Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

Unbiased Drug Target Prediction Reveals Sensitivity to Ferroptosis Inducers, HDAC and RTK Inhibitors in Melanoma Subtypes

BACKGROUND

The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem.

OBJECTIVE

Here, we mine large-scale MM proteogenomic data to identify druggable targets and forecast treatment efficacy and resistance.

METHODS

Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC, and AKT1, across five distinct MM subtypes. These proteins are potential drug targets applicable to one or multiple MM subtypes. Additionally, by integrating proteogenomic profiles obtained from MM subtypes with MM cell line dependency and drug sensitivity data, we identified a total of 162 potentially targetable genes. Lastly, we identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype.

RESULTS

Employing these unbiased approaches, we have uncovered compounds targeting ferroptosis demonstrating a striking 30× fold difference in sensitivity among different subtypes.

CONCLUSIONS

Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy.

A multi-omics analysis of effector and resting treg cells in pan-cancer

Regulatory T cells (Tregs) are critical for maintaining the stability of the immune system and facilitating tumor escape through various mechanisms. Resting T cells are involved in cell-mediated immunity and remain in a resting state until stimulated, while effector T cells promote immune responses. Here, we investigated the roles of two gene signatures, one for resting Tregs (FOXP3 and IL2RA) and another for effector Tregs (FOXP3, CTLA-4, CCR8 and TNFRSF9) in pan-cancer. Using data from The Cancer Genome Atlas (TCGA), The Cancer Proteome Atlas (TCPA) and Gene Expression Omnibus (GEO), we focused on the expression profile of the two signatures, the existence of single nucleotide variants (SNVs) and copy number variants (CNVs), methylation, infiltration of immune cells in the tumor and sensitivity to different drugs. Our analysis revealed that both signatures are differentially expressed across different cancer types, and correlate with patient survival. Furthermore, both types of Tregs influence important pathways in cancer development and progression, like apoptosis, epithelial-to-mesenchymal transition (EMT) and the DNA damage pathway. Moreover, a positive correlation was highlighted between the expression of gene markers in both resting and effector Tregs and immune cell infiltration in adrenocortical carcinoma, while mutations in both signatures correlated with enrichment of specific immune cells, mainly in skin melanoma and endometrial cancer. In addition, we reveal the existence of widespread CNVs and hypomethylation affecting both Treg signatures in most cancer types. Last, we identified a few correlations between the expression of CCR8 and TNFRSF9 and sensitivity to several drugs, including COL-3, Chlorambucil and GSK1070916, in pan-cancer. Overall, these findings highlight new evidence that both Treg signatures are crucial regulators of cancer progression, providing potential clinical outcomes for cancer therapy.

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment.

RalB promotes lymph node metastasis in tongue squamous cell carcinoma

BACKGROUND

Lymph nodes metastasis is the main metastasis mode of tongue squamous cell carcinoma (TSCC). Ras related GTP binding protein B (RalB) have been recently described that it was involved in tumor growth and metastasis, but the effect in TSCC is still ill-defined.

OBJECTIVE

This study provides insights into the role of RALB as a prognostic factor in head and neck squamous cell carcinoma (HNSCC) and demonstrates its involvement in promoting lymph node metastasis in TSCC.

METHODS

Firstly, the expression level of RALB and the relationship with clinical features were examined. Subsequently, RALB knockdown Cal-27 cells orthotopic xenotransplantation in the tongue of BALB/c nude mice were established. Finally, using Connectivity Map (CMAP) database to find possible drugs.

RESULTS

Firstly, RALB could not only predict the cancer patients' prognosis and survival and but also act as a potential prognostic factor, particularly in HNSCC by pan-cancer bioinformatics analysis. In addition, we found that RalB promoted tumor growth and lymph node metastasis. Finally, we identified Tirabrutinib (ONO-4059) targeting RalB with good binding properties.

CONCLUSIONS

RalB act as a prognostic gene in HNSCC, and promote lymph node metastasis in early stage of TSCC.

Multi-omic analyses reveal PTPN6's impact on tumor immunity across various cancers

Protein Tyrosine Phosphatase Non-Receptor Type 6 (PTPN6) plays a crucial regulatory role in cellular processes and has been implicated in oncogenesis. This pan-cancer analysis aimed to elucidate PTPN6's involvement across various cancer types, with a particular emphasis on its association with tumor immunity. We analyzed PTPN6 expression data from open access databases using various statistical techniques, including survival analysis, genetic heterogeneity analysis, immune profiling, single-cell analysis, drug sensitivity analysis, and protein interaction analysis. We also conducted in vitro experiments utilizing colorectal cancer cell lines to validate PTPN6's functional role. PTPN6 exhibited distinct expression patterns across cancers, and its prognostic significance was apparent in several cancer types, particularly in glioblastoma, sarcoma, and melanoma. We observed correlations between PTPN6 and immune genes/cell infiltration in these cancers, suggesting a potential role in modulating the tumor immune microenvironment. Single-cell analysis revealed that PTPN6 is predominantly localized in macrophages, B cells, and dendritic cells within the tumor microenvironment, implying its involvement in regulating immune cell function. Enrichment analysis highlighted PTPN6's role in immune-related pathways. Drug sensitivity analysis identified specific drugs, including PAC-1, SNX-2112, BELINOSTAT, VORINOSTAT, TPCA-1, and PHA-893,888, whose efficacy may be influenced by PTPN6 expression. Knocking down PTPN6 expression inhibited the proliferation and migration of colorectal cancer cells in vitro, confirming its oncogenic role in this cancer type. This pan-cancer analysis establishes PTPN6's multifaceted influence on tumor immunity and its potential as a biomarker and therapeutic target.

New and Effective Inhibitor of Class I HDACs, Eimbinostat, Reduces the Growth of Hematologic Cancer Cells and Triggers Apoptosis

Background: Histone deacetylases (HDACs) are critical epigenetic modulators involved in regulating various molecular mechanisms essential for cell development and growth. Alterations in HDAC activity have been linked to the progression of numerous cancers, including lymphoma. Over the past decade, the FDA has approved several HDAC inhibitors for lymphoma treatment, leading to heightened interest in this emerging class of drugs. Methods: In our research, we developed a novel HDAC inhibitor that exhibits high selectivity for class I HDACs. Results: Our in vitro findings indicate that treating lymphoma/leukemia cells with this inhibitor results in a marked suppression of cell growth and promotes apoptosis, while leaving the cell cycle unaffected. Conclusions: We propose that our new inhibitor, named eimbinostat, holds significant promise as a potential therapeutic agent for the treatment of hematologic malignancies such as lymphoma or leukemia.

Sex Differences in Cancer Functional Genomics: Gene Dependency and Drug Sensitivity

Patient sex influences a wide range of cancer phenotypes, including prevalence, response to therapy and survival endpoints. Molecular sex differences have been identified at all levels of the central dogma. It is hypothesized that these molecular differences may drive the observed clinical sex differences. Yet despite a growing catalog of molecular sex differences in a range of cancer types, their specific functional consequences remain unclear. To directly assess how patient sex impacts cancer cell function, we evaluated 1,209 cell lines subjected to CRISPR knockout, RNAi knockdown or drug exposures. Despite limited statistical power, we identified pan- and per-cancer sex differences in gene essentiality in six sex-linked and fourteen autosomal genes, and in drug sensitivity for two compounds. These data fill a gap in our understanding of the link between sex-differential molecular effects and patient phenotypes. They call for much more careful and systematic consideration of sex-specific effects in mechanistic and functional studies.

