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Introduction Thyroid dysfunction is the most frequent endocrine immune related adverse event (irAE) in non-small cell lung cancer (NSCLC), typically arising 3–6 months into immune checkpoint inhibitor (ICI) therapy, but arising after ICI cessation, in some cases. Due to limited post-treatment adverse event reporting requirements on ICI trials, the incidence of ICI-induced thyroid dysfunction arising after therapy is unclear. We investigated ICI-induced thyroid dysfunction in a cohort of 294 NSCLC patients, with a specific focus on the post-treatment setting. Methods Retrospective analysis of ICI-induced thyroid dysfunction (clinically acted upon or laboratory only) was performed in 294 UCLA NSCLC patients treated 2012–2018. Clinically acted upon thyroid dysfunction was defined as thyroid diagnosis documentation and/or thyroid medication administration. Laboratory only dysfunction was defined as abnormal thyroid labs in the absence of clinical action. Timing of thyroid dysfunction relative to ICI treatment and thyroid monitoring patterns were also assessed. Results 82% (241/294) of ICI treated NSCLC patients had thyroid labs during treatment. Of these 241 patients, 13% (31/241) had clinically acted upon thyroid dysfunction prior to, 8% (18/241) during, and 4% (9/241) after ICI. Most patients, 66% (159/241), did not have thyroid labs after ICI, but in the 53 patients with labs and no prior clinical dysfunction, 17% (9/53) developed clinical dysfunction after ICI. In these 9 patients, median time from ICI initiation to dysfunction was 253 days. Two patients with post-treatment laboratory only dysfunction were observed. Conclusions ICI-induced thyroid dysfunction arising post-treatment appears more common than previously appreciated, warranting additional evaluation.

Identifying patients who respond to immune checkpoint blockade (ICB) is a significant challenge in oncology. While PD-L1 expression by immunohistochemistry (IHC) is the current diagnostic gold standard for patient selection, it nevertheless does not capture all patients who may respond to ICB. Recent gene expression studies in high-grade serous ovarian carcinoma have defined an immunoreactive molecular subtype that has a measurable difference in patient survival compared with non-immunoreactive subtypes, but no studies have yet demonstrated its impact on predicting response to ICB. As a step toward establishing the predictive value of gene expression classifiers in ICB, we assessed the relationship between PD-L1 IHC and molecular subtypes of ovarian epithelial cancer. This was done by analyzing a total of 93 tissue specimens from patients with stage III and IV disease, and comparing PD-L1 IHC with gene expression by Agilent microarrays using TCGA-defined subtypes. We showed that ovarian tumors with elevated IHC PD-L1 expression are most strongly associated with immunoreactive subtype as compared with other molecular subtypes, reaching statistical significance against differentiated (Dunn’s test, 33.39, p = 0.0003) and mesenchymal (39.63, p

Introduction Tyrosine kinase inhibitors (TKI) targeting epidermal growth factor receptor (EGFR) are approved for use in metastatic non-small cell lung cancer (NSCLC). Case presentation Here we present a case of a African American patient with stage IIIA NSCLC treated with osimertinib in the neoadjuvant setting with concurrent radiation, followed by resection. The patient remains disease-free 4 months after surgery. Conclusion This case report suggests that osimertinib may be effective as neoadjuvant therapy in resectable stage III disease. Additionally, we provide a summary of previous case reports and ongoing clinical trials for neoadjuvant EGFR inhibition in stage III NSCLC patients.

Purpose of review Immune checkpoint blockade (ICB) is a promising area of cancer therapeutic research. Therapies targeting the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) mechanism of tumor immune evasion have resulted in durable responses in many difficult-to-treat tumor types. While these inhibitors are being actively investigated in clinical trials for ovarian cancer, most patients fail to respond to initial treatment with immune therapy. This review focuses on biomarkers for predicting response to treatment, and discusses clinical trials using ICB for recurrent ovarian cancer. Recent findings While PD-L1 detection by immunohistochemistry (IHC) is approved as a companion or complementary diagnostic in some cancers, there are many limitations with its use as a predictive marker. Recent research has explored biomarkers beyond PD-L1 that assess for somatic mutations, immune cell infiltrate, and gene signatures. Summary With improved understanding of the tumor microenvironment and genomic classifications of ovarian tumors, new diagnostics and biomarkers that supplement conventional IHC may help predict response to therapy.

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.