Prediction of Tumor-Associated Macrophages and Immunotherapy Benefits Using Weakly Supervised Contrastive Learning in Breast Cancer Pathology Images

Shiravand, Y. et al. Immune checkpoint inhibitors in cancer therapy. Current Oncology 29, 3044–3060 (2022).

Wang, Z. et al. Niraparib activates interferon signaling and potentiates anti-pd-1 antibody efficacy in tumor models. Scientific Reports 9, 1853 (2019).

Qianhui, X., Shaohuai, C., Yuanbo, H. & Wen, H. Landscape of immune microenvironment under immune cell infiltration pattern in breast cancer. Frontiers in Immunology 12, 711433 (2021).

Fuchou, T. et al. mrna-seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377–382 (2009).

Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12, 453–457 (2015).

Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 102, 15545–15550 (2005).

Yoshihara, K., Shahmoradgoli, M., Martínez, E. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications 4, 2612 (2013).

Yann, L., Yoshua., B. & Geoffrey, H. Deep learning. Nature 521, 436–444 (2015).

He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (2016).

Deng, J. et al. Imagenet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition 248–255 (2009).

Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Commun.ACM 60, 84–90 (2017).

Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014).

Wang, S. et al. Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer Research 80, 2056–2066 (2020).

Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine 25, 1301–1309 (2019).

Ilse, M., Tomczak, J. M. & Welling, M. in Chapter 22 - deep multiple instance learning for digital histopathology (eds Zhou, S. K., Rueckert, D. & Fichtinger, G.) Handbook of Medical Image Computing and Computer Assisted Intervention 521–546 (Academic Press, 2020).

Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nature Methods 12, 453–457 (2015).

Chen, C. L. et al. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nature Communications 12, 1193 (2021).

Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering 5, 555–570 (2021).

Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nature Medicine 25, 1054–1056 (2019).

Kather, J. N. et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nature Cancer 1, 789–799 (2020).

Schmauch, B. et al. A deep learning model to predict rna-seq expression of tumours from whole slide images. Nature Communications 11, 1193 (2020).

Yoo, S. Y. et al. Whole-slide image analysis reveals quantitative landscape of tumor-immune microenvironment in colorectal cancers. Clinical Cancer Research 26, 870–881 (2020).

Saltz, J. et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports 23, 181–193 (2018).

Oord, A. v. d., Li, Y. & Vinyals, O. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).

Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. International conference on machine learning 1597–1607 (2020).

He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2020).

He, K. et al. Masked autoencoders are scalable vision learners. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 16000–16009 (2022).

Huang, H. et al. Contrastive learning-based computational histopathology predict differential expression of cancer driver genes. Briefings in Bioinformatics 23 (2022).

Zhou, Z. H. A brief introduction to weakly supervised learning. National Science Review 5, 44–53 (2017).

Otsu, N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 62–66 (1979).

Cao, X. et al. Effect of cabazitaxel on macrophages improves cd47-targeted immunotherapy for triple-negative breast cancer. Journal for ImmunoTherapy of Cancer 9 (2021).

Yunna, C., Mengru, H., Lei, W. & Weidong, C. Macrophage m1/m2 polarization. European Journal of Pharmacology 877, 173090 (2020).

Hu, Q., Wang, X., Hu, W. & Qi, G.-J. Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 1074–1083 (2021).

Shao, Z. et al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Advances in Neural Information Processing Systems 34 (2021).

Tekguc, M., Wing, J. B., Osaki, M., Long, J. & Sakaguchi, S. Treg-expressed ctla-4 depletes cd80/cd86 by trogocytosis, releasing free pd-l1 on antigen-presenting cells. Proceedings of the National Academy of Sciences 118, e2023739118 (2021).

Xu, Q., Chen, S., Hu, Y. & Huang, W. Clinical m2 macrophages-related genes to aid therapy in pancreatic ductal adenocarcinoma. Cancer Cell International 21, 582 (2021).

Wang, Z. Q., Milne, K., Webb, J. R. & Watson, P. H. Cd74 and intratumoral immune response in breast cancer. Oncotarget 8, 12664–12674 (2017).

Liao, L. et al. A potent pgk1 antagonist reveals pgk1 regulates the production of il-1 and il-6. Acta Pharmaceutica Sinica B 12, 4180–4192 (2022).

Edwards, N. J. et al. The cptac data portal: A resource for cancer proteomics research. Journal of Proteome Research 14, 2707–2713 (2015).

Mlynska, A. et al. A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors. American Journal of Reproductive Immunology 84, e13244 (2020).

Galon, J. & Bruni, D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nature Reviews Drug Discovery 18, 197–218 (2019).

Charoentong, P. et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Reports 18, 248–262 (2017).

留言 (0)

沒有登入
gif