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John Le Quesne

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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: 12
    • Now Available
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      MA15.01 - Cellular Prion Protein Transcriptionally Regulated by NFIL3 Enhances Lung Cancer Cell Lamellipodium Formation and Migration (Now Available) (ID 151)

      15:45 - 17:15  |  Presenting Author(s): Shin-Chih vincent Lin  |  Author(s): Chia-Hung Lin, Nien-Chu Shih, Hsin-Ling Liu, Wen-Chao Wang, Kun-Yang Lin, Yi-Cheng Lin, Yi-Chen Yeh, Hiroshi Minato, Takeshi Fujii, Yu-Chung Wu, Mei-Yu Chen, Teh-Ying Chou

      • Abstract
      • Presentation
      • Slides

      Background

      Tumor invasion and metastasis are the major causes of treatment failure and mortality in lung cancer patients. However, the precise molecular targets responsible for tumor invasion remain unclear.

      Method

      In this study, we identified a group of genes with differential expression in in situ and invasive lung adenocarcinoma tissues by cDNA microarray analysis; among these genes we further characterized the association of the upregulation of PRNP, the gene encoding cellular Prion protein (PrPc), with lung adenocarcinoma invasiveness through immunohistochemistry and in situ hybridization analysis on clinical tissues. The roles of PrPc in lung cancer cell lines were also verified by using immunofluorescence staining, in vitro transwell assay and in vivo metastasis mouse model. In addition, the impact of PrPc on the activation of the JNK signaling pathway was investigated by Western blot analysis. Finally, luciferase reporter assay and chromatin immunoprecipitation assay were used to identify the transcriptional activators of PRNP.

      Result

      Immunohistochemistry on clinical specimens showed association of PrPc expression with invasive but not in situ lung adenocarcinoma. Consistently, the expression of PrPc was higher in the highly invasive than in the lowly invasive lung adenocarcinoma cell lines. Knockdown of PrPc expression in cultured lung adenocarcinoma cells decreased their lamellipodium formation, in vitro migration and invasion, and in vivo experimental lung metastasis. Phosphorylation of JNKs was found to correlate with PrPc expression and the inhibition of JNKs suppressed the PrPc-induced up-regulation of lamellipodium formation, cell migration, and invasion. Moreover, we identified the nuclear factor, interleukin 3 regulated (NFIL3) protein as a transcriptional activator of the PRNP promoter. Accordingly, NFIL3 promoted lung cancer cell migration and invasion in a PrPc-dependent manner. High NFIL3 expression in clinical specimens of lung adenocarcinoma was also associated with tumor invasiveness and poor survival of patients.

      Conclusion

      Our observations suggest that PRNP expression is associated with the invasiveness of lung adenocarcinoma, and cell line model demonstrated that PrPc serves as a critical factor for lung cancer cell lamellipodia formation, migration and invasion via JNK signaling. A novel transcription factor, NFIL3, was identified to upregulate PRNP expression in lung cancer cells; further characterizations showed that NFIL3 promotes lung cancer cell migration through PrPc-dependent manner. Moreover, high NFIL3 expression was found to be associated with lung cancer invasiveness in clinical tissues. Overall, NFIL3/PrPc axis plays a critical role in lung cancer invasiveness and metastasis, and may be the potential therapeutic targets in the future.

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      MA15.02 - Deep Learning Approach for Automated Tumor Cells Detection and Estimation of PD-L1 22C3 Assay Expression in Lung Adenocarcinoma (Now Available) (ID 577)

      15:45 - 17:15  |  Presenting Author(s): Jianghua Wu  |  Author(s): Dongmei Lin, Wei Sun, Changling Liu, Xiaoqing Liu, Yajun Zhang, Junjie Zhang, Haiyue Wang, Xinying Liu, Xin Yang, Shaoping Ling

      • Abstract
      • Presentation
      • Slides

      Background

      It is vital and challenging to assess an accurate PD-L1 expression status on tumor cells for immunotherapy in lung cancer. The purpose of this study was to set up an automated system to detect the tumor cells and estimate the tumor proportion score (TPS) of PD-L1 immunohistochemistry (IHC) expression for lung adenocarcinoma based on deep learning, and provide a potential Artificial Intelligences (AI) assistive diagnostic tool in the quantification of PD-L1 interpretation.

