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Takashi Kohno



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    MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Pathology
    • Presentations: 1
    • Now Available
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      MA15.03 - Exploring Digital Pathology-Based Morphological Biomarkers for a Better Patients’ Selection to the Immune Checkpoint Inhibitor of Lung Cancer (Now Available) (ID 1777)

      15:45 - 17:15  |  Author(s): Takashi Kohno

      • Abstract
      • Presentation
      • Slides

      Background

      For eligible patients’ selection for immune checkpoint inhibitor therapy (ICI), it is important to establish more accurate predicting biomarkers, in addition to PD-L1 IHC and MSI-high. We hypothesized that morphological characteristics should reflect genetic alteration, thus could predict ICI responsiveness. In this study, we examined the predictive potential of morphological characteristics using digital whole-slide images as a new biomarker for ICI-treatment on non-small cell lung cancer (NSCLC) and their relationship to PD-L1 IHC and genetic alterations.

      Method

      71 NSCLC who received ICI therapy were recruited. Digital images of H&E and PD-L1 (22C3) IHC stained slides of pre-treatment biopsied or resected materials were examined by previously reported image analysis techniques using e-Pathologist ® (NEC, Japan). Morphological characteristics of cancer cells (three and six parameters of nuclear shape and chromatin texture) were extracted as MC-scores. Of 11 cases (pilot cohort), PD-L1 IHC (22C3) and tumor mutation burden (TMB) by the NGS-based target sequence (NCC oncopanel ®) were examined. Correlation between MC-score, PD-L1 IHC, TMB status, and clinical outcome was calculated. A p-value of less than 0.05 was defined as statistically significant.

      Decision tree analysis for evaluating predicting ICI-responsiveness was built using statistically significant MC-scores. We also tested the predictive value of a deep learning analysis (AI model) with 5-fold cross-validation. AUC (area under the curve) of ROC analysis was calculated.

      Result

      Of the responders, the MC-score of cancer cell were statistically different from those of the non-responders; nuclear texture contour complexity (11.8 vs. 8.25, median value of responder vs. non-responders; p<0.01), homogeneity (0.396 vs. 0.421; p<0.01), angular second moment (ASM) (0.0203 vs. 0.0214; p=0.049) and nuclear circularity (0.878 vs. 0.885, p=0.026). Circularity (p=0.011) and texture homogeneity (p=0.048) correlated with TMB. ASM texture correlated with PD-L1 expression (p=0.018). The decision tree model for predictive and screening purposes resulted in 0.83 and 0.62 accuracies, respectively. AUC of AI-model for ICI responsiveness resulted in fair (0.74 on average, range 0.55-0.81).

      Conclusion

      Our results indicate the substantial value of the morphological feature as a biomarker for ICI therapy. Morphological characteristics are eligible from archived FFPE samples, showed good correlation to the underlying genetic alteration. Digital pathology can serve useful predictive morphological biomarkers for precision medicine of lung cancer patients, and promising the power of AI-assisted pathology.

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    P1.01 - Advanced NSCLC (ID 158)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Advanced NSCLC
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.01-102 - Actionable Gene Aberration and the Response of Matched Therapy Among Patients with Non-Small-Cell Lung Carcinoma (Now Available) (ID 1177)

      09:45 - 18:00  |  Author(s): Takashi Kohno

      • Abstract
      • Slides

      Background

      Tumor genotyping using multiplex gene panel is now standard for precision medicine in non-small-cell lung carcinoma (NSCLC). We sought to assess the prevalence of actionable genomic alterations among NSCLC patients using our next-generation sequencing panel (NCC Oncopanel) and the response of matched therapy.

      Method

      This is a post-hoc analysis of prospective study in which patients with advanced solid cancer were prospectively enrolled to undergo the comprehensive genomic profiling panel (NCC Oncopanel) conducted between July 2013 and March 2018 in National Cancer Center Hospital. The NCC Oncopanel assay, a multiplexed next-generation sequencing (NGS) assay of 114 cancer-associated genes, was performed in a CLIA-compliant laboratory in National Cancer Center. Subjects were primarily patients without any known actionable alteration such as EGFR or ALK. Patients with NSCLC were extracted into this analysis. Clinical data and treatment outcomes were retrospectively collected.

      Result

      In total, 100 patients were extracted. Sufficient tumor tissue for NGS analysis were available in 91 patients; median age was 57 (range 30‒77); 74 (81.3%) adenocarcinoma; 44 (48.4%) female; 42 (46.2%) never smoker. According to the OncoKB and CIViC database, and the Clinical Practice Guidelines for NGS in Cancer Diagnosis and Treatment issued by three major Japanese cancer-related societies, 85 patients (93.4%) had at least one potential pathogenic alteration. Actionable gene aberrations were identified in 49 (53.9%). Evidence levels were ranked as follows: 24 (26%) harbored level 1 aberrations (ALK, EGFR, ROS1, BRAF); 15 (16%) harbored level 2 (RET, DDR2, MET, ATM, BRCA2, CDK4, CTNNB1, EZH2, JAK2, NRAS, TSC1); 10 (11%) harbored level 3A (CDKN2A, ERBB2, HRAS, PTEN, SMARCA4, STK11). Matched therapy was administered into 29 (31.9%) leading to the objective response rate of 58.6% and the disease control rate of 79.3% with the median progression-free survival of 10.5 months (95%CI; 5.1‒15.8).

      Conclusion

      Multiplex gene panel is feasible and useful in screening candidates for matched therapy among NSCLC patients. NSCLC patients without any known actionable mutations should be considered to undergo comprehensive genomic profiling.

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