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Title: Machine Learning of Single Cell Transcriptomic Data From anti-PD-1 Responders and Non-responders Reveals Distinct Resistance Mechanisms in Skin Cancers and PDAC
Immune checkpoint therapies such as PD-1 blockade have vastly improved the treatment of numerous cancers, including basal cell carcinoma (BCC). However, patients afflicted with pancreatic ductal carcinoma (PDAC), one of the deadliest malignancies, overwhelmingly exhibit negative responses to checkpoint therapy. We sought to combine data analysis and machine learning to differentiate the putative mechanisms of BCC and PDAC non-response. We discover that increased MHC-I expression in malignant cells and suppression of MHC and PD-1/PD-L expression in CD8 + T cells is associated with nonresponse to treatment. Furthermore, we leverage machine learning to predict response to PD-1 blockade on a cellular level. We confirm divergent resistance mechanisms between BCC, PDAC, and melanoma and highlight the potential for rapid and affordable testing of gene expression in BCC patients to accurately predict response to checkpoint therapies. Our findings present an optimistic outlook for the use of quantitative cross-cancer analyses in characterizing immune responses and predicting immunotherapy outcomes.  more » « less
Award ID(s):
1763272
NSF-PAR ID:
10322224
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Frontiers in Genetics
Volume:
12
ISSN:
1664-8021
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Brain metastases are one of the most common intracranial neoplasms. Increasing evidence have indicated that systemic immunotherapy may provide long‐term benefits for brain metastases. Herein, we presented the results of an immune oncology panel RNA sequencing platform for patients with brain metastases from different primary sites.

    Methods

    We investigated 25 samples of human brain metastases from lung cancer (n = 12), breast cancer (n = 6), and colorectal cancer (n = 7). Besides, 13 paired samples of adjacent noncancerous brain tissue (10 from patients with lung cancer and 3 from patients with breast cancer) were collected as controls. By comparing the brain metastases and paired samples of adjacent noncancerous brain tissue from 13 patients, we detected three upregulated and six downregulated genes, representing the malignant properties of cancer cells and increased immune infiltration in the microenvironment. Next, we profiled the immune‐related genes in brain metastases from three primary cancer types.

    Results

    A group of genes were significantly overexpressed in the microenvironment of brain metastases from lung cancer, covering the checkpoint pathways, lymphocyte infiltration, and TCR‐coexpression. Especially, immune checkpoint molecules, PD‐L1, PD‐L2, and IDO1 were expressed at higher levels in brain metastases from lung cancer than those from the other two cancer types.

    Conclusions

    This study presents an immune landscape of brain metastases from different cancer types. With high RNA expression levels of PD‐1/PD‐L1 axis and immune infiltration in brain metastases, it would be worthwhile to explore the efficacy of immune checkpoint blockade for lung cancer patients with intracranial metastases.

     
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