title: DO CANCER CLINICAL TRIAL POPULATIONS TRULY REPRESENT CANCER PATIENTS? A COMPARISON OF OPEN CLINICAL TRIALS TO THE CANCER GENOME ATLAS.
abstract: Open clinical trial data offer many opportunities for the scientific community to independently verify published results, evaluate new hypotheses and conduct meta-analyses. These data provide a springboard for scientific advances in precision medicine but the question arises as to how representative clinical trials data are of cancer patients overall. Here we present the integrative analysis of data from several cancer clinical trials and compare these to patient-level data from The Cancer Genome Atlas (TCGA). Comparison of cancer type-specific survival rates reveals that these are overall lower in trial subjects. This effect, at least to some extent, can be explained by the more advanced stages of cancer of trial subjects. This analysis also reveals that for stage IV cancer, colorectal cancer patients have a better chance of survival than breast cancer patients. On the other hand, for all other stages, breast cancer patients have better survival than colorectal cancer patients. Comparison of survival in different stages of disease between the two datasets reveals that subjects with stage IV cancer from the trials dataset have a lower chance of survival than matching stage IV subjects from TCGA. One likely explanation for this observation is that stage IV trial subjects have lower survival rates since their cancer is less likely to respond to treatment. To conclude, we present here a newly available clinical trials dataset which allowed for the integration of patient-level data from many cancer clinical trials. Our comprehensive analysis reveals that cancer-related clinical trials are not representative of general cancer patient populations, mostly due to their focus on the more advanced stages of the disease. These and other limitations of clinical trials data should, perhaps, be taken into consideration in medical research and in the field of precision medicine.
authors.affiliation: Institute for Computational Health Sciences, University of California, San Francisco Mission Hall, 550 16th Street, San Francisco, CA 94158-2549, USA, Nophar.Geifman@ucsf.edu.
authors.forename: Nophar
authors.initials: N
authors.lastname: Geifman
authors.forename: Atul J
authors.initials: AJ
authors.lastname: Butte
mesh_terms: D001271:Atlases as Topic; D001943:Breast Neoplasms / Q000235:genetics / Q000628:therapy; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D015179:Colorectal Neoplasms / Q000235:genetics / Q000628:therapy; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D016208:Databases, Factual / Q000706:statistics & numerical data; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D009369:Neoplasms / Q000235:genetics* / Q000628:therapy*; D010190:Pancreatic Neoplasms / Q000235:genetics / Q000628:therapy; D057285:Precision Medicine / Q000379:methods / Q000706:statistics & numerical data; D011471:Prostatic Neoplasms / Q000235:genetics / Q000628:therapy