RNA Sequencing of the NCI-60: Integration into CellMiner and CellMiner CDB

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Acetalax (Oxyphenisatin Acetate, NSC 59687) and Bisacodyl Cause Oncosis in Triple-Negative Breast Cancer Cell Lines by Poisoning the Ion Exchange Membrane Protein TRPM4

Triple-negative breast cancer (TNBC) is clinically aggressive and relatively unresponsive to current therapies. Therefore, the development of new anticancer agents is needed to satisfy clinical needs. Oxyphenisatin acetate (Acetalax), which had been used as a laxative, has recently been reported to have anticancer activity in murine models. In this study, we demonstrate that Acetalax and its diphenolic laxative structural analogue bisacodyl (Dulcolax) exhibit potent antiproliferative activity in TNBC cell lines and cause oncosis, a nonapoptotic cell death characterized by cellular and nuclear swelling and cell membrane blebbing, leading to mitochondrial dysfunction, ATP depletion, and enhanced immune and inflammatory responses. Mechanistically, we provide evidence that transient receptor potential melastatin member 4 (TRPM4) is poisoned by Acetalax and bisacodyl in MDA-MB468, BT549, and HS578T TNBC cells. MDA-MB231 and MDA-MB436 TNBC cells without endogenous TRPM4 expression as well as TRPM4-knockout TNBC cells were found to be Acetalax- and bisacodyl-resistant. Conversely, ectopic expression of TRPM4 sensitized MDA-MB231 and MDA-MB436 cells to Acetalax. TRPM4 was also lost in cells with acquired Acetalax resistance. Moreover, TRPM4 is rapidly degraded by the ubiquitin-proteasome system upon acute exposure to Acetalax and bisacodyl. Together, these results demonstrate that TRPM4 is a previously unknown target of Acetalax and bisacodyl and that TRPM4 expression in cancer cells is a predictor of Acetalax and bisacodyl efficacy and could be used for the clinical development of these drugs as anticancer agents.

SIGNIFICANCE

Acetalax and bisacodyl kill cancer cells by causing oncosis following poisoning of the plasma membrane sodium transporter TRPM4 and represent a new therapeutic approach for TNBC.

Sarcoma_CellminerCDB: A tool to interrogate the genomic and functional characteristics of a comprehensive collection of sarcoma cell lines

Sarcomas are a diverse group of rare malignancies composed of multiple different clinical and molecular subtypes. Due to their rarity and heterogeneity, basic, translational, and clinical research in sarcoma has trailed behind that of other cancers. Outcomes for patients remain generally poor due to an incomplete understanding of disease biology and a lack of novel therapies. To address some of the limitations impeding preclinical sarcoma research, we have developed Sarcoma_CellMinerCDB, a publicly available interactive tool that merges publicly available sarcoma cell line data and newly generated omics data to create a comprehensive database of genomic, transcriptomic, methylomic, proteomic, metabolic, and pharmacologic data on 133 annotated sarcoma cell lines. The reproducibility, functionality, biological relevance, and therapeutic applications of Sarcoma_CellMinerCDB described herein are powerful tools to address and generate biological questions and test hypotheses for translational research. Sarcoma_CellMinerCDB (https://discover.nci.nih.gov/SarcomaCellMinerCDB) aims to contribute to advancing the preclinical study of sarcoma.

Imaging analysis of six human histone H1 variants reveals universal enrichment of H1.2, H1.3, and H1.5 at the nuclear periphery and nucleolar H1X presence

Histone H1 participates in chromatin condensation and regulates nuclear processes. Human somatic cells may contain up to seven histone H1 variants, although their functional heterogeneity is not fully understood. Here, we have profiled the differential nuclear distribution of the somatic H1 repertoire in human cells through imaging techniques including super-resolution microscopy. H1 variants exhibit characteristic distribution patterns in both interphase and mitosis. H1.2, H1.3, and H1.5 are universally enriched at the nuclear periphery in all cell lines analyzed and co-localize with compacted DNA. H1.0 shows a less pronounced peripheral localization, with apparent variability among different cell lines. On the other hand, H1.4 and H1X are distributed throughout the nucleus, being H1X universally enriched in high-GC regions and abundant in the nucleoli. Interestingly, H1.4 and H1.0 show a more peripheral distribution in cell lines lacking H1.3 and H1.5. The differential distribution patterns of H1 suggest specific functionalities in organizing lamina-associated domains or nucleolar activity, which is further supported by a distinct response of H1X or phosphorylated H1.4 to the inhibition of ribosomal DNA transcription. Moreover, H1 variants depletion affects chromatin structure in a variant-specific manner. Concretely, H1.2 knock-down, either alone or combined, triggers a global chromatin decompaction. Overall, imaging has allowed us to distinguish H1 variants distribution beyond the segregation in two groups denoted by previous ChIP-Seq determinations. Our results support H1 variants heterogeneity and suggest that variant-specific functionality can be shared between different cell types.

Single-cell sequencing and transcriptome analyses in the construction of a liquid-liquid phase separation-associated gene model for rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a disabling autoimmune disease that affects multiple joints. Accumulating evidence suggests that imbalances in liquid-liquid phase separation (LLPS) can lead to altered spatiotemporal coordination of biomolecular condensates, which play important roles in carcinogenesis and inflammatory diseases. However, the role of LLPS in the development and progression of RA remains unclear. Methods: We screened RA and normal samples from GSE12021, GSE55235, and GSE55457 transcriptome datasets and GSE129087 and GSE109449 single-cell sequencing datasets from Gene Expression Omnibus database to investigate the pathogenesis of LLPS-related hub genes at the transcriptome and single cell sequencing levels. Machine learning algorithms and weighted gene co-expression network analysis were applied to screen hub genes, and hub genes were validated using correlation studies. Results: Differential analysis showed that 36 LLPS-related genes were significantly differentially expressed in RA, further random forest and support vector machine identified four and six LLPS-related genes, respectively, and weighted gene co-expression network analysis identified 396 modular genes. Hybridization of the three sets revealed two hub genes, MYC and MAP1LC3B, with AUCs of 0.907 and 0.911, respectively. Further ROC analysis of the hub genes in the GSE55457 dataset showed that the AUCs of MYC and MAP1LC3B were 0.815 and 0.785, respectively. qRT-PCR showed that the expression of MYC and MAP1LC3B in RA synovial tissues was significantly lower than that in the normal control synovial tissues. Correlation analysis between hub genes and the immune microenvironment and single-cell sequencing analysis revealed that both MYC and MAP1LC3B were significantly correlated with the degree of infiltration of various innate and acquired immune cells. Conclusion: Our study reveals a possible mechanism for LLPS in RA pathogenesis and suggests that MYC and MAP1LC3B may be potential novel molecular markers for RA with immunological significance.

Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning

Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-β signaling pathway. Consensus clustering of The Cancer Genome Atlas (TCGA) BRCA dataset using mCAF single-cell characteristic gene signatures segregated samples into high-fibrotic and low-fibrotic groups. Patients in the high-fibrotic group exhibited a significantly poor prognosis. A weighted gene co-expression network analysis and univariate Cox analysis of bulk RNA-seq data revealed 17 differential genes with prognostic values. The mCAF risk prognosis signature (mRPS) was developed using 10 machine learning algorithms. The clinical outcome predictive accuracy of the mRPS was higher than that of the conventional TNM staging system. mRPS was correlated with the infiltration level of anti-tumor effector immune cells. Based on consensus prognostic genes, BRCA samples were classified into the following two subtypes using six machine learning algorithms (accuracy > 90%): interferon (IFN)-γ-dominant (immune C2) and TGF-β-dominant (immune C6) subtypes. Patients with mRPS downregulation were associated with improved prognosis, suggesting that they can potentially benefit from immunotherapy. Thus, the mRPS model can stably predict BRCA prognosis, reflect the local immune status of the tumor, and aid clinical decisions on tumor immunotherapy.

CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations

Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships.

SIGNIFICANCE

CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.

The genomic landscape of sensitivity to arsenic trioxide uncovered by genome-wide CRISPR-Cas9 screening

Introduction

Arsenic trioxide (ATO) is a promising anticancer drug for hematological malignancy. Given the dramatic efficacy of acute promyelocytic leukemia (APL), ATO has been utilized in other types of cancers, including solid tumors. Unfortunately, the results were not comparable with the effects on APL, and the resistance mechanism has not been clarified yet. This study intends to identify relevant genes and pathways affecting ATO drug sensitivity through genome-wide CRISPR-Cas9 knockdown screening to provide a panoramic view for further study of ATO targets and improved clinical outcomes.

Methods

A genome-wide CRISPR-Cas9 knockdown screening system was constructed for ATO screening. The screening results were processed with MAGeCK, and the results were subjected to pathway enrichment analysis using WebGestalt and KOBAS. We also performed protein-protein interaction (PPI) network analysis using String and Cytoscape, followed by expression profiling and survival curve analysis of critical genes. Virtual screening was used to recognize drugs that may interact with the hub gene.

Results

We applied enrichment analysis and identified vital ATO-related pathways such as metabolism, chemokines and cytokines production and signaling, and immune system responses. In addition, we identified KEAP1 as the top gene relating to ATO resistance. We found that KEAP1 expression was higher in the pan-cancer, including ALL, than in normal tissue. Patients with acute myeloid leukemia (AML) with higher KEAP1 expression had worse overall survival (OS). A virtual screen showed that etoposide and eltrombopag could bind to KEAP1 and potentially interact with ATO.

Discussion

ATO is a multi-target anticancer drug, and the key pathways regulating its sensitivity include oxidative stress, metabolism, chemokines and cytokines, and the immune system. KEAP1 is the most critical gene regulating ATO drug sensitivity, which is related to AML prognosis and may bind to some clinical drugs leading to an interaction with ATO. These integrated results provided new insights into the pharmacological mechanism of ATO and potentiate for further applications in cancer treatments.

Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

BACKGROUND

The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool.

RESULTS

We evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively).

CONCLUSION

The optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Dissecting the heterogeneities of the tumor microenvironment between metastatic and nonmetastatic primary colorectal cancer patients by single-cell RNA sequencing

AIMS

One of the main factors hampering the long-term prognosis of colorectal cancer (CRC) patients is distant metastasis. However, the driving factors of CRC metastasis have not been clarified at the single-cell level, which limits the in-depth study of accurate prediction and prevention of CRC metastasis to improve the prognosis.

MATERIALS AND METHODS

Heterogeneities in the tumor microenvironment (TME) between metastatic and nonmetastatic CRC were investigated by single-cell RNA (scRNA) sequencing data. In detail, 50,462 single cells from 20 primary CRC samples, including 40,910 cells from nonmetastatic CRC (M0 group) and 9552 cells from metastatic CRC (M1 group), were systematically analyzed in this study.

KEY FINDINGS

Based on the single-cell atlas, we revealed that cancer cells and fibroblasts accounted for relatively high proportions in metastatic CRC compared with nonmetastatic CRC. Moreover, two specific cancer cell subtypes (FGGY⁺SLC6A6⁺ and IGFBP3⁺KLK7⁺ cancer cells) and three specific fibroblast subtypes (ADAMTS6⁺CAPG⁺, PIM1⁺SGK1⁺ and CA9⁺UPP1⁺ fibroblasts) in metastatic CRC were identified. The functional and differentiation characteristics of these specific cell subclusters were elucidated by enrichment and trajectory analyses.

