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Pisanic, T. R.

Publications and source records attributed to Pisanic, T. R..

2 recordsLinked to original sources

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 cells1-4. Aging-associated loss of stemness is posited to suppress tumorigenesis5,6, but this hypothesis has not been tested in vivo. Here, using physiologically aged autochthonous genetically engineered mouse models and primary cells7,8, 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.

cancer biology↗

A Methylation Density Binary Classifier for Predicting and Optimizing the Performance of Methylation Biomarkers in Clinical Samples

BackgroundVariation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass), that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification. ResultsWe developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-mL) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers. ConclusionsOur results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitate the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically-diverse ovarian carcinomas, indicating the broad utility of ZNF154 for use as a biomarker of ovarian cancer.

bioinformatics↗