Research into basic PD-1 biology, together with the clinical picture of cancer patients treated with PD-1 blockade, converge to emphasize that PD-1 engages complex signaling networks to support T cell homeostasis, differentiation and immune responses. However, there is a lack of comprehensive understanding in the molecular cascades engaged by PD-1 in functionally distinct T cell subpopulations.
We use a combination of functional immuno-assays, transcriptional and proteomic profiling of human T cells to demonstrate that effector T cells acquire programs of resistance to PD-1 signaling as they progress through the trajectory from naïve-to-memory differentiation.
Overall, PD-1 mediated stronger inhibition via PD-L2 compared to PD-L1. However, we observed differences in the functional responses to PD-1 signaling driven by T cell subset heterogeneity independent of the level of PD-1 expression. In naïve and central memory T cells, PD-1 inhibited cytokine production, cell cycle progression and cellular metabolism. Functional inhibition by PD-1 in these subsets was observed in the presence and absence of CD28 co-stimulation. In contrast, PD-1 ligation led to small inhibition of cytokine production in effector T cells. Critically, the functional, proliferative and metabolic signatures of terminally differentiated effector CD8 T cells were not affected upon stimulation in the presence of PD-1. Integrated transcriptomic and proteomic profiling of highly purified naïve and memory T cells demonstrated that effector T cell differentiation is associated with the development of resistance programs to PD-1-mediated inhibition. Central and effector memory T cells sharing the same T cell receptor and stimulated in the presence of PD-1 confirmed gradual loss of sensitivity to PD-1 signaling in effector T cells.
Together, our findings elucidate the cellular and molecular etiologies associated with sensitivity and resistance to PD-1 signaling.
The Mathers Foundation, NIH NIGMS
Immune Response Regulation: Cellular Mechanisms (IRC)
Subhasree Sridhar, Woo-Seung Lee, E. Kanshin et al.· Journal of Immunology· 0 citations
Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage malignancies can be subtle on chest X-rays, creating challenges for radiologists. This study evaluates Vision Transformers (ViTs) for predicting lung cancer one to two years before clinical diagnosis. We analyzed 259,361 chest X-rays from 91,020 imaging studies at the Jamaica Plains VA Hospital in Boston, MA. The dataset showed extreme class imbalance, approximately 1:150 cancer to non-cancer, which was addressed using hybrid under- and over-sampling and class-weighted loss optimization. Three ViT configurations were evaluated: a model trained from scratch, an ImageNet-pretrained model, and a Corona-pretrained model fine-tuned on the lung cancer dataset. Transfer learning improved performance, with pretrained models exceeding the scratch baseline by 6-10 percentage points in AUC and about 10-12 percent in balanced accuracy. ImageNet-pretrained models showed the most stable overall performance, while Corona-pretrained models achieved higher sensitivity in some settings but greater variability. Moderate resampling ratios, including 1:1 undersampling and 1.5:2 oversampling, provided favorable trade-offs between sensitivity, precision, and computational efficiency, reducing runtime by up to 70 percent without major performance loss. These findings demonstrate the potential of ViTs for early lung cancer risk prediction from routine chest X-rays. Although performance remains below clinical deployment thresholds, the results support further development of ViT-based triage systems to flag high-risk patients for earlier evaluation.
O. Kotevska, Ian Goethert, Michael McGee et al.· 0 citations
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