Biology subjects

Putter, H.

Publications and source records attributed to Putter, H..

4 recordsLinked to original sources

bioRxiv2024

Whole-Slide ECM Imaging Reveals Dense Fibrous Matrix as a High-Risk Factor for Recurrence in Stage II Colon Cancer

IntroductionThe extracellular matrix (ECM) supports tumor progression by influencing tumor cell migration and invasion. This study examines the link between peritumoral ECM morphology and five-year recurrence risk in TNM Stage II colon cancer, using quantitative whole-slide ECM imaging. We hypothesize that loose ECM regions are associated with increased recurrence risk due to enhanced tumor budding (TB) or poorly differentiated clusters (PDC). MethodsIn a case-control study of 100 TNM Stage II colon cancer patients (25 with recurrence and 75 controls matched by lymph node sampling and tumor extent), Picrosirius red-stained sections were imaged to quantify ten ECM parameters across 798 regions of interest (ROIs). Conditional logistic regression assessed associations between ECM morphologies, TB, PDCs, and recurrence. ResultsUnsupervised clustering identified three ECM morphologies: dense fibrous, loose sparse, and complex tortuous. Dense fibrous ECM correlated strongly with recurrence (aOR 9.43, 95% CI 3.29-29.30, p < 0.001), while loose sparse and complex tortuous ECMs were associated with reduced recurrence risk (aOR 0.33, 95% CI 0.11-0.91, p = 0.040, and aOR 0.14, 95% CI 0.02-0.53, p = 0.012, respectively). TB was highest in loose sparse ECM (mean 9.3), and PDCs were highest in dense fibrous ECM (mean 5.5). DiscussionOur findings suggest that ECM morphology, particularly dense fibrous ECM, predicts recurrence in Stage II colon cancer, highlighting ECM profiling as a promising tool for patient stratification beyond traditional staging.

cancer biology↗

bioRxiv2024

Penalized reduced rank regression for multi-outcome survival data supports a common metabolic risk score for age-related diseases

The increasing availability of multi-outcome data in health research presents new opportunities for understanding complex health processes, such as ageing. Ageing is a multifaceted process, encompassing both lifespan and healthspan, as well as the onset of age-related diseases. To model this complexity, we propose the penalized reduced rank regression model for multi-outcome survival data (penalized survRRR), which identifies shared latent factors driving multiple outcomes. The model imposes a rank constraint on the coefficient matrix to capture underlying mechanisms of ageing, while accommodating high-dimensional and correlated predictors and outcomes by introducing penalization. We discuss the statistical properties of this doubly-regularized approach and show how the optimal number of ranks can be estimated from the data. We apply a lasso-penalized reduced rank regression model to 78,553 participants of the UK Biobank, using over 200 metabolic variables as predictors and the onset of seven age-related diseases and mortality as the outcomes of interest. Our results indicate that a rank 1 model provides the best fit to the data, resulting in a single metabolite-based score of age-related disease susceptibility. This highlights the potential of the penalized survRRR model to provide new insights into the nature of the relationship between metabolomics and age-related diseases.

molecular biology↗

bioRxiv2023

The AccelerAge framework: A new statistical approach to predict biological age based on time-to-event data

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individuals survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individuals aging status.

molecular biology↗

bioRxiv2023

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors

There is variability in the rate of aging among people of the same chronological age. The concept of biological age is postulated to capture this variability, and hence to better represent an individuals true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of ones chronological age-independent rate of aging {Delta}. We show that the most popular cross-sectional biological age predictors--based on multiple linear regression, the Klemera-Doubal method or principal component analysis--rely on the same strong underlying assumption, namely that a candidate marker of agings association with chronological age is directly informative of its association with the aging rate {Delta}. We call this the identical-association assumption and prove that it is untestable in a cross-sectional setting. Using synthetic data, we illustrate the consequences if the assumption does not hold: in such scenarios, there is no guarantee that the weights that a cross-sectional method assigns to candidate markers are informative of the underlying truth. Using real data we illustrate that the extent to which the identical-association assumption holds is of direct practical relevance for anyone interested in developing or interpreting cross-sectional biological age predictors.

molecular biology↗