      Method

      Fifty PD-L1 22C3 IHC slides of lung adenocarcinoma samples on digitized whole-slide images (WSI) database was employed. We first designed a model with a fully convolutional neural network (FCNN) based on U-ResNet architecture to obtain the cancer segmentation. Representative regions were selected from each slide, and 100 regions were collected for manual annotations as a training set for cancer detection. Another 50 regions were used to validate the performance of automated cancer detection and TPS estimation as a test set. After the quality control, a whole model of automated cancer cell segmentation and membrane positive estimation was set up on standard PD-L1 22C3 IHC staining. TPS could be automatically predicted by AI tool and then compared with the interpretations of pathologists.

      Result

      The results of automated lung adenocarcinoma cells segmentation on the test set of 22C3 IHC staining showed a moderate sensitivity (71.46%) with a high specificity (95.94%) which was much more crucial for TPS counting. In rest 43 out of 50 regions after a quality control, TPS estimated by the automated PD-L1 analysis based on cancer segmentation showed a significant correlation with the average scores (r=0.9609, p<0.001) and the median scores (r=0.9523, p<0.001) of pathologists' interpretations.

      results.png

      Conclusion

      We provide an automated tumor cells detection and TPS estimation model for lung adenocarcinoma and demonstrate the potential of using machine learning methods to access PD-L1 IHC status conveniently. A further validation of AI tool for automated scoring PD-L1 in diagnostic routine is highly recommended in the future.

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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  |  Presenting Author(s): Noriko Motoi  |  Author(s): Hiroshi Yoshida, Tomoharu Kiyuna, Hidehito Horinouchi, Takashi Kohno, Shun-ichi Watanabe, Yuichiro Ohe, Atsushi Ochiai

      • 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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      MA15.04 - Discussant - MA15.01, MA15.02, MA15.03 (Now Available) (ID 3780)

      15:45 - 17:15  |  Presenting Author(s): Anja C. Roden

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MA15.05 - Computerized Measurements of Cellular Diversity on H&amp;E Tissue Are Prognostic of OS and Associated with Mutational Status in NSCLC (Now Available) (ID 1975)

      15:45 - 17:15  |  Presenting Author(s): Vamsidhar Velcheti  |  Author(s): Cheng Lu, Kaustav Bera, Xiangxue Wang, Pingfu Fu, Michael Yang, David Rimm, Kurt A Schalper, Anant Madabhushi

      • Abstract
      • Presentation
      • Slides

      Background

      Tumor heterogeneity is known to be implicated in chemotherapeutic resistance and poor prognosis for non-small cell lung cancer (NSCLC). In this study we sought to evaluate the role of computer extracted features reflecting the intrinsic cellular morphological diversity (ICMD) of tumors from digitized H&E stained images of early-stage NSCLC patients. Additionally, we sought to evaluate the association of these ICMD features in adenocarcinomas with the ALK and EGFR mutational status.

      Method

      Two cohorts, D1 and D2, of digitized H&E stained tissue microarray images (TMA) of NSCLC, n=395 and n=91, respectively, were used for modeling the ICMD predictor. A pretrained deep learning model was used for segmentation of nuclei, and clusters of proximally located nuclei were identified. The ICMD features were then extracted as the variations in shape, size, and texture measurements of nuclei within the clusters. A Cox proportional hazard model using the ICMD features was then trained for lung adenocarcinomas (LUAD, n=270), and squamous cell carcinomas (LUSC, n=216), separately, and was validated on independent cohort from (D3) The Cancer Genome Atlas (TCGA) (n=473) to predict Overall Survival (OS). Univariate and multivariate analyses were performed on (D3).

      Result

      In (D3), high risk patients predicted by the ICMD features had significantly poorer survival (HR (95% CI) = 1.48 (1.06-2.06), p=0.021 for LUSC, HR (95% CI) = 1.59 (1.11-2.29), p=0.006 for LUAD) in univariate analysis. In multivariate analysis, controlling for major clinical variables, ICMD was independently associated with 5-year OS (p<0.016). (See Table 1) We also found that ICMD features were associated with driver mutations ALK (p=0.0204) and EGFR (p=0.0017) in LUAD.

      Table 1| Multivariate analysis for overall survival on the validation set D3.

