Citations

LBA110 Background: There are limited data on COVID-19 in patients with cancer. We characterize the outcomes of patients with cancer and COVID-19 and identify potential prognostic factors. Methods: The COVID-19 and Cancer Consortium (CCC19) cohort study includes patients with active or prior hematologic or invasive solid malignancies reported across academic and community sites. Results: We included 1,018 cases accrued March-April 2020. Median age was 66 years (range, 18-90). Breast (20%) and prostate (16%) cancers were most prevalent; 43% of patients were on active anti-cancer treatment. At time of data analysis, 106 patients (10.4%) have died and 26% met the composite outcome of death, severe illness requiring hospitalization, and/or mechanical ventilation. In multivariable logistic regression analysis, independent factors associated with increased 30-day mortality were age, male sex, former smoking, ECOG performance status (2 versus 0/1: partially adjusted odds ratio (pAOR) 2.74, 95% CI 1.31-5.7; 3/4 versus 0/1: pAOR 5.34, 95% CI 2.44-11.69), active malignancy (stable/responding, pAOR 1.93, 95% CI 1.06-3.5; progressing, pAOR 3.79, 95% CI 1.78-8.08), and receipt of azithromycin and hydroxychloroquine. Tumor type, race/ethnicity, obesity, number of comorbidities, recent surgery, and type of active cancer therapy were not significant factors for mortality. Conclusions: All-cause 30-day mortality and severe illness in this cohort were significantly higher than previously reported for the general population and were associated with general risk factors as well as those unique to patients with cancer. Cancer type and treatment were not independently associated with increased 30-day mortality. Longer follow-up is needed to better understand the impact of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.

PURPOSE NCI-MATCH is a nationwide, histology-agnostic, signal-finding, molecular profile–driven trial for patients with refractory cancers, lymphomas, or myelomas. Patients with tumors harboring actionable aberration(s) in fibroblast growth factor receptor ( FGFR) 1-3 were treated with AZD4547, an oral FGFR1-3 inhibitor. METHODS Patients’ tumors were screened by next-generation sequencing for predefined FGFR amplification, activating mutations, or fusions. Patients were treated with AZD4547, 80 mg orally twice daily until progression of disease or drug intolerance. A response rate of 16% was considered promising. RESULTS Between July 2016 and June 2017, 70 patients were assigned and 48 received protocol therapy and are eligible for analysis. Patients’ tumors harbored FGFR1 or FGFR2 amplification (n = 20), FGFR2 or FGFR3 single-nucleotide variants (n = 19), or FGFR1 or FGFR3 fusions (n = 9). The most common primary tumors were breast (33.3%), urothelial (12.5%), and cervical cancer (10.4%).Grade 3 adverse events were consistent with those described in previous clinical trials. Confirmed partial responses were seen in 8% (90% CI, 3% to 18%) and were observed only in patients whose tumors harbored FGFR1-3 point mutations or fusions. Stable disease was observed in 37.5% (90% CI, 25.8% to 50.4%). The median progression-free survival (PFS) was 3.4 months, and the 6-month PFS rate was 15% (90% CI, 8% to 31%). For patients with tumors harboring FGFR fusions, the response rate was 22% (90% CI, 4.1% to 55%), and 6-month PFS rate was 56% (90% CI, 31% to 100%). CONCLUSION Preliminary signals of activity appeared to be limited to cancers harboring FGFR activating mutations and fusions, although AZD4547 did not meet the primary end point. Different FGFR somatic alterations may confer different levels of signaling potency and/or oncogene dependence.

PURPOSE Electronic health records (EHRs) are created primarily for nonresearch purposes; thus, the amounts of data are enormous, and the data are crude, heterogeneous, incomplete, and largely unstructured, presenting challenges to effective analyses for timely, reliable results. Particularly, research dealing with clinical notes relevant to patient care and outcome is seldom conducted, due to the complexity of data extraction and accurate annotation in the past. RECIST is a set of widely accepted research criteria to evaluate tumor response in patients undergoing antineoplastic therapy. The aim for this study was to identify textual sources for RECIST information in EHRs and to develop a corpus of pharmacotherapy and response entities for development of natural language processing tools. METHODS We focused on pharmacotherapies and patient responses, using 55,120 medical notes (n = 72 types) in Mayo Clinic’s EHRs from 622 randomly selected patients who signed authorization for research. Using the Multidocument Annotation Environment tool, we applied and evaluated predefined keywords, and time interval and note-type filters for identifying RECIST information and established a gold standard data set for patient outcome research. RESULTS Key words reduced clinical notes to 37,406, and using four note types within 12 months postdiagnosis further reduced the number of notes to 5,005 that were manually annotated, which covered 97.9% of all cases (n = 609 of 622). The resulting data set of 609 cases (n = 503 for training and n = 106 for validation purpose), contains 736 fully annotated, deidentified clinical notes, with pharmacotherapies and four response end points: complete response, partial response, stable disease, and progressive disease. This resource is readily expandable to specific drugs, regimens, and most solid tumors. CONCLUSION We have established a gold standard data set to accommodate development of biomedical informatics tools in accelerating research into antineoplastic therapeutic response.

