Explore, Visualize, and Analyze Functional Cancer Proteomic Data Using the Cancer Proteome Atlas

The Prognostic Significance of Collagen VI in Pancreatic Ductal Adenocarcinoma

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and lack of biomarkers. A rich desmoplastic tumor stroma is considered a hallmark of PDAC and previous studies have indicated upregulated expression of collagen VI (COL6) in PDAC. COL6 is shown to associate with prognosis in many cancers but has been less extensively studied in PDAC.

MATERIALS AND METHODS

The expression of COL6 was analyzed by immunohistochemistry in tissue microarrays containing resected tumor tissue samples from PDAC patients (n = 164). Significance of COL6 was estimated with Kaplan-Meier survival estimates and multivariable Cox regression analysis. COL6 protein and mRNA expression patterns were further investigated in publicly available datasets.

RESULTS

There were no statistically significant ( P < 0.05) differences in survival when comparing high and low protein expression of any of the analyzed COL6 α-chains (α1(VI): hazard ratio [HR] 0.90, 95% confidence interval [CI] 0.64-1.28; α2(VI): HR 1.28, 95% CI 0.86-1.89; α3(VI): HR 0.91, 95% CI 0.64-1.29). Similar results were obtained when assessing public data from the Cancer Proteome Atlas, Clinical Proteomic Tumor Analysis Consortium, and The Cancer Genome Atlas.

CONCLUSIONS

In contrast with previous studies and some other cancers, we did not find any association of COL6 tissue expression and PDAC survival.

Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer

BACKGROUND

Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy.

METHODS

The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics.

RESULTS

We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group.

CONCLUSIONS

The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies.

Comprehensive pan-cancer analysis and experiments revealed R3HDM1 as a novel predictive biomarker for prognosis and immune therapy response

Background

R3HDM1, an RNA binding protein with one R3H domain, remains uncharacterized in terms of its association with tumor progression, malignant cell regulation, and the tumor immune microenvironment. This paper aims to fill this gap by analyzing the potential of R3HDM1 in diagnosis, prognosis, chemotherapy, and immune function across various cancers.

Methods

Data was collected from the Firehost database (http://gdac.broadinstitute.org) to obtain the TCGA pan-cancer queue containing tumor and normal samples. Additional data on miRNA, TCPA, mutations, and clinical information were gathered from the UCSC Xena database (https://xenabrowser.net/datapages/). The mutation frequency and locus of R3HDM1 in the TCGA database were examined using the cBioPortal. External validation through GEO data was conducted to assess the differential expression of R3HDM1 in different cancers. Protein expression levels were evaluated using the Clinical Proteomics Tumor Analysis Alliance (CPTAC). The differential expression of R3HDM1 was verified in lung adenocarcinoma cell lines and normal lung glandular epithelial cells via RT-qPCR. Cell migration and proliferation experiments were conducted by knocking down the expression of R3HDM1 in two lung adenocarcinoma cell lines using small interfering RNA. The biological role of R3HDM1 in pan-cancer was explored using the GSEA method. Multiple immune infiltration algorithms from the TIMER2.0 database was employed to investigate the correlation between R3HDM1 expression and the tumor immune microenvironment. Validation of transcriptome immune infiltration was based on 140 single-cell datasets from the TISCH database. The study also characterized a pan-cancer survival profile and analyzed the differential expression of R3HDM1 in different molecular subtypes. The relationship between R3HDM1 and drug resistance was investigated using four chemotherapy data sources: CellMiner, GDSC, CTRP and PRISM. The impact of chemicals on the expression of R3HDM1 was explored through the CTD database.

Result

The study revealed differential expression of R3HDM1 in various tumors, indicating its potential as an early diagnostic marker. Changes in somatic copy number (SCNA) and DNA methylation were identified as factors contributing to abnormal expression levels. Additionally, the study found that R3HDM1 expression is associated with clinical features, metabolic pathways, and important pathways related to metastasis and the immune system. High expression of R3HDM1 was linked to poor prognosis across different tumors and altered drug sensitivity. Furthermore, the expression of R3HDM1 showed significant correlations with immune modulatory molecules and biomarkers of lymphocyte subpopulation infiltration. Finally, the study highlighted four chemicals that could influence the expression of R3HDM1.

Conclusion

Overall, this study proposes that R3HDM1 expression is a promising biomarker for predicting the prognosis of cancer, especially lung adenocarcinoma, and the efficacy of immunotherapy, demonstrating the rationale for further exploration in the development of anti-tumor therapies.

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

Comprehensive pan-cancer analysis of ACSS3 as a biomarker for prognosis and immunotherapy response

Background

ACSS3 (acyl-CoA synthetase short-chain family member 3) is found in numerous tissues and is linked to tumor cell type development and metastasis.

Methods

We conducted a comprehensive pan-cancer analysis of ACSS3. The TCGA (Cancer Genome Atlas), CPTAC (Clinical Proteomic Tumor Analysis Consortium), and HPA databases were used to ascertain the connection between ACSS3 and various types of tumors. Genes in the TCGA database would be identified using cBioPortal queries, and their transcriptome expression would then be verified using GEO data. ACSS3 expression and cellular localization in various tumor tissues of most cancer types were analyzed using single-cell sequencing data from the TISCH database. According to HPA and CPTAC databases, we analyzed and evaluated protein expression levels. Predictive analysis based on precise survival data of ACSS3 expression levels for 26 cancer types predicted using the TCGA database. Furthermore, we investigated the relationship between ACSS3 and immune microenvironments in different tumor tissues using the TIMER and TISCH databases. CellMiner, GDSC, and CTRP data would clarify the relationship between ACSS3 and drug resistance and explore the chemicals that affect ACSS3 expression. The final part of our study explored and validated the role ACSS3 played in glioma proliferation, migration, and invasion.

Results

ACSS3 is differentially expressed in various tumors and exhibits early diagnostic value. ACSS3 expression is associated with clinical features, and high ACSS expression anticipates a worse prognosis in multiple tumors and may impact drug sensitivity. The changes in the immunosuppressive microenvironment of gliomas are closely related to the upregulation of ACSS3.

Conclusions

ACSS3 is a novel biomarker for forecasting different human cancer prognoses, as it can influence the biological process by modulating the immune microenvironment. ACSS3 is a critical prognostic factor for glioma and is related to its proliferation, migration, and invasion.

IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show that IBPGNET outperforms other classification methods in 5-fold cross-validation. IBPGNET identified PSMC1 and PSMD11 as genes associated with LUAD recurrence, and their expression levels were significantly higher in LUAD cells than in normal cells. The knockdown of PSMC1 and PSMD11 in LUAD cells increased their sensitivity to afatinib and decreased cell migration, invasion and proliferation. In addition, the cells showed significantly lower EGFR expression, indicating that PSMC1 and PSMD11 may mediate therapeutic sensitivity through EGFR expression.

Repurposing of Zika virus live-attenuated vaccine (ZIKV-LAV) strains as oncolytic viruses targeting human glioblastoma multiforme cells

Glioblastoma multiforme (GBM) is the most common malignant primary brain cancer affecting the adult population. Median overall survival for GBM patients is poor (15 months), primarily due to high rates of tumour recurrence and the paucity of treatment options. Oncolytic virotherapy is a promising treatment alternative for GBM patients, where engineered viruses selectively infect and eradicate cancer cells by inducing cell lysis and eliciting robust anti-tumour immune response. In this study, we evaluated the oncolytic potency of live-attenuated vaccine strains of Zika virus (ZIKV-LAV) against human GBM cells in vitro. Our findings revealed that Axl and integrin αvβ5 function as cellular receptors mediating ZIKV-LAV infection in GBM cells. ZIKV-LAV strains productively infected and lysed human GBM cells but not primary endothelia and terminally differentiated neurons. Upon infection, ZIKV-LAV mediated GBM cell death via apoptosis and pyroptosis. This is the first in-depth molecular dissection of how oncolytic ZIKV infects and induces death in tumour cells.

HPV-negative head and neck cancers with adverse pathological features carry specific molecular changes that are associated with survival

BACKGROUND

Adverse pathological features following surgery in head and neck squamous cell carcinoma (HNSCC) are strongly associated with survival and guide adjuvant therapy. We investigated molecular changes associated with these features.

METHODS

We downloaded data from the Cancer Genome Atlas and Cancer Proteome Atlas HNSCC cohorts. We compared tumors positive versus negative for perineural invasion (PNI), lymphovascular invasion (LVI), extracapsular spread (ECS), and positive margins (PSM), with multivariable analysis.

RESULTS

All pathological features were associated with poor survival, as were the following molecular changes: low cyclin E1 (HR = 1.7) and high PKC-alpha (HR = 1.8) in tumors with PNI; six of 13 protein abundance changes with LVI; greater tumor hypoxia and high Raptor (HR = 2.0) and Rictor (HR = 1.6) with ECS; and low p38 (HR = 2.3), high fibronectin (HR = 1.6), low annexin A1 (HR = 3.1), and high caspase-9 (HR = 1.6) abundances with PSM.

CONCLUSIONS

Pathological features in HNSCC carry specific molecular changes that may explain their poor prognostic associations.

High-Resolution Profiling of Head and Neck Squamous Cells Carcinoma Identifies Specific Biomarkers and Expression Subtypes of Clinically Relevant Vulnerabilities

BACKGROUND

Head and neck squamous cell carcinoma (HNSC) is the seventh most common cancer worldwide. Although there are several options for the treatment of HNSC, there is still a lack of better biomarkers to accurately predict the response to treatment and thus be more able to correctly treat the therapeutic modality.

METHODS

First, we typed cases from the TCGA-HNSC cohort into subtypes by a Bayesian non-negative matrix factorization (BayesNMF)-based consensus clustering approach. Subsequently, genomic and proteomic data from HNSC cell lines were integrated to identify biomarkers of response to targeted therapies and immunotherapies. Finally, associations between HNSC subtypes and CD8 T-cell-associated effector molecules, common immune checkpoint genes, were compared to assess the potential of HNSC subtypes as clinically predictive immune checkpoint blockade therapy.

RESULTS

The 500 HNSC cases from TCGA were put through a consensus clustering approach to identify six HNSC expression subtypes. In addition, subtypes with unique proteomics and dependency profiles were defined based on HNSC cell line histology and proteomics data. Subtype 4 (S4) exhibits hyperproliferative and hyperimmune properties, and S4-associated cell lines show specific vulnerability to ADAT2, EIF5AL1, and PAK2. PD-L1 and CASP1 inhibitors have therapeutic potential in S4, and we have also demonstrated that S4 is more responsive to immune checkpoint blockade therapy.

CONCLUSION

Overall, our HNSC typing approach identified robust tumor-expressing subtypes, and data from multiple screens also revealed subtype-specific biology and vulnerabilities. These HNSC expression subtypes and their biomarkers will help develop more effective therapeutic strategies.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods

Cancer is a heterogeneous disease, and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroups of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification, irrespective of the tissue of origin. This work proposes a machine learning (ML) based pipeline for subgroup identification in pan-cancer. Here, mRNA, miRNA, DNA methylation, and protein expression features from pan-cancer samples were concatenated and non-linearly projected to a lower dimension using an ML algorithm. This data was then clustered to identify multi-omics-based novel subgroups. The clinical characterization of these ML subgroups indicated significant differences in overall survival (OS) and disease-free survival (DFS) (p-value<0.0001). The subgroups formed by the patients from different tumors shared similar molecular alterations in terms of immune microenvironment, mutation profile, and enriched pathways. Further, decision-level and feature-level fused classification models were built to identify the novel subgroups for unseen samples. Additionally, the classification models were used to obtain the class labels for the validation samples, and the molecular characteristics were verified. To summarize, this work identified novel ML subgroups using multi-omics data and showed that the patients with different tumor types could be similar molecularly. We also proposed and validated the classification models for subgroup identification. The proposed classification models can be used to identify the novel multi-omics subgroups, and the molecular characteristics of each subgroup can be used to design appropriate treatment regimen.

