L'Imperio Vincenzo, Cazzaniga Giorgio, Mannino Mauro, Seminati Davide, Mascadri Francesco, Ceku Joranda, Casati Gabriele, Bono Francesca, Eloy Catarina, Rocco Elena Guerini, Frascarelli Chiara, Fassan Matteo, Malapelle Umberto, Pagni Fabio
Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy.
Pathology Laboratory, Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP), Porto, Portugal.
Virchows Arch. 2025 Feb;486(2):277-286. doi: 10.1007/s00428-024-03794-9. Epub 2024 Mar 26.
The estimation of tumor cellular fraction (TCF) is a crucial step in predictive molecular pathology, representing an entry adequacy criterion also in the next-generation sequencing (NGS) era. However, heterogeneity of quantification practices and inter-pathologist variability hamper the robustness of its evaluation, stressing the need for more reliable results. Here, 121 routine histological samples from non-small cell lung cancer (NSCLC) cases with complete NGS profiling were used to evaluate TCF interobserver variability among three different pathologists (pTCF), developing a computational tool (cTCF) and assessing its reliability vs ground truth (GT) tumor cellularity and potential impact on the final molecular results. Inter-pathologist reproducibility was fair to good, with overall Wk ranging between 0.46 and 0.83 (avg. 0.59). The obtained cTCF was comparable to the GT (p = 0.129, 0.502, and 0.130 for surgical, biopsies, and cell block, respectively) and demonstrated good reliability if elaborated by different pathologists (Wk = 0.9). Overall cTCF was lower as compared to pTCF (30 ± 10 vs 52 ± 19, p < 0.001), with more cases < 20% (17, 14%, p = 0.690), but none containing < 100 cells for the algorithm. Similarities were noted between tumor area estimation and pTCF (36 ± 29, p < 0.001), partly explaining variability in the human assessment of tumor cellularity. Finally, the cTCF allowed a reduction of the copy number variations (CNVs) called (27 vs 29, - 6.9%) with an increase of effective CNVs detection (13 vs 7, + 85.7%), some with potential clinical impact previously undetected with pTCF. An automated computational pipeline (Qupath Analysis of Nuclei from Tumor to Uniform Molecular tests, QuANTUM) has been created and is freely available as a QuPath extension. The computational method used in this study has the potential to improve efficacy and reliability of TCF estimation in NSCLC, with demonstrated impact on the final molecular results.
肿瘤细胞分数(TCF)的估计是预测性分子病理学中的关键步骤,在下一代测序(NGS)时代也是衡量样本合格性的一项标准。然而,量化方法的异质性和病理学家之间的差异阻碍了其评估的稳健性,因此需要更可靠的结果。在这里,我们使用了121例具有完整NGS分析结果的非小细胞肺癌(NSCLC)病例的常规组织学样本,评估了三位不同病理学家(pTCF)之间的TCF观察者间变异性,开发了一种计算工具(cTCF),并评估了其与真实肿瘤细胞含量(GT)的可靠性以及对最终分子结果的潜在影响。病理学家之间的再现性从中等到良好,总体肯德尔一致性系数(Wk)在0.46至0.83之间(平均0.59)。获得的cTCF与GT相当(手术样本、活检样本和细胞块样本的p值分别为0.129、0.502和0.130),并且如果由不同病理学家进行分析,显示出良好的可靠性(Wk = 0.9)。总体而言,cTCF低于pTCF(30±10 vs 52±19,p < 0.001),<20%的病例更多(17例,14%,p = 0.690),但算法中没有病例包含<100个细胞。肿瘤面积估计与pTCF之间存在相似性(36±29,p < 0.001),部分解释了人类对肿瘤细胞含量评估的变异性。最后,cTCF使得检测到的拷贝数变异(CNV)减少(27 vs 29,-6.9%),同时有效CNV检测增加(13 vs 7,+85.7%),其中一些具有潜在临床影响,而之前pTCF未检测到。我们创建了一个自动化计算流程(从肿瘤细胞核到统一分子检测的Qupath分析,QuANTUM),并作为QuPath扩展免费提供。本研究中使用的计算方法有潜力提高NSCLC中TCF估计的效率和可靠性,并对最终分子结果产生影响。