Biology subjects

Eng, J. R.

Publications and source records attributed to Eng, J. R..

5 recordsLinked to original sources

bioRxiv2026

Epigenetic progression of pancreatic cancer to aggressive subtypes involves alternate routes of lineage reprogramming in subtype-intermediate progenitor cells

Pancreatic ductal adenocarcinoma (PDAC) progression involves malignant cell state plasticity. Epigenetic changes underlie this plasticity, yet the PDAC cis-regulatory landscape remains understudied. To address this, we profiled 33 primary tumors and 7 metastases from 39 patients with single-cell ATAC-seq, paired with 10 single-cell RNA-seq profiles. We found that epigenetic GATA6+/KRT17+ co-accessibility identifies a classical-basal subtype-intermediate progenitor state (SIP) associated with better clinical outcomes. SIP cells display limited epigenetic reprogramming from premalignant epithelium and retain gastric-intestinal differentiation reminiscent of neoplastic precursors. Lineages without GATA6+/KRT17+ co-accessibility exhibit greater lineage and epithelial-mesenchymal plasticity. Classical PDACs that repress basal gene accessibility activate neural-like progenitor (NRP) and tuft lineage enhancers, whereas basal committed tumors display esophageal transdifferentiation. Compared to SIP, classical-NRP and basal committed tumors have poorer outcomes, and show distinct PD-1/PD-L1 immune proteomic phenotypes and prognostic myofibroblast epigenetic states, respectively. Our work reveals links between lineage reprogramming, EMT, and epigenetic progression in human PDAC.

cancer biology↗

bioRxiv2025

A window trial in metastatic pancreatic ductal adenocarcinoma reveals resistance mechanisms to targeting the KRAS-MEK pathway

Copy number alterations of KRAS, mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), and MYC occur in 30-40% of PDAC. Here we demonstrate that KRAS and MYC are frequently co-gained and accompanied with worse prognosis in PDAC. In a Window-of-Opportunity clinical trial for metastatic PDAC, serial biopsies and deep multi-omics analyses were utilized to explore resistance mechanisms to MEK inhibition, as a surrogate for KRAS inhibition. Tumors from four of 14 patients showed Ki-67/CA19-9-based biomarker response (BR). Non-BR tumors were enriched for KRAS/MYC co-gain and KRASG12D variant. A transcriptomic signature of BR tumors was inversely correlated with KRASG12D/MYC co-gain in a large PDAC dataset and predictive for KRAS inhibitor response in multiple models. Finally, co-targeting KRAS and MYC was synergistic in KRASG12D/MYC co-gain PDAC. Together, this study provides insight into KRAS inhibitor resistance and supports MYC as an important target to improve patient outcomes in this deadly disease.

cancer biology↗

bioRxiv2025

Myc and Kras cooperate in adult acinar cells to drive phenotypic heterogeneity, metastasis, and therapeutic resistance in a novel pancreatic cancer mouse model

Pancreatic ductal adenocarcinoma (PDAc) is a deadly malignancy, most commonly diagnosed in advanced stages when no curative treatments are available. The development of new models that aid ongoing investigation into the mechanisms by which it initiates, disseminates, and evades treatment is of the utmost importance. In vivo models that accurately recapitulate the features and spectrum of human pancreatic cancer are paramount to make a dent in this disease as two decades of the standard-of-care have failed to substantially improve survival. Here, we take advantage of our finding that post-translational stabiliziation of MYC downstream of the canonical PDAc driver, mutant KRAS, is an early event in PDAc progression to design a novel mouse model of PDAc progression based on deregulated, constituitive expression of Myc and mutant Kras in adult pancreatic acinar cells. Tumors from this KMC model histologically and molecularly recapitulate heterogeneity seen in human PDAc, with a high rate of metastasis to the liver. Cell lines derived from KMC autochthonous PDAc provide new models for orthotopic primary tumors that reliably metastasize to the liver and lung, providing important new tools to efficiently study the metastatic cascade and aid in the develoment of new therapeutics addressing metastatic disease. Cell lines represent distinct molecular subtypes with corresponding differential drug sensitivity. Toghether, this model provides a new and additional tool in the study of pancreatic cancer and the means by which it so deftly evades our best efforts at treatment.

cancer biology↗

bioRxiv2023

Robust biomarker discovery through multiplatform multiplex image analysis of breast cancer clinical cohorts

AO_SCPLOWBSTRACTC_SCPLOWSpatial profiling of tissues promises to elucidate tumor-microenvironment interactions and enable development of spatial biomarkers to predict patient response to immunotherapy and other therapeutics. However, spatial biomarker discovery is often carried out on a single patient cohort or imaging technology, limiting statistical power and increasing the likelihood of technical artifacts. In order to analyze multiple patient cohorts profiled on different platforms, we developed methods for comparative data analysis from three disparate multiplex imaging technologies: 1) cyclic immunofluorescence data we generated from 102 breast cancer patients with clinical follow-up, in addition to publicly available 2) imaging mass cytometry and 3) multiplex ion-beam imaging data. We demonstrate similar single-cell phenotyping results across breast cancer patient cohorts imaged with these three technologies and identify cellular abundance and proximity-based biomarkers with prognostic value across platforms. In multiple platforms, we identified lymphocyte infiltration as independently associated with longer survival in triple negative and high-proliferation breast tumors. Then, a comparison of nine spatial analysis methods revealed robust spatial biomarkers. In estrogen receptor-positive disease, quiescent stromal cells close to tumor were more abundant in good prognosis tumors while tumor neighborhoods of mixed fibroblast phenotypes were enriched in poor prognosis tumors. In triple-negative breast cancer (TNBC), macrophage proximity to tumor and B cell proximity to T cells were greater in good prognosis tumors, while tumor neighborhoods of vimentin-positive fibroblasts were enriched in poor prognosis tumors. We also tested previously published spatial biomarkers in our ensemble cohort, reproducing the positive prognostic value of isolated lymphocytes and lymphocyte occupancy and failing to reproduce the prognostic value of tumor-immune mixing score in TNBC. In conclusion, we demonstrate assembly of larger clinical cohorts from diverse platforms to aid in prognostic spatial biomarker identification and validation. SO_SCPLOWTATEMENTC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWOFC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWSIGNIFICANCEC_SCPLOWOur single-cell spatial analysis of multiple clinical cohorts uncovered novel biomarkers of patient outcome in breast cancer. Additionally, our data, software, and methods will help advance spatial characterization of the tumor microenvironment.

cancer biology↗

bioRxiv2021

A framework for multiplex imaging optimization and reproducible analysis

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.

cancer biology↗