Cytoskeleton regulator RNA expression on cancer-associated fibroblasts is associated with prognosis and immunotherapy response in bladder cancer

Background

Dysregulation of long noncoding RNAs (lncRNAs) has been reported to be associated with multiple tumors where they act as tumor suppressors or accelerators. The lncRNA CYTOR was identified as an oncogene involved in many cancers, such as gastric cancer, colorectal cancer, hepatocellular carcinoma, and renal cell carcinoma. However, the role of CYTOR in bladder cancer (BCa) has rarely been reported.

Methods

Using cancer datasets from The Cancer Genome Atlas (TCGA) program, we analyzed the association between CYTOR expression and prognostic value, oncogenic pathways, antitumor immunity and immunotherapy response in BCa. The influence of CYTOR on the immune infiltration pattern in the urothelial carcinoma microenvironment was further verified in our dataset. Single-cell analysis revealed the role of CYTOR in the tumor microenvironment (TME) of BCa. Finally, we evaluated the expression of CYTOR in BCa in the Peking University First Hospital (PKU-BCa) dataset and its correlation with the malignant phenotype of BCa in vitro and in vivo.

Results

The results indicated that CYTOR was highly expressed in multiple cancer samples, including BCa, and increased CYTOR expression contributed to poor overall survival (OS). Additionally, elevated CYTOR expression was significantly correlated with clinicopathological features of BCa, such as female sex, advanced TNM stage, high histological grade and non-papillary subtype. Functional characterization revealed that CYTOR may be involved in immune-related pathways and the epithelial mesenchymal transformation (EMT) process. Moreover, CYTOR had a significant association with infiltrating immune cells, including M2 macrophages and regulatory T cells (Tregs). CYTOR facilitates the crosstalk between cancer-associated fibroblasts (CAFs) and macrophages, and mediates M2 polarization of macrophages. Correlation analysis revealed a positive correlation between CYTOR expression and programmed cell death-1 (PD-1)/programmed death ligand 1 (PD-L1)/expression and other targets for specific immunotherapy in BCa, which are recognized to predict the efficacy of immunotherapy.

Conclusions

These results suggest that CYTOR serves as a potential biomarker for predicting survival outcome, TME cell infiltration characteristics and immunotherapy response in BCa.

Identification and verification of eight cancer-associated fibroblasts related genes as a prognostic signature for head and neck squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) can exert their immunosuppressive effects by secreting various effectors that are involved in the regulation of tumor-infiltrating immune cells as well as other immune components in the tumor immune microenvironment (TIME), thereby promoting tumorigenesis, progression, metastasis, and drug resistance. Although a large number of studies suggest that CAFs play a key regulatory role in the development of head and neck squamous cell carcinoma (HNSCC), there are limited studies on the relevance of CAFs to the prognosis of HNSCC. In this study, we identified a prognostic signature containing eight CAF-related genes for HNSCC by univariate Cox analysis, lasso regression, stepwise regression, and multivariate Cox analysis. Our validation in primary cultures of CAFs from human HNSCC and four human HNSCC cell lines confirmed that these eight genes are indeed characteristic markers of CAFs. Immune cell infiltration differences analysis between high-risk and low-risk groups according to the eight CAF-related genes signature hinted at CAFs regulatory roles in the TIME, further revealing its potential role on prognosis. The signature of the eight CAF-related genes was validated in different independent validation cohorts and all showed that it was a valid marker for prognosis. The significantly higher overall survival (OS) in the low-risk group compared to the high-risk group was confirmed by Kaplan-Meier (K-M) analysis, suggesting that the signature of CAF-related genes can be used as a non-invasive predictive tool for HNSCC prognosis. The low-risk group had significantly higher levels of tumor-killing immune cell infiltration, as confirmed by CIBERSORT analysis, such as CD8⁺ T cells, follicular helper T cells, and Dendritic cells (DCs) in the low-risk group. In contrast, the level of infiltration of pro-tumor cells such as M0 macrophages and activated Mast cells (MCs) was lower. It is crucial to delve into the complex mechanisms between CAFs and immune cells to find potential regulatory targets and may provide new evidence for subsequently targeted immunotherapy. These results suggest that the signature of the eight CAF-related genes is a powerful indicator for the assessment of the TIME of HNSCC. It may provide a new and reliable potential indicator for clinicians to predict the prognosis of HNSCC, which may be used to guide treatment and clinical decision-making in HNSCC patients. Meanwhile, CAF-related genes are expected to become tumor biomarkers and effective targets for HNSCC.

Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Deep-learning based approach to identify substrates of human E3 ubiquitin ligases and deubiquitinases

E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) play key roles in protein degradation. However, a large number of E3 substrate interactions (ESIs) and DUB substrate interactions (DSIs) remain elusive. Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and number of epochs, have been systematically assessed. The performance of DeepUSI was thoroughly evaluated by multiple metrics, based on internal and external validation. Application of DeepUSI to cancer-associated E3 and DUB genes identified a list of druggable substrates with functional implications, warranting further investigation. Together, DeepUSI presents a new framework for predicting substrates of E3 ubiquitin ligases and deubiquitinates.

ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients. The obtained results demonstrated that ALDH2 gene and protein expression significantly downregulated in LUAD patient samples. Furthermore, The American Joint Committee on Cancer (AJCC) reported that diminished ALDH2 expression was closely linked to worse overall survival (OS) in different stages of LUAD. Considerably, ALDH2 showed aberrant DNA methylation status in LUAD cancer. ALDH2 was found to be downregulated in the proteomic expression profile of several cell biology signaling pathways, particularly stem cell-related pathways. Finally, the relationship of ALDH2 activity with stem cell-related factors and immune system were reported. In conclusion, the downregulation of ALDH2, abnormal DNA methylation, and the consequent deficit of stemness signaling pathways are relevant prognostic and therapeutic markers in LUAD.

Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning

Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

Integrative network analysis reveals subtype-specific long non-coding RNA regulatory mechanisms in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is one of most common malignancies with high mortality worldwide. Importantly, the molecular heterogeneity of HNSC complicates the clinical diagnosis and treatment, leading to poor overall survival outcomes. To dissect the complex heterogeneity, recent studies have reported multiple molecular subtyping systems. For instance, HNSC can be subdivided to four distinct molecular subtypes: atypical, basal, classical, and mesenchymal, of which the mesenchymal subtype is characterized by upregulated epithelial-mesenchymal transition (EMT) and associated with poorer survival outcomes. Despite a wealth of studies into the complex molecular heterogeneity, the regulatory mechanism specific to this aggressive subtype remain largely unclear. Herein, we developed a network-based bioinformatics framework that integrates lncRNA and mRNA expression profiles to elucidate the subtype-specific regulatory mechanisms. Applying the framework to HNSC, we identified a clinically relevant lncRNA LNCOG as a key master regulator mediating EMT underlying the mesenchymal subtype. Five genes with strong prognostic values, namely ANXA5, ITGA5, CCBE1, P4HA2, and EPHX3, were predicted to be the putative targets of LNCOG and subsequently validated in other independent datasets. By integrative analysis of the miRNA expression profiles, we found that LNCOG may act as a ceRNA to sponge miR-148a-3p thereby upregulating ITGA5 to promote HNSC progression. Furthermore, our drug sensitivity analysis demonstrated that the five putative targets of LNCOG were also predictive of the sensitivities of multiple FDA-approved drugs. In summary, our bioinformatics framework facilitates the dissection of cancer subtype-specific lncRNA regulatory mechanisms, providing potential novel biomarkers for more optimized treatment of HNSC.