      Multivariate Cox Proportional Hazard Model Analysis Controlling for Other Variables

      TCGA-LUSC

      TCGA-LUAD

      Variable

      HR (95% CI)

      p value

      HR (95% CI)

      p value

      Age (>65 vs <=65)

      1.14(0.81-1.61)

      0.451

      0.89(0.63-1.28)

      0.540

      Smoking status

      1.36(0.83-2.23)

      0.221

      1.14(0.64-2.01)

      0.661

      Overall Stage (Stage II vs I)

      1.13(0.66-1.94)

      0.651

      1.86(1.04-3.32)

      0.037

      T-Stage (T2,3 vs T1)

      1.26(0.85-1.87)

      0.244

      1.25(0.85-1.85)

      0.263

      N-Stage (N1 vs N0)

      1.36(0.77-2.41)

      0.292

      3.11(1.55-6.23)

      0.001

      Developed Model

      High risk vs. Low risk

      1.52(1.08-2.13)

      0.016

      1.55(1.09-2.22)

      0.015

      CI = 95% confidence interval; HR = Mantel-Haenszel Hazard ratio. Values in bold are statistically significant, p<=0.05.

      Conclusion

      Computer extracted image features of cellular diversity were able to predict OS in NSCLC and were also associated with the ALK and EGFR mutational status. Future work will entail evaluating ICMD features in predicting added benefit of adjuvant therapy in early stage NSCLCs as well as correlating with gene expression data.

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      MA15.06 - Stage I Lung Adenocarcinoma Gene Expression Associated with Aggressive Histologic Features for Guiding Precision Surgery and Therapy (Now Available) (ID 1124)

      15:45 - 17:15  |  Presenting Author(s): Jiarui Zhang  |  Author(s): Eric Burks, Travis Sullivan, Jacob Sands, Shawn Regis, Brady McKee, Andrea McKee, Sherry Zhang, Hanqiao Liu, Gang Liu, Steven Dubinett, Avrum Spira, Jennifer Beane, Kimberly Christ, Marc Lenburg

      • Abstract
      • Presentation
      • Slides

      Background

      Stage I lung adenocarcinomas (LUADs) show heterogeneity in histologic patterns which correlate with malignant behavior. Solid, micropapillary and cribriform patterns are associated with worse survival whereas lepidic (in situ) predominance has the best prognosis. In this study, we sought to characterize histologic pattern specific gene expression in resected clinical stage I LUADs. We also aimed to train and validate a genomic biomarker predictive of histologic aggressive patterns with the ultimate goal of being able to impact surgical and therapeutic decision making for post-biopsy management.

      Method

      A training cohort of 56 tumors from patients meeting NCCN high-risk screening criteria with stage I LUAD was included for pathologic annotation and whole exome RNA sequencing. Histologic pattern subtyping in 5% increments including all diagnostic slides was performed. A single representative FFPE block was chosen for RNA library-prep with Illumina TruSeq Access Kit and sequencing. Negative binomial models were used to identify gene expression differences associated with percent solid, cribriform, or micropapillary histology, and EnrichR was used for gene pathway enrichment analysis. Ss-GSEA was used to predict tumor infiltration of 20 immune cell types. A random-forest classifier for predicting aggressive histologic patterns was trained using 5-fold cross validation. A set of tumors from 16 independent patients with ≤2.0 cm clinical stage I LUAD was macro-dissected into 32 paired components (lepidic + non-lepidic regions) and subjected to RNAseq. Six tumors were defined as non-aggressive (lepidic + acinar/papillary) and ten tumors were defined as aggressive (lepidic + solid/micropapillary/cribriform). Four aggressive tumors were upstaged after surgical resection.

      Result

      In the training cohort, we identified 1322 genes associated with tumor histologic composition(FDR q <0.05 and fold-change > 2). Genes whose expression differs with solid histology% are enriched for involvement in DNA replication, cell cycle regulation and inflammation (FDR q<0.001). Genes whose expression is associated with micropapillary% are enriched for involvement in tRNA-aminoacylation and decrease of T-cell activity (FDR q<0.001). The functional enrichment of genes whose expression is associated with cribiform% was less informative. LUADs with micropapillary patterns exhibited gene expression consistent with decreased antigen presentation and low T-cell infiltration, and solid patterns exhibited gene expression consistent with increased infiltration of T-regulatory and Th2 cells (FDR q<0.05).

