肿瘤演化重建在很大程度上受到算法和实验选择的影响。

IF 12.5 1区医学 Q1 ONCOLOGY

Cancer research Pub Date : 2024-10-02 DOI:10.1158/0008-5472.CAN-24-3530

Rija Zaidi, Simone Zaccaria

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引用次数: 0

摘要

肿瘤进展是一个进化过程，在这一过程中，细胞会获得不同的基因改变。一些癌症进化研究通过对肿瘤样本进行大量 DNA 测序，利用各种肿瘤进化算法推断基因改变的存在，从而重建这一进化过程。通过对这些算法进行全面的基准测试，Salcedo 及其同事最近的一项研究发现，算法和实验选择是影响肿瘤进化重建准确性的主要因素，这为解读以前的研究提供了新的思路，并为研究界提出了一条有用的前进道路。

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Tumor evolution reconstruction is heavily influenced by algorithmic and experimental choices.

Tumor progression is an evolutionary process during which cells acquire distinct genetic alterations. Several cancer evolutionary studies reconstruct this evolutionary process by applying bulk DNA sequencing to a tumor sample to infer the presence of genetic alterations using various tumor evolutionary algorithms. Through a comprehensive benchmarking effort of these algorithms, a recent study by Salcedo and colleagues found that algorithmic and experimental choices are the main drivers of the accuracy of tumor evolution reconstruction, shedding new light on interpreting previous studies and suggesting a useful path forward for the research community.

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来源期刊

Cancer research 医学-肿瘤学

CiteScore

16.10

自引率

0.90%

发文量

7677

审稿时长

2.5 months

期刊介绍： Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.