Biology subjects

Schulman, A.

Publications and source records attributed to Schulman, A..

5 recordsLinked to original sources

bioRxiv2025

Longitudinal Analysis of Matched Patient Biospecimens Reveals Neural Reprogramming of Cancer-Associated Fibroblasts Following Chemotherapy in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), the most common subtype of pancreatic cancer, is a deadly disease with a complex tumor microenvironment (TME). How chemotherapy alters the TME, and whether these changes drive chemoresistance, is poorly understood. We examined matched pre- and post-treatment tissue specimens and found near-universal enrichment of axonal guidance genes in cancer-associated fibroblasts (CAFs) after treatment. These CAFs were enriched near sites of perineural invasion, coinciding with regions of increased tumor cell proliferation, and were enriched in tumor areas distant from nerves after chemotherapy. Metastatic recurrence lesions had the highest prevalence of these CAFs versus primary tumors and untreated metastasis. These CAFs showed elevated non-canonical Wnt mediators and axonal guidance genes, which complemented matching cognate binding partners in tumor epithelial cells, suggesting a role in tumor-stroma crosstalk. Our findings implicate fibroblast-derived axonal-guidance genes in promoting PDAC invasion and point to a promising target for this disease. Statement of SignificanceTherapeutic resistance remains a major challenge in pancreatic cancer. Our findings shed light on the changes in the complex tumor microenvironment in response to chemotherapy and identify fibroblasts high in axonal-guidance genes that may drive tumor progression and chemoresistance, thus uncovering a potential avenue for targeting pancreatic cancer.

cancer biology↗

bioRxiv2025

Off-target mapping enhances selectivity of machine learning-predicted CK2 inhibitors

A key challenge in drug development is identification of druggable targets, the modulation of which attenuates disease progression, while avoiding inhibition of proteins that lead to dose-limiting toxicities. Here, we investigate a drug target casein kinase 2 (CK2) - a serine/threonine kinase implicated in cancer, for which existing inhibitors have so far failed in clinical trials. Using molecular and pharmacoepidemiology approaches, we show that small molecules targeting cyclin-dependent kinase (CDK) family members CDK1/2/7/9, including the existing CK2 inhibitors, have a higher risk to induce adverse effects or fail in clinical trials. Based on this finding, we establish a machine learning (ML) assisted discovery pipeline to redesign more specific and allosteric lead compounds against CK2, with a more selective on-target binding and favourable off-target profile. Importantly, we show that such selective design is possible when standard molecular docking and ML algorithms are combined with an error prediction model. In conclusion, our study reports a simple yet efficient ML-powered drug discovery pipeline and novel submicromolar inhibitors targeting clinically relevant CK2 kinase with no clinically approved antagonists available. Our prediction pipeline was able to achieve a 90% hit-rate, significantly reducing the need for subsequent wet-lab validation.

pharmacology and toxicology↗

bioRxiv2025

Spatial analysis of IPMNs defines a paradoxical KRT17-positive, low-grade epithelial population harboring malignant features

Background & AimsIntraductal papillary mucinous neoplasms (IPMNs) are pancreatic cysts that represent one of the few radiologically identifiable precursors to pancreatic ductal adenocarcinoma (PDAC). Though the IPMN-bearing patient population represents a unique opportunity for early detection and interception, current guidelines provide insufficient accuracy in determining which patients should undergo resection versus surveillance, resulting in a sizable fraction of resected IPMNs only harboring low-grade dysplasia, suggesting that there may be overtreatment of this clinical entity. MethodsTo investigate the transcriptional changes that occur during IPMN progression, we performed spatial transcriptomics using the Nanostring GeoMx on patient samples containing the entire spectrum of IPMN disease including low-grade dysplasia, high-grade dysplasia, and IPMN-derived carcinoma. Single cell RNA sequencing was performed on side branch and main duct IPMN biospecimens. ResultsWe identified a subpopulation of histologically low-grade IPMN epithelial cells that express malignant transcriptional features including KRT17, S100A10 and CEACAM5, markers that are enriched in PDAC. We validated this high-risk gene signature in both single-cell RNA sequenced samples and an external ST dataset containing a larger number of IPMN samples including non-tumor bearing IPMN (i.e. low-grade IPMN in isolation). Immunofluorescence staining of a large cohort of patient tissues confirmed the presence of KRT17-positive cells, which were found to comprise a small subset of epithelial cells within histologically low-grade IPMN in a patchy distribution. ConclusionsOur study demonstrates that KRT17 marks a distinct transcriptional signature in a subpopulation of epithelial cells within histologically low-grade IPMN. This population of cells likely represents a transitional state of histologically low-grade epithelial cells undergoing progression to a higher grade of dysplasia and thus may represent a higher risk of progression to carcinoma. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/643943v2_ufig1.gif" ALT="Figure 1000"> View larger version (43K): org.highwire.dtl.DTLVardef@47d380org.highwire.dtl.DTLVardef@18e7ab2org.highwire.dtl.DTLVardef@1193ffeorg.highwire.dtl.DTLVardef@b84a98_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

cancer biology↗

bioRxiv2024

Attention-based approach to predict drug-target interactions across seven target superfamilies

MotivationDrug-target interactions (DTIs) hold pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary, and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets. Yet, most FDA-approved drugs bind with only a small fraction of disease targets. ResultsThis study introduces an attention-based method to predict drug-target bioactivities for all human proteins across seven superfamilies. Nine different descriptor sets were meticulously examined to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P<0.001) for six out of seven superfamilies. The proposed method outperformed nine state-of-the-art deep learning and graph-based methods, and importantly, maintained relatively high performance for most target superfamilies when tested in independent sources of bioactivity data. We computationally validated 185,676 drug-target pairs from ChEMBL-V33, that were not available in model training, with a reasonable performance of Spearman correlation > 0.57 (P<0.001) for most superfamilies. This justifies the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing activities among 3,492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications. Availability and implementationThe codes and training datasets to reproduce and extend the results are available at https://github.com/AronSchulman/MMAtt-DTA.

systems biology↗

bioRxiv2019

TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools

Chromosome-scale genome sequence assemblies underpin pan-genomic studies. Recent genome assembly efforts in the large-genome Triticeae crops wheat and barley have relied on the commercial closed-source assembly algorithm DeNovoMagic. We have developed TRITEX, an open-source computational workflow that combines paired-end, mate-pair, 10X Genomics linked-read with chromosome conformation capture sequencing data to construct sequence scaffolds with megabase-scale contiguity ordered into chromosomal pseudomolecules. We evaluated the performance of TRITEX on publicly available sequence data of tetraploid wild emmer and hexaploid bread wheat, and constructed an improved annotated reference genome sequence assembly of the barley cultivar Morex as a community resource.

genomics↗