A dissected LMO2 functional analysis and clinical relevance in brain gliomas

Brain glioma is one of the cancer types with worst prognosis, and LMO2 has been reported to play oncogenic functions in brain gliomas. Herein, analysis of datasets from The Cancer Genome Atlas (TCGA) indicated that higher LMO2 level in patient samples indicated worse prognosis in lower grade gliomas (LGG) but not glioblastoma multiforme (GBM). Further, in tumor tissues consisting of a variety of cell types, LMO2 level indicated intratumoral endothelium and pattern recognition receptor (PRR) response in both LGGs and GBMs, and additionally indicated cytotoxic T-lymphocyte, M2 macrophage infiltration and fibroblast specifically in LGGs. Moreover, only in LGGs these aspects were significantly associated with patient survival, in either risky or protective manner, and these dissected associations can give a better prediction on patient prognosis than LMO2 alone. This study not only provided more detailed understandings of LMO2 functional representatives in brain gliomas but also demonstrated that dealing with certain gene (LMO2 in this study) in transcriptome data with the Weighted Gene Co-Expression Network Analysis (WGCNA) method was a robust strategy for dissecting exact and reasonable gene functions/associations in a complicated tumor environment.

Identifies microtubule-binding protein CSPP1 as a novel cancer biomarker associated with ferroptosis and tumor microenvironment

Centrosome and spindle pole-associated protein (CSPP1) is a centrosome and microtubule-binding protein that plays a role in cell cycle-dependent cytoskeleton organization and cilia formation. Previous studies have suggested that CSPP1 plays a role in tumorigenesis; however, no pan-cancer analysis has been performed. This study systematically investigates the expression of CSPP1 and its potential clinical outcomes associated with diagnosis, prognosis, and therapy. CSPP1 is widely present in tissues and cells and its aberrant expression serves as a diagnostic biomarker for cancer. CSPP1 dysregulation is driven by multi-dimensional mechanisms involving genetic alterations, DNA methylation, and miRNAs. Phosphorylation of CSPP1 at specific sites may play a role in tumorigenesis. In addition, CSPP1 correlates with clinical features and outcomes in multiple cancers. Take brain low-grade gliomas (LGG) with a poor prognosis as an example, functional enrichment analysis implies that CSPP1 may play a role in ferroptosis and tumor microenvironment (TME), including regulating epithelial-mesenchymal transition, stromal response, and immune response. Further analysis confirms that CSPP1 dysregulates ferroptosis in LGG and other cancers, making it possible for ferroptosis-based drugs to be used in the treatment of these cancers. Importantly, CSPP1-associated tumors are infiltrated in different TMEs, rendering immune checkpoint blockade therapy beneficial for these cancer patients. Our study is the first to demonstrate that CSPP1 is a potential diagnostic and prognostic biomarker associated with ferroptosis and TME, providing a new target for drug therapy and immunotherapy in specific cancers.

Aristolochic acids exposure was not the main cause of liver tumorigenesis in adulthood

Aristolochic acids (AAs) have long been considered as a potent carcinogen due to its nephrotoxicity. Aristolochic acid I (AAI) reacts with DNA to form covalent aristolactam (AL)–DNA adducts, leading to subsequent A to T transversion mutation, commonly referred as AA mutational signature. Previous research inferred that AAs were widely implicated in liver cancer throughout Asia. In this study, we explored whether AAs exposure was the main cause of liver cancer in the context of HBV infection in mainland China. Totally 1256 liver cancer samples were randomly retrieved from 3 medical centers and a refined bioanalytical method was used to detect AAI–DNA adducts. 5.10% of these samples could be identified as AAI positive exposure. Whole genome sequencing suggested 8.41% of 107 liver cancer patients exhibited the dominant AA mutational signature, indicating a relatively low overall AAI exposure rate. In animal models, long-term administration of AAI barely increased liver tumorigenesis in adult mice, opposite from its tumor-inducing role when subjected to infant mice. Furthermore, AAI induced dose-dependent accumulation of AA–DNA adduct in target organs in adult mice, with the most detected in kidney instead of liver. Taken together, our data indicate that AA exposure was not the major threat of liver cancer in adulthood.

Violations of proportional hazard assumption in Cox regression model of transcriptomic data in TCGA pan-cancer cohorts

Background

Cox proportional hazard regression (CPH) model relies on the proportional hazard (PH) assumption: the hazard of variables is independent of time. CPH has been widely used to identify prognostic markers of the transcriptome. However, the comprehensive investigation on PH assumption in transcriptomic data has lacked.

Results

The whole transcriptomic data of the 9,056 patients from 32 cohorts of The Cancer Genome Atlas and the 3 lung cancer cohorts from Gene Expression Omnibus were collected to construct CPH model for each gene separately for fitting the overall survival. An average of 8.5% gene CPH models violated the PH assumption in TCGA pan-cancer cohorts. In the gene interaction networks, both hub and non-hub genes in CPH models were likely to have non-proportional hazards. Violations of PH assumption for the same gene models were not consistent in 5 non-small cell lung cancer datasets (all kappa coefficients < 0.2), indicating that the non-proportionality of gene CPH models depended on the datasets. Furthermore, the introduction of log(t) or sqrt(t) time-functions into CPH improved the performance of gene models on overall survival fitting in most tumors. The time-dependent CPH changed the significance of log hazard ratio of the 31.9% gene variables.

Conclusions

Our analysis resulted that non-proportional hazards should not be ignored in transcriptomic data. Introducing time interaction term ameliorated performance and interpretability of non-proportional hazards of transcriptome data in CPH.

Unraveling unique and common cell type-specific mechanisms in glioblastoma multiforme

Glioblastoma multiforme persists to be an enigmatic distress in neuro-oncology. Its untethering capacity to thrive in a confined microenvironment, metastasize intracranially, and remain resistant to the systemic treatments, renders this tumour incurable. The glial cell type specificity in GBM remains exploratory. In our study, we aimed to address this problem by studying the GBM at the cell type level in the brain. The cellular makeup of this tumour is composed of genetically altered glial cells which include astrocyte, microglia, oligodendrocyte precursor cell, newly formed oligodendrocyte and myelinating oligodendrocyte. We extracted cell type-specific solid tumour as well as recurrent solid tumour glioma genes, and studied their functional networks and contribution towards gliomagenesis. We identified the principal transcription factors that are found to be regulating vital tumorigenic processes. We also assessed the protein-protein interaction networks at their domain level to get a more microscopic view of the structural and functional operations that transpire in these cells. This yielded the eminent protein regulators exhibiting their regulation in signaling pathways. Overall, our study unveiled regulatory mechanisms in glioma cell types that can be targeted for a more efficient glioma therapy.

Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Analysis of the immune landscape in virus-induced cancers using a novel integrative mechanism discovery approach

Background

The mechanisms of carcinogenesis from viral infections are extraordinarily complex and not well understood. Traditional methods of analyzing RNA-sequencing data may not be sufficient for unraveling complicated interactions between viruses and host cells. Using RNA and DNA-sequencing data from The Cancer Genome Atlas (TCGA), we aim to explore whether virus-induced tumors exhibit similar immune-associated (IA) dysregulations using a new algorithm we developed that focuses on the most important biological mechanisms involved in virus-induced cancers. Differential expression, survival correlation, and clinical variable correlations were used to identify the most clinically relevant IA genes dysregulated in 5 virus-induced cancers (HPV-induced head and neck squamous cell carcinoma, HPV-induced cervical cancer, EBV-induced stomach cancer, HBV-induced liver cancer, and HCV-induced liver cancer) after which a mechanistic approach was adopted to identify pathways implicated in IA gene dysregulation.

Results

Our results revealed that IA dysregulations vary with the cancer type and the virus type, but cytokine signaling pathways are dysregulated in all virus-induced cancers. Furthermore, we also found that important similarities exist between all 5 virus-induced cancers in dysregulated clinically relevant oncogenic signatures and IA pathways. Finally, we also discovered potential mechanisms for genomic alterations to induce IA gene dysregulations using our algorithm.

Conclusions

Our study offers a new approach to mechanism identification through integrating functional annotations and large-scale sequencing data, which may be invaluable to the discovery of new immunotherapy targets for virus-induced cancers.

Deubiquitinase JOSD2 stabilizes YAP/TAZ to promote cholangiocarcinoma progression

Cholangiocarcinoma (CCA) has emerged as an intractable cancer with scanty therapeutic regimens. The aberrant activation of Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) are reported to be common in CCA patients. However, the underpinning mechanism remains poorly understood. Deubiquitinase (DUB) is regarded as a main orchestrator in maintaining protein homeostasis. Here, we identified Josephin domain-containing protein 2 (JOSD2) as an essential DUB of YAP/TAZ that sustained the protein level through cleavage of polyubiquitin chains in a deubiquitinase activity-dependent manner. The depletion of JOSD2 promoted YAP/TAZ proteasomal degradation and significantly impeded CCA proliferation in vitro and in vivo. Further analysis has highlighted the positive correlation between JOSD2 and YAP abundance in CCA patient samples. Collectively, this study uncovers the regulatory effects of JOSD2 on YAP/TAZ protein stabilities and profiles its contribution in CCA malignant progression, which may provide a potential intervention target for YAP/TAZ-related CCA patients.

Activation of TMEM16A Ca2+-activated Cl- channels by ROCK1/moesin promotes breast cancer metastasis

Introduction

Transmembrane protein 16A (TMEM16A) is a Ca²⁺-activated chloride channel that plays a role in cancer cell proliferation, migration, invasion, and metastasis. However, whether TMEM16A contributes to breast cancer metastasis remains unknown.

Objective

In this study, we investigated whether TMEM16A channel activation by ROCK1/moesin promotes breast cancer metastasis.

Methods

Wound healing assays and transwell migration and invasion assays were performed to study the migration and invasion of MCF-7 and T47D breast cancer cells. Western blotting was performed to evaluate the protein expression, and whole-cell patch clamp recordings were used to record TMEM16A Cl⁻ currents. A mouse model of breast cancer lung metastasis was generated by injecting MCF-7 cells via the tail vein. Metastatic nodules in the lung were assessed by hematoxylin and eosin staining. Lymph node metastasis, overall survival, and metastasis-free survival of breast cancer patients were assessed using immunohistochemistry and The Cancer Genome Atlas dataset.

Results

TMEM16A activation promoted breast cancer cell migration and invasion in vitro as well as breast cancer metastasis in mice. Patients with breast cancer who had higher TMEM16A levels showed greater lymph node metastasis and shorter survival. Mechanistically, TMEM16A promoted migration and invasion by activating EGFR/STAT3/ROCK1 signaling, and the role of the TMEM16A channel activity was important in this respect. ROCK1 activation by RhoA enhanced the TMEM16A channel activity via the phosphorylation of moesin at T558. The cooperative action of TMEM16A and ROCK1 was supported through clinical findings indicating that breast cancer patients with high levels of TMEM16A/ROCK1 expression showed greater lymph node metastasis and poor survival.

Conclusion

Our findings revealed a novel mechanism underlying TMEM16A-mediated breast cancer metastasis, in which ROCK1 increased TMEM16A channel activity via moesin phosphorylation and the increase in TMEM16A channel activities promoted cell migration and invasion. TMEM16A inhibition may be a novel strategy for treating breast cancer metastasis.

MicroRNA-regulated transcriptome analysis identifies four major subtypes with prognostic and therapeutic implications in prostate cancer

MicroRNA (miRNA) deregulation plays a critical role in the heterogeneous development of prostate cancer (PCa) by tuning mRNA levels. Herein, we aimed to characterize the molecular features of PCa by clustering the miRNA-regulated transcriptome with non-negative matrix factorization. Using 478 PCa samples from The Cancer Genome Atlas, four molecular subtypes (S-I, S-II, S-III, and S-IV) were identified and validated in two merged microarray and RNAseq datasets with 656 and 252 samples, respectively. Interestingly, the four subtypes showed distinct clinical and biological features after comprehensive analyses of clinical features, multiomic profiles, immune infiltration, and drug sensitivity. S-I is basal/stem/mesenchymal-like and immune-excluded with marked transforming growth factor β, epithelial-mesenchymal transition and hypoxia signals, increased sensitivity to olaparib, and intermediate prognosis. S-II is luminal/metabolism-active and responsive to androgen deprivation therapy with frequent TMPRSS2-ERG fusion and a good prognosis. S-III is characterized by moderate proliferative and metabolic activity, sensitivity to taxane-based chemotherapy, and intermediate prognosis. S-IV is highly proliferative with moderate EMT and stemness, frequent deletions of TP53, PTEN and RB, and the poorest prognosis; it is also immune-inflamed and sensitive to anti-PD-L1 therapy. Overall, based on miRNA-regulated gene profiles, this study identified four distinct PCa subtypes that could improve risk stratification at diagnosis and provide therapeutic guidance.

Machine learning and deep learning methods that use omics data for metastasis prediction

Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

The molecular feature of macrophages in tumor immune microenvironment of glioma patients

Background

Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth.

Methods

Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore.

Results

Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate.

Conclusion

Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.

