Project description:Cisplatin, a platinum-based chemotherapeutic drug, has been used for over 30 years in a wide variety of cancers with varying degrees of success. In particular, cisplatin has been used to treat late stage non-small cell lung cancer (NSCLC) as the standard of care. However, therapeutic outcomes vary from patient to patient. Considerable efforts have been invested to identify biomarkers that can be used to predict cisplatin sensitivity in NSCLC. Here we reviewed current evidence for cisplatin sensitivity biomarkers in NSCLC. We focused on several key pathways, including nucleotide excision repair, drug transport and metabolism. Both expression and germline DNA variation were evaluated in these key pathways. Current evidence suggests that cisplatin-based treatment could be improved by the use of these biomarkers.
Project description:Novel oncology drugs often fail to progress from preclinical experiments to FDA approval. Therefore, determining which preclinical or clinical factors associate with drug activity could accelerate development of effective therapies. We investigated whether preclinical metrics and patient characteristics are associated with objective response rate (ORR) in phase II clinical trials of targeted therapies for non-small cell lung cancer (NSCLC). We developed a reproducible process to select a single phase II trial and supporting preclinical publication for a given drug-indication pair, which we defined as the pairing of a small molecule inhibitor (e.g., crizotinib) with the specific patient population for which it was designed to work (e.g., patients with an ALK aberration). We demonstrated that robust drug activity in mice, as measured by change in tumor size, is independently associated with improved ORR in phase II clinical trials. The number of mice utilized in experiments, the number of publications referencing the drug for NSCLC before the phase II clinical trial, and whether the drug was approved for a cancer other than NSCLC also significantly correlated with ORR. Among clinical characteristics, sex, race, histology, and smoking history were significantly associated with ORR. Further research into metrics that correlate with drug activity has the potential to optimize selection of novel therapies for clinical trials and enrich the drug development pipeline, particularly for patients with targetable genetic aberrations and rare cancers.
Project description:The current standard therapies for advanced, recurrent or metastatic colon cancer are the 5-fluorouracil and oxaliplatin or irinotecan schedules (FOxFI) +/- targeted drugs cetuximab or bevacizumab. Treatment with the FOxFI cytotoxic chemotherapy regimens causes significant toxicity and might induce secondary cancers. The overall low efficacy of the targeted drugs seen in colon cancer patients still is hindering the substitution of the chemotherapy. The ONCOTRACK project developed a strategy to identify predictive biomarkers based on a systems biology approach, using omics technologies to identify signatures for personalized treatment based on single drug response data. Here, we describe a follow-up project focusing on target-specific drug combinations. Background for this experimental preclinical study was that, by analyzing the tumor growth inhibition in the PDX models by cetuximab treatment, a broad heterogenic response from complete regression to tumor growth stimulation was observed. To provide confirmation of the hypothesis that drug combinations blocking alternatively activated oncogenic pathways may improve therapy outcomes, 25 models out of the well-characterized ONCOTRACK PDX panel were subjected to treatment with a drug combination scheme using four approved, targeted cancer drugs.
Project description:It is infeasible to test many different chemotherapy drugs on actual patients in large clinical trials, which motivates computational methods with the ability to learn and exploit associations between drug effectiveness and patient characteristics. This work proposes a machine learning approach to infer robust predictors of drug responses from patient genomic information. Rather than predicting the exact drug response on a given cell line, we introduce an elastic-net regression methodology to compare a drug-cell line pair against an alternative pair. Using predicted pairwise comparisons we rank the effectiveness of different drugs on the same cell line. A total of 173 cell lines and 100 drug responses were used in various settings for training and testing the proposed models. By comparing our approach against twelve baseline methods, we demonstrate that it outperforms the state-of-the-art methods in the literature. In contrast to most other methods, the algorithm is able to maintain its high performance even when we use a large number of drugs and few cell lines.
Project description:The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.
Project description:Cancer drug development has been riddled with high attrition rates, in part, due to poor reproducibility of preclinical models for drug discovery. Poor experimental design and lack of scientific transparency may cause experimental biases that in turn affect data quality, robustness and reproducibility. Here, we pinpoint sources of experimental variability in conventional 2D cell-based cancer drug screens to determine the effect of confounders on cell viability for MCF7 and HCC38 breast cancer cell lines treated with platinum agents (cisplatin and carboplatin) and a proteasome inhibitor (bortezomib). Variance component analysis demonstrated that variations in cell viability were primarily associated with the choice of pharmaceutical drug and cell line, and less likely to be due to the type of growth medium or assay incubation time. Furthermore, careful consideration should be given to different methods of storing diluted pharmaceutical drugs and use of DMSO controls due to the potential risk of evaporation and the subsequent effect on dose-response curves. Optimization of experimental parameters not only improved data quality substantially but also resulted in reproducible results for bortezomib- and cisplatin-treated HCC38, MCF7, MCF-10A, and MDA-MB-436 cells. Taken together, these findings indicate that replicability (the same analyst re-performs the same experiment multiple times) and reproducibility (different analysts perform the same experiment using different experimental conditions) for cell-based drug screens can be improved by identifying potential confounders and subsequent optimization of experimental parameters for each cell line.