DrBioRight 2.0: an LLM-powered bioinformatics chatbot for large-scale cancer functional proteomics analysis

Functional proteomics provides critical insights into cancer mechanisms, facilitating the discovery of novel biomarkers and therapeutic targets. We have developed a comprehensive cancer functional proteomics resource using reverse phase protein arrays, incorporating data from nearly 8000 patient samples from The Cancer Genome Atlas and approximately 900 samples from the Cancer Cell Line Encyclopedia. Our dataset includes a curated panel of  nearly 500 high-quality antibodies, covering all major cancer hallmark pathways. To enhance the accessibility and analytic power of this resource, we introduce DrBioRight 2.0 ( https://drbioright.org ), an intuitive bioinformatic platform powered by state-of-the-art large language models. DrBioRight enables researchers to explore protein-centric cancer omics data, perform advanced analyses, visualize results, and engage in interactive discussions using natural language. By streamlining complex proteogenomic analyses, this tool accelerates the translation of large-scale functional proteomics data into meaningful biomedical insights.

Characterization of Exhausted T Cell Signatures in Pan-Cancer Settings

T cells play diverse roles in cancer immunology, acting as tumor suppressors, cytotoxic effectors, enhancers of cytotoxic T lymphocyte responses and immune suppressors; providing memory and surveillance; modulating the tumor microenvironment (TME); or activating innate immune cells. However, cancer cells can disrupt T cell function, leading to T cell exhaustion and a weakened immune response against the tumor. The expression of exhausted T cell (Tex) markers plays a pivotal role in shaping the immune landscape of multiple cancers. Our aim was to systematically investigate the role of known T cell exhaustion (Tex) markers across multiple cancers while exploring their molecular interactions, mutation profiles, and potential implications for immunotherapy. The mRNA expression profile of six Tex markers, LAG-3, PDCD1, TIGIT, HAVCR2, CXCL13, and LAYN was investigated in pan-cancer. Utilizing data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), The Cancer Proteome Atlas (TCPA), and other repositories, we characterized the differential expression of the Tex markers, their association with the patients' survival outcome, and their mutation profile in multiple cancers. Additionally, we analyzed the effects on cancer-related pathways and immune infiltration within the TME, offering valuable insights into mechanisms of cancer immune evasion and progression. Finally, the correlation between their expression and sensitivity to multiple anti-cancer drugs was investigated extensively. Differential expression of all six markers was significantly associated with KIRC and poor prognosis in several cancers. They also played a potential activating role in apoptosis, EMT, and hormone ER pathways, as well as a potential inhibitory role in the DNA damage response and RTK oncogenic pathways. Infiltration of different immune cells was also found to be associated with the expression of the Tex-related genes in most cancer types. These findings underline that the reviving of exhausted T cells can be used to enhance the efficacy of immunotherapy in cancer patients.

Decoding Drug Response With Structurized Gridding Map-Based Cell Representation

A thorough understanding of cell-line drug response mechanisms is crucial for drug development, repurposing, and resistance reversal. While targeted anticancer therapies have shown promise, not all cancers have well-established biomarkers to stratify drug response. Single-gene associations only explain a small fraction of the observed drug sensitivity, so a more comprehensive method is needed. However, while deep learning models have shown promise in predicting drug response in cell lines, they still face significant challenges when it comes to their application in clinical applications. Therefore, this study proposed a new strategy called DD-Response for cell-line drug response prediction. First, a limitation of narrow modeling horizons was overcome to expand the model training domain by integrating multiple datasets through source-specific label binarization. Second, a modified representation based on a two-dimensional structurized gridding map (SGM) was developed for cell lines & drugs, avoiding feature correlation neglect and potential information loss. Third, a dual-branch, multi-channel convolutional neural network-based model for pairwise response prediction was constructed, enabling accurate outcomes and improved exploration of underlying mechanisms. As a result, the DD-Response demonstrated superior performance, captured cell-line characteristic variations, and provided insights into key factors impacting cell-line drug response. In addition, DD-Response exhibited scalability in predicting clinical patient responses to drug therapy. Overall, because of DD-response's excellent ability to predict drug response and capture key molecules behind them, DD-response is expected to greatly facilitate drug discovery, repurposing, resistance reversal, and therapeutic optimization.

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network

Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have been a key driver of research in this area. However, these biological datasets, which are typically high dimensional but have small sample sizes, present challenges such as overfitting and poor generalization in predictive models. As a complicating matter, gene expression (GE) data must capture complex inter-gene relationships, exacerbating these issues. In this article, we tackle these challenges by introducing a drug response prediction method, called drug response graph attention network (DRGAT), which combines a denoising diffusion implicit model for data augmentation with a recently introduced graph attention network (GAT) with high-order neighbor propagation (HO-GATs) prediction module. Our proposed approach achieved almost 5% improvement in the area under receiver operating characteristic curve compared with state-of-the-art models for the many studied drugs, indicating our method's reasonable generalization capabilities. Moreover, our experiments confirm the potential of diffusion-based generative models, a core component of our method, to mitigate the inherent limitations of omics datasets by effectively augmenting GE data.

Biological and therapeutic implications of sex hormone-related gene clustering in testicular cancer

BACKGROUND

Gonadotropin dysregulation seems to play a potential role in the carcinogenesis of testicular germ cell tumor (TGCT). The aim of this study was to explore the expression of specific genes related to sex hormone regulation, synthesis, and metabolism in TGCT and to define specific hormonal clusters. Two publicly available databases were used for this analysis (TCGA and GSE99420). By means of hard-threshold regularized KMEANS clustering, we assigned TGCT samples into four clusters defined in respect to different expression of the sex hormone-related genes. We analysed clinical data, protein and gene expression, signaling regarding hormonal clusters. Based on whole-transcriptome gene expression, prediction of anti-cancer drug response was made by RIDGE models.

RESULTS

Cluster #1 (12-16%) consisted primarily of non-seminomatous germ cell tumor (NSGCT), characterized by high expression of PRL, GNRH1, HSD17B2 and SRD5A1. Cluster #2 (42-50%) included predominantly seminomas with high expression of SRD5A3, being highly infiltrated by T and B cells. Cluster #3 (8.3-18%) comprised of NSGCT with high expression of CGA, CYP19A1, HSD17B12, HSD17B1, SHBG. Cluster #4 (23-30%), which consisted primarily of NSGCT with a small fraction of seminomas, was outlined by increased expression of STAR, POMC, CYP11A1, CYP17A1, HSD3B2 and HSD17B3. Elevated fibroblast levels and increased extracellular matrix- and growth factor signaling-related gene signature scores were described in cluster #1 and #3. In the combined model of progression-free survival, S2/S3 tumor marker status, hormonal cluster #1 or #3 and teratoma histology, were independently associated with 25-30% increase of progression risk. Based on the increased receptor tyrosine kinase and growth factor signaling, cluster #1, #3 and #4 were predicted to be sensitive to tyrosine kinase inhibitors, FGFR inhibitors or EGFR/ERBB inhibitors. Cluster #2 and #4 were responsive to compounds interfering with DNA synthesis, cytoskeleton, cell cycle and epigenetics. Response to apoptosis modulators was predicted only for cluster #2.

CONCLUSIONS

Hormonal cluster #1 or #3 is an independent prognostic factor regarding poor progression-free survival. Hormonal cluster assignment also affects the predicted drug response with cluster-dependent susceptibility to specific novel therapeutic compounds.

