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Zsolt Megyesfalvi



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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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      P1.04-49 - Quantitative Computed Tomography (CT) Based Texture Analysis (QTA) Might Identify Responders to Immunotherapy in Non-Small Cell Lung Cancer (ID 2762)

      09:45 - 18:00  |  Presenting Author(s): Zsolt Megyesfalvi

      • Abstract
      • Slides

      Background

      Quantitative computed tomography (CT) based texture analysis (QTA) can characterize tumor heterogeneity, that might be associated with tumor infiltrating immune cells, including T-cells, a hallmark of ongoing immune surveillance with potential therapeutic importance. Therefore, QTA might represent prognostic and predictive biomarker in terms of immunotherapy administration. In this study, we investigate the potential of tumoral QTA and a novel machine learning approach to differentiate responders from non-responders using baseline pre-treatment CT imaging in advanced-stage NSCLC patients treated with immune checkpoint inhibitors.

      Method

      The QTA was applied separately on 50 contrast and 50 non-contrast CT images of histologically confirmed NSCLC patients. All patients included were treated with second-line immunotherapy (nivolumab, pembrolizumab or atezolizumab). Three-dimensional tumor segmentation was performed using the 4.10 version of 3D Slicer, and a total of 104 CT parameters from each CT image was obtained. For data pre-processing and standardization, we used the Sklearn machine learning library in Python programming language, reducing the number of CT parameters by Principal Component Analysis (PCA). The components thus obtained were further analyzed with hierarchical cluster analysis. According to QTA, responders were differentiated from non-responders based on naïve Bayes and k-means clustering. Response was defined as complete response, partial response, or stable disease at the first follow-up CT scan after immunotherapy initiation while non-response was defined as either visible signs of progression at time of or death prior to first follow-up CT scan. To verify the accurateness of the machine learning algorithms, leave-one-out cross-validation was performed.

      Result

      Overall, we analyzed the CT scans of 88 advanced-stage NSCLC patients including 40 woman and 48 men. PCA identified eight major principal components which characterize the 104 CT features with an accuracy of 90%, suggesting no loss of essential CT-related information. We further evaluated these eight principal components and hierarchical cluster analysis clearly identified two major subgroups both in the contrast and non-contrast CT image group. According to the QTA, based on naïve Bayes clustering, the machine learning algorithm was able to differentiate responders from non-responders with an accuracy of 66.6% in the contrast CT image group but was not predictive in the non-contrast group. K-means clustering also showed an accuracy of 66.6%, thus confirming the results of the naïve Bayes clustering.

      Conclusion

      Advanced-stage NSCLC patients can be classified into two major subgroups according to their CT features principal components with hierarchical cluster analysis using both contrast and non-contrast CT images. The clinical relevance of these principal component related subgroups should be further investigated in future prospective studies. By evaluating the contrast CT images, machine learning algorithms based on naïve Bayes and k-means clustering may predict the response to immunotherapy, however, further studies on “Big Data” are needed to define the exact prognostic value of QTA in NSCLC patients regarding immunotherapy administration. Application of QTA to prognostication for progression-free survival and overall survival is in progress.

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    P1.12 - Small Cell Lung Cancer/NET (ID 179)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Small Cell Lung Cancer/NET
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.12-09 - RNA Sequencing in Small Cell Lung Carcinoma Reveals Change in Neuroendocrine Pattern in Primary Tumor Versus Lymph Node Metastases (ID 2820)

      09:45 - 18:00  |  Author(s): Zsolt Megyesfalvi

      • Abstract
      • Slides

      Background

      Recent preclinical cell line data presented at World Conference on Lung Cancer 2018 suggest that neuroendocrine (NE) pattern of small cell lung cancer (SCLC) has strong therapeutic relevance. NE high tumors are associated with immune desert and NE low tumors are considered immune oasis phenotype.

      Method

      Targeted RNA-sequencing of 2560 genes was performed on 32 matched surgically resected SCLC patients primary tumors and lymph node (LN) metastases. We performed a cluster analysis and heat map to divide patients into NE high and NE low subtypes by using the top NE associated genes.

      Result

      Cluster analysis clearly identified SCLC NE subtypes according to primary tumor (NE high vs. low, 20 vs. 12, respectively) and LNs (NE high vs. low, 23 vs. 9, respectively). In case of five patients, a change in NE pattern was observed, suggesting a possible inter-tumor heterogeneity regarding NE differentiation. Moreover, a significant downregulation of NE associated genes CAV1, CAV2 and ANXA3 was found in LN metastases compared to primary tumor. A lower expression of NE associated key RNA genes REST and Myc, and the higher expression of DLL3 in NE high subtype are in accordance with the preclinical findings, and confirms the accuracy of the cluster analysis performed.

      Conclusion

      Our data confirm the results of preclinical studies and show NE low and high differentiation clusters in SCLC. NE pattern of the LN metastatic lesions might not reflect the NE phenotype of the primary tumor, consequently, treatment decisions including immunotherapy administration needs to be further investigated.

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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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      P2.12-02 - Intertumoral Heterogeneity in Small Cell Lung Carcinoma According to the Primary Tumor Versus Lymph Node Metastases Delineated by RNA Sequencing (ID 2748)

      10:15 - 18:15  |  Presenting Author(s): Zsolt Megyesfalvi

      • Abstract
      • Slides

      Background

      The pathological diagnosis of small cell lung cancer (SCLC) was determined mainly based on the simple morphological pattern for decades with no significant therapeutic advancements. Exploring the gene expression profile of matched primary and lymph node (LN) metastatic SCLC tumors might provide unique insights into new potential therapeutic approaches.

      Method

      A total of 32 histologically confirmed SCLC patients underwent surgical resection with available tumor tissue specimen were included in our study. We performed targeted RNA sequencing, to analyze tumor heterogeneity in terms of differences in gene expression and relevant pathways according to primary tumor and LN metastases.

      Result

      We found 6% (n=154) RNA genes with significant differences and only 13.1% (n=336) of all genes in the entire panel had a strong correlation between the primary tumor and LN metastases. Transcription factors had higher percentage of correlation compared to genes in importance in extracellular and cell adhesion receptors and signaling. According to the top 25 RNA genes in our gene panel with significant difference in RNA gene expression the majority of these RNA genes were downregulated (n=20) in LN metastases, having a wide range of functions including proliferation, growth and survival. In contrast, the upregulated top RNA genes (n=5) have a major role in cell adhesion, lymphoid tissue development and inflammatory response.

      Conclusion

      Our findings highlight the RNA gene discordance between primary tumors and corresponding LN metastases indicating a non-homogeneous nature of the tumor mass at different anatomical locations with potential therapeutic applications.

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