SIGNIFICANCE

These results provide fundamental knowledge for future in-depth research to screen effective methods and drugs to predict and prevent CRC metastasis to improve prognosis.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Identification of the molecular characteristics associated with microsatellite status of colorectal cancer patients for the clinical application of immunotherapy

Background: Mismatch repair-proficient (pMMR) microsatellite stability (MSS) in colorectal cancer (CRC) indicates an unfavorable therapeutic response to immunotherapy with immune checkpoint inhibitors (ICIs). However, the molecular characteristics of CRC patients with pMMR MSS remain largely unknown. Methods: Heterogeneities between mismatch repair-deficient (dMMR) microsatellite instability (MSI) and pMMR MSS CRC patients were investigated at the single-cell level. Next, an MSS-related risk score was constructed by single-sample gene set enrichment analysis (ssGSEA). The differences in immune and functional characteristics between the high- and low-score groups were systematically analyzed. Results: Based on the single-cell RNA (scRNA) atlas, an MSS-specific cancer cell subpopulation was identified. By taking the intersection of the significant differentially expressed genes (DEGs) between different cancer cell subtypes of the single-cell training and validation cohorts, 29 MSS-specific cancer cell marker genes were screened out for the construction of the MSS-related risk score. This risk score signature could efficiently separate pMMR MSS CRC patients into two subtypes with significantly different immune characteristics. The interactions among the different cell types were stronger in the MSS group than in the MSI group, especially for the outgoing signals of the cancer cells. In addition, functional differences between the high- and low-score groups were preliminarily investigated. Conclusion: In this study, we constructed an effective risk model to classify pMMR MSS CRC patients into two completely different groups based on the specific genes identified by single-cell analysis to identify potential CRC patients sensitive to immunotherapy and screen effective synergistic targets.

Large inherent variability in data derived from highly standardised cell culture experiments

Cancer drug development is hindered by high clinical attrition rates, which are blamed on weak predictive power by preclinical models and limited replicability of preclinical findings. However, the technically feasible level of replicability remains unknown. To fill this gap, we conducted an analysis of data from the NCI60 cancer cell line screen (2.8 million compound/cell line experiments), which is to our knowledge the largest depository of experiments that have been repeatedly performed over decades. The findings revealed profound intra-laboratory data variability, although all experiments were executed following highly standardised protocols that avoid all known confounders of data quality. All compound/ cell line combinations with > 100 independent biological replicates displayed maximum GI50 (50% growth inhibition) fold changes (highest/ lowest GI50) > 5% and 70.5% displayed maximum fold changes > 1000. The highest maximum fold change was 3.16 × 10¹⁰ (lowest GI50: 7.93 ×10^-10 µM, highest GI50: 25.0 µM). FDA-approved drugs and experimental agents displayed similar variation. Variability remained high after outlier removal, when only considering experiments that tested drugs at the same concentration range, and when only considering NCI60-provided quality-controlled data. In conclusion, high variability is an intrinsic feature of anti-cancer drug testing, even among standardised experiments in a world-leading research environment. Awareness of this inherent variability will support realistic data interpretation and inspire research to improve data robustness. Further research will have to show whether the inclusion of a wider variety of model systems, such as animal and/ or patient-derived models, may improve data robustness.

Mitochondrial RNA methyltransferase TRMT61B is a new, potential biomarker and therapeutic target for highly aneuploid cancers

Despite being frequently observed in cancer cells, chromosomal instability (CIN) and its immediate consequence, aneuploidy, trigger adverse effects on cellular homeostasis that need to be overcome by anti-stress mechanisms. As such, these safeguard responses represent a tumor-specific Achilles heel, since CIN and aneuploidy are rarely observed in normal cells. Recent data have revealed that epitranscriptomic marks catalyzed by RNA-modifying enzymes change under various stress insults. However, whether aneuploidy is associated with such RNA modifying pathways remains to be determined. Through an in silico search for aneuploidy biomarkers in cancer cells, we found TRMT61B, a mitochondrial RNA methyltransferase enzyme, to be associated with high levels of aneuploidy. Accordingly, TRMT61B protein levels are increased in tumor cell lines with an imbalanced karyotype as well as in different tumor types when compared to control tissues. Interestingly, while TRMT61B depletion induces senescence in melanoma cell lines with low levels of aneuploidy, it leads to apoptosis in cells with high levels. The therapeutic potential of these results was further validated by targeting TRMT61B in transwell and xenografts assays. We show that TRM61B depletion reduces the expression of several mitochondrial encoded proteins and limits mitochondrial function. Taken together, these results identify a new biomarker of aneuploidy in cancer cells that could potentially be used to selectively target highly aneuploid tumors.

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.

Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma

Background: The mechanism of copper-induced cell death, which is called cuproptosis, has recently been clarified. However, the integrated role of cuproptosis-related genes in hepatocellular carcinoma (HCC) and its relationship with immune characteristics are still completely unknown.

Methods: In this study, the expression, genetic, and transcriptional regulation states of 16 cuproptosis-related genes in HCC were systematically investigated. An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. Differences in functional characteristics among different expression clusters were clarified by gene set variation analysis (GSVA). The abundances of immune cells in each HCC sample were calculated by the CIBERSORT algorithm. Next, a cuproptosis-related risk score was established based on the significant differentially expressed genes (DEGs) among different expression clusters.

Results: A specific cluster of HCC patients with poor prognosis, an inhibitory immune microenvironment, and high expression levels of immune checkpoint molecules was identified based on the expression of the 16 cuproptosis-related genes. This cluster of patients could be well-identified by a cuproptosis-related risk score system. The prognostic value of this risk score was validated in the training and two validation cohorts (TCGA-HCC, China-HCC, and Japan-HCC cohorts). Moreover, the overall expression status of the cuproptosis-related genes and the genes used to establish the cuproptosis-related risk score in specific cell types of the tumor microenvironment were preliminarily clarified by single-cell RNA (scRNA) sequencing data.

Conclusion: These results indicated that cuproptosis-related genes play an important role in HCC, and targeting these genes may ameliorate the inhibitory immune microenvironment to improve the efficacy of immunotherapy with immune checkpoint inhibitors (ICIs).

Identification of two immune-related risk score signatures through integrated analysis of multi-omics data in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Immunotherapy has become a major treatment for advanced HCC, but the therapeutic effects remain unsatisfactory. In this study, we constructed an immune cell risk score (ICS) and an immune cell-related gene risk score (ICRGS) for the prognosis prediction of HCC through integrated analysis of bulk and single-cell RNA (scRNA) sequencing data. These two risk score signatures both showed good predictive values in the training and validation cohorts. The potential interactions among these prognostic immune cell types were elucidated by cell-cell communication analysis. The results of enrichment analysis and gene set enrichment analysis (GSEA) of the prognostic genes showed that metabolic-related processes were involved in the immune response of HCC. Furthermore, the results of correlation analyses further confirmed the hub genes that were strongly correlated with immune cells. Finally, potential therapeutic drugs targeting these hub genes were screened by CellMiner based on NCI-60 cell line set. Taken together, two useful models for the prognosis prediction of HCC patients were constructed in this study. The functional differences between the two groups of HCC patients separated by ICS or ICRGS provide fundamental knowledge for finding synergistic therapeutic targets for HCC immunotherapy.

