ABSTRACT
The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors.
ABSTRACT
Previous studies in pulmonary artery smooth muscle cells (PASMCs) showed that acute hypoxia activates capacitative Ca(2+) entry (CCE) but the molecular candidate(s) mediating CCE caused by acute hypoxia remain unclear. The present study aimed to determine if transient receptor potential canonical 1 (TRPC1) and Orai1 interact with stromal interacting molecule 1 (STIM1) and mediate CCE caused by acute hypoxia in mouse PASMCs. In primary cultured PASMCs loaded with fura-2, acute hypoxia caused a transient followed by a sustained rise in intracellular Ca(2+) concentration ([Ca(2+)](i)). The transient but not sustained rise in [Ca(2+)](i) was partially inhibited by nifedipine. Acute hypoxia also increased the rate of Mn(2+) quench of fura-2 fluorescence that was inhibited by SKF 96365, Ni(2+), La(3+), and Gd(3+), exhibiting pharmacological properties characteristic of CCE. The nifedipine-insensitive rise in [Ca(2+)](i) and the increase in Mn(2+) quench rate were both inhibited in cells treated with TRPC1 antibody or TRPC1 small interfering (si)RNA, in STIM1 siRNA-transfected cells and in Orai1 siRNA-transfected cells. Moreover, overexpression of STIM1 resulted in a marked increase in [Ca(2+)](i) and Mn(2+) quench rate caused by acute hypoxia, and they were reduced in cells treated with TRPC1 antibody and in cells transfected with Orai1 siRNA. Furthermore, TRPC1 and Orai1 coimmunoprecipitated with STIM1 and the precipitation levels of TRPC1 and Orai1 were increased in cells exposed to acute hypoxia. Immunostaining showed colocalizations of TRPC1-STIM1 and Orai1-STIM1, and the colocalizations of these proteins were more apparent in acute hypoxia. These data provide direct evidence that TRPC1 and Orai1 channels mediate CCE through activation of STIM1 in acute hypoxic mouse PASMCs.
