Drug Sensitivity Prediction Research Articles

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer

BackgroundPlatelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications.MethodsWe downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR).ResultsA ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750.ConclusionThe novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based on multi-stage multi-modal drug representations (ModDRDSP) to reflect the properties of drugs more comprehensively, and to better model the complex interactions between cells and drugs. Specifically, we adopt the SMILES representation learning method based on the deep hierarchical bi-directional GRU network (DSBiGRU) and the molecular graph representation learning method based on the deep message-crossing network (DMCN) for the multi-modal information of drugs. Additionally, we integrate the multi-omics information of cell lines based on a convolutional neural network (CNN). Finally, we use an ensemble deep forest algorithm for the prediction of drug sensitivity. After validation, the ModDRDSP shows impressive performance which outperforms the four current industry-leading models. More importantly, ablation experiments demonstrate the validity of each module of the proposed model, and case studies show the good results of ModDRDSP for predicting drug sensitivity, further establishing the superiority of ModDRDSP in terms of performance.

EPCO-36. INTEGRATIVE DEEP-LEARNING AND PHARMACOGENOMIC PROFILING OF MALIGNANT GLIOMAS USING PATIENT-DERIVED TUMOR CELLS

Abstract Malignant glioma stands as a formidable cancer with limited therapeutic options due to many factors including pronounced genetic heterogeneity, the blood-brain barrier, among others. Precision medicine, tailoring treatment based on a patient’s unique molecular profiles, is a fitting approach for addressing intra- and inter-tumor heterogeneity. Previously, we have shown the advantage of using patient-derived tumor cells (PDCs) to evaluate drug efficacy across various cancer types. Unlike cancer cell lines, which significantly differ from primary tumors, PDCs capture the molecular features of primary tumors, showing predictive clinical responses in a retrospective study. Building on these encouraging results, we have expanded PDC-based drug screening to specifically target malignant gliomas. We have established an initial cohort of over 500 malignant glioma PDCs (~ 370 patients) and screened more than 100 drugs. We have complemented our cohort with molecular profiles, including genomic and transcriptomic data to infer the potential pharmacogenomic associations of malignant gliomas. We constructed a multi-modal deep-learning model that seamlessly integrates molecular features and drug structure to predict the drug sensitivity. By employing an explainable machine learning method, we retrospectively analyzed the model to investigate the potential molecular biomarkers and their contribution to drug sensitivity. Collectively, our study demonstrates the efficacy of using patient-derived tumor cells (PDCs) combined with deep-learning models to predict drug sensitivity in malignant gliomas. This integrative approach can potentially refine personalized treatment strategies and improve clinical outcomes for glioma patients.

Development of a novel disulfidptosis-correlated m6A/m1A/m5C/m7G gene signature to predict prognosis and therapeutic response for lung adenocarcinoma patients by integrated machine-learning

BackgroundLung adenocarcinoma (LUAD) represents a significant global health burden, necessitating advanced prognostic tools for improved patient management. RNA modifications (m6A, m1A, m5C, m7G), and disulfidptosis, a novel cell death mechanism, have emerged as promising biomarkers and therapeutic targets in cancer.MethodsWe systematically compiled disulfidptosis-correlated genes and RNA modification-related genes from existing literature. A novel disulfidptosis-correlated m6A/m1A/m5C/m7G riskscore was computed using integrated machine-learning algorithms. Transcriptomic data from TCGA and GEO databases were downloaded analyzed. Single-cell RNA-sequencing data from the TISCH database was processed using the Seurat package. Genes’ protein–protein interaction network was constructed using the String database. Functional phenotype analysis was performed using GSVA, ClusterProfiler, and IOBR packages. Consensus clustering divided patients into two distinct groups. Drug sensitivity predictions were obtained from the GDSC1 database and predicted using the Oncopredict package.ResultsThe disulfidptosis-correlated m6A/m1A/m5C/m7G risk score effectively stratified LUAD patients into prognostically distinct groups, demonstrating superior predictive accuracy compared to conventional clinical parameters. Patients in different risk groups exhibited significant molecular and clinical differences. Subsequent analyses identified two molecular subtypes associated with RNA modification and disulfidptosis, revealing differences in immune infiltration and prognosis. Functional enrichment analyses highlighted pathways involving RNA modification and disulfidptosis, underscoring their roles in LUAD pathogenesis. Single-cell analysis revealed distinct features between high- and low-risk status cells.ConclusionThis study introduces a novel disulfidptosis-correlated m6A/m1A/m5C/m7G risk score as a robust prognostic tool for LUAD, integrating insights from RNA modifications and cell death mechanisms. The risk score enhances prognostic stratification and identifies potential targets for personalized therapeutic strategies in LUAD. This comprehensive approach emphasizes the critical roles of RNA modifications and disulfidptosis in LUAD biology, paving the way for future research and clinical applications aimed at improving patient outcomes.

