Biology subjects

Strasser, M. K.

Publications and source records attributed to Strasser, M. K..

4 recordsLinked to original sources

bioRxiv2026

Upregulation of Genetic Markers of Poor Prognosis following Chemotherapy in Acute Myeloid Leukemia Cells

Chemoresistance, a leading cause of treatment failure in cancer, is commonly explained by Darwinian selection of treatment-resistant cell clones. However, recurring resistant tumors invariably display complex phenotypes that contribute to increased malignancy, unlikely to have been selected for by chemotherapy. The growing awareness of (non - genetic) phenotypic plasticity led to the hypothesis that chemotherapy, while reducing tumor burden, also inflicts cell stress that induces stem-like states in cells that survive treatment. Here we examined the transcriptomes of HL-60 leukemic cancer cells that survived exposure to three commonly used drugs at submaximal doses for two to four days, and compared differentially expressed genes to those associated with prognosis in public transcriptome databases of acute myeloid leukemia cohorts. While among the genes differentially upregulated in surviving cells, some reflect the therapeutic effect of chemotherapy as they were associated with favorable outcomes in cohort data, many genes upregulated were associated with poor survival, notably genes involved in stemness, epithelial-mesenchymal transition (EMT), inflammation, drug resistance, and apoptosis evasion. These findings support the idea that treatment effectiveness is the net result of an intrinsic tradeoff: Cytocidal treatment, intended to quantitatively reduce cancer cells, also qualitatively increases the malignancy of non-killed cells, which could contribute to residual disease and relapse. This result has implications for drug screening of new therapeutics, as well as in vitro profiling of patient-derived tumor cell susceptibility to existing drugs, which only assess suppression of cancer cell growth and survival. Statement of SignificanceCells surviving chemotherapy upregulate many genes that may affect prognosis. Therefore, drug screening must embrace a more holistic assessment of the biological quality of cell response beyond the rate of cell killing.

cancer biology↗

bioRxiv2025

A Fibroblast State Choreographs an Epithelial YAP-dependent Regenerative Program Essential to (Pre)malignancy via ECM-mediated Mechanotransduction

Chronic lung injury generates metaplasia which occasionally, but ominously, progresses to squamous dysplasia and squamous lung cancer. To identify mechanisms through which disrupted tissue homeostasis contributes to malignant initiation and progression, we used in