Biology subjects

Cramer, E. M.

Publications and source records attributed to Cramer, E. M..

3 recordsLinked to original sources

bioRxiv2026

Tumor-immune trajectory context connects static tissue architecture to clinical outcomes

Multiplexed tissue imaging (MTI) has revealed recurrent tumor microenvironment (TME) architectures with prognostic value, yet these measurements are inherently static, obscuring dynamic changes in the TME that govern therapeutic response. Here, we introduce a trajectory-centric framework that reconstructs continuous TME dynamics by integrating agent-based mathematical modeling and simulation with state space analysis. This approach yields a mechanistically constrained reference landscape built entirely from in silico simulation, and onto which static patient biospecimens can be projected and mapped onto simulated TME trajectories. Systematic simulation of tumor-immune interactions in triple-negative breast cancer identifies six metastable TME states connected by transition pathways spanning immune control to immune escape. Mapping MTI data from two independent patient cohorts, including longitudinal samples from a randomized immunotherapy trial, validates this landscape by positioning individual biospecimens along inferred TME trajectories rather than in static states. We show that treatment-phase TME states, but not pre-treatment configurations, robustly predict immunotherapy response, and identical terminal states can arise from distinct trajectory histories corresponding to immune failure or resolved inflammation. Thus, this framework enables mechanistic simulations to define a reference dynamical landscape that serves as a coordinate system for interpreting static clinical spatial data, providing a principled basis for evaluating consistency, predictiveness, and clinical relevance across independent patient cohorts. Altogether, this study advances spatial tumor profiling from static state classification of human tissues to dynamic trajectory inference, establishing a quantitative framework for trajectory-informed, state-guided, and temporally adaptive immunotherapy strategies.

cancer biology↗

bioRxiv2024

Towards Digital Quantification of Ploidy from Pan-Cancer Digital Pathology Slides using Deep Learning

Abnormal DNA ploidy, found in numerous cancers, is increasingly being recognized as a contributor in driving chromosomal instability, genome evolution, and the heterogeneity that fuels cancer cell progression. Furthermore, it has been linked with poor prognosis of cancer patients. While next-generation sequencing can be used to approximate tumor ploidy, it has a high error rate for near-euploid states, a high cost and is time consuming, motivating alternative rapid quantification methods. We introduce PloiViT, a transformer-based model for tumor ploidy quantification that outperforms traditional machine learning models, enabling rapid and cost-effective quantification directly from pathology slides. We trained PloiViT on a dataset of fifteen cancer types from The Cancer Genome Atlas and validated its performance in multiple independent cohorts. Additionally, we explored the impact of self-supervised feature extraction on performance. PloiViT, using self-supervised features, achieved the lowest prediction error in multiple independent cohorts, exhibiting better generalization capabilities. Our findings demonstrate that PloiViT predicts higher ploidy values in aggressive cancer groups and patients with specific mutations, validating PloiViT potential as complementary for ploidy assessment to next-generation sequencing data. To further promote its use, we release our models as a user-friendly inference application and a Python package for easy adoption and use.

bioinformatics↗

bioRxiv2024

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial tissue sectioning to comprehensively profile samples by imaging each section with unique biomarker panels or assays. The dependence on serial sections is attributed to technological limitations of MTI panel size or incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due to inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities and spatial information, which are critical for effectively completing this task. To address this problem, we first use Monte-Carlo simulations to estimate the overlap between serial 5m-thick sections. We then introduce COEXIST, a novel algorithm that synergistically combines shared molecular profiles with spatial information to seamlessly integrate serial sections at the single-cell level. We demonstrate COEXIST necessity and performance across several applications. These include combining MTI panels for improved spatial single-cell profiling, rectification of miscalled cell phenotypes using a single MTI panel, and the comparison of MTI platforms at single-cell resolution. COEXIST not only elevates MTI platform validation but also overcomes the constraints of MTIs panel size and the limitation of full nuclei on a single slide, capturing more intact nuclei in consecutive sections and thus enabling deeper profiling of cell lineages and functional states.

bioinformatics↗