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Reinders, M. J.

Publications and source records attributed to Reinders, M. J..

2 recordsLinked to original sources
bioRxiv2019

A NINE-YEAR LONGITUDINAL STUDY IN THE BLOOD OF A SUPERCENTENARIAN REVEALS AN EXTENSIVE SUBCLONAL ARCHITECTURE AND ONGOING SUBCLONAL DYNAMICS

The aged hematopoietic system is characterized by decreased immuno-competence and by a reduced number of hematopoietic stem cells (HSCs) that actively generates new blood cell (age-related clonal hematopoiesis, ARCH). While both aspects are commonly associated with an increased risk of aging-related diseases, it is currently unknown to what extent these aspects co-occur during exceptional longevity. Here, we investigated these aspects in blood cells of an immuno-hematopoietically normal female who reached 111 years. Blood samples were collected across a 9-year period at ages 103, 110 and 111 years. We applied several genetic sequencing approaches to investigate clonality in peripheral blood samples and sorted cell subsets. Immuno-competence was characterized using flow cytometry, T-cell receptor excision circle (TREC) assays, and in vitro proliferation assays. We identified a single DNMT3A-mutated HSC clone with a complex subclonal architecture and observed ongoing subclonal dynamics within the 9-year timeframe of our sampling. The mutated HSC generated 78-87% myeloid cells, 6-7% of the B-cells, 6% of CD8+ T-cells, and notably 22% of the CD4+ T-cells. Intriguingly, we found that T-cells were capable of robust proliferation when challenged in vitro. Moreover, we observed a surprisingly high TREC content, indicative of recent generation of naive T-cells. Concluding, we observed long-term stability of extreme ARCH with ongoing clonal dynamics combined with functional T-cell immunity. Our results indicate that extreme ARCH does not compromise immuno-competence and that a clonally expanded CD4+ T-cell subset may serve as a potential hallmark of the supercentenarian immune system. Key pointsO_LILongitudinal blood sampling from a female aged 103-111 revealed a dynamic clonal hematopoiesis contributing to myeloid and lymphoid subsets C_LIO_LIDespite the highly advanced age and extreme clonal hematopoiesis we observed functional T-cell immunity C_LI

immunology↗
bioRxiv2019

PRECISE: A domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors

MotivationCell lines and patient-derived xenografts (PDX) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response, remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data is often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting. ResultsWe show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors. AvailabilityPRECISE and the scripts for running our experiments are available on our GitHub page (https://github.com/NKI-CCB/PRECISE). Contactl.wessels@nki.nl Supplementary informationSupplementary data are available. online.

bioinformatics↗