Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines
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ABSTRACT:
PROVIDER: PRJNA1056027 | ENA |
REPOSITORIES: ENA
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Project description:Colorectal cancer is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for treatment of metastatic colorectal cancer are 5-FU, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct predictive gene expression-based classifier to separate responders and non-responders. In this study we used 7 established colorectal cancer organoids to conduct correlational analysis of the expression and drug resistance. We also included in the study publicly available datasets of colorectal cancer cell lines. The obtained results demonstrated that the expression of just a small group of genes consistently correlate with resistance to standard chemotherapeutic drugs in different datasets. Some of these genes have been previously connected to prognosis or response to anticancer drugs, but some of them were linked to the drug resistance for the first time. The classifier constructed on the expression levels of some of these genes allows to discriminate between patients with favorable and unfavorable 5-year OS prognosis and can be further validated in clinic to improve the results of the treatment.
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Project description:Purpose: Follicular lymphoma is a common lymphoma of adults. Although its course is often indolent, a substantial proportion of patients have a poor prognosis, often due to rapid progression or transformation to a more aggressive lymphoma. Currently available clinical prognostic scores, such as the follicular lymphoma international prognostic index, are not able to optimally predict transformation or poor outcome. Experimental Design: Gene expression profiling was done on primary lymphoma biopsy samples. Results: Using a statistically conservative approach, predictive interaction analysis, we have identified pairs of interacting genes that predict poor outcome, measured as death within 5 years of diagnosis. The best gene pair performs >1,000-fold better than any single gene or the follicular lymphoma international prognostic index in our data set. Many gene pairs achieve outcome prediction accuracies exceeding 85% in extensive cross-validation and noise sensitivity computational analyses.Many genes repeatedly appear in top-ranking pairs, suggesting that they reproducibly provide predictive capability. Conclusions:The evidence reported here may provide the basis for an expression-based, multigene test for predicting poor follicular lymphoma outcomes. Keywords: Comparative genomics
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Project description:Breast carcinoma (BC) is the leading cause of death in women worldwide, making up 23% of all cancers in women, with 1.38 million new cases worldwide annually and responsible for 460,000 deaths. Despite the significant advances in the identification of molecular markers and different modalities of treatment in primary BC, the ability to predict the metastatic behavior in breast cancer is still limited. The purpose of this study was to help identify novel molecular markers associated with clinical outcome in a cohort of Brazilian BC patients. We generated global gene expression profiles from 24 patients with invasive ductal BC followed for ⥠5-years, including 15 samples from patients classified as presenting good prognosis based on traditional markers and clinical criteria and 9 patients that developed metastasis. We identified a set of 58 differentially expressed genes (p â¤0.01) between groups of patients with good and poor prognosis. Up-regulation of B3GNT7, PPM1D, TNKS2, PHB and GTSE1 in patients with poor prognosis was confirmed by quantitative RT-PCR in an independent sample set from patients with BC (47 with good prognosis and 8 that presented metastasis). Expression of BAD protein was investigated by immunohistochemistry in 1276 BC samples and confirmed the reduced expression levels in metastatic cases observed in the oligoarray data. These findings point to novel prognostic markers that can distinguish breast carcinoma samples according to clinical course and progression of the disease. Global expression profiles from 38 ductal breast tumor patient samples were used to search for molecular signatures correlated with current prognostic markers. A subset of 24 cases comprising 15 patients that remained free of disease after surgery and 9 patients that developed metastasis was used to identify candidate biomarkers associated with metastatic progression. Candidates were subsequently validated in additional independent samples by RT-qPCR or immunohistochemistry.
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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.
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