Systematic Comparison of FBS and Medium Variation Effect on Key Cellular Processes Using Morphological Profiling

Although every cell biologist knows the importance of selecting the right growth conditions and it is well known that the composition of growth medium may vary depending on a product brand or lot affecting many cellular processes, still those effects are poorly systematized. We addressed this issue by comparing the effect of 12 fetal bovine sera (FBS) and eight growth media from different brands on the morphological and functional parameters of five cell types: lung adenocarcinoma, neuroblastoma, glioblastoma, embryonic kidney, and colorectal cancer cells. Using high-throughput imaging, we compared cell proliferation; performed morphological profiling based on the imaging of 561,519 cells; measured extracellular regulated kinases (ERK1/2) activity, mitochondria potential, and lysosome accumulation; and compared cell sensitivity to drugs, response to EGF stimulation, and ability to differentiate. We found that changes in cell proliferation and morphology were independent, and morphological changes were associated with differences in mitochondria potential or the cell's ability to differentiate. Surprisingly, the most drastic differences were detected in serum-free conditions, where medium choice affected cell survival and response to EGF. Overall, our data may be used to improve the reproducibility of experiments involving cell cultures, and the effects of 28 growth conditions on proliferation and 44 morphological parameters can be explored through a Shinyapp.

Attenuated growth factor signaling during cell death initiation sensitizes membranes towards peroxidation

Cell death programs such as apoptosis and ferroptosis are associated with aberrant redox homeostasis linked to lipid metabolism and membrane function. Evidence for cross-talk between these programs is emerging. Here, we show that cytotoxic stress channels polyunsaturated fatty acids via lysophospholipid acyltransferase 12 into phospholipids that become susceptible to peroxidation under additional redox stress. This reprogramming is associated with altered acyl-CoA synthetase isoenzyme expression and caused by a decrease in growth factor receptor tyrosine kinase (RTK)-phosphatidylinositol-3-kinase signaling, resulting in suppressed fatty acid biosynthesis, for specific stressors via impaired Akt-SREBP1 activation. The reduced availability of de novo synthesized fatty acids favors the channeling of polyunsaturated fatty acids into phospholipids. Growth factor withdrawal by serum starvation mimics this phenotype, whereas RTK ligands counteract it. We conclude that attenuated RTK signaling during cell death initiation increases cells' susceptibility to oxidative membrane damage at the interface of apoptosis and alternative cell death programs.

A Multimodal Framework to Uncover Drug-Responsive Subpopulations in Triple-Negative Breast Cancer

Understanding how individual cancer cells adapt to drug treatment is a fundamental challenge limiting precision medicine cancer therapy strategies. While single-cell technologies have advanced our understanding of cellular heterogeneity, efforts to connect the behavior of individual cells to broader tumor drug responses and uncover global trends across diverse systems remain limited. There is a growing availability of single-cell and bulk omics data, but a lack of centralized tools and repositories makes it difficult to study drug response globally, especially at the level of single-cell adaptation. To address this, we present a multimodal framework that integrates bulk and single-cell treated and untreated transcriptomics data to identify drug responsive cell populations in triple-negative breast cancer (TNBC). Our framework leverages population-scale bulk transcriptomics data from TNBC samples to define seven main "identities", each representing unique combinations of biologically relevant genes. These identities are dynamic and trackable, allowing us to map them onto single cells and uncover global patterns of how cell populations respond to drug treatment. Unlike static classifications, this approach captures the evolving nature of cellular states, revealing that a select few identities dominate and drive population-level responses during treatment. Crucially, our ability to decode these trends through the inherent noise of single-cell data provides a clearer picture of how heterogeneous cell populations adapt to therapy. By identifying the dominant identities and their dynamics, we can better predict how entire tumors respond to treatment. This insight is essential for designing precise combination therapies tailored to the unique heterogeneity of patient tumors, addressing the single-cell variations that ultimately determine therapeutic outcomes.

Serum Starvation Enhances the Antitumor Activity of Natural Matrices: Insights into Bioactive Molecules from Dromedary Urine Extracts

Natural matrices have historically been a cornerstone in drug discovery, offering a rich source of structurally diverse and biologically active compounds. However, research on natural products often faces significant challenges due to the complexity of natural matrices, such as urine, and the limitations of bioactivity assessment assays. To ensure reliable insights, it is crucial to optimize experimental conditions to reveal the bioactive potential of samples, thereby improving the validity of statistical analyses. Approaches in metabolomics further strengthen this process by identifying and focusing on the most promising compounds within natural matrices, enhancing the precision of bioactive metabolite prioritization. In this study, we assessed the bioactivity of 17 dromedary urine samples on human renal cells under serum-reduced conditions (1%FBS) in order to minimize possible FBS-derived interfering factors. Using viability assays and Annexin V/PI staining, we found that the tumor renal cell lines Caki-1 and RCC-Shaw were more sensitive to the cytotoxic effects of the small molecules present in dromedary urine compared to non-tumor HK-2 cells. Employing NMR metabolomics analysis combined with detected in vitro activity, our statistical model highlights the presence of bioactive compounds in dromedary urine, such as azelaic acid and phenylacetyl glycine, underscoring its potential as a sustainable source of bioactive molecules within the framework of green chemistry and circular economy initiatives.

The Hallmarks of Predictive Oncology

SIGNIFICANCE

As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the progress and timely adoption of predictive modeling in precision oncology. Through these hallmarks, we hope to establish standards and guidelines that enable the symbiotic development of artificial intelligence and precision oncology.

Comprehensive Analyses of PANoptosome with Potential Implications in Cancer Prognosis and Immunotherapy

Cell death resistance significantly contributes to poor therapeutic outcomes in various cancers. PANoptosis, a unique inflammatory programmed cell death (PCD) pathway activated by specific triggers and regulated by the PANoptosome, possesses key features of apoptosis, pyroptosis, and necroptosis, but these cannot be accounted for by any of the three PCD pathways alone. While existing studies on PANoptosis have predominantly centered on infectious and inflammatory diseases, its role in cancer malignancy has been understudied. In this comprehensive investigation, we conducted pan-cancer analyses of PANoptosome component genes across 33 cancer types. We characterized the genetic, epigenetic, and transcriptomic landscapes, and introduced a PANoptosome-related potential index (PANo-RPI) for evaluating the intrinsic PANoptosome assembly potential in cancers. Our findings unveil PANo-RPI as a prognostic factor in numerous cancers, including KIRC, LGG, and PAAD. Crucially, we established a significant correlation between PANo-RPI and tumor immune responses, as well as the infiltration of diverse lymphoid and myeloid cell subsets across nearly all cancer types. Moreover, a high PANo-RPI was consistently associated with improved immunotherapy response and efficacy, as evidenced by re-analysis of multiple immunotherapy cohorts. In conclusion, our study suggests that targeting PANoptosome components and modulating PANoptosis may hold tremendous therapeutic potential in the context of cancer.