Bioinformatic analysis of m6A "reader" YTH family in pan-cancer as a clinical prognosis biomarker

The m6A methylation of mRNA has been demonstrated to interact with the "Reader". YTH domain family is one of the readers containing five members involved in the progression of multiple tumors. The present study aimed to explore the YTH family's role in seventeen cancer types. Data were downloaded from The Cancer Genome Atlas (TCGA) dataset and analyzed by Software R 3.6.3. Using different bioinformatics methods, including analyses of the overall survival (OS) and disease-free survival (DFS), Gene Set Variation Analysis (GSVA) enrichment. Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT algorithm, multivariate and lasso cox regression analysis our results reveal that, while the expression of the YTH domain family varies distinctively in different cancer types the expression of YTH family is upregulated in most cancer types, especially in liver cancer, and the liver cancer prediction model established herein includes YTHDF1 and YTHDF2. Therefore, the results of the present study have demonstrated that the YTH domain family has the potential to predict the prognosis of cancer and the sensitivity to immunotherapy.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Hypoxanthine phosphoribosyl transferase 1 metabolizes temozolomide to activate AMPK for driving chemoresistance of glioblastomas

Temozolomide (TMZ) is a standard treatment for glioblastoma (GBM) patients. However, TMZ has moderate therapeutic effects due to chemoresistance of GBM cells through less clarified mechanisms. Here, we demonstrate that TMZ-derived 5-aminoimidazole-4-carboxamide (AICA) is converted to AICA ribosyl-5-phosphate (AICAR) in GBM cells. This conversion is catalyzed by hypoxanthine phosphoribosyl transferase 1 (HPRT1), which is highly expressed in human GBMs. As the bona fide activator of AMP-activated protein kinase (AMPK), TMZ-derived AICAR activates AMPK to phosphorylate threonine 52 (T52) of RRM1, the catalytic subunit of ribonucleotide reductase (RNR), leading to RNR activation and increased production of dNTPs to fuel the repairment of TMZ-induced-DNA damage. RRM1 T52A expression, genetic interruption of HPRT1-mediated AICAR production, or administration of 6-mercaptopurine (6-MP), a clinically approved inhibitor of HPRT1, blocks TMZ-induced AMPK activation and sensitizes brain tumor cells to TMZ treatment in mice. In addition, HPRT1 expression levels are positively correlated with poor prognosis in GBM patients who received TMZ treatment. These results uncover a critical bifunctional role of TMZ in GBM treatment that leads to chemoresistance. Our findings underscore the potential of combined administration of clinically available 6-MP to overcome TMZ chemoresistance and improve GBM treatment.

Dimension reduction for integrative survival analysis

We propose a constrained maximum partial likelihood estimator for dimension reduction in integrative (e.g., pan-cancer) survival analysis with high-dimensional predictors. We assume that for each population in the study, the hazard function follows a distinct Cox proportional hazards model. To borrow information across populations, we assume that each of the hazard functions depend only on a small number of linear combinations of the predictors (i.e., "factors"). We estimate these linear combinations using an algorithm based on "distance-to-set" penalties. This allows us to impose both low-rankness and sparsity on the regression coefficient matrix estimator. We derive asymptotic results that reveal that our estimator is more efficient than fitting a separate proportional hazards model for each population. Numerical experiments suggest that our method outperforms competitors under various data generating models. We use our method to perform a pan-cancer survival analysis relating protein expression to survival across 18 distinct cancer types. Our approach identifies six linear combinations, depending on only 20 proteins, which explain survival across the cancer types. Finally, to validate our fitted model, we show that our estimated factors can lead to better prediction than competitors on four external datasets.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

Activation of Vitamin D/VDR Signaling Reverses Gemcitabine Resistance of Pancreatic Cancer Cells Through Inhibition of MUC1 Expression

BACKGROUND

Pancreatic ductal adenocarcinoma (PDA) has a poor prognosis due to its therapeutic resistance. Inactivation of vitamin D/vitamin D receptor (VDR) signaling may contribute to the malignant phenotype of PDA and altered expression of oncoprotein mucin 1 (MUC1) may be involved in drug resistance of cancer cells.

AIM

To determine whether vitamin D/VDR signaling regulates the expression and function of MUC1 and its effect on acquired gemcitabine resistance of pancreatic cancer cells.

METHODS

Molecular analyses and animal models were used to determine the impact of vitamin D/VDR signaling on MUC1 expression and response to gemcitabine treatment.

RESULTS

RPPA analysis indicated that MUC1 protein expression was significantly reduced in human PDA cells after treatment with vitamin D3 or its analog calcipotriol. VDR regulated MUC1 expression in both gain- and loss-of-function assays. Vitamin D3 or calcipotriol significantly induced VDR and inhibited MUC1 expression in acquired gemcitabine-resistant PDA cells and sensitized the resistant cells to gemcitabine treatment, while siRNA inhibition of MUC1 was associated with paricalcitol-associated sensitization of PDA cells to gemcitabine treatment in vitro. Administration of paricalcitol significantly enhanced the therapeutic efficacy of gemcitabine in xenograft and orthotopic mouse models and increased the intratumoral concentration of dFdCTP, the active metabolite of gemcitabine.

CONCLUSION

These findings demonstrate a previously unidentified vitamin D/VDR-MUC1 signaling axis involved in the regulation of gemcitabine resistance in PDA and suggests that combinational therapies that include targeted activation of vitamin D/VDR signaling may improve the outcomes of patients with PDA.

Identification of a Novel Protein-Based Prognostic Model in Gastric Cancers

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10-7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.

Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer

Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.

Setd2 inactivation sensitizes lung adenocarcinoma to inhibitors of oxidative respiration and mTORC1 signaling

SETD2 is a tumor suppressor that is frequently inactivated in several cancer types. The mechanisms through which SETD2 inactivation promotes cancer are unclear, and whether targetable vulnerabilities exist in these tumors is unknown. Here we identify heightened mTORC1-associated gene expression programs and functionally higher levels of oxidative metabolism and protein synthesis as prominent consequences of Setd2 inactivation in KRAS-driven mouse models of lung adenocarcinoma. Blocking oxidative respiration and mTORC1 signaling abrogates the high rates of tumor cell proliferation and tumor growth specifically in SETD2-deficient tumors. Our data nominate SETD2 deficiency as a functional marker of sensitivity to clinically actionable therapeutics targeting oxidative respiration and mTORC1 signaling.

Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures

(1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.

AKT-driven epithelial-mesenchymal transition is affected by copper bioavailability in HER2 negative breast cancer cells via a LOXL2-independent mechanism

BACKGROUND

The main mechanism underlying cancer dissemination is the epithelial to mesenchymal transition (EMT). This process is orchestrated by cytokines like TGFβ, involving "non-canonical" AKT- or STAT3-driven pathways. Recently, the alteration of copper homeostasis seems involved in the onset and progression of cancer.

