Differential frequency in imaging-based outcome measurement: Bias in real-world oncology comparative-effectiveness studies.

BACKGROUND: Comparative-effectiveness studies using real-world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of endpoints such as progression-free survival (PFS) are based on progression events detected by imaging assessments. This study aimed to evaluate the potential bias introduced by differential imaging assessment frequency when using electronic health record (EHR)-derived data to investigate the comparative effectiveness of cancer therapies. METHODS: Using a nationwide de-identified EHR-derived database, we first analyzed imaging assessment frequency patterns in patients diagnosed with advanced non-small cell lung cancer (aNSCLC). We used those RWD inputs to develop a discrete event simulation model of two treatments where disease progression was the outcome and PFS was the endpoint. Using this model, we induced bias with differential imaging assessment timing and quantified its effect on observed versus true treatment effectiveness. We assessed percent bias in the estimated hazard ratio (HR). RESULTS: The frequency of assessments differed by cancer treatment types. In simulated comparative-effectiveness studies, PFS HRs estimated using real-world imaging assessment frequencies differed from the true HR by less than 10% in all scenarios (range: 0.4% to -9.6%). The greatest risk of biased effect estimates was found comparing treatments with widely different imaging frequencies, most exaggerated in disease settings where time to progression is very short. CONCLUSIONS: This study provided evidence that the frequency of imaging assessments to detect disease progression can differ by treatment type in real-world patients with cancer and may induce some bias in comparative-effectiveness studies in some situations.

Technology-Enabled Clinical Trials: Transforming Medical Evidence Generation.

The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.

Immunotherapy: New Strategies for the Treatment of Gynecologic Malignancies.

Over the past decade, the ability of cancer cells to evade immune destruction has become recognized as one of the hallmarks of cancer. This understanding has paved the way for the development of novel therapeutic agents that can enhance activation of antitumor immune responses or reverse immunosuppressive mechanisms through which tumors escape immune-mediated rejection. The treatment of gynecologic cancers remains a therapeutic challenge, as these malignancies are often diagnosed in advanced stages, and many patients relapse despite appropriate management. Clinical trials have shown efficacy for various immunotherapeutic strategies, especially the use of tumor-targeting antibodies; enhancement of tumor antigen presentation, such as with vaccines and toll-like receptor agonists; and the targeting of immunosuppressive mechanisms, such as via checkpoint blockade. Emerging data on new and combination approaches currently under investigation provide a strong rationale for these approaches.

Concordance of Clinician-Documented and Imaging Response in Patients With Stage IV Non-Small Cell Lung Cancer Treated With First-Line Therapy.

Importance: In observational oncology studies of solid tumors, response to treatment can be evaluated based on electronic health record (EHR) documentation (clinician-assessed response [CAR]), an approach different from standardized radiologist-measured response (Response Evaluation Criteria in Solid Tumours [RECIST] 1.1). Objective: To evaluate the feasibility of an imaging response based on RECIST (IRb-RECIST) and the concordance between CAR and imaging response based on RECIST assessments, and investigate discordance causes. Design, Setting, and Participants: This cohort study used an EHR-derived, deidentified database that included patients with stage IV non-small cell lung cancer (NSCLC) diagnosed between January 1, 2011, to June 30, 2019, selected from 3 study sites. Data analysis was conducted in August, 2020. Exposures: Undergoing first-line therapy and imaging assessments of response to treatment. Main Outcomes and Measures: In this study, CAR assessments (referred to in prior publications as "real-world response" [rwR]) were defined as clinician-documented changes in disease burden at radiologic evaluation time points; they were abstracted manually and assigned to response categories. The RECIST-based assessments accommodated routine practice patterns by using a modified version of RECIST 1.1 (IRb-RECIST), with independent radiology reads. Concordance was calculated as the percent agreement across all response categories and across a dichotomous stratification (response [complete or partial] vs no response), unconfirmed or confirmed. Results: This study found that, in 100 patients evaluated for concordance, agreement between CAR and IRb-RECIST was 71% (95% CI, 61%-80%), and 74% (95% CI, 64%-82%) for confirmed and unconfirmed response, respectively. There were more responders using CAR than IRb-RECIST (40 vs 29 with confirmation; 64 vs 43 without confirmation). The main sources of discordance were the different use of thresholds for tumor size changes by RECIST vs routine care, and unavailable baseline or follow-up scans resulting in inconsistent anatomic coverage over time. Conclusions and Relevance: In this cohort study of patients with stage IV NSCLC, we collected routine-care imaging, showing the feasibility of response evaluation using IRb-RECIST criteria with independent centralized review. Concordance between CAR and centralized IRb-RECIST was moderate. Future work is needed to evaluate the generalizability of these results to broader populations, and investigate concordance in other clinical settings.

Analysis of a Real-World Progression Variable and Related Endpoints for Patients with Five Different Cancer Types.

