Cellular senescence (CS), manifested as the irreversible arrest of the cell cycle, is a
critical component of the aging hallmarks. Cancer is considered as an aging-related disease
and remains the leading cause of death in the aged population. Senescent cells have been
observed in the murine and human tumor microenvironments, accumulating evidence has
linked the cell senescence in tumor microenvironment with cancer progression and
metastasis, whereas conflicting conclusions have been made across various cancer types.
One of senescent characteristic is the increased secretion of multiple cytokines,
chemokines and proteinases, which is termed as the senescence associated secretory
phenotype (SASP). SASP has been reported to accelerate tumor growth by facilitating
immune evasion and the destruction of extracellular matrix barrier, but it can also protect
against tumor development by inhibiting tumor cell proliferation and stimulating the
immune response in different contexts. Thus, it is of particular important to investigate the
roles of cellular senescence in diverse cancer types, which would improve the tailoring of
senescence-targeted therapy in specific tumors.
Defining cellular senescence levels remains a critical unanswered question due to
the absence of universal and specific markers. Current researchers are more interested in
how to recognizing common features of cellular senescence by integrating several
transcriptional profiles of senescent cells, but the accurate quantification of senescence
levels in tumor environment remain poorly characterized. Thus, it is urgently needed to
develope a computational method to quantify cellular senescence levels in patients, and
apply the method to explore phenotypes associated with cellular senescence during tumor
development.
Our study defined computational metrics of senescence levels as CS scores to
delineate CS landscape across 33 cancer types and explored CS-associated phenotypes by
integrating multi-platform data from ∼20,000 patients and ∼212,000 single-cell profiles.
Our analyses revealed the cancer type-specific associations of senescence levels with
genomic variations and immune molecular features. Deciphering single-cell profiles in
prostate cancer cells revealed that cellular senescence levels maintained intratumor
heterogeneity and were closely associated with activated or inhibited immune features.
Importantly, CS score also predicted immunotherapy response in multiple cohorts and
significantly associated with prolonged patient survival. Furthermore, three prognosis
related genes from cellular signature gene set were identified by machine learning
algorithms in prostate cancer and were further validated in four independent cohorts and
an in-house cohort of 72 prostate cancer clinical samples. The senescence quantification
and related analyses are available on an interactive online website, TCSER
(http://tcser.bmicc.org).
Overall, our comprehensive analysis of transcriptional profiles in 33 cancer types
has established a comprehensive framework for better understanding of the context
dependent regulatory functions of cellular senescence in different cancer types, and we
screened prognosis-related cellular senescence biomarkers in prostate cancer. Our study
unraveled the unique changes of molecular features associated with cellular senescence in
various cancers, providing new ideas and valuable therapeutic targets for cancer typespecific precision therapy. The interactive platform also facilitates the investigation of
widely research field, and promote in-depth exploration of cellular senescence.
