Virtual Library

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    EX04 - Mini Oral Abstract Session - MA08.06, MA18.02, MA19.02, MA20.11 (ID 1006)

    • Event: WCLC 2018
    • Type: Exhibit Showcase
    • Track: Advanced NSCLC
    • Presentations: 4
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      EX04.01 - Outcomes Among Patients with EGFR-Mutant Metastatic NSCLC with and without Brain Metastases (ID 13671)

      09:55 - 10:00  |  Presenting Author(s): Janet L Espirito  |  Author(s): Eric Nadler, Melissa Pavilack, Bismark Baidoo, Ancilla W. Fernandes

      • Abstract
      • Slides

      Background

      Brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) are common, with reported frequencies of up to 44%. The epidermal growth factor receptor-mutant (EGFRm) subtype of NSCLC is known to have specific pathologic features that may influence patterns of metastases and outcomes. The prevalence of BM in patients with EGFRm NSCLC is unknown; however, incidence is expected to increase as new treatments emerge that prolong survival. Therefore, an understanding of the impact of BM and burden of illness in this population is needed.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      A retrospective observational matched cohort study of adults with EGFRm metastatic NSCLC, with BM and without BM (NBM), between January 1, 2014, and July 31, 2016, with follow-up to March 31, 2017. Data were extracted from the iKnowMedTM electronic health record database of patients in the US Oncology Network of community oncology practices, supplemented with chart review. Patients were matched 1:1 by age categories and sex. Patients enrolled in interventional clinical trials or with other cancer diagnoses were excluded. Categorical variables were analyzed using chi-squared tests; time to treatment failure (TTF) and overall survival (OS) were assessed using the Kaplan-Meier method starting from the matched line of therapy for TTF and from NSCLC diagnosis for OS.

      4c3880bb027f159e801041b1021e88e8 Result

      A total of 402 (BM, 201; NBM, 201) patients were included; median age was 70 and 77 years, respectively (p<.05). Patients in both cohorts were predominantly female (65%), Caucasian (69%), non-smokers (42%), and had adenocarcinoma (92%). Over 90% of patients were treated in the first-line setting. Median TTF in the BM and NBM cohorts following initial treatment was 10.9 months (95% confidence interval [CI], 9.5-12.0) and 10.4 months (95% CI, 8.9-12.2), respectively. Median OS for the BM and NBM cohorts was 11.9 months (95% CI, 9.7-13.4) and 16.0 months (95% CI, 9.1-20.6), respectively (log-rank p=.017). Median OS from onset of BM in the BM cohort was 10.0 months (95% CI, 7.4-11.2). While CNS symptoms were present in both cohorts, they were significantly higher in the BM cohort; patients in the BM cohort had significantly greater use of home healthcare, physical therapy, and social work services (p<.05).

      8eea62084ca7e541d918e823422bd82e Conclusion

      Median TTF was similar in patients with EGFRm metastatic NSCLC with BM and NBM; however, OS was significantly worse in the BM cohort. Symptom burden and healthcare resource utilization were also higher in the BM cohort. These findings highlight an unmet treatment need for patients with EGFRm NSCLC with BM.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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      EX04.02 - The Impact of Treatment Evolution in NSCLC (iTEN) Model: Development and Validation (ID 13468)

      10:00 - 10:05  |  Presenting Author(s): Manjusha Hurry  |  Author(s): Daniel Moldaver, Diana Tran, William Kenneth Evans, Parneet Kaur Cheema, Randeep Sangha, Ronald Burkes, Barbara Melosky, Erik Orava, Daniel Grima

      • Abstract
      • Slides

      Background

      Background: The iTEN model was developed to estimate the survival impact of new treatments for advanced NSCLC (aNSCLC) patients. The structure and key assumptions of the iTEN model and outputs validated against published real-world survival data are presented.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Methods: The iTEN model is a discrete event simulation of aNSCLC patients’ treatment plans. Individual patient characteristics (histology, molecular subtypes (EGFR, ALK, ROS1, BRAF, PD-L1), and performance status) are generated by random sampling from Canadian prevalence estimates. All Health Canada approved agents for treatment of aNSCLC are included. Simulated patients start on first-line therapy and move to subsequent lines of therapy in modelled progression events. Up to six-lines of therapy can be included. Time-of-event for progression or death for each patient is calculated based on random probabilities and progression-free survival (PFS) and overall survival (OS) curves modelled from published clinical trials. For example, a simulated ALK+ patient might receive first-line crizotinib, followed by second-line ceritinib and BSC, based on PFS/OS data from PROFILE 1014 and ASCEND-5. Predicted OS is calculated as the cumulative time spent on active therapy and BSC. PFS/OS data were extrapolated using best practices. Treatment on previous therapies was assumed to have no impact on the efficacy of subsequent therapies. Model survival predictions were validated against published real-world estimates from the Ontario Cancer and Austrian (TYROL) registries, by reproducing the same treatment mix in the simulated patients as in the publications.