METHODS

We expose different breast cancer cell lines, including two triple negative (TNBC) ones, an HER2 enriched and one cell line representative of the Luminal A molecular subtype, to short- or long-term copper-chelation by triethylenetetramine (TRIEN). We analyse changes in the expression of EMT markers (E-cadherin, fibronectin, vimentin and αSMA), in the levels and activity of extracellular matrix components (LOXL2, fibronectin and MMP2/9) and of copper homeostasis markers by Western blot analyses, immunofluorescence, enzyme activity assays and RT-qPCR. Boyden Chamber and wound healing assays revealed the impact of copper chelation on cell migration. Additionally, we explored whether perturbation of copper homeostasis affects EMT prompted by TGFβ. Metabolomic and lipidomic analyses were applied to search the effects of copper chelation on the metabolism of breast cancer cells. Finally, bioinformatics analysis of data on breast cancer patients obtained from different databases was employed to correlate changes in kinases and copper markers with patients' survival.

RESULTS

Remarkably, only HER2 negative breast cancer cells differently responded to short- or long-term exposure to TRIEN, initially becoming more aggressive but, upon prolonged exposure, retrieving epithelial features, reducing their invasiveness. This phenomenon may be related to the different impact of the short and prolonged activation of the AKT kinase and to the repression of STAT3 signalling. Bioinformatics analyses confirmed the positive correlation of breast cancer patients' survival with AKT activation and up-regulation of CCS. Eventually, metabolomics studies demonstrate a prevalence of glycolysis over mitochondrial energetic metabolism and of lipidome changes in TNBC cells upon TRIEN treatment.

CONCLUSIONS

We provide evidence of a pivotal role of copper in AKT-driven EMT activation, acting independently of HER2 in TNBC cells and via a profound change in their metabolism. Our results support the use of copper-chelators as an adjuvant therapeutic strategy for TNBC.

Protein expression profiling identifies a prognostic model for ovarian cancer

Background

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

Methods

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

Results

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

Conclusions

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

Constitutively Active Androgen Receptor in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant type of liver cancer and a leading cause of cancer-related death globally. It is also a sexually dimorphic disease with a male predominance both in HCC and in its precursors, non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH). The role of the androgen receptor (AR) in HCC has been well documented; however, AR-targeted therapies have failed to demonstrate efficacy in HCC. Building upon understandings of AR in prostate cancer (PCa), this review examines the role of AR in HCC, non-androgen-mediated mechanisms of induced AR expression, the existence of AR splice variants (AR-SV) in HCC and concludes by surveying current AR-targeted therapeutic approaches in PCa that show potential for efficacy in HCC in light of AR-SV expression.

Src: coordinating metabolism in cancer

Metabolism must be tightly regulated to fulfil the dynamic requirements of cancer cells during proliferation, migration, stemness and differentiation. Src is a node of several signals involved in many of these biological processes, and it is also an important regulator of cell metabolism. Glucose uptake, glycolysis, the pentose-phosphate pathway and oxidative phosphorylation are among the metabolic pathways that can be regulated by Src. Therefore, this oncoprotein is in an excellent position to coordinate and finely tune cell metabolism to fuel the different cancer cell activities. Here, we provide an up-to-date summary of recent progress made in determining the role of Src in glucose metabolism as well as the link of this role with cancer cell metabolic plasticity and tumour progression. We also discuss the opportunities and challenges facing this field.

Integration of transcriptomics, proteomics, and metabolomics data to reveal HER2-associated metabolic heterogeneity in gastric cancer with response to immunotherapy and neoadjuvant chemotherapy

Background

Currently available prognostic tools and focused therapeutic methods result in unsatisfactory treatment of gastric cancer (GC). A deeper understanding of human epidermal growth factor receptor 2 (HER2)-coexpressed metabolic pathways may offer novel insights into tumour-intrinsic precision medicine.

Methods

The integrated multi-omics strategies (including transcriptomics, proteomics and metabolomics) were applied to develop a novel metabolic classifier for gastric cancer. We integrated TCGA-STAD cohort (375 GC samples and 56753 genes) and TCPA-STAD cohort (392 GC samples and 218 proteins), and rated them as transcriptomics and proteomics data, resepectively. 224 matched blood samples of GC patients and healthy individuals were collected to carry out untargeted metabolomics analysis.

Results

In this study, pan-cancer analysis highlighted the crucial role of ERBB2 in the immune microenvironment and metabolic remodelling. In addition, the metabolic landscape of GC indicated that alanine, aspartate and glutamate (AAG) metabolism was significantly associated with the prevalence and progression of GC. Weighted metabolite correlation network analysis revealed that glycolysis/gluconeogenesis (GG) and AAG metabolism served as HER2-coexpressed metabolic pathways. Consensus clustering was used to stratify patients with GC into four subtypes with different metabolic characteristics (i.e. quiescent, GG, AAG and mixed subtypes). The GG subtype was characterised by a lower level of ERBB2 expression, a higher proportion of the inflammatory phenotype and the worst prognosis. However, contradictory features were found in the mixed subtype with the best prognosis. The GG and mixed subtypes were found to be highly sensitive to chemotherapy, whereas the quiescent and AAG subtypes were more likely to benefit from immunotherapy.

Conclusions

Transcriptomic and proteomic analyses highlighted the close association of HER-2 level with the immune status and metabolic features of patients with GC. Metabolomics analysis highlighted the co-expressed relationship between alanine, aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC. The novel integrated multi-omics strategy used in this study may facilitate the development of a more tailored approach to GC therapy.

Progressing Towards a Human-Centric Approach in Cancer Research

Despite the advancement in research methodologies and technologies for cancer research, there is a high rate of anti-cancer drug attrition. In this review, we discuss different conventional and modern approaches in cancer research and how human-centric models can improve on the voids conferred by more traditional animal-centric models, thereby offering a more reliable platform for drug discovery. Advanced three-dimensional cell culture methodologies, along with in silico computational analysis form the core of human-centric cancer research. This can provide a holistic understanding of the research problems and help design specific and accurate experiments that could lead to the development of better cancer therapeutics. Here, we propose a new human-centric research roadmap that promises to provide a better platform for cancer research and drug discovery.

Construction and validation of a prognostic risk model for breast cancer based on protein expression

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

Patient-level proteomic network prediction by explainable artificial intelligence

Understanding the pathological properties of dysregulated protein networks in individual patients’ tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring “patient-level” oncogenic mechanisms.

