Dataset Information

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.

ABSTRACT: Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case−control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

SUBMITTER: Gonzalez-Bosquet J

PROVIDER: S-EPMC9785370 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.

Gonzalez-Bosquet Jesus J Gabrilovich Sofia S McDonald Megan E ME Smith Brian J BJ Leslie Kimberly K KK Bender David D DD Goodheart Michael J MJ Devor Eric E

International journal of molecular sciences 20221216 24

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case−control study evaluating patients with EC, endo ...[more]

PMID: 36555654

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Project description:Cryptococcal meningitis causes an estimated 112,000 global deaths per annum. Genomic and phenotypic features of the infecting strain of Cryptococcus spp. have been associated with outcomes from cryptococcal meningitis. Additionally, population-level pharmacokinetic variability is well documented in these patient cohorts. The relative contribution of these factors to clinical outcomes is unknown. Based in Malawi, we conducted a sub-study of the phase 3 Ambition-CM trial (ISRCTN72509687), collecting plasma and cerebrospinal fluid at serial time points during the first 14 days of antifungal therapy. We explored the relative contribution of pathogen genotype, drug resistance phenotype, and pharmacokinetics on clinical outcomes including lumbar opening pressure, pharmacodynamic effect, and mortality. We report remarkable genomic homogeneity among infecting strains of Cryptococcus spp., within and between patients. There was no evidence of acquisition of antifungal resistance in our isolates. Genotypic features of the infecting strain were not consistently associated with adverse or favorable clinical outcomes. However, baseline fungal burden and early fungicidal activity (EFA) were associated with mortality. The strongest predictor of EFA was the level of exposure to amphotericin B. Our analysis suggests the most effective means of improving clinical outcomes from HIV-associated cryptococcal meningitis is to optimize exposure to potent antifungal therapy.ImportanceHIV-associated cryptococcal meningitis is associated with a high burden of mortality. Research into the different strain types causing this disease has yielded inconsistent findings in terms of which strains are associated with worse clinical outcomes. Our study suggests that the exposure of patients to potent anti-cryptococcal drugs has a more significant impact on clinical outcomes than the strain type of the infecting organism. Future research should focus on optimizing drug exposure, particularly in the context of novel anticryptococcal drugs coming into clinical use.

Dataset Information

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.

Publications

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.

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