Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma
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PROVIDER: S-EPMC10926146 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
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Project description:BackgroundIt is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labour intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. Therefore, they miss indirect effectors or the effects from tissue-specific interactions.ResultsWe developed cell line specific functional modules. Enriched scores of functional modules are utilized as cell line specific features to predict the efficacy of drugs. Cell line specific functional modules are clusters of genes, which have similar biological functions in cell line specific networks. We used linear regression for drug efficacy prediction. We assessed the prediction performance in leave-one-out cross-validation (LOOCV). Our method was compared with elastic net model, which is a popular model for drug efficacy prediction. In addition, we analysed drug sensitivity-associated functions of five drugs - lapatinib, erlotinib, raloxifene, tamoxifen and gefitinib- by our model.ConclusionsOur model can provide cell line specific drug efficacy prediction and also provide functions which are associated with drug sensitivity. Therefore, we could utilize drug sensitivity associated functions for drug repositioning or for suggesting secondary drugs for overcoming drug resistance.
| S-EPMC4965733 | biostudies-literature
Project description:Pediatric high-grade gliomas (pHGG) are lethal, incurable brain tumors frequently driven by clonal mutations in histone genes. They often harbor a range of additional genetic alterations that correlate with different ages, anatomic locations, and tumor subtypes. We developed models representing 16 pHGG subtypes driven by different combinations of alterations targeted to specific brain regions. Tumors developed with varying latencies and cell lines derived from these models engrafted in syngeneic, immunocompetent mice with high penetrance. Targeted drug screening revealed unexpected selective vulnerabilities-H3.3G34R/PDGFRAC235Y to FGFR inhibition, H3.3K27M/PDGFRAWT to PDGFRA inhibition, and H3.3K27M/PDGFRAWT and H3.3K27M/PPM1DΔC/PIK3CAE545K to combined inhibition of MEK and PIK3CA. Moreover, H3.3K27M tumors with PIK3CA, NF1, and FGFR1 mutations were more invasive and harbored distinct additional phenotypes, such as exophytic spread, cranial nerve invasion, and spinal dissemination. Collectively, these models reveal that different partner alterations produce distinct effects on pHGG cellular composition, latency, invasiveness, and treatment sensitivity.SignificanceHistone-mutant pediatric gliomas are a highly heterogeneous tumor entity. Different histone mutations correlate with different ages of onset, survival outcomes, brain regions, and partner alterations. We have developed models of histone-mutant gliomas that reflect this anatomic and genetic heterogeneity and provide evidence of subtype-specific biology and therapeutic targeting. See related commentary by Lubanszky and Hawkins, p. 1516. This article is highlighted in the In This Issue feature, p. 1501.
| S-EPMC10326601 | biostudies-literature
Project description:Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM's results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.
| S-EPMC6872169 | biostudies-literature
Project description:Glioma is a highly fatal cancer with prognostically significant molecular subtypes and few known risk factors. Multiple studies have implicated infections in glioma susceptibility, but evidence remains inconsistent. Genetic variants in the human leukocyte antigen (HLA) region modulate host response to infection and have been linked to glioma risk. In this study, we leveraged genetic predictors of antibody response to 12 viral antigens to investigate the relationship with glioma risk and survival. Genetic reactivity scores (GRSs) for each antigen were derived from genome-wide-significant (p < 5 × 10-8) variants associated with immunoglobulin G antibody response in the UK Biobank cohort. We conducted parallel analyses of glioma risk and survival for each GRS and HLA alleles imputed at two-field resolution by using data from 3,418 glioma-affected individuals subtyped by somatic mutations and 8,156 controls. Genetic reactivity scores to Epstein-Barr virus (EBV) ZEBRA and EBNA antigens and Merkel cell polyomavirus (MCV) VP1 antigen were associated with glioma risk and survival (Bonferroni-corrected p < 0.01). GRSZEBRA and GRSMCV were associated in opposite directions with risk of IDH wild-type gliomas (ORZEBRA = 0.91, p = 0.0099/ORMCV = 1.11, p = 0.0054). GRSEBNA was associated with both increased risk for IDH mutated gliomas (OR = 1.09, p = 0.040) and improved survival (HR = 0.86, p = 0.010). HLA-DQA1∗03:01 was significantly associated with decreased risk of glioma overall (OR = 0.85, p = 3.96 × 10-4) after multiple testing adjustment. This systematic investigation of the role of genetic determinants of viral antigen reactivity in glioma risk and survival provides insight into complex immunogenomic mechanisms of glioma pathogenesis. These results may inform applications of antiviral-based therapies in glioma treatment.
