Cancer cell receives extracellular signal inputs to obtain a stem-like status, yet how tumor microenvironmental (TME) neural signals steer cancer stemness to establish the hierarchical tumor architectures remains elusive. Here, a pan-cancer transcriptomic screening for 10852 samples of 33 TCGA cancer types reveals that cAMP-responsive element (CRE) transcription factors are convergent activators for cancer stemness. Deconvolution of transcriptomic profiles, specification of neural markers and illustration of norepinephrine dynamics uncover a bond between TME neural signals and cancer-cell CRE activity. Specifically, neural signal norepinephrine potentiates the stemness of proximal cancer cells by activating cAMP-CRE axis, where ATF1 serves as a conserved hub. Upon activation by norepinephrine, ATF1 potentiates cancer stemness by coordinated trans-activation of both nuclear pluripotency factors MYC/NANOG and mitochondrial biogenesis regulators NRF1/TFAM, thereby orchestrating nuclear reprograming and mitochondrial rejuvenating. Accordingly, single-cell transcriptomes confirm the coordinated activation of nuclear pluripotency with mitochondrial biogenesis in cancer stem-like cells. These findings elucidate that cancer cell acquires stemness via a norepinephrine-ATF1 driven nucleus-mitochondria collaborated program, suggesting a spatialized stemness acquisition by hijacking microenvironmental neural signals.
Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.
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