Homologous recombination deficiency and molecular subtype are associated with immunogenicity in ovarian cancer

Aim: There is an unmet need for predictive biomarkers for immune checkpoint blockade in ovarian cancer. Homologous recombination deficiency (HRD) and immunoreactive molecular subtype may be associated with determinants of immunogenicity. Materials & methods: Neoantigen load, tumor inflammation signature (TIS), immune cell infiltrates and individual immune checkpoints were assessed based on HRD status and molecular subtype. Results: Tumors with HRD demonstrated significantly higher expression of neoantigens and multiple immune check points, but not higher TIS scores or increased immune cell infiltrates. Immunoreactive tumors had significantly higher neoantigen expression, TIS scores, immune cell infiltrate and immune checkpoint expression compared with other subtypes. Conclusion: HRD and the immunoreactive molecular subtype signature were associated with multiple determinants of immunogenicity and deserve further exploration as predictive biomarkers.

Deciphering miR-520c-3p as a probable target for immunometabolism in non-small cell lung cancer using systems biology approach

Background: Non-small cell lung cancer (NSCLC) is considered to have more than 80% of all lung cancer cases, making it the leading cause of cancer-related deaths globally. MicroRNA (miRNA) deregulation has been seen often in NSCLC and has been linked to the disease’s genesis, progression, and metastasis via affecting their target genes.

Materials and Methods: Our study focused on the functionality of down-regulated miRNAs in NSCLC. For this study, we used 91 miRNAs reported to be down-regulated in NSCLC. The targets of these miRNAs were chosen from miRNA databases with functionality in NSCLC, including miRBase, miRDB, miRTV, and others. Inter-regulatory miRNA-NSCLC networks were generated. Simulated annealing was used to improve the network’s resilience and understandability. GSEA was used to examine 24607 genes reported experimentally in order to gain physiologically relevant information about the target miR-520c-3p.

Results: The study revealed functional prominence on miR-520c-3p, down-regulated during NSCLC. The involvement of miR-520c-3p in the PI3K/AKT/mTOR signaling pathway was recognized.

Conclusions: The therapeutic usage by designing a synthetic circuit of miR-520c-3p was explored, which may help in suppressing tumors in NSCLC. Our study holds promise for the successful deployment of currently proposed miRNA-based therapies for malignant disorders, which are still in the early pre-clinical stages of development.

Visual barcodes for clonal-multiplexing of live microscopy-based assays

While multiplexing samples using DNA barcoding revolutionized the pace of biomedical discovery, multiplexing of live imaging-based applications has been limited by the number of fluorescent proteins that can be deconvoluted using common microscopy equipment. To address this limitation, we develop visual barcodes that discriminate the clonal identity of single cells by different fluorescent proteins that are targeted to specific subcellular locations. We demonstrate that deconvolution of these barcodes is highly accurate and robust to many cellular perturbations. We then use visual barcodes to generate ‘Signalome’ cell-lines by mixing 12 clones of different live reporters into a single population, allowing simultaneous monitoring of the activity in 12 branches of signaling, at clonal resolution, over time. Using the ‘Signalome’ we identify two distinct clusters of signaling pathways that balance growth and proliferation, emphasizing the importance of growth homeostasis as a central organizing principle in cancer signaling. The ability to multiplex samples in live imaging applications, both in vitro and in vivo may allow better high-content characterization of complex biological systems.

Multiplex analyses of samples allow understanding complex processes in cancer initiation, progression and therapy response. Here, the authors present a fluorescence imaging-based visual barcode for livecell clonal-multiplexing which allows identifying signalling pathways clusters in response to different chemotherapy compounds.

Tumor molecular differences associated with outcome disparities of Black patients with head and neck cancer

BACKGROUND

Numerous studies of head and neck squamous cell carcinoma (HNSCC) have demonstrated disparate outcomes by race and ethnicity. Beyond known associations with socioeconomic variables, whether these are also associated with differences in tumor molecular composition has thus far been poorly explored.

METHODS

We downloaded clinical and multiplatform molecular data from The Cancer Genome Atlas and other published studies. These were compared between non-Hispanic Black (n = 43) and White (n = 354) patients with non-HPV-related tumors, using multivariable models. Publicly available validation cohorts were used.

RESULTS

Black patients had poorer progression-free survival than White patients. Tumors of Black patients had greater copy number aberrations, and increased SFRP1 methylation and miRNA-mediated PRG4 silencing associated with poor survival. PI3K/AkT/mTOR pathway proteins were differentially expressed.

CONCLUSIONS

There are molecular differences between tumors of Black and White patients that may partially account for differences in survival. These may inform targeted treatment decisions to achieve equitable outcomes.

Prognostic Landscape of Tumor-Infiltrating T and B Cells in Human Cancer

Recently, immunotherapy targeting tumor-infiltrating lymphocytes (TILs) has emerged as a critical and promising treatment in several types of cancer. However, not all cancer types have been tested in immunotherapeutic trials, and different patients and cancer types may have unpredictable clinical outcomes. This situation has created a particular exigency for analyzing the prognostic significance of tumor-infiltrating T cells (TIL-T) and B cells (TIL-B) across different cancer types. To address the critical role of TILs, the abundances of TIL-T and TIL-B cells, as determined by the protein levels of LCK and CD20, were analyzed across heterogeneous human malignancies. TIL-T and TIL-B cells showed varying prognostic significances across heterogeneous cancer types. Additionally, distinct distributions of TIL-T and TIL-B cells were observed in different cancer and tumor microenvironment (TME) subtypes. Next, we analyzed the cellular context for the TME communication network involving the well-acknowledgeable chemokine receptors of TIL-T and TIL-B cells, implying the functional interactions with TME. Additionally, these chemokine receptors, expressed by TIL-T and TIL-B cells, were remarkably correlated with the levels of TIL-T or TIL-B cell infiltrations across nearly all the cancer types, indicating these chemokine receptors as universal targets for up- and down-regulating the TIL-T and TIL-B cells. Lastly, we provide the prognostic landscape of TIL-T and TIL-B cells across 30 cancer types and the subgroups defined by gender, histopathology, histological grade, therapeutic approach, drug, and TME subtype, which are intended to be a resource to fuel the investigations of TILs, with important implications for cancer immunotherapy.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Cyclin E1 in Murine and Human Liver Cancer: A Promising Target for Therapeutic Intervention during Tumour Progression

Simple Summary

The cell cycle regulator Cyclin E1 is a key mediator and biomarker of liver cancer progression in mice and man independent of its canonical interacting partner Cyclin-dependent kinase 2. Over-expression of Cyclin E1 during hepatocarcinogenesis modulates several distinct biological processes such as proliferation, DNA damage response, stemness, invasion and the tumour microenvironment. Interventional depletion of Cyclin E1 in the course of liver cancer progression significantly reduces tumour burden. In contrast, the expression of Cyclin-dependent kinase 2 is dispensable for the progression of liver cancer in mice and lacked diagnostic or prognostic value in patients. Thus, specific inhibition of Cyclin E1 expression represents a promising strategy for the treatment of liver cancer.

