Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise for unraveling tumor heterogeneity and understanding treatment resistance. However, conventional methods, especially in cancers like non-small cell lung cancer (NSCLC), often yield low CTC numbers, hindering comprehensive analyses. This study addresses this limitation by employing diagnostic leukapheresis (DLA) to cancer patients, enabling the screening of larger blood volumes. To leverage DLA's full potential, this study introduces a novel approach for CTC enrichment from DLAs. METHODS: DLA was applied to six advanced stage NSCLC patients. For an unbiased CTC enrichment, a two-step approach based on negative depletion of hematopoietic cells was used. Single-cell (sc) whole-transcriptome sequencing was performed, and CTCs were identified based on gene signatures and inferred copy number variations. RESULTS: Remarkably, this innovative approach led to the identification of unprecedented 3,363 CTC transcriptomes. The extensive heterogeneity among CTCs was unveiled, highlighting distinct phenotypes related to the epithelial-mesenchymal transition (EMT) axis, stemness, immune responsiveness, and metabolism. Comparison with sc transcriptomes from primary NSCLC cells revealed that CTCs encapsulate the heterogeneity of their primary counterparts while maintaining unique CTC-specific phenotypes. CONCLUSIONS: In conclusion, this study pioneers a transformative method for enriching CTCs from DLA, resulting in a substantial increase in CTC numbers. This allowed the creation of the first-ever single-cell whole transcriptome in-depth characterization of the heterogeneity of over 3,300 NSCLC-CTCs. The findings not only confirm the diagnostic value of CTCs in monitoring tumor heterogeneity but also propose a CTC-specific signature that can be exploited for targeted CTC-directed therapies in the future. This comprehensive approach signifies a major leap forward, positioning CTCs as a key player in advancing our understanding of cancer dynamics and paving the way for tailored therapeutic interventions.

Exploring Negated Entites for Named Entity Recognition in Italian Lung Cancer Clinical Reports.

This paper explores the potential of leveraging electronic health records (EHRs) for personalized health research through the application of artificial intelligence (AI) techniques, specifically Named Entity Recognition (NER). By extracting crucial patient information from clinical texts, including diagnoses, medications, symptoms, and lab tests, AI facilitates the rapid identification of relevant data, paving the way for future care paradigms. The study focuses on Non-small cell lung cancer (NSCLC) in Italian clinical notes, introducing a novel set of 29 clinical entities that include both presence or absence (negation) of relevant information associated with NSCLC. Using a state-of-the-art model pretrained on Italian biomedical texts, we achieve promising results (average F1-score of 80.8%), demonstrating the feasibility of employing AI for extracting biomedical information in the Italian language.

Non-Small Cell Lung Cancer, Version 4.2024.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Non-Small Cell Lung Cancer (NSCLC) provide recommendations for the treatment of patients with NSCLC, including diagnosis, primary disease management, surveillance for relapse, and subsequent treatment. The panel has updated the list of recommended targeted therapies based on recent FDA approvals and clinical data. This selection from the NCCN Guidelines for NSCLC focuses on treatment recommendations for advanced or metastatic NSCLC with actionable molecular biomarkers.

Can hypoxia marker carbonic anhydrase IX serve as a potential new diagnostic marker and therapeutic target of non-small cell lung cancer?

Lung cancer represents the leading cause of cancer-related deaths. Non-small cell lung cancer (NSCLC), the most common form of lung cancer, is a molecularly heterogeneous disease with intratumoral heterogeneity and a significant mutational burden associated with clinical outcome. Tumor microenvironment (TME) plays a fundamental role in the initiation and progression of primary de novo lung cancer and significantly influences the response of tumor cells to therapy. Hypoxia, an integral part of the tumor microenvironment and a serious clinical phenomenon, is associated with increased genetic instability and a more aggressive phenotype of NSCLC, which correlates with the risk of metastasis. Low oxygen concentration influences all components of TME including the immune microenvironment. Hypoxia-inducible pathway activated in response to low oxygen supply mediates the expression of genes important for the adaptation of tumor cells to microenvironmental changes. A highly active transmembrane hypoxia-induced metalloenzyme - carbonic anhydrase IX (CAIX), as a part of transport metabolon, contributes to the maintenance of intracellular pH within physiological values and to the acidification of the extracellular space. CAIX supports cell migration and invasion and plays an important role in NSCLC tumor tissue and pleural effusion. Due to its high expression, it also represents a potential diagnostic differential biomarker and therapeutic target in NSCLC. To test new potential targeted therapeutic compounds, suitable models are required that more faithfully simulate tumor tissue, TME components, and spatial architecture.

