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Balázs Döme



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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  |  Author(s): Balázs Döme

      • 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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    P1.09 - Pathology (ID 173)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Pathology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.09-24 - Tumor Necrosis Correlates with PD-L1 and PD-1 Expression in Lung Adenocarcinoma (ID 1917)

      09:45 - 18:00  |  Author(s): Balázs Döme

      • Abstract

      Background

      Predictive biomarkers for immunotherapy in lung cancer are intensively investigated, however, correlations between PD-L1/PD-1 expressions and clinical features or histopathological tumor characteristics determined on hematoxylin and eosin stained sections have not extensively been studied.

      Method

      We determined PD-L1 expression of tumor cells (TC) and immune cells (IC), and PD-1 expression of IC by immunohistochemistry in 268 lung adenocarcinoma (LADC) patients, and correlated the data with smoking, COPD, tumor grade, necrosis, lepidic growth pattern, vascular invasion, density of stromal IC, and EGFR/KRAS status of the tumors.

      Result

      There was a positive correlation between PD-L1 expression of TC and IC, as well as PD-L1 and PD-1 expression of IC. Tumor necrosis was associated with higher PD-L1 expression of TC and PD-1 expression of IC. A negative correlation was observed between lepidic growth pattern and PD-L1 expression of TC and PD-L1/PD-1 expression of IC. EGFR mutation seemed to negatively correlate with PD-1 expression of IC, but this tendency could not be verified when applying corrections for multiple comparisons. No significant effect of the KRAS mutation on any of the studied variables could be established.

      Conclusion

      Here we first demonstrate that the presence of necrosis correlates with higher PD-L1 expression of TC and PD-1 expression of IC in LADC. Further studies are required to determine the predictive value of this observation in LADC patients receiving immunotherapy.

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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): Balázs Döme

      • 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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    P2.04 - Immuno-oncology (ID 167)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.04-25 - Gut Mycobiome and Metabolic Interactions with Bacteria in Lung Cancer Patients Reveals Potential Therapeutic Vulnerabilities (ID 2080)

      10:15 - 18:15  |  Author(s): Balázs Döme

      • Abstract

      Background

      There is a lack in our understanding of pathogenesis and mechanisms accounting for large variability in response to systemic therapy. Recent data suggest that the gut-lung axis regulates systemic immune function. Moreover, in vivo and clinical studies, mainly in melanoma and mixed epithelial tumors, suggest the role of gut bacteria and response to systemic therapy. However, the presence and potential theranostic role of gastrointestinal (GI) mycobiome in lung cancer has not been explored. Here, we aimed to evaluate the associations of GI bacteria and Candida species in lung cancer patients.

      Method

      We included 124 stool samples from 98 lung cancer patients (adenocarcinoma (n=48), squamous cell (n=24), small cell lung cancer (n=15) and other (n=11). Patients underwent lung resection surgery (n=20) or treated with first line chemotherapy CHT (n=78). We analyzed the gut microbiome according clinicopathological variables using Internal transcribed spacer (ITS) and shotgun metagenomic sequencing technique. We performed Spearman correlation between gut microbial species and continuous variables and random forest model (RF) for feature selection. Pathway analysis was done using HUmann2 pipeline, bacterial species annotation was performed with Metaphaln2 pipeline and for fungi ITS, we used PIPITS pipeline. We compared Candida diversity with healthy controls from the Human Microbiome Project.

      Result

      We identified Candida species in 65% (71 of 124) of the stool samples. Of these, 48 were baseline 23 were follow-up samples (treatment). 77% of the patients included were diagnosed with advanced stage disease. There were significant differences in Candida abundance in healthy controls vs. cancer patients. In contrast, there were no significant differences in alpha and beta diversity between baseline and follow-up samples (treatment). In total 46 significant species and 140 pathways were significant different and fed into RF. Species belonging to Actinobacteria, Bacteroidetes and Firmicutes phyla are important features in RF and found to be negatively correlated (r <= -0.3, and p < 0.05) with Candida species. Certain bacteria pathways were significantly different according to the presence of Candida.

      Conclusion

      Candida is present in stool samples in the majority of lung cancer patients both at diagnosis and during systemic therapy. There were associations with certain gut bacteria and Candida species that may have potential future therapeutic implications. Further biomarker studies in well-defined homogenous subgroups are ongoing in order to identify the exact biomarker role of the mycobiome in lung cancer.

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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  |  Author(s): Balázs Döme

      • 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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