Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States of America.
Department of Pathology, UT Southwestern Medical Center, Dallas, TX, United States of America.
PLoS One. 2020 Mar 5;15(3):e0229569. doi: 10.1371/journal.pone.0229569. eCollection 2020.
We previously showed, in a pilot study with publicly available data, that T cell receptor (TCR) repertoires from tumor infiltrating lymphocytes (TILs) could be distinguished from adjacent healthy tissue repertoires by the presence of TCRs bearing specific, biophysicochemical motifs in their antigen binding regions. We hypothesized that such motifs might allow development of a novel approach to cancer detection. The motifs were cancer specific and achieved high classification accuracy: we found distinct motifs for breast versus colorectal cancer-associated repertoires, and the colorectal cancer motif achieved 93% accuracy, while the breast cancer motif achieved 94% accuracy. In the current study, we sought to determine whether such motifs exist for ovarian cancer, a cancer type for which detection methods are urgently needed. We made two significant advances over the prior work. First, the prior study used patient-matched TILs and healthy repertoires, collecting healthy tissue adjacent to the tumors. The current study collected TILs from patients with high-grade serous ovarian carcinoma (HGSOC) and healthy ovary repertoires from cancer-free women undergoing hysterectomy/salpingo-oophorectomy for benign disease. Thus, the classification task is distinguishing women with cancer from women without cancer. Second, in the prior study, classification accuracy was measured by patient-hold-out cross-validation on the training data. In the current study, classification accuracy was additionally assessed on an independent cohort not used during model development to establish the generalizability of the motif to unseen data. Classification accuracy was 95% by patient-hold-out cross-validation on the training set and 80% when the model was applied to the blinded test set. The results on the blinded test set demonstrate a biophysicochemical TCR motif found overwhelmingly in women with HGSOC but rarely in women with healthy ovaries, strengthening the proposal that cancer detection approaches might benefit from incorporation of TCR motif-based biomarkers. Furthermore, these results call for studies on large cohorts to establish higher classification accuracies, as well as for studies in other cancer types.
我们之前在一项针对公开数据的初步研究中表明,肿瘤浸润淋巴细胞(TIL)中的 T 细胞受体(TCR)谱可以通过其抗原结合区域中存在带有特定生物物理化学基序的 TCR 来与相邻的健康组织谱区分开来。我们假设,这种基序可能为癌症检测方法的发展提供一种新的途径。这些基序是癌症特异性的,并且具有很高的分类准确性:我们发现了乳腺癌与结直肠癌相关 TIL 谱之间存在不同的基序,结直肠癌基序的准确率达到 93%,而乳腺癌基序的准确率达到 94%。在本研究中,我们试图确定是否存在卵巢癌相关的基序,因为迫切需要开发针对这种癌症的检测方法。与之前的研究相比,我们有两个重大进展。首先,之前的研究使用了患者匹配的 TIL 和健康的 T 细胞受体谱,从肿瘤附近采集健康组织。而本研究则从患有高级别浆液性卵巢癌(HGSOC)的患者中采集 TIL,并从因良性疾病接受子宫切除术/输卵管卵巢切除术的无癌妇女中采集健康卵巢 T 细胞受体谱。因此,分类任务是区分患有癌症的妇女和没有癌症的妇女。其次,在之前的研究中,分类准确性是通过在训练数据上进行患者留一交叉验证来衡量的。在本研究中,还在一个未用于模型开发的独立队列上评估了分类准确性,以建立基序对未见数据的泛化能力。在训练集上的患者留一交叉验证的分类准确性为 95%,当模型应用于盲测集时为 80%。盲测集上的结果表明,一种生物物理化学 TCR 基序在患有 HGSOC 的女性中发现的频率远远高于健康卵巢的女性,这进一步证明了癌症检测方法可能受益于 TCR 基序标志物的纳入。此外,这些结果呼吁进行更大规模队列的研究以提高分类准确性,并进行其他癌症类型的研究。