Robust Prediction of Prognosis and Immunotherapy Response for Bladder Cancer through Machine Learning Algorithm

The important roles of machine learning and ferroptosis in bladder cancer (BCa) are still poorly understood. In this study, a comprehensive analysis of 19 ferroptosis-related genes (FRGs) was performed in 1322 patients with BCa from four independent patient cohorts and a pan-cancer cohort of 9824 patients. Twelve FRGs were selected through machine learning algorithm to construct the prognosis model. Significantly differential survival outcomes (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.55−2.82, p < 0.0001) were observed between patients with high and low ferroptosis scores in the TCGA cohort, which was also verified in the E-MTAB-4321 cohort (HR = 4.71, 95% CI: 1.58−14.03, p < 0.0001), the GSE31684 cohort (HR = 1.76, 95% CI: 1.08−2.87, p = 0.02), and the pan-cancer cohort (HR = 1.15, 95% CI: 1.07−1.24, p < 0.0001). Tumor immunity-related pathways, including the IL-17 signaling pathway and JAK-STAT signaling pathway, were found to be associated with the ferroptosis score in BCa through a functional enrichment analysis. Further verification in the IMvigor210 cohort revealed the BCa patients with high ferroptosis scores tended to have worse survival outcome after receiving tumor immunotherapy. Significantly different ferroptosis scores could also be found between BCa patients with different reactions to treatment with immune checkpoint inhibitors.

Identification of the Immune Subtype of Hepatocellular Carcinoma for the Prediction of Disease-Free Survival Time and Prevention of Recurrence by Integrated Analysis of Bulk- and Single-Cell RNA Sequencing Data

Background

The main factors affecting the long-term prognosis of hepatocellular carcinoma (HCC) patients undergoing radical surgery are recurrence and metastasis. However, the methods for predicting disease-free survival (DFS) time and preventing postoperative recurrence of HCC are still very limited.

Methods

In this study, immune cell abundances in HCC samples were analyzed by single-sample gene set enrichment analysis (ssGSEA), while the prognostic values of immune cells for DFS time prediction were evaluated by the least absolute shrinkage and selection operator (LASSO) and subsequent univariate and multivariate Cox analyses. Next, a risk score was constructed based on the most prognostic immune cells and their corresponding coefficients. Interactions among prognostic immune cells and the specific targets for the prevention of recurrence were further identified by single-cell RNA (scRNA) sequencing data and CellMiner.

Results

A novel efficient T cell risk score (TCRS) was constructed based on data from the three most prognostic immune cell types (effector memory CD8 T cells, regulatory T cells and follicular helper T cells) for identifying an immune subtype of HCC patients with longer DFS times and inflammatory immune characteristics. Functional differences between the high- and low-score groups separated by TCRS were clarified, and the cell-cell communication among these immune cells was elucidated. Finally, fifteen hub genes that may be potential therapeutic targets for the prevention of recurrence were identified.

Conclusions

We constructed and verified a useful model for the prediction of DFS time of HCC after surgery. In addition, fifteen hub genes were identified as candidates for the prevention of recurrence, and a preliminarily investigation of potential drugs targeting these hub genes was carried out.

H3K4 demethylase KDM5B regulates cancer cell identity and epigenetic plasticity

The H3K4 demethylase KDM5B is overexpressed in multiple cancer types, and elevated expression levels of KDM5B is associated with decreased survival. However, the underlying mechanistic contribution of dysregulated expression of KDM5B and H3K4 demethylation in cancer is poorly understood. Our results show that loss of KDM5B in multiple types of cancer cells leads to increased proliferation and elevated expression of cancer stem cell markers. In addition, we observed enhanced tumor formation following KDM5B depletion in a subset of representative cancer cell lines. Our findings also support a role for KDM5B in regulating epigenetic plasticity, where loss of KDM5B in cancer cells with elevated KDM5B expression leads to alterations in activity of chromatin states, which facilitate activation or repression of alternative transcriptional programs. In addition, we define KDM5B-centric epigenetic and transcriptional patterns that support cancer cell plasticity, where KDM5B depleted cancer cells exhibit altered epigenetic and transcriptional profiles resembling a more primitive cellular state. This study also provides a resource for evaluating associations between alterations in epigenetic patterning upon depletion of KDM5B and gene expression in a diverse set of cancer cells.

Robust Validation and Comprehensive Analysis of a Novel Signature Derived from Crucial Metabolic Pathways of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a dismal prognosis. PDAC have extensively reprogrammed metabolic characteristics influenced by interactions with normal cells, the effects of the tumor microenvironment and oncogene-mediated cell-autonomous pathways. In this study, we found that among all cancer hallmarks, metabolism played an important role in PDAC. Subsequently, a 16-gene prognostic signature was established with genes derived from crucial metabolic pathways, including glycolysis, bile acid metabolism, cholesterol homeostasis and xenobiotic metabolism (gbcx). The signature was used to distinguish overall survival in multiple cohorts from public datasets as well as a validation cohort followed up by us at Shanghai Cancer Center. Notably, the gbcx-related risk score (gbcxMRS) also accurately predicted poor PDAC subtypes, such as pure-basal-like and squamous types. At the same time, it also predicted PDAC recurrence. The gbcxMRS was also associated with immune cells, especially CD8 T cells, Treg cells. Furthermore, a high gbcxMRS may indicate high drug sensitivity to irinotecan and docetaxel and CTLA4 inhibitor immunotherapy. Taken together, these results indicate a robust and reproducible metabolic-related signature based on analysis of the overall pathogenesis of pancreatic cancer, which may have excellent prognostic and therapeutic implications for PDAC.

