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Lisa Le
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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): Lisa Le
- Abstract
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): Lisa Le
- Abstract
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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P1.01 - Advanced NSCLC (ID 158)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Advanced NSCLC
- Presentations: 2
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.01-30 - Non-Small Cell Lung Cancer (NSCLC) Next Generation Sequencing (NGS): Integrating Genomic Sequencing into a Publicly Funded Health Care Model (Now Available) (ID 2588)
09:45 - 18:00 | Author(s): Lisa Le
- Abstract
Background
Standard of care (SOC) molecular diagnostics for stage IV NSCLC patients in Ontario, Canada includes publicly reimbursed EGFR/ALK, and BRAF/ ROS-1 testing in selected cases. Other genomic alterations are not tested routinely at all institutions; however, enhanced molecular testing may broaden treatment options for patients by identifying actionable targets. This study evaluated costs, identified actionable targets, and determined clinical trial eligibility as a result of using the Oncomine Comprehensive Assay v3 (OCA v3, ThermoFisher) NGS in stage IV NSCLC patients at a single institution.
Method
This prospective study of stage IV NSCLC out-patients at Princess Margaret Cancer Centre (Toronto) began in February 2018 and recruitment is ongoing (NCT03558165). NSCLC patients without EGFR/ALK/KRAS/BRAF alteration (unless failure of prior targeted therapy and tissue rebiopsy), had diagnostic samples tested by OCAv3 (ThermoFisher; 161 genes: hotspots, fusions, and copy number variations). Primary endpoints were identification of incremental actionable targets and clinical trial opportunities as a result of broader OCAv3 testing. Secondary endpoints include feasibility and cost from the Canadian public healthcare perspective.
Result
From Feb 2018- Jan 2019 65 patients were enrolled [62% (N=40) completed/ 21% (N=14) screen fail/ 17% (N=11) pending], median age of completed cohort was 65, 60% (N=24) female, never/light smokers 68% (N=27), Asian 38% (N=15), previously treated 33% (N=13). Actionable targets beyond SOC were identified in 33% (N=13): ERBB2 (N=8), BRAFV600 (N=3), NRG fusion (N=1), MET exon 14 (N=1). Failure of NGS was secondary to insufficient tissue. 91% (N=10) of screen failures was secondary to tissue exhaustion from prior sequential SOC molecular testing. New clinical trial options were identified in 70% as a result of OCA v3 testing. Incremental costs per case beyond EGFR/ALK are estimated at $540 CAD. If ROS-1 and BRAF testing were publicly reimbursed at current rates, the incremental profiling cost with OCAv3 would be $90 CAD per case.
Conclusion
The OCAv3 consolidates genomic testing, identifies additional actionable targets, and substantially increases clinical trial eligibility for patients at a small incremental cost. Sample failures are reflective of exhausted diagnostic tissue as a result of prior sequential genomic testing. The key barrier to implementation of NGS remains funding in the Canadian health care system.
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P1.01-70 - Dominant Circulating Myeloid Populations Are Associated with Poor Response in NSCLC Treated with 1st Line PD-1 Monotherapy (Now Available) (ID 2295)
09:45 - 18:00 | Author(s): Lisa Le
- Abstract
Background
Immune subpopulations within the tumor microenvironment (TME) play a central role in determining response to checkpoint inhibitors. Myeloid derived suppressor cells (MDSC), a heterogeneous population of immature myeloid cells, have a predominantly immunosuppressive role by stimulating T regulatory cells. We hypothesize that elevated myeloid-to-lymphocyte measures in the peripheral blood predict for greater numbers of myeloid derived suppressor cells in the TME and worse outcomes.
Method
We identified all advanced NSCLC patients treated with immunotherapy between 2010-2019 at the Princess Margaret Cancer Center. Patients who received first line monotherapy with a PD-1 inhibitor were reviewed for clinical information including age, sex, histology, stage, smoking status, ethnicity, PD-L1 expression and tumor genotype. Myeloid cells lines analyzed included neutrophils, monocytes and platelets, expressed as ratios to peripheral lymphocytes. Multivariate analyses were conducted using the cox and logistic regression models to adjust for confounders.
Result
We identified 75 patients who were eligible for analysis. Disproportionate increases in the different myeloid cell types were highly correlated with each other (all Pearson’s rho>0.8) and the neutrophil to lymphocyte ratio (NLR) was selected as representative. A high NLR (>5) was associated with shorter time-to-treatment-failure (median TTF 9.7 vs 29.4 months) that remained significant after adjusting for confounders including PD-L1 and presence of liver metastases (p=0.004). High NLR was also an independent predictor of poor OS (median 11.3 vs 56.8 months, HR 3.02, p=0.04). Although NLR was not predictive of radiographic response, there was a trend to association with a rapidly progressive phenotype defined by primary progressive disease and a duration of therapy ≤2 months (p=0.06). Other predictive factors included the presence of liver metastases, which was associated with a worse OS (HR3.37 p=0.05) but not TTF (p=0.14). An association was also seen between NLR and liver metastases (mean NLR 6.6 vs 25.2 in the absence and presence of liver metastases respectively, p<0.001).
Conclusion
A disproportionate increase in peripheral immune myeloid populations may represent a systemic, myeloid-driven, immunosuppressive state that is significantly associated with primary refractory disease, rapid progression, and poor survival. A subset of about 50 patients with biobanked tissue are presently being analyzed using multiplex immunofluorescence to assess for MDSCs in the TME to correlate with peripheral blood findings.
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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): Lisa Le
- Abstract
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.
