Search

We retrospectively analyzed non–small cell lung cancer (NSCLC) patients from a single center treated with pembrolizumab on the KEYNOTE-001 trial and evaluated the association between treatment-related adverse events (trAEs) and clinical outcomes. Investigators reported AEs on trial and graded them according to Common Terminology Criteria for Adverse Events v4.0, labeling them as unlikely, possibly, or probably treatment-related. AEs labeled as possibly/probably related were considered trAEs for this analysis. The relationship between the incidence of a trAE and clinical outcomes was evaluated. Ninety-seven NSCLC patients treated on KEYNOTE-001 at the University of California, Los Angeles were evaluated. Ten percent (85/826) of AEs were trAEs, occurring in 40% (39/97) of patients. The most frequent trAEs were rash (21% patients), fatigue (6% patients), and hypothyroidism (6% patients). The 39 patients that experienced a trAE had increased objective response rate (ORR, 38.5%), progression-free survival (PFS: median, 248 days), and overall survival (OS: median, 493 days), compared with the 58 patients that did not (ORR: 8.9%, PFS: median 60 days, OS: median 144.5 days). The observed association between trAEs and improved clinical outcome persisted when using Cox proportional hazards regression models to assess the confounding effect of covariates and mitigate guarantee-time bias. The association also remained when data were substratified by grade, degree of association, and treatment-related select AE designation. This single-center analysis revealed that trAEs predicted for improved clinical outcome with pembrolizumab, and when controlling for guarantee-time bias and plausible confounders, this association remained. This observed relationship adds to our understanding of anti–PD-1 therapy and could aid clinicians in identifying patients most likely to benefit from therapy. Cancer Immunol Res; 6(3); 288–94. ©2018 AACR.

Table S2: Demographics of the 97 NSCLC patients treated at UCLA on KEYNOTE-001 (N=97) compared with the demographics of the total KEYNOTE-001 study population (n=495). Of note, the UCLA cohort of 97 patients is included in the total KEYNOTE-001 study population of 495 patients.

Supplementary Figure S1A-B. ORR, PFS, and OS analysis for UCLA NSCLC cohort of KEYNOTE-001 trial. ORR, PFS, and OS for all 97 patients in the UCLA cohort of the KEYNOTE-001 trial stratified by the occurrence of a (A) treatment related adverse event (trAE) or (B) a treatment related select AE.

Lung cancer is a heterogeneous disease, and the availability of comprehensive genomic profiling has allowed for the characterization of its molecular subtypes. This has increased the ability to deliver “personalized medicines” by tailoring therapies to target driver mutations in a patient's cancer. The development of targeted therapies for non-small cell lung cancer (NSCLC) has helped define the era of precision medicine throughout oncology. This article aims to contextualize recent research and provide an updated summary of targeted therapies available for patients with NSCLC. With practitioners and clinical researchers in mind, we note standard of care therapies, important approvals, practice guidelines, and treatments in development. The first section discusses mutations in the epidermal growth factor receptor (EGFR) gene, and the second section examines rearrangements in the anaplastic lymphoma kinase (ALK) and ROS1 fusions. Finally, we explore the rarer molecular alterations in BRAF, RET, MET, HER2, and KRAS. Given the many available therapies, it is important to understand the molecular alterations in NSCLC, and how to target them.

Purpose Health care research increasingly relies on assessment of data extracted from electronic medical records (EMRs). Clinical trial adverse event (AE) logs and patient-reported outcomes (PROs) are sources of data often available in the context of specific research projects. The aim of this study was to evaluate the extent of data concordance from these sources. Patients and Methods Patients enrolled in clinical trials or receiving standard treatment for lung cancer (n = 62) completed validated questionnaires on physical and psychological symptoms at up to three assessment points. Temporally matched documentation was extracted from EMR notes and, for clinical trial participants (n = 41), AE logs. Evaluated data included symptom assessment, vital signs, medication logs, and laboratory values. Agreement (positive, negative) and Cohen’s κ coefficients were calculated to assess concordance of symptoms among sources, with PROs considered the gold standard. Results Patient-reported weight loss correlated significantly with clinical measurements ( t = 2.90; P = .02), and average number of PROs correlated negatively with albumin concentration, supporting PROs as the gold standard. Comparisons of PROs versus EMR yielded poor concordance across 11 physical symptoms, anxiety, and depressive symptoms (all κ < 0.40). Providers under-reported the presence of each symptom in the EMR compared with PROs. AE logs showed similarly poor concordance with PROs (all κ < 0.40, except shortness of breath). Negative agreement among sources was higher than positive agreement for all symptoms except pain. Conclusion There was poor concordance between EMR notes and AE logs with PROs. Findings suggest that EMR notes and AE logs may not be reliable sources for capturing physical and psychological symptoms experienced by patients with lung cancer, supporting use of PRO assessments in oncology practices.