A Neutrophil Extracellular Traps Signature Predicts the Clinical Outcomes and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

Background: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis and immune escape of cancers. This study aimed to investigate NET-related genes, their clinical prognostic value and their correlation with immunotherapy and anticancer drugs in patients with head and neck squamous cell carcinoma (HNSCC).

Methods: Differentially expressed NET-related genes in HNSCC were identified based on multiple public databases. To improve the clinical practicability and avoid overfitting, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were used to construct a prognostic risk model. A nomogram was further used to explore the clinical value of the model. Internal and external validation were conducted to test the model. Furthermore, the immune microenvironment, immunophenoscore (IPS) and sensitivity to anticancer drugs in HNSCC patients with different prognostic risks were explored.

Results: Six NET-related genes were screened to construct the risk model. In the training cohort, Kaplan–Meier (K-M) analysis showed that the overall survival (OS) of low-risk HNSCC patients was significantly better than that of high-risk HNSCC patients (p < 0.001). The nomogram also showed a promising prognostic value with a better C-index (0.726 vs 0.640) and area under the curve (AUC) (0.743 vs 0.706 at 3 years, 0.743 vs 0.645 at 5 years) than those in previous studies. Calibration plots and decision curve analysis (DCA) also showed the satisfactory predictive capacity of the nomogram. Internal and external validation further strengthened the credibility of the clinical prognostic model. The level of tumor mutational burden (TMB) in the high-risk group was significantly higher than that in the low-risk group (p = 0.017), and the TMB was positively correlated with the risk score (R = 0.11; p = 0.019). Moreover, the difference in immune infiltration was significant in HNSCC patients with different risks (p < 0.05). Furthermore, the IPS analysis indicated that anti-PD-1 (p < 0.001), anti-CTLA4 (p < 0.001) or combining immunotherapies (p < 0.001) were more beneficial for low-risk HNSCC patients. The response to anticancer drugs was also closely correlated with the expression of NET-related genes (p < 0.001).

Conclusion: This study identified a novel prognostic model that might be beneficial to develop personalized treatment for HNSCC patients.

Overexpression of abnormal spindle-like microcephaly-associated (ASPM) increases tumor aggressiveness and predicts poor outcome in patients with lung adenocarcinoma

Background

Cumulative evidence points to abnormal spindle-like microcephaly-associated (ASPM) protein being overexpressed in various cancers, and the aberrant expression of ASPM has been shown to promote cancer tumorigenicity and progression. However, its role and clinical significance in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the expression patterns of ASPM and its clinical significance in LUAD.

Methods

In total, 4 original worldwide LUAD microarray mRNA expression datasets (N=1,116) with clinical and follow-up annotations were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The expression of ASPM protein in LUAD patients was detected by immunohistochemistry. Survival analysis and Cox regression analysis were used to examine the prognostic value of ASPM expression. Gene set enrichment analysis (GSEA) was performed to investigate the relationship between ASPM and LUAD.

Results

Dataset analyses and immunohistochemistry revealed that ASPM expression was significantly higher in the LUAD tissues compared with normal lung tissues, especially in the advanced tumor stage. Additionally, overexpression of ASPM was significantly correlated with shorter overall survival (OS) and relapse-free survival (RFS) in LUAD. Univariate and multivariate Cox regression analyses revealed that the overexpression of ASPM was a potential independent predictor of poor OS and RFS. However, ASPM overexpression was not significantly associated with predicting OS in lung squamous cell carcinoma. GSEA analysis demonstrated that ASPM was significantly enriched in the cell cycle, DNA replication, homologous recombination, RNA degradation, mismatch repair, and p53 signaling pathways.

Conclusions

These findings demonstrate the important role of ASPM in the tumorigenesis and progression of LUAD.

The Potential Mechanism of Cancer Patients Appearing More Vulnerable to SARS-CoV-2 and Poor Outcomes: A Pan-Cancer Bioinformatics Analysis

To explore the potential mechanism of cancer patients appearing more vulnerable to SARS-CoV-2 infection and poor COVID-19 outcomes, we conducted an integrative bioinformatics analysis for SARS-CoV-2-required genes and host genes and variants related to SARS-CoV-2 susceptibility and COVID-19 severity. BLCA, HNSC, KIRC, KIRP, LGG, PCPG, PRAD, TGCT, and THCA patients carrying rs10774671-A (OAS1) genotype may be more likely to have poor COVID-19 outcomes relative to those who carry rs10774671-G, because individuals carrying rs10774671-A will have lower expression of OAS1, which serves as a protective factor against SARS-CoV-2 processes and poor COVID-19 outcomes. SARS-CoV-2-required genes were correlated with TME, immune infiltration, overall survival, and anti-cancer drug sensitivity. CHOL patients may have a higher risk of SARS-CoV-2 infection than healthy subjects. SARS-CoV-2-induced ACE2 and NPC1 elevation may have a negative influence on the immune responses of LUSC and CD8+T infiltration of LUAD, and negatively affect the sensitivity of anti-lung cancer drugs. LUSC and LUAD patients may have a varying degree of adverse outcomes if they are infected with SARS-CoV-2. miR-760 may target and inhibit ACE2 expression. Cancer patients appearing vulnerable to SARS-CoV-2 infection and having poor COVID-19 outcomes may be partly due to host genetic factors and dysregulation of SARS-CoV-2-required genes. OAS1, ACE2, and miR-760 could serve as the treatment and intervention targets for SARS-CoV-2.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug-response analysis such as tissue distribution of dose-response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug-response phenotypes of cancer models.

