R. Tudor

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer.

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.

Impact of number versus location of metastases on survival in stage IV M1b non-small cell lung cancer

BackgroundTo assess the impact of location versus number of extra-pulmonary metastatic sites (EPMS) on survival in stage IV non-small cell lung cancer (NSCLC).Methods and materialsRetrospective analysis was conducted on patients diagnosed during 1999–2013 with stage IV, M1b (AJCC 7th edition) NSCLC using the large, institutional Glans-Look Database, which contains patient demographic, clinical, pathological, treatment, and outcome information. We assessed the impact of location and number of EPMS and identified correlates of overall survival using the Kaplan–Meier method and Cox regression.ResultsWe identified a total of 2065 NSCLC patients with EPMS. Median age was 67 (IQR 58–75) years, 52% were men, and 78% were current or former smokers. 60% had one EPMS, and 40% had two or more EPMS. Among those with only one EPMS, most frequent organ involvement included bone (40%), brain (32%), and liver (13%). Median overall survival (mOS) was worst in those with liver metastasis and best in those with adrenal metastasis (2.0 vs. 5.2xa0months, pu2009=u20090.015). However, outcomes based on site of organ involvement were not significantly different in multivariable analysis. Compared to patients with one EPMS, individuals with two or more EPMS experienced worse outcomes (mOSu2009≤u20092.9 vs. 3.9 months, pu2009<u20090.001), and were associated with worse prognosis in Cox regression analysis (HR 1.5, 95% CI 1.3–1.7, pu2009<u20090.001).ConclusionsNumber 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.

Beyond disease-progression: Clinical outcomes after EGFR -TKIs in a cohort of EGFR mutated NSCLC patients

Purpose Treatment and clinical-outcomes were described in a sub-cohort of non-small-cell lung cancer (NSCLC) patients with disease-progression (PD) after epidermal growth factor tyrosine kinase inhibitors (EGFR-TKIs) treatment. Patients and methods We retrospectively analyzed a single-institutional EGFR mutation positive (EGFRmut+) NSCLC cohort for post-TKI-PD management, and assessed overall survival (OS) and post-progression survival (PPS). All de-novo (first lung-cancer occurrence) stage IIIA-IV patients, as well as de-novo stage IV subset was analyzed. Multi-state modeling (MSM) and a Cox PH regression model with propensity score weights adjusted for clinicopathological variables between: diagnosis and PD and PD to death. Results 123 stage IIIA-IV patients were identified with 104 meeting RECIST-1.1-PD criteria. This RECIST-1.1-PD criteria subset included females (64.6%), Asians (39.4%), never/non-smokers (55.8%), and exon 19 deletion carriers (44.2%). Commonest treatment beyond initial-PD was continuing TKI alone (46/104), with another 21 patients continuing TKI plus additional systemic therapy. The median OS for patients who continued TKI treatment at initial-PD was 21.1 months versus 15.6 months for patients who discontinued TKI, p = 0.006. Via MSM analysis, continuing TKI at initial-PD followed by other systemic therapy was associated with an 83% reduced death risk, adjusted HR: 0.17 (95% CI: 0.07, 0.39). In the Cox PH model, ever-smokers with an exon 19 deletion had increased risk of death after PD (adjusted HR: 3.19, 95% CI: 1.54, 6.58), as did exon 21 mutation carriers, (adjusted HR: 2.10, 95% CI: 1.10, 4.00) and females (adjusted HR: 3.19, 95% CI: 1.54, 6.58). Conclusion Subsequent systemic therapy after continuing TKI at initial-PD reduced the risk of death. Additionally, our data suggest that positive smoking history increases death risk for some EGFR mutation types and females.