Integrins as attractive targets for cancer therapeutics

Integrins are transmembrane receptors that have been implicated in the biology of various human physiological and pathological processes. These molecules facilitate cell–extracellular matrix and cell–cell interactions, and they have been implicated in fibrosis, inflammation, thrombosis, and tumor metastasis. The role of integrins in tumor progression makes them promising targets for cancer treatment, and certain integrin antagonists, such as antibodies and synthetic peptides, have been effectively utilized in the clinic for cancer therapy. Here, we discuss the evidence and knowledge on the contribution of integrins to cancer biology. Furthermore, we summarize the clinical attempts targeting this family in anti-cancer therapy development.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Identification of subtypes correlated with tumor immunity and immunotherapy in cutaneous melanoma

Because immune checkpoint inhibitors (ICIs) are effective for a subset of melanoma patients, identification of melanoma subtypes responsive to ICIs is crucial. We performed clustering analyses to identify immune subtypes of melanoma based on the enrichment levels of 28 immune cells using transcriptome datasets for six melanoma cohorts, including four cohorts not treated with ICIs and two cohorts treated with ICIs. We identified three immune subtypes (Im-H, Im-M, and Im-L), reproducible in these cohorts. Im-H displayed strong immune signatures, low stemness and proliferation potential, genomic stability, high immunotherapy response rate, and favorable prognosis. Im-L showed weak immune signatures, high stemness and proliferation potential, genomic instability, low immunotherapy response rate, and unfavorable prognosis. The pathways highly enriched in Im-H included immune, MAPK, apoptosis, calcium, VEGF, cell adhesion molecules, focal adhesion, gap junction, and PPAR. The pathways highly enriched in Im-L included Hippo, cell cycle, and ErbB. Copy number alterations correlated inversely with immune signatures in melanoma, while tumor mutation burden showed no significant correlation. The molecular features correlated with favorable immunotherapy response included immune-promoting signatures and pathways of PPAR, MAPK, VEGF, calcium, and glycolysis/gluconeogenesis. Our data recapture the immunological heterogeneity in melanoma and provide clinical implications for the immunotherapy of melanoma.

Multi-omics approaches identify SF3B3 and SIRT3 as candidate autophagic regulators and druggable targets in invasive breast carcinoma

Autophagy is a critical cellular homeostatic mechanism, and its dysfunction is linked to invasive breast carcinoma (BRCA). Recently, several omics methods have been applied to explore autophagic regulators in BRCA; however, more reliable and robust approaches for identifying crucial regulators and druggable targets remain to be discovered. Thus, we report here the results of multi-omics approaches to identify potential autophagic regulators in BRCA, including gene expression (EXP), DNA methylation (MET) and copy number alterations (CNAs) from The Cancer Genome Atlas (TCGA). Newly identified candidate genes, such as SF3B3, TRAPPC10, SIRT3, MTERFD1, and FBXO5, were confirmed to be involved in the positive or negative regulation of autophagy in BRCA. SF3B3 was identified firstly as a negative autophagic regulator, and siRNA/shRNA-SF3B3 were shown to induce autophagy-associated cell death in in vitro and in vivo breast cancer models. Moreover, a novel small-molecule activator of SIRT3, 1-methylbenzylamino amiodarone, was discovered to induce autophagy in vitro and in vivo. Together, these results provide multi-omics approaches to identify some key candidate autophagic regulators, such as the negative regulator SF3B3 and positive regulator SIRT3 in BRCA, and highlight SF3B3 and SIRT3 as new druggable targets that could be used to fill the gap between autophagy and cancer drug development.

Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system

The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer.

Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system.

Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score.

The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

ceRNA network development and tumour-infiltrating immune cell analysis of metastatic breast cancer to bone

PURPOSE

Advanced breast cancer commonly metastasises to bone; however, the molecular mechanisms underlying the affinity for breast cancer cells to bone remains unclear. Thus, we developed nomograms based on a competing endogenous RNA (ceRNA) network and analysed tumour-infiltrating immune cells to elucidate the molecular pathways that may predict prognosis in patients with breast cancer.

METHODS

We obtained the RNA expression profile of 1091 primary breast cancer samples included in The Cancer Genome Atlas database, 58 of which were from patients with bone metastasis. We analysed the differential RNA expression patterns between breast cancer with and without bone metastasis and developed a ceRNA network. Cibersort was employed to differentiate between immune cell types based on tumour transcripts. Nomograms were then established based on the ceRNA network and immune cell analysis. The value of prognostic factors was evaluated by Kaplan-Meier survival analysis and a Cox proportional risk model.

RESULTS

We found significant differences in long non-coding RNAs (lncRNAs), 18 microRNAs (miRNAs), and 20 messenger RNAs (mRNAs) between breast cancer with and without bone metastasis, which were used to construct a ceRNA network. We found that the protein-coding genes GJB3, CAMMV, PTPRZ1, and FBN3 were significantly differentially expressed by Kaplan-Meier analysis. We also observed significant differences in the abundance of plasma cell and follicular helper T cell populations between the two groups. In addition, the proportion of mast cells, gamma delta T cells, and plasma cells differed depending on disease location and stage. Our analysis showed that a high proportion of follicular helper T cells and a low proportion of eosinophils promoted survival and that DLX6-AS1, Wnt6, and GABBR2 expression may be associated with bone metastasis in breast cancer.

CONCLUSIONS

We developed a bioinformatic tool for exploring the molecular mechanisms of bone metastasis in patients with breast cancer and identified factors that may predict the occurrence of bone metastasis.

The SARS-CoV-2 host cell receptor ACE2 correlates positively with immunotherapy response and is a potential protective factor for cancer progression. Comput Struct Biotechnol J. 2020;18:2438-2444. [PMID: 32905022 PMCID: PMC7462778 DOI: 10.1016/j.csbj.2020.08.024]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 29 million people and has caused more than 900,000 deaths worldwide as of September 14, 2020. The SARS-CoV-2 human cell receptor ACE2 has recently received extensive attention for its role in SARS-CoV-2 infection. Many studies have also explored the association between ACE2 and cancer. However, a systemic investigation into associations between ACE2 and oncogenic pathways, tumor progression, and clinical outcomes in pan-cancer remains lacking. Using cancer genomics datasets from the Cancer Genome Atlas (TCGA) program, we performed computational analyses of associations between ACE2 expression and antitumor immunity, immunotherapy response, oncogenic pathways, tumor progression phenotypes, and clinical outcomes in 13 cancer cohorts. We found that ACE2 upregulation was associated with increased antitumor immune signatures and PD-L1 expression, and favorable anti-PD-1/PD-L1/CTLA-4 immunotherapy response. ACE2 expression levels inversely correlated with the activity of cell cycle, mismatch repair, TGF-β, Wnt, VEGF, and Notch signaling pathways. Moreover, ACE2 expression levels had significant inverse correlations with tumor proliferation, stemness, and epithelial-mesenchymal transition. ACE2 upregulation was associated with favorable survival in pan-cancer and in multiple individual cancer types. These results suggest that ACE2 is a potential protective factor for cancer progression. Our data may provide potential clinical implications for treating cancer patients infected with SARS-CoV-2.

Artificial intelligence (AI) and big data in cancer and precision oncology

Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery. NGS generates large datasets that demand specialised bioinformatics resources to analyse the data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images. Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical application of NGS remains to be validated. By continuing to enhance the progression of innovation and technology, the future of AI and precision oncology show great promise.

Good or not good: Role of miR-18a in cancer biology

miR-18a is a member of primary transcript called miR-17-92a (C13orf25 or MIR17HG) which also contains five other miRNAs: miR-17, miR-19a, miR-20a, miR-19b and miR-92a. This cluster as a whole shows specific characteristics, where miR-18a seems to be unique. In contrast to the other members, the expression of miR-18a is additionally controlled and probably functions as its own internal controller of the cluster. miR-18a regulates many genes involved in proliferation, cell cycle, apoptosis, response to different kinds of stress, autophagy and differentiation. The disturbances of miR-18a expression are observed in cancer as well as in different diseases or pathological states. The miR-17-92a cluster is commonly described as oncogenic and it is known as 'oncomiR-1', but this statement is a simplification because miR-18a can act both as an oncogene and a suppressor. In this review we summarize the current knowledge about miR-18a focusing on its regulation, role in cancer biology and utility as a potential biomarker.

Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning

The effective non-invasive diagnosis and prognosis are critical for cancer treatment. The plasma cell-free DNA (cfDNA) provides a good material for cancer liquid biopsy and its worth in this field is increasingly explored. Here we describe a new pipeline for effectively finding new cfDNA-based biomarkers for cancers by combining SALP-seq and machine learning. Using the pipeline, 30 cfDNA samples from 26 esophageal cancer (ESCA) patients and 4 healthy people were analyzed as an example. As a result, 103 epigenetic markers (including 54 genome-wide and 49 promoter markers) and 37 genetic markers were identified for this cancer. These markers provide new biomarkers for ESCA diagnosis, prognosis and therapy. Importantly, these markers, especially epigenetic markers, not only shed important new insights on the regulatory mechanisms of this cancer, but also could be used to classify the cfDNA samples. We therefore developed a new pipeline for effectively finding new cfDNA-based biomarkers for cancers by combining SALP-seq and machine learning. In this study, we also discovered new clinical worth of cfDNA distinct from other reported characters.

Identification of a novel PAK1 inhibitor to treat pancreatic cancer

Pancreatic cancer is one of the most aggressive cancers with poor prognosis and a low 5-year survival rate. The family of P21-activated kinases (PAKs) appears to modulate many signaling pathways that contribute to pancreatic carcinogenesis. In this work, we demonstrated that PAK1 is a critical regulator in pancreatic cancer cell growth. PAK1-targeted inhibition is therefore a new potential therapeutic strategy for pancreatic cancer. Our small molecule screening identified a relatively specific PAK1-targeted inhibitor, CP734. Pharmacological and biochemical studies indicated that CP734 targets residue V342 of PAK1 to inhibit its ATPase activity. Further in vitro and in vivo studies elucidated that CP734 suppresses pancreatic tumor growth through depleting PAK1 kinase activity and its downstream signaling pathways. Little toxicity of CP734 was observed in murine models. Combined with gemcitabine or 5-fluorouracil, CP734 also showed synergistic effects on the anti-proliferation of pancreatic cancer cells. All these favorable results indicated that CP734 is a new potential therapeutic candidate for pancreatic cancer.

PreMSIm: An R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer

Microsatellite instability (MSI) is a genomic property of the cancers with defective DNA mismatch repair and is a useful marker for cancer diagnosis and treatment in diverse cancer types. In particular, MSI has been associated with the active immune checkpoint blockade therapy response in cancer. Most of computational methods for predicting MSI are based on DNA sequencing data and a few are based on mRNA expression data. Using the RNA-Seq pan-cancer datasets for three cancer cohorts (colon, gastric, and endometrial cancers) from The Cancer Genome Atlas (TCGA) program, we developed an algorithm (PreMSIm) for predicting MSI from the expression profiling of a 15-gene panel in cancer. We demonstrated that PreMSIm had high prediction performance in predicting MSI in most cases using both RNA-Seq and microarray gene expression datasets. Moreover, PreMSIm displayed superior or comparable performance versus other DNA or mRNA-based methods. We conclude that PreMSIm has the potential to provide an alternative approach for identifying MSI in cancer.

The landscape of bacterial presence in tumor and adjacent normal tissue across 9 major cancer types using TCGA exome sequencing

Identification of microbial composition directly from tumor tissue permits studying the relationship between microbial changes and cancer pathogenesis. We interrogated bacterial presence in tumor and adjacent normal tissue strictly in pairs utilizing human whole exome sequencing to generate microbial profiles. Profiles were generated for 813 cases from stomach, liver, colon, rectal, lung, head & neck, cervical and bladder TCGA cohorts. Core microbiota examination revealed twelve taxa to be common across the nine cancer types at all classification levels. Paired analyses demonstrated significant differences in bacterial shifts between tumor and adjacent normal tissue across stomach, colon, lung squamous cell, and head & neck cohorts, whereas little or no differences were evident in liver, rectal, lung adenocarcinoma, cervical and bladder cancer cohorts in adjusted models. Helicobacter pylori in stomach and Bacteroides vulgatus in colon were found to be significantly higher in adjacent normal compared to tumor tissue after false discovery rate correction. Computational results were validated with tissue from an independent population by species-specific qPCR showing similar patterns of co-occurrence among Fusobacterium nucleatum and Selenomonas sputigena in gastric samples. This study demonstrates the ability to identify bacteria differential composition derived from human tissue whole exome sequences. Taken together our results suggest the microbial profiles shift with advanced disease and that the microbial composition of the adjacent tissue can be indicative of cancer stage disease progression.

Modulation of non-coding RNAs by resveratrol in ovarian cancer cells: In silico analysis and literature review of the anti-cancer pathways involved

Background and aim

Non-coding RNAs control cell functioning through affecting gene expression and translation and their dysregulation is associated with altered cell homeostasis and diseases, including cancer. Nutraceuticals with anti-cancer therapeutic potential have been shown to modulate non-coding RNAs expression that could impact on the expression of genes involved in the malignant phenotype.

Experimental procedure

Here, we report on the microarray profiling of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) and on the associated biochemical pathways and functional processes potentially modulated in OVCAR-3 ovarian cancer cells exposed for 24 h to Resveratrol (RV), a nutraceutical that has been shown to inhibit carcinogenesis and cancer progression in a variety of human and animal models, both in vitro and in vivo. Diana tools and Gene Ontology (GO) pathway analyses along with Pubmed literature search were employed to identify the cellular processes possibly affected by the dysregulated miRNAs and lncRNAs.

Results and conclusion

The present data consistently support the contention that RV could exert anti-neoplastic activity via non-coding RNAs epigenetic modulation of the pathways governing cell homeostasis, cell proliferation, cell death and cell motility.

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Nutraceuticals with anti-cancer therapeutic potential have been shown to modulate non-coding RNAs expression that could impact on the expression of genes involved in the malignant phenotype.

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Here, we report on the microarray profiling of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) and on the associated biochemical pathways and functional processes potentially modulated in OVCAR-3 ovarian cancer cells exposed for 24 h to Resveratrol (RV), a nutraceutical that has been shown to inhibit carcinogenesis and cancer progression in a variety of human and animal models.

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The data here reported consistently support the contention that RV could exert anti-neoplastic activity via non-coding RNAs epigenetic modulation of the pathways governing cell homeostasis, cell proliferation, cell death and cell motility.