INTRODUCTION: We previously demonstrated that real-world progression (rwP) can be ascertained from unstructured electronic health record (EHR)-derived documents using a novel abstraction approach for patients with advanced non-small cell lung cancer (base case). The objective of this methodological study was to assess the reliability, clinical relevance, and the need for disease-specific adjustments of this abstraction approach in five additional solid tumor types. METHODS: Patients with metastatic breast cancer (mBC), advanced melanoma (aMel), small cell lung cancer (SCLC), metastatic renal cell carcinoma (mRCC), and advanced gastric/esophageal cancer (aGEC) were selected from a real-world database. Disease-specific additions to the base case were implemented as needed. The resulting abstraction approach was applied to each disease cohort to capture rwP events and dates. To provide comprehensive clinical context, real-world progression-free survival (rwPFS) and time to progression (rwTTP) were compared to real-world overall survival (rwOS), time to next treatment (rwTTNT), and time to treatment discontinuation (rwTTD). Endpoint estimates were assessed using the Kaplan-Meier method. Correlations between real-world endpoints and rwOS were calculated using Spearman's ρ. RESULTS: Additions to the base-case rwP abstraction approach were required for mBC, aMel, and SCLC. Inter-abstractor agreement for rwP occurrence, irrespective of date, ranged from 88% to 97%. Occurrence of clinically relevant downstream events (new antineoplastic systemic therapy start, antineoplastic systemic therapy end, or death relative to the rwP event) ranged from 59% (aMel) to 72% (mBC). Median rwPFS ranged from 3.7 (aMel) to 7.7 (mBC) months, and median rwTTP ranged from 4.6 (aMel) to 8.3 (mRCC) months. Correlations between rwOS and rwPFS ranged from 0.52 (aMel) to 0.82 (SCLC). The correlation between rwOS and rwTTD was often lower relative to other comparisons (range 0.40-0.62). CONCLUSION: Derivation of a rwP variable from EHR documentation is feasible and reliable across the five solid tumors. Endpoint analyses show that rwP produces clinically meaningful information.

Complementary Roles for Tissue- and Blood-Based Comprehensive Genomic Profiling for Detection of Actionable Driver Alterations in Advanced NSCLC.

Introduction: Whereas tumor biopsy is the reference standard for genomic profiling of advanced NSCLC, there are now multiple assays approved by the Food and Drug Administration for liquid biopsy testing of circulating tumor DNA. Here, we study the incremental value that liquid biopsy comprehensive genomic profiling (CGP) adds to tissue molecular testing. Methods: Patients with metastatic NSCLC were enrolled in a prospective diagnostic study to receive circulating tumor DNA CGP; tissue CGP was optional in addition to their standard tissue testing. Focusing on nine genes listed per the National Comprehensive Cancer Network (NCCN) guidelines, liquid CGP was compared with available tissue testing results across three subcohorts: tissue CGP, standard-of-care testing of up to five biomarkers, or no tissue testing. Results: A total of 515 patients with advanced nonsquamous NSCLC received liquid CGP. Among 131 with tissue CGP results, NCCN biomarkers were detected in 86 (66%) with tissue CGP and 56 (43%) with liquid CGP (p < 0.001). Adding liquid CGP to tissue CGP detected no additional patients with NCCN biomarkers, whereas tissue CGP detected NCCN biomarkers in 30 patients (23%) missed by liquid CGP. Studying 264 patients receiving tissue testing of up to five genes, 102 (39%) had NCCN biomarkers detected in tissue, with an additional 48 (18%) detected using liquid CGP, including 18 with RET, MET, or ERBB2 drivers not studied in tissue. Conclusions: For the detection of patients with advanced nonsquamous NSCLC harboring 9 NCCN biomarkers, liquid CGP increases detection in patients with limited tissue results, but does not increase detection in patients with tissue CGP results available. In contrast, tissue CGP can add meaningfully to liquid CGP for detection of NCCN biomarkers and should be considered as a follow-up when an oncogenic driver is not identified by liquid biopsy.

Bridging the divide between clinical research and clinical care in oncology: An integrated real-world evidence generation platform.

Real world data (RWD) are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources; real-world evidence (RWE) generated by RWD analyses can become an important component of drug development programs and, potentially, regulatory decision-making. As a RWD source, electronic health records (EHRs) can now provide patient-level data at unparalleled depth and granularity. We propose a RWE generation framework that could maximize the synergy between RWD and prospective clinical trials by capitalizing on an emerging data curation infrastructure that may be applied to both retrospective and prospective research. In this platform, centralized data collection and monitoring could be enabled via routine EHR use, and seamlessly integrated with select intentional data capture during prospective study periods. By bridging the divide between routine care and clinical research, this integrated platform aggregates retrospective and prospective data, collected both routinely and intentionally. This approach makes clinical trial participation more available to patients, increasing the potential depth of data, representativeness and efficiency of clinical research.

Characterization of a Real-World Response Variable and Comparison with RECIST-Based Response Rates from Clinical Trials in Advanced NSCLC.

