A Paradigm Shift in Non-Small-Cell Lung Cancer (NSCLC) Diagnostics: From Single Gene Tests to Comprehensive Genomic Profiling.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2024-04-05 eCollection Date: 2024-01-01 DOI:10.1177/11769351241243243

Ushna Zameer, Wajiha Shaikh, Abdul Moiz Khan

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引用次数: 0

Abstract

Lung cancer imposes a burden on the health care system worldwide affecting 2 million people and causing 1.8 million deaths in 2021.More than 85% of all lung cancer cases are reported under Non-small-cell lung cancer (NSCLC). It is critical to discover gene alterations to treat non-small cell lung cancer successfully. The CAP/IASLC/AMP recommendations supported use of polymerase chain reaction (PCR) and fluorescent in situ hybridization (FISH) EGFR (epidermal growth factor receptor) mutations and ALK (Anaplastic lymphoma kinase) rearrangements, respectively. A study presented in the annual meeting of the American Society of Clinical Oncology (ASCO) in Chicago emphasized the need for comprehensive genomic profiling (CGP) before single gene tests (SGTs) since it demonstrated that SGT can result in the depletion of precious biopsy samples. As a result, the efficacy of thorough genetic Profiling (CGP) is reduced, preventing patients from receiving valuable genetic information about their tumors.

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非小细胞肺癌（NSCLC）诊断范式的转变：从单一基因测试到综合基因组分析。

肺癌给全球医疗系统带来了沉重负担，2021 年全球将有 200 万人罹患肺癌，180 万人因此死亡。发现基因改变对成功治疗非小细胞肺癌至关重要。CAP/IASLC/AMP建议分别支持使用聚合酶链反应（PCR）和荧光原位杂交（FISH）检测表皮生长因子受体（EGFR）突变和ALK（无性淋巴瘤激酶）重排。在芝加哥举行的美国临床肿瘤学会（ASCO）年会上发表的一项研究强调，在进行单基因检测（SGTs）之前，有必要先进行全面基因组分析（CGP），因为该研究表明，单基因检测会导致珍贵的活检样本损耗殆尽。因此，全面基因组分析（CGP）的效果会降低，使患者无法获得有关其肿瘤的宝贵基因信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.