36. Development of a Software Support System for Targeted Treatment of Non-Small Cell Lung Cancer Utilizing Genetic Mutation Analysis Data

Le Tu Linh, Nguyen Viet Nhung, Trinh Le Huy, Le Van Quang, Dinh Van Luong, Nguyen Thi Trang

Nội dung chính của bài viết

Tóm tắt

This study presents the development of a software support system aimed at improving targeted treatment outcomes in non-small cell lung cancer (NSCLC) by utilizing genetic mutation analysis data. PubMed BERT model were generated to identify tumor gene mutated features associated with gene-drug responses. Then the best classifier with highest accuracy served for the development of the support software. A multi-center retrospective cohort study was conducted to evaluate of treatment outcomes based on software support to identify and predict targeted therapeutic in NSCLC. The results demonstrated the potential of leveraging genetic mutation analysis data and AI technology to optimize treatment strategies and improve outcomes for NSCLC patients with PubMed BERT model in extracting and categorizing keywords to build the database (a Recall (sensitivity) score of 98.12%). The study revealed that among the 109 patients with EGFR mutations identified through NGS analysis, 72% showed partial responsiveness, overall response rate (ORR) was 67.0%, and the disease control rate (DCR) was 82.6%. The software support system developed in this study holds promise for enhancing personalized treatment approaches in NSCLC by leveraging genetic mutation analysis data to guide targeted therapies and improve the treatment outcome for patients.

Chi tiết bài viết

Tài liệu tham khảo

1. Lee CK, Davies L, Wu YL, et al. Gefitinib or Erlotinib vs Chemotherapy for EGFR Mutation-Positive Lung Cancer: Individual Patient Data Meta-Analysis of Overall Survival. J Natl Cancer Inst. 2017; 109(6). doi:10.1093/jnci/djw279
2. Huang RX, Siriwanna D, Cho WC, et al. Lung adenocarcinoma-related target gene prediction and drug repositioning. Front Pharmacol. 2022; 13:936758. doi:10.3389/fphar.2022.936758.
3. Paz-Ares L, Tan EH, O’Byrne K, et al. Afatinib versus gefitinib in patients with EGFR mutation-positive advanced non-small-cell lung cancer: overall survival data from the phase IIb LUX-Lung 7 trial. Ann Oncol Off J Eur Soc Med Oncol. 2017; 28(2): 270-277. doi:10.1093/annonc/mdw611.
4. Lou NN, Zhang XC, Chen HJ, et al. Clinical outcomes of advanced non-small-cell lung cancer patients with EGFR mutation, ALK rearrangement and EGFR/ALK co-alterations. Oncotarget. 2016; 7(40): 65185-65195. doi:10.18632/oncotarget.11218.
5. Lee CK, Kim S, Lee JS, et al. Next-generation sequencing reveals novel resistance mechanisms and molecular heterogeneity in EGFR-mutant non-small cell lung cancer with acquired resistance to EGFR-TKIs. Lung Cancer Amst Neth. 2017; 113: 106-114. doi:10.1016/j.lungcan.2017.09.005.
6. Girard N. New Strategies and Novel Combinations in EGFR TKI-Resistant Non-small Cell Lung Cancer. Curr Treat Options Oncol. 2022; 23(11): 1626-1644. doi:10.1007/s11864-022-01022-7.
7. Huang J, Zhuang C, Chen J, et al. Targeted Drug/Gene/Photodynamic Therapy via a Stimuli-Responsive Dendritic-Polymer-Based Nanococktail for Treatment of EGFR-TKI-Resistant Non-Small-Cell Lung Cancer. Adv Mater Deerfield Beach Fla. 2022; 34(27): e2201516. doi:10.1002/adma.202201516.
8. Forbes SA, Beare D, Bindal N, et al. COSMIC: High-Resolution Cancer Genetics Using the Catalogue of Somatic Mutations in Cancer. Curr Protoc Hum Genet. 2016;91:10.11.1-10.11.37. doi:10.1002/cphg.21.
9. Agarwal SM, Nandekar P, Saini R. Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach. RSC Adv. 2022; 12(26): 16779-16789. doi:10.1039/d2ra00373b.
10. Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med. 2018; 15(11):e1002711. doi:10.1371/journal.pmed.1002711.
11. Uthman OA, Court R, Enderby J, et al. Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning. Health Technol Assess Winch Engl. Published online November 30, 2022. doi:10.3310/UDIR6682.
12. Doppalapudi S, Qiu RG, Badr Y. Lung cancer survival period prediction and understanding: Deep learning approaches. Int J Med Inf. 2021; 148:104371. doi:10.1016/j.ijmedinf.2020.104371.
13. Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 2019; 47(D1): D941-D947. doi:10.1093/nar/gky1015.
14. Mocellin S, Shrager J, Scolyer R, et al. Targeted Therapy Database (TTD): a model to match patient’s molecular profile with current knowledge on cancer biology. PloS One. 2010; 5(8): e11965. doi:10.1371/journal.pone.0011965.
15. Li S, Li L, Zhu Y, et al. Coexistence of EGFR with KRAS, or BRAF, or PIK3CA somatic mutations in lung cancer: a comprehensive mutation profiling from 5125 Chinese cohorts. Br J Cancer. 2014; 110(11): 2812-2820. doi:10.1038/bjc.2014.210.
16. Irham LM, Wong HSC, Chou WH, et al. Integration of genetic variants and gene network for drug repurposing in colorectal cancer. Pharmacol Res. 2020; 161: 105203. doi:10.1016/j.phrs.2020.105203.
17. Chen M, Xu Y, Zhao J, et al. Concurrent Driver Gene Mutations as Negative Predictive Factors in Epidermal Growth Factor Receptor-Positive Non-Small Cell Lung Cancer. EBioMedicine. 2019; 42: 304-310. doi:10.1016/j.ebiom.2019.03.023.
18. Hu W, Liu Y, Chen J. Concurrent gene alterations with EGFR mutation and treatment efficacy of EGFR-TKIs in Chinese patients with non-small cell lung cancer. Oncotarget. 2017; 8(15): 25046-25054. doi:10.18632/oncotarget.15337.