ACSL4 and polyunsaturated lipids support metastatic extravasation and colonization

Metastatic dissemination to distant organs demands that cancer cells possess high morphological and metabolic adaptability. However, contributions of the cellular lipidome to metastasis remain elusive. Here, we uncover a correlation between metastasis potential and ferroptosis susceptibility in multiple cancers. Metastases-derived cancer cells exhibited higher ferroptosis sensitivity and polyunsaturated fatty acyl (PUFA)-lipid contents than primary-tumor-derived cells from ovarian cancer patients. Metabolism-focused CRISPR screens in a mouse model for ovarian cancer distant metastasis established via two rounds of in vivo selection revealed the PUFA-lipid biosynthesis enzyme acyl-coenzyme A (CoA) synthetase long-chain family member 4 (ACSL4) as a pro-hematogenous metastasis factor. ACSL4 promotes metastatic extravasation by enhancing membrane fluidity and cellular invasiveness. While promoting metastasis, the high PUFA-lipid state creates dependencies on abhydrolase-domain-containing 6, acylglycerol lipase (ABHD6), enoyl-CoA delta isomerase 1 (ECI1), and enoyl-CoA hydratase 1 (ECH1)-rate-limiting enzymes preparing unsaturated fatty acids (UFAs) for β-oxidation. ACSL4/ECH1 co-inhibition achieved potent suppression of metastasis. Our work establishes the dual functions of PUFA-lipids in tumor progression and metastasis that may be exploitable for therapeutic development.

AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power. Key challenges in predictive modeling include scarcity of labeled data, generalization across different domains, and disentangling causation from correlation. In light of recent advances in multi-omics data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale modeling framework to tackle these issues. This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict genotype-environment-phenotype relationships under various conditions. AI models inspired by biology may identify novel molecular targets, biomarkers, pharmaceutical agents, and personalized medicines for presently unmet medical needs.

Synthetic lethal strategies for the development of cancer therapeutics

Synthetic lethality is a genetic phenomenon whereby the simultaneous presence of two different genetic alterations impairs cellular viability. Importantly, targeting synthetic lethal interactions offers potential therapeutic strategies for cancers with alterations in pathways that might otherwise be considered undruggable. High-throughput screening methods based on modern CRISPR-Cas9 technologies have emerged and become crucial for identifying novel synthetic lethal interactions with the potential for translation into biologically rational cancer therapeutic strategies as well as associated predictive biomarkers of response capable of guiding patient selection. Spurred by the clinical success of PARP inhibitors in patients with BRCA-mutant cancers, novel agents targeting multiple synthetic lethal interactions within DNA damage response pathways are in clinical development, and rational strategies targeting synthetic lethal interactions spanning alterations in epigenetic, metabolic and proliferative pathways have also emerged and are in late preclinical and/or early clinical testing. In this Review, we provide a comprehensive overview of established and emerging technologies for synthetic lethal drug discovery and development and discuss promising therapeutic strategies targeting such interactions.

Longitudinal phosphoproteomics reveals the PI3K-PAK1 axis as a potential target for recurrent colorectal liver metastases

The resistance of colorectal cancer liver metastases (CRLMs) to 5-fluorouracil (5-FU) chemotherapy remains a significant global health challenge. We investigated the phosphoproteomic dynamics of serial tissue sections obtained from initial metastases and recurrent tumors collected from 24 patients to address this unmet need for innovative therapeutic strategies for patients with CRLM with a poor prognosis. Our analysis revealed the activation of PAK kinase in patients with CRLM with a poor prognosis. Using an unbiased computational approach, we conducted a correlation analysis between PAK1 kinase activity and 545 drug sensitivity profiles across 35 colorectal cancer cell lines and identified PI3K inhibitors as potential therapeutic candidates. The efficacy of the FDA-approved PI3K inhibitor copanlisib was validated in 5-FU-resistant cell lines with high PAK1 kinase activity both in vitro and in vivo. This study presents an effective strategy for drug target discovery based on kinase activity, and the concept of this approach is widely applicable.

Systematic pan-cancer analysis identifies ZBTB11 as a potential pan-cancer biomarker and immunotherapy target in multiple tumor types

BACKGROUND

ZBTB11 is a putative transcription factor with an N-terminal BTB domain and tandem C-terminal zinc finger motifs. Recent studies have suggested a potential role for ZBTB11 in tumorigenesis. However, the biological significance of ZBTB11 in different cancer types remains uncertain.

METHODS

The expression levels, prognostic values, genetic mutations, and DNA promoter methylation of ZBTB11 across tumor types were explored via various online websites and databases, including TIMER2.0, GEPIA2, cBioPortal, UALCAN, GSCA, CancerSEA, and others. Additionally, a competing lncRNA-miRNA network of ZBTB11 was constructed, and its interaction with chemicals and genes was investigated.

RESULTS

Our findings revealed that ZBTB11 was aberrantly expressed in a multitude of tumor types and exhibited variability across various tumor stages. A survival analysis revealed that ZBTB11 predicted a poor prognosis in BRCA, KIRP, LIHC, PCPG, PRAD, SARC, UCEC, and a good prognosis in CHOL, ESCA, GBM, KIRC, and READ. We also found that the most frequent genetic alterations type of ZBTB11 was mutation, and the DNA methylation level of ZBTB11 decreased in various cancers. Furthermore, ZBTB11 expression correlated with immune cells infiltration and genetic markers of immunodulators in cancers. Moreover, the results of single-cell sequencing demonstrated that ZBTB11 could regulate several tumor biological behaviors, including apoptosis, DNA damage, and angiogenesis. A lncRNA-miRNA network regulating ZBTB11 expression in tumor development and progression was constructed. It is of particular significance that ZBTB11 demonstrated a correlation with the CTRP and GDSC drug sensitivity, and that it served as a mediator between chemicals and cancers.

CONCLUSION

These findings demonstrate that ZBTB11 is associated with multiple tumor types and disease prognosis. ZBTB11 may represent a potential key biomarker and therapeutic target in cancers.

SOCS1: A potential diagnostic and prognostic marker for aggressive gliomas and a new target for immunotherapy

Gliomas, the most common and deadly cancers of the central nervous system, present a unique immunological barrier that severely undermines the effectiveness of immunotherapies. Suppressor of cytokine signaling 1 (SOCS1), belonging to the SOCS protein family and playing a pivotal role in various cancer treatment strategies and is abundant in high-grade gliomas. This study conducted a comparative analysis of SOCS1 and glioma immune checkpoints. It underscores the feasibility of leveraging SOCS1 as a promising diagnostic and prognostic marker for aggressive gliomas, thus offering novel targets for glioma immunotherapy. Comprehensive gene expression analyses and clinical data validations were performed across multiple databases. The expression and biological functions of SOCS1 were examined through an array of techniques including pan-cancer analysis, functional enrichment, gene set variation analysis, and immune microenvironment examination. This was done alongside a comparison of the similarities between SOCS1 and various glioma immune checkpoints. Utilizing clinical information from patients, a bespoke predictive model was developed to further corroborate the prognostic capabilities of SOCS1. The investigation revealed considerable similarities between SOCS1 and several immune checkpoints such as CTLA4, demonstrating SOCS1's role as an independent prognostic factor positively influencing glioma patient outcomes. The inclusion of SOCS1 in the developed predictive model significantly enhanced its precision. Our findings highlight SOCS1's potential as an innovative target for glioma immunotherapy, providing a novel strategy to overcome the immunological barriers posed by gliomas. Furthermore, identifying SOCS1 as a viable diagnostic marker for aggressive gliomas improves the accuracy of prognostic predictions for affected patients.

Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

Breast cancer patients are categorized into three subtypes with distinct treatment approaches. Precision oncology has increased patient outcomes by targeting the specific molecular alterations of tumours, yet challenges remain. Treatment failure persists due to the coexistence of several malignant subpopulations with different drug sensitivities within the same tumour, a phenomenon known as intratumour heterogeneity (ITH). This heterogeneity has been extensively studied from a tumour-centric view, but recent insights underscore the role of the tumour microenvironment in treatment response. Our research utilizes spatial transcriptomics data from breast cancer patients to predict drug sensitivity. We observe diverse response patterns across tumour, interphase and microenvironment regions, unveiling a sensitivity and functional gradient from the tumour core to the periphery. Moreover, we find tumour therapeutic clusters with different drug responses associated with distinct biological functions driven by unique ligand-receptor interactions. Importantly, we identify genetically identical subclones with different responses depending on their location within the tumour ducts. This research underscores the significance of considering the distance from the tumour core and microenvironment composition when identifying suitable treatments to target ITH. Our findings provide critical insights into optimizing therapeutic strategies, highlighting the necessity of a comprehensive understanding of tumour biology for effective cancer treatment.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.7% in the number of multi-omic profiles and thereby generating a complete DepMap for 1523 cancer cell lines. The synthetically enhanced data increases statistical power, uncovering less studied mechanisms associated with drug resistance, and refines the identification of genetic associations and clustering of cancer cell lines. By applying SHapley Additive exPlanations (SHAP) for model interpretation, MOSA reveals multi-omic features essential for cell clustering and biomarker identification related to drug and gene dependencies. This understanding is crucial for developing much-needed effective strategies to prioritize cancer targets.

Discovery, Validation and Mechanistic Study of XPO1 Inhibition in the Treatment of Triple-Negative Breast Cancer

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer with limited treatment options. The nuclear export protein XPO1 has emerged as a potential therapeutic target in cancer, but its role in TNBC has not been fully characterized. This study investigates the potential of repurposing selinexor, an FDA-approved XPO1 inhibitor, as a novel therapeutic options for TNBC. Methods: A computational drug repurposing pipeline was used to predict patient tumor responses to hundreds of drugs. We identified XPO1 inhibitors as a candidate drug and validated its efficacy on an independent patient dataset and across various TNBC cell lines. RNA-sequencing after longitudinal XPO1 inhibition and further mechanistic studies were performed to explore and confirm the leading causes of TNBC cell sensitivity to XPO1 inhibition. Results: Selinexor significantly reduce the viability of a variety of TNBC cell lines. Mechanistically, selinexor induces TNBC cell death by inhibiting the NF-kB pathway through nuclear retention of NFKBIA. This effect was consistent across multiple TNBC cell lines. Conclusions: XPO1 inhibitors show promise as targeted therapies for TNBC patients. New mechanistic insight into the causes leading to TNBC sensitivity to XPO1-inhibition-mediated cell death warrant further clinical trials to evaluate the safety and efficacy in TNBC.

Precision oncology platforms: practical strategies for genomic database utilization in cancer treatment

In recent years, the expansion of molecularly targeted cancer therapies has significantly advanced precision oncology. Parallel developments in next-generation sequencing (NGS) technologies have also improved precision oncology applications, making genomic analysis of tumors more affordable and accessible. Targeted NGS panels now enable the rapid identification of diverse actionable mutations, requiring clinicians to efficiently assess the predictive value of cancer biomarkers for specific treatments. The urgency for timely and accurate decision-making in oncology emphasizes the importance of reliable precision oncology software. Online clinical decision-making tools and associated cancer databases have been designed by consolidating genomic data into standardized, accessible formats. These new platforms are highly integrated and crucial for identifying actionable somatic genomic biomarkers essential for tumor survival, determining corresponding drug targets, and selecting appropriate treatments based on the mutational profile of each patient's tumor. To help oncologists and translational cancer researchers unfamiliar with these tools, we review the utility, accuracy, and comprehensiveness of several commonly used precision medicine software options currently available. Our analysis categorized selected genomic databases based on their primary content, utility, and how well they provide practical guidance for interpreting somatic biomarker data. We identified several comprehensive, mostly open-access platforms that are easy to use for genetic biomarker searches, each with unique features and limitations. Among the precision oncology tools we evaluated, we found MyCancerGenome and OncoKB to be the first choice, offering comprehensive, accurate up-to-date information on the clinical significance of somatic mutations. To illustrate the application of these precision oncology tools in clinical settings, we evaluated three case studies to see how use of the platforms could have influenced treatment planning. Most of the precision oncology software evaluated could be easily streamlined into clinical workflows to provide updated information on approved drugs and clinical trials related the actionable mutations detected. Some platforms were very intuitive and easy to use, while others, often developed in smaller academic settings, were more difficult to navigate and may not be updated consistently. Future enhancements, incorporating artificial intelligence algorithms, are likely to improve integration of the platforms with diverse big data sources, enabling more accurate predictions of potential therapeutic responses.

AI-empowered perturbation proteomics for complex biological systems

The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.

Connections Between Endoplasmic Reticulum Stress and Prognosis of Hepatocarcinoma

Endoplasmic reticulum (ER) stress is a state in which misfolded or unfolded proteins accumulate in the lumen of the ER as a result of some exogenous or endogenous factors. It plays a crucial role in the pathogenesis of malignancies, affecting cell survival, proliferation, and metastasis in cancer. ER stress genes could provide new ideas for potential therapeutic targets in cancer. In our study, we aimed to construct an ER stress-related genes (ERGs) model for hepatocellular carcinoma (HCC). ERGs with differential expression and significant survival were screened to construct a prognostic model. The effectiveness of the model was successfully validated by external datasets. High and low-risk groups were classified based on risk scores. Functional analysis showed risk groups involved in the unfolded protein response, DNA repair, and other differential pathways. When compared to patients with low risk, the prognosis for HCC patients in the high-risk group might be worsened by disruptions in these pathways. Importantly, we considered genomic druggability and predicted drugs. Sorafenib-induced autophagy in HCC cells through an ES stress mechanism. Sorafenib was more sensitive for high-risk patients. In brief, our model predicted the prognosis of HCC and provided novel treatment strategies for the study of other cancers.

Developmental-status-aware transcriptional decomposition establishes a cell state panorama of human cancers

BACKGROUND

Cancer cells evolve under unique functional adaptations that unlock transcriptional programs embedded in adult stem and progenitor-like cells for progression, metastasis, and therapeutic resistance. However, it remains challenging to quantify the stemness-aware cell state of a tumor based on its gene expression profile.

METHODS

We develop a developmental-status-aware transcriptional decomposition strategy using single-cell RNA-sequencing-derived tissue-specific fetal and adult cell signatures as anchors. We apply our method to various biological contexts, including developing human organs, adult human tissues, experimentally induced differentiation cultures, and bulk human tumors, to benchmark its performance and to reveal novel biology of entangled developmental signaling in oncogenic processes.

RESULTS

Our strategy successfully captures complex dynamics in developmental tissue bulks, reveals remarkable cellular heterogeneity in adult tissues, and resolves the ambiguity of cell identities in in vitro transformations. Applying it to large patient cohorts of bulk RNA-seq, we identify clinically relevant cell-of-origin patterns and observe that decomposed fetal cell signals significantly increase in tumors versus normal tissues and metastases versus primary tumors. Across cancer types, the inferred fetal-state strength outperforms published stemness indices in predicting patient survival and confers substantially improved predictive power for therapeutic responses.

CONCLUSIONS

Our study not only provides a general approach to quantifying developmental-status-aware cell states of bulk samples but also constructs an information-rich, biologically interpretable, cell-state panorama of human cancers, enabling diverse translational applications.

