Joshu, C. | Search bioRxiv

bioRxiv2026

Subclonal Complete Loss of CDKN1B as a Common Genomic Alteration in Prostate Cancer: Associations with Race and Prostate Cancer Outcomes

BackgroundHomozygous biallelic inactivation of CDKN1B is thought to be rare in cancer. Herein we evaluate the prevalence of intratumoral (subclonal) complete p27 protein loss (IPPL) in primary prostate cancer. Experimental DesignWe used immunohistochemistry (IHC) for p27 in a large cohort of whole tissue sections from radical prostatectomy (n=412) and metastases from self-identified African American (AA) and European American (EA) individuals. IPPL was evaluated alongside CDKN1B mRNA in-situ hybridization and next generation sequencing of laser captured cancer regions. Cox proportional hazards analyses assessed the association of IPPL with biochemical recurrence and development of metastases after radical prostatectomy. ResultsIPPL was detected in 18.1% of AA versus 12.2% of EA cases and was tightly correlated with CDKN1B mRNA loss and biallelic genomic loss. IPPL was associated with [≥]pT3 pathologic stage and pN1 disease, however these associations were only significant among AA participants. IPPL was further associated in both univariate and multivariate analyses with the development of biochemical recurrence and metastasis after primary treatment, specifically in AA individuals. The prevalence of p27 genomic alterations in metastatic disease is higher than that of primary prostate cancer in publicly available datasets as well as our analysis of autopsy cases via IHC, indicating that complete p27 loss may be selected for in metastatic disease. ConclusionsSubclonal biallelic loss of CDKN1B resulting in complete p27 protein loss is one of the most commonly occurring biallelic tumor suppressor genomic alterations in primary prostate cancer, and could contribute to worse prostate cancer outcomes, specifically in AA males.

cancer biology↗

bioRxiv2024

The microanatomy of human skin in aging

Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales.

pathology↗