Abstract Background The proportion of tumors of various histologies that may respond to drugs targeted to molecular alterations is unknown. NCI-MATCH, a collaboration between ECOG-ACRIN Cancer Research Group and the National Cancer Institute, was initiated to find efficacy signals by matching patients with refractory malignancies to treatment targeted to potential tumor molecular drivers regardless of cancer histology. Methods Trial development required assumptions about molecular target prevalence, accrual rates, treatment eligibility, and enrollment rates as well as consideration of logistical requirements. Central tumor profiling was performed with an investigational next-generation DNA–targeted sequencing assay of alterations in 143 genes, and protein expression of protein expression of phosphatase and tensin homolog, mutL homolog 1, mutS homolog 2, and RB transcriptional corepressor 1. Treatments were allocated with a validated computational platform (MATCHBOX). A preplanned interim analysis evaluated assumptions and feasibility in this novel trial. Results At interim analysis, accrual was robust, tumor biopsies were safe (<1% severe events), and profiling success was 87.3%. Actionable molecular alteration frequency met expectations, but assignment and enrollment lagged due to histology exclusions and mismatch of resources to demand. To address this lag, we revised estimates of mutation frequencies, increased screening sample size, added treatments, and improved assay throughput and efficiency (93.9% completion and 14-day turnaround). Conclusions The experiences in the design and implementation of the NCI-MATCH trial suggest that profiling from fresh tumor biopsies and assigning treatment can be performed efficiently in a large national network trial. The success of such trials necessitates a broad screening approach and many treatment options easily accessible to patients.

Abstract Oncology has undergone major changes in systemic treatment options, from chemotherapy to antiangiogenic agents to tyrosine-kinase inhibitors to immunotherapy. Historically, a therapeutic agent is tested in separate trials as monotherapy or combined with investigational or approved agents/modalities, to determine antitumor activity in each histology based on the premise that each histology responds differently to the same treatment. This paradigm is time consuming and may or may not make use of molecular characterization to test each agent in each histology. However, with the expansion of biologic understanding and development of various biomarkers, e.g., estrogen receptor and Her 2 amplification, we move beyond the basic paradigm of general histology to one in which treatment is based upon molecular characteristics of the tumor. Examples of recent discoveries from this more modern paradigm are (1) multiple histologies sharing a common molecular profile and (2) a subset within a single histology having a given molecular characteristic. Various agents have been tested singularly in terms of molecular aberrations and histology, e.g., ALK inhibitors and NSCLC. However, with the rapid increase in the number of targeted agents in development, more facile and efficient clinical trial designs are needed. The National Cancer Institute (NCI) and ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) collaborated in designing the Molecular Analysis for Therapy Choice trial (NCI-MATCH or EAY131), the first large-scale signal-finding precision medicine oncology trial in the United States to incorporate centralized NGS testing to direct patients to parallel (nonrandomized) phase II treatment arms under a master protocol. The trial is being conducted by the NCI National Clinical Trials Network (NCTN), with ECOG-ACRIN leading the trial. More than 100 investigators from across the NCTN adult cancer-oriented member groups (Alliance for Clinical Trials in Oncology, ECOG-ACRIN, NRG Oncology, and SWOG) have worked collaboratively to design and lead what is currently 30 treatment arms (subprotocols), with more arms in development. Wide-scale NCTN investigator involvement ensures use of the latest knowledge to define the actionable mutations required for eligibility and to make evidence-based selections of experimental agents. In NCI-MATCH, patients are assigned treatment based on the genomic alterations found in their tumors through genomic sequencing and other tests at the time of initial enrollment for screening. Unlike other phase II trials, each arm is open to advanced solid tumors, lymphomas, or myeloma that share a set of molecular aberrations, not restricted to a single histology. This strategy accommodates and encourages enrollment of rare tumors for which there are often no standard treatments and limited clinical trial options. NCI-MATCH has 10-30 concurrent treatment arms available to patients at any given time, testing both investigational agents and FDA-approved drugs for new indications. Most treatment arms have an enrollment goal of 35 patients, with some arms that address tumor gene variants of higher prevalence expanded to 70 patients. Together, the NCTN and NCI Community Oncology Research Program provide infrastructure for many clinical sites (>1100) to participate, providing ready access to physicians and patients; screening enrollment has occurred in all 50 states in the U.S, the District of Columbia, and Puerto Rico. A specific assay was developed for the trial (the MATCH assay), and a laboratory network was organized to rapidly process the tissue and efficiently interrogate for mutations. NCI and ECOG-ACRIN statisticians developed the analysis plan and are currently maintaining data that are closely monitored by safety experts for any adverse event signals. Lastly, a bioinformatics system (MATCHbox) was developed to coordinate molecular data collection and support rule-based decision-making based on those data. Because genomics in oncology is rapidly expanding and evolving, NCI-MATCH required flexibility to accommodate brisker screening accrual than anticipated, and adaptation to constantly emerging information about new drugs and new molecular alterations. The goal to sequence the tumors of 6,000 patients with NCI funding was achieved two years ahead of schedule, but that cohort was not sufficient to fill all the arms—in particular, those aimed at the most rarely occurring tumor gene aberrations. Currently, the goal for the trial is to complete the open treatment arms by allowing for designated commercial and academic laboratories to notify ordering physicians when genomic tests they ordered to guide clinical care indicate a potential eligibility to a NCI-MATCH arm with a rare variant and allow for enrollment to the relevant treatment subprotocol if the patient meets all subprotocol eligibility criteria. The tissue confirmation of the molecular abnormality on the MATCH assay by the central laboratories will be accomplished after subprotocol enrollment so that patient treatment will not be delayed for confirmation. A demonstration project is now under way to test the applicability of this approach and potentially develop common standards for future use. With tumor sequencing becoming a more and more common practice in oncology, this may allow patients with mutations of low prevalence to have investigational treatment options. NCI-MATCH is expanding its panel and definition of actionable mutations to allow greater flexibility to incorporate in real time new mutations supported with adequate levels of evidence. The valuable genomic and clinical outcome data collected in the trial will permit evaluation of efficacy of targeting certain actionable mutations with a specific agent. In addition, biospecimens are being collected to build a rich resource for conducting auxiliary biology studies that may answer questions about prevalence of mutations in the metastatic setting, prevalence of mutations in rare histologies, and resistance mechanisms of various targeted therapy. At this meeting, two abstracts will provide an overall trial update and present information about the prevalence of mismatch repair deficiency (dMMR) in the centrally screened population. Citation Format: Alice P. Chen, Peter J. O'Dwyer, Lyndsay Harris, Barbara A. Conley, Stanley R. Hamilton, Mickey Williams, Robert J. Gray, Shuli Li, Lisa M. McShane, Lawrence V. Rubinstein, Susanna I. Lee, Shaji Kumar, Edith P. Mitchell, James A. Zwiebel, Constantine A. Gatsonis, Lalitha K. Shankar, Paolo F. Caimi, Carlos L. Arteaga, A John Iafrate, Jeffrey Sklar, Richard F. Little, Keith T. Flaherty. NCI-MATCH: A new paradigm in the era of genomic oncology [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr PL03-01.