Small Molecule Screening Identifies HSP90 as a Modifier of RNA Foci in Myotonic Dystrophy Type 1

Myotonic dystrophy type 1 (DM1) is a multisystemic disorder caused by a CTG triplet repeat expansion within the 3' untranslated region of the DMPK gene. Expression of the expanded allele generates RNA containing long tracts of CUG repeats (CUGexp RNA) that form hairpin structures and accumulate in nuclear RNA foci; however, the factors that control DMPK expression and the formation of CUGexp RNA foci remain largely unknown. We performed an unbiased small molecule screen in an immortalized human DM1 skeletal muscle myoblast cell line and identified HSP90 as a modifier of endogenous RNA foci. Small molecule inhibition of HSP90 leads to enhancement of RNA foci and upregulation of DMPK mRNA levels. Knockdown and overexpression of HSP90 in undifferentiated DM1 myoblasts validated the impact of HSP90 with upregulation and downregulation of DMPK mRNA, respectively. Furthermore, we identified p-STAT3 as a downstream mediator of HSP90 impacting levels of DMPK mRNA and RNA foci. Interestingly, differentiated cells exhibited an opposite effect of HSP90 inhibition displaying downregulation of DMPK mRNA through a mechanism independent of p-STAT3 involvement. This study has revealed a novel mediator for DMPK mRNA and foci regulation in DM1 cells with the potential to identify targets for future therapeutic intervention.

A Novel Mitochondrial-Related Gene Signature for the Prediction of Prognosis and Therapeutic Efficacy in Lower-Grade Glioma

Lower-grade glioma (LGG) is a common primary brain tumor with a highly heterogeneous clinical presentation, and its prognosis cannot be accurately predicted by current histopathology. It has been found that mitochondria play an important role in hypoxia, angiogenesis, and energy metabolism in glioma, and mitochondrial function may have an important impact on LGG prognosis. The goal of this study was to develop a novel prognostic model based on Mitochondrial-related genes (MRGs). We first analyzed the somatic alterations profiles of MRGs in patients with LGG and found that somatic alterations were common in LGG and correlated with prognosis. Using RNA-seq data from TCGA and CGGA, 12 prognosis-related MRGs were identified to construct a mitochondrial activation score (MiAS) model by combining univariate regression and LASSO regression analysis. The model and nomogram were evaluated using the area under the ROC curve with AUC = 0.910. The model was closely correlated with the clinical characteristics of LGG patients and performed well in predicting the prognosis of LGG patients with significantly shorter overall survival (OS) time in the high-MiAS group. GSVA and GSEA results showed that oxidative stress, pro-cancer, and immune-related pathways were significantly enriched in the high-MiAS group. CIBERSORT results showed that MiAS was significantly associated with immune cell infiltration in LGG. Macrophage M1 and follicular helper T cells had increased infiltration in the high-MiAS group. TIDE predicted a better immunotherapy outcome in patients in the low-MiAS group. Finally, using data from the CTRPv2 and GDSC2 datasets to assess chemotherapy response in LGG, it was predicted that the chemotherapeutic agents AZD6482, MG-132, and PLX-4720 might be potential agents for patients in the high-MiAS group of LGG. In addition, we performed in vitro experiments and found that knockdown of OCIAD2 expression reduced the abilities of glioma cells to proliferate, migrate, and invade. In contrast, overexpression of OCIAD2 enhanced these abilities of glioma cells. This study found that MRGs were correlated with LGG patient prognosis, which is expected to provide new treatment strategies for LGG patients with different MiAS.

Development of a polygenic score predicting drug resistance and patient outcome in breast cancer

Gene expression profiles of hundreds of cancer cell-lines and the cell-lines' response to drug treatment were analyzed to identify genes whose expression correlated with drug resistance. In the GDSC dataset of 809 cancer cell lines, expression of 36 genes were associated with drug resistance (increased IC50) to many anti-cancer drugs. This was validated in the CTRP dataset of 860 cell lines. A polygenic score derived from the correlation coefficients of the 36 genes in cancer cell lines, UAB36, predicted resistance of cell lines to Tamoxifen. Although the 36 genes were selected from cell line behaviors, UAB36 successfully predicted survival of breast cancer patients in three different cohorts of patients treated with Tamoxifen. UAB36 outperforms two existing predictive gene signatures and is a predictor of outcome of breast cancer patients independent of the known clinical co-variates that affect outcome. This approach should provide promising polygenic biomarkers for resistance in many cancer types against specific drugs.

NFKBIE is a predictive factor of survival and is correlated with immune infiltration and antigen processing and presentation in hepatocellular carcinoma

The important role of the nuclear factor κB (NFκB) pathway in tumour development has long been recognized; however, the role of the NFκB inhibitor family in liver cancer has not been elucidated. Hepatocellular carcinoma (HCC) is a serious public health burden with a high incidence, poor prognosis, and early detection, especially in Asia, where hepatitis is prevalent. In the present study, the mRNA expression level of the NFκB inhibitor family was assessed in HCC and normal tissues using the Metabolic Gene Rapid Visualizer, University of Alabama at Birmingham Cancer Data Analysis Portal, and the Tumor Immune Estimation Resource database (TIMER). Survival curves of nuclear factor of κ light polypeptide gene enhancer in B-cells inhibitor (NFKBI)E were obtained using the Kaplan-Meier method. Genes co-expressed with NFKBIE in HCC samples were studied using data from the LinkedOmics and the Hepatocellular Carcinoma Databases. Protein-protein interaction networks, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment pathway analyses were used to assess the NFKBIE mechanism in HCC. Using the TIMER database, the association between immune infiltration and NFKBIE was determined. RNA-sequencing (RNA-seq) was used to evaluate the function of NFKBIE in HCC and its impact on proliferation and migration. Western blotting was used to confirm the expression of NFKBIE in HCC cell lines. In addition, NFKBIE overexpression in HCC was demonstrated using tissue microarrays encompassing 80 pairs of HCC and normal liver tissues. NFKBIE was the only NFκB inhibitor with high expression and an improved prognosis in HCC compared with other NFκB inhibitors. NFKBIE was correlated with clinical characteristics, such as tumour grade, tumour protein P53 mutation status and tumour stage. Data obtained from Gene Set Cancer Analysis suggested that NFKBIE may inhibit the PI3K/AKT, RAS/MAPK, RTK and TSC/mTOR pathways. In addition, NFKBIE was significantly associated with B-cell immune infiltration and the RNA-seq data demonstrated that knockdown of NFKBIE significantly affected 'Antigen processing and presentation' and 'hepatocellular carcinoma' pathways. Immunohistochemistry of microarrays of tissue samples revealed that NFKBIE was overexpressed in several stages of HCC. Finally, inhibition of NFKBIE decreased the proliferation and migration of HCC cells. In conclusion, due to its prognostic value and overexpression in HCC, NFKBIE distinguished itself from other NFκB inhibitors. As such, it may provide a novel prognostic indicator and immunotherapeutic target for HCC.

