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Ju Hwan Cho



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    OA13 - Ideal Approach to Lung Resection and Novel Perioperative Therapy (ID 146)

    • Event: WCLC 2019
    • Type: Oral Session
    • Track: Treatment of Early Stage/Localized Disease
    • Presentations: 1
    • Now Available
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      OA13.07 - Neoadjuvant Atezolizumab in Resectable NSCLC Patients: Immunophenotyping Results from the Interim Analysis of the Multicenter Trial LCMC3 (Now Available) (ID 1755)

      11:30 - 13:00  |  Author(s): Ju Hwan Cho

      • Abstract
      • Presentation
      • Slides

      Background

      The immune mechanisms dictating response and resistance to PD-(L)1 blockade are not well understood in early stage non-small cell lung cancer (NSCLC). Understanding these mechanisms will be key to improve outcomes and identify the next generation of predictive biomarkers of response to these therapies. Here, we present updated immunophenotyping at time of interim analysis of LCMC3, a multicenter trial of neoadjuvant atezolizumab in resectable NSCLC (NCT02927301).

      Method

      Patients received 2 cycles of atezolizumab before resection. Tumor, LN biopsies and PB were obtained pre-atezolizumab and at surgery. Paired PB, screening and surgical LN were analyzed using IMMUNOME flow cytometry. Plasma-based cytokine arrays were performed on a subset of patients. Immunophenotypic analyses were correlated with treatment effect, major pathologic response (MPR, primary endpoint) and preoperative treatment-related adverse events (preop-TRAE).

      Result

      We report on 55 patients with paired PB samples (analyzed within 72h after collection) and completed surgery. We observed preop-TRAE in 32/55 patients (18 grade 1, 13 grade 2, 1 grade 3). CD1c+ and CD141+ myeloid cells (MC) were lower at baseline in patients developing preop-TRAEs, while monocytic M-MDSCs were higher in those patients. Senescent T cells decreased in patients with preop-TRAE and increased in patients with non-preop-TRAE. After treatment, the absolute cell counts of late activated CD4+and CD8+T cells decreased in patients achieving MPR. LN IMMUNOME data, cytokine data and 12-month follow-up (DFS, OS) will be reported.

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      Conclusion

      Preliminary immunophenotyping data from the interim analysis showed significantly lower baseline immunosuppressive cell subsets in patients with preop-TRAE and decreased late activated CD4+and CD8+T cells from PB in patients with MPR.These results, together with additional LN IMMUNOME and cytokine analyses, may improve our understanding of immunophenotypic features associated with outcome, and changes induced by neoadjuvant atezolizumab in early stage NSCLC patients.

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    P1.04 - Immuno-oncology (ID 164)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.04-15 - Smoking Status Is Not a Replacement Biomarker for Tumor Mutation Burden in Non-Small Lung Cancer (ID 454)

      09:45 - 18:00  |  Author(s): Ju Hwan Cho

      • Abstract
      • Slides

      Background

      Recent clinical studies suggest tumor mutation burden (TMB) as a promising therapeutic biomarker of anti-tumor immune checkpoint blockade (ICB). Given the causal link between cancer-causing mutations and tobacco smoking, patients with a significant smoking history may respond better to ICB. However, it is not clear if smoking history is an adequate surrogate biomarker for TMB. Here, we sought assess the clinical utility of smoking history in predicting tumor mutation burden.

      Method

      Publicly available smoking history and DNA somatic alteration data from NSCLC were downloaded from The Cancer Genome Atlas and a large dataset of lung adenocarcinoma tumors published by Imielinski, et al (Cell, 2012) Tumor mutation burden was calculated as the sum of all somatic mutations divided by the exome sequencing coverage. Smoking history was analyzed both as categorical (ever, never, former) and semi-continous variables (pack years). Hypermutancy was defined as greater than or equal to 10 mutations per megabase.

      Result

      A total of 395 LUAD and 419 LUSC patients were included in this analysis. Smokers had significantly higher tumor mutation burdens than non-smokers; however, in both LUAD and LUSC, there were smokers with low TMB and non-smokers with high TMB. Smoking pack year history (SPY) was weakly positively correlated (Spearman ρ = 0.20, p = 2.5x10-4) in LUAD but uncorrelated (Spearman ρ = -0.026, p = 0.61) in LUSC. Non-smokers and patients without a recorded SPY were excluded from the SPY analysis. We calculated AUCs for predicting hypermutancy in tumors, using variable thresholds of SPY. In LUAD and LUSC, SPY had an AUC of 0.38 and 0.47 in predicting TMB, showing that SPY was not better than random prediction. We also sought to predict TMB from smoking as a binary variable. In LUSC, 8/18 (44%) non-smokers and 253/447 (57%) smokers were hypermutant. In LUAD, 9/61 (15%) non-smokers and 219/391 (56%) smokers were hypermutant. Additionally, we repeated this analysis on matched smoking history and TMB from an independent cohort of 162 LUAD tumors published by Imielinski, et al. Similarly, we found that 1/27 (4%) of nonsmokers and 66/135 (49%) of smokers were hypermutant. In this cohort, the AUC in predicting TMB with SPY was 0·21.

      Conclusion

      In this study, we investigated the relationship between tobacco smoking and TMB. While the average lung cancer patient with a history of tobacco smoking has a higher TMB than the average never-smoker, there is not a clear relationship between the extent of exposure in pack years and TMB. In general, smoking is not an informative biomarker for TMB, however, non-smokers who develop LUAD are unlikely to have high TMB. This study highlights the value of next generation sequencing for TMB in predicting therapeutic response to ICB.

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