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.

Concordance of real-world versus conventional progression-free survival from a phase 3 trial of endocrine therapy as first-line treatment for metastatic breast cancer.

There is growing interest in leveraging real-world data to complement knowledge gained from randomized clinical trials and inform the design of prospective randomized studies in oncology. The present study compared clinical outcomes in women with metastatic breast cancer who received letrozole as first-line monotherapy in oncology practices across the United States versus patients in the letrozole-alone cohort of the PALOMA-2 phase 3 trial. The real-world cohort (N = 107) was derived from de-identified patient data from the Flatiron Health electronic health record database. The clinical trial cohort (N = 222) comprised postmenopausal women in the letrozole-alone arm of PALOMA-2. Patients in the real-world cohort received letrozole monotherapy per labeling and clinical judgment; patients in PALOMA-2 received letrozole 2.5 mg/d, continuous. Real-world survival and response rates were based on evidence of disease burden curated from clinician notes, radiologic reports, and pathology reports available in the electronic health record. Progression-free survival and objective response rate in PALOMA-2 were based on Response Evaluation Criteria in Solid Tumors v1.1. Concordance of survival and response rates were retrospectively assessed using inverse probability of treatment weighting-adjusted Cox regression analysis. Inverse probability of treatment weighting-adjusted Cox regression results showed similar median progression-free survival in the real-world and PALOMA-2 cohorts (18.4 and 16.6 months, respectively): the hazard ratio using real-world data as reference was 1.04 (95% CI, 0.69-1.56). No significant difference was observed in response rates: 41.8% in the real-world cohort vs 39.4% in the PALOMA-2 cohort (odds ratio using real-world data as reference: 0.91 [95% CI, 0.57-1.44]). These findings indicate that data abstracted from electronic health records with proper quality controls can yield meaningful information on clinical outcomes. These data increase confidence in the use of real-world assessments of progression and response as efficacy endpoints. Trial registration NCT01740427; Funding: Pfizer.

Characterizing the Feasibility and Performance of Real-World Tumor Progression End Points and Their Association With Overall Survival in a Large Advanced Non-Small-Cell Lung Cancer Data Set.

PURPOSE: Large, generalizable real-world data can enhance traditional clinical trial results. The current study evaluates reliability, clinical relevance, and large-scale feasibility for a previously documented method with which to characterize cancer progression outcomes in advanced non-small-cell lung cancer from electronic health record (EHR) data. METHODS: Patients who were diagnosed with advanced non-small-cell lung cancer between January 1, 2011, and February 28, 2018, with two or more EHR-documented visits and one or more systemic therapy line initiated were identified in Flatiron Health's longitudinal EHR-derived database. After institutional review board approval, we retrospectively characterized real-world progression (rwP) dates, with a random duplicate sample to ascertain interabstractor agreement. We calculated real-world progression-free survival, real-world time to progression, real-world time to next treatment, and overall survival (OS) using the Kaplan-Meier method (index date was the date of first-line therapy initiation), and correlations between OS and other end points were assessed at the patient level (Spearman's ρ). RESULTS: Of 30,276 eligible patients,16,606 (55%) had one or more rwP event. Of these patients, 11,366 (68%) had subsequent death, treatment discontinuation, or new treatment initiation. Correlation of real-world progression-free survival with OS was moderate to high (Spearman's ρ, 0.76; 95% CI, 0.75 to 0.77; evaluable patients, n = 20,020), and for real-world time to progression correlation with OS was lower (Spearman's ρ, 0.69; 95% CI, 0.68 to 0.70; evaluable patients, n = 11,902). Interabstractor agreement on rwP occurrence was 0.94 (duplicate sample, n = 1,065) and on rwP date 0.85 (95% CI, 0.81 to 0.89; evaluable patients n = 358 [patients with two independent event captures within 30 days]). Median rwP abstraction time from individual EHRs was 18.0 minutes (interquartile range, 9.7 to 34.4 minutes). CONCLUSION: We demonstrated that rwP-based end points correlate with OS, and that rwP curation from a large, contemporary EHR data set can be reliable, clinically relevant, and feasible on a large scale.

Lighting the fires within: the cell biology of autoinflammatory diseases.

Autoinflammatory diseases are characterized by seemingly unprovoked pathological activation of the innate immune system in the absence of autoantibodies or autoreactive T cells. Discovery of the causative mutations underlying several monogenic autoinflammatory diseases has identified key regulators of innate immune responses. Recent studies have highlighted the role of misfolding, oligomerization and abnormal trafficking of pathogenic mutant proteins in triggering autoinflammation, and suggest that more common rheumatic diseases may have an autoinflammatory component. This coincides with recent discoveries of new links between endoplasmic reticulum stress and inflammatory signalling pathways, which support the emerging view that autoinflammatory diseases may be due to pathological dysregulation of stress-sensing pathways that normally function in host defence.