      4c3880bb027f159e801041b1021e88e8 Result

      Results: iTEN estimated two- to five-year survival rates were similar to those reported by the Ontario Cancer and TYROL registries.

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      8eea62084ca7e541d918e823422bd82e Conclusion

      Conclusions: While further analyses are required, the iTEN model produces survival estimates comparable to published real-world data; therefore, the iTEN model may be a valid tool to estimate aNSCLC patient survival.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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      EX04.03 - Prior Therapy and Increased Expression of PD-L1 in NSCLC Tumor Samples (ID 11881)

      10:05 - 10:10  |  Presenting Author(s): Anne-Marie Boothman  |  Author(s): Marietta Scott, Marianne Ratcliffe, Jessica Whiteley, Phillip A. Dennis, Catherine Wadsworth, Alan Sharpe, Naiyer A Rizvi, Marina Chiara Garassino, Jill Walker

      • Abstract
      • Slides

      Background

      Tumor PD-L1 expression has been shown to enrich for response to immunotherapy in several indications, including advanced NSCLC. However, the stability of PD-L1 expression over time and its relationship with non-immunotherapy cancer treatment is currently uncertain. We hypothesized that prior chemotherapy or radiotherapy would increase PD-L1 expression.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      In the Phase 2, open-label, single-arm durvalumab ATLANTIC study (NCT02087423), patients who had received ≥2 prior systemic regimens in the treatment of Stage IIIB or IV NSCLC were screened for tumor PD-L1 expression by immunohistochemistry using the VENTANA PD-L1 (SP263) Assay (25% tumor cell [TC] cutoff). PD-L1 expression was assessed using either a recent (<3 months) or archival sample; a subset of patients provided both. The relationship between non-immunotherapy cancer treatment and prevalence of tumor PD-L1 expression ≥25% (TC≥25%) was assessed in patients who received therapy prior to sample acquisition versus those who did not. Pearson’s chi-squared test was used to examine the differences between patient subgroups.

      4c3880bb027f159e801041b1021e88e8 Result

      Of the patients screened for participation in ATLANTIC, 1590 were successfully assessed for PD-L1 expression. PD-L1 TC≥25% prevalence was higher in patients who had received prior radiotherapy or chemotherapy before sample acquisition, with prevalence noticeably higher in those who had received ≥2 lines of prior chemotherapy. Prior EGFR inhibitor treatment did not have any noticeable relationship to TC≥25% prevalence (Table). In the subset of patients with paired recent and archival samples, TC≥25% prevalence remained the same in 74% of cases, increased over time in 19.5%, and decreased in 6.5%.

      Treatment regimen

      Subgroup (n)

      PD-L1 TC≥25% prevalence (%)

      P-value

      Prior tyrosine kinase inhibitor (TKI)

      No prior TKI (607)

      39.9

      0.947

      Prior TKI (411)

      39.7

      Prior EGFR inhibitor (379)

      38.5

      0.154

      Prior ALK inhibitor (15)

      60.0

      Prior chemotherapy

      No prior chemotherapy (145)

      29.0

      0.004

      Prior chemotherapy (873)

      41.6

      Number of lines of prior chemotherapy

      0 (155)

      29.0

      0.031

      1 (10)

      30.0

      2 (138)

      42.8

      >2 (725)

      41.5

      Prior radiotherapy

      No prior radiotherapy (599)

      37.1

      0.034

      Prior radiotherapy (419)

      43.7

      8eea62084ca7e541d918e823422bd82e Conclusion

      PD-L1 expression may increase in response to chemotherapy or radiotherapy and is unlikely to decrease over time. Re-biopsy may provide a more accurate assessment of current tumor PD-L1 expression status when a low/negative result is seen in an archival sample, particularly if the patient has received multiple lines of intervening radiotherapy or chemotherapy.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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      EX04.04 - Automatic Nodule Size Measurements Can Improve Prediction Accuracy Within a Brock Risk Model (ID 14018)

      10:10 - 10:15  |  Presenting Author(s): Timor Kadir  |  Author(s): Carlos Arteta, Nick Dowson, Petr Novotny, Catarina Santos, Lyndsey Pickup, Sarim Ather, Jerome Declerck, Fergus V Gleeson