Prognostic characteristics and drug sensitivity analysis of hepatocellular carcinoma based on histone modification-related genes: a multi-omics integrated study revealing potential therapeutic targets and individualized treatment strategies

BackgroundHepatocellular carcinoma (HCC) ranks among the most prevalent and lethal malignancies worldwide. Histone modifications (HMs) play a pivotal role in the initiation and progression of HCC. However, our understanding of HMs in HCC remains limited due to the disease’s heterogeneity and the complexity of HMs.MethodsWe integrated multi-omics data from multiple cohorts, including single-cell RNA sequencing, bulk RNA sequencing, and clinical information. Weighted gene co-expression network analysis (WGCNA) and consensus clustering were employed to identify histone-related genes. We developed a histone modification-related signature (HMRS) using 117 machine learning methods. Comprehensive analyses of molecular characteristics, immune landscape, and drug sensitivity associated with the HMRS were performed.ResultsThrough integrative analysis, we defined 110 histone-related genes and identified 45 HCC-HM-related genes (HCC-HMRgenes). The HMRS demonstrated robust prognostic value across multiple cohorts. Patients with high HMRS scores exhibited distinct genomic alterations, including higher tumor heterogeneity and TP53 mutations. The high-risk group showed enrichment in cell cycle, DNA repair, and metabolic pathways. Immune landscape analysis revealed significant differences in immune cell infiltration and pathway activities between high- and low-risk groups. Drug sensitivity prediction suggested potential therapeutic strategies for different risk groups.ConclusionOur study provides a comprehensive understanding of HMs in HCC and establishes a robust prognostic signature. The HMRS not only stratifies patients into distinct risk groups but also offers insights into underlying molecular mechanisms, immune characteristics, and potential therapeutic strategies, paving the way for personalized medicine in HCC.

Prognostic and Immunotherapeutic Role of TACC3 in Pancancer, and its Impact on Proliferation and Docetaxel Resistance in Lung Adenocarcinoma.

Transforming acidic coiled-coil containing protein 3 (TACC3) exerts a vital role in cancer progression by modulating cell division and facilitating tumor growth. Given the lack of comprehensive research on the pan-cancer implications of TACC3, our study aimed to analyze the functional role of TACC3 in pan-cancer and validate it through experimental investigations in lung adenocarcinoma. We first employed various bioinformatics techniques to investigate the expression and prognostic significance of TACC3 in pancancer. Subsequently, we analyzed the correlation between TACC3 and immune infiltration, immune checkpoints, drug sensitivity, as well as the prediction of immune therapy response. Finally, we validated the association between TACC3 and the proliferation of lung adenocarcinoma, as well as its resistance to docetaxel, through in vitro experiments. Here, TACC3 exhibited high expression in human cancers and was associated with poor prognosis in various cancer types. It was also involved in immune infiltration and demonstrated a strong predictive ability for immune therapy response. Through drug sensitivity prediction, we further identified a potential association between TACC3 and docetaxel resistance, which was subsequently validated in lung adenocarcinoma. Our investigation of TACC3 revealed its potential as a promising target both for immunosuppression and docetaxel resistance in pan-cancer, especially in lung adenocarcinoma.