Loss of Tenascin-X expression during tumor progression: A new pan-cancer marker

Cancer is a systemic disease involving multiple components produced from both tumor cells themselves and surrounding stromal cells. The pro- or anti-tumoral role of the stroma is still under debate. Indeed, it has long been considered the main physical barrier to the diffusion of chemotherapy by its dense and fibrous nature and its poor vascularization. However, in murine models, the depletion of fibroblasts, the main ExtraCellular Matrix (ECM)-producing cells, led to more aggressive tumors even though they were more susceptible to anti-angiogenic and immuno-modulators. Tenascin-C (TNC) is a multifunctional matricellular glycoprotein (i.e. an ECM protein also able to induce signaling pathway) and is considered as a marker of tumor expansion and metastasis. However, the status of other tenascin (TN) family members and particularly Tenascin-X (TNX) has been far less studied during this pathological process and is still controversial. Herein, through (1) in silico analyses of the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and (2) immunohistochemistry staining of Tissue MicroArrays (TMA), we performed a large and extensive study of TNX expression at both mRNA and protein levels (1) in the 6 cancers with the highest incidence and mortality in the world (i.e. lung, breast, colorectal, prostate, stomach and liver) and (2) in the cancers for which sparse data regarding TNX expression already exist in the literature. We thus demonstrated that, in most cancers, TNX expression is significantly downregulated during cancer progression and we also highlighted, when data were available, that high TNXB mRNA expression in cancer is correlated with a good survival prognosis.

Applications and analysis of targeted genomic sequencing in cancer studies

Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.

Elevated HOX gene expression in acute myeloid leukemia is associated with NPM1 mutations and poor survival

Acute myeloid leukemia (AML) is a clonal disorder of hematopoietic progenitor cells and the most common malignant myeloid disorder in adults. Several gene mutations such as in NPM1 (nucleophosmin 1) are involved in the pathogenesis and progression of AML. The aim of this study was to identify genes whose expression is associated with driver mutations and survival outcome. Genotype data (somatic mutations) and gene expression data including RNA-seq, microarray, and qPCR data were used for the analysis. Multiple datasets were utilized as training sets (GSE6891, TCGA, and GSE1159). A new clinical sample cohort (Semmelweis set) was established for in vitro validation. Wilcoxon analysis was used to identify genes with expression alterations between the mutant and wild type samples. Cox regression analysis was performed to examine the association between gene expression and survival outcome. Data analysis was performed in the R statistical environment. Eighty-five genes were identified with significantly altered expression when comparing NPM1 mutant and wild type patient groups in the GSE6891 set. Additional training sets were used as a filter to condense the six most significant genes associated with NPM1 mutations. Then, the expression changes of these six genes were confirmed in the Semmelweis set: HOXA5 (P = 3.06E-12, FC = 8.3), HOXA10 (P = 2.44E-09, FC = 3.3), HOXB5 (P = 1.86E-13, FC = 37), MEIS1 (P = 9.82E-10, FC = 4.4), PBX3 (P = 1.03E-13, FC = 5.4) and ITM2A (P = 0.004, FC = 0.4). Cox regression analysis showed that higher expression of these genes - with the exception of ITM2A - was associated with worse overall survival. Higher expression of the HOX genes was identified in tumors harboring NPM1 gene mutations by computationally linking genotype and gene expression. In vitro validation of these genes supports their potential therapeutic application in AML.

Large-scale analysis reveals the specific clinical and immune features of B7-H3 in glioma

Background: B7-H3 is an immune checkpoint member that belongs to B7-CD28 families and plays a vital role in the inhibition of T-cell function. Importantly, B7-H3 is widely overexpressed on solid tumors, making it become an attractive target for cancer immunotherapy. To clarify the expression panel of B7-H3 in glioma, we explored the clinical and immune features of B7-H3 expression in a large-scale study. Methods and patients: Totally, 1323 glioma samples from Chinese Glioma Genome Atlas (CGGA) dataset, including 325 RNAseq data and 301 mRNA microarray data, and The Cancer Genome Atlas (TCGA) dataset, including 697 RNAseq data, were gathered into our research. The statistical analysis and graphical work were mainly realized by R language. Results: B7-H3 expression was found positively correlated with the grade of malignancy, which might be caused by hypomethylation. The expression level of B7-H3 was consistently up-regulated in IDH wild-type glioma and highly enriched in mesenchymal subtype. GSEA analysis suggested that B7-H3 related genes were more involved in immune response and angiogenesis in glioma. Moreover, B7-H3 showed a consistent positive relationship with stromal and immune cell populations. Further analysis confirmed that B7-H3 played an important role in T-cell-mediated immunity, especially in T-cell-mediated immune response to tumor cell. Circos plots revealed that B7-H3 was tightly associated with most B7 members and other immune checkpoints. Univariate and multivariate cox analysis demonstrated that B7-H3 was an independent prognosticator for glioma patients. Conclusion: B7-H3 represents the malignant phenotype of glioma and independently predicted worse prognosis in glioma patients. Moreover, B7-H3 collaborating with other checkpoint members may contribute to the dysfunctional phenotype of T cell. These findings will be helpful for further optimizing immunotherapies for glioma.

siRNA Library Screening Identifies a Druggable Immune-Signature Driving Esophageal Adenocarcinoma Cell Growth

BACKGROUND & AIMS

Effective therapeutic approaches are urgently required to tackle the alarmingly poor survival outcomes in esophageal adenocarcinoma (EAC) patients. EAC originates from within the intestinal-type metaplasia, Barrett's esophagus, a condition arising on a background of gastroesophageal reflux disease and associated inflammation.

METHODS

This study used a druggable genome small interfering RNA (siRNA) screening library of 6022 siRNAs in conjunction with bioinformatics platforms, genomic studies of EAC tissues, somatic variation data of EAC from The Cancer Genome Atlas data of EAC, and pathologic and functional studies to define novel EAC-associated, and targetable, immune factors.

RESULTS

By using a druggable genome library we defined genes that sustain EAC cell growth, which included an unexpected immunologic signature. Integrating Cancer Genome Atlas data with druggable siRNA targets showed a striking concordance and an EAC-specific gene amplification event associated with 7 druggable targets co-encoded at Chr6p21.1. Over-representation of immune pathway-associated genes supporting EAC cell growth included leukemia inhibitory factor, complement component 1, q subcomponent A chain (C1QA), and triggering receptor expressed on myeloid cells 2 (TREM2), which were validated further as targets sharing downstream signaling pathways through genomic and pathologic studies. Finally, targeting the triggering receptor expressed on myeloid cells 2-, C1q-, and leukemia inhibitory factor-activated signaling pathways (TYROBP-spleen tyrosine kinase and JAK-STAT3) with spleen tyrosine kinase and Janus-activated kinase inhibitor fostamatinib R788 triggered EAC cell death, growth arrest, and reduced tumor burden in NOD scid gamma mice.

CONCLUSIONS

These data highlight a subset of genes co-identified through siRNA targeting and genomic studies of expression and somatic variation, specifically highlighting the contribution that immune-related factors play in support of EAC development and suggesting their suitability as targets in the treatment of EAC.

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.

Identification of the Consistently Altered Metabolic Targets in Human Hepatocellular Carcinoma

BACKGROUND & AIMS

Cancer cells rely on metabolic alterations to enhance proliferation and survival. Metabolic gene alterations that repeatedly occur in liver cancer are largely unknown. We aimed to identify metabolic genes that are consistently deregulated, and are of potential clinical significance in human hepatocellular carcinoma (HCC).

