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A network-based framework to discover treatment-response-predicting biomarkers for complex diseases

ABSTRACT: A network-based framework to discover treatment-response-predicting biomarkers for complex diseases

PROVIDER: PRJNA1085755 | ENA |

REPOSITORIES: ENA

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A network-based framework to discover treatment-response-predicting biomarkers for complex diseases

Project description:Precision medicine's potential to transform complex autoimmune-disease treatment is often challenged by limited data availability and inadequate sample size when compared to the number of molecular features found in high-throughput multi-omics datasets. Addressing this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. ProBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein–protein interaction network to reverse disease states. ProBeNet prioritizes biomarkers by considering (1) therapy-targeted proteins, (2) disease-specific molecular signatures, and (3) an underlying network of interactions among cellular components (the human interactome). With ProBeNet, biomarkers were discovered predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a MAPK3/1 inhibitor). Predictive power of ProBeNet biomarkers was validated with retrospective gene-expression data from ulcerative-colitis and rheumatoid-arthritis patients and prospective data from ulcerative-colitis and Crohn’s disease patient-derived tissues. Machine-learning models using ProBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited (fewer than 20 samples). These results illustrate the value of ProBeNet for reducing features and for constructing robust machine-learning models when limited data are available. ProBeNet may be used to develop companion and complementary diagnostic assays for complex autoimmune-disease therapies, which may help stratify suitable patient subgroups in clinical trials, approve new drugs, and improve patient outcomes.

2024-03-13 | GSE261205 | GEO

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Biomarkers in Predicting Neurotoxicity in Patients With Colorectal Cancer Receiving Oxaliplatin

Project description:RATIONALE: Studying samples of blood in the laboratory from patients receiving oxaliplatin for cancer may help doctors learn more about changes that occur in DNA and identify biomarkers related to neurotoxicity. PURPOSE: This phase II trial is studying biomarkers in predicting neurotoxicity in patients with colorectal cancer receiving oxaliplatin.

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Identifying biomarkers predicting response to chemotherapy in Stage II and Stage III colorectal cancer patients

Project description:Defining molecular features that can predict the response to chemotherapy for stage II-III colorectal cancer (CRC) patients remains challenging in cancer research. Most available clinical samples are Formalin-Fixed and Paraffin-Embedded (FFPE). Affymetrix GeneChip® Human Transcriptome Array 2.0 (HTA) is one platform marketed for high-throughput gene expression profiling for FFPE tissue samples. In this study, we analyzed the whole transcriptom gene expression of 156 CRC patient samples measured by this platform to identify biomarkers predicting the response to chemotherapy for stage II-III CRC patients.

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