The Prognostic Value and Immune Landscapes of a m6A/m5C/m1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma

Background: N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether m6A/m5C/m1A-related long non-coding RNAs (lncRNAs) affect the prognosis of head and neck squamous cell carcinoma (HNSCC). Methods: We summarized 52 m6A/m5C/m1A-related genes, downloaded 44 normal samples and 501 HNSCC tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and then searched for m6A/m5C/m1A-related genes co-expressed lncRNAs. We adopt the least absolute shrinkage and selection operator (LASSO) Cox regression to obtain m6A/m5C/m1A-related lncRNAs to construct a prognostic signature of HNSCC. Results: This prognostic signature is based on six m6A/m5C/m1A-related lncRNAs (AL035587.1, AC009121.3, AF131215.5, FMR1-IT1, AC106820.5, PTOV1-AS2). It was found that the high-risk subgroup has worse overall survival (OS) than the low-risk subgroup. Moreover, the results showed that most immune checkpoint genes were significantly different between the two risk groups (p < 0.05). Immunity microenvironment analysis showed that the contents of NK cell resting, macrophages M2, and neutrophils in samples of low-risk group were significantly lower than those of high-risk group (p < 0.05), while the contents of B cells navie, plasma cells, and T cells regulatory (Tregs) were on the contrary (p < 0.05). In addition, patients with high tumor mutational burden (TMB) had the worse overall survival than those with low tumor mutational burden. Conclusion: Our study elucidated how m6A/m5C/m1A-related lncRNAs are related to the prognosis, immune microenvironment, and TMB of HNSCC. In the future, these m6A/m5C/m1A-related lncRNAs may become a new choice for immunotherapy of HNSCC.

Prognostic Value of a Ferroptosis-Related Gene Signature in Patients With Head and Neck Squamous Cell Carcinoma

Background: Ferroptosis is an iron-dependent programmed cell death (PCD) form that plays a crucial role in tumorigenesis and might affect the antitumor effect of radiotherapy and immunotherapy. This study aimed to investigate distinct ferroptosis-related genes, their prognostic value and their relationship with immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The differentially expressed ferroptosis-related genes in HNSCC were filtered based on multiple public databases. To avoid overfitting and improve clinical practicability, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were performed to construct a prognostic risk model. Moreover, a nomogram was constructed to forecast individual prognosis. The differences in tumor mutational burden (TMB), immune infiltration and immune checkpoint genes in HNSCC patients with different prognoses were investigated. The correlation between drug sensitivity and the model was firstly analyzed by the Pearson method. Results: Ten genes related to ferroptosis were screened to construct the prognostic risk model. Kaplan-Meier (K-M) analysis showed that the prognosis of HNSCC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001), and the area under the curve (AUC) of the 1-, 3- and 5-year receiver operating characteristic (ROC) curve increased year by year (0.665, 0.743, and 0.755). The internal and external validation further verified the accuracy of the model. Then, a nomogram was build based on the reliable model. The C-index of the nomogram was superior to a previous study (0.752 vs. 0.640), and the AUC (0.729 vs. 0.597 at 1 year, 0.828 vs. 0.706 at 3 years and 0.853 vs. 0.645 at 5 years), calibration plot and decision curve analysis (DCA) also shown the satisfactory predictive capacity. Furthermore, the TMB was revealed to be positively correlated with the risk score in HNSCC patients (R = 0.14; P < 0.01). The differences in immune infiltration and immune checkpoint genes were significant (P < 0.05). Pearson analysis showed that the relationship between the model and the sensitivity to antitumor drugs was significant (P < 0.05). Conclusion: Our findings identified potential novel therapeutic targets, providing further potential improvement in the individualized treatment of patients with HNSCC.

Laurent CD, Khoo KH, Mahal LK, Zandberg WF, Huang X, Klassen JS, Macauley MS. Carbohydrate Sulfation As a Mechanism for Fine-Tuning Siglec Ligands

The immunomodulatory family of Siglecs recognizes sialic acid-containing glycans as "self", which is exploited in cancer for immune evasion. The biochemical nature of Siglec ligands remains incompletely understood, with emerging evidence suggesting the importance of carbohydrate sulfation. Here, we investigate how specific sulfate modifications affect Siglec ligands by overexpressing eight carbohydrate sulfotransferases (CHSTs) in five cell lines. Overexpression of three CHSTs─CHST1, CHST2, or CHST4─significantly enhance the binding of numerous Siglecs. Unexpectedly, two other CHSTs (Gal3ST2 and Gal3ST3) diminish Siglec binding, suggesting a new mode to modulate Siglec ligands via sulfation. Results are cell type dependent, indicating that the context in which sulfated glycans are presented is important. Moreover, a pharmacological blockade of N- and O-glycan maturation reveals a cell-type-specific pattern of importance for either class of glycan. Production of a highly homogeneous Siglec-3 (CD33) fragment enabled a mass-spectrometry-based binding assay to determine ≥8-fold and ≥2-fold enhanced affinity for Neu5Acα2-3(6-O-sulfo)Galβ1-4GlcNAc and Neu5Acα2-3Galβ1-4(6-O-sulfo)GlcNAc, respectively, over Neu5Acα2-3Galβ1-4GlcNAc. CD33 shows significant additivity in affinity (≥28-fold) for the disulfated ligand, Neu5Acα2-3(6-O-sulfo)Galβ1-4(6-O-sulfo)GlcNAc. Moreover, joint overexpression of CHST1 with CHST2 in cells greatly enhanced the binding of CD33 and several other Siglecs. Finally, we reveal that CHST1 is upregulated in numerous cancers, correlating with poorer survival rates and sodium chlorate sensitivity for the binding of Siglecs to cancer cell lines. These results provide new insights into carbohydrate sulfation as a general mechanism for tuning Siglec ligands on cells, including in cancer.

Inhibition of glucose transport synergizes with chemical or genetic disruption of mitochondrial metabolism and suppresses TCA cycle-deficient tumors

Efforts to target glucose metabolism in cancer have been limited by the poor potency and specificity of existing anti-glycolytic agents and a poor understanding of the glucose dependence of cancer subtypes in vivo. Here, we present an extensively characterized series of potent, orally bioavailable inhibitors of the class I glucose transporters (GLUTs). The representative compound KL-11743 specifically blocks glucose metabolism, triggering an acute collapse in NADH pools and a striking accumulation of aspartate, indicating a dramatic shift toward oxidative phosphorylation in the mitochondria. Disrupting mitochondrial metabolism via chemical inhibition of electron transport, deletion of the malate-aspartate shuttle component GOT1, or endogenous mutations in tricarboxylic acid cycle enzymes, causes synthetic lethality with KL-11743. Patient-derived xenograft models of succinate dehydrogenase A (SDHA)-deficient cancers are specifically sensitive to KL-11743, providing direct evidence that TCA cycle-mutant tumors are vulnerable to GLUT inhibitors in vivo.

