Quantitative Imaging Assessment for Clinical Trials in Oncology.

BACKGROUND: Objective radiographic assessment is crucial for accurately evaluating therapeutic efficacy and patient outcomes in oncology clinical trials. Imaging assessment workflow can be complex; can vary with institution; may burden medical oncologists, who are often inadequately trained in radiology and response criteria; and can lead to high interobserver variability and investigator bias. This article reviews the development of a tumor response assessment core (TRAC) at a comprehensive cancer center with the goal of providing standardized, objective, unbiased tumor imaging assessments, and highlights the web-based platform and overall workflow. In addition, quantitative response assessments by the medical oncologists, radiologist, and TRAC are compared in a retrospective cohort of patients to determine concordance. PATIENTS AND METHODS: The TRAC workflow includes an image analyst who pre-reviews scans before review with a board-certified radiologist and then manually uploads annotated data on the proprietary TRAC web portal. Patients previously enrolled in 10 lung cancer clinical trials between January 2005 and December 2015 were identified, and the prospectively collected quantitative response assessments by the medical oncologists were compared with retrospective analysis of the same dataset by a radiologist and TRAC. RESULTS: This study enlisted 49 consecutive patients (53% female) with a median age of 60 years (range, 29-78 years); 2 patients did not meet study criteria and were excluded. A linearly weighted kappa test for concordance for TRAC versus radiologist was substantial at 0.65 (95% CI, 0.46-0.85; standard error [SE], 0.10). The kappa value was moderate at 0.42 (95% CI, 0.20-0.64; SE, 0.11) for TRAC versus oncologists and only fair at 0.34 (95% CI, 0.12-0.55; SE, 0.11) for oncologists versus radiologist. CONCLUSIONS: Medical oncologists burdened with the task of tumor measurements in patients on clinical trials may introduce significant variability and investigator bias, with the potential to affect therapeutic response and clinical trial outcomes. Institutional imaging cores may help bridge the gap by providing unbiased and reproducible measurements and enable a leaner workflow.

Novel Common Genetic Susceptibility Loci for Colorectal Cancer.

BACKGROUND: Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10-8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. METHODS: We conducted a GWAS in European descent CRC cases and control subjects using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10-8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. RESULTS: The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10-8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. CONCLUSIONS: This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.