Browsing by Author "Mileham, Kathryn F."

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    Defining comprehensive biomarker‐related testing and treatment practices for advanced non‐small‐cell lung cancer: Results of a survey of U.S. oncologists
    (Wiley, 2022) Mileham, Kathryn F.; Schenkel, Caroline; Bruinooge, Suanna S.; Freeman-Daily, Janet; Basu Roy, Upal; Moore, Amy; Smith, Robert A.; Garrett-Mayer, Elizabeth; Rosenthal, Lauren; Garon, Edward B.; Johnson, Bruce E.; Osarogiagbon, Raymond U.; Jalal, Shadia; Virani, Shamsuddin; Weber Redman, Mary; Silvestri, Gerard A.; Medicine, School of Medicine
    Background: An ASCO taskforce comprised of representatives of oncology clinicians, the American Cancer Society National Lung Cancer Roundtable (NLCRT), LUNGevity, the GO2 Foundation for Lung Cancer, and the ROS1ders sought to: characterize U.S. oncologists' biomarker ordering and treatment practices for advanced non-small-cell lung cancer (NSCLC); ascertain barriers to biomarker testing; and understand the impact of delays on treatment decisions. Methods: We deployed a survey to 2374 ASCO members, targeting U.S. thoracic and general oncologists. Results: We analyzed 170 eligible responses. For non-squamous NSCLC, 97% of respondents reported ordering tests for EGFR, ALK, ROS1, and BRAF. Testing for MET, RET, and NTRK was reported to be higher among academic versus community providers and higher among thoracic oncologists than generalists. Most respondents considered 1 (46%) or 2 weeks (52%) an acceptable turnaround time, yet 37% usually waited three or more weeks to receive results. Respondents who waited ≥3 weeks were more likely to defer treatment until results were reviewed (63%). Community and generalist respondents who waited ≥3 weeks were more likely to initiate non-targeted treatment while awaiting results. Respondents <5 years out of training were more likely to cite their concerns about waiting for results as a reason for not ordering biomarker testing (42%, vs. 19% with ≥6 years of experience). Conclusions: Respondents reported high biomarker testing rates in patients with NSCLC. Treatment decisions were impacted by test turnaround time and associated with practice setting and physician specialization and experience.
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    Prognostic Mutational Signatures of NSCLC Patients treated with chemotherapy, immunotherapy and chemoimmunotherapy
    (Springer Nature, 2023-03-27) Smith, Margaret R.; Wang, Yuezhu; D’Agostino, Ralph, Jr.; Liu, Yin; Ruiz, Jimmy; Lycan, Thomas; Oliver, George; Miller, Lance D.; Topaloglu, Umit; Pinkney, Jireh; Abdulhaleem, Mohammed N.; Chan, Michael D.; Farris, Michael; Su, Jing; Mileham, Kathryn F.; Xing, Fei; Biostatistics and Health Data Science, School of Medicine
    Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.