Single-cell RNA-seq reveals heterogeneity in metastatic renal cell carcinoma and effect of anti-angiogenesis therapy in the pancreas metastatic lesion

Metastatic clear cell renal cell carcinoma has heterogenous tumor microenvironment (TME). Among the metastatic lesions, pancreas metastasis is rare and controversy in treatment approaches. Here, extensive primary and metastatic lesion samples were included by single-cell RNA-seq to decipher the distinct metastasis TME. The hypoxic and inflammatory TME of pancreas metastasis was decoded in this study, and the activation of PAX8-myc signaling, and metabolic reprogramming were observed. The active components including endothelial cells, fibroblasts and T cells were profiled. Meanwhile, we also evaluated the effect of anti-angiogenesis treatment in the pancreas metastasis patient. The potential mechanisms of pancreatic tropism, instability of genome, and the response of immunotherapy were also discussed in this work. Taken together, our findings suggest a clue to the heterogeneity in metastasis TME and provide evidence for the treatment of pancreas metastasis in renal cell carcinoma patients.

SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification

The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used graph deep learning methods to identify cancer driver genes based on biological networks. However, incompleteness and the noise of the networks will weaken the performance of models. To address this, we propose a cancer driver gene identification method based on self-supervision for graph convolutional networks, which can efficiently enhance the structure of the network and further improve predictive accuracy. The reliability of SSCI is verified by the area under the receiver operating characteristic curves (AUROC), the area under the precision-recall curves (AUPRC), and the F1 score, with respective values of 0.966, 0.964, and 0.913. The results show that our method can identify cancer driver genes with strong discriminative power and biological interpretability.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Learning chemical sensitivity reveals mechanisms of cellular response

Chemical probes interrogate disease mechanisms at the molecular level by linking genetic changes to observable traits. However, comprehensive chemical screens in diverse biological models are impractical. To address this challenge, we develop ChemProbe, a model that predicts cellular sensitivity to hundreds of molecular probes and drugs by learning to combine transcriptomes and chemical structures. Using ChemProbe, we infer the chemical sensitivity of cancer cell lines and tumor samples and analyze how the model makes predictions. We retrospectively evaluate drug response predictions for precision breast cancer treatment and prospectively validate chemical sensitivity predictions in new cellular models, including a genetically modified cell line. Our model interpretation analysis identifies transcriptome features reflecting compound targets and protein network modules, identifying genes that drive ferroptosis. ChemProbe is an interpretable in silico screening tool that allows researchers to measure cellular response to diverse compounds, facilitating research into molecular mechanisms of chemical sensitivity.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Multi-task deep latent spaces for cancer survival and drug sensitivity prediction

MOTIVATION

Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has driven many developments in drug development, however, there are important aspects crucial to precision medicine that are often overlooked, namely the inherent differences between tumours in patients and the cell-lines used to model them in vitro. Recent developments in transfer learning methods for patient and cell-line data have shown progress in translating results from cell-lines to individual patients in silico. However, transfer learning can be forceful and there is a risk that clinically relevant patterns in the omics profiles of patients are lost in the process.

RESULTS

We present MODAE, a novel deep learning algorithm to integrate omics profiles from cell-lines and patients for the purposes of exploring precision medicine opportunities. MODAE implements patient survival prediction as an additional task in a drug-sensitivity transfer learning schema and aims to balance autoencoding, domain adaptation, drug-sensitivity prediction, and survival prediction objectives in order to better preserve the heterogeneity between patients that is relevant to survival. While burdened with these additional tasks, MODAE performed on par with baseline survival models, but struggled in the drug-sensitivity prediction task. Nevertheless, these preliminary results were promising and show that MODAE provides a novel AI-based method for prioritizing drug treatments for high-risk patients.

AVAILABILITY AND IMPLEMENTATION

https://github.com/UEFBiomedicalInformaticsLab/MODAE.

Zeb1 mediates EMT/plasticity-associated ferroptosis sensitivity in cancer cells by regulating lipogenic enzyme expression and phospholipid composition

Therapy resistance and metastasis, the most fatal steps in cancer, are often triggered by a (partial) activation of the epithelial-mesenchymal transition (EMT) programme. A mesenchymal phenotype predisposes to ferroptosis, a cell death pathway exerted by an iron and oxygen-radical-mediated peroxidation of phospholipids containing polyunsaturated fatty acids. We here show that various forms of EMT activation, including TGFβ stimulation and acquired therapy resistance, increase ferroptosis susceptibility in cancer cells, which depends on the EMT transcription factor Zeb1. We demonstrate that Zeb1 increases the ratio of phospholipids containing pro-ferroptotic polyunsaturated fatty acids over cyto-protective monounsaturated fatty acids by modulating the differential expression of the underlying crucial enzymes stearoyl-Co-A desaturase 1 (SCD), fatty acid synthase (FASN), fatty acid desaturase 2 (FADS2), elongation of very long-chain fatty acid 5 (ELOVL5) and long-chain acyl-CoA synthetase 4 (ACSL4). Pharmacological inhibition of selected lipogenic enzymes (SCD and FADS2) allows the manipulation of ferroptosis sensitivity preferentially in high-Zeb1-expressing cancer cells. Our data are of potential translational relevance and suggest a combination of ferroptosis activators and SCD inhibitors for the treatment of aggressive cancers expressing high Zeb1.

Cytochrome P450 2J2 is required for the natural compound austocystin D to elicit cancer cell toxicity

Austocystin D is a natural compound that induces cytochrome P450 (CYP) monooxygenase-dependent DNA damage and growth inhibition in certain cancer cell lines. Cancer cells exhibiting higher sensitivity to austocystin D often display elevated CYP2J2 expression. However, the essentiality and the role of CYP2J2 for the cytotoxicity of this compound remain unclear. In this study, we demonstrate that CYP2J2 depletion alleviates austocystin D sensitivity and DNA damage induction, while CYP2J2 overexpression enhances them. Moreover, the investigation into genes involved in austocystin D cytotoxicity identified POR and PGRMC1, positive regulators for CYP activity, and KAT7, a histone acetyltransferase. Through genetic manipulation and analysis of multiomics data, we elucidated a role for KAT7 in CYP2J2 transcriptional regulation. These findings strongly suggest that CYP2J2 is crucial for austocystin D metabolism and its subsequent cytotoxic effects. The potential use of austocystin D as a therapeutic prodrug is underscored, particularly in cancers where elevated CYP2J2 expression serves as a biomarker.

Unveiling the unique role of TSPAN7 across tumors: a pan-cancer study incorporating retrospective clinical research and bioinformatic analysis

BACKGROUND

TSPAN7 is an important factor in tumor progression. However, the precise function of TSPAN7 and its role in pan-cancer are not clear.

METHODS

Based on Xinhua cohort incorporating 370 patients with kidney neoplasm, we conducted differential expression analysis by immunohistochemistry between tumor and normal tissues, and explored correlations of TSPAN7 with patients' survival. Subsequently, we conducted a pan-cancer study, and successively employed differential expression analysis, competing endogenous RNA (ceRNA) analysis, protein-protein interaction (PPI) analysis, correlation analysis of TSPAN7 with clinical characteristics, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. Last but not least, gene set enrichment analysis was applied to identify enriched pathways of TSPAN7.

RESULTS

In Xinhua cohort, TSPAN7 expression was significantly up-regulated (P-value = 0.0019) in tumor tissues of kidney neoplasm patients. High TSPAN7 expression was associated with decreases in overall survival (OS) (P-value = 0.009) and progression-free survival (P-value = 0.009), and it was further revealed as an independent risk factor for OS (P-value = 0.0326, HR = 5.66, 95%CI = 1.155-27.8). In pan-cancer analysis, TSPAN7 expression was down-regulated in most tumors, and it was associated with patients' survival, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. The ceRNA network and PPI network of TSPAN7 were also constructed. Last but not least, the top five enriched pathways of TSPAN7 in various tumors were identified.

