Biology subjects

Seppala, T. T.

Publications and source records attributed to Seppala, T. T..

3 recordsLinked to original sources

bioRxiv2025

Colorectal cancer heterogeneity co-evolves with tumor architecture to determine disease outcome

Intratumoral heterogeneity, originating from genetic, epigenetic, and phenotypic cellular diversity, is pervasive in cancer. As these heterogeneous states employ diverse mechanisms to promote tumor progression, metastasis, and therapy resistance, emergence of cancer heterogeneity is one of the most significant barriers to curative treatment. Here we leverage deep learning approaches to develop a high-throughput image-analysis paradigm with subcellular resolution that quantifies and predicts colorectal cancer (CRC) patient outcome based on tissue architecture and nuclear morphology. We further combine this approach with spatial transcriptomics, multiplex immunohistochemistry, and patient-derived organoids to uncover a dynamic, co-evolutionary relationship between tumor architecture and cell states. We identify clinically relevant architectural interfaces in CRC tissue that diversify cellular identities by favoring distinct cancer stem cell states and thus promote evolution of tumor heterogeneity. Specifically, tissue fragmentation and associated compressive forces promote loss of classic stem cell signature and acquisition of fetal/regenerative stem cell states, which initially emerges as a hybrid state with features of epithelial-to-mesenchyme (EMT) transition. Additional tumor stroma communication then diversifies these states into distinct stem cell and EMT states at the invasive margins of tumors. Reciprocally, Wnt signaling state of tumor cells tunes their responsiveness to tissue architecture. Collectively, this work uncovers a feedback loop between cell states and tissue architecture that drives cancer heterogeneity and cell state diversification. Machine learning-based analyses harness this co-dependency, independently of mutational status or cancer stage, to predict patient outcome.

cancer biology↗

bioRxiv2024

In vivo CRISPR screening identifies geranylgeranyl diphosphate as a pancreatic cancer tumor growth dependency

Cancer cells must maintain lipid supplies for their proliferation and do so by upregulating lipogenic gene programs. The sterol regulatory element-binding proteins (SREBPs) act as modulators of lipid homeostasis by acting as transcriptional activators of genes required for fatty acid and cholesterol synthesis and uptake. SREBPs have been recognized as chemotherapeutic targets in multiple cancers, however it is not well understood which SREBP target genes are essential for tumorigenesis. Using parallel in vitro and in vivo CRISPR knockout screens, we identified terpenoid backbone biosynthesis genes as essential for pancreatic ductal adenocarcinoma (PDAC) tumor development. Specifically, we identified the non-sterol isoprenoid product of the mevalonate pathway, geranylgeranyl diphosphate (GGPP), as an essential lipid for tumor growth. Mechanistically, we observed that restricting mevalonate pathway activity using statins and SREBP inhibitors synergistically induced apoptosis and caused disruptions in small G protein prenylation that have pleiotropic effects on cellular signaling pathways. Finally, we demonstrated that geranylgeranyl diphosphate synthase 1 (GGPS1) knockdown significantly reduces tumor burden in an orthotopic xenograft mouse model. These findings indicate that PDAC tumors selectively require GGPP over other lipids such as cholesterol and fatty acids and that this is a targetable vulnerability of pancreatic cancer cells.

cell biology↗

bioRxiv2022

Inflammatory Signaling and Fibroblast-Cancer Cell Interactions Transfer from a Harmonized Human Single-cell RNA Sequencing Atlas of Pancreatic Ductal Adenocarcinoma to Organoid Co-Culture

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a heterogeneous tumor microenvironment (TME) that is enriched with cancer associated fibroblasts (CAFs)1. Cell-cell interactions involving these CAFs promote an immunosuppressive phenotype with altered inflammatory gene expression. While single-cell transcriptomics provides a tool to dissect the complex intercellular pathways that regulate cancer-associated inflammation in human tumors, complementary experimental systems for mechanistic validation remain limited. This study integrated single-cell data from human tumors and novel organoid co-cultures to study the PDAC TME. We derived a comprehensive atlas of PDAC gene expression from six published human single-cell RNA sequencing (scRNA-seq) datasets2-7 to characterize intercellular signaling pathways between epithelial tumor cells and CAFs that regulate the inflammatory TME. Analysis of the epithelial cell compartment identified global gene expression pathways that modulate inflammatory signaling and are correlated with CAF composition. We then generated patient-derived organoid-CAF co-cultures to serve as a biological model of the cellular interactions learned from human tissue in the atlas. Transfer learning analysis to additional scRNA-seq data of this co-culture system and mechanistic experiments confirmed the epithelial response to fibroblast signaling. This bidirectional approach of complementary computational and in vitro applications provides a framework for future studies identifying important mechanisms of intercellular interactions in PDAC.

cancer biology↗