Systematic multiomics analysis and in vitro experiments suggest that ITGA5 could serve as a promising therapeutic target for ccRCC

BackgroundIntegrin alpha 5 (ITGA5) was previously confirmed to be related to prognosis in several cancer types; however, its function in clear cell renal cell carcinoma (ccRCC) and how this molecule regulates tumor progression and the tumor microenvironment (TME) remain to be elucidated.MethodsWe investigated the prognostic implications of ITGA5 with a machine learning model and evaluated biological behaviors of different levels of ITGA5 expression in vitro. Bioinformatic analysis was performed to explain the comprehensive effect of ITGA5 on the TME and drug sensitivity.ResultsWe constructed a machine learning model to elaborate the prognostic implication of ITGA5. As tumorigenesis of ccRCC was tightly relevant with several mutant genes, we investigated the correlation between ITGA5 expression and frequent mutations and found ITGA5 upregulation in VHL mutant ccRCC (P = 0.016). Through overexpressing, silencing, and blocking ITGA5, we verified the role of ITGA5 in promoting ccRCC adverse biological activities; and the potential functions of ITGA5 in ccRCC were bioinformatically demonstrated, summarizing as cell proliferation, migration, and angiogenesis. The localization of ITGA5 primarily in endothelia and macrophages further verified its magnitude in angiogenesis and aroused our excavation in ITGA5 regulation of immune infiltration landscape. Generally, ITGA5-high ccRCC presented an immunosuppressive TME by inducing a lower level of CD8 + T cell infiltration. For the last part we predicted drug sensitivity relevant to ITGA5 and concluded that a joint medication of ITGA5 inhibitors and VEGFR-target drugs (including sunitinib, axitinib, pazopanib, and motesanib) might be a promising therapeutic strategy.ConclusionOur findings clarified the adverse outcome induced by high expression of ITGA5 in ccRCC patients. In vitro experiments and bioinformatical analysis identified ITGA5 function as predominantly cell proliferation, migration, angiogenesis, and macrophage recruitment. Further, we predicted immune infiltration and medication sensitivity regulation by ITGA5 and proposed a joint use of ITGA5 inhibitors and anti-angiogenetic drugs as a potential potent therapeutic strategy.

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer

BackgroundAbnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.MethodsSingle-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.ResultsA total of 153 SMRGs were acquired, of which 48 were linked to PC patients’ prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.ConclusionThis study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

Integrative analysis of single-cell and bulk multi-omics data to reveal subtype-specific characteristics and therapeutic strategies in clear cell renal cell carcinoma patients.

Background: Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of malignant renal cell carcinoma and is well known as a common genitourinary cancer. Stratifying tumors based on heterogeneity is essential for better treatment options. Methods: In this study, consensus clusters were constructed based on gene expression, DNA methylation, and gene mutation data, which were combined with multiple clustering algorithms. After identifying two heterogeneous subtypes, we analyzed the molecular characteristics, immunotherapy response, and drug sensitivity differences of each subtype. And we further integrated bulk data and single-cell RNA sequencing (scRNA-Seq) data to infer the immune cell composition and malignant tumor cell proportion of subtype-related cell subpopulations. Results: Among the two identified consensus subtypes (CS1 and CS2), CS1 was enriched in more inflammation-related and oncogenic pathways than CS2. Simultaneously, CS1 showed a worse prognosis and we found more copy number variations and BAP1 mutations in CS1. Although CS1 had a high immune infiltration score, it exhibited high expression of suppressive immune features. Based on the prediction of immunotherapy and drug sensitivity, we inferred that CS1 may respond poorly to immunotherapy and be less sensitive to targeted drugs. The analysis of bulk data integrated with single-cell data further reflected the high expression of inhibitory immune features in CS1 and the high proportion of malignant tumor cells. And CS2 contained a large number of plasmacytoid B cells, presenting an activated immune microenvironment. Finally, the robustness of our subtypes was successfully validated in four external datasets. Conclusion: In summary, we conducted a comprehensive analysis of multi-omics data with 10 clustering algorithms to reveal the molecular characteristics of KIRC patients and validated the relevant conclusions by single-cell analysis and external data. Our findings discovered new KIRC subtypes and may further guide personalized and precision treatments.

