Biology subjects

Tolkach, Y.

Publications and source records attributed to Tolkach, Y..

2 recordsLinked to original sources

bioRxiv2024

Tissue clocks derived from histological signatures of biological aging enable tissue-specific aging predictions from blood

Aging, the leading risk factor for numerous diseases, manifests through diverse structural and architectural changes in human tissues, providing an opportunity to quantify and interpret tissue-specific aging. To address this, we present a comprehensive assessment of tissue changes occurring during human aging, utilizing a vast array of whole slide histopathological images from the Genotype-Tissue Expression Project (GTEx), primarily reflecting non-diseased tissue samples. Using deep learning, we analyzed 25,712 images from 40 distinct tissue types across 983 individuals, quantifying nuanced morphological changes that tissues undergo with age. We developed tissue clocks--predictors of biological age based on tissue images--which achieved a mean prediction error of 4.9 years. These clocks were associated with established aging markers, including telomere attrition, subclinical pathologies, and comorbidities. In a systematic assessment of biological age rates across organs, we identified pervasive non-uniform rates of aging across the human lifespan, with some organs exhibiting earlier changes (20-40 years old) and others showing bimodal patterns of age-related changes. We also uncovered several associations between demographic, lifestyle, and medical history factors and tissue-specific acceleration or deceleration of biological age, highlighting potential modifiable risk factors that influenced the aging process at the tissue level. Finally, by combining paired histological images and gene expression data, we developed a strategy to predict tissue-specific age gaps from blood samples. This approach was validated in independent cohorts covering eight diseases, ranging from acute conditions like stroke to chronic diseases such as cystic fibrosis and Alzheimers disease. It successfully recovered significant associations with disease-relevant organs and revealed patterns of systemic and tissue-specific aging that may reflect broader physiological changes in health and disease. This work offers a new perspective on the aging process by positioning tissue structure as an integrator of cellular and molecular changes that reflect the physiological state of organs in health and disease. It underscores the value of histopathological imaging as a tool for understanding human aging and provides a foundation for the monitoring of tissue-specific aging processes in age-associated diseases.

physiology↗

bioRxiv2019

Biomarker-based outcome prediction in prostate adenocarcinoma depends on the TMPRSS2-ERG status

BackgroundProstate adenocarcinoma (PCa) with/without the TMPRSS2-ERG (T2E)-fusion represent distinct molecular subtypes. ObjectiveTo investigate gene-signatures associated with metastasis in T2E-positive and -negative PCa, and to identify and validate subtype-specific prognostic biomarkers. Design, setting and participantsGene expression and clinicopathological data of two discovery PCa cohorts (total n=783) were separately analyzed regarding the T2E-status. Selected subtype-specific biomarkers were validated in two additional cohorts (total n=405). Outcome measurements and statistical analysisFrom both discovery cohorts, we generated two gene lists ranked by their differential intratumoral expression in patients with/without metastases stratified by T2E-status, which were subjected to gene set enrichment and leading-edge analyses. The resulting top 20 gene-signatures of both gene lists associated with metastasis were analyzed for overlaps between T2E-positive and -negative cases. Genes shared by several functional gene-signatures were tested for their association with event-free survival using the Kaplan-Meier method in a validation cohort. Immunohistochemistry was performed in another validation cohort. Results and limitationsMetastatic T2E-positive and -negative PCa are characterized by different gene-signatures. Five genes (ASPN, BGN, COL1A1, RRM2 and TYMS) were identified whose high expression was significantly associated with worse outcome exclusively in T2E-negative PCa. This was validated in an independent cohort for all genes and additionally for RRM2 by immunohistochemistry in a separate validation cohort. No prognostic biomarkers were identified exclusively for T2E-positive tumors. ConclusionsOur study demonstrates that the prognostic value of biomarkers critically depends on the molecular subtype, i.e. the T2E-status, which should be considered when screening for and applying novel prognostic biomarkers for outcome prediction in PCa. Patient summaryOutcome prediction for PCa is complex. The results of this study highlight that the validity of prognostic biomarkers depends on the molecular subtype, specifically the presence/absence of T2E. The reported new subtype-specific biomarkers exemplify that biomarker-based outcome prediction in PCa should consider the T2E-status.

cancer biology↗