Applications of Artificial Intelligence in Lung Pathology.

Artificial intelligence/machine learning tools are being created for use in pathology. Some examples related to lung pathology include acid-fast stain evaluation, programmed death ligand-1 (PDL-1) interpretation, evaluating histologic patterns of non-small-cell lung carcinoma, evaluating histologic features in mesothelioma associated with adverse outcomes, predicting response to anti-PDL-1 therapy from hematoxylin and eosin-stained slides, evaluation of tumor microenvironment, evaluating patterns of interstitial lung disease, nondestructive methods for tissue evaluation, and others. There are still some frameworks (regulatory, workflow, and payment) that need to be established for these tools to be integrated into pathology.

Molecular Testing in Lung Cancer: Recommendations and Update.

Adoption of molecular testing in lung cancer is increasing. Molecular testing for staging and prediction of response for targeted therapy remain the main indications, and although utilization of blood-based testing for tumor is growing, the use of the diagnostic cytology and tissue specimens is equally important. The pathologist needs to optimize reflex testing, incorporate stage-based algorithms, and understand types of tests for timely and complete assessment in the majority of cases. When tissue is limited, testing should capture the most frequent alterations to maximize the yield of what are largely mutually exclusive alterations, avoiding the need for repeat biopsy.

CRISPR/Cas13a-based supersensitive circulating tumor DNA assay for detecting EGFR mutations in plasma.

Despite recent technological advancements in cell tumor DNA (ctDNA) mutation detection, challenges persist in identifying low-frequency mutations due to inadequate sensitivity and coverage of current procedures. Herein, we introduce a super-sensitivity and specificity technique for detecting ctDNA mutations, named HiCASE. The method utilizes PCR-based CRISPR, coupled with the restriction enzyme. In this work, HiCASE focuses on testing a series of EGFR mutations to provide enhanced detection technology for non-small cell lung cancer (NSCLC), enabling a detection sensitivity of 0.01% with 40 ng cell free DNA standard. When applied to a panel of 140 plasma samples from 120 NSCLC patients, HiCASE exhibits 88.1% clinical sensitivity and 100% specificity with 40 µL of plasma, higher than ddPCR and Super-ARMS assay. In addition, HiCASE can also clearly distinguish T790M/C797S mutations in different positions at a 1% variant allele frequency, offering valuable guidance for drug utilization. Indeed, the established HiCASE assay shows potential for clinical applications.

Comprehensive analysis and validation reveal DEPDC1 as a potential diagnostic biomarker associated with tumor immunity in non-small-cell lung cancer.

