Pan-cancer Transcriptomic Predictors of Perineural Invasion Improve Occult Histopathologic Detection.
Pan-cancer Transcriptomic Predictors of Perineural Invasion Improve Occult Histopathologic Detection.
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DOI：
10.1158/1078-0432.ccr-20-4382
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发表时间：
2021-05-15
期刊：
影响因子：
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通讯作者：
Hwang WL
中科院分区：
文献类型：
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作者：
Guo JA;Hoffman HI;Shroff SG;Chen P;Hwang PG;Kim DY;Kim DW;Cheng SW;Zhao D;Mahal BA;Alshalalfa M;Niemierko A;Wo JY;Loeffler JS;Fernandez-Del Castillo C;Jacks T;Aguirre AJ;Hong TS;Mino-Kenudson M;Hwang WL
神经周围浸润（PNI）与肿瘤的侵袭性、复发和转移有关，并可影响辅助治疗的实施。然而，标准的组织病理学检查在检测PNI方面具有有限的灵敏度，并且不能提供对其机制基础的见解。进行多变量考克斯回归以验证12种癌症类型的2029例患者中PNI与生存率之间的相关性。差异表达和基因集富集分析用于学习PNI相关程序。应用机器学习模型构建PNI基因表达分类器。由委员会认证的病理学家对H&E载玻片进行盲法重新审查，有助于确定分类器是否可以改善PNI的隐匿性组织病理学检测。PNI与OS（风险比，1.73; 95%CI，1.27-2.36; P < 0.001）和DFS（风险比，1.79; 95%CI，1.38-2.32; P < 0.001）均相关。神经元样、促存活和侵袭性程序在PNI阳性肿瘤中富集（Padj < 0.001）。虽然PNI相关的功能可能部分反映了神经的存在增加，但许多差异表达的基因特异性地映射到来自单细胞图谱的恶性细胞。使用随机森林推导出PNI基因表达分类器，并作为隐匿性组织病理学检测的工具进行评估。在对最初描述为PNI阴性的切片进行设盲H&E复检时，与低评分队列相比，高分类器评分队列中更多的标本被重新注释为PNI阳性（P = 0.03，Fisher精确检验）。这项研究提供了有关PNI的显着生物学见解，并证明了基因表达分类器在增强组织病理学特征检测方面的作用。
Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathological examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic underpinnings. A multi-variate Cox regression was performed to validate associations between PNI and survival in 2029 patients across 12 cancer types. Differential expression and gene set enrichment analysis were used to learn PNI-associated programs. Machine learning models were applied to build a PNI gene expression classifier. A blinded re-review of H&E slides by a board-certified pathologist helped determine whether the classifier could improve occult histopathological detection of PNI. PNI associated with both poor OS (hazard ratio, 1.73; 95% CI, 1.27–2.36; P < 0.001) and DFS (hazard ratio, 1.79; 95% CI, 1.38–2.32; P < 0.001). Neural-like, pro-survival, and invasive programs were enriched in PNI-positive tumors (Padj < 0.001). Although PNI-associated features likely reflect in part the increased presence of nerves, many differentially-expressed genes mapped specifically to malignant cells from single-cell atlases. A PNI gene expression classifier was derived using random forest and evaluated as a tool for occult histopathological detection. On a blinded H&E re-review of sections initially described as PNI-negative, more specimens were re-annotated as PNI-positive in the high classifier score cohort compared to the low-scoring cohort (P = 0.03, Fisher’s exact test). This study provides salient biological insights regarding PNI and demonstrates a role for gene expression classifiers to augment detection of histopathological features.