Biology subjects

Luijts, T.

Publications and source records attributed to Luijts, T..

3 recordsLinked to original sources

bioRxiv2026

Predicting subclonal TP53 mutations from tumor spatial transcriptomics data using a graph convolutional neural network

Spatial transcriptomics (ST) has revolutionized our understanding of tumor biology but inherently lacks information on the upstream somatic driver mutations. We developed a spatially-aware graph convolutional neural network (MuT-GCNN) that infers TP53 clones directly from ST data. MuT-GCNN was trained on virtual ST slides with clones simulated from a large collection of existing RNA and matched DNA sequencing data. The model is highly performant with precision and recall values exceeding 95% in most analysed cancer types. It is sensitive for single hit mutations and is primarily informed by the expression of p53 signalling genes in cancer cells. After demonstrating the potential of the model on publicly available squamous cell carcinoma (SCC) data, a direct validation was performed using ST and matched DNA sequencing from serial slices obtained from 4 cutaneous SCC samples. With the increasing availability of ST data and upcoming ST atlases, MuT-GCNN can unveil the location of (sub)clonal alterations in TP53, the most frequently mutated gene in human cancer.

cancer biology↗

bioRxiv2025

Early initiation of small intestine neuroendocrine tumors

Small intestine neuroendocrine tumor (SI-NET) is normally diagnosed late in life and has several unusual properties, including low proliferation rate, low mutation burden, lack of driver mutations, and frequent multifocality in the form of polyclonal tumor clusters. This sets SI-NET aside from other adult cancers and raises questions about the timeline of initiation and progression. Here, we investigated the evolutionary history of multifocal and unifocal SI-NET using whole genome sequencing data, obtaining timing information on primary tumors, metastases and key genetic events. Despite the late onset, the results indicated that major genetic alterations and the establishment of advanced metastatic tumor cell clones can often be traced back to childhood or adolescence in SINET. This was validated by re-examination of archival CT/MR scans, allowing longitudinal tracking of individual tumors up to 12 years prior. Metastases were detected at a high degree of consistency in the historical imaging data and estimated growth rates suggested several additional decades of tumor expansion. Collectively, our data support that slow growth of advanced lesions over half a century or more may precede SI-NET diagnosis in late adulthood.

cancer biology↗

bioRxiv2022

A clinically annotated post-mortem approach to study multi-organ somatic mutational clonality in histologically healthy tissues.

Recent research on histologically healthy human tissues identified omnipresent mutational microclones, driven by somatic mutations known to be responsible for carcinogenesis (e.g., in TP53 or NOTCH1). These new insights are fundamentally changing current tumour evolution models, with broad oncological implications. Most studies are based on surgical remnant tissues, which are not available for many organs and rarely in a pan-organ setting (multiple organs from the same individual). Here, we describe an approach based on clinically annotated post-mortem tissues, derived from whole-body donors that are routinely used for educational purposes at human anatomy units. We validated this post-mortem approach using UV-exposed and unexposed epidermal skin tissues and confirm the presence of positively selected NOTCH1/2-, TP53- and FAT1-driven clones. No selection signals were detected in a set of immune genes or housekeeping genes. Additionally, we provide the first evidence for smoking-induced clonal changes in oral epithelia, likely underlying the origin of head and neck carcinogenesis. In conclusion, the whole-body donor-based approach provides a nearly unlimited healthy tissue resource to study mutational clonality and gain fundamental mutagenic insights in the presumed earliest stages of tumour evolution.

genomics↗