Current evidence suggests that DEP domain containing 1 (DEPDC1) has an important effect on non-small-cell lung cancer (NSCLC). However, the diagnostic value and the regulatory function within NSCLC are largely unclear. This work utilized publicly available databases and in vitro experiments for exploring, DEPDC1 expression, clinical features, diagnostic significance and latent molecular mechanism within NSCLC. According to our results, DEPDC1 was remarkably upregulated in the tissues of NSCLC patients compared with non-carcinoma tissues, linked with gender, stage, T classification and N classification based on TCGA data and associated with smoking status and stage according to GEO datasets. Meanwhile, the summary receiver operating characteristic (sROC) curve analysis result showed that DEPDC1 had a high diagnostic value in NSCLC (AUC = 0.96, 95% CI: 0.94-0.98; diagnostic odds ratio = 99.08, 95%CI: 31.91-307.65; sensitivity = 0.89, 95%CI: 0.81-0.94; specificity = 0.92, 95%CI: 0.86-0.96; positive predictive value = 0.94, 95%CI: 0.89-0.98; negative predictive value = 0.78, 95%CI: 0.67-0.90; positive likelihood ratio = 11.77, 95%CI: 6.11-22.68; and negative likelihood ratio = 0.12, 95%CI: 0.06-0.22). Subsequently, quantitative real-time PCR (qRT-PCR) and western blotting indicated that DEPDC1 was high expressed in NSCLC cells. According to the in vitro MTS and apoptotic assays, downregulated DEPDC1 expression targeting P53 signaling pathway inhibited the proliferation of NSCLC cells while promoting apoptosis of NSCLC cells. Moreover, DEPDC1 was significantly correlated with immune cell infiltrating levels in NSCLC based on TCGA data, which were primarily associated with T cells CD4 memory activated, macrophages M1, B cells memory, mast cells resting, T cells regulatory, monocytes, and T cells CD4 memory resting. Compared with the group with high expression of DEPDC1, the group with low expression level had higher scores for immune checkpoint inhibitors (ICIs) treatment. GSEA confirmed that DEPDC1 was involved in gene expression and tumor-related signaling pathways. Finally, DEPDC1 and its associated immune-related genes were shown to be enriched in 'receptor ligand activity', 'external side of plasma membrane', 'regulation of innate immune response', and 'Epstein-Barr virus infection' pathways. The present study demonstrates that DEPDC1 may contribute to NSCLC tumorigenesis and can be applied as the biomarker for diagnosis and immunology.

Comparison between next-generation sequencing and multiplex polymerase chain reaction assays for nonsmall-cell lung cancer molecular diagnosis.

PURPOSE: Genetic mutation detection has become an important step in nonsmall-cell lung cancer (NSCLC) treatment because of the increasing number of drugs that target genomic rearrangements. A multiplex test that can detect multiple gene mutations prior to treatment is thus necessary. Currently, either next-generation sequencing (NGS)-based or polymerase chain reaction (PCR)-based tests are used. We evaluated the performance of the Oncomine Dx Target Test (ODxTT), an NGS-based multiplex biomarker panel test, and the AmoyDx Pan Lung Cancer PCR Panel (AmoyDx PLC panel), a real-time PCR-based multiplex biomarker panel test. MATERIALS AND METHODS: Patients with histologically diagnosed NSCLC and a sufficient sample volume to simultaneously perform the AmoyDx PLC panel and ODxTT-M were included in the study. The success and detection rates of both tests were evaluated. RESULTS: Biopsies revealed 116 cases of malignancies, 100 of which were NSCLC. Of these, 59 met the inclusion criteria and were eligible for analysis. The success rates were 100% and 98% for AmoyDx PLC panel and ODxTT-M, respectively. Nine driver mutations were detected in 35.9% and 37.3% of AmoyDx PLC and ODxTT-M panels, respectively. EGFR mutations were detected in 14% and 12% of samples using the AmoyDx PLC panel and ODxTT-M, respectively. Of the 58 cases in which both NGS and AmoyDx PLC panels were successful, discordant results were observed in seven cases. These differences were mainly due to different sensitivities of the detection methods used and the gene variants targeted in each test. DISCUSSION: The AmoyDx PLC panel, a PCR-based multiplex diagnostic test, exhibits a high success rate. The frequency of the nine genes targeted for treatment detected by the AmoyDx PLC panel was comparable to the frequency of mutations detected by ODxTT-M. Clinicians should understand and use the AmoyDx PLC panel and ODxTT-M with respect to their respective performances and limitations.

Six-Gene Signature for Differential Diagnosis and Therapeutic Decisions in Non-Small-Cell Lung Cancer-A Validation Study.

