深度學習種系基因檢測篩查前列腺癌和黑色素瘤患者的準確性更高
作者：
小柯機器人發布時間：2020/11/21 22:59:42
美國哈佛大學Eliezer M. Van Allen團隊比較了前列腺癌和黑色素瘤患者種系基因檢測與標準方法檢測的準確性。2020年11月17日，該研究發表在《美國醫學會雜誌》上。
不到10%的癌症患者可檢測到致病性種系改變，這可能部分是由於不完整的致病性變異檢測所致。
為了評估深度學習方法是否能在癌症患者中發現更多種系致病性變異，2010-2017年，研究組在美國和歐洲進行了一項標準種系檢測方法和深度學習方法的橫斷面研究，選擇了兩個前列腺癌和黑色素瘤患者隊列。主要結局包括118個癌症易感基因的致病性變異檢測表現，評估為敏感性、特異性、陽性預測值（PPV）和陰性預測值（NPV）。
前列腺癌隊列包括1072名男性，確診時的平均年齡為63.7歲，其中79.9%具有歐洲血統；黑色素瘤隊列包括1295名患者，確診時的平均年齡為59.8歲，其中37.7%為女性，81.9%具有歐洲血統。深度學習法比標準方法相比，可識別出更多癌症易感基因的致病性突變患者，其中前列腺癌隊列分別識別198例和182例，黑色素瘤隊列分別識別93例和74例；敏感性（前列腺癌：94.7%對87.1%；黑色素瘤：74.4%對59.2%），特異性（前列腺癌：64.0%對36.0%；黑色素瘤：63.4%對36.6%），PPV（前列腺癌：95.7%對91.9%；黑色素瘤：54.4%對35.4%），NPV（前列腺癌：59.3%對25.0%；黑色素瘤：80.8%對60.5%）。
對於美國醫學遺傳學和基因組學學會（ACMG）認定的致病性變異基因，在前列腺癌隊列中，深度學習法的敏感性為94.9%，標準方法為90.6%，差異不顯著；但在黑色素瘤隊列中，深度學習法的敏感性為71.6%，顯著高於標準方法（53.7%）。與標準方法相比，深度學習法對孟德爾基因有較高的敏感性（前列腺癌：99.7%對95.1%；黑色素瘤：91.7%對86.2%）。
研究結果表明，對於兩個獨立的前列腺癌和黑色素瘤患者隊列，使用深度學習的種系基因檢測與現行標準的基因檢測方法相比，在檢測致病性變異方面具有更高的敏感性和特異性。
附：英文原文
Title: Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma
Author: Saud H. AlDubayan, Jake R. Conway, Sabrina Y. Camp, Leora Witkowski, Eric Kofman, Brendan Reardon, Seunghun Han, Nicholas Moore, Haitham Elmarakeby, Keyan Salari, Hani Choudhry, Abdullah M. Al-Rubaish, Abdulsalam A. Al-Sulaiman, Amein K. Al-Ali, Amaro Taylor-Weiner, Eliezer M. Van Allen
Issue&Volume: 2020/11/17
Abstract:
Importance Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.
Objective To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer.
Design, Setting, and Participants A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017.
Exposures Germline variant detection using standard or deep learning methods.
Main Outcomes and Measures The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach.
Results The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, –1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, –2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]).
Conclusions and Relevance Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.
DOI: 10.1001/jama.2020.20457
Source: https://jamanetwork.com/journals/jama/article-abstract/2772962