CHG: A Systematically Integrated Database of Cancer Hallmark Genes

COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms

Recent research on multi-view clustering algorithms for complex disease subtyping often overlooks aspects like clustering stability and critical assessment of prognostic relevance. Furthermore, current frameworks do not allow for a comparison between data-driven and pathway-driven clustering, highlighting a significant gap in the methodology. We present the COPS R-package, tailored for robust evaluation of single and multi-omics clustering results. COPS features advanced methods, including similarity networks, kernel-based approaches, dimensionality reduction, and pathway knowledge integration. Some of these methods are not accessible through R, and some correspond to new approaches proposed with COPS. Our framework was rigorously applied to multi-omics data across seven cancer types, including breast, prostate, and lung, utilizing mRNA, CNV, miRNA, and DNA methylation data. Unlike previous studies, our approach contrasts data- and knowledge-driven multi-view clustering methods and incorporates cross-fold validation for robustness. Clustering outcomes were assessed using the ARI score, survival analysis via Cox regression models including relevant covariates, and the stability of the results. While survival analysis and gold-standard agreement are standard metrics, they vary considerably across methods and datasets. Therefore, it is essential to assess multi-view clustering methods using multiple criteria, from cluster stability to prognostic relevance, and to provide ways of comparing these metrics simultaneously to select the optimal approach for disease subtype discovery in novel datasets. Emphasizing multi-objective evaluation, we applied the Pareto efficiency concept to gauge the equilibrium of evaluation metrics in each cancer case-study. Affinity Network Fusion, Integrative Non-negative Matrix Factorization, and Multiple Kernel K-Means with linear or Pathway Induced Kernels were the most stable and effective in discerning groups with significantly different survival outcomes in several case studies.

Comparative Quantitative Proteomic Analysis of Melanoma Subtypes, Nevus-Associated Melanoma, and Corresponding Nevi

A substantial part of cutaneous malignant melanomas develops from benign nevi. However, the precise molecular events driving the transformation from benign to malignant melanoma are not well-understood. We used laser microdissection and mass spectrometry to analyze the proteomes of melanoma subtypes, including superficial spreading melanomas (n = 17), nodular melanomas (n = 17), and acral melanomas (n = 15). Furthermore, we compared the proteomes of nevi cells with those of melanoma cells within the same specimens (nevus-associated melanoma (n = 14)). In total, we quantified 7935 proteins. Despite the genomic and clinical differences of the melanoma subtypes, our analysis revealed relatively similar proteomes, except for the upregulation of proteins involved in immune activation in nodular melanomas versus acral melanomas. Examining nevus-associated melanoma versus nevi, we found 1725 differentially expressed proteins (false discovery rate < 0.05). Among these proteins were 140 that overlapped with cancer hallmarks, tumor suppressors, and regulators of metabolism and cell cycle. Pathway analysis indicated aberrant activation of the phosphoinositide 3-kinase-protein kinase B-mTOR pathways and the Hippo-YAP pathway. Using a classifier, we identified six proteins capable of distinguishing melanoma from nevi samples. Our study represents a comprehensive comparative analysis of the proteome in melanoma subtypes and associated nevi, offering insights into the biological behavior of these distinct entities.

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

INTRODUCTION

Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels.

METHODS

In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.

RESULTS

Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.

CONCLUSIONS

This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

The coupling of transcription and translation enables prokaryotes to regulate mRNA stability and reduce nonfunctional transcripts. Eukaryotes evolved other means to perform these functions. Here, we quantify the disparity between gene expression and protein levels and attempt to explain its origins. We collected publicly available simultaneous measurements of gene expression, protein level, division rate, and growth inhibition of breast cancer cells under drug perturbation. We used the cell lines as entities with shared origin, different evolutionary trajectories, and cancer hallmarks to define tasks subject to specializing and trading-off. We observed varying average mRNA and protein correlation across cell lines, and it was consistently higher for the gene products in the cancer hallmarks. The enrichment of hallmark gene products signifies the resources invested in it as a task. Enrichment based on mRNA or protein abundance corresponds to the relative resources dedicated to transcription and translation. The differences in gene- and protein-based enrichment correlated with nominal division rates but not growth inhibition under drug perturbations. Comparing the range of enrichment scores of the hallmarks within each cell signifies the resources dedicated to each. Cells appear to have a wider range of enrichment in protein synthesis relative to gene transcription. The difference and range of enrichment of the hallmark genes and proteins correlated with cell division and inhibition in response to drug treatments. We posit that cancer cells may express the genes coding for seemingly nonspecialized tasks but do not translate them to the corresponding proteins. This trade-off may cost the cells under normal conditions but confer benefits during stress.