Non-small-cell lung cancer (NSCLC) poses a challenge due to its heterogeneity, necessitating precise histopathological subtyping and prognostication for optimal treatment decision-making. Molecular markers emerge as a potential solution, overcoming the limitations of conventional methods and supporting the diagnostic-therapeutic interventions. In this study, we validated the expression of six genes (MIR205HG, KRT5, KRT6A, KRT6C, SERPINB5, and DSG3), previously identified within a 53-gene signature developed by our team, utilizing gene expression microarray technology. Real-time PCR on 140 thoroughly characterized early-stage NSCLC samples revealed substantial upregulation of all six genes in squamous cell carcinoma (SCC) compared to adenocarcinoma (ADC), regardless of clinical factors. The decision boundaries of the logistic regression model demonstrated effective separation of the relative expression levels between SCC and ADC for most genes, excluding KRT6C. Logistic regression and gradient boosting decision tree classifiers, incorporating all six validated genes, exhibited notable performance (AUC: 0.8930 and 0.8909, respectively) in distinguishing NSCLC subtypes. Nevertheless, our investigation revealed that the gene expression profiles failed to yield predictive value regarding the progression of early-stage NSCLC. Our molecular diagnostic models manifest the potential for an exhaustive molecular characterization of NSCLC, subsequently informing personalized treatment decisions and elevating the standards of clinical management and prognosis for patients.

[Current Status and Issues of Companion Diagnostics for Non-Small Cell Lung Cancer].

There have been many driver gene abnormalities identified in NSCLC, and the development and clinical adoption of targeting drugs for such are progressing. This has, as a result, complicated the situation surrounding companion diagnostics (CDx). Lung cancer treatment guidelines recommend the use of Multiplex testing that allow for CDx for many driver gene abnormalities to be used prior to 1L treatment for advanced lung cancer. The problem is that insurance rules stipulate that only one of Multiplex test is deemed reimbursable per junction in time, particular CDx are linked to particular targeted drugs instead of particular driver gene abnormalities, and none of the currently available Multiplex tests contain the CDx for all genetic mutations for which drugs have been approved. This results in a fair number of cases in which regulatory issues ensue. It is also very difficult to obtain enough of a tissue sample to get accurate results in Multiplex testing for advanced NSCLC. CDx are not full-proof, as results must be interpreted with a consideration of the potential for a false-negative due to the nature of tests. This paper, therefore, will focus on the issues with CDx from the viewpoint of medical oncologists in the clinical setting.

Advancing Nonsmall Cell Lung Cancer Diagnosis Accuracy via Dual Detection Fluorescent Nanoprobes.

Among the primary threats to human health worldwide, nonsmall cell lung cancer (NSCLC) remains a significant factor and is a leading cause of cancer-related deaths. Due to subtle early symptoms, NSCLC patients are diagnosed at advanced stages, resulting in low survival rates. Herein, novel Au-Se bond nanoprobes (NPs) designed for the specific detection of Calpain-2 (CAPN2) and Human Neutrophil Elastase (HNE), pivotal biomarkers in NSCLC, were developed. The NPs demonstrated exceptional specificity and sensitivity toward CAPN2 and HNE, enabling dual-color fluorescence imaging to distinguish between NSCLC cells and normal lung cells effectively. The NPs' performance was consistent across a wide pH range (6.2 to 8.0), and it exhibited remarkable resistance to biological thiol interference, indicating its robustness in complex physiological environments. These findings suggest the nanoprobe is a promising tool for early NSCLC diagnosis, offering a novel approach for enhancing the accuracy of cancer detection.

Circulating biomarkers as predictors of response to immune checkpoint inhibitors in NSCLC: Are we on the right path?

Immune checkpoints inhibitors (ICIs) have markedly improved the therapeutic management of advanced NSCLC and, more recently, they have demonstrated efficacy also in the early-stage disease. Despite better survival outcomes with ICIs compared to standard chemotherapy, a large proportion of patients can derive limited clinical benefit from these agents. So far, few predictive biomarkers, including the programmed death-ligand 1 (PD-L1), have been introduced in clinical practice. Therefore, there is an urgent need to identify novel biomarkers to select patients for immunotherapy, to improve efficacy and avoid unnecessary toxicity. A deeper understanding of the mechanisms involved in antitumor immunity and advances in the field of liquid biopsy have led to the identification of a wide range of circulating biomarkers that could potentially predict response to immunotherapy. Herein, we provide an updated overview of these circulating biomarkers, focusing on emerging data from clinical studies and describing modern technologies used for their detection.