METHODS

We studied the expression of 2,761 metabolic genes in 8 microarray datasets comprising 521 human HCC tissues. Genes exclusively up-regulated or down-regulated in 6 or more datasets were defined as consistently deregulated. The consistent genes that correlated with tumor progression markers (ECM2 and MMP9) (Pearson correlation P < .05) were used for Kaplan-Meier overall survival analysis in a patient cohort. We further compared proteomic expression of metabolic genes in 19 tumors vs adjacent normal liver tissues.

RESULTS

We identified 634 consistent metabolic genes, ∼60% of which are not yet described in HCC. The down-regulated genes (n = 350) are mostly involved in physiologic hepatocyte metabolic functions (eg, xenobiotic, fatty acid, and amino acid metabolism). In contrast, among consistently up-regulated metabolic genes (n = 284) are those involved in glycolysis, pentose phosphate pathway, nucleotide biosynthesis, tricarboxylic acid cycle, oxidative phosphorylation, proton transport, membrane lipid, and glycan metabolism. Several metabolic genes (n = 434) correlated with progression markers, and of these, 201 predicted overall survival outcome in the patient cohort analyzed. Over 90% of the metabolic targets significantly altered at the protein level were similarly up- or down-regulated as in genomic profile.

CONCLUSIONS

We provide the first exposition of the consistently altered metabolic genes in HCC and show that these genes are potentially relevant targets for onward studies in preclinical and clinical contexts.

Gastrin and Gastric Cancer

Gastric cancer is the third leading cause of cancer-related mortality worldwide. Despite progress in understanding its development, challenges with treatment remain. Gastrin, a peptide hormone, is trophic for normal gastrointestinal epithelium. Gastrin also has been shown to play an important role in the stimulation of growth of several gastrointestinal cancers including gastric cancer. We sought to review the role of gastrin and its pathway in gastric cancer and its potential as a therapeutic target in the management of gastric cancer. In the normal adult stomach, gastrin is synthesized in the G cells of the antrum; however, gastrin expression also is found in many gastric adenocarcinomas of the stomach corpus. Gastrin's actions are mediated through the G-protein-coupled receptor cholecystokinin-B (CCK-B) on parietal and enterochromaffin cells of the gastric body. Gastrin blood levels are increased in subjects with type A atrophic gastritis and in those taking high doses of daily proton pump inhibitors for acid reflux disease. In experimental models, proton pump inhibitor-induced hypergastrinemia and infection with Helicobacter pylori increase the risk of gastric cancer. Understanding the gastrin:CCK-B signaling pathway has led to therapeutic strategies to treat gastric cancer by either targeting the CCK-B receptor with small-molecule antagonists or targeting the peptide with immune-based therapies. In this review, we discuss the role of gastrin in gastric adenocarcinoma, and strategies to block its effects to treat those with unresectable gastric cancer.

Genomics, Endoscopy, and Control of Gastroesophageal Cancers: A Perspective

In The Cancer Genome Atlas the goals were to define how to treat advanced cancers with targeted therapy. However, the challenges facing cancer interception for early detection and prevention include length bias in which current screening and surveillance approaches frequently miss rapidly progressing cancers that then present at advanced stages in the clinic with symptoms (underdiagnosis). In contrast, many early detection strategies detect benign conditions that may never progress to cancer during a lifetime, and the patient dies of unrelated causes (overdiagnosis). This challenge to cancer interception is believed to be due to the speed at which the neoplasm evolves, called length bias sampling; rapidly progressing cancers are missed by current early detection strategies. In contrast, slowly or non-progressing cancers or their precursors are selectively detected. This has led to the concept of cancer interception, which can be defined as active interception of a biological process that drives cancer development before the patient presents in the clinic with an advanced, symptomatic cancer. The solutions needed to advance strategies for cancer interception require assessing the rate at which the cancer evolves over time and space. This is an essential challenge that needs to be addressed by robust study designs including normal and non-progressing controls when known to be appropriate.

Gastric Cancer in the Era of Precision Medicine

Gastric cancer (GC) remains the third most common cause of cancer death worldwide, with limited therapeutic strategies available. With the advent of next-generation sequencing and new preclinical model technologies, our understanding of its pathogenesis and molecular alterations continues to be revolutionized. Recently, the genomic landscape of GC has been delineated. Molecular characterization and novel therapeutic targets of each molecular subtype have been identified. At the same time, patient-derived tumor xenografts and organoids now comprise effective tools for genetic evolution studies, biomarker identification, drug screening, and preclinical evaluation of personalized medicine strategies for GC patients. These advances are making it feasible to integrate clinical, genome-based and phenotype-based diagnostic and therapeutic methods and apply them to individual GC patients in the era of precision medicine.

Gastric Cancer Genomics: Advances and Future Directions

Advancement in the field of cancer genomics is revolutionizing the molecular characterization of a wide variety of different cancers. Recent application of large-scale, next-generation sequencing technology to gastric cancer, which remains a major source of morbidity and mortality throughout the world, has helped better define the complex genomic landscape of this cancer. These studies also have led to the development of novel genomically based molecular classification systems for gastric cancer, reinforced the importance of classic driver mutations in gastric cancer pathogenesis, and led to the discovery of new driver gene mutations that previously were not known to be associated with gastric cancer. This wealth of genomic data has significant potential to impact the future management of this disease, and the challenge remains to effectively translate this genomic data into better treatment paradigms for gastric cancer.

Hyperpolarized (13)C MR imaging detects no lactate production in mutant IDH1 gliomas: Implications for diagnosis and response monitoring

Metabolic imaging of brain tumors using (13)C Magnetic Resonance Spectroscopy (MRS) of hyperpolarized [1-(13)C] pyruvate is a promising neuroimaging strategy which, after a decade of preclinical success in glioblastoma (GBM) models, is now entering clinical trials in multiple centers. Typically, the presence of GBM has been associated with elevated hyperpolarized [1-(13)C] lactate produced from [1-(13)C] pyruvate, and response to therapy has been associated with a drop in hyperpolarized [1-(13)C] lactate. However, to date, lower grade gliomas had not been investigated using this approach. The most prevalent mutation in lower grade gliomas is the isocitrate dehydrogenase 1 (IDH1) mutation, which, in addition to initiating tumor development, also induces metabolic reprogramming. In particular, mutant IDH1 gliomas are associated with low levels of lactate dehydrogenase A (LDHA) and monocarboxylate transporters 1 and 4 (MCT1, MCT4), three proteins involved in pyruvate metabolism to lactate. We therefore investigated the potential of (13)C MRS of hyperpolarized [1-(13)C] pyruvate for detection of mutant IDH1 gliomas and for monitoring of their therapeutic response. We studied patient-derived mutant IDH1 glioma cells that underexpress LDHA, MCT1 and MCT4, and wild-type IDH1 GBM cells that express high levels of these proteins. Mutant IDH1 cells and tumors produced significantly less hyperpolarized [1-(13)C] lactate compared to GBM, consistent with their metabolic reprogramming. Furthermore, hyperpolarized [1-(13)C] lactate production was not affected by chemotherapeutic treatment with temozolomide (TMZ) in mutant IDH1 tumors, in contrast to previous reports in GBM. Our results demonstrate the unusual metabolic imaging profile of mutant IDH1 gliomas, which, when combined with other clinically available imaging methods, could be used to detect the presence of the IDH1 mutation in vivo.

