In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher’s exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

中文翻译：

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基于集成变换器的多实例学习从子宫内膜癌和结直肠癌的组织病理学全玻片图像中预测病理亚型和肿瘤突变负荷


在子宫内膜癌 （EC） 和结直肠癌 （CRC） 中，除了微卫星不稳定性外，肿瘤突变负荷 （TMB） 作为一种基因组生物标志物逐渐受到关注，可用于临床确定哪些患者可能受益于免疫检查点抑制剂。高 TMB 的特征是大量突变基因，这些基因编码异常的肿瘤新抗原，意味着对免疫治疗的反应更好。因此，部分与高 TMB 相关的 EC 和 CRC 患者可能有更高的机会接受免疫治疗。TMB 测量主要通过全外显子组测序或二代测序进行评估，成本高且难以在所有临床病例中广泛应用。因此，迫切需要一种有效、高效、低成本且易于访问的工具来区分 EC 和 CRC 患者的 TMB 状态。在本研究中，我们提出了一个深度学习框架，即基于集成 Transformer 的多实例学习与自监督学习 Vision Transformer 特征编码器 （ETMIL-SSLViT），以直接从 EC 和 CRC 患者的 H&E 染色全玻片图像 （WSI） 预测病理亚型和 TMB 状态，这有助于病理分类和癌症治疗计划。我们的框架在两个不同的癌症队列上进行了评估，包括一个 EC 队列，其中包含来自 529 名患者的 918 个组织病理学 WSI，以及一个 CRC 队列，来自癌症基因组图谱的 594 名患者，具有 1495 个 WSI。实验结果表明，所提出的方法在两个癌症数据集上的癌症亚型分类和 TMB 预测方面都取得了优异的性能，并优于 7 种最先进的 （SOTA） 方法。 Fisher 精确检验进一步验证了所提出的模型的预测与实际癌症亚型或 TMB 状态之间的关联都非常强 （p<0.001）。这些有希望的发现表明，我们提出的方法具有通过准确预测 EC 和 CRC 亚型以及 TMB 状态来指导个性化治疗决策的潜力，以便为 EC 和 CRC 患者制定有效的免疫治疗计划。