| S-EPMC9247888 | biostudies-literature
Project description:Although paediatric high grade gliomas resemble their adult counterparts in many ways, there appear to be distinct clinical and biological differences. One important factor hampering the development of new targeted therapies is the relative lack of cell lines derived from childhood glioma patients, as it is unclear whether the well-established adult lines commonly used are representative of the underlying molecular genetics of childhood tumours. We have carried out a detailed molecular and phenotypic characterisation of a series of paediatric high grade glioma cell lines in comparison to routinely used adult lines.
| S-ECPF-TABM-579 | biostudies-other
Project description:When evaluating anti-cancer drugs, two different measurements are used: relative viability, which scores an amalgam of proliferative arrest and cell death, and fractional viability, which specifically scores the degree of cell killing. We quantify relationships between drug-induced growth inhibition and cell death by counting live and dead cells using quantitative microscopy. We find that most drugs affect both proliferation and death, but in different proportions and with different relative timing. This causes a non-uniform relationship between relative and fractional response measurements. To unify these measurements, we created a data visualization and analysis platform called drug GRADE, which characterizes the degree to which death contributes to an observed drug response. GRADE captures drug- and genotype-specific responses, which are not captured using traditional pharmacometrics. This study highlights the idiosyncratic nature of drug-induced proliferative arrest and cell death. Furthermore, we provide a metric for quantitatively evaluating the relationship between these behaviors.
| S-EPMC7394473 | biostudies-literature
Project description:Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.
| S-EPMC10777918 | biostudies-literature
Project description:Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC50]) of an individual drug by inputting pharmacogenomics in disease models remains critical. Machine learning (ML) has been predominantly applied for prediction, despite the advent of deep learning (DL). Moreover, whether DL or traditional ML models are superior for predicting cell viability IC50s has to be established. Herein, we constructed ML and DL drug response prediction models for 24 individual drugs and compared the performance of the models by employing gene expression and mutation profiles of cancer cell lines as input. We observed no significant difference in drug response prediction performance between DL and ML models for 24 drugs [root mean squared error (RMSE) ranging from 0.284 to 3.563 for DL and from 0.274 to 2.697 for ML; R2 ranging from -7.405 to 0.331 for DL and from -8.113 to 0.470 for ML]. Among the 24 individual drugs, the ridge model of panobinostat exhibited the best performance (R2 0.470 and RMSE 0.623). Thus, we selected the ridge model of panobinostat for further application of explainable artificial intelligence (XAI). Using XAI, we further identified important genomic features for panobinostat response prediction in the ridge model, suggesting the genomic features of 22 genes. Based on our findings, results for an individual drug employing both DL and ML models were comparable. Our study confirms the applicability of drug response prediction models for individual drugs.
| S-EPMC10366128 | biostudies-literature
Project description:Although paediatric high grade gliomas resemble their adult counterparts in many ways, there appear to be distinct clinical and biological differences. One important factor hampering the development of new targeted therapies is the relative lack of cell lines derived from childhood glioma patients, as it is unclear whether the well-established adult lines commonly used are representative of the underlying molecular genetics of childhood tumours. We have carried out a detailed molecular and phenotypic characterisation of a series of paediatric high grade glioma cell lines in comparison to routinely used adult lines.
2009-09-21 | E-TABM-579 | biostudies-arrayexpress
Project description:Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.
| S-EPMC10519600 | biostudies-literature