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Mitchell Machtay



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    P1.18 - Treatment of Locoregional Disease - NSCLC (ID 190)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Treatment of Locoregional Disease - NSCLC
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.18-12 - PACIFIC-4/RTOG 3515: Phase III Study of Durvalumab Following SBRT for Unresected Stage I/II, Lymph-Node Negative NSCLC (ID 1363)

      09:45 - 18:00  |  Author(s): Mitchell Machtay

      • Abstract
      • Slides

      Background

      Approximately 20% of non-small-cell lung cancer (NSCLC) patients (pts) present with localized disease and this percentage is expected to increase with routine computerized tomography screening. While surgery remains standard of care (SoC) for operable pts, for unresected pts stereotactic body radiation therapy (SBRT) is now the standard. However, locoregional and distant failures occur in >30% of pts after SBRT, with higher failure rates associated with larger tumors. Durvalumab is a selective high-affinity, human IgG1 monoclonal antibody that blocks PD-L1 binding to PD-1 and CD80. In the Phase 3 PACIFIC trial of durvalumab vs placebo in patients with unresected, Stage III NSCLC without progression on concurrent chemoradiotherapy (cCRT), durvalumab significantly improved both primary endpoints of progression-free survival (PFS) and overall survival (OS) versus placebo and the two treatment arms had similar safety profiles (Antonia et al, NEJM 2017; 2018). Accordingly, the PACIFIC regimen is becoming the SoC for Stage III NSCLC. Accumulating evidence suggests potential benefit with immunotherapy at early stage NSCLC. PACIFIC-4 will assess the efficacy and safety of durvalumab versus placebo following SBRT in pts with unresected Stage I/II lymph-node negative NSCLC.

      Method

      PACIFIC-4 is a Phase 3, randomized, placebo-controlled, double-blind, international study of durvalumab in pts with clinical Stage I/II node-negative (T1 to T3N0M0) NSCLC following definitive SBRT. Approximately 630 pts will be randomized 1:1 to receive durvalumab (1500 mg intravenously) or placebo every 4 weeks for 24 months, or until discontinuation due to disease progression, toxicity or withdrawal of consent. Eligible pts are adults with unresected Stage I/II NSCLC, are node-negative, ECOG PS 0–2, and have completed SoC SBRT. The primary endpoint is PFS using BICR assessments and the key secondary endpoint is OS. Other endpoints include health-related quality of life, lung cancer mortality, pharmacokinetics, immunogenicity, and safety. Recruitment for this trial is ongoing.

      Result

      Section not applicable

      Conclusion

      Section not applicable

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    P2.12 - Small Cell Lung Cancer/NET (ID 180)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Small Cell Lung Cancer/NET
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.12-03 - Building and Validating a Lymphocyte Nadir Based Model to Predict Survival in Patients with Limited Stage-Small Cell Lung Cancer (ID 2931)

      10:15 - 18:15  |  Author(s): Mitchell Machtay

      • Abstract

      Background

      Increasing evidence indicates host immunity participate in cancer progression and metastases. It has been reported that the lymphocyte nadir is an independent prognostic marker for survival in various cancers. We have previously reported a survival significance of baseline lymphocyte in limited stage small cell lung cancer (LS-SCLC). Here we hypothesize that treatment induced lymphopenia, like the lymphocyte nadir during the course of treatment, in combination of clinical factors can predict survival better than conventional models in patients with LS-SCLC.

      Method

      This is a retrospective study of 616 patients from a single Institution. Consecutive patients with LS- SCLC treated with thoracic radiation (with or without concurrent chemotherapy) from 2013 to 2017 were included. Additional eligibility included availability of complete-blood-count data from baseline and at least two time points during the course of radiotherapy. These 616 patients were randomly divided into a training dataset (n=308) and a validation dataset (n=308). The primary endpoint was overall OS. Univariate proportional hazard (PH) cox model was used to assess potential clinicopathological predictors on OS. The multivariable Cox PH model was constructed by the forward selection. According to the final Cox model built using significant variables from training dataset, we calculated the risk score for every patient and validate the predictive valuable of the risk score on OS in the validation set.

      Result

      Under univariate analysis, younger age (HR 1.02 per 1 yr, 95%CI 1.006-1.043, p=0.008), female gender (HR 1.40, 95%CI 0.95-2.07, p=0.09), earlier stage (stage I-II vs stage III, HR 2.2, 95%CI 1.11-4.33, p=0.02), concurrent chemotherapy (concurrent vs. not, HR 0.61, 95%CI 0.42-0.88, p=0.01) and a higher lymphocyte nadir ( HR 0.48, 95% CI 0.20-1.14, per 103 lymphocytes/ μL, p=0.097) was significantly associated with increased OS in the training dataset. Using lymphocyte nadir in combination of significant clinical factors from univariate analysis, we developed a multivariable Cox PH model (lymphocyte nadir: HR 0.39, 95% CI 0.16-0.99, per 103 lymphocytes/ μL, p=0.048) with concordance (C)-index of 0.63. In the validation dataset, the multivariable model revealed that lymphocyte nadir had a borderline significance on OS (HR 0.45, 95% CI 0.19-1.06, per 103 lymphocytes/ μL, p=0.067) with a comparable c-index of 0.60. Moreover, the risk score calculated using the coefficients from the final Cox model built using the training dataset remained to be a significant predictor for OS (HR 2.04, 95% CI 1.36-3.07, per 1 risk score increase, p<0.0001) in validation dataset.

      Conclusion

      This may be the first study validated a survival predictive model based on lymphocyte nadir in a large sample of patients with LS-SCLC. Should it should be validated in an external dataset, this model might provide some prediction for each patient and provide an opportunity to individualize treatment based on the individual’s survival probability.