      A gene expression classifier was trained to predict the presence of aggressive histologic patterns. We validated this classifier on a set of 16 tumor specimens from which we macro-dissected and analyzed tissue from the most aggressive histologic pattern (AUC = 1.00). We also found that this classifier could differentiate lepidic regions isolated from aggressive tumors from lepidic regions isolated from non-aggressive tumors (AUC = 0.74).

      Conclusion

      We identified solid-, micropapillary- and cribriform-specific gene expression and associated immune response among clinical stage I LUADs, and developed a classifier predictive of aggressive histologic features using either lepidic (in situ) or non-lepidic components. As such, this biomarker has the potential to predict histologic aggressiveness even from pre-surgical tumor biopsies where all histologic patterns may not be represented. Such a biomarker may be useful in guiding clinical decision making including extent of surgical resection.

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      MA15.07 - Circulating miRNA: A Biomarker for Classification of Lung Cancer and Benign Lung Disease (Now Available) (ID 2218)

      15:45 - 17:15  |  Presenting Author(s): Alvida Qvick  |  Author(s): Anders Wirén, Maria Rönnqvist, Gisela Helenius

      • Abstract
      • Presentation
      • Slides

      Background

      Circulating biomarkers for cancer have great potential for diagnosis as well as follow up of treatment. MicroRNAs (miRNA) are involved in the expression of a majority of proteins with different cell types having different miRNA expression. The aim of this study was to create a circulating miRNA-based model to discriminate patients with lung cancer from patients with benign lung disease.

      Method

      Samples were collected from patients under investigation for lung cancer at Örebro University hospital. Patients were then divided into groups based on diagnosis, which resulted in NSCLC adenocarcinoma (n=24), NSCLC squamous cell carcinoma (n=13), SCLC (n=4) and a heterogeneous group consisting of different benign lung diseases (n=19). Healthy controls were collected separately (n=17).

      Circulating miRNA was processed using the extraction-free library preparation miRNA Whole Transcriptome Assay with probes for 2083 human mature miRNAs and analyzed with massive parallel sequencing.

      Differential expression between groups was estimated using edgeR. MiRNAs that had the highest impact on patient grouping were used in a sPLS discriminant analysis. The resulting classification model was validated using the leave-one-out method.

      Result

      The final model for comparison between patients with benign lung disease and patients with lung cancer contained 19 miRNAs. The model had an error rate of 15 % with errors distributed evenly between groups. A sub-analysis of patients with mutations in EGFR (n=5) and KRAS (n=6) was performed showing two distinct patterns in miRNA expression.classifyer.png

      Conclusion

      MiRNA shows promise as a circulating biomarker for lung cancer but may not be sufficient as an independent classifier. The predictive power may be improved by using several biomarkers in combination. The difference in expression between tumors with different mutations may be derived from alternate driving processes in these tumors.

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      MA15.08 - Discussant - MA15.05, MA15.06, MA15.07 (Now Available) (ID 3781)

      15:45 - 17:15  |  Presenting Author(s): John Longshore

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MA15.09 - PD-L1 Status in Relation with Non-Small Cell Lung Cancer Major Subtypes, Differentiation, Molecular Profiling and Smoking History (Now Available) (ID 2874)

      15:45 - 17:15  |  Presenting Author(s): Zhaolin Xu  |  Author(s): Alexi Surette, Mathieu Castonguay, Drew Bethune, Arik Drucker, Daniel French, Harry Henteleff, Mary Macneil, Wojciech Morzycki, Madelaine Plourde, Stephanie Snow

      • Abstract
      • Presentation
      • Slides

      Background

      Continued advances in lung cancer precision medicine have allowed targeted therapies based on an individual tumor’s genetic makeup. Recent advances in immune therapy based on immune checkpoint inhibitors have provided additional promising results. Currently, the majority of lung cancer mutational data available in the literature are from advanced stage non-small cell lung cancer. Mutational data from early stage lung cancer patients is limited. There is also limited data on PD-L1 tumor status is relation to mutational status along with other pathological and clinical characteristics. In this study, we evaluated these issues in 871 cases of surgically resected lung cancer.

