Background and Objective The Surveillance, Epidemiology, and End Results Program (SEER) program and the National Program of Cancer Registries (NPCR), are authoritative sources for population cancer surveillance and research in the US. An increasing number of recent oncology studies are based on the electronic health record (EHR)-derived de-identified databases created and maintained by Flatiron Health. This report describes the differences in the originating sources and data development processes, and compares baseline demographic characteristics in the cancer-specific databases from Flatiron Health, SEER, and NPCR, to facilitate interpretation of research findings based on these sources. Methods Patients with documented care from January 1, 2011 through May 31, 2019 in a series of EHR-derived Flatiron Health de-identified databases covering multiple tumor types were included. SEER incidence data (obtained from the SEER 18 database) and NPCR incidence data (obtained from the US Cancer Statistics public use database) for malignant cases diagnosed from January 1, 2011 to December 31, 2016 were included. Comparisons of demographic variables were performed across all disease-specific databases, for all patients and for the subset diagnosed with advanced-stage disease. Results As of May 2019, a total of 201,570 patients with 19 different cancer types were included in Flatiron Health datasets. In an overall comparison to national cancer registries, patients in the Flatiron Health databases had similar sex and geographic distributions, but appeared to be diagnosed with later stages of disease and their age distribution differs from the other datasets. For variables such as stage and race, Flatiron Health databases had a greater degree of incompleteness. There are variations in these trends by cancer types. Conclusions These three databases present general similarities in demographic and geographic distribution, but there are overarching differences across the populations they cover. Differences in data sourcing (medical oncology EHRs vs cancer registries), and disparities in sampling approaches and rules of data acquisition may explain some of these divergences. Furthermore, unlike the steady information flow entered into registries, the availability of medical oncology EHR-derived information reflects the extent of involvement of medical oncology clinics at different points in the specialty management of individual diseases, resulting in inter-disease variability. These differences should be considered when interpreting study results obtained with these databases.

PurposeAn association of Lynch syndrome (LS) with breast cancer has been long suspected; however, there have been insufficient data to address this question for each of the LS genes individually.MethodsWe conducted a retrospective review of personal and family history in 423 women with pathogenic or likely pathogenic germ-line variants in MLH1 (N = 65), MSH2 (N = 94), MSH6 (N = 140), or PMS2 (N = 124) identified via clinical multigene hereditary cancer testing. Standard incidence ratios (SIRs) of breast cancer were calculated by comparing breast cancer frequencies in our study population with those in the general population (Surveillance, Epidemiology, and End Results 18 data).ResultsWhen evaluating by gene, the age-standardized breast cancer risks for MSH6 (SIR = 2.11; 95% confidence interval (CI), 1.56–2.86) and PMS2 (SIR = 2.92; 95% CI, 2.17–3.92) were associated with a statistically significant risk for breast cancer whereas no association was observed for MLH1 (SIR = 0.87; 95% CI, 0.42–1.83) or MSH2 (SIR = 1.22; 95% CI, 0.72–2.06).ConclusionOur data demonstrate that two LS genes, MSH6 and PMS2, are associated with an increased risk for breast cancer and should be considered when ordering genetic testing for individuals who have a personal and/or family history of breast cancer.

There remains debate about whether risk-reducing salpingo-oophorectomy (RRSO), which reduces ovarian cancer risk, also reduces breast cancer risk. We examined the association between RRSO and breast cancer risk using a prospective cohort of 17 917 women unaffected with breast cancer at baseline (7.2% known carriers of BRCA1 or BRCA2 mutations). During a median follow-up of 10.7 years, 1046 women were diagnosed with incident breast cancer. Modeling RRSO as a time-varying exposure, there was no association with breast cancer risk overall (hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 0.87 to 1.24) or by tertiles of predicted absolute risk based on family history (HR = 0.68, 95% CI = 0.32 to 1.47, HR = 0.94, 95% CI = 0.70 to 1.26, and HR = 1.10, 95% CI = 0.88 to 1.39, for lowest, middle, and highest tertile of risk, respectively) or for BRCA1 and BRCA2 mutation carriers when examined separately. There was also no association after accounting for hormone therapy use after RRSO. These findings suggest that RRSO should not be considered efficacious for reducing breast cancer risk.

Introduction Effectiveness metrics for real-word research, analogous to clinical trial ones, are needed. This study aimed to develop a real-world response (rwR) variable applicable to solid tumors and to evaluate its clinical relevance and meaningfulness. Methods This retrospective study used patient cohorts with advanced non-small cell lung cancer from a nationwide, de-identified electronic health record (EHR)-derived database. Disease burden information abstracted manually was classified into response categories anchored to discrete therapy lines (per patient-line). In part 1, we quantified the feasibility and reliability of data capture, and estimated the association between rwR status and real-world progression-free survival (rwPFS) and real-world overall survival (rwOS). In part 2, we investigated the correlation between published clinical trial overall response rates (ORRs) and real-world response rates (rwRRs) from corresponding real-world patient cohorts. Results In part 1, 85.4% of patients ( N = 3248) had  at least one radiographic assessment documented. Median abstraction time per patient-line was 15.0 min (IQR 7.8–28.1). Inter-abstractor agreement on presence/absence of at least one assessment was 0.94 (95% CI 0.92–0.96; n = 503 patient-lines abstracted in duplicate); inter-abstractor agreement on best confirmed response category was 0.82 (95% CI 0.78–0.86; n = 384 with at least one captured assessment). Confirmed responders at a 3-month landmark showed significantly lower risk of death and progression in rwOS and rwPFS analyses across all line settings. In part 2, rwRRs (from 12 rw cohorts) showed a high correlation with trial ORRs (Spearman’s ρ = 0.99). Conclusions We developed a rwR variable generated from clinician assessments documented in EHRs following radiographic evaluations. This variable provides clinically meaningful information and may provide a real-world measure of treatment effectiveness. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-021-01659-0.

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Purpose There is no optimal prognostic model for T-cell lymphoblastic lymphoma (T-LBL). Here, we discussed the predictive value of total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) measured on 18F-fluorodeoxyglucose positron emission tomography–computed tomography (PET-CT) in T-LBL.Materials and Methods Thirty-seven treatment naïve T-LBL patients with PET-CT scans were enrolled. TMTV was obtained using the 41% maximum standardized uptake value (SUVmax) threshold method, and TLG was measured as metabolic tumor volume multiplied by the mean SUV. Progression-free survival (PFS) and overall survival (OS) were analyzed by Kaplan-Meier curves and compared by the log-rank test.Results The optimal cutoff values for SUVmax, TMTV, and TLG were 12.7, 302 cm3, and 890, respectively. A high SUVmax, TMTV, and TLG indicated a shorten PFS and OS. On multivariable analysis, TMTV ≥ 302 cm3, and central nervous system (CNS) involvement predicted inferior PFS, while high SUVmax, TLG and CNS involvement were associated with worse OS. Subsequently, we generated a risk model comprising high SUVmax, TMTV or TLG and CNS involvement, which stratified the population into three risk groups, which had significantly different median PFS of not reached, 14 months, and 7 months for low-risk group, mediate-risk group, and high-risk group, respectively (p < 0.001). Median OS were not reached, 27 months, and 13 months, respectively (p < 0.001).Conclusion Baseline SUVmax, TMTV, and TLG measured on PET-CT are strong predictors of worse outcome in T-LBL. A risk model integrating these three parameters with CNS involvement identifies patients at high risk of disease progression.