Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.
Number rather than location of EPMS is a prognostic factor in patients with stage IV M1b NSCLC. This information is relevant for accurate prognostication, stratification of participants in future clinical trials, and timely and appropriate advanced care planning.
PURPOSE Plasma detection of EGFR T790M mutations is an emerging alternative to tumor rebiopsy in acquired epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance. Validation of analytical sensitivity and clinical utility is required before routine diagnostic use in clinical laboratories. PATIENTS AND METHODS Sixty-three patients with advanced EGFR-mutant lung cancer at 7 Canadian centers, who were being screened for the ASTRIS trial (ClinicalTrials.gov identifier: NCT02474355 ), participated in this companion study. Plasma T790M mutation was detected using droplet digital polymerase chain reaction, Cobas (Roche Diagnostics, Indianapolis, IN), or next-generation sequencing in 4 laboratories. T790M concordance was assessed between plasma and tumor samples. RESULTS Assessment of T790M in tumor biopsy tissue was successful in 81% of patients; 49% had confirmed T790M results (tumor or plasma) for ASTRIS. Plasma testing in this companion study yielded T790M results in 97% of patients; 62% had T790M-positive results, 36% had negative results, and 2% had indeterminate results. Of 38 patients with negative or indeterminate biopsy results, 55% had positive plasma T790M results, increasing the proportion with T790M-positive results to 73%. Sensitivity of plasma T790M testing was 75%. Overall concordance between tissue and plasma was 64%, and concordance among laboratories was 90.3%. Response to osimertinib and duration of therapy were similar irrespective of testing method (overall response rate, 62.5% for tissue, 66.7% for plasma, and 70.6% for both). CONCLUSION This multicenter validation study demonstrates that plasma EGFR T790M testing can identify significantly more patients than biopsy alone who may benefit from targeted therapy.
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