Trends in oligomannosylation and α1,2-mannosidase expression in human cancers

Aberrant protein glycosylation is a prominent cancer feature. While many tumour-associated glycoepitopes have been reported, advances in glycoanalytics continue to uncover new associations between glycosylation and cancer. Guided by a comprehensive literature survey suggesting that oligomannosylation (Man_5–9 GlcNAc₂) is a widespread and often regulated glycosignature in human cancers, we here revisit a valuable compilation of nearly 500 porous graphitized carbon LC-MS/MS N-glycomics datasets acquired across 11 human cancer types to systematically test for oligomannose-cancer associations. Firstly, the quantitative glycomics data obtained across 34 cancerous cell lines demonstrated that oligomannosylation is a pan-cancer feature spanning in a wide abundance range. In keeping with literature, our quantitative glycomics data of tumour and matching control tissues and new MALDI-MS imaging data of tissue microarrays showed a strong cancer-associated elevation of oligomannosylation in both basal cell (p = 1.78 × 10^–12) and squamous cell (p = 1.23 × 10^–11) skin cancer and colorectal cancer (p = 8.0 × 10^–4). The glycomics data also indicated that some cancer types including gastric and liver cancer exhibit unchanged or reduced oligomannose levels, observations also supported by literature and MALDI-MS imaging data. Finally, expression data from public cancer repositories indicated that several α1,2-mannosidases are regulated in tumour tissues suggesting that these glycan-processing enzymes may contribute to the cancer-associated modulation of oligomannosylation. This omics-centric study has compiled robust glycomics and enzyme expression data revealing interesting molecular trends that open avenues to better understand the role of oligomannosylation in human cancers.

Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

Background

Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made.

Results

In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC₅₀) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug.

Conclusions

We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07581-7.

A pan-cancer analysis of the HER family gene and their association with prognosis, tumor microenvironment, and therapeutic targets

AIMS

The human epidermal growth factor receptor (HER) family gene is involved in a wide range of biological functions in human cancers. Nevertheless, there is little research that comprehensively analysis the correlation between HER family members and prognosis, tumor microenvironment (TME) in different cancers.

MATERIALS AND METHODS

Based on updated public databases and integrated several bioinformatics analysis methods, we evaluated expression level, prognostic values of HER family gene and explore the association between expression of HER family gene and TME, Stemness score, immune subtype, drug sensitivity in pan-cancer.

KEY FINDINGS

EGFR, ERBB2, ERBB3, and ERBB4 were higher expressed in four cancers, five cancers, ten cancers, and two cancers, respectively. HER family gene expression is related to the prognosis in several cancers from TCGA and has a significant correlation with stromal and immune scores in pan-cancer also has a significant correlation with RNA stemness score and DNA stemness score in pan-cancer. Expression level of HER family gene is associated with immune subtype in head and neck squamous cell carcinoma and kidney renal clear cell carcinoma. EGFR expression was negatively associated with drug sensitivity of Pipamperone, Tamoxifen, Bafetinib and positively related to drug sensitivity of Dasatinib and Staurosporine. ERBB2 expression was negatively related to drug sensitivity of Ifosfamide, Imexon, and Oxaliplatin. ERBB4 expression was positively related to drug sensitivity of E-7820.

SIGNIFICANCE

These findings may elucidate the roles played by HER family gene in cancer progression and providing insights for further investigation of the HER family gene as potential targets in pan-cancer.

Integrative pan cancer analysis reveals epigenomic variation in cancer type and cell specific chromatin domains

Epigenetic mechanisms contribute to the initiation and development of cancer, and epigenetic variation promotes dynamic gene expression patterns that facilitate tumor evolution and adaptation. While the NCI-60 panel represents a diverse set of human cancer cell lines that has been used to screen chemical compounds, a comprehensive epigenomic atlas of these cells has been lacking. Here, we report an integrative analysis of 60 human cancer epigenomes, representing a catalog of activating and repressive histone modifications. We identify genome-wide maps of canonical sharp and broad H3K4me3 domains at promoter regions of tumor suppressors, H3K27ac-marked conventional enhancers and super enhancers, and widespread inter-cancer and intra-cancer specific variability in H3K9me3 and H4K20me3-marked heterochromatin domains. Furthermore, we identify features of chromatin states, including chromatin state switching along chromosomes, correlation of histone modification density with genetic mutations, DNA methylation, enrichment of DNA binding motifs in regulatory regions, and gene activity and inactivity. These findings underscore the importance of integrating epigenomic maps with gene expression and genetic variation data to understand the molecular basis of human cancer. Our findings provide a resource for mining epigenomic maps of human cancer cells and for identifying epigenetic therapeutic targets.

CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics

CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.

Deep learning of pharmacogenomics resources: moving towards precision oncology

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

Near-Infrared Light Irradiation Induced Mild Hyperthermia Enhances Glutathione Depletion and DNA Interstrand Cross-Link Formation for Efficient Chemotherapy

DNA alkylating agents generally kill tumor cells by covalently binding with DNA to form interstrand or intrastrand cross-links. However, in the case of cisplatin, only a few DNA adducts (<1%) are highly toxic irreparable interstrand cross-links. Furthermore, cisplatin is rapidly detoxified by high levels of intracellular thiols such as glutathione (GSH). Since the discovery of its mechanism of action, people have been looking for ways to directly and efficiently remove intracellular GSH and increase interstrand cross-links to improve drug efficacy and overcome resistance, but there has been little breakthrough. Herein, we hypothesized that the anticancer efficiency of cisplatin can be enhanced through iodo-thiol click chemistry mediated GSH depletion and increased formation of DNA interstrand cross-links via mild hyperthermia triggered by near-infrared (NIR) light. This was achieved by preparing an amphiphilic polymer with platinum(IV) (Pt(IV)) prodrugs and pendant iodine atoms (iodides). The polymer was further used to encapsulate IR780 and assembled into Pt-I-IR780 nanoparticles. Induction of mild hyperthermia (43 °C) at the tumor site by NIR light irradiation had three effects: (1) it accelerated the GSH-mediated reduction of Pt(IV) in the polymer main chain to platinum(II) (Pt(II)); (2) it boosted the iodo-thiol substitution click reaction between GSH and iodide, thereby attenuating the GSH-mediated detoxification of cisplatin; (3) it increased the proportion of highly toxic and irreparable Pt-DNA interstrand cross-links. Therefore, we find that mild hyperthermia induced via NIR irradiation can enhance the killing of cancer cells and reduce the tumor burden, thus delivering efficient chemotherapy.