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BACKGROUND

The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance.

OBJECTIVE

The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers.

METHODS

Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC).

RESULTS

The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance.

CONCLUSIONS

The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

Cell cycle protein BORA is associated with colorectal cancer progression by AURORA-PLK1 cascades: a bioinformatics analysis

Colorectal cancer (CRC) is the third most diagnosed cancer in the world. A better understanding of the molecular mechanism of CRC is essential for making novel strategies for the CRC management and its prevention. The present study aims to explore the molecular mechanism through integrated bioinformatics analysis by analyzing genes and their co-expression pattern in normal and CRC states. GSE110223, GSE110224 and GSE113513 gene expression profiles were analyzed in this study. The co-expression networks for normal and tumor samples were constructed separately and analyzed to identify the modules, sub-networks and key genes. Gene regulatory network analysis was done to understand the regulatory mechanism of selected genes. Survival analysis was performed for the identified sub-networks and key genes to understand their role in CRC progression. A total of seven modules were detected and the KEGG pathway analysis revealed these modules were mainly enriched with cell cycle, metabolism and signaling-related pathways. E2F6 and ETV4 transcription factors regulating the activity of multiple genes of identified modules were found to be up-regulated in CRC. Six Sub-networks and seven key genes, BORA, CCT7, DTL, RUVBL1, RUVBL2, THEM6 and TMEM97 associated with the CRC progression were identified. Disease-gene association analysis identified a novel association of the BORA gene with CRC that activates and regulates the AURORA-PLK1 cascades in the cell cycle. Survival analysis indicates that the overexpressed BORA is associated with unfavourable overall survival in CRC. The mechanistic role of BORA in the regulation of cell cycle progression suggests that BORA might act as a potential therapeutic target for CRC.

An Updated Overview of Existing Cancer Databases and Identified Needs

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

The Versatility of Plectin in Cancer: A Pan-Cancer Analysis on Potential Diagnostic and Prognostic Impacts of Plectin Isoforms

Plectin, encoded by PLEC, is a cytoskeletal and scaffold protein with a number of unique isoforms that act on various cellular functions such as cell adhesion, signal transduction, cancer cell invasion, and migration. While plectin has been shown to display high expression and mislocalization in tumor cells, our knowledge of the biological significance of plectin and its isoforms in tumorigenesis remain limited. In this study, we first performed pathway enrichment analysis to identify cancer hallmark proteins associated with plectin. Then, a pan-cancer analysis was performed using RNA-seq data collected from the Cancer Genome Atlas (TCGA) to detect the mRNA expression levels of PLEC and its transcript isoforms, and the prognostic as well as diagnostic significance of the transcript isoforms was evaluated considering cancer stages. We show here that several tissue specific PLEC isoforms are dysregulated in different cancer types and stages but not the expression of PLEC. Among them, PLEC 1d and PLEC 1f are potential biomarker candidates and call for further translational and personalized medicine research. This study makes a contribution as a stride to unravel the molecular mechanisms underpinning plectin isoforms in cancer development and progression by revealing the potent plectin isoforms in different stages of cancer as potential early cancer detection biomarkers. Importantly, uncovering how plectin isoforms guide malignancy and particular cancer types by comprehensive functional studies might open new avenues toward novel cancer therapeutics.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms.

SIGNIFICANCE

BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.

Early contribution of germline and nevi genetic alterations to a rapidly-progressing cutaneous melanoma patient: a case report

BACKGROUND

Cutaneous melanoma is the skin cancer with the highest mutational burden and metastatic rate. Early genetic alterations and biomarkers of distant progression are a point of interest. In addition to germline-susceptibility loci, almost 30% of melanomas arise from precursor benign nevi lesions, providing a source for malignant transformation.

