Text analysis framework for identifying mutations among non-small cell lung cancer patients from laboratory data.

BACKGROUND: Laboratory data can provide great value to support research aimed at reducing the incidence, prolonging survival and enhancing outcomes of cancer. Data is characterized by the information it carries and the format it holds. Data captured in Alberta's biomarker laboratory repository is free text, cluttered and rouge. Such data format limits its utility and prohibits broader adoption and research development. Text analysis for information extraction of unstructured data can change this and lead to more complete analyses. Previous work on extracting relevant information from free text, unstructured data employed Natural Language Processing (NLP), Machine Learning (ML), rule-based Information Extraction (IE) methods, or a hybrid combination between them. METHODS: In our study, text analysis was performed on Alberta Precision Laboratories data which consisted of 95,854 entries from the Southern Alberta Dataset (SAD) and 6944 entries from the Northern Alberta Dataset (NAD). The data covers all of Alberta and is completely population-based. Our proposed framework is built around rule-based IE methods. It incorporates topics such as Syntax and Lexical analyses to achieve deterministic extraction of data from biomarker laboratory data (i.e., Epidermal Growth Factor Receptor (EGFR) test results). Lexical analysis compromises of data cleaning and pre-processing, Rich Text Format text conversion into readable plain text format, and normalization and tokenization of text. The framework then passes the text into the Syntax analysis stage which includes the rule-based method of extracting relevant data. Rule-based patterns of the test result are identified, and a Context Free Grammar then generates the rules of information extraction. Finally, the results are linked with the Alberta Cancer Registry to support real-world cancer research studies. RESULTS: Of the original 5512 entries in the SAD dataset and 5017 entries in the NAD dataset which were filtered for EGFR, the framework yielded 5129 and 3388 extracted EGFR test results from the SAD and NAD datasets, respectively. An accuracy of 97.5% was achieved on a random sample of 362 tests. CONCLUSIONS: We presented a text analysis framework to extract specific information from unstructured clinical data. Our proposed framework has shown that it can successfully extract relevant information from EGFR test results.

The impact of population-based EGFR testing in non-squamous metastatic non-small cell lung cancer in Alberta, Canada.

OBJECTIVES: While Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitors have been shown to be effective in phase III randomized trials, the value of targeted therapies has been challenging to evaluate at the population-level. We examined the impact of population-level EGFR testing and treatment on survival outcomes among non-squamous metastatic Non-Small Cell Lung Cancer (NSCLC) patients. MATERIALS AND METHODS: Real-world, population-level data were collected from all de novo non-squamous metastatic NSCLC patients in Alberta, Canada from 2004 to 2020. EGFR testing data were collected through Alberta Precision Laboratories. Differences in survival rates and overall survival (OS) pre (2004-2012) and post initiation (post) (2013-2019) testing periods were evaluated using interrupted time series analyses. The impact of testing and subsequent treatment was evaluated using multivariable Cox Proportional Hazards models. RESULTS: In total, 4,578 non-squamous metastatic NSCLC patients were diagnosed pre-EGFR testing and 4,457 patients were diagnosed post-EGFR testing (2013-2019). Among patients diagnosed in the pre-EGFR testing period, the 6-month, 1-year, and 2-year survival probabilities were 0.39 (95 % CI: 0.38-0.41), 0.22 (95 % CI: 0.21-0.23), and 0.09 (95 % CI: 0.08-0.10), while the survival probabilities for patients diagnosed in the post-EGFR testing period were 0.45 (95 % CI: 0.43-0.46), 0.29 (95 % CI: 0.27-0.30), and 0.16 (95 % CI: 0.15-0.17), respectively. After adjusting for baseline patient and clinical characteristics, OS in the post-EGFR period was significantly improved compared to the pre-EGFR period (HR: 0.81; 95 % CI: 0.78-0.85). Among patients who were treated with systemic therapy, those tested for an EGFR mutation had significantly greater survival than patients who were not tested HR of 0.81 (95 % CI: 0.70-0.95). CONCLUSION: These results show the considerable impact of population-based molecular testing and subsequent targeted therapies on survival among metastatic NSCLC patients. The estimates here can be used in future studies to evaluate the population-level cost-effectiveness of testing and treatment.

Prevalence, Treatment Patterns, and Outcomes of Individuals with EGFR Positive Metastatic Non-Small Cell Lung Cancer in a Canadian Real-World Setting: A Comparison of Exon 19 Deletion, L858R, and Exon 20 Insertion EGFR Mutation Carriers.

Real-world evidence surrounding EGFR positive NSCLC patients in Canada is limited. Administrative databases in Alberta, Canada were used to evaluate EGFR testing and mutation prevalence in de novo metastatic NSCLC, as well as the characteristics, treatment patterns, and outcomes of individuals with Exon 19, L858R and Exon20ins mutations. Between 2013-2019, 2974 individuals underwent EGFR testing, of which 451 (15.2%) were EGFR positive. Among EGFR positive individuals, 221 (49.0%) had an Exon 19 mutation, 159 (35.3%) had an L858R mutation, and 18 (4%) had an Exon20ins mutation. The proportion of individuals who initiated 1L systemic therapy was 89.1% for Exon19, 85.5% for L858R, and 72.2% for Exon20ins carriers. The primary front-line systemic therapy was gefitinib or afatinib monotherapy for individuals with Exon 19 (93.4%) and L858R (94.1%) mutations versus platinum combination therapy for individuals with Exon20ins mutations (61.5%). The Exon20ins cohort had worse median overall survival from initiation of 1L systemic therapy (10.5 months [95% CI: 8.0-not estimable]) than the Exon19 (20.6 months [95% CI: 18.4-24.9]), and L858R cohorts (19.1 months [95% CI: 14.5-23.1]). These findings highlight that Exon20ins mutations represent a rare subset of NSCLC in which treatment options are limited and survival outcomes are worse relative to individuals with more common types of EGFR mutations.