Digital Circulating Tumor Cells Quantification.

Circulating tumor cells (CTCs) are an emerging but vital biomarker for cancer management. An efficient methodology for accurately quantifying CTCs remains challenging due to their rareness. Here, we develop a digital CTC detection strategy using partitioning instead of enrichment to quantify CTCs. By utilizing the characteristics of droplet microfluidics that can rapidly generate a large number of parallel independent reactors, combined with Poisson distribution, we realize the quantification of CTCs in the blood directly. The limit of detection of our digital CTCs quantification assay is five cells per 5 mL of whole blood. By simultaneously detecting multiple genetic mutations, our approach achieves highly sensitive and specific detection of CTCs in peripheral blood from NSCLC patients (AUC = 1). Our digital platform offers a potential approach and strategy for the quantification of CTCs, which could contribute to the advancement of cancer medical management.

The biomarkers ATLAS: An audit on 1100 non-small cell lung cancer from an Italian knowledge-based database.

AIMS: To date, precision medicine has revolutionized the clinical management of Non-Small Cell Lung Cancer (NSCLC). International societies approved a rapidly improved mandatory testing biomarkers panel for the clinical stratification of NSCLC patients, but harmonized procedures are required to optimize the diagnostic workflow. In this context a knowledge-based database (Biomarkers ATLAS, https://biomarkersatlas.com/) was developed by a supervising group of expert pathologists and thoracic oncologists collecting updated clinical and molecular records from about 80 referral Italian institutions. Here, we audit molecular and clinical data from n = 1100 NSCLC patients collected from January 2019 to December 2020. METHODS: Clinical and molecular records from NSCLC patients were retrospectively collected from the two coordinating institutions (University of Turin and University of Naples). Molecular biomarkers (KRAS, EGFR, BRAF, ROS1, ALK, RET, NTRK, MET) and clinical data (sex, age, histological type, smoker status, PD-L1 expression, therapy) were collected and harmonized. RESULTS: Clinical and molecular data from 1100 (n = 552 mutated and n = 548 wild-type) NSCLC patients were systematized and annotated in the ATLAS knowledge-database. Molecular records from biomarkers testing were matched with main patients' clinical variables. CONCLUSIONS: Biomarkers ATLAS (https://biomarkersatlas.com/) represents a unique, easily managing, and reliable diagnostic tool aiming to integrate clinical records with molecular alterations of NSCLC patients in the real-word Italian scenario.

Gold Nanoprobes for Detection of a Crucial EGFR Deletion for Early Diagnosis of Non-Small-Cell Lung Cancer.

Gold nanoparticles (AuNPs) exhibit improved optical and spectral properties compared to bulk materials, making them suitable for the detection of DNA, RNA, antigens, and antibodies. Here, we describe a simple, selective, and rapid non-cross linking detection assay, using approx. 35 nm spherical Au nanoprobes, for a common mutation occurring in exon 19 of the epidermal growth factor receptor (EGFR), associated with non-small-cell lung cancer cells. AuNPs were synthesized based on the seed-mediated growth method and functionalized with a specific 16 bp thiolated oligonucleotide using a pH-assisted method. Both AuNPs and Au nanoprobes proved to be highly stable and monodisperse through ultraviolet-visible spectrophotometry, dynamic light scattering (DLS), and electrophoretic light scattering (ELS). Our results indicate a detection limit of 1.5 µg mL-1 using a 0.15 nmol dm-3 Au nanoprobe concentration. In conclusion, this work presents an effective possibility for a straightforward, fast, and inexpensive alternative for the detection of DNA sequences related to lung cancer, leading to a potential platform for early diagnosis of lung cancer patients.

Small extracellular vesicles: Multi-functional aspects in non-small cell lung carcinoma.

