Identifying Oncology Clinical Trial Candidates Using Artificial Intelligence Predictions of Treatment Change: A Pilot Implementation Study.

PURPOSE: Precision oncology clinical trials often struggle to accrue, partly because it is difficult to find potentially eligible patients at moments when they need new treatment. We piloted deployment of artificial intelligence tools to identify such patients at a large academic cancer center. PATIENTS AND METHODS: Neural networks that process radiology reports to identify patients likely to start new systemic therapy were applied prospectively for patients with solid tumors that had undergone next-generation sequencing at our center. Model output was linked to the MatchMiner tool, which matches patients to trials using tumor genomics. Reports listing genomically matched patients, sorted by probability of treatment change, were provided weekly to an oncology nurse navigator (ONN) coordinating recruitment to nine early-phase trials. The ONN contacted treating oncologists when patients likely to change treatment appeared potentially trial-eligible. RESULTS: Within weekly reports to the ONN, 60,199 patient-trial matches were generated for 2,150 patients on the basis of genomics alone. Of these, 3,168 patient-trial matches (5%) corresponding to 525 patients were flagged for ONN review by our model, representing a 95% reduction in review compared with manual review of all patient-trial matches weekly. After ONN review for potential eligibility, treating oncologists for 74 patients were contacted. Common reasons for not contacting treating oncologists included cases where patients had already decided to continue current treatment (21%); the trial had no slots (14%); or the patient was ineligible on ONN review (12%). Of 74 patients whose oncologists were contacted, 10 (14%) had a consult regarding a trial and five (7%) enrolled. CONCLUSION: This approach facilitated identification of potential patients for clinical trials in real time, but further work to improve accrual must address the many other barriers to trial enrollment in precision oncology research.

Uterine Inflammatory Myofibroblastic Tumors: Proposed Risk Stratification Model Using Integrated Clinicopathologic and Molecular Analysis.

Inflammatory myofibroblastic tumor (IMT) of the uterus is a rare mesenchymal tumor with largely benign behavior; however, a small subset demonstrate aggressive behavior. While clinicopathologic features have been previously associated with aggressive behavior, these reports are based on small series, and these features are imperfect predictors of clinical behavior. IMTs are most commonly driven by ALK fusions, with additional pathogenic molecular alterations being reported only in rare examples of extrauterine IMTs. In this study, a series of 11 uterine IMTs, 5 of which demonstrated aggressive behavior, were evaluated for clinicopathologic variables and additionally subjected to capture-based next-generation sequencing with or without whole-transcriptome RNA sequencing. In the 6 IMTs without aggressive behavior, ALK fusions were the sole pathogenic alteration. In contrast, all 5 aggressive IMTs harbored pathogenic molecular alterations and numerous copy number changes in addition to ALK fusions, with the majority of the additional alterations present in the primary tumors. We combined our series with cases previously reported in the literature and performed statistical analyses to propose a novel clinicopathologic risk stratification score assigning 1 point each for: age above 45 years, size≥5 cm,≥4 mitotic figures per 10 high-power field, and infiltrative borders. No tumors with 0 points had an aggressive outcome, while 21% of tumors with 1 to 2 points and all tumors with ≥3 points had aggressive outcomes. We propose a 2-step classification model that first uses the clinicopathologic risk stratification score to identify low-risk and high-risk tumors, and recommend molecular testing to further classify intermediate-risk tumors.

AACR Project GENIE: 100,000 Cases and Beyond.

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain. Here, we demonstrate the use of these real-world data, harmonized through a centralized data resource, to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharmaceutical collaborations and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledge base of increasing impact to inform clinical decision-making and bring together cancer researchers internationally. SIGNIFICANCE: AACR Project GENIE has now accrued data from >110,000 tumors, placing it among the largest repository of publicly available, clinically annotated genomic data in the world. GENIE has emerged as a powerful resource to evaluate genome-guided clinical trial design, uncover drivers of cancer subtypes, and inform real-world use of genomic data. This article is highlighted in the In This Issue feature, p. 2007.

Plasma copper and zinc concentration in individuals with autism correlate with selected symptom severity.

AIM: To assess plasma zinc and copper concentration in individuals with autism and correlate these levels with symptom severity. SUBJECTS AND METHODS: Plasma from 102 autistic individuals, and 18 neurotypical controls, were tested for plasma zinc and copper using inductively-coupled plasma-mass spectrometry. Copper and zinc levels and Cu/Zn were analyzed for possible correlation with severity of 19 symptoms. RESULTS: Autistic individuals had elevated plasma levels of copper and Cu/Zn and lower, but not significantly lower, plasma Zn compared to neurotypical controls. There was a correlation between Cu/Zn and expressive language, receptive language, focus attention, hyperactivity, fine motor skills, gross motor skills and Tip Toeing. There was a negative correlation between plasma zinc concentration and hyperactivity, and fine motor skills severity. DISCUSSION: These results suggest an association between plasma Cu/Zn and severity of symptoms associated with autism.

Psychosocial variables in children and teens of extended families identified through bipolar affective disorder probands.

OBJECTIVES: This multi-site study investigated the frequency of risk-related variables for developing an affective disorder using a within-pedigree control group. We wished to determine the effect of life events, social relationships, self-perceived competence, and aspects of home environment for youngsters from extended families with loading for bipolar disorder. Using a within-family contrast group, we address the following two issues: (1) Do offspring or their parents from families who do and do not have an affected parent report differences (i) in home environment? (ii) in frequency and type of offspring life events? and (iii) in social relations and self-perception? and (2) Do children or their parents who do or do not have an affective disorder report differently on these areas? METHODS: Juvenile offspring (n = 50) and their parents from 14 bipolar pedigrees were assessed. Structured interviews and self- or parent-reported instruments were used to compare offspring with an affected first-degree relative to those without and to compare offspring with or without an affective disorder. RESULTS: Only one significant psychosocial difference was found between offspring with or without a parent with an affective disorder but several were found between offspring who themselves did or did not have an affective disorder. These differences are in the areas of the need for discipline, social support, and dependent negative life events. CONCLUSIONS: The findings identify potential early psychosocial markers for affective disorder in high risk offspring.