CASE PRESENTATION

Patient#009 developed a cutaneous melanoma over a nevus, followed by progression to regional and distant metastases in months, unresponsive to targeted therapy. To search for the genetic contribution to this rapid progression, a longitudinal analysis was performed through WES of germline, nevi, primary tumor, and a metastatic lymph node. Differential SNP/INDEL and CNV gene alterations, with functional impact on key pathways and cancer hallmarks in each step of evolution, were discerned. Tumor-associated nevus was, for the first time, split into two sections, distant and adjacent to the primary tumor, to study its heterogeneity. Shared SNP alterations, with stable allele fraction from germline to metastasis were detected, mainly affecting DNA repair genes and promoting genome instability. Early somatic alterations, shared by nevi and primary and metastatic tumors, included BRAF^V600E and focal copy-loss of several genes, acquiring additional cancer hallmarks. Phylogenetic analyses revealed that these common somatic alterations would provide a "bridge", allowing progression from a benign to a malignant state. Distant and adjacent nevi were rich in alterations, presenting differential SNP and CNV alterations. Upon tumor transformation, a marked increase in CNV over SNP alterations was determined. Both the number of SNP and CNV-affected genes, including known driver genes, increased throughout progression, although TMB levels remained lower than expected for melanoma. Typical alterations in BRAF^V600E tumors related to intrinsic resistance to targeted therapy were found, including BRAF amplification and loss of PTEN, CDKN2A/B, and TP53 surveillance genes. Finally, numerous metastatic alterations were detected, further promoting tumor progression.

CONCLUSIONS

In this patient, longitudinal WES analysis revealed a sequential and cumulative pattern of genetic alterations, where germline and nevi somatic events contributed early to its rapid clinical progression. In this case report, we found tumor-associated nevi as genetically heterogeneous precursor entities, in which potential prognostic biomarkers should be studied prospectively.

Single cell profiling of γδ hepatosplenic T-cell lymphoma unravels tumor cell heterogeneity associated with disease progression

PURPOSE

Hepatosplenic T-cell lymphoma (HSTCL), mostly derived from γδ T cells, is a rare but very aggressive lymphoma with poor outcomes. In this study, we generated the first single cell landscape for this rare disease and characterized the molecular pathogenesis underlying the disease progression.

METHODS

We performed paired single cell RNA-seq and T cell receptor (TCR) sequencing on biopsies from a HSTCL patient pre- and post- chemotherapy treatments. Following by a series of bioinformatics analysis, we investigated the gene expression profile of γδ HSTCS as well as its tumor microenvironment (TME).

RESULTS

We characterized the unique gene expressing signatures of malignant γδ T cells with a set of marker genes were newly identified in HSTCL (AREG, PLEKHA5, VCAM1 etc.). Although the malignant γδ T cells were expanded from a single TCR clonotype, they evolved into two transcriptionally distinct tumor subtypes during the disease progression. The Tumor_1 subtype was dominant in pre-treatment samples with highly aggressive phenotypes. While the Tumor_2 had relative mild cancer hallmark signatures but expressed genes associated with tumor survival signal and drug resistance (IL32, TOX2, AIF1, AKAP12, CD38 etc.), and eventually became the main tumor subtype post-treatment. We further dissected the tumor microenvironment and discovered the dynamically rewiring cell-cell interaction networks during the treatment. The tumor cells had reduced communications with the microenvironment post-treatment.

CONCLUSIONS

Our study reveals heterogenous and dynamic tumor and microenvironment underlying pathogenesis of HSTCL and may contribute to identify novel targets for diagnosis and treatment of HSTCL in the future.

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems' biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.

MCU controls melanoma progression through a redox‐controlled phenotype switch

Melanoma is the deadliest of skin cancers and has a high tendency to metastasize to distant organs. Calcium and metabolic signals contribute to melanoma invasiveness; however, the underlying molecular details are elusive. The MCU complex is a major route for calcium into the mitochondrial matrix but whether MCU affects melanoma pathobiology was not understood. Here, we show that MCU_A expression correlates with melanoma patient survival and is decreased in BRAF kinase inhibitor‐resistant melanomas. Knockdown (KD) of MCU_A suppresses melanoma cell growth and stimulates migration and invasion. In melanoma xenografts, MCU_{A_KD} reduces tumor volumes but promotes lung metastases. Proteomic analyses and protein microarrays identify pathways that link MCU_A and melanoma cell phenotype and suggest a major role for redox regulation. Antioxidants enhance melanoma cell migration, while prooxidants diminish the MCU_{A_KD}‐induced invasive phenotype. Furthermore, MCU_{A_KD} increases melanoma cell resistance to immunotherapies and ferroptosis. Collectively, we demonstrate that MCU_A controls melanoma aggressive behavior and therapeutic sensitivity. Manipulations of mitochondrial calcium and redox homeostasis, in combination with current therapies, should be considered in treating advanced melanoma.