Abstract

Cyclin E1 (CCNE1) is a regulatory subunit of Cyclin-dependent kinase 2 (CDK2) and is thought to control the transition of quiescent cells into cell cycle progression. Recently, we identified CCNE1 and CDK2 as key factors for the initiation of hepatocellular carcinoma (HCC). In the present study, we dissected the contributions of CCNE1 and CDK2 for HCC progression in mice and patients. Therefore, we generated genetically modified mice allowing inducible deletion of Ccne1 or Cdk2. After initiation of HCC, using the hepatocarcinogen diethylnitrosamine (DEN), we deleted Ccne1 or Cdk2 and subsequently analysed HCC progression. The relevance of CCNE1 or CDK2 for human HCC progression was investigated by in silico database analysis. Interventional deletion of Ccne1, but not of Cdk2, substantially reduced the HCC burden in mice. Ccne1-deficient HCCs were characterised by attenuated proliferation, impaired DNA damage response and downregulation of markers for stemness and microinvasion. Additionally, the tumour microenvironment of Ccne1-deficient mice showed a reduction in immune mediators, myeloid cells and cancer-associated fibroblasts. In sharp contrast, Cdk2 was dispensable for HCC progression in mice. In agreement with our mouse data, CCNE1 was overexpressed in HCC patients independent of risk factors, and associated with reduced disease-free survival, a common signature for enhanced chromosomal instability, proliferation, dedifferentiation and invasion. However, CDK2 lacked diagnostic or prognostic value in HCC patients. In summary, CCNE1 drives HCC progression in a CDK2-independent manner in mice and man. Therefore, interventional inactivation of CCNE1 represents a promising strategy the treatment of liver cancer.

3p Arm Loss and Survival in Head and Neck Cancer: An Analysis of TCGA Dataset

Loss of the 3p chromosome arm has previously been reported to be a biomarker of poorer outcome in both human papillomavirus (HPV)-positive and HPV-negative head and neck cancer. However, the precise operational measurement of 3p arm loss is unclear and the mutational profile associated with the event has not been thoroughly characterized. We downloaded the clinical, single nucleotide variation (SNV), copy number aberration (CNA), RNA sequencing, and reverse phase protein assay (RPPA) data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas HNSCC cohorts. Survival data and hypoxia scores were downloaded from published studies. In addition, we report the inclusion of an independent Memorial Sloan Kettering cohort. We assessed the frequency of loci deletions across the 3p arm separately in HPV-positive and -negative disease. We found that deletions on chromosome 3p were almost exclusively an all or none event in the HPV-negative cohort; patients either had <1% or >97% of the arm deleted. 3p arm loss, defined as >97% deletion in HPV-positive patients and >50% in HPV-negative patients, had no impact on survival (p > 0.05). However, HPV-negative tumors with 3p arm loss presented at a higher N-category and overall stage and developed more distant metastases (p < 0.05). They were enriched for SNVs in TP53, and depleted for point mutations in CASP8, HRAS, HLA-A, HUWE1, HLA-B, and COL22A1 (false discovery rate, FDR < 0.05). 3p arm loss was associated with CNAs across the whole genome (FDR < 0.1), and pathway analysis revealed low lymphoid-non-lymphoid cell interactions and cytokine signaling (FDR < 0.1). In the tumor microenvironment, 3p arm lost tumors had low immune cell infiltration (FDR < 0.1) and elevated hypoxia (FDR < 0.1). 3p arm lost tumors had lower abundance of proteins phospho-HER3 and ANXA1, and higher abundance of miRNAs hsa-miR-548k and hsa-miR-421, which were all associated with survival. There were no molecular differences by 3p arm status in HPV-positive patients, at least at our statistical power level. 3p arm loss is largely an all or none phenomenon in HPV-negative disease and does not predict poorer survival from the time of diagnosis in TCGA cohort. However, it produces tumors with distinct molecular characteristics and may represent a clinically useful biomarker to guide treatment decisions for HPV-negative patients.

Integrative pan-cancer analysis of MEK1 aberrations and the potential clinical implications

Alterations of mitogen-activated protein kinase kinase 1 (MEK1) are commonly associated with tumorigenesis, and MEK1 is thought to be a suitable targeted therapy for various cancers. However, abnormal MEK1 alterations and their relevant clinical implications are unknown. Our research comprehensively analyzed the MEK1 alteration spectrum and provided novel insight for targeted therapies. There were 7694 samples covering 32 types of cancer from The Cancer Genome Atlas (TCGA) database. They were used to conduct an integrative analysis of MEK1 expression, alterations, functional impacts and clinical significance. There was a dramatic difference in the alteration frequency and distribution and clinical implications in 32 types of cancer from the TCGA. Skin cutaneous melanoma (SKCM) has the most alterations and has therapeutic targets located in the protein kinase domain, and the growing expression of SKCM is positively related to patient prognosis. MEK1 expression in lung adenocarcinoma (LUAD), kidney renal papillary cell carcinoma (KIRP), esophageal carcinoma (ESCA) and liver hepatocellular carcinoma (LIHC) is decreased, which is associated with better prognosis, while MEK1 expression in thymoma (THYM), stomach adenocarcinoma (STAD), kidney renal clear cell carcinoma (KIRC), testicular germ cell tumors (TGCTs) and head and neck squamous cell carcinoma (HNSC) is increased, which is associated with better prognosis. Mesothelioma (MESO) has the second highest alterations but has no therapy targets. This study provided a great and detailed interpretation of MEK1 expression, alterations and clinical implications in 32 types of cancer and reminded us to fill the gap in MEK1 research from a new perspective.