      Method

      Multiplexed molecular profiling in 871 surgically resected lung cancer specimens was performed. A panel of genes including EGFR, KRAS, BRAF, PIK3CA, HER2 and ALK was tested. Tumor PD-L1 status was also evaluated by immunohistochemistry using pharmDx22C3. PD-L1 status was measured by tumor proportional score (TPS): <1%, 1-49% and ≥50% tumor cell positivity. Correlations between PD-L1 and gene mutation status, smoking history, histological grade, gender and age of paraffin embedded blocks were analyzed.

      Result

      This cohort includes adenocarcinoma (68%), squamous cell carcinoma (SCC) (22%) and other subtypes (10%). The average age is 67. Females account for 52%. A positive smoking history was present in 93%. Well differentiated tumors (G1) account for 11%, moderately differentiated (G2) 37% and poorly and undifferentiated (G3) 52%. EGFR mutations were identified in 7.4% and KRAS mutations in 31.7%. TPS <1% accounted for 48.8%, 1-49% for 34.6% and ≥50% for 16.5%.

      There was no statistically significant difference in PD-L1 TPS between histological subtypes or gender. Significantly more G1 tumors had a TPS <1% (76.7%) compared to G2 (57.4%, p=0.0013) and G3 tumors (41.8%, p<0.0001). Fewer G1 tumors had a TPS 1-49% (20.9%) than G2 (34.1%, p=0.015) and G3 (35.2%, p=0.01) tumors. G3 tumors were more likely to have a TPS ≥50% (24.6%) than G1 (2.3%, p<0.0001) and G2 (7.63%, p<0.0001) tumors. Never smokers were more likely to have a TPS <1% (71.1% vs 50.6%, p=0.04) and less likely to have a TPS ≥50% (5.8% vs 16.5%, p=0.04). Tumors with EGFR mutation were more likely to have a TPS <1% than those without EGFR mutation (70.7% vs 47.3%, p=0.0003) and less likely to have a TPS 1-49% (20.0 vs 35.5%, p=0.011). Tumors with KRAS mutations were less likely to have a TPS <1% (36.6% vs 54.9%, p<0.0001) and more likely to have a TPS 1-49% (40.6% vs 31.5%, p=0.0086) and ≥50% (22.8% vs 13.6%, p=0.0007). PD-L1 IHC performed on blocks stored for 2 years or longer had a statistically significant higher rate of TPS <1% compared to blocks stored for less than 2 years.

      Conclusion

      This study provides information relating to the relationship between PD-L1 levels and tumor molecular profile, histological grade and patient demographics. Additionally, we raise the possibility of false negatives on IHC performed for PD-L1 on paraffin embedded blocks stored for 2 years or more.

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      MA15.10 - Stromal Markers of Activated Tumor Associated Fibroblasts Predict Poor Survival and Are Associated with Necrosis in Non-Small Cell Lung Cancer (Now Available) (ID 2212)

      15:45 - 17:15  |  Presenting Author(s): Jordi Alcaraz  |  Author(s): Josep Lluís Carrasco, Millares Laura, Anabel Martínez-Romero, Julio Sánchez De Cos, Ramon Rami-Porta, Luis M Seijo, Jose Ramirez, Noemi Reguart, Esther Barreiro, Eduard Monsó

      • Abstract
      • Presentation
      • Slides

      Background

      Tumor associated fibroblasts (TAFs) are essential contributors of the progression of non-small cell lung cancer (NSCLC). Most lung TAFs exhibit an activated phenotype characterized by the expression of α-SMA and fibrillar collagens. However, the prognostic value of these activation markers in NSCLC remains unclear.

      Method

      We conducted a retrospective multicentric study of the prognostic value of the standard markers of activated fibroblasts. For this purpose, we conducted a quantitative image analysis of α-SMA immunostaining and picrosirius red staining of fibrillar collagens imaged by bright-field and polarized microscopy, respectively, using tissue microarrays with samples from 220 surgical patients, which elicited a percentage of positive staining area for each marker and patient.

      Result

      Kaplan-Meier curves showed that all TAF activation markers were significantly associated with poor survival, and their prognostic value was independent of TNM staging as revealed by multivariate analysis, which elicited an adjusted increased risk of death after 3 years of 129% and 94% for fibrillar collagens imaged with bright-field (p = 0.004) and polarized light (p = 0.003), respectively, and of 89% for α-SMA (p = 0.009). We also found a significant association between all TAF activation markers and tumor necrosis, which is often indicative of hypoxia, supporting a pathologic link between tumor desmoplasia and necrosis/hypoxia.

