Background In the U.S., lung cancer accounts for 14% of cancer diagnoses and 28% of cancer deaths annually. Since no cure exists for advanced lung cancer, the main treatment goal is to prolong survival. Chemotherapy regimens produce side effects with different profiles. Coupling this with individual patient’s preferred side effects could result in patient-centered choices leading to better treatment outcomes. There are apparently no previous studies of or tools for assessing and utilizing patient chemotherapy preferences in clinical settings. The long-term goal of the study was to facilitate patients’ treatment choices for advanced-stage lung cancer. A primary aim was to determine how preferences for chemotherapy side effects relate to chemotherapy choices. Methods An observational, longitudinal, open cohort study of patients with advanced-stage non-small cell lung cancer (NSCLC) was conducted. Data sources included patient medical records and from one to three interviews per subject. Data were analyzed using Chi-square, Fisher’s Exact and McNamara’s test, and logistic regression. Results Patients identified the top three chemotherapy side effects that they would most like to avoid: shortness of breath, bleeding, and fatigue. These side effects were similar between first and last interviews, although the rank order changed after patients experienced chemotherapy. Conclusions Patients ranked drug side effects that they would most like to avoid. Patient-centered clinical care and patient-centered outcomes research are feasible and may be enhanced by stakeholder commitment. The study results are limited to patients with advanced NSCLC. Most of the subjects were White, since patients were drawn from the U.S. Midwest, a predominantly White population.

PURPOSE: In the United States, lung cancer accounts for 14% of cancer diagnoses and 28% of cancer deaths annually. Because no cure exists for advanced lung cancer, the primary treatment goal is to prolong survival. OBJECTIVES: The study aim was to determine whether individual preferences, characteristics, and treatment experiences affect the meaning of treatment success. MATERIALS AND METHODS: A quantitative study using an observational, longitudinal cohort of patients with advanced stage non–small-cell lung cancer was conducted. Data sources included medical records and patient interviews. Data were analyzed using χ2, Fisher’s exact, and McNemar’s tests, as well as logistic regressions. RESULTS: At the first interview of 235 individuals, 12% considered survival alone as their definition of treatment success; others defined treatment success as survival plus other aspects, such as quality of life and reaching an important personal goal. As they moved through chemotherapy, 47% of the patients changed their definition of treatment success. Bivariate analysis showed that patients with lower incomes tended to be more likely to change their definition of treatment success compared with their counterparts with higher income ( P = .0245). CONCLUSION: By taking chemotherapy, patients expect to increase their odds of survival and want to maintain the quality of life and functionality. A patient’s definition of treatment success is often changing as treatment continues, making it appropriate to ensure patient-provider communication throughout their clinical care. The study results are limited to patients with advanced non–small-cell lung cancer and drawn from a predominantly white patient population, mainly from the US Midwest.

Background: COX-2 inhibitors, such as celecoxib, and ubiquitin-proteasome pathway inhibitors, such as bortezomib, can down-regulate NF-κB, a transcription factor implicated in tumor growth. The objective of this study was to determine the maximum tolerated dose and dose-limiting toxicities of bortezomib in combination with celecoxib in patients with advanced solid tumors.

1539 Background: Clinical trial eligibility increasingly requires information found in NGS tests; lack of structured NGS results hinders the automation of trial matching for this criterion, which may be a deterrent to open biomarker-driven trials in certain sites. We developed a machine learning tool that infers the presence of NGS results in the EHR, facilitating clinical trial matching. Methods: The Flatiron Health EHR-derived database contains patient-level pathology and genetic counseling reports from community oncology practices. An internal team of clinical experts reviewed a random sample of patients across this network to generate labels of whether each patient had been NGS tested. A supervised ML model was trained by scanning documents in the EHR and extracting n-gram features from text snippets surrounding relevant keywords (i.e. 'Lung biomarker', 'Biomarker negative'). Through k-fold cross-validation and l2-regularization, we found that a logistic regression was able to classify patients' NGS testing status. The model's offline performance on a 20% hold-out test set was measured with standard classification metrics: sensitivity, specificity, positive predictive value (PPV) and NPV. In an online setting, we integrated the tool into Flatiron's clinical trial matching software OncoTrials by including in each patient's profile an indicator of "likely NGS tested" or "unlikely NGS tested" based on the classifier's prediction. For patients inferred as tested, the model linked users to a test report view in the EHR. In this online setting, we measured sensitivity and specificity of the model after user review in two community oncology practices. Results: This NGS testing status inference model was characterized using a test sample of 15,175 patients. The model sensitivity and specificity (95%CI) were 91.3% (90.2, 92.3) and 96.2% (95.8, 96.5), respectively; PPV was 84.5% (83.2, 85.8) and NPV was 98.0% (97.7, 98.2). In the validation sample (N = 200 originated from 2 distinct care sites), users identified NGS testing status with a sensitivity of 95.2% (88.3%, 98.7%). Conclusions: This machine learning model facilitates the screening for potential patient enrollment in biomarker-driven trials by automatically surfacing patients with NGS test results at high sensitivity and specificity into a trial matching application to identify candidates. This tool could mitigate a key barrier for participation in biomarker-driven trials for community clinics.