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilized for the development of immune-related molecular markers in DR. Based on the datasets GSE102485 and GSE160306, differentially expressed genes (DEGs) were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Five ML algorithms including Bayesian, Learning Vector Quantization (LVQ), Wrapper (Boruta), Random Forest (RF), and Logistic Regression were employed to select immune-related genes associated with DR (DR.Sig). Seven ML algorithms including Naive Bayes (NB), RF, Support Vector Machine (SVM), AdaBoost Classification Trees (AdaBoost), Boosted Logistic Regressions (LogitBoost), K-Nearest Neighbors (KNN), and Cancerclass were utilized to construct a predictive model for DR. The relationship between DR.Sig genes and immune cells was analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA). Additionally, drug sensitivity prediction of DR.Sig genes and molecular docking were performed. Through the utilization of 5 ML algorithms, 6 immune-related biomarkers closely related to the occurrence of DR were identified, including FCGR2B, CSRP1, EDNRA, SDC2, TEK, and CIITA. The DR predictive model constructed based on these 6 DR.Sig genes using the Cancerclass algorithm demonstrated superior predictive performance compared to 4 previously published DR-related biomarkers. In vivo and in vitro experiments also provided strong validation of the expression of the 6 genes in DR. Positive correlations were observed between these genes and 22 types of immune cells. Molecular docking results revealed that CSRP1, EDNRA, and TEK exhibited the highest affinities with the small molecule compounds etoposide, FR-139317, and camptothecin, respectively. The models constructed based on various ML algorithms can effectively predict the occurrence of DR events and hold potential for targeted drug therapies, providing a basis for the early diagnosis and targeted treatment of DR.

ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer

Breast cancer remains a leading cause of mortality among women worldwide, with drug resistance driven by transcription factors and mutations posing significant challenges. To address this, we present ResisenseNet, a predictive model for drug sensitivity and resistance. ResisenseNet integrates transcription factor expression, genomic markers, drugs, and molecular descriptors, employing a hybrid architecture of 1D-CNN + LSTM and DNN to effectively learn long-range and temporal patterns from amino acid sequences and transcription factor data. The model demonstrated exceptional predictive accuracy, achieving a validation accuracy of 0.9794 and a loss value of 0.042. Comprehensive validation included comparisons with state-of-the-art models and ablation studies, confirming the robustness of the developed architecture. ResisenseNet has been applied to repurpose existing anticancer drugs across 14 different cancers, with a focus on breast cancer. Among the malignancies studied, drugs targeting Low-grade Glioma (LGG) and Lung Adenocarcinoma (LUAD) showed increased sensitivity to breast cancer as per ResisenseNet’s assessment. Further evaluation of the predicted sensitive drugs revealed that 14 had no prior history of anticancer activity against breast cancer. These drugs target key signaling pathways involved in breast cancer, presenting novel therapeutic opportunities. ResisenseNet addresses drug resistance by filtering ineffective compounds and enhancing chemotherapy for breast cancer. In vitro studies on sensitive drugs provide valuable insights into breast cancer prognosis, contributing to improved treatment strategies.

Identification and experimental validation of a sialylation–related long noncoding RNA signature for prognosis of bladder cancer

BackgroundThe dysregulation of sialylation plays a pivotal role in cancer progression and metastasis, impacting various aspects of tumor behavior. This study aimed to investigate the prognostic significance of long non-coding RNAs (lncRNAs) in relation to sialylation. Additionally, we aimed to develop a signature of sialylation-related lncRNAs in the context of bladder cancer.MethodsThis study used transcriptomic data and clinical information from the TCGA (the Cancer Genome Atlas) database to screen for sialylation-related lncRNAs and constructed a prognostic model. The relationships between these lncRNAs and biological pathways, immune cell infiltration, drug sensitivity, etc., were analyzed, and the expression of some lncRNAs was validated at the cellular level.ResultsThis study identified 6 prognostic lncRNAs related to sialylation and constructed a risk score model with high predictive accuracy and reliability. The survival period of patients in the high-risk group was significantly lower than that of the low-risk group, and it was related to various biological pathways and immune functions. In addition, this study found differences in the sensitivity of patients in different risk groups to chemotherapy drugs, providing a reference for personalized treatment.ConclusionIn this study, we examined the relationship between sialylation-related lncRNA and the prognosis of bladder cancer, providing new molecular markers and potential targets for diagnosis and treatment. Our research revealed correlations between sialylation-related lncRNA characteristics and clinicopathological features, potential mechanisms, somatic mutations, immune microenvironment, chemotherapy response, and predicted drug sensitivity in bladder cancer. Additionally, in vitro cellular studies were conducted to validate these findings and lay the groundwork for future clinical applications.