DNA hypomethylation-mediated activation of Cancer/Testis Antigen 45 (CT45) genes is associated with disease progression and reduced survival in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a highly lethal malignancy due to a lack of early detection approaches coupled with poor outcomes for patients with clinically advanced disease. Cancer-testis (CT) or cancer-germline genes encode antigens known to generate spontaneous anti-tumor immunity in cancer patients. CT45 genes are a recently discovered 6-member family of X-linked CT genes with oncogenic function. Here, we determined CT45 expression in EOC and fully defined its epigenetic regulation by DNA methylation. CT45 was silent and hypermethylated in normal control tissues, but a large subset of EOC samples showed increased CT45 expression in conjunction with promoter DNA hypomethylation. In contrast, copy number status did not correlate with CT45 expression in the TCGA database for EOC. CT45 promoter methylation inversely correlated with both CT45 mRNA and protein expression, the latter determined using IHC staining of an EOC TMA. CT45 expression was increased and CT45 promoter methylation was decreased in late-stage and high-grade EOC, and both measures were associated with poor survival. CT45 hypomethylation was directly associated with LINE-1 hypomethylation, and CT45 was frequently co-expressed with other CT antigen genes in EOC. Decitabine treatment induced CT45 mRNA and protein expression in EOC cells, and promoter transgene analyses indicated that DNA methylation directly represses CT45 promoter activity. These data verify CT45 expression and promoter hypomethylation as possible prognostic biomarkers, and suggest CT45 as an immunological or therapeutic target in EOC. Treatment with decitabine or other epigenetic modulators could provide a means for more effective immunological targeting of CT45.

Analysis of DNA methylation and gene expression in radiation-resistant head and neck tumors

Resistance to radiation therapy constitutes a significant challenge in the treatment of head and neck squamous cell cancer (HNSCC). Alteration in DNA methylation is thought to play a role in this resistance. Here, we analyzed DNA methylation changes in a matched model of radiation resistance for HNSCC using the Illumina HumanMethylation450 BeadChip. Our results show that compared to radiation-sensitive cells (SCC-61), radiation-resistant cells (rSCC-61) had a significant increase in DNA methylation. After combining these results with microarray gene expression data, we identified 84 differentially methylated and expressed genes between these 2 cell lines. Ingenuity Pathway Analysis revealed ILK signaling, glucocorticoid receptor signaling, fatty acid α-oxidation, and cell cycle regulation as top canonical pathways associated with radiation resistance. Validation studies focused on CCND2, a protein involved in cell cycle regulation, which was identified as hypermethylated in the promoter region and downregulated in rSCC-61 relative to SCC-61 cells. Treatment of rSCC-61 and SCC-61 with the DNA hypomethylating agent 5-aza-2'deoxycitidine increased CCND2 levels only in rSCC-61 cells, while treatment with the control reagent cytosine arabinoside did not influence the expression of this gene. Further analysis of HNSCC data from The Cancer Genome Atlas found increased methylation in radiation-resistant tumors, consistent with the cell culture data. Our findings point to global DNA methylation status as a biomarker of radiation resistance in HNSCC, and suggest a need for targeted manipulation of DNA methylation to increase radiation response in HNSCC.

Copy number variants associated with 18p11.32, DCC and the promoter 1B region of APC in colorectal polyposis patients

Familial Adenomatous Polyposis (FAP) is the second most common inherited predisposition to colorectal cancer (CRC) associated with the development of hundreds to thousands of adenomas in the colon and rectum. Mutations in APC are found in ~ 80% polyposis patients with FAP. In the remaining 20% no genetic diagnosis can be provided suggesting other genes or mechanisms that render APC inactive may be responsible. Copy number variants (CNVs) remain to be investigated in FAP and may account for disease in a proportion of polyposis patients. A cohort of 56 polyposis patients and 40 controls were screened for CNVs using the 2.7M microarray (Affymetrix) with data analysed using ChAS (Affymetrix). A total of 142 CNVs were identified unique to the polyposis cohort suggesting their involvement in CRC risk. We specifically identified CNVs in four unrelated polyposis patients among CRC susceptibility genes APC, DCC, MLH1 and CTNNB1 which are likely to have contributed to disease development in these patients. A recurrent deletion was observed at position 18p11.32 in 9% of the patients screened that was of particular interest. Further investigation is necessary to fully understand the role of these variants in CRC risk given the high prevalence among the patients screened.

Inhibition of autophagy as a new means of improving chemotherapy efficiency in high-LC3B triple-negative breast cancers

The triple-negative breast cancer (TN BC) subtype is the most aggressive form of invasive BC. Despite intensive efforts to improve BC treatments, patients with TN BC continue to exhibit poor survival, with half developing resistance to chemotherapy. Here we identify autophagy as a key mechanism in the progression and chemoresistance of a subset of TN tumors. We demonstrate that LC3B, a protein involved in autophagosome formation, is a reliable marker of poor prognosis in TN BC, validating this prognostic value at both the mRNA and protein levels in several independent cohorts. We also show that LC3B has no prognostic value for other BC subtypes (Luminal or HER2 BC), thus revealing a specific impact of autophagy on TN tumors. Autophagy is essential for the proliferative and invasive properties in 3D of TN BC cells characterized by high LC3B levels. Interestingly, the activity of the transcriptional co-activator YAP1 (Yes-associated protein 1) is regulated by the autophagy process and we identify YAP1 as a new actor in the autophagy-dependent proliferative and invasive properties of high-LC3B TN BC. Finally, inhibiting autophagy by silencing ATG5 or ATG7 significantly impaired high-LC3B TN tumor growth in vivo. Moreover, using a patient-derived TN tumor transplanted into mice, we show that an autophagy inhibitor, chloroquine, potentiates the effects of chemotherapeutic agents. Overall, our data identify LC3B as a new prognostic marker for TN BC and the inhibition of autophagy as a promising therapeutic strategy for TN BC patients.

EZH2 inhibition enhances the efficacy of an EGFR inhibitor in suppressing colon cancer cells

Metastatic colon cancer has a 5-year survival of less than 10% despite the use of aggressive chemotherapeutic regimens. As signaling from epidermal growth factor receptor (EGFR) is often enhanced and epigenetic regulation is often altered in colon cancer, it is desirable to enhance the efficacy of EGFR-directed therapy by co-targeting an epigenetic pathway. We showed that the histone methyltransferase EZH2, which catalyzes methylation of histone H3 lysine 27 (H3K27), was upregulated in colon cancers in The Cancer Genome Atlas (TCGA) database. Since co-inhibition of both EGFR and EZH2 has not been studied in colon cancer, we examined the effects of co-inhibition of EGFR and EZH2 on 2 colon cancer cell lines, HT-29 and HCT-15. Co-inhibition of EZH2 and EGFR with the small molecules UNC1999 and gefitinib, led to a significant decrease in cell number and increased apoptosis compared to inhibition of either pathway alone, and similar results were noted after EZH2 shRNA knockdown. Moreover, co-inhibition of EZH2 and EGFR also significantly induced autophagy, indicating that autophagy may play a role in the observed synergy. Together, these findings suggest that inhibition of both EZH2 and EGFR serves as an effective method to increase the efficacy of EGFR inhibitors in suppressing colon cancer cells.