Aging limits stemness and tumorigenesis in the lung by reprogramming iron homeostasis.

Aging is associated with a decline in the number and fitness of adult stem cells 1-4 . Aging-associated loss of stemness is posited to suppress tumorigenesis 5,6 , but this hypothesis has not been tested in vivo . Here, using physiologically aged autochthonous genetically engineered mouse models and primary cells 7,8 , we demonstrate aging suppresses lung cancer initiation and progression by degrading stemness of the alveolar cell of origin. This phenotype is underpinned by aging-associated induction of the transcription factor NUPR1 and its downstream target lipocalin-2 in the cell of origin in mice and humans, leading to a functional iron insufficiency in the aged cells. Genetic inactivation of the NUPR1-lipocalin-2 axis or iron supplementation rescue stemness and promote tumorigenic potential of aged alveolar cells. Conversely, targeting the NUPR1- lipocalin-2 axis is detrimental to young alveolar cells via induction of ferroptosis. We find that aging-associated DNA hypomethylation at specific enhancer sites associates with elevated NUPR1 expression, which is recapitulated in young alveolar cells by inhibition of DNA methylation. We uncover that aging drives a functional iron insufficiency, which leads to loss of stemness and tumorigenesis, but promotes resistance to ferroptosis. These findings have significant implications for the therapeutic modulation of cellular iron homeostasis in regenerative medicine and in cancer prevention. Furthermore, our findings are consistent with a model whereby most human cancers initiate in young individuals, revealing a critical window for such cancer prevention efforts.

Multi-modal quantification of pathway activity with MAYA.

Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.