Dotto, G. P. | bioRxiv Science

bioRxiv2026

p63 and p73 regulate convergent and factor-specific transcriptional programs in cutaneous squamous cell carcinoma

Aberrant transcriptional regulation is a defining feature of squamous cell carcinoma (SCC), yet how lineage transcription factors coordinate shared and factor-specific oncogenic programs remains poorly understood. Although TP63 (p63) is frequently amplified in SCC, the contribution of its paralog TP73 (p73) has remained unclear. Here we show that p73, together with p63, is upregulated in skin SCC and is required for tumorigenesis. Mechanistically, p63 and p73 form heteromeric complexes and co-occupy distal enhancer elements, establishing a shared chromatin regulatory framework. Integration of chromatin and transcriptomic profiling reveals that this common enhancer landscape supports both convergent and divergent transcriptional outputs. Both factors cooperatively sustain core proliferation but also exert regulatory biases, with p63 preferentially reinforcing epithelial lineage circuits and p73 contributing to DNA replication and stress-associated pathways. Among shared downstream targets, p63/p73 co-regulation of multiple epidermal growth factor receptor (EGFR) ligands establishes a feed-forward signaling module that amplifies mitogenic signaling. Amphiregulin emerges as a dominant functional mediator, and its depletion phenocopies key aspects of p63/p73 loss, including impaired proliferation and tumor formation. Together, these findings support a model in which shared enhancer occupancy by p63 and p73 drives cooperative and factor-specific transcriptional programs, linking chromatin regulation to signaling and tumor maintenance.

molecular biology↗

bioRxiv2025

CocycleHunter: cohomology-based circular gene setenrichment and genetic phase estimation in single-cell RNA-seq data.

Standard single-cell RNA-seq analysis frameworks aggregate over-lapping biological processes and impose a single parametrization, conflating distinct programs. Here, we introduce a topological framework that detects and disentangles multiple cyclic processes directly from single-cell transcriptomic data. We validate this approach on synthetic datasets and scRNA-seq profiles of human dermal fibroblasts under control conditions and following androgen receptor (AR) silencing, as well as in vivo mouse prostate regeneration under androgen receptor add-back. We show robust cell cycle structure across conditions, identify an unbiased AR-linked stress signature related to the senescence and proliferation across organisms, and uncover cholesterol homeostasis as an AR-linked program in tissue regeneration. This framework enables identification and separation of concurrent cyclic processes from snapshot single-cell data, revealing complex multi-dimensional regulatory dynamics inaccessible to standard clustering analysis.

bioinformatics↗