Project description:IntroductionLung cancer in never smokers represents a distinct epidemiological, clinical, and molecular entity.ResultsMost 712 never smoking lung cancer patients were female (72%) with a median age at diagnosis of 62.2 years (18-94). Caucasians (46%), East Asians (42%), adenocarcinoma histology (87%) and presentation with metastatic disease at diagnosis (59%) were common. Of 515 patients with available archival tissue, the most common identified single mutations were EGFR (52.2%), followed by ALK (7.5%), KRAS (2.3%), TP53 (1.3%), ERBB2 (1%), BRAF (0.4%), PIK3CA (0.4%), SMAD4 (0.4%), CTNNB1 (0.2%), AKT1 (0.2%), and NRAS (0.2%); 8% tumors had multiple mutations, while 25.8% had none identified. Median overall survival (mOS) was 42.2 months (mo) for the entire cohort. Patients with mutations in their tumors had significantly better mOS (69.5 mo) when compared to those without (31.0 mo) (HR = 0.59; 95% CI: 0.44-0.79; p < 0.001). Earlier stage (p < 0.001), adenocarcinoma histology (p = 0.012), good performance status (p < 0.001) and use of targeted therapy (p < 0.001) were each independently associated with longer survival. Patients with ALK-translocation-positive tumours have significantly longer OS compared to those without any mutations (p = 0.0029) and to those with other and null mutations (p = 0.022).ConclusionsLung cancer in never smokers represents a distinct clinical and molecular entity characterized by a high incidence of targetable mutations and long survival.MethodsWe analyzed retrospectively the data from electronic patient records of never smokers diagnosed with lung cancer treated at the Princess Margaret Cancer Centre (Toronto) between 1988-2015 to characterize demographic and clinical features, pathology, molecular profile (using hotspot or targeted sequencing panels), treatment and survival.

| S-EPMC5978248 | biostudies-literature

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

| PRJNA1018662 | ENA

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

| PRJNA1018664 | ENA

Brain Tumour Research Program Telethon Kids Institute Data Access Committee

Project description:Data Access Committee EGAC00001002507

| EGAC00001002507 | EGA

A mechanism for controlled access to GWAS data: experience of the GAIN Data Access Committee.

Project description:The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.

| S-EPMC3617375 | biostudies-literature

RNA-seq of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

Project description:The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches.

2021-01-19 | GSE156914 | GEO

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