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- Gatto, Francesco, 1987, et al.
(författare)
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Plasma and Urine Free Glycosaminoglycans as Monitoring and Predictive Biomarkers in Metastatic Renal Cell Carcinoma: A Prospective Cohort Study
- 2023
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Ingår i: JCO PRECISION ONCOLOGY. - 2473-4284. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSENo liquid biomarkers are approved in metastatic renal cell carcinoma (mRCC) despite the need to predict and monitor response noninvasively to tailor treatment choices. Urine and plasma free glycosaminoglycan profiles (GAGomes) are promising metabolic biomarkers in mRCC. The objective of this study was to explore if GAGomes could predict and monitor response in mRCC.PATIENTS AND METHODSWe enrolled a single-center prospective cohort of patients with mRCC elected for first-line therapy (ClinicalTrials.gov identifier: NCT02732665) plus three retrospective cohorts (ClinicalTrials.gov identifiers: NCT00715442 and NCT00126594) for external validation. Response was dichotomized as progressive disease (PD) versus non-PD every 8-12 weeks. GAGomes were measured at treatment start, after 6-8 weeks, and every third month in a blinded laboratory. We correlated GAGomes with response and developed scores to classify PD versus non-PD, which were used to predict response at treatment start or after 6-8 weeks.RESULTSFifty patients with mRCC were prospectively included, and all received tyrosine kinase inhibitors (TKIs). PD correlated with alterations in 40% of GAGome features. We developed plasma, urine, and combined glycosaminoglycan progression scores that monitored PD at each response evaluation visit with the area under the receiving operating characteristic curve (AUC) of 0.93, 0.97, and 0.98, respectively. For internal validation, the scores predicted PD at treatment start with the AUC of 0.66, 0.68, and 0.74 and after 6-8 weeks with the AUC of 0.76, 0.66, and 0.75. For external validation, 70 patients with mRCC were retrospectively included and all received TKI-containing regimens. The plasma score predicted PD at treatment start with the AUC of 0.90 and at 6-8 weeks with the AUC of 0.89. The pooled sensitivity and specificity were 58% and 79% at treatment start. Limitations include the exploratory study design.CONCLUSIONGAGomes changed in association with mRCC response to TKIs and may provide biologic insights into mRCC mechanisms of response.
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| 2. |
- Limeta, Angelo, 1996, et al.
(författare)
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Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors
- 2023
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Ingår i: Computational and Structural Biotechnology Journal. - 2001-0370. ; 21, s. 3912-3919
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Forskningsöversikt (refereegranskat)abstract
- A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.
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