Project description:A reliable animal model that can mimic the GBM intracranial infiltration and Blood-brain barrier (BBB) interaction is necessary for effective therapeutics development. Here, we report a zebrafish-based orthotopic GBM xenograft model, in which GBM cells from different species and even patients, can robustly propagate and faithfully reproduce their histological characteristics. Single-cell RNA-seq indicates a transcriptomic adaption of GBM xenografts to infiltrative phenotype within the zebrafish brains. We also provide evidence that the BBB in zebrafish larva is molecularly and functionally intact and can interact with GBM cells in similar ways as in mammals, which together enables this model to accurately identify BBB penetrating drugs. Using GBM patients’ samples, we further generate zebrafish patient-derived orthotopic xenografts (z-PDOX) and proof-of-concept experiments indicate the short-term temozolomide response in z-PDOX can predict the long-term prognosis of corresponding GBM patients. These together illustrate the value of zebrafish GBM model in drug discovery and precision medicine.
Project description:BackgroundDisulfidptosis is a recently discovered form of cell death. However, its biological mechanisms in bladder cancer (BCa) are yet to be understood.MethodsDisulfidptosis-related clusters were identified by consensus clustering. A disulfidptosis-related gene (DRG) prognostic model was established and verified in various datasets. A series of experiments including qRT-PCR, immunoblotting, IHC, CCK-8, EdU, wound-healing, transwell, dual-luciferase reporter, and ChIP assays were used to study the biological functions.ResultsWe identified two DRG clusters, which exhibited distinct clinicopathological features, prognosis, and tumor immune microenvironment (TIME) landscapes. A DRG prognostic model with ten features (DCBLD2, JAM3, CSPG4, SCEL, GOLGA8A, CNTN1, APLP1, PTPRR, POU5F1, CTSE) was established and verified in several external datasets in terms of prognosis and immunotherapy response prediction. BCa patients with high DRG scores may be characterized by declined survival, inflamed TIME, and elevated tumor mutation burden. Besides, the correlation between DRG score and immune checkpoint genes and chemoradiotherapy-related genes indicated the implication of the model in personalized therapy. Furthermore, random survival forest analysis was performed to select the top important features within the model: POU5F1 and CTSE. qRT-PCR, immunoblotting, and immunohistochemistry assays showed the enhanced expression of CTSE in BCa tumor tissues. A series of phenotypic assays revealed the oncogenetic roles of CTSE in BCa cells. Mechanically, POU5F1 can transactivate CTSE, promoting BCa cell proliferation and metastasis.ConclusionsOur study highlighted the disulfidptosis in the regulation of tumor progression, sensitivity to therapy, and survival of BCa patients. POU5F1 and CTSE may be potential therapeutic targets for the clinical treatment of BCa.
Project description:BackgroundAlternative splicing (AS) plays an essential role in tumorigenesis and progression. This study intended to construct an innovative prognostic model based on AS events to gain more precise survival prediction and search for potential therapeutic targets in ovarian cancer.MethodsSeven types of AS events in ovarian serous cystadenocarcinoma (OV) patients with RNA-seq were obtained using The Cancer Genome Atlas (TCGA) SpliceSeq tool and database. Cox and Kaplan-Meier curve analyses were employed to establish the prognostic models. Relying on drug sensitivity data from the CellMiner database, Genomics of Drug Sensitivity (GDS) was adopted to estimate the platinum-sensitive analysis. Furthermore, a prognostic splicing factor (SF)-AS network was constructed using Cytoscape. Finally, in order to explore the influence of the tumor microenvironment on the prognosis of OV patients, we first combined a similar network fusion and consensus clustering (SNF-CC) algorithm to identify three OV subtypes based on survival-related AS events and then utilized single-sample Gene Set Enrichment Analysis (ssGSEA) method to perform immune cell infiltration analysis.ResultsA total of 48,049 AS events and 21,841 related genes were selected from 318 OV samples, and 2,206 AS events associated with disease-free survival (DFS) were identified. Multivariate Cox and Kaplan-Meier curve analyses were then employed to establish the prognostic models. Receiver operating characteristic (ROC) analysis from 0.59 to 0.75 showed that these models were highly efficient in distinguishing patient survival. GDS was adopted with the CellMiner database to provide some insights for platinum-sensitive analysis of OV. Furthermore, a prognostic SF-AS network, which discovered a significant connection between SFs and prognostic AS genes, was constructed using Cytoscape. The combined SNF-CC algorithm revealed three distinct OV subtypes based on the prognostic AS events, and the associations between this novel molecular classification and immune cell infiltration were further explored.ConclusionsWe developed a powerful prognostic AS signature for OV and provided a deeper understanding of SF-AS network regulatory mechanisms, as well as platinum-sensitive and cancer immune microenvironments. These results revealed various candidate biomarkers and potential targets for OV treatment strategies.
Project description:Considerable progress has been made in identifying genetic risk factors for idiosyncratic adverse drug reactions in the past 30 years. These reactions can affect various tissues and organs, including liver, skin, muscle and heart, in a drug-dependent manner. Using both candidate gene and genome-wide association studies, various genes that make contributions of varying extents to each of these forms of reactions have been identified. Many of the associations identified for reactions affecting the liver and skin involve human leukocyte antigen (HLA) genes and for reactions relating to the drugs abacavir and carbamazepine, HLA genotyping is now in routine use prior to drug prescription. Other HLA associations are not sufficiently specific for translation but are still of interest in relation to underlying mechanisms for the reactions. Progress on non-HLA genes affecting adverse drug reactions has been less, but some important associations, such as those of SLCO1B1 and statin myopathy, KCNE1 and drug-induced QT prolongation and NAT2 and isoniazid-induced liver injury, are considered. Future prospects for identification of additional genetic risk factors for the various adverse drug reactions are discussed.