Prognostic implications of metabolism-related genes in acute myeloid leukemia.

Acute myeloid leukemia(AML) is a diverse malignancy with a prognosis that varies, being especially unfavorable in older patients and those with high-risk characteristics. Metabolic reprogramming has become a significant factor in AML development , presenting new opportunities for prognostic assessment and therapeutic intervention. Metabolism-related differentially expressed genes (mDEGs) were identified by integrating KEGG metabolic gene lists with AML gene expression data from GSE63270. Using TCGA data, we performed consensus clustering and survival analysis to investigate the prognostic significance of mDEGs. A metabolic risk model was constructed using LASSO Cox reg ression and enhanced by a nomogram incorporated clinical characteristics. The model was validated through receiver operating characteristic (ROC) curves and survival statistics. Gene network analysis was conducted to identify critical prognostic factors. The tumor immune microenvironment was evaluated using CIBERSORT and ESTIMATE algorithms, followed by correlation analysis between immune checkpoint gene expression and risk scores. Drug sensitivity predictions and in vitro assays were performed to explore the effects of mDEGs on cell proliferation and chemoresistance. An 11-gene metabolic prognostic model was established and validated. High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05). The risk score was an independent prognostic factor. High-risk patients showed increased immune cell infiltration and potential response to checkpoint inhibitors but decreased drug sensitivity. The model correlated with sensitivity to drugs such as venetoclax. Carbonic anhydrase 13 (CA13) was identified as a key gene related to prognosis and doxorubicin resistance. Knocking down CA13 reduced proliferation and increased cell death with doxorubicin treatment. A novel metabolic gene signature was developed to stratify risk and predict prognosis in AML, serving as an independent prognostic factor. CA13 was identified as a potential therapeutic target. This study provides new insights into the prognostic and therapeutic implications of metabolic genes in AML.

Non-cytotoxic levels of resveratrol enhance the anticancer effects of cisplatin by increasing the methyltransferase activity of CARM1 in human cancer cells

BackgroundResveratrol (RSVL) is a plant-derived polyhydroxyphenolic compound with excellent anticancer properties, alone or in combination with other chemotherapeutic drugs. However, the anticancer mechanism of RSVL is diverse and high concentrations are often required for RSVL to exert its anticancer effect, which would also adversely affect normal cells. PurposeThe main objective of this study is to investigate the molecular mechanism of how non-cytotoxic concentrations of RSVL enhance the anticancer effect of cisplatin involving a newly identified RSVL-binding protein. MethodsCell viability of cell lines from three cancer types exposed to RSVL and/or cisplatin was measured by NBB staining assay. RSVL-binding proteins were identified using RSVL-bound CNBr-activated Sepharose 4B beads coupled with LC-MS/MS, and the binding between RSVL and novel RSVL-binding protein was further confirmed with an in vitro pull-down assay. The expression of proteins was examined by immunoblot analysis, and the activity of methyltransferase was evaluated by in vitro methylation assay. The methylation level of H3R17 in the gene promoter was investigated using ChIP-qPCR. Bioinformatics analysis was conducted to identify pathway enrichment of genes, predict drug sensitivity, and analyze the survival of cancer patients. ResultsLow doses of RSVL might promote cancer cell growth whereas high doses of RSVL showed cytotoxic effects on normal cells. When co-treated with a lower cisplatin dose, non-cytotoxic RSVL levels showed synergistic anticancer effects. Here, coactivator-associated arginine methyltransferase 1 (CARM1) was identified as a novel RSVL-binding protein, and we showed that the upregulation of CARM1 increased the sensitivity of cancer cells to RSVL. Interestingly, we found that CARM1 was essential in the RSVL-induced sensitivity of cisplatin. Further molecular mechanistic studies revealed that RSVL could stabilize CARM1 protein, resulting in the upregulation and increased methyltransferase activity of CARM1. Additionally, we showed that the methylation levels of H3R17 in the promoter of p21, a downstream gene of CARM1 involving cell cycle arrest, were significantly increased after RSVL treatment. Finally, data from our bioinformatics analysis suggested that CARM1 could be utilized as a potential biomarker for chemotherapeutic drug sensitivity and prognosis in cancers. ConclusionsThis study identified CARM1 as a RSVL-binding protein for the first time and elucidated the potential roles of CARM1 in enhancing the efficacy of cisplatin by low doses of RSVL, which could have important clinical implications.

