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Pradnya Dinkar Patil



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    MA03 - Clinomics and Genomics (ID 119)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Advanced NSCLC
    • Presentations: 1
    • Now Available
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      MA03.01 - The Impact of Early Steroids on Clinical Outcomes in Patients with Advanced NSCLC Treated with Immune Checkpoint Inhibitors- A Cancerlinq Cohort (Now Available) (ID 2807)

      10:30 - 12:00  |  Author(s): Pradnya Dinkar Patil

      • Abstract
      • Presentation
      • Slides

      Background

      Immune checkpoint inhibitors (ICIs) have changed the treatment paradigm for patients with NSCLC, however only a fraction of patients have objective responses to these agents. Identifying clinical factors that influence efficacy of ICIs is crucial for optimal patient selection for treatment. Since ICIs produce anti-tumor responses by reinvigorating cytotoxic effector T cells, one can surmise that patients who receive steroids within a short interval of initiating ICIs will have less robust anti-tumor responses. Clinical trials usually exclude patients receiving steroids for this reason. In clinical practice, patients with NSCLC often receive corticosteroids for various indications such as brain metastases, appetite stimulation, autoimmune disorders, or COPD. By analyzing data obtained from a large real world cohort of patients with NSCLC, we aim to study the impact of early steroids (within 30 days) on clinical outcomes in patients with advanced NSCLC treated with ICIs.

      Method

      Using the Cancerlinq Discovery Database which consists of data aggregated from the electronic medical records of oncology practices, 11,143 patients with advanced NSCLC treated with ICIs were identified. Of these, 1581 patients were prescribed or administered ≥ 10 mg of prednisone or equivalent corticosteroid dose within the first 30 days of initiating ICIs. To account for prognostic heterogeneity within the population, we created matched cohorts of patients that exhibited similar prognostic clinical characteristics such as age (using 65 years as a cutoff) and gender. Association between time on treatment with ICIs and early steroid use was evaluated using the Student’s t-test. Overall survival (OS) was estimated using the Kaplan-Meier method and analyzed using the Cox proportional-hazards model.

      Result

      The cohort consisted of a predominantly white population (53.4%), with a median age of 76 years and a slight male predominance (54.9%). The median time on ICI treatment was 3.8 months. Patients who received steroids within the first 30 days had a shorter time on treatment- median of 3.36 months vs 3.86 months for those without steroid use (p= 0.023). Early steroid use was also associated with significantly worse overall survival [HR 1.16, 95% CI (1.05, 1.28) p<0.002].

      Figure: Kaplan-Meier survival analyses of patients with NSCLC treated with ICIs according to early steroid use

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      Conclusion

      The use of ≥ 10 mg of prednisone equivalent corticosteroid dose within 30 days of initiating ICIs was associated with shorter time on treatment and worse overall survival in this large real world cohort of NSCLC patients. It is prudent that clinicians judiciously prescribe corticosteroids upon initiation of ICIs.

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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): Pradnya Dinkar Patil

      • 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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    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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      P1.04-25 - CT Based Vessel Tortuosity Features Are Prognostic of Overall Survival and Predictive of Immunotherapy Response in NSCLC Patients (ID 826)

      09:45 - 18:00  |  Author(s): Pradnya Dinkar Patil

      • Abstract

      Background

      Recently majority of patients with advanced non-small cell lung cancer (NSCLC) without targetable mutations are treated with immune checkpoint inhibitors (ICI). Since there are currently no validated biomarkers for predicting benefit of immunotherapy (IO), there is an unmet clinical need for development of such biomarkers. The tumor vasculature is a key component of the tumor micro-environment that can influence its behavior and therapeutic refractoriness. We aimed to evaluate the prognostic and predictive potential of quantitative vessel trotuosity (QVT), in the NSCLC patients treated with ICI drugs. Two hypotheses were established: first, the QVT on pre-treatment CT scans of NSCLC patients are associated with overall survival (OS). Second, the prognostic QVT features can lead to identify the patients who will benefit from IO.

      Method

      This study include 128 patients with advanced NSCLC. All patients underwent a baseline contrast CT imaging. Patients who did not receive IO drugs after 2 cycles due to a lack of response or progression as per RECIST were classified as non-responders. The dataset was splitted into a discovery (N=64) and validation sets (N=64). A set of 74 QVT features pertaining to tortuosity and curvature of tumor vasculature was extracted in CT scans. The initial set of QVT features were reduced to 8 features using least absolute shrinkage and selection operator (LASSO) in conjunction with OS data of the patients. Then, cox proportional hazard model was used to determine the contribution of each feature for categorizing survival groups. The weighted sum of selected 8 features gave a risk score (QRS) per patient. Patients in validation set were stratified based on QRS using the cutoff and feature weights learned in the discovery set. Prognostic features in conjunction with a linear discriminant machine learning model and OS were used to build a model to predict the response to IO. The prognostic features were also used for unsupervised clustering of the patients.

      Result

      The QRS risk score was able to stratify patients into two survival groups in validation set (Fig1. a-b) with p-value=0.022, Hazard ratio (HR)=0.47 and concordance index (CI)=0.61. The response prediction model yielded an AUC of 0.64±0.03 (Fig1.c). The agreement between patients with high OS and positive response to therapy was found to be 0.62 on unsupervised clustering method (Fig1. d).

