用于抗 PD1 免疫疗法反应的肿瘤不可知性预测的随机森林基因组分类器。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2022-11-22 eCollection Date: 2022-01-01 DOI:10.1177/11769351221136081
Emma Bigelow, Suchi Saria, Brian Piening, Brendan Curti, Alexa Dowdell, Roshanthi Weerasinghe, Carlo Bifulco, Walter Urba, Noam Finkelstein, Elana J Fertig, Alex Baras, Neeha Zaidi, Elizabeth Jaffee, Mark Yarchoan
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引用次数: 0

摘要

肿瘤突变负荷(TMB)是肿瘤新表位负荷的替代物,它被用作一种泛肿瘤生物标志物,用于识别可能从抗程序性细胞死亡1(PD1)免疫疗法中获益的患者,但它是一种不完善的生物标志物。还有多种基因组特征与抗 PD1 反应相关,但这些特征的综合预测价值以及每个特征的附加信息量仍不清楚。我们评估了使用全外显子组测序(WES)得出的抗 PD1 反应决定因素的机器学习(ML)方法是否能比单独使用 TMB 更好地预测抗 PD1 反应者。随机森林分类器在公开的抗PD1数据(n = 104)上进行了训练,随后在独立的抗PD1队列(n = 69)上进行了测试。训练和测试数据集包括一系列癌症类型,如非小细胞肺癌(NSCLC)、头颈部鳞状细胞癌(HNSCC)、黑色素瘤,以及少量其他肿瘤类型的患者。使用的特征包括 TMB 和换框突变数量等摘要,以及更多基因层面的特征,如与免疫检查点反应和耐药性相关的突变计数。两种 ML 算法的接受者操作曲线下面积(AUC)都超过了单纯的 TMB("人类指导 "算法的 AUC 为 0.63,"群集 "算法的 AUC 为 0.64,单纯的 TMB 算法的 AUC 为 0.58)。相对于其对肿瘤新表位负担的总体贡献,癌基因内的突变不成比例地调节了抗PD1反应。使用 ML 算法评估抗 PD1 反应的多个拟议基因组决定因素,比单独使用 TMB 稍微提高了性能,这突出表明有必要整合其他生物标记物,以进一步提高模型性能。
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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A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy.

Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 "human-guided," 0.64 "cluster," and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
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.
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