Biology subjects

Savignano, H.

Publications and source records attributed to Savignano, H..

5 recordsLinked to original sources

bioRxiv2026

Transcription Factor Subtype Governs Response and Resistance to DLL3-Directed T-Cell Engagement in Small Cell Lung Cancer

Although small cell lung cancer (SCLC) comprises transcription factor (TF)-defined molecular subtypes (ASCL1, NEUROD1, POU2F3), the extent to which these subtypes predict response to clinically effective therapy in patients--and whether therapy can select for subtype switching--remains unknown. The recent approval of the DLL3xCD3 bispecific T-cell engager tarlatamab represents one of the first meaningful advances in relapsed small cell lung cancer (SCLC) in decades, yet responses remain heterogeneous and resistance is inevitable. Here, we inferred SCLC gene expression from circulating chromatin in prospectively collected patient plasma (46 patients; 167 samples), enabling interrogation of response and acquired resistance to tarlatamab. Parallel development of the first immunocompetent syngeneic mouse model to study tarlatamab response and resistance enabled functional validation. Across species, findings converged on a central principle: TF subtype governs both initial response and acquired resistance. Therapeutic response was significantly associated with ASCL1-subtype tumors, whereas NEUROD1-subtype tumors exhibited inferior responses and POU2F3-subtype tumors were uniformly resistant, consistent with DLL3 being a direct ASCL1 transcriptional target and most highly expressed in ASCL1-positive tumors. Strikingly, one mode of acquired resistance revealed therapeutic selection for a NEUROD1-high state with concomitant DLL3 downregulation. Other resistant tumors exhibited enrichment of regulatory and exhausted T-cell programs, highlighting tarlatamabs dual-targeting mechanism of action. Together, these results reveal that tarlatamab exerts selective pressure against ASCL1-driven lineages, facilitating resistance through loss of an antigen intrinsically linked to that state. These findings underscore the clinical relevance of TF-defined molecular subtypes in human SCLC. More broadly, they highlight the power of integrating longitudinal in vivo plasma transcriptional profiling from patient plasma with functional mouse modeling to uncover clinical and biological mechanisms of response and resistance to cell-surface-targeted therapies.

cancer biology↗

bioRxiv2026

Integrated inference of cancer gene expression from cell-free plasma chromatin

Gene expression is a defining determinant of tumor identity, behavior, and therapeutic response, yet remains challenging to measure noninvasively. Here, we introduce APEX (Associating Plasma Epigenomic features with eXpression), a framework for inferring expression from circulating cell-free chromatin. Trained on [~]270,000 gene-sample pairs from matched tumor RNA-seq and plasma cfChIP-seq across multiple cancers and validated on >15 unseen cancer subtypes, APEX accurately infers cancer gene expression across a range of tumor fractions and outperforms existing plasma-based approaches by integrating positional histone mark and DNA fragmentation patterns across promoters and gene bodies. Using plasma alone, APEX enables classification of prognostically relevant basal and classical pancreatic cancer subtypes and identifies plasma-inferred NECTIN4 expression as a biomarker of response to enfortumab vedotin in metastatic bladder cancer. Together, these findings establish APEX as a biopsy-free approach for profiling tumor transcriptional states and extend liquid biopsy beyond genomic alterations to clinically relevant gene expression programs.

genomics↗

bioRxiv2025

SNAP: Streamlined Nextflow Analysis Pipeline for Immunoprecipitation-Based Epigenomic Profiling of Circulating Chromatin

Epigenomic profiling of circulating chromatin is a powerful and minimally invasive approach for detecting and monitoring disease, but there are no bioinformatics pipelines tailored to the unique characteristics of cell-free chromatin. We present SNAP (Streamlined Nextflow Analysis Pipeline), a reproducible, scalable, and modular workflow specifically designed for immunoprecipitation-based methods for profiling cell-free chromatin. SNAP incorporates quality control metrics optimized for circulating chromatin, including enrichment score and fragment count thresholds, as well as direct estimation of circulating tumor DNA (ctDNA) content from fragment length distributions. It also includes SNP fingerprinting to enable sample identity verification. When applied to cfChIP-seq and cfMeDIP-seq data across multiple cancer types, SNAPs quality filters significantly improved classification performance while maintaining high data retention. Independent validation using plasma from patients with osteosarcoma confirmed the detection of tumor-associated epigenomic signatures that correlated with ctDNA levels and reflected disease biology. SNAPs modular architecture enables straightforward extension to additional cell-free immunoprecipitation-based assays, providing a robust framework to support studies of circulating chromatin broadly. SNAP is compatible with cloud and high-performance computing environments and is publicly available at https://github.com/prc992/SNAP/. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/694452v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@47c734org.highwire.dtl.DTLVardef@674af8org.highwire.dtl.DTLVardef@16ae938org.highwire.dtl.DTLVardef@1f57a01_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

A chromatin fiber model explains cell-free DNA fragmentation signatures of active regulatory elements

Circulating cell-free DNA (cfDNA) assays are being widely adopted in oncology and maternal-fetal medicine. Patterns of cfDNA fragmentation can provide useful information about gene regulation and expression in human disease from a blood draw. Here, we demonstrate that enhancer RNA expression - a marker of enhancer activity - can be inferred from local patterns of cfDNA fragmentation. We define a transcriptional activation score (TAS) that predicts expression of enhancers and genes based on cfDNA fragment sizes and positions near transcriptional start sites (TSSs). The TAS identifies activity of cancer-associated enhancers in patients with cancer, distinguishes clinically relevant cancer subtypes, and identifies activation of enhancers associated with treatment resistance and therapy response. We propose a simple model to account for our findings based on chromatin fiber structure and the depletion of H1 histone proteins near active TSSs. Our model provides a unified framework that reconciles seemingly conflicting observations from prior fragmentomics studies. Broadly, this work enables blood-based assessments of gene regulation in cancer and non-oncologic diseases to inform pathobiology, diagnosis, and treatment selection.

genomics↗

bioRxiv2025

Detection and monitoring of translocation renal cell carcinoma via plasma cell-free epigenomic profiling

TFE3 translocation renal cell carcinoma (tRCC), an aggressive kidney cancer driven by TFE3 gene fusions, is frequently misdiagnosed owing to morphologic overlap with other kidney cancer subtypes. Conventional liquid biopsy assays that detect tumor DNA via somatic mutations or copy number alterations are unsuitable for tRCC, since it often lacks recurrent genetic alterations and because fusion breakpoints are highly variable between patients. We reasoned that epigenomic profiling could more effectively detect tRCC, because the driver fusion constitutes an oncogenic transcription factor that alters gene regulation. By defining a TFE3-driven epigenomic signature in tRCC cell lines and detecting it in patient plasma using chromatin immunoprecipitation and sequencing, we distinguished tRCC from clear cell RCC (AUC=0.87) and healthy controls (AUC=0.91) at low tumor fractions (<1%). This work establishes a framework for non-invasive epigenomic detection, diagnosis and monitoring of tRCC, with implications for other mutationally quiet, fusion-driven cancers. SIGNIFICANCETranslocation renal cell carcinoma (tRCC) is an aggressive fusion-driven subtype of kidney cancer that is frequently misdiagnosed due to morphologic overlap with other kidney cancer subtypes. Conventional liquid biopsy assays targeting DNA alterations are suboptimal for use in tRCC due to its paucity of genomic changes. We demonstrate the utility of cell-free chromatin profiling to noninvasively detect and monitor tRCC with high accuracy, a method that could have applicability to other genomically quiet cancers.

genomics↗