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Kurt A Schalper



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    MA11 - Immunotherapy in Special Populations and Predictive Markers (ID 135)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Immuno-oncology
    • Presentations: 1
    • Now Available
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      MA11.11 - STK11/LKB1 Genomic Alterations Are Associated with Inferior Clinical Outcomes with Chemo-Immunotherapy in Non-Squamous NSCLC (Now Available) (ID 2898)

      14:00 - 15:30  |  Author(s): Kurt A Schalper

      • Abstract
      • Presentation
      • Slides

      Background

      Addition of pembrolizumab (P) to platinum-doublet chemotherapy [carboplatin (or cisplatin) and pemetrexed (CP)] prolongs overall survival and is a standard of care (SOC) for the 1st line treatment of metastatic EGFR/ALK wild-type (wt) non-squamous non-small cell lung cancer (mnsNSCLC). Despite widespread use of the CPP regimen, molecular determinants of clinical benefit from the addition of P to CP remain poorly defined. We previously identified genomic alterations in STK11/LKB1 as a major driver of primary resistance to PD-1/PD-L1 blockade in mnsNSCLC. Here, we present updated data on the impact of STK11/LKB1 alterations on clinical outcomes with CPP chemo-immunotherapy from a large retrospective multi-institution international study.

      Method

      620 pts with mnsNSCLC and tumor genomic profiling encompassing STK11/LKB1 from 21 academic institutions in the US and Europe were included in this study. Clinical outcomes were collected for two distinct patient cohorts: a) 468 pts treated with first-line CPP (or >1st line following FDA-approved TKIs) that were alive for 14 days thereafter and b) 152 STK11/LKB1-mt pts that received CP prior to regulatory approval of CPP.

      Result

      Among 468 CPP-treated pts, STK11/LKB1 genomic alterations (N=118) were associated with significantly shorter PFS (mPFS 5.0m vs 6.8m, HR 1.45, 95% CI 1.11 to 1.91; P=0.007) and shorter OS (mOS 10.6m vs 16.7m, HR 1.46, 95% CI 1.04 to 2.07; P=0.031) compared with STK11/LKB1-wt tumors (N=350). The likelihood of disease progression as BOR to CPP differed significantly between the two groups (29.5% vs 17%, P= 0.006). Similar results were obtained when limiting the analysis to EGFR and ALK-wt tumors (N=435) (mPFS 5.0m vs 6.9m, HR 1.48, 95% CI 1.12-1.95, P=0.006 and mOS 10.6m vs 16.7m, HR 1.45, 95% CI 1.02-2.05, P=0.036). Importantly, in pts with STK11/LKB1-mt mnsNSCLC, addition of pembrolizumab to CP did not result in significant improvement of PFS (mPFS 5.0m vs 3.9m, HR 0.82, 95% CI 0.63 to 1.07, P=0.14) or OS (mOS 10.6m vs 9.1m, HR 0.93, 95% CI 0.67 to 1.30, P=0.69) compared to CP alone.

      Conclusion

      In mnsNSCLC, STK11/LKB1 alterations define a subgroup of pts with inferior clinical outcomes with CPP and lack of benefit from the addition of pembrolizumab to CP chemotherapy. Novel therapeutic strategies are required to establish effective antitumor immunity in STK11/LKB1-mutant NSCLC.

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

      15:45 - 17:15  |  Author(s): Kurt A Schalper

      • 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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    MA25 - Precision Medicine in Advanced NSCLC (ID 352)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Advanced NSCLC
    • Presentations: 1
    • Now Available
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      MA25.02 - Arrangement and Architecture of Tumor-Infiltrating Lymphocyte on H&amp;E Slides Predict OS in Nivolumab Treated Non-Small Cell Lung Cancer (Now Available) (ID 2911)

      14:30 - 16:00  |  Author(s): Kurt A Schalper

      • Abstract
      • Presentation
      • Slides

      Background

      Immune checkpoint inhibitors (ICI) are a promising and novel approach to treating chemotherapy refractory advanced NSCLC as well as first-line combination therapy in certain NSCLC. Nivolumab, a PD-L1 inhibitor is a promising ICI showing durable benefit with low toxicity in these patients. While PD-L1 positivity is an established tissue based biomarker for response to Nivolumab, studies have shown response rates ranging from 20-50%. Recent research has shown that TILs have been implicated in cancer aggressiveness as well as immune response. In this work, we go beyond simply counting TILs, and apply novel computer-extracted features characterizing the interaction and spatial co-localization of TILs and cancer nuclei (SpaTIL) in stratifying patients based on OS following nivolumab therapy.

      Method

      H&E tissue slides obtained from pre-treatment biopsies of 96 NSCLC patients treated with nivolumab were digitized and included for this study from 3 different institutions with the tumor region annotated by pathologists. Then 85 SpaTIL features related to TIL density, architecture and co-localization with tumor cells have been extracted to represent each patient. The most discriminative and uncorrelated features were selected by Elastic-Net regularized Cox-regression model to predict OS. The model was trained on D1 (n=25) and independently validated in D2 (n=32) and D3 (n=64). Multivariate analysis with clinico-pathologic factors was also performed.

