Low-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries.
Low-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries.
复制标题
DOI：
10.1182/bloodadvances.2021004347
复制
发表时间：
2021-05
期刊：
影响因子：
7.5
通讯作者：
F. Valvert;Oscar Silva;E. Solórzano-Ortíz;M. Puligandla;Marcos Mauricio Siliézar Tala;Timothy Guyon;Samuel L. Dixon;Nelly López;Francisco López;César Camilo Carías Alvarado;R. Terbrueggen;K. Stevenson;Y. Natkunam;D. Weinstock;Edward L Briercheck
中科院分区：
文献类型：
--
作者：
F. Valvert;Oscar Silva;E. Solórzano-Ortíz;M. Puligandla;Marcos Mauricio Siliézar Tala;Timothy Guyon;Samuel L. Dixon;Nelly López;Francisco López;César Camilo Carías Alvarado;R. Terbrueggen;K. Stevenson;Y. Natkunam;D. Weinstock;Edward L Briercheck
诊断不足影响了低收入和中等收入国家（LMIC）的癌症护理。我们假设，一个廉价的基因表达检测使用石蜡包埋活检标本LMIC可以区分淋巴瘤亚型，而无需病理学家的输入。我们审查了2006年至2018年期间在危地马拉城癌症研究所和医院Bernardo Del瓦莱博士获得的所有疑似淋巴瘤的活检标本。根据世界卫生组织分类建立诊断，然后将其分为9类：非恶性、侵袭性B细胞、弥漫性大B细胞、滤泡性、霍奇金、套细胞、边缘区、自然杀伤/T细胞或成熟T细胞淋巴瘤。我们建立了一种基于化学连接探针的检测（CLPA），通过毛细管电泳定量37个基因的表达，试剂/耗材成本约为10美元/样品。为了基于基因表达分配bin，将13个模型作为候选基础学习器进行评估，然后将每个模型的类概率用作极端梯度提升超级学习器中的预测器。呼叫概率< 60%的病例被归类为不确定。194份储存时间<3年的活检标本中有4份（2%）发生了检测失败。诊断样本分为70%（n = 397）训练组和30%（n = 163）验证组。验证队列的总体准确度为86%（95%置信区间[CI]：80%-91%）。排除28个（17%）不确定呼叫后，准确性增加到94%（95% CI：89%-97%）。一致性为97%的一组高概率调用（n = 37）在美国和危地马拉由CLPA测定。排除不确定病例后，复发/难治性活检标本队列（n = 39）的准确性分别为79%和88%。基因表达的机器学习分析可以准确地对石蜡包埋的淋巴瘤活检标本进行分类，并可以改变LMIC的诊断。
Inadequate diagnostics compromise cancer care across lower- and middle-income countries (LMICs). We hypothesized that an inexpensive gene expression assay using paraffin-embedded biopsy specimens from LMICs could distinguish lymphoma subtypes without pathologist input. We reviewed all biopsy specimens obtained at the Instituto de Cancerología y Hospital Dr. Bernardo Del Valle in Guatemala City between 2006 and 2018 for suspicion of lymphoma. Diagnoses were established based on the World Health Organization classification and then binned into 9 categories: nonmalignant, aggressive B-cell, diffuse large B-cell, follicular, Hodgkin, mantle cell, marginal zone, natural killer/T-cell, or mature T-cell lymphoma. We established a chemical ligation probe-based assay (CLPA) that quantifies expression of 37 genes by capillary electrophoresis with reagent/consumable cost of approximately $10/sample. To assign bins based on gene expression, 13 models were evaluated as candidate base learners, and class probabilities from each model were then used as predictors in an extreme gradient boosting super learner. Cases with call probabilities < 60% were classified as indeterminate. Four (2%) of 194 biopsy specimens in storage <3 years experienced assay failure. Diagnostic samples were divided into 70% (n = 397) training and 30% (n = 163) validation cohorts. Overall accuracy for the validation cohort was 86% (95% confidence interval [CI]: 80%-91%). After excluding 28 (17%) indeterminate calls, accuracy increased to 94% (95% CI: 89%-97%). Concordance was 97% for a set of high-probability calls (n = 37) assayed by CLPA in both the United States and Guatemala. Accuracy for a cohort of relapsed/refractory biopsy specimens (n = 39) was 79% and 88%, respectively, after excluding indeterminate cases. Machine-learning analysis of gene expression accurately classifies paraffin-embedded lymphoma biopsy specimens and could transform diagnosis in LMICs.