BACKGROUND: Despite the significant antitumor activity of pembrolizumab in non-small cell lung cancer (NSCLC), clinical benefit has been less frequently observed in patients whose tumors harbor epidermal growth factor receptor (EGFR) mutations compared to EGFR wild-type patients. Our single center experience on the KEYNOTE-001 trial suggested that pembrolizumab-treated EGFR-mutant patients, who were tyrosine kinase inhibitor (TKI) naïve, had superior clinical outcomes to those previously treated with a TKI. As TKI naïve EGFR-mutants have generally been excluded from pembrolizumab studies, data to guide treatment decisions in this patient population is lacking, particularly in patients with PD-L1 expression ≥50%. METHODS: We conducted a phase II trial (NCT02879994) of pembrolizumab in TKI naive patients with EGFR mutation positive, advanced NSCLC and PD-L1 positive (≥1%, 22C3 antibody) tumors. Pembrolizumab was administered 200mg q3wks. The primary endpoint was objective response rate. Secondary endpoints included safety of pembrolizumab, additional pembrolizumab efficacy endpoints, and efficacy and safety of an EGFR TKI after pembrolizumab. RESULTS: Enrollment was ceased due to lack of efficacy after 11 of 25 planned patients were treated. 82% of trial patients were treatment naïve, 64% had sensitizing EGFR mutations, and 73% had PD-L1 expression ≥50%. Only 1 patient had an objective response (ORR: 9%), but repeat analysis of this patient’s tumor definitively showed the original report of an EGFR mutation to be erroneous. Observed treatment related adverse events were similar to prior experience with pembrolizumab, but two deaths within 6 months of enrollment, including one attributed to pneumonitis, were of concern. CONCLUSIONS: Pembrolizumab’s lack of efficacy in TKI naïve, PD-L1+, EGFR-mutant patients with advanced NSCLC, including those with PD-L1 expression ≥50%, suggests that it is not an appropriate therapeutic choice in this setting.

We retrospectively analyzed non–small cell lung cancer (NSCLC) patients from a single center treated with pembrolizumab on the KEYNOTE-001 trial and evaluated the association between treatment-related adverse events (trAEs) and clinical outcomes. Investigators reported AEs on trial and graded them according to Common Terminology Criteria for Adverse Events v4.0, labeling them as unlikely, possibly, or probably treatment-related. AEs labeled as possibly/probably related were considered trAEs for this analysis. The relationship between the incidence of a trAE and clinical outcomes was evaluated. Ninety-seven NSCLC patients treated on KEYNOTE-001 at the University of California, Los Angeles were evaluated. Ten percent (85/826) of AEs were trAEs, occurring in 40% (39/97) of patients. The most frequent trAEs were rash (21% patients), fatigue (6% patients), and hypothyroidism (6% patients). The 39 patients that experienced a trAE had increased objective response rate (ORR, 38.5%), progression-free survival (PFS: median, 248 days), and overall survival (OS: median, 493 days), compared with the 58 patients that did not (ORR: 8.9%, PFS: median 60 days, OS: median 144.5 days). The observed association between trAEs and improved clinical outcome persisted when using Cox proportional hazards regression models to assess the confounding effect of covariates and mitigate guarantee-time bias. The association also remained when data were substratified by grade, degree of association, and treatment-related select AE designation. This single-center analysis revealed that trAEs predicted for improved clinical outcome with pembrolizumab, and when controlling for guarantee-time bias and plausible confounders, this association remained. This observed relationship adds to our understanding of anti–PD-1 therapy and could aid clinicians in identifying patients most likely to benefit from therapy. Cancer Immunol Res; 6(3); 288–94. ©2018 AACR.

Objective It is crucial for clinicians to stay up to date on current literature in order to apply recent evidence to clinical decision making. Automatic summarization systems can help clinicians quickly view an aggregated summary of literature on a topic. Casama, a representation and summarization system based on “contextualized semantic maps,” captures the findings of biomedical studies as well as the contexts associated with patient population and study design. This paper presents a user-oriented evaluation of Casama in comparison to a context-free representation, SemRep. Materials and methods The effectiveness of the representation was evaluated by presenting users with manually annotated Casama and SemRep summaries of ten articles on driver mutations in cancer. Automatic annotations were evaluated on a collection of articles on EGFR mutation in lung cancer. Seven users completed a questionnaire rating the summarization quality for various topics and applications. Results Casama had higher median scores than SemRep for the majority of the topics (p ≤ 0.00032), all of the applications (p ≤ 0.00089), and in overall summarization quality (p ≤ 1.5e-05). Casama's manual annotations outperformed Casama's automatic annotations (p = 0.00061). Discussion Casama performed particularly well in the representation of strength of evidence, which was highly rated both quantitatively and qualitatively. Users noted that Casama's less granular, more targeted representation improved usability compared to SemRep. Conclusion This evaluation demonstrated the benefits of a contextualized representation for summarizing biomedical literature on cancer. Iteration on specific areas of Casama's representation, further development of its algorithms, and a clinically-oriented evaluation are warranted.

This paper describes the information retrieval step in Casama (Contextualized Semantic Maps), a project that summarizes and contextualizes current research papers on driver mutations in non-small cell lung cancer. Casama?s representation of lung cancer studies aims to capture elements that will assist an end-user in retrieving studies and, importantly, judging their strength. This paper focuses on two types of study metadata: study objective and study design. 430 abstracts on EGFR and ALK mutations in lung cancer were annotated manually. Casama?s support vector machine (SVM) automatically classified the abstracts by study objective with as much as 129% higher F-scores compared to PubMed?s built-in filters. A second SVM classified the abstracts by epidemiological study design, suggesting strength of evidence at a more granular level than in previous work. The classification results and the top features determined by the classifiers suggest that this scheme would be generalizable to other mutations in lung cancer, as well as studies on driver mutations in other cancer domains. HighlightsWe propose to improve retrieval by representing and extracting study metadata.Multiple expert readers produced a gold standard of 430 abstracts on lung cancer.Automatic classification performed better than or comparable to PubMed?s filters.Study design classification was robust to differences in vocabulary across corpora.Top-ranked features were not domain-specific and could generalize to other domains.

e20662Background: The 6 FDA drug approvals for non-small cell lung cancer (NSCLC) in 2015 included 2 programmed cell death protein 1 (PD-1) inhibitors and a 3rd generation (gen) epidermal growth fa...

7517 Background: Trials for NSCLC have increasingly targeted specific molecular abnormalities. This approach has led to approval of multiple drugs based on results in specific BioM populations prio...