Extracellular vesicles (EVs) impact normal and pathological cellular signaling through bidirectional trafficking. Exosomes, a subset of EVs possess biomolecules including proteins, lipids, DNA fragments and various RNA species reflecting a speculum of their parent cells. The involvement of exosomes in bidirectional communication and their biological constituents substantiate its role in regulating both physiology and pathology, including multiple cancers. Non-small cell lung cancer (NSCLC) is the most common lung cancers (85%) with high incidence, mortality and reduced overall survival. Lack of efficient early diagnostic and therapeutic tools hurdles the management of NSCLC. Interestingly, the exosomes from body fluids similarity with parent cells or tissue offers a potential future multicomponent tool for the early diagnosis of NSCLC. The structural twinning of exosomes with a cell/tissue and the competitive tumor derived exosomes in tumor microenvironment (TME) promotes the unpinning horizons of exosomes as a drug delivery, vaccine, and therapeutic agent. Exosomes in clinical point of view assist to trace: acquired resistance caused by various therapeutic agents, early diagnosis, progression, and surveillance. In an integrated approach, EV biomarkers offer potential cutting-edge techniques for the detection and diagnosis of cancer, though the purification, characterization, and biomarker identification processes for the translational research regarding EVs need further optimization.

The cost-effectiveness of including liquid biopsy into molecular profiling strategies for newly diagnosed advanced non-squamous non-small cell lung cancer in an Asian population.

OBJECTIVES: Liquid biopsy is complementary to tissue biopsy for lung cancer profiling, yet evidence of the cost-effectiveness is limited. This could retard implementation and reimbursement in clinical practice. The aim of this study is to estimate the cost-effectiveness of profiling strategies that include liquid biopsy and to identify the optimal profiling approach for newly diagnosed advanced non-squamous non-small cell lung cancer (NSCLC) in an Asian population using Singapore as an example. MATERIALS AND METHODS: A decision tree and partitioned-survival model was developed from the Singapore healthcare system's perspective to evaluate the cost-effectiveness of five molecular profiling strategies: either tissue or plasma next-generation sequencing (NGS) alone, a concurrent, and two sequential approaches. Model inputs were informed by local data or published literature. Sensitivity analyses and scenario analyses were undertaken to understand the robustness of the conclusions for decision making. The optimal strategy at different willingness-to-pay (WTP) thresholds was presented by cost-effectiveness acceptability frontier and the expected loss curve. RESULTS: The sequential tissue-plasma NGS approach revealed an additional 0.0981 quality adjusted life years (QALYs) for an extra cost of S$3,074 over a 20-year time horizon compared to tissue NGS alone, resulting in an incremental cost-effectiveness ratio (ICER) of S$31,318/QALY and an incremental net monetary benefit of S$1,343 per patient. The findings were sensitive to the costs of pembrolizumab and osimertinib and the probabilities of re-biopsy after tissue NGS. Sequential plasma-tissue NGS and plasma NGS alone were more costly and less effective than alternatives. CONCLUSION: The sequential tissue-plasma NGS approach generated the highest net monetary benefit and was the optimal testing strategy when WTP was S$45,000/QALY. It retained superiority but understandably with a higher ICER when expensive, non-first line treatments were included. Overall, its routine clinical practice should be proactively considered for newly diagnosed advanced non-squamous NSCLC in an Asian population.

Biphenotypic lung carcinoma with coexpression of TTF-1 and ΔNP63/P40 within most of the same individual cells: a further case confirming poor prognosis and a review of literature.

The WHO Classification of Tumors, Thoracic Tumors, 5th edition, has outlined the use of TTF-1 and ΔNP63/P40 to discriminate between adenocarcinoma and squamous cell carcinoma. In 2015, the first description of a rare non-small cell lung carcinoma featuring co-expression of glandular and squamous differentiation within most of the same individual tumor cells was reported on, with ultrastructural and molecular demonstration of such a biphenotypic differentiation. We herein describe an additional case of this rare tumor entity, which is confirmed to be an aggressive neoplasm despite potential targets of therapy.

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.