SummaryWeight loss is an effective intervention for diminishing disease burden in obese older adults. Pharmacological interventions that reduce food intake and thereby promote weight loss may offer effective strategies to reduce age‐related disease. We previously reported that 17α‐estradiol (17α‐E2) administration elicits beneficial effects on metabolism and inflammation in old male mice. These observations were associated with reduced calorie intake. Here, we demonstrate that 17α‐E2 acts through pro‐opiomelanocortin (Pomc) expression in the arcuate nucleus (ARC) to reduce food intake and body mass in mouse models of obesity. These results confirm that 17α‐E2 modulates appetite through selective interactions within hypothalamic anorexigenic pathways. Interestingly, some peripheral markers of metabolic homeostasis were also improved in animals with near complete loss of ARC Pomc transcription. This suggests that 17α‐E2 might have central and peripheral actions that can beneficially affect metabolism cooperatively or independently.

OBJECTIVE Intensive treatment (INT) of type 1 diabetes reduces the incidence of cardiovascular disease (CVD) events compared with conventional treatment (CONV), but it also results in more weight gain. Our objective was to examine whether excessive weight gain from INT of type 1 diabetes is independently associated with subsequent CVD events. RESEARCH DESIGN AND METHODS Quartiles (Q) of weight gain in 1,213 participants aged 18 years and older at enrollment in the Diabetes Control and Complications Trial (DCCT) were determined within randomized treatment groups (INT vs. CONV) using change in BMI from baseline to the closeout DCCT visits. Effects of this weight gain on CVD risk factors and outcomes during an additional 20 years of observational follow-up were then determined. RESULTS The Q4 INT group experienced greater proportional weight gain (median change in BMI, 6.08 kg/m2), increases in CVD risk factors, and need for medications for hypertension and lipids compared with the Q1–3 INT and comparable CONV groups. Over a mean of 26 years of follow-up, the numbers of major and total CVD events were not statistically different in Q4 compared with Q1–3 of either the INT or CONV group. By year 14, however, the incident CVD event curve became significantly higher in the Q4 INT group than in the Q1–3 INT groups (P = 0.024) and was similar to that for the CONV group. CONCLUSIONS For the first 13 years after DCCT, INT for type 1 diabetes reduced macrovascular events compared with CONV, even when excessive weight gain occurred. After this, total CVD events significantly increased in the Q4 INT group, becoming equivalent to those in the CONV group. Longer follow-up is needed to determine whether this trend continues and results in more major CVD events.

This article provides nomenclature recommendations developed by an international workgroup to increase transparency and standardization of pharmacogenetic (PGx) result reporting. Presently, sequence variants identified by PGx tests are described using different nomenclature systems. In addition, PGx analysis may detect different sets of variants for each gene, which can affect interpretation of results. This practice has caused confusion and may thereby impede the adoption of clinical PGx testing. Standardization is critical to move PGx forward.