A small protein encoded by a putative lncRNA regulates apoptosis and tumorigenicity in human colorectal cancer cells

Long noncoding RNAs (lncRNAs) are often associated with polysomes, indicating coding potential. However, only a handful of endogenous proteins encoded by putative lncRNAs have been identified and assigned a function. Here, we report the discovery of a putative gastrointestinal-tract-specific lncRNA (LINC00675) that is regulated by the pioneer transcription factor FOXA1 and encodes a conserved small protein of 79 amino acids which we termed FORCP (FOXA1-Regulated Conserved Small Protein). FORCP transcript is undetectable in most cell types but is abundant in well-differentiated colorectal cancer (CRC) cells where it functions to inhibit proliferation, clonogenicity, and tumorigenesis. The epitope-tagged and endogenous FORCP protein predominantly localizes to the endoplasmic reticulum (ER). In response to ER stress, FORCP depletion results in decreased apoptosis. Our findings on the initial characterization of FORCP demonstrate that FORCP is a novel, conserved small protein encoded by a mis-annotated lncRNA that regulates apoptosis and tumorigenicity in well-differentiated CRC cells.

SCLC-CellMiner: A Resource for Small Cell Lung Cancer Cell Line Genomics and Pharmacology Based on Genomic Signatures

CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB/) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell lung cancer (SCLC) cell lines, providing a resource for research into this "recalcitrant cancer." We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.

Integrative analysis of the common genetic characteristics in ovarian cancer stem cells sorted by multiple approaches

BACKGROUND

Ovarian cancer is the second fatal malignancy of the female reproductive system. Based on the cancer stem cell (CSC) theory, its poor prognosis of ovarian cancer attributed to tumor recurrence caused by CSCs. A variety of cell surface-specific markers have been employed to identify ovarian cancer stem cells (OCSCs). In this study, we attempted to explore the common feature in ovarian cancer stem cells sorted by multiple approaches.

METHODS

We collected the gene expression profiles of OCSCs were from 5 public cohorts and employed R software and Bioconductor packages to establish differently expressed genes (DEGs) between OCSCs and parental cells. We extracted the integrated DEGs by protein-protein interaction (PPI) network construction and explored potential treatment by the Cellminer database.

RESULTS

We identified and integrated the DEGs of OCSCs sorted by multiple isolation approaches. Besides, we identified OCSCs share characteristics in the lipid metabolism and extracellular matrix changes. Moreover, we obtained 16 co-expressed core genes, such as FOXQ1, MMP7, AQP5, RBM47, ETV4, NPW, SUSD2, SFRP2, IDO1, ANPEP, CXCR4, SCNN1A, SPP1 and IFI27 (upregulated) and SERPINE1, DUSP1, CD40, and IL6 (downregulated). Through correlation analysis, we screened out ten potential drugs to target the core genes.

CONCLUSION

Based on the comprehensive analysis of the genomic datasets with different sorting methods of OCSCs, we figured out the common driving genes to regulating OCSC and obtained ten new potential therapies for eliminating ovarian cancer stem cells. Hence, the findings of our study might have potential clinical significance.

Candidate biomarker assessment for pharmacological response

Using the information from our CellMiner (https://discover.nci.nih.gov/cellminer/) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) web-based applications, we identified 3978 molecular events with significant links to pharmacological response for genes that are either targets, biomarkers, or have established causal linkage to drugs. Molecular events included DNA copy number, methylation and mutation; and transcript; and whole or phospho-protein expression for the NCI-60 human cancer cell lines. While all forms of molecular data were informative in some (gene-drug) pairings, the type of significantly linked molecular events was found to vary widely by drug. Some forms of molecular data were found to have more frequent significant correlation than others. Leading were phosphoproteins as measured by antibody (31%), followed by transcript as measured by microarray (16%), and total protein levels as measured by mass spectrometry or antibody (14%). All other measurements ranged between 5 and 11%. Data reliability was underscored by concordant results when using differing drugs with the same targets, as well as different measurements of the same molecular parameter. The significance of correlations of the various molecular parameters to the pharmacological responses provides functional indication of those parameters that are biologically relevant for each gene-drug pairing, as well as comparisons between measurement types.

Widespread transcript shortening through alternative polyadenylation in secretory cell differentiation

Most eukaryotic genes produce alternative polyadenylation (APA) isoforms. Here we report that, unlike previously characterized cell lineages, differentiation of syncytiotrophoblast (SCT), a cell type critical for hormone production and secretion during pregnancy, elicits widespread transcript shortening through APA in 3'UTRs and in introns. This global APA change is observed in multiple in vitro trophoblast differentiation models, and in single cells from placentas at different stages of pregnancy. Strikingly, the transcript shortening is unrelated to cell proliferation, a feature previously associated with APA control, but instead accompanies increased secretory functions. We show that 3'UTR shortening leads to transcripts with higher mRNA stability, which augments transcriptional activation, especially for genes involved in secretion. Moreover, this mechanism, named secretion-coupled APA (SCAP), is also executed in B cell differentiation to plasma cells. Together, our data indicate that SCAP tailors the transcriptome during formation of secretory cells, boosting their protein production and secretion capacity.

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.

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High-quality NCI-60 proteotypes created using pressure cycling technology and SWATH-MS

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Proteotypes improve drug response prediction in multi-omics regression analysis

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∼3000 measured proteins allow investigation into protein complex stoichiometry

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CellMinerCDB (discover.nci.nih.gov/cellminercdb) portal allows dataset exploration