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Mike R Sung



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    EP1.04 - Immuno-oncology (ID 194)

    • Event: WCLC 2019
    • Type: E-Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 08:00 - 18:00, Exhibit Hall
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      EP1.04-24 - Smoking History May Help Predict Immunotherapy Response in PDL1+ Lung Cancer Patients (Now Available) (ID 2688)

      08:00 - 18:00  |  Author(s): Mike R Sung

      • Abstract
      • Slides

      Background

      Tumour PD-L1 expression is a key predictor of benefit from anti-PD-1 therapy in NSCLC. Clinical factors associated with benefit include smoking status. We explored the additional predictive impact of smoking status added to tumour PD-L1 expression.

      Method

      A prospective cohort of 125 patients with advanced NSCLC treated at a single institution with anti-PD-1 therapy and outcome data including treatment response was explored. Ordinal logistic regression was performed to test factors associated with treatment response, including age, sex, ethnicity, pathology, PD-L1 expression (>=1%) and smoking status.

      Result

      Median age of the cohort at the time of anti-PD-1 therapy was 65.3 years (range 28-88.2); 55.2% were male, 21.2% were East Asian, 76.8% had adenocarcinoma (11.8% EGFR mutant, 15.2% squamous, 8% other). In univariable analysis, smoking status was associated with higher response rate (current versus never smoker: OR 3.73, 95% CI 1.40-9.97, p=0.004; past versus never: OR 1.09; 95% CI 0.52-2.30, p=0.09). Patients with EGFR mutant lung cancer were unlikely to respond (OR 0.25; 95% CI 0.07-0.83, p=0.02). In multivariable analysis, smoking remained significantly associated with response in patients with positive tumour PD-L1 expression (current vs. never smoker OR 4.01; 95% CI 1.31-12.27, p=0.01).

      Conclusion

      Clinical factors such as smoking status may enrich our ability to select patients with PD-L1 positive lung cancer that respond to immunotherapy.

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    EP1.16 - Treatment in the Real World - Support, Survivorship, Systems Research (ID 206)

    • Event: WCLC 2019
    • Type: E-Poster Viewing in the Exhibit Hall
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 08:00 - 18:00, Exhibit Hall
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      EP1.16-05 - Real World Outcomes of Advanced NSCLC Patients with Liver Metastases  (Now Available) (ID 2632)

      08:00 - 18:00  |  Author(s): Mike R Sung

      • Abstract
      • Slides

      Background

      Patients with advanced lung cancer represent a heterogenous population with varying patterns of metastasis. Those with liver metastases may represent a unique cohort with differential response to therapy, including immunotherapy in NSCLC. Novel Natural Language Processing (NLP) and Artificial Intelligence (AI) technology enables automated extraction of real-world data to examine these populations at greater scale than current manual chart abstraction processes, helping clinicians make more informed treatment decisions.

      Method

      Patients diagnosed with stage IIIB/IV lung cancer who received first-line systemic therapy at the Princess Margaret Cancer Centre between 2015 and 2018 were reviewed using the DARWEN™ NLP and AI data abstraction platform developed by Pentavere. Data extracted include tumour histology, patient age, sex, ECOG performance status, smoking status, biomarker status, PD-L1 expression, sites of metastases, treatment details and survival.

      Result

      Of 615 patients with accessible electronic pathology records, 540 (87.8%) had NSCLC and 333 (54.1%) received systemic therapy. In those patients treated with first-line therapy (immunotherapy 10.2%, targeted therapy 30.9%, chemotherapy 62.7%), 27.3% (91/333) had liver metastasis at any point from baseline to end of follow up (median follow up 8 months).

      280 patients had NSCLC and received systemic therapy and were included in subsequent analysis. Of these, 69 (24.6%) had liver metastases at any point and overall survival was worse in those patients 544 vs 715 days (p=0.006).

      Liver metastases were more commonly seen in those with more metastatic sites (OR: 1.42, 95% CI: 1.19-1.70, p= < 0.001). By contrast, those with EGFR mutant lung cancer were less likely to develop liver metastasis (OR: 0.45, 95% CI: 0.23-0.87, p=0.02).

      Using Cox regression analyses, after controlling for age, sex, baseline performance status, baseline smoking status, first line treatment, total number of metastatic sites and baseline LDH, presence of liver metastasis remained significantly associated with worse survival (HR: 1.78, 95% CI: 1.14-2.76, p=0.01). Elevated baseline LDH, a known poor prognostic factor, was also associated with worse overall survival (HR: 1.58, 95% CI: 10.6-2.35), p=0.02). No differential effect by type of therapy was seen.

      Conclusion

      The presence of liver metastases confers worse prognosis in advanced non-small cell lung cancer patients. This effect was observed irrespective of treatment type and highlights the need for additional treatment options which are efficacious in this patient population. Larger cohort studies may help identify patients with liver metastases that may benefit from specific therapeutic strategies in the future. NLP and AI technologies like DARWEN™ can rapidly generate population-based datasets and provide clinicians with timely access to previously unavailable information on treatment patterns and outcomes which can lead to improved care.

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    P1.16 - Treatment in the Real World - Support, Survivorship, Systems Research (ID 186)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.16-07 - Real World Evidence of the Impact of Immunotherapy in Patients with Advanced Lung Cancer (Now Available) (ID 2731)

      09:45 - 18:00  |  Author(s): Mike R Sung

      • Abstract
      • Slides

      Background

      PD-1 axis inhibitors have become a standard treatment modality in the management of advanced lung cancer. Novel Natural Language Processing (NLP) and Artificial Intelligence (AI) technology enables automated extraction of real-world data at greater scale than current manual chart abstraction processes, which can be used to further explore the impact of these agents in the general population irrespective of PDL1 tumour expression.

      Method

      Patients diagnosed with stage IIIB/IV lung cancer at the Princess Margaret Cancer Centre between 2015 and 2018 were reviewed using the DARWEN™ NLP and AI data abstraction platform developed by Pentavere. Data extracted include patient age, smoking status, ECOG performance status, tumour histology, biomarker status, PDL1 expression, sites of metastases, treatment information and survival.

      Result

      Of 615 patients with accessible electronic pathology records, 540 (87.8%) had NSCLC and 280 (51.8%) of those received systemic therapy and were included in the analysis.

      86 (30.7%) were EGFR sensitizing mutation positive, 18 (6.4%) ALK rearranged, PDL1>50%/1-49/<1/unknown in 21/8/10/61%. Almost one third (31.7%) of those that received treatment received immunotherapy for any line of therapy (12.1% first-line). Chemotherapy was used first-line in 56.1% and targeted therapy in 36.1% of those receiving systemic therapy

      Patients that were more likely to receive immunotherapy any line were smokers (OR: 2.7, 95% CI: 1.43-5.10, p=0.002) with a higher number of metastatic sites (OR: 1.23, 95% CI: 1.06-1.43, p=0.005). Those with EGFR sensitizing mutation and ALK rearrangement were less likely to be given immunotherapy (OR: 0.07, 95% CI: 0.03-0.19, p<0.001 and OR: 0.11, 95% CI: 0.01-0.84, p=0.03 respectively). There was no difference in the rates of immunotherapy being given in those with PDL1>50%/1-49/<1 (52/52/44%, p=0.8).

      Using Cox regression analyses after controlling for ALK, EGFR, PD-L1, age, sex, baseline ECOG, smoking status and number of metastatic sites, patients that received immunotherapy at any point had longer survival (HR: 0.28, 95%CI: 0.12-0.67, p=0.004) in a complete case analysis.

      Conclusion

      Novel NLP and AI technologies like DARWEN™ gives clinicians access to previously unavailable information on real world treatment strategies and outcomes. Increasing uptake of immunotherapy may further improve outcomes for patients with this challenging to treat cancer. This study demonstrates that the benefit of immunotherapy seen in clinical trials can be translated into the general advanced lung cancer population. Larger population studies will be needed to further analyze the impact of new treatments in the real world and will be facilitated by automated data abstraction to rapidly generate large datasets.

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