      Conclusion

      Our findings identify patients with large histologic coverage of fibrillar collagens and α-SMA+ TAFs to be at higher risk of recurrence and death, supporting that they could be considered for adjuvant therapy. Moreover it supports that antifibrotic drugs aiming to target tumor fibrosis may be an effective therapeutic approach to improve survival in NSCLC.

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      MA15.11 - Establishing a Cell Sociology Platform for the Assessment of Targetable Interactions to Predict Lung Cancer Outcome (Now Available) (ID 2652)

      15:45 - 17:15  |  Presenting Author(s): Erin A Marshall  |  Author(s): Katey SS Enfield, Paul Gallagher, Martial Guillaud, Calum Macaulay, Wan Lam

      • Abstract
      • Presentation
      • Slides

      Background

      The tumor microenvironment (TME) is a complex mixture of tumor epithelium, stroma and immune cells. The immune component of the TME is highly prognostic for tumor progression and patient outcome. Immune functionality, however, is often dictated by direct cell-to-cell contacts and cannot be resolved by simple metrics of cell density (for example, number of cells per mm2 or flow cytometry). For example, direct contact between CD8+ T cells and target cells is necessary for CD8+ T cell activity, and direct contact between PD1+ and PD-L1+ cells is necessary for the efficacy of immune checkpoint inhibitors. Current immunohistochemistry (IHC) techniques identify immune cell numbers and densities, but lack assessment of spatial relationships (or “cell sociology”). Here, we develop a platform to examine these direct interactions within the TME, and assess their relationship with patient outcome in two independent non-small cell lung cancer (NSCLC) cohorts.

      Method

      Tissue sections of primary tumors from lung adenocarcinoma (LUAD) patients with known clinical outcome were stained using 2 multiplex IHC panels: CD3/CD8/CD79a (Panel 1) and PD1/PDL1/CD8 (Panel 2). Hyperspectral image analysis determined the phenotype of all cells. Using the same IHC panels, these observations were assessed in a secondary NSCLC dataset (n=674). Deconvolution of these images was used to identify cell types, and cellular ‘neighborhoods’ were assessed using a Voronoi approach. This cohort was also profiled by for gene expression to validate immune subset fractions. We further identified other tumor features, including the presence of tertiary lymphoid organs (TLOs; transient immune structures necessary for antibody production from B cells).

      Result

      High density of intra-tumoral CD8+ T cells was associated with non-recurrence of tumors. However, we find that a non-random cell sociology pattern of CD8+ T cells directly surrounded by tumor cells was more significantly associated with non-recurrence compared to density alone. Monte Carlo re‐sampling analysis determined that these cell sociology patterns were non-random.

      Conclusion

      Hyperspectral cell sociology expands our understanding of the complex interplay between tumor cells and immune infiltrate. This technology improves our understanding of the tumour microenvironment and allows us to directly quantify interactions that dictate immune responses to cancers. Consequently, the implementation of this platform could improve predictions of responses to immunotherapy and lead to a deeper understanding of anti-tumor immunity.

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      MA15.12 - Discussant - MA15.09, MA15.10, MA15.11 (Now Available) (ID 3782)

      15:45 - 17:15  |  Presenting Author(s): David Hwang

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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    P1.03 - Biology (ID 161)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Biology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.03-10 - High-Risk Growth Patterns of Lung Adenocarcinoma Show Distinct Modes of Metastasis and Recurrence (ID 1267)

      09:45 - 18:00  |  Author(s): John Le Quesne

      • Abstract
      • Slides

      Background

      Following curative surgery for non-small cell lung cancer, around half of patients ultimately succumb to recurrent/metastatic disease. This may occur in many ways, such as recurrence at the surgical site, within the pleura, or by metastatic spread via lymphatics or blood vessels. This study aims to identify tumour-intrinsic pathological risk factors which predict particular modes of recurrence of lung adenocarcinoma, to improve our understanding of both the clinical course of disease and the biology underlying tumour recurrence.