      • Abstract
      • Slides

      Background

      The intrinsic variance of manually measured nodule diameters may limit the predictive accuracy of the Brock University Cancer Prediction Model, especially given the relative weight of its coefficient within the model. Size measurements that are automatically derived may improve this prediction performance. This study aims to examine whether automatic nodule segmentation can improve the predictive efficacy of the Brock model.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      A retrospective analysis was performed on all images from the third annual screening study (T2) of the US National Lung Cancer Screening Trial (NLST). Following BTS guidelines, all nodules >=5mm were selected with no other exclusions whether on type, attenuation margin, or otherwise. This resulted in 7551 benign and 314 malignant nodules from 5373 patients with mean age 62±5 years and 3180 males. An automatic segmentation method, based on Deep Learning, was used to segment the nodule volume using a single point placed within the nodule on the CT image as an initialization. The nodule volume, V, was calculated from the segmentation and then converted to an equivalent spherical diameter, Dsph=∛(6/π V).

      We evaluated four implementations: 1) the original Brock model using as input the manual nodule sizes provided in the NLST dataset (BaselineManual); 2) the original Brock model using as input automatically calculated equivalent spherical diameter, Dsph, (BaselineAuto); 3) a Brock model re-fitted to the NLST dataset, using the manual nodule sizes (OptimManual); 4) a Brock model re-fitted to the NLST dataset, using the automatic nodule sizes (OptimAuto).

      Statistics were computed by bootstrapping across 1000 draws without replacement with 70%/30% training/testing per-patient splits. The performance of each combination was measured using Area-Under-the-Receiver-Operating-Curve (AUC-ROC) of predicting nodule malignancy. The relative weighting of size coefficients was also compared.

      4c3880bb027f159e801041b1021e88e8 Result

      The AUC-ROC was 86.5% (95% confidence interval (CI): 83.2, 89.7) for BaselineManual, 87.4% for OptManual (84.3, 90.5), 88.5% for BaselineAuto (85.7, 91.4), and 89.0% for OptAuto (86.2, 91.8). The relative absolute weight of the size coefficient is 0.46 in OptManual (95% CI: 0.42,0.49) increasing to 0.53 (0.49, 0.57) in OptAuto, an increase of 0.07 (0.06, 0.09)

      8eea62084ca7e541d918e823422bd82e Conclusion

      Automatic segmentation appears to improve the prediction accuracy of both the original Brock model and the NLST optimized version. The benefits of repeatable measurements from automatic segmentations are apparent even as direct replacements within the original model, i.e. without re-fitting. However, re-fitting the parameters increases the relative weight of the nodule size coefficient and further improves performance.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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    EXH01 - AstraZeneca Exhibit Showcase Session - The Visions and Science for Earlier Treatment (Not IASLC CME Accredited) (ID 990)

    • Event: WCLC 2018
    • Type: Exhibit Showcase
    • Track:
    • Presentations: 2
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      EXH01.01 - Developing and Preparing for Earlier Treatments in Lung Cancer (ID 14699)

      09:55 - 10:10  |  Presenting Author(s): Jean-Charles Soria

      • Abstract

      Abstract not provided

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      EXH01.02 - Early Detection and Monitoring of Lung Cancer (ID 14700)

      10:10 - 10:25  |  Presenting Author(s): J Carl Barrett

      • Abstract

      Abstract not provided

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    EXH02 - Thermo Fisher Exhibit Showcase Session - The Way We Test Matters for Patient Outcomes (Not IASLC CME Accredited) (ID 1001)

    • Event: WCLC 2018
    • Type: Exhibit Showcase
    • Track:
    • Presentations: 1
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      EXH02.01 - Thermo Fisher Exhibit Showcase Session - The Way We Test Matters for Patient Outcomes (ID 14774)

      12:15 - 13:15  |  Presenting Author(s): Anagh Vora

      • Abstract

      Abstract not provided

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    EXH03 - Inivata Exhibit Showcase Session - Liquid Biopsy: Providing Accurate Insights for Advanced NSCLC Patients - A Closer Look at the Validation and Utility of the InVision First - Lung Test (Not IASLC CME Accredited) (ID 1002)

    • Event: WCLC 2018
    • Type: Exhibit Showcase
    • Track:
    • Presentations: 1
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      EXH03.01 - Liquid Biopsy - Providing Accurate Insights for Advanced NSCLC Patients - A Closer Look at the Validation and Utility of the InVision FirstTM - Lung Test (ID 14775)

      17:00 - 17:30  |  Presenting Author(s): Ramaswamy Govindan, Clive Morris

      • Abstract
      • Slides

      Abstract not provided

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    PA01 - Patients and Advocacy DRIVING Research Together (ID 1005)

    • Event: WCLC 2018
    • Type: Patient and Advocacy Panel
    • Track: Advocacy
    • Presentations: 1