      Result

      The top features consisted of the abundance of TILs around tumor cells and the distribution of the TILs. On the validation set, SpaTIL classifier yielded a HR=3.03 (95%CI=1.1 -8.35; p=0.042) on D2 and HR=4.12 (95%CI=1.87-9.09; p=0.02) on D3 by a log-rank test. On multivariate analysis with stage, smoking, histologic type, total lymphocyte count (See Table 1) SpaTIL was independently prognostic of OS (HR=7.88; 95%CI=1.66 – 37.216; p=0.009).wlc19 (2).png

      Table 1. Multivariate analysis for overall survival on the validation sets D2 and D3

      Variables

      HR(95% CI)

      p value

      Age (>65 vs <=65 yrs)

      0.99(0.97-1.03)

      0.67

      Gender (Male vs Female)

      1.05(0.75-2.79)

      0.88

      Smoking Status

      (Former vs Never smoker)

      3.19(0.92-11.061)

      0.07

      Histological Subtypes (Adeno vs Squamous)1

      1.06(0.13-8.54)

      0.95

      EGFR status

      1.32(0.49-3.52)

      0.58

      ALK status

      0.63(0.36-1.10)

      0.10

      Total lymphocyte count

      0.99(0.99-1.00)

      0.33

      SpaTIL Classifier

      7.88(1.66-37.216)

      0.009

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

      Conclusion

      Spatial interaction of TILs and cancer are independently prognostic of OS in nivolumab treated NSCLC. Further validation needs to be done to evaluate its utility.

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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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      P1.04-23 - Characterizing the Tumor Immune Microenvironment of Non-Small Cell Lung Carcinoma in People Living with HIV Using Imaging Mass Cytometry (ID 2675)

      09:45 - 18:00  |  Author(s): Kurt A Schalper

      • Abstract
      • Slides

      Background

      Non-AIDS defining cancers (NADCs) have become a leading cause of cancer incidence and mortality in people living with HIV (PLWH). Lung cancer is the most common NADC in this population, with non-small cell lung cancer (NSCLC) as the most common histologic subtype. PLWH develop NSCLC at younger ages, present with later stage of disease, and have worse outcomes. Global immune dysfunction caused by HIV infection, independent of absolute CD4 count, is hypothesized to contribute to this phenomenon. Imaging Mass Cytometry (IMC) provides for multidimensional protein detection using metal-conjugated antibodies and mass spectrometry. Here, we use IMC to investigate the NSCLC tumor microenvironment of HIV+ and HIV- patients, identifying differences in immune function.

      Method

      Paraffin-embedded tumor tissue from 18 HIV+ patients and 19 HIV- matched patients were obtained from the Pathology department of Yale-New Haven Hospital. Controls were matched on the basis of age, sex, histology, stage at presentation, and year of cancer diagnosis. An antibody panel consisting of 36 structural, phenotypic, and functional targets was used to characterize these tissues by IMC. Unsupervised clustering with PhenoGraph was used to identify major cell populations. Chi-square test and t-test were used to compare categorical and continuous variables, respectively.

      Result

      Between HIV+ and HIV- patients, median age was 53 and 59 years, 61% and 58% presented at stages III/IV, and median overall survival was 8 and 89 months respectively (log rank; P = 0.006). Among HIV + patients, 78% were on antiretroviral therapy and 56% had an undetectable viral load at time of cancer diagnosis. No significant difference in overall CD3, CD4, CD8, CD20, or CD68 signal was detected between HIV+ vs HIV- cases. Similarly, no significant difference was seen in expression of inhibitory T-cell receptors (PD1, TIM3, LAG3) on CD4+ or CD8+ cells. CD68 cells from HIV+ cases demonstrated increased PD-L1 expression compared with CD68 cells from HIV- cases (P = 0.006). Three unique subpopulations of epithelial tumor cells were identified by PhenoGraph; these subsets differed in expression of PD-L1, MHC class I and II antigen presentation proteins (HLA-ABC, HLA-DR), and cellular proliferation markers (KI67). Distribution among these three tumor cell subsets varied significantly between HIV+ and HIV- cases (P =0.005).

      Conclusion

      In this well-matched cohort of NSCLC, we investigated the tumor microenvironment using highly multiplexed imaging mass cytometry. Despite having similar stages at presentation as HIV- cases, overall survival was markedly decreased among HIV + cases. Though no significant difference in tumor infiltrating immune cells or lymphocyte T cell inhibitory receptor expression was seen, monocytes from HIV+ patients demonstrated increased PD-L1 expression. In addition, distinct subpopulations of tumor cells were identified, with tumors from PLWH having different proportional representation among these subsets. Further genomic and transcriptomic analysis of HIV-associated NSCLC tumors may provide further insight into the effect of HIV infection on tumor phenotype and prognosis.

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