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      Conclusion

      The CT extracted QVT features was found to be prognostic of OS and also showed predictive value that could be used to identify patients who will benefit from IO.

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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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      P2.04-16 - Novel CT Based Radiomic Features are Prognostic and Predictive of Benefit of Chemoimmunotherapy in Advanced Non-Squamous NSCLC (ID 2769)

      10:15 - 18:15  |  Author(s): Pradnya Dinkar Patil

      • Abstract

      Background

      Carboplatin, pemetrexed and pembrolizumab (C/P/P) is currently approved for patients with advanced non-squamous carcinoma of the lung (NS-NSCLC) based on superior survival outcomes noted in KEYNOTE-189. Since clinical benefit was observed across all PD-L1 expression categories, there are currently no robust predictive biomarkers that can identify subsets of patients likely to derive benefit from this regimen. We sought to evaluate whether radiomic features extracted from within and outside the nodule on pre-therapy CT scans could predict response to C/P/P.

      Method

      We retrospectively identified 52 patients with stage IV NS-NSCLC who received C/P/P. Of these, 6 were excluded because of non-evaluable thoracic lesions. Lung tumors were contoured on 3D SLICER software by an expert reader. Textural and shape radiomic features were extracted from intra/peritumoral regions using MATLAB® 2018b platform (Mathworks, Natick, MA). The primary endpoint of our study was RECIST response and secondary end point was overall survival (OS). A linear discriminant analysis classifier (LDA) was used to predict response across 100 iterations of threefold cross validation in the dataset. Performance of classifier on response was measured by area under receiver operating characteristic curve (AUC). To build the multivariate radiomic signature for OS, least absolute shrinkage and selection operator (LASSO) Cox regression model was used and a risk score was computed according to a linear combination of selected features. Patients were divided into high-risk or low-risk groups based on median risk score.

      Result

      The top five radiomic features (intra/peritumoral textural patterns) predictive of response to C/P/P were identified by mRMR feature selection method. LDA classifier using these features could discriminate responders from non-responders with an AUC of 0.77 ± 0.05.

      The radiomic risk score was calculated using a linear combination of top six selected features from LASSO with corresponding coefficients. In a multivariate Cox proportional hazards model using a combination of clinicopathologic and radiomic features, the radiomics signature was found to be significantly associated with OS (averaged on 100 iteration of CV) (HR 10.42; 95% CI: 4.18-26; P = 4.92e-07). Kaplan-Meier survival analyses according to the radiomics signature risk-score showed significantly worse survival in the high risk category.

      Conclusion

      Textural features within and outside the nodule on pre-treatment CT images of patients with NS-NSCLC treated with C/P/P were predictive of responses and OS. Additional validation of these quantitative image-based biomarkers in independent cohorts is warranted.

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      Figure: Kaplan-Meier survival analyses of patients (N = 46) with NS-NSCLC treated with C/P/P using the radiomics signature risk-score.

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    P2.17 - Treatment of Early Stage/Localized Disease (ID 189)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Treatment of Early Stage/Localized Disease
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.17-35 - Integrating CT Radiomic &amp; Quantitative Histomorphometric Whole Slide Image Features Predicts Disease Free Survival in ES-NSCLC (ID 2910)

      10:15 - 18:15  |  Author(s): Pradnya Dinkar Patil

      • Abstract

      Background

      Early-Stage non-small cell lung cancer (ES-NSCLC) accounts for approximately 40% of NSCLC cases, with 5-year survival rates varying between 31-49%. Radiomic textural features from pre-treatment CT scans and QH features from H&E stained WSIs have been shown to be independently prognostic of outcome. With diagnostic CT scans and surgical resection, the standard of care in ES-NSCLC, in this work we seek to take a multimodality approach using routine imaging to improve the predictive performance in determining DFS following resection.

      Method

      A retrospective chart review of Stage I and II (ES-NSCLC) pts undergoing surgical resection between 2005-14 with available CT and resected tissue yielded 70 pts. A total of 248 radiomic CT textural features from inside the tumor (Intratumoral –IT) and outside the tumor (Peritumoral – PT) and 242 QH features related to the nuclear shape, texture and spatial orientation and architecture from H&E WSI were extracted. We developed two risk models, Radiomic and QH using the most stable, discriminative and uncorrelated features from CT and WSI respectively determined by Lasso-regularized Cox regression to predict Disease free survival (DFS). Model performances were analyzed using Hazard Ratios (HR), Concordance Index (C-index) and Decision curve analysis. We built a nomogram to calculate the DFS based around the individual models as well as an integration of the QH and Radiomic models.

      Result

      Top 6 Radiomic features included 2 IT and 4 PT features from the Haralick and Collage families. The QH model comprised 6 nuclear shape and graph features. In predicting DFS, While the Radiomic model had a HR of 2.4 (p <0.01) with C-index – 0.67, the QH model had HR – 3.1 (p <0.01) with C-index – 0.74. Integration of the Radiomic and QH model yielded a C-index of 0.78 (p< 0.01). After addition of prognostic clinical factors (LVI, AJCC stage) to the model, the C-index was 0.80, almost doubling either modalities alone. The constructed nomogram visualized the apparent benefits of the three models while a decision curve clearly demonstrated the increased benefit of combined integrated model.

      Conclusion

      Integration of CT-derived radiomic and tissue-derived QH features was found to show improved performance in predicting RFS when compared to either radiomics or QH alone.

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