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Disruption of cancer cell functions by task-specific drug perturbations

Cancer expands clonally, capitalizing on the variations between growing cells. Cancer cells specialize in one or more functions to gain an advantage. This study examined the prediction that cells would be vulnerable to drugs that perturb their specific tasks. We analyzed the correlation between gene expression and the response to drug perturbations in different cancer cells. Next, we assigned every cancer cell to an archetype based on gene expression. Finally, we calculated the enrichment of the cancer hallmark gene sets in each cell, archetypes, and response to drug treatment. We found that the extremes of gene expression were susceptible to change in response to perturbations. This correlation predicted the growth rate inhibition of breast cancer cells. Cancer hallmarks were enriched differently in the archetypes, and this enrichment predicted the cell’s response to perturbations. We present evidence that specialized cancer cells are sensitive to compounds that perturb their tasks.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

Integrated computational analysis reveals HOX genes cluster as oncogenic drivers in head and neck squamous cell carcinoma

Alterations in homeobox (HOX) gene expression are involved in the progression of several cancer types including head and neck squamous cell carcinoma (HNSCC). However, regulation of the entire HOX cluster in the pathophysiology of HNSCC is still elusive. By using different comprehensive databases, we have identified the significance of differentially expressed HOX genes (DEHGs) in stage stratification and HPV status in the cancer genome atlas (TCGA)-HNSCC datasets. The genetic and epigenetic alterations, druggable genes, their associated functional pathways and their possible association with cancer hallmarks were identified. We have performed extensive analysis to identify the target genes of DEHGs driving HNSCC. The differentially expressed HOX cluster-embedded microRNAs (DEHMs) in HNSCC and their association with HOX-target genes were evaluated to construct a regulatory network of the HOX cluster in HNSCC. Our analysis identified sixteen DEHGs in HNSCC and determined their importance in stage stratification and HPV infection. We found a total of 55 HNSCC driver genes that were identified as targets of DEHGs. The involvement of DEHGs and their targets in cancer-associated signaling mechanisms have confirmed their role in pathophysiology. Further, we found that their oncogenic nature could be targeted by using the novel and approved anti-neoplastic drugs in HNSCC. Construction of the regulatory network depicted the interaction between DEHGs, DEHMs and their targets genes in HNSCC. Hence, aberrantly expressed HOX cluster genes function in a coordinated manner to drive HNSCC. It could provide a broad perspective to carry out the experimental investigation, to understand the underlying oncogenic mechanism and allow the discovery of new clinical biomarkers for HNSCC.

Transcriptome analysis of clock disrupted cancer cells reveals differential alternative splicing of cancer hallmarks genes

Emerging evidence points towards a regulatory role of the circadian clock in alternative splicing (AS). Whether alterations in core-clock components may contribute to differential AS events is largely unknown. To address this, we carried out a computational analysis on recently generated time-series RNA-seq datasets from three core-clock knockout (KO) genes (ARNTL, NR1D1, PER2) and WT of a colorectal cancer (CRC) cell line, and time-series RNA-seq datasets for additional CRC and Hodgkin’s lymphoma (HL) cells, murine WT, Arntl KO, and Nr1d1/2 KO, and murine SCN WT tissue. The deletion of individual core-clock genes resulted in the loss of circadian expression in crucial spliceosome components such as SF3A1 (in ARNTL^KO), SNW1 (in NR1D1^KO), and HNRNPC (in PER2^KO), which led to a differential pattern of KO-specific AS events. All HCT116^KO cells showed a rhythmicity loss of a crucial spliceosome gene U2AF1, which was also not rhythmic in higher progression stage CRC and HL cancer cells. AS analysis revealed an increase in alternative first exon events specific to PER2 and NR1D1 KO in HCT116 cells, and a KO-specific change in expression and rhythmicity pattern of AS transcripts related to cancer hallmarks genes including FGFR2 in HCT116_ARNTL^KO, CD44 in HCT116_NR1D1^KO, and MET in HCT116_PER2^KO. KO-specific changes in rhythmic properties of known spliced variants of these genes (e.g. FGFR2 IIIb/FGFR2 IIIc) correlated with epithelial-mesenchymal-transition signalling. Altogether, our bioinformatic analysis highlights a role for the circadian clock in the regulation of AS, and reveals a potential impact of clock disruption in aberrant splicing in cancer hallmark genes.