CONCLUSION

TSPAN7 served as a promising biomarker of various tumors, especially kidney neoplasms, and it was closely associated with tumor purity, tumor genomics, tumor immunology, and drug sensitivity in pan-cancer level.

A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level

Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce "efficacious combination benefit" (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.

Multi-dimensional characterization of apoptosis in the tumor microenvironment and therapeutic relevance in melanoma

PURPOSE

Melanoma is widely utilized as a prominent model for the development of immunotherapy, thought an inadequate immune response can occur. Moreover, the development of apoptosis-related therapies and combinations with other therapeutic strategies is impeded by the limited understanding of apoptosis's role within diverse tumor immune microenvironments (TMEs).

METHODS

Here, we constructed an apoptosis-related tumor microenvironment signature (ATM) and employ multi-dimensional analysis to understand the roles of apoptosis in tumor microenvironment. We further assessed the clinical applications of ATM in nine independent cohorts, and anticipated the impact of ATM on cellular drug response in cultured cells.

RESULTS

Our ATM model exhibits robust performance in survival prediction in multiple melanoma cohorts. Different ATM groups exhibited distinct molecular signatures and biological processes. The low ATM group exhibited significant enrichment in B cell activation-related pathways. What's more, plasma cells showed the lowest ATM score, highlighting their role as pivotal contributors in the ATM model. Mechanistically, the analysis of the interplay between plasma cells and other immune cells elucidated their crucial role in orchestrating an effective anti-tumor immune response. Significantly, the ATM signature exhibited associations with therapeutic efficacy of immune checkpoint blockade and the drug sensitivity of various agents, including FDA-approved and clinically utilized drugs targeting the VEGF signaling pathway. Finally, ATM was associated with tertiary lymphoid structures (TLS), exhibiting stronger patient stratification ability compared to classical "hot tumors".

CONCLUSION

Our findings indicate that ATM is a prognostic factor and is associated with the immune response and drug sensitivity in melanoma.

Novel mitochondrial-related gene signature predicts prognosis and immunological status in glioma

Background

Mitochondria are the center of cellular metabolism. The relationship between mitochondria and diseases has also been studied for a long time. However, the prognostic role of mitochondrial-related genes (MRGs) in patients with glioma and their biological effects are still unclear. The aim of the study was to construct a mitochondria-related model to assess prognosis and potential biological effects like immune infiltration, gene pathway and mutation, and give some predictive chemotherapeutic agents.

Methods

The data of 675 patients from The Cancer Genome Atlas (TCGA) database were used to identify MRG signature and construct a prognostic model. After validating its robustness in Chinese Glioma Genome Atlas (CGGA), two risk groups derived from the prognostic model were then conducted with Gene Set Enrichment Analysis (GSEA), immune status, mutation status and chemotherapeutic agents prediction.

Results

The prognostic model built from six gene signatures can successfully predict the prognosis and reflect clinicopathological characteristics. Patients in high-risk group displayed significantly worse overall survival (OS), immunosuppression effects, and mutation markers with worse prognosis. Twelve chemotherapeutic agents with strongly correlated sensitivity and risk scores were selected as potential agents.

Conclusions

The novel MRG signatures (TYMP, TSFM, MGME1, BOLA3, TRMT5, NDUFA9) can predict prognosis and immunological status in glioma.

DeepDRA: Drug repurposing using multi-omics data integration with autoencoders

Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consuming. Some computational methods, such as machine learning and deep learning, have been suggested to solve these challenges using drug repurposing. Despite the promise of classical machine-learning methods in repurposing cancer drugs and predicting responses, deep-learning methods performed better. This study aims to develop a deep-learning model that predicts cancer drug response based on multi-omics data, drug descriptors, and drug fingerprints and facilitates the repurposing of drugs based on those responses. To reduce multi-omics data's dimensionality, we use autoencoders. As a multi-task learning model, autoencoders are connected to MLPs. We extensively tested our model using three primary datasets: GDSC, CTRP, and CCLE to determine its efficacy. In multiple experiments, our model consistently outperforms existing state-of-the-art methods. Compared to state-of-the-art models, our model achieves an impressive AUPRC of 0.99. Furthermore, in a cross-dataset evaluation, where the model is trained on GDSC and tested on CCLE, it surpasses the performance of three previous works, achieving an AUPRC of 0.72. In conclusion, we presented a deep learning model that outperforms the current state-of-the-art regarding generalization. Using this model, we could assess drug responses and explore drug repurposing, leading to the discovery of novel cancer drugs. Our study highlights the potential for advanced deep learning to advance cancer therapeutic precision.

Synergistic drug interactions of the histone deacetylase inhibitor givinostat (ITF2357) in CRLF2-rearranged pediatric B-cell precursor acute lymphoblastic leukemia identified by high-throughput drug screening

Combining multiple drugs broadens the window of therapeutic opportunities and is crucial for diseases that are currently lacking fully curative treatments. A powerful emerging tool for selecting effective drugs and combinations is the high-throughput drug screening (HTP). The histone deacetylase inhibitor (HDACi) givinostat (ITF2357) has been shown to act effectively against CRLF2-rearranged pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL), a subtype characterized by poor outcome and enriched in children with Down Syndrome, very fragile patients with a high susceptibility to treatment-related toxicity. The aim of this study is to investigate possible synergies with givinostat for these difficult-to-treat patients by performing HTP screening with a library of 174 drugs, either approved or in preclinical studies. By applying this approach to the CRLF2-r MHH-CALL-4 cell line, we identified 19 compounds with higher sensitivity in combination with givinostat compared to the single treatments. Next, the synergy between givinostat and the promising candidates was further validated in CRLF2r cell lines with a broad matrix of concentrations. The combinations with trametinib (MEKi) or venetoclax (BCL2i) were found to be the most effective and with the greatest synergy across three metrics (ZIP, HAS, Bliss). Their efficacy was confirmed in primary blasts treated ex vivo at concentration ranges with a safe profile on healthy cells. Finally, we described givinostat-induced modifications in gene expression of MAPK and BCL-2 family members, supporting the observed synergistic interactions. Overall, our study represents a model of drug repurposing strategy using HTP screening for identifying synergistic, efficient, and safe drug combinations.

Threonine fuels glioblastoma through YRDC-mediated codon-biased translational reprogramming

Cancers commonly reprogram translation and metabolism, but little is known about how these two features coordinate in cancer stem cells. Here we show that glioblastoma stem cells (GSCs) display elevated protein translation. To dissect underlying mechanisms, we performed a CRISPR screen and identified YRDC as the top essential transfer RNA (tRNA) modification enzyme in GSCs. YRDC catalyzes the formation of N6-threonylcarbamoyladenosine (t6A) on ANN-decoding tRNA species (A denotes adenosine, and N denotes any nucleotide). Targeting YRDC reduced t6A formation, suppressed global translation and inhibited tumor growth both in vitro and in vivo. Threonine is an essential substrate of YRDC. Threonine accumulated in GSCs, which facilitated t6A formation through YRDC and shifted the proteome to support mitosis-related genes with ANN codon bias. Dietary threonine restriction (TR) reduced tumor t6A formation, slowed xenograft growth and augmented anti-tumor efficacy of chemotherapy and anti-mitotic therapy, providing a molecular basis for a dietary intervention in cancer treatment.