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

Survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method for discovering gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for patient dichotomization when using continuous gene expression values. There is some optimization software for cutoff determination. However, the software's predetermined cutoffs are usually set at the medians or quantiles of gene expression values. There are also few clinicopathological features available in pre-processed datasets. We applied an in-house workflow, including data retrieving and pre-processing, feature selection, sliding-window cutoff selection, Kaplan-Meier survival analysis, and Cox proportional hazard modeling for biomarker discovery. In our approach for the TCGA HNSCC cohort, we scanned human protein-coding genes to find optimal cutoff values. After adjustments with confounders, clinical tumor stage and surgical margin involvement were found to be independent risk factors for prognosis. According to the results tables that show hazard ratios with Bonferroni-adjusted p values under the optimal cutoff, three biomarker candidates, CAMK2N1, CALML5, and FCGBP, are significantly associated with overall survival. We validated this discovery by using the another independent HNSCC dataset (GSE65858). Thus, we suggest that transcriptomic analysis could help with biomarker discovery. Moreover, the robustness of the biomarkers we identified should be ensured through several additional tests with independent datasets.

miR-138-5p induces aggressive traits by targeting Trp53 expression in murine melanoma cells, and correlates with poor prognosis of melanoma patients

Deregulation of miRNAs contributes to the development of distinct cancer types, including melanoma, an aggressive form of skin cancer characterized by high metastatic potential and poor prognosis. The expression of a set of 580 miRNAs was investigated in a model of murine melanoma progression, comprising non-metastatic (4C11-) and metastatic melanoma (4C11+) cells. A significant increase in miR-138-5p expression was found in the metastatic 4C11+ melanoma cells compared to 4C11-, which prompted us to investigate its role in melanoma aggressiveness. Functional assays, including anoikis resistance, colony formation, collective migration, serum-deprived growth capacity, as well as in vivo tumor growth and experimental metastasis were performed in 4C11- cells stably overexpressing miR-138-5p. miR-138-5p induced an aggressive phenotype in mouse melanoma cell lines leading to increased proliferation, migration and cell viability under stress conditions. Moreover, by overexpressing miR-138-5p, low-growing and non-metastatic 4C11- cells became highly proliferative and metastatic in vivo, similar to the metastatic 4C11+ cells. Luciferase reporter analysis identified the tumor suppressor Trp53 as a direct target of miR-138-5p. Using data sets from independent melanoma cohorts, miR-138-5p and P53 expression were also found deregulated in human melanoma samples, with their levels negatively and positively correlated with prognosis, respectively. Our data shows that the overexpression of miR-138-5p contributes to melanoma metastasis through the direct suppression of Trp53.

The Proteomic Landscape of Growth Factor Signaling Networks Associated with FAT1 Mutations in Head and Neck Cancers

FAT1 is frequently mutated in head and neck squamous cell carcinoma (HNSCC), but the biological and clinical effects of FAT1 mutations in HNSCC remain to be fully elucidated. We investigated the landscape of altered protein and gene expression associated with FAT1 mutations and clinical outcomes of patients with HNSCC. FAT1 mutation was stratified with clinical information from The Cancer Genome Atlas HNSCC databases with more than 200 proteins or phosphorylated sites. FAT1 mutation was significantly more prevalent among HPV(-), female, and older patients and was enriched in oral, larynx, and hypopharynx primary tumors. FAT1 mutation was also significantly associated with lower FAT1 gene expression and increased protein expression of HER3_pY1289, IRS1, and CAVEOLIN1. From an independent International Cancer Genome Consortium dataset, FAT1 mutation in oral cancer co-occurred with top mutated genes TP53 and CASP8. Poorer overall survival or progression-free survival was observed in patients with FAT1 mutation or altered HER3_pY1289, IRS1, or CAVEOLIN1. Pathway analysis revealed dominant ERBB/neuregulin pathways linked to FAT1 mutations in HNSCC, and protein signature panels uncovered the heterogeneity of patient subgroups. Decreased pEGFR, pHER2, and pERK and upregulated pHER3 and HER3 proteins were observed in two FAT1 knockout HNSCC cell lines, supporting that FAT1 alterations lead to altered EGFR/ERBB signaling. In squamous cancers of the lung and cervix, a strong association of FAT1 and EGFR gene expressions was identified. Collectively, these results suggest that alteration of FAT1 appears to involve mostly HPV(-) HNSCC and may contribute to resistance to EGFR-targeted therapy. SIGNIFICANCE: Integrative bioinformatics and statistical analyses reveal a panel of genes and proteins associated with FAT1 mutation in HNSCC, providing important insights into prospective clinical investigations with targeted therapies.

E6-mediated activation of JNK drives EGFR signalling to promote proliferation and viral oncoprotein expression in cervical cancer

Human papillomaviruses (HPV) are a major cause of malignancy worldwide, contributing to ~5% of all human cancers including almost all cases of cervical cancer and a growing number of ano-genital and oral cancers. HPV-induced malignancy is primarily driven by the viral oncogenes, E6 and E7, which manipulate host cellular pathways to increase cell proliferation and enhance cell survival, ultimately predisposing infected cells to malignant transformation. Consequently, a more detailed understanding of viral-host interactions in HPV-associated disease offers the potential to identify novel therapeutic targets. Here, we identify that the c-Jun N-terminal kinase (JNK) signalling pathway is activated in cervical disease and in cervical cancer. The HPV E6 oncogene induces JNK1/2 phosphorylation in a manner that requires the E6 PDZ binding motif. We show that blockade of JNK1/2 signalling using small molecule inhibitors, or knockdown of the canonical JNK substrate c-Jun, reduces cell proliferation and induces apoptosis in cervical cancer cells. We further demonstrate that this phenotype is at least partially driven by JNK-dependent activation of EGFR signalling via increased expression of EGFR and the EGFR ligands EGF and HB-EGF. JNK/c-Jun signalling promoted the invasive potential of cervical cancer cells and was required for the expression of the epithelial to mesenchymal transition (EMT)-associated transcription factor Slug and the mesenchymal marker Vimentin. Furthermore, JNK/c-Jun signalling is required for the constitutive expression of HPV E6 and E7, which are essential for cervical cancer cell growth and survival. Together, these data demonstrate a positive feedback loop between the EGFR signalling pathway and HPV E6/E7 expression, identifying a regulatory mechanism in which HPV drives EGFR signalling to promote proliferation, survival and EMT. Thus, our study has identified a novel therapeutic target that may be beneficial for the treatment of cervical cancer.

