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Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine.

ABSTRACT: Targeted cancer therapy requires the rapid and accurate identification of genetic abnormalities predictive of therapeutic response. We sought to develop a high-throughput genotyping platform that would allow prospective patient selection to the best available therapies, and that could readily and inexpensively be adopted by most clinical laboratories. We developed a highly sensitive multiplexed clinical assay that performs very well with nucleic acid derived from formalin fixation and paraffin embedding (FFPE) tissue, and tests for 120 previously described mutations in 13 cancer genes. Genetic profiling of 250 primary tumours was consistent with the documented oncogene mutational spectrum and identified rare events in some cancer types. The assay is currently being used for clinical testing of tumour samples and contributing to cancer patient management. This work therefore establishes a platform for real-time targeted genotyping that can be widely adopted. We expect that efforts like this one will play an increasingly important role in cancer management.

SUBMITTER: Dias-Santagata D

PROVIDER: S-EPMC3377316 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine.

Dias-Santagata Dora D Akhavanfard Sara S David Serena S SS Vernovsky Kathy K Kuhlmann Georgiana G Boisvert Susan L SL Stubbs Hannah H McDermott Ultan U Settleman Jeffrey J Kwak Eunice L EL Clark Jeffrey W JW Isakoff Steven J SJ Sequist Lecia V LV Engelman Jeffrey A JA Lynch Thomas J TJ Haber Daniel A DA Louis David N DN Ellisen Leif W LW Borger Darrell R DR Iafrate A John AJ

EMBO molecular medicine 20100501 5

Targeted cancer therapy requires the rapid and accurate identification of genetic abnormalities predictive of therapeutic response. We sought to develop a high-throughput genotyping platform that would allow prospective patient selection to the best available therapies, and that could readily and inexpensively be adopted by most clinical laboratories. We developed a highly sensitive multiplexed clinical assay that performs very well with nucleic acid derived from formalin fixation and paraffin e ...[more]

PMID: 20432502

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Project description:ObjectivesThe authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE) in an individual patient.BackgroundPatient similarity analysis is an evolving paradigm for precision medicine in which patients are clustered or classified based on their similarities in several clinical features.MethodsA retrospective cohort of 866 patients was used to develop a network architecture using 9 echocardiographic features of LV structure and function. The data for 468 patients from 2 prospective cohort registries were then added to test the model's generalizability.ResultsThe map of cross-sectional data in the retrospective cohort resulted in a looped patient network that persisted even after the addition of data from the prospective cohort registries. After subdividing the loop into 4 regions, patients in each region showed unique differences in LV function, with Kaplan-Meier curves demonstrating significant differences in MACE-related rehospitalization and death (both p < 0.001). Addition of network information to clinical risk predictors resulted in significant improvements in net reclassification, integrated discrimination, and median risk scores for predicting MACE (p < 0.05 for all). Furthermore, the network predicted the cardiac disease cycle in each of the 96 patients who had second echocardiographic evaluations. An improvement or remaining in low-risk regions was associated with lower MACE-related rehospitalization rates than worsening or remaining in high-risk regions (3% vs. 37%; p < 0.001).ConclusionsPatient similarity analysis integrates multiple features of cardiac function to develop a phenotypic network in which patients can be mapped to specific locations associated with specific disease stage and clinical outcomes. The use of patient similarity analysis may have relevance for automated staging of cardiac disease severity, personalized prediction of prognosis, and monitoring progression or response to therapies.

Dataset Information

Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine.

Publications

Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine.

Similar Datasets

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