# Integrative Multimodal Profiling of TAp73 and DNp73 Reveals Isoform-Specific Transcriptomic Coregulator Landscapes in Cancer Programs

## TL;DR

This study maps how two p73 isoforms interact with other proteins to influence cancer progression, revealing new insights into their roles in tumor development.

## Contribution

The study provides a novel integrative workflow to map isoform-specific coTF landscapes and identifies PATZ1 as a new DNp73β interactor.

## Key findings

- TAp73 and DNp73 have distinct coTF repertoires with different functional roles in cancer.
- A shared EMT-associated coTF signature correlates with poor patient survival.
- PATZ1 was validated as a novel direct interactor of DNp73β.

## Abstract

(1) Background: The transcription factor p73 exists in multiple isoforms with divergent functions in cancer. While DNp73 promotes stemness, epithelial–mesenchymal transition (EMT), and metastasis, the tumor-suppressive isoform TAp73 can also switch to promoting cancer progression. How isoforms sharing the same DNA-binding domain produce divergent outcomes remains unclear. (2) Methods: Here, we performed CUT&RUN in combination with JASPAR, transcriptomics, proteomics, patient survival and gene expression data to map genome-wide and promoter-associated DNA-binding and coregulatory transcription factor (coTF) profiles of TAp73α and DNp73β in melanoma cells. (3) Results: Systematic screening for motif enrichment in cancer hallmark gene sets revealed TAp73- and DNp73-specific coTF repertoires with distinct functions. We identified a coregulator signature for EMT genes enriched for both isoforms that has tumor context-dependent effects on survival and correlates with unfavorable patient prognosis. Of these EMT-associated coTFs, PATZ1 was validated as a novel direct interactor of DNp73β. (4) Conclusions: Our results provide a comprehensive reference map of p73 isoform-specific binding and coregulator recruitment and establish a workflow to model their influence on cancer reprogramming with implications for AI-based individualized therapy.