Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology.

PURPOSE: Matching patients to investigational therapies requires new tools to support physician decision making. We designed and implemented Precision Insight Support Engine (PRECISE), an automated, just-in-time, clinical-grade informatics platform to identify and dynamically track patients on the basis of molecular and clinical criteria. Real-world use of this tool was analyzed to determine whether PRECISE facilitated enrollment to early-phase, genome-driven trials. MATERIALS AND METHODS: We analyzed patients who were enrolled in genome-driven, early-phase trials using PRECISE at Memorial Sloan Kettering Cancer Center between April 2014 and January 2018. Primary end point was the proportion of enrolled patients who were successfully identified using PRECISE before enrollment. Secondary end points included time from sequencing and PRECISE identification to enrollment. Reasons for a failure to identify genomically matched patients were also explored. RESULTS: Data were analyzed from 41 therapeutic trials led by 19 principal investigators. In total, 755 patients were accrued to these studies during the period that PRECISE was used. PRECISE successfully identified 327 patients (43%) before enrollment. Patients were diagnosed with 29 tumor types and harbored alterations in 43 oncogenes, most commonly ERBB2 (21.3%), PIK3CA (14.1%), and BRAF (8.7%). Median time from sequencing to enrollment was 163 days (interquartile range, 66 to 357 days), and from PRECISE identification to enrollment 87 days (interquartile range, 37 to 180 days). Common reasons for failing to identify patients before enrollment included accrual on the basis of molecular alterations that did not match pre-established PRECISE genomic eligibility (140 [33%] of 428) and external sequencing not available for parsing (127 [30%] of 428). CONCLUSION: PRECISE identified 43% of all patients accrued to a diverse cohort of early-phase, genome-matched studies. Purpose-built informatics platforms represent a novel and potentially effective method for matching patients to molecularly selected studies.

Toxicity Attribution in Phase I Trials: Evaluating the Effect of Dose on the Frequency of Related and Unrelated Toxicities.

PURPOSE: Phase I studies rely on investigators to accurately attribute adverse events as related or unrelated to study drug. This information is ultimately used to help establish a safe dose. Attribution in the phase I setting has not been widely studied and assessing the accuracy of attribution is complicated by the lack of a gold standard. We examined dose-toxicity relationships as a function of attribution and toxicity category to evaluate for evidence of toxicity misattribution. EXPERIMENTAL DESIGN: Individual patient records from 38 phase I studies activated between 2000 and 2010 were used. Dose was defined as a percentage of maximum dose administered on each study. Relationships between dose and patient-level toxicity were explored graphically and with logistic regression. All P values were two-sided. RESULTS: 11,909 toxicities from 1,156 patients were analyzed. Unrelated toxicity was not associated with dose (P = 0.0920 for grade ≥ 3, P = 0.4194 for grade ≥ 1), whereas related toxicity increased with dose (P < 0.0001, both grade ≥ 3 and ≥ 1). Similar results were observed across toxicity categories. In the five-tier system, toxicities attributed as "possibly," "probably," or "definitely" related were associated with dose (all P < 0.0001), whereas toxicities attributed as "unlikely" or "unrelated" were not (all P > 0.1). CONCLUSIONS: Reassuringly, we did not observe an association between unrelated toxicity rate and dose, an association that could only have been explained by physician misattribution. Our findings also confirmed our expectation that related toxicity rate increases with dose. Our analysis supports simplifying attribution to a two-tier system by collapsing "possibly," "probably," and "definitely" related.

Predictors of early treatment discontinuation in patients enrolled on Phase I oncology trials.

PURPOSE: Patients who do not complete one cycle of therapy on Phase I trials for reasons other than dose limiting toxicity (DLT) are considered inevaluable for toxicity and must be replaced. METHODS: Individual records from patients enrolled to NCI-sponsored Phase I trials activated between 2000 and 2010 were used. Early discontinuation was defined as the failure to begin cycle 2 for reasons other than a DLT during cycle 1. A multinomial logistic regression with a 3-level nominal outcome (early discontinuation, DLT during cycle 1, and continuation to cycl1e 2) was used with continuation to cycle 2 serving as the reference category. The final model was used to create two risk scores. An independent external cohort was used to validate these models. RESULTS: Data from 3079 patients on 127 Phase I trials were analyzed. ECOG performance status (1, ≥ 2, two-sided P = .0315 and P = .0007), creatinine clearance (<60 ml/min, P = .0455), alkaline phosphatase (>2.5xULN, P = .0026), AST (>ULN, P = .0076), hemoglobin (<10 g/dL, P < .0001), albumin (<3.5 g/dL, P < .0001), and platelets (<400x109/L, P = .0732) were predictors of early discontinuation. The c-index of the final model was 0.63. CONCLUSION: Knowledge of risk factors for early treatment discontinuation in conjunction with clinical judgment can help guide Phase I patient selection.

Nomogram to predict cycle-one serious drug-related toxicity in phase I oncology trials.

PURPOSE: All patients in phase I trials do not have equivalent susceptibility to serious drug-related toxicity (SDRT). Our goal was to develop a nomogram to predict the risk of cycle-one SDRT to better select appropriate patients for phase I trials. PATIENTS AND METHODS: The prospectively maintained database of patients with solid tumor enrolled onto Cancer Therapeutics Evaluation Program-sponsored phase I trials activated between 2000 and 2010 was used. SDRT was defined as a grade ≥ 4 hematologic or grade ≥ 3 nonhematologic toxicity attributed, at least possibly, to study drug(s). Logistic regression was used to test the association of candidate factors to cycle-one SDRT. A final model, or nomogram, was chosen based on both clinical and statistical significance and validated internally using a bootstrapping technique and externally in an independent data set. RESULTS: Data from 3,104 patients enrolled onto 127 trials were analyzed to build the nomogram. In a model with multiple covariates, Eastern Cooperative Oncology Group performance status, WBC count, creatinine clearance, albumin, AST, number of study drugs, biologic study drug (yes v no), and dose (relative to maximum administered) were significant predictors of cycle-one SDRT. All significant factors except dose were included in the final nomogram. The model was validated both internally (bootstrap-adjusted concordance index, 0.60) and externally (concordance index, 0.64). CONCLUSION: This nomogram can be used to accurately predict a patient's risk for SDRT at the time of enrollment. Excluding patients at high risk for SDRT should improve the safety and efficiency of phase I trials.