A Risk Model Developed based on Homologous Recombination Deficiency Genes for Evaluating the Drug Sensitivity and Prognostic Prediction of Lung Adenocarcinoma.

This study was designed to construct a risk model based on homologous recombination deficiency (HRD) to evaluate the prognosis and drug sensitivity for patients with lung adenocarcinoma (LUAD). LUAD is a subtype of lung cancer with unfavorable overall survival (OS) and prognosis. HRD has been widely studied in various tumors, but its role in LUAD has not been fully understood. We aimed to construct an HRD-related risk model for predicting the prognosis and drug sensitivity of patients with LUAD. Gene expression data of the LUAD samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We extracted HRD genes from previous literature and performed univariate COX analysis to select those closely associated with LUAD prognosis. ConsensusClusterPlus was employed to stratify the samples in the TCGA-LUAD cohort into different subtypes. A RiskScore model was established applying random forest method. Furthermore, immunotherapy response and drug sensitivity were predicted using Tumor Immune Dysfunction and Exclusion (TIDE) software and pRRophytic R package, respectively. Finally, the clinical features between High- and Low- RiskScore groups were compared. A total of 16 HRD genes relevant to LUAD prognosis were selected and used to classify 3 LUAD clusters (C1, C2, and C3). Specifically, C1, with a lower TIDE score displayed higher immune infiltration and immunotherapy benefit and the optimal OS, while C2 was closely correlated with tumor-relevant pathways and had the worst OS. Finally, 4 HRD genes (RAD51AP1, BRCA1, H2AFX, and FANCL) were determined to develop a RiskScore signature. It was found that a higher RiskScore was related to more advanced stages, worse OS, and tumor development pathways. Additionally, the High-RiskScore group with a higher TIDE score was sensitive to 44 traditional chemotherapy drugs. A nomogram combined with RiskScore exhibited an accurate survival prediction ability. The HRD-based RiskScore played a crucial role in LUAD development, showing a strong potential to serve as a prognostic indicator for LUAD. Our findings contributed to the diagnosis of LUAD and its treatment.

Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach

Background and ObjectiveThe standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents for most mutational events, implementing individualized treatment for AML has proven difficult. We reanalysed the BeatAML dataset employing Machine Learning algorithms. The BeatAML project entails patients extensively characterized at the molecular and clinical levels and linked to drug sensitivity outputs. Our approach capitalizes on the molecular and clinical data provided by the BeatAML dataset to predict the ex vivo drug sensitivity for the 122 drugs evaluated by the project. MethodsWe utilized ElasticNet, which produces fully interpretable models, in combination with a two-step training protocol that allowed us to narrow down computations. We automated the genes’ filtering step by employing two metrics, and we evaluated all possible data combinations to identify the best training configuration settings per drug. ResultsWe report a Pearson correlation across all drugs of 0.36 when clinical and RNA sequencing data were combined, with the best-performing models reaching a Pearson correlation of 0.67. When we trained using the datasets in isolation, we noted that RNA Sequencing data (Pearson: 0.36) attained three times the predictive power of whole exome sequencing data (Pearson: 0.11), with clinical data falling somewhere in between (Pearson 0.26). Lastly, we present a paradigm of clinical significance. We used our models’ prediction as a drug sensitivity score to rank an individual's expected response to treatment. We identified 78 patients out of 89 (88 %) that the proposed drug was more potent than the administered one based on their ex vivo drug sensitivity data. ConclusionsIn conclusion, our reanalysis of the BeatAML dataset using Machine Learning algorithms demonstrates the potential for individualized treatment prediction in Acute Myeloid Leukemia patients, addressing the longstanding challenge of treatment personalization in this disease. By leveraging molecular and clinical data, our approach yields promising correlations between predicted drug sensitivity and actual responses, highlighting a significant step forward in improving therapeutic outcomes for AML patients.

