion events, respectively. Comparison with human data sets revealed novel and known driver genes for B-cell development, disease, and signaling pathways: PI3K-AKT-mTOR, MAPK, NFκB, and B-cell receptor (BCR). Finally, functional data indicate that modulating Ras-responsive element-binding protein 1 (RREB1) expression in human DLBCL cell lines in vitro alters KRAS expression, signaling, and proliferation; thus, suggesting that this proto-oncogene is a common mechanism of RAS/MAPK hyperactivation in human DLBCL. IMPLICATIONS: A forward genetic screen identified new genetic drivers of human B-cell lymphoma and uncovered a RAS/MAPK-activating mechanism not previously appreciated in human lymphoid disease. Overall, these data support targeting the RAS/MAPK pathway as a viable therapeutic target in a subset of human patients with DLBCL.