PHLPPing the balance: restoration of protein kinase C in cancer

Protein kinase signalling, which transduces external messages to mediate cellular growth and metabolism, is frequently deregulated in human disease, and specifically in cancer. As such, there are 77 kinase inhibitors currently approved for the treatment of human disease by the FDA. Due to their historical association as the receptors for the tumour-promoting phorbol esters, PKC isozymes were initially targeted as oncogenes in cancer. However, a meta-analysis of clinical trials with PKC inhibitors in combination with chemotherapy revealed that these treatments were not advantageous, and instead resulted in poorer outcomes and greater adverse effects. More recent studies suggest that instead of inhibiting PKC, therapies should aim to restore PKC function in cancer: cancer-associated PKC mutations are generally loss-of-function and high PKC protein is protective in many cancers, including most notably KRAS-driven cancers. These recent findings have reframed PKC as having a tumour suppressive function. This review focusses on a potential new mechanism of restoring PKC function in cancer - through targeting of its negative regulator, the Ser/Thr protein phosphatase PHLPP. This phosphatase regulates PKC steady-state levels by regulating the phosphorylation of a key site, the hydrophobic motif, whose phosphorylation is necessary for the stability of the enzyme. We also consider whether the phosphorylation of the potent oncogene KRAS provides a mechanism by which high PKC expression may be protective in KRAS-driven human cancers.

A Non-canonical Wnt Signature Correlates With Lower Survival in Gastric Cancer

Genetic evidence suggests a role for the Wnt/β-catenin pathway in gastric cancer. However, Wnt5a, regarded as a prototypical non-canonical Wnt ligand, has also been extensively associated with this disease. Therefore, the roles of the Wnt signaling pathway in gastric cancer initiation and progression, and particularly the precise mechanisms by which the non-canonical Wnt pathway might promote the development and progression of gastric cancer, are not entirely well understood. This article analyzes publicly available gene and protein expression data and reveals the existence of a WNT5A/FZD2/FZD7/ROR2 signature, which correlates with tumor-infiltrating and mesenchymal cell marker expression. High expression of FZD7 and ROR2 correlates with a shared gene and protein expression profile, which in turn correlates with poor prognosis. In summary, the findings presented in this article provide an updated view of the relative contributions of the Wnt/β-catenin and non-canonical Wnt pathways in gastric cancer.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

Subgroup-Independent Mapping of Renal Cell Carcinoma-Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries

Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.

Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).

Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.

Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

Fatty Acid Synthase Confers Tamoxifen Resistance to ER+/HER2+ Breast Cancer

Simple Summary

Overactivation of the human epidermal growth factor receptor 2 (HER2) is one of the main drivers of tamoxifen resistance in estrogen receptor (ER)-positive breast cancer patients. Combined targeting of HER2 and ER, however, has yielded disappointing results in the clinical setting. Therefore, other potential mechanisms for tamoxifen resistance would not be overcome by solely blocking the cross-talk between ER and HER2 at the receptor(s) level. Using cell lines, animal models, and clinical data, we provide evidence to support a critical role of fatty acid synthase (FASN)—the major site for endogenous fat synthesis—in HER2-driven tamoxifen resistance. Importantly, treatment with a FASN inhibitor impeded the estrogen-like tumor-promoting effects of tamoxifen and fully restored the anti-estrogenic activity of tamoxifen in ER+/HER2-overexpressing breast cancer xenografts. We postulate FASN as a biological determinant of HER2-driven tamoxifen resistance and FASN inhibition as a novel therapeutic approach to restore tamoxifen sensitivity in endocrine-resistant breast cancer.

Abstract

The identification of clinically important molecular mechanisms driving endocrine resistance is a priority in estrogen receptor-positive (ER+) breast cancer. Although both genomic and non-genomic cross-talk between the ER and growth factor receptors such as human epidermal growth factor receptor 2 (HER2) has frequently been associated with both experimental and clinical endocrine therapy resistance, combined targeting of ER and HER2 has failed to improve overall survival in endocrine non-responsive disease. Herein, we questioned the role of fatty acid synthase (FASN), a lipogenic enzyme linked to HER2-driven breast cancer aggressiveness, in the development and maintenance of hormone-independent growth and resistance to anti-estrogens in ER/HER2-positive (ER+/HER2+) breast cancer. The stimulatory effects of estradiol on FASN gene promoter activity and protein expression were blunted by anti-estrogens in endocrine-responsive breast cancer cells. Conversely, an AKT/MAPK-related constitutive hyperactivation of FASN gene promoter activity was unaltered in response to estradiol in non-endocrine responsive ER+/HER2+ breast cancer cells, and could be further enhanced by tamoxifen. Pharmacological blockade with structurally and mechanistically unrelated FASN inhibitors fully impeded the strong stimulatory activity of tamoxifen on the soft-agar colony forming capacity—an in vitro metric of tumorigenicity—of ER+/HER2+ breast cancer cells. In vivo treatment with a FASN inhibitor completely prevented the agonistic tumor-promoting activity of tamoxifen and fully restored its estrogen antagonist properties against ER/HER2-positive xenograft tumors in mice. Functional cancer proteomic data from The Cancer Proteome Atlas (TCPA) revealed that the ER+/HER2+ subtype was the highest FASN protein expressor compared to basal-like, HER2-enriched, and ER+/HER2-negative breast cancer groups. FASN is a biological determinant of HER2-driven endocrine resistance in ER+ breast cancer. Next-generation, clinical-grade FASN inhibitors may be therapeutically relevant to countering resistance to tamoxifen in FASN-overexpressing ER+/HER2+ breast carcinomas.

Human epidermal growth factor receptor 2 expression in head and neck squamous cell carcinoma: Variation within and across primary tumor sites, and implications for antigen-specific immunotherapy

BACKGROUND

The purpose of this study is to describe human epidermal growth factor 2 (HER2) overexpression in head and neck squamous cell carcinoma (HNSCC) and re-evaluate its potential as a target for HER2-directed immunotherapies.

METHODS

A retrospective cohort of patients with HNSCC receiving curative treatment was identified, and HER2 expression evaluated in archival tissue by immunohistochemistry and correlated with clinicopathological characteristics. HER2 expression data were also determined for HNSCC patients in The Cancer Genome Atlas.

RESULTS

Nineteen percent of HNSCC and 39% of oropharyngeal HNSCC (OPSCC) were HER2 positive. HER2 expression positively correlated with nodal metastasis (p = 0.035). Patients with HER2-positive tumors had decreased overall survival (p = 0.012), including within the human papilloma virus-positive OPSCC subgroup (p = 0.007).

CONCLUSIONS

A substantial fraction of HNSCC overexpresses HER2 protein, suggesting it may be a suitable target for antigen-directed immunotherapy. HER2 expression and its correlation with survival vary across HNSCC subsites, making it unsuitable as a prognostic marker.