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

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

Prognostic signature and immune landscape of 5-methylcytosine-related long non-coding RNAs in gastric cancer

BackgroundLong non-coding RNAs (lncRNAs) have been demonstrated to be useful in assessing the prognosis of cancer patients. However, few studies have focused on 5-methylcytosine-related lncRNAs (m5C-lncRNAs) in gastric cancer (GC). In this study, we aimed to establish a m5C-lncRNAs prognostic signature (m5C-LPS) and explore its potential impact on guiding clinical practice for GC. MethodsRNA-sequence and clinicopathological data were retrieved from The Cancer Genome Atlas (TCGA) database, while the coexpression of long non-coding RNAs (lncRNAs) was determined using Pearson's correlation analysis. A m5C-LPS model was constructed using univariate and Lasso Cox regression, and its prognostic value and accuracy were subsequently validated. Subsequently, the expression of 11 m5C-lncRNAs was verified via quantitative real-time PCR (qRT-PCR) in gastric cancer (GC) cell lines. The potential biological mechanism of this signature was elucidated using Gene Set Enrichment Analysis (GSEA). Based on the GSEA findings, CIBERSORT and ESTIMATE algorithms were utilized to conduct a comprehensive investigation of the tumor immune microenvironment (TIME) in GC. Additionally, pRRophetic and TIDE algorithms were employed to predict drug sensitivity and the efficacy of immunotherapy for GC patients. Results280 lncRNAs were identified as m5C-lncRNAs, including RHPN1-AS1, AC093752.3, TSC22D1-AS1, AL391152.1, MAGI2-AS3, AC048382.2, AL033527.3, AC007405.2, AC036103.1, CCDC183-AS1, and ADORA2A-AS1. Their prognostic value was validated, and the expression of these 11 lncRNAs was confirmed in four gastric cancer cell lines using quantitative reverse transcription PCR (qRT-PCR). A nomogram incorporating a risk score was developed to provide more precise clinical decision-making. Gene Set Enrichment Analysis (GSEA) showed that many classical signaling pathways related to tumor progression were enriched in this signature. Analyses related to immunity and drug sensitivity demonstrated distinct differences in features between high-risk and low-risk subgroups. ConclusionThe m5C-LPS can predict the survival of gastric cancer (GC) patients, provide novel therapeutic targets, and thus offer more thoughtful perspectives for future clinical decisions regarding GC.

DrugFormer: Graph-Enhanced Language Model to Predict Drug Sensitivity.

Drug resistance poses a crucial challenge in healthcare, with response rates to chemotherapy and targeted therapy remaining low. Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To address this challenge, DrugFormer, a novel graph-augmented large language model designed to predict drug resistance at single-cell level is proposed. DrugFormer integrates both serialized gene tokens and gene-based knowledge graphs for the accurate predictions of drug response. After training on comprehensive single-cell data with drug response information, DrugFormer model presents outperformance, with higher F1, precision, and recall in predicting drug response. Based on the scRNA-seq data from refractory multiple myeloma (MM) and acute myeloid leukemia (AML) patients, DrugFormer demonstrates high efficacy in identifying resistant cells and uncovering underlying molecular mechanisms. Through pseudotime trajectory analysisunique drug-resistant cellular states associated with poor patient outcomes are revealed. Furthermore, DrugFormer identifies potential therapeutic targets, such as COX8A, for overcoming drug resistance across different cancer types. In conclusion, DrugFormer represents a significant advancement in the field of drug resistance prediction, offering a powerful tool for unraveling the heterogeneity of cellular response to drugs and guiding personalized treatment strategies.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted the critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, NETotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, in tumor development. Therefore, by investigating these pathways, we aimed to develop a predictive model for HCC prognosis and drug sensitivity. We analyzed transcriptome, single-cell transcriptome, genomic, and clinical information using data from the TCGA-LIHC, GSE14520, GSE45436, and GSE166635 datasets. Machine learning algorithms were used to establish a cell death index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates with poorer prognosis. Unsupervised clustering revealed three molecular subtypes of HCC with distinct biological processes. Furthermore, a nomogram integrating CDI and clinical information demonstrated good predictive performance. CDI was associated with immune checkpoint genes and tumor microenvironment components using single-cell transcriptome analysis. Drug sensitivity analysis indicated that patients with high CDI may be resistant to oxaliplatin and cisplatin but sensitive to axitinib and sorafenib. In summary, our model offers a precise prediction of clinical outcomes and drug sensitivity for patients with HCC, providing valuable insights for personalized treatment strategies.