Piantadosi, Steven

Abstract Background Reference sets are needed to evaluate performance of multi-cancer detection (MCD) assays. The National Cancer Institute funded the Alliance reference set study to assess MCDs for use in future trials. Methods Individuals with cancer and controls were recruited; blood specimens were collected prior to cancer treatment. A performance evaluation study was designed utilizing reference set samples. Companies (n = 6) were selected to participate based on review of performance data and ability to utilize the blood collection tube. Companies received samples from cancer types their assay was designed to detect (“targeted”), plus additional “non-targeted” and control samples. Companies reported positive/negative calls, risk scores, and tissue-of-origin (TOO) predictions. Sensitivity was computed for early (I-II) and late (III-IV) stage cases, based on positive/negative calls (SEPN) and at fixed 98% specificity (SE98). Specificity and TOO accuracy were computed. Results Five hundred and forty nine cases (encompassing 13 cancer types) and 413 controls from the reference set were included in the study. Companies assessed samples from median 6 (range 5-9) targeted cancer types and median 8 (range: 7-11) overall cancer types. Median (range) specificity was 92.3% (76.5%-98.5%). Median (range) SEPN was 32% (25%-42%) for early stage and 73% (48%-89%) for late stage; while median (range) SE98 was 19% (8%-35%) for early stage and 66% (13%-79%) for late stage. Median sensitivity for non-targeted types was 40% (early stage) and 52% (late stage). Median (range) TOO accuracy (primary predicted site) was 75% (64%-78%). Conclusions Sensitivity and specificity varied widely across assays with early-stage sensitivity substantially lower than late-stage sensitivity. Clinical Trial Registration Number ClinicalTrials.gov identifier NCT05334069.

AbstractBackgroundThe use of electronic health record (EHR) data for research is limited by a lack of structure and a standard data model. The objective of the ICAREdata (Integrating Clinical Trials and Real‐World Endpoints Data) project was to structure key research data elements in EHRs using a minimal Common Oncology Data Elements (mCODE) data model to extract and transmit data.MethodsThe ICAREdata project captured two EHR data elements essential to clinical trials: cancer disease status and treatment plan change. The project was implemented in clinical sites participating in Alliance for Clinical Trials in Oncology trials. Data were extracted from EHRs and sent by secure Fast Healthcare Interoperability Resource messaging (a standard for exchanging EHRs) to a database. Selected elements were compared with corresponding data from the trial's electronic data capture (EDC) system, Medidata Rave.ResultsBy December 2023, data were extracted and transmitted from 10 sites for 35 patients, involving 367 clinical encounters across 15 clinical trials. Data through March 2023 demonstrated that concordance for the elements treatment plan change and cancer disease status was 79% and 34%, respectively. When disease evaluation was reported by both EHR and EDC (n = 15), there was 87% agreement on cancer disease status.ConclusionsDocumentation, extraction, and aggregation of structured data elements in EHRs using mCODE and ICAREdata methods is feasible in multi‐institutional cancer clinical trials. EDC as a reference data set allowed assessment of the completeness of EHR data capture. Future initiatives will focus on elements with shared definitions in clinical and research environments and efficient workflows.Plain Language Summary Clinical trials use electronic case report forms to report data, and data must be manually entered on these forms, which is costly and time consuming. ICAREdata methods use structured, organized data from clinical trials that can be more easily shared instead having to enter free text into electronic health records.

Abstract Immunotherapy has revolutionized treatment for many hard-to-treat cancers but has yet to produce significant improvement in outcomes for patients with glioblastoma. This reflects the multiple and unique mechanisms of immune evasion and escape in this highly heterogeneous tumor. Glioblastoma engenders profound local and systemic immunosuppression and is remarkably effective at inducing T-cell dysfunction, posing a challenge to any immunotherapy-based approach. To overcome these mechanisms, multiple disparate modes of immune-oriented therapy will be required. However, designing trials that can evaluate these combinatorial approaches requires careful consideration. In this review, we explore the immunotherapy resistance mechanisms that have been encountered to date and how combinatorial approaches may address these. We also describe the unique aspects of trial design in both preclinical and clinical settings and consider endpoints and markers of response best suited for an intervention involving multiple agents.