      Method

      A single-centre cohort of 1025 patients who had surgically resected primary lung adenocarcinoma between the years of 1998 and 2015 was used. Full clinicopathological data were collected from patient records and slide review. All available radiology image data were examined to identify first recurrence site(s) for each case; these were grouped as surgical site, ipsilateral lung, ipsilateral pleura, nodal recurrence and distant recurrence. 824 cases with complete recurrence data were included in the final analysis. Univariate and multivariate Cox models of time to recurrence after surgery for each recurrence site were constructed. Depending on recurrence site, models included growth pattern (predominance or presence), tumour size, nodal involvement, vascular invasion, pleural involvement, surgical procedure and R stage.

      Result

      408 patients (49.5%) had recurrent disease, with 200 (24.2%) patients showing simultaneous recurrence at multiple sites. Of the patients who developed recurrences, 99 (24.3%) recurred at the surgical site. 118 (28.9%) patients recurred within the ipsilateral lung and 75 (18.4%) within the ipsilateral pleura. There were 168 instances of nodal recurrence, most often within N2 lymph nodes (n=95, 56.5%). 316 cases of distant recurrence were reported, with the most common sites being the contralateral lung (n=110, 34.8%), bone (n=71, 22.5%) and brain (n=67, 21.2%). Distant recurrence is associated with significantly shorter post-recurrence survival (PRS) than locoregional recurrence (50% PRS 7.9 vs. 11.1 months, P=0.008).

      Surgical site recurrence is related to subanatomical wedge resection (HR=4.98, 95% CI: 2.45-10.12, P<0.001), tumour size (HR=1.01, 95% CI: 1.00-1.03, P=0.042) and positive resection margins (HR=4.05, 95% CI: 2.01-8.15, P<0.001). Ipsilateral pleural recurrence is strongly predicted by advancing PL stage (PL2 vs. PL0, HR=2.61, 95% CI: 1.18-5.77, P=0.018; PL3 vs. PL0, HR=3.74, 95% CI: 1.64-8.51, P=0.002).

      Notably, recurrence in the ipsilateral lung (HR=2.08, 95% CI: 1.29-3.35, P=0.003) and nodal recurrence (HR=2.11, 95% CI: 1.29-3.46, P=0.003) are both independently predicted by the presence of any micropapillary component of tumour growth. In contrast, distant recurrence is related to solid pattern predominance (HR=2.50, 95% CI: 1.43-4.35, P=0.001) univariately, and with near independence in multivariate analysis (HR=1.79, 95% CI: 0.99-3.24, P=0.054). In multivariate models, only solid-predominant tumours are significantly associated with recurrence in the brain (HR=8.52, 95% CI: 1.13-64.01, P=0.037).

      Conclusion

      We present evidence that the two high-risk patterns of tumour growth, solid and micropapillary, have predilections for recurrence/metastasis via haematogenous and lymphatic vascular systems respectively. This distinction raises important biological questions about mechanisms of tumour spread, and may help to inform future treatment and prevention strategies.

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    P2.06 - Mesothelioma (ID 170)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Mesothelioma
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.06-11 - MEDUSA: Phylogenetic Analysis of Mesothelioma Tumours by Multiregional Sampling, Whole Exome Sequencing, and Copy Number Analysis (ID 921)

      10:15 - 18:15  |  Author(s): John Le Quesne

      • Abstract

      Background

      The Mesothelioma evolution: Drugging somatic alterations (MEDUSA) project aims to investigate the genomic evolution and heterogeneity of malignant pleural mesothelioma and identify genomic changes early in mesothelioma evolution that can be targeted by drugs. For 20 malignant pleural mesothelioma patients, we have analysed the exomes of at least four regions of the tumour and paired whole blood.

      Method

      Using paired tumour-normal analysis with the software Sequenza, we have called copy number alterations specific to the tumour, and used the software Tumult to reconstruct a phylogeny of the tumour for each of the 20 patients.

      Result

      We show that mesothelioma shows extensive heterogeneity in copy number changes, and accumulates typically between 100-200 copy number gains and losses while evolving in a branching pattern. We identify and validate copy number alterations that occur truncally, early in the evolution of the tumour, and are recurrent across patients, including homozygous loss of CDKN2A and MTAP in 4/20 patients, heterozygous loss of MTOR in 6/20 patients and heterozygous loss of BAP1 in 4/20 patients. Losses of these key genes are observed in some other patients, but only in a subset of regions, suggesting that they have occurred later in the evolution of the tumour compared to truncal changes.

      Conclusion

      As truncal changes are likely to be present throughout the tumour, identifying them highlights potential Achilles’ heels for drug targeting and treatment.