The human immune response depends on the binding of T-cell receptors (TCRs)
to antigens (pTCR), which elicits the T cells to eliminate viruses, tumor
cells, and other pathogens. The ability of human immunity system responding to
unknown viruses and bacteria stems from the TCR diversity. However, this vast
diversity poses challenges on the TCR-antigen binding prediction methods. In
this study, we propose a Masked Language Model (MLM), referred to as tcrLM, to
overcome limitations in model generalization. Specifically, we randomly masked
sequence segments and train tcrLM to infer the masked segment, thereby extract
expressive feature from TCR sequences. Meanwhile, we introduced virtual
adversarial training techniques to enhance the model's robustness. We built the
largest TCR CDR3 sequence dataset to date (comprising 2,277,773,840 residuals),
and pre-trained tcrLM on this dataset. Our extensive experimental results
demonstrate that tcrLM achieved AUC values of 0.937 and 0.933 on independent
test sets and external validation sets, respectively, which remarkably
outperformed four previously published prediction methods. On a large-scale
COVID-19 pTCR binding test set, our method outperforms the current
state-of-the-art method by at least 8%, highlighting the generalizability of
our method. Furthermore, we validated that our approach effectively predicts
immunotherapy response and clinical outcomes on a clinical cohorts. These
findings clearly indicate that tcrLM exhibits significant potential in
predicting antigenic immunogenicity.

我们提出了一种蒙版语言模型（tcrLM），用于预测 T 细胞受体（TCR）与抗原的结合，通过训练 tcrLM 来推断被屏蔽的片段，从 TCR 序列中提取表达特征，并在大规模 COVID-19 pTCR 结合测试集上超过当前最先进的方法，有效预测免疫疗法反应和临床结果。

用于预测 T 细胞受体 - 抗原结合特异性的大型语言模型

A large language model for predicting T cell receptor-antigen binding  specificity

Efficient and precise quantification of lymphocytes in histopathology slides
is imperative for the characterization of the tumor microenvironment and
immunotherapy response insights. We developed a data-centric optimization
pipeline that attain great lymphocyte detection performance using an
off-the-shelf YOLOv5 model, without any architectural modifications. Our
contribution that rely on strategic dataset augmentation strategies, includes
novel biological upsampling and custom visual cohesion transformations tailored
to the unique properties of tissue imagery, and enables to dramatically improve
model performances. Our optimization reveals a pivotal realization: given
intensive customization, standard computational pathology models can achieve
high-capability biomarker development, without increasing the architectural
complexity. We showcase the interest of this approach in the context of breast
cancer where our strategies lead to good lymphocyte detection performances,
echoing a broadly impactful paradigm shift. Furthermore, our data curation
techniques enable crucial histological analysis benchmarks, highlighting
improved generalizable potential.

淋巴细胞在组织病理学切片中的高效、准确量化对于肿瘤微环境和免疫治疗反应的表征至关重要。我们开发了一个基于数据的优化流程，利用现成的 YOLOv5 模型在不进行任何结构修改的情况下实现了出色的淋巴细胞检测性能。我们的工作依赖于策略性的数据集增强策略，包括新颖的生物上采样和定制的视觉连贯转换，针对组织图像的独特特性进行设计，从而大大提高了模型的性能。我们的优化揭示了一个关键的实现：在进行了密集定制化之后，标准的计算病理学模型可以实现高能力的生物标志物开发，而不增加架构复杂性。我们展示了这种方法在乳腺癌背景下的兴趣，证明我们的策略可以实现良好的淋巴细胞检测性能，引发了广泛的影响性的范式转变。此外，我们的数据整理技术提供了关键的组织学分析基准，突显了提高的通用潜力。

通过数据中心策略优化乳腺癌全切片图像中的淋巴细胞检测

Optimizing Lymphocyte Detection in Breast Cancer Whole Slide Imaging  through Data-Centric Strategies

Radiomics has shown a capability for different types of cancers such as
glioma to predict the clinical outcome. It can have a non-invasive means of
evaluating the immunotherapy response prior to treatment. However, the use of
deep convolutional neural networks (CNNs)-based radiomics requires large
training image sets. To avoid this problem, we investigate a new imaging
features that model distribution with a Gaussian mixture model (GMM) of learned
3D CNN features. Using these deep radiomic features (DRFs), we aim to predict
the immune marker status (low versus high) and overall survival for glioma
patients. We extract the DRFs by aggregating the activation maps of a
pre-trained 3D-CNN within labeled tumor regions of MRI scans that corresponded
immune markers of 151 patients. Our experiments are performed to assess the
relationship between the proposed DRFs, three immune cell markers (Macrophage
M1, Neutrophils and T Cells Follicular Helper), and measure their association
with overall survival. Using the random forest (RF) model, DRFs was able to
predict the immune marker status with area under the ROC curve (AUC) of 78.67,
83.93 and 75.67\% for Macrophage M1, Neutrophils and T Cells Follicular Helper,
respectively. Combined the immune markers with DRFs and clinical variables,
Kaplan-Meier estimator and Log-rank test achieved the most significant
difference between predicted groups of patients (short-term versus long-term
survival) with p\,=\,4.31$\times$10$^{-7}$ compared to p\,=\,0.03 for Immune
cell markers, p\,=\,0.07 for clinical variables , and
p\,=\,1.45$\times$10$^{-5}$ for DRFs. Our findings indicate that the proposed
features (DRFs) used in RF models may significantly consider prognosticating
patients with brain tumour prior to surgery through regularly acquired imaging
data.

使用随机森林模型并结合深度辐射学特征（DRFs）和临床变量，我们可以显著地预测胶质瘤患者免疫标记物的状态和总体生存期，并推断这些特征可用于在手术前通过定期获​​取的成像数据进行脑肿瘤患者的预测。