Copy number alterations and epithelial‑mesenchymal transition genes in diffuse and intestinal gastric cancers in Mexican patients

Gastric cancer (GC) is a common malignancy with the highest mortality rate among diseases of the digestive system, worldwide. The present study of GC alterations is crucial to the understanding of tumor biology and the establishment of important aspects of cancer prognosis and treatment response. In the present study, DNA from Mexican patients with diffuse GC (DGC), intestinal GC (IGC) or non‑atrophic gastritis (NAG; control) was purified and whole‑genome analysis was performed with high‑density arrays. Shared and unique copy number alterations (CNA) were identified between the different tissues involving key genes and signaling pathways associated with cancer. This led to the molecular distinction and identification of the most relevant molecular functions to be identified. A more detailed bioinformatics analysis of epithelial‑mesenchymal transition (EMT) genes revealed that the altered network associated with chromosomal alterations included 11 genes that were shared between DGC, IGC and NAG, as well as 19 DGC‑ and 7 IGC‑exclusive genes. Furthermore, the main molecular functions included adhesion, angiogenesis, migration, metastasis, morphogenesis, proliferation and survival. The present study provided the first whole‑genome high‑density array analysis in Mexican patients with GC and revealed shared and exclusive CNA‑associated genes in DGC and IGC. In addition, a bioinformatics‑predicted network was generated, focusing on CNA‑altered genes associated with EMT and the hallmarks of cancer, as well as precancerous alterations that may lead to GC. Molecular signatures of diffuse and intestinal GC, predicted bioinformatically, involve common and distinct CNA‑EMT genes related to the hallmarks of cancer that are potential candidates for screening biomarkers of GC, including early stages.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

NetRank Recovers Known Cancer Hallmark Genes as Universal Biomarker Signature for Cancer Outcome Prediction

Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.

New insights on familial colorectal cancer type X syndrome

Familial colorectal cancer type X (FCCTX) is a heterogeneous colorectal cancer predisposition syndrome that, although displays a cancer pattern similar to Lynch syndrome, is mismatch repair proficient and does not exhibit microsatellite instability. Besides, its genetic etiology remains to be elucidated. In this study we performed germline exome sequencing of 39 cancer-affected patients from 34 families at risk for FCCTX. Variant classification followed the American College of Medical Genetics and Genomics (ACMG) guidelines. Pathogenic/likely pathogenic variants were identified in 17.65% of the families. Rare and potentially pathogenic alterations were identified in known hereditary cancer genes (CHEK2), in putative FCCTX candidate genes (OGG1 and FAN1) and in other cancer-related genes such as ATR, ASXL1, PARK2, SLX4 and TREX1. This study provides novel important clues that can contribute to the understanding of FCCTX genetic basis.

Integrating the Epigenome and Transcriptome of Hepatocellular Carcinoma to Identify Systematic Enhancer Aberrations and Establish an Aberrant Enhancer-Related Prognostic Signature

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.

A Transcriptional Signature of IL-2 Expanded Natural Killer Cells Predicts More Favorable Prognosis in Bladder Cancer

Activation of natural killer (NK) cell function is regulated by cytokines, such as IL-2, and secreted factors upregulated in the tumor microenvironment, such as platelet-derived growth factor D (PDGF-DD). In order to elucidate a clinical role for these important regulators of NK cell function in antitumor immunity, we generated transcriptional signatures representing resting, IL-2-expanded, and PDGF-DD-activated, NK cell phenotypes and established their abundance in The Cancer Genome Atlas bladder cancer (BLCA) dataset using CIBERSORT. The IL-2-expanded NK cell phenotype was the most abundant in low and high grades of BLCA tumors and was associated with improved prognosis. In contrast, PDGFD expression was associated with numerous cancer hallmark pathways in BLCA tumors compared with normal bladder tissue, and a high tumor abundance of PDGFD transcripts and the PDGF-DD-activated NK cell phenotype were associated with a poor BLCA prognosis. Finally, high tumor expression of transcripts encoding the activating NK cell receptors, KLRK1 and the CD160-TNFRSF14 receptor-ligand pair, was strongly correlated with the IL-2-expanded NK cell phenotype and improved BLCA prognosis. The transcriptional parameters we describe may be optimized to improve BLCA patient prognosis and risk stratification in the clinic and potentially provide gene targets of therapeutic significance for enhancing NK cell antitumor immunity in BLCA.

A Computational Analysis in a Cohort of Parkinson's Disease Patients and Clock-Modified Colorectal Cancer Cells Reveals Common Expression Alterations in Clock-Regulated Genes

Simple Summary

Cancer and neurodegenerative diseases are two aging-related pathologies with differential developmental characteristics, but they share altered cellular pathways. Interestingly, dysregulations in the biological clock are reported in both diseases, though the extent and potential consequences of such disruption have not been fully elucidated. In this study, we aimed at characterizing global changes on common cellular pathways associated with Parkinson’s disease (PD) and colorectal cancer (CRC). We used gene expression data retrieved from an idiopathic PD (IPD) patient cohort and from CRC cells with unmodified versus genetically altered clocks. Our results highlight common differentially expressed genes between IPD patients and cells with disrupted clocks, suggesting a role for the circadian clock in the regulation of pathways altered in both pathologies. Interestingly, several of these genes are related to cancer hallmarks and may have an impact on the overall survival of colon cancer patients, as suggested by our analysis.

Abstract

Increasing evidence suggests a role for circadian dysregulation in prompting disease-related phenotypes in mammals. Cancer and neurodegenerative disorders are two aging related diseases reported to be associated with circadian disruption. In this study, we investigated a possible effect of circadian disruption in Parkinson’s disease (PD) and colorectal cancer (CRC). We used high-throughput data sets retrieved from whole blood of idiopathic PD (IPD) patients and time course data sets derived from an in vitro model of CRC including the wildtype and three core-clock knockout (KO) cell lines. Several gene expression alterations in IPD patients resembled the expression profiles in the core-clock KO cells. These include expression changes in DBP, GBA, TEF, SNCA, SERPINA1 and TGFB1. Notably, our results pointed to alterations in the core-clock network in IPD patients when compared to healthy controls and revealed variations in the expression profile of PD-associated genes (e.g., HRAS and GBA) upon disruption of the core-clock genes. Our study characterizes changes at the transcriptomic level following circadian clock disruption on common cellular pathways associated with cancer and neurodegeneration (e.g., immune system, energy metabolism and RNA processing), and it points to a significant influence on the overall survival of colon cancer patients for several genes resulting from our analysis (e.g., TUBB6, PAK6, SLC11A1).

Exosomes in the Tumor Microenvironment: From Biology to Clinical Applications

Cancer is one of the most important health problems and the second leading cause of death worldwide. Despite the advances in oncology, cancer heterogeneity remains challenging to therapeutics. This is because the exosome-mediated crosstalk between cancer and non-cancer cells within the tumor microenvironment (TME) contributes to the acquisition of all hallmarks of cancer and leads to the formation of cancer stem cells (CSCs), which exhibit resistance to a range of anticancer drugs. Thus, this review aims to summarize the role of TME-derived exosomes in cancer biology and explore the clinical potential of mesenchymal stem-cell-derived exosomes as a cancer treatment, discussing future prospects of cell-free therapy for cancer treatment and challenges to be overcome.

SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions

microRNAs (miRNAs) are post-transcriptional regulators involved in many biological processes and human diseases, including cancer. The majority of transcripts compete over a limited pool of miRNAs, giving rise to a complex network of competing endogenous RNA (ceRNA) interactions. Currently, gene-regulatory networks focus mostly on transcription factor-mediated regulation, and dedicated efforts for charting ceRNA regulatory networks are scarce. Recently, it became possible to infer ceRNA interactions genome-wide from matched gene and miRNA expression data. Here, we inferred ceRNA regulatory networks for 22 cancer types and a pan-cancer ceRNA network based on data from The Cancer Genome Atlas. To make these networks accessible to the biomedical community, we present SPONGEdb, a database offering a user-friendly web interface to browse and visualize ceRNA interactions and an application programming interface accessible by accompanying R and Python packages. SPONGEdb allows researchers to identify potent ceRNA regulators via network centrality measures and to assess their potential as cancer biomarkers through survival, cancer hallmark and gene set enrichment analysis. In summary, SPONGEdb is a feature-rich web resource supporting the community in studying ceRNA regulation within and across cancer types.

Comprehensive Exome Analysis of Immunocompetent Metastatic Head and Neck Cancer Models Reveals Patient Relevant Landscapes

Immunocompetent metastatic head and neck cancer (HNC) models, although scarce, can help understanding cancer progression and therapy responses in vivo. Their comprehensive genome characterizations are essential for translational research. We first exome-sequenced the two most widely used spontaneous metastatic immunocompetent models, namely AT-84 and SCC VII, followed by comprehensive genomic analyses with three prior-sequenced models (MOC2, MOC2-10, and 4MOSC2), together with patient tumors for utility assessment. AT-84 and SCC VII bear high HNC tumor resemblance regarding mutational signatures-Trp53, Fanconi anemia, and MAPK and PI3K pathway defects. Collectively, the five models harbor genetic aberrations across 10 cancer hallmarks and 14 signaling pathways and machineries (metabolic, epigenetic, immune evasion), to extents similar in patients. Immune defects in HLA-A (H2-Q10, H2-Q4, H2-Q7, and H2-K1), Pdcd1, Tgfb1, Il2ra, Il12a, Cd40, and Tnfrsf14 are identified. Invasion/metastatic genome analyses first highlight potential druggable ERBB4 and KRAS mutations, for advanced/metastatic oral cavity cancer, as well as known metastasis players (Muc5ac, Trem3, Trp53, and Ttn) frequently captured by all models. Notable immunotherapy and precision druggable targets (Pdcd1, Erbb4, Fgfr1, H/Kras, Jak1, and Map2k2) and three druggable hubs (RTK family, MAPK, and DNA repair pathways) are frequently represented by these models. Immunocompetent metastatic HNC models are worth developing to address therapy- and invasion/metastasis-related questions in host immunity contexts.

Landscape of active enhancers developed de novo in cirrhosis and conserved in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) patients always have a background of cirrhosis. Aberrant epigenetic changes in cirrhosis provide a conductive environment for HCC tumorigenesis. Active enhancers (AEs) are essential for epigenetic regulation and play an important role in cell development and the progression of many diseases. However, the role of AEs in the progression from cirrhosis to HCC remains unclear. We systemically constructed a landscape of AEs that developed de novo in cirrhosis and were conserved in HCC, referred to as CL-HCC AEs. We observed significant upregulation of these CL-HCC AE-associated genes in cirrhosis and HCC, with no other epigenetic changes. Enrichment analysis of these CL-HCC AE-associated genes revealed enrichment in both hepatocyte-intrinsic tumorigenesis and tumor immune response, which might contribute to HCC tumorigenesis. Analysis of the diagnostic ability of these CL-HCC AE-associated genes provided a five-gene (THBS4, OLFML2B, CDKN3, GABRE, and HDAC11) diagnostic biomarker for HCC. Molecular subtype (MS) identification based on the CL-HCC AE-associated genes identified 3 MSs. Samples representing the 3 MSs showed differences in CL-HCC AE-associated gene expression levels, prognosis, copy number variation (CNV)/mutation frequencies, functional pathways, tumor microenvironment (TME) cell subtypes, immunotherapy responses and putative drug responses. We also found that the BET bromodomain inhibitor JQ1 downregulated the expression of CL-HCC AE-associated genes. Collectively, our results suggest that CL-HCC AEs and their associated genes contribute to HCC tumorigenesis and evolution, and could be used to distinguish the different landscapes of HCC and help explore the mechanism, classification, prediction, and precision therapy of HCC.