Dataset Information

Integrated Molecular Profilting in Advanced Cancers Trial

ABSTRACT: The present protocol seeks to provide molecular profiling data to the treating physician for patients with advanced breast, non-small cell lung, colorectal, genitourinary, pancreatobiliary gastrointestinal, upper aerodigestive tract, gynecological, melanoma, unknown primary, and rare carcinomas, as well as patients who are phase I trial candidates, in order to help identify which standard regimens or clinical trials of molecularly targeted therapies may be most appropriate for the individual patient.

PROVIDER: EGAS00001001897 | EGA |

REPOSITORIES: EGA

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Publications

Testing ERBB2 p.L755S kinase domain mutation as a druggable target in a patient with advanced colorectal cancer.

Aung Kyaw L KL Stockley Tracy L TL Serra Stefano S Kamel-Reid Suzanne S Bedard Philippe L PL Siu Lillian L LL

Cold Spring Harbor molecular case studies 20160901 5

Recent advances in molecular profiling technologies allow genetic driver events in individual tumors to be identified. The hypothesis behind this ongoing molecular profiling effort is that improvement in patients' clinical outcomes will be achieved by inhibiting these discovered genetic driver events with matched targeted drugs. This hypothesis is currently being tested in oncology clinics with variable early results. Herein, we present our experience with a case of advanced colorectal cancer (C ...[more]

PMID: 27626067

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Project description:Pulmonary enteric adenocarcinoma (PEAD) is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas (ADCs) by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome wide methylation profiles of 600 primary pulmonary, colorectal and upper gastrointestinal ADCs obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal ADCs from TCGA and the GEO database, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed DNA methylation data of 15 PEADs as well as four pulmonary metastases and four primary colorectal ADCs with the algorithm. All 15 PEADs were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal ADC samples were identified as primary colorectal ADCs. In a t-distributed stochastic neighbor embedding analysis, the PEAD samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary ADCs. Additional characterization of the PEAD series using fluorescence in-situ hybridization, next generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (66.7%) the molecular data was mostly reminiscent of standard pulmonary ADCs. In conclusion, we provide sound evidence of the pulmonary origin of PEAD and in addition provide a publicly available machine learning based algorithm to reliably distinguish PEAD from metastatic colorectal cancer and upper gastrointestinal adenocarcinomas.

Dataset Information

Integrated Molecular Profilting in Advanced Cancers Trial

Publications

Testing ERBB2 p.L755S kinase domain mutation as a druggable target in a patient with advanced colorectal cancer.

Similar Datasets

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