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Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.

Matching insurance claims data with EMR molecular status data in non-small cell lung cancer (NSCLC) patients: Understanding real-world molecular testing and prevalence rates at the site and investigator level.

Sub-category:
Metastatic Non-Small Cell Lung Cancer

Category:
Lung Cancer—Non-Small Cell Metastatic

Meeting:
2019 ASCO Annual Meeting

Abstract No:
e20568

Citation:
J Clin Oncol 37, 2019 (suppl; abstr e20568)

Author(s): Michael Gregory Cushion, Bhavani Krishnan, Jeff Paul Hodge, Jaya Chandra Balusu, Joseph Wagner, Cristina Oliva, Dana Edwards; IQVIA, London, United Kingdom; IQVIA, Durham, NC; IQVIA, Raleigh, NC; IQVIA, Plymouth Meeting, PA; IQVIA, Reading, United Kingdom

Abstract Disclosures

Abstract:

Background: Many targeted therapy clinical trials require a somatic gene mutation/alteration for eligibility. We assessed the feasibility of leveraging Real-World Data (RWD) to enrol NSCLC patients into clinical trials. Methods: US insurance claims data were extracted to identify lung cancer patients. These data were matched with EMR data also containing NSCLC patients’ details regarding the occurrence and results of molecular testing for EGFR, ALK, ROS1, JAK2, HER2 and RET somatic alterations, achieving a level of granular detail beyond that available in each individual dataset. A one-year extraction period was applied, with no gender or age restrictions. Results: Results for the matched dataset are summarised in the table below - the overall patient record match was 89.6%. Conclusions: The observed prevalence correlated reasonably well with literature reported prevalence for the molecular biomarkers associated commercially available targeted therapies in NSCLC (EGFR, ALK, ROS1). The sample size for the remaining biomarkers was too small to draw conclusions, though the presence of data correlating to these is of interest, considering that there are no currently approved targeted therapies in NSCLC tailored to these predictive biomarkers. This approach could be expanded upon to recruit patients into targeted therapy clinical trials as the dataset is fully linkable to sites and investigators. With the emergence of broad genomic profiling, the availability of molecular data to support clinical trial enrolment is also expected to grow.

Molecular biomarkerNo. of patient records from EMR data with molecular testNo. of patient records from EMR data with positive resultPositive result prevalence (%)Literature reported prevalence (%)No. of patient records from insurance claims data with lung cancer diagnosis
EGFR436368.310-35274
ALK328113.43-7243
ROS115895.71107
JAK211Sample size too
small to infer
11
HER260Sample size too
small to infer
2-43
RET50Sample size too
small to infer
15

 
Other Abstracts in this Sub-Category:

 

1. Association of STK11/LKB1 genomic alterations with lack of benefit from the addition of pembrolizumab to platinum doublet chemotherapy in non-squamous non-small cell lung cancer.

Meeting: 2019 ASCO Annual Meeting Abstract No: 102 First Author: Ferdinandos Skoulidis
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer

 

2. Real-world outcomes of patients with advanced non-small cell lung cancer (aNSCLC) and autoimmune disease (AD) receiving immune checkpoint inhibitors (ICIs).

Meeting: 2019 ASCO Annual Meeting Abstract No: 110 First Author: Sean Khozin
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer

 

3. RELAY: A multinational, double-blind, randomized Phase 3 study of erlotinib (ERL) in combination with ramucirumab (RAM) or placebo (PL) in previously untreated patients with epidermal growth factor receptor mutation-positive (EGFRm) metastatic non-small cell lung cancer (NSCLC).

Meeting: 2019 ASCO Annual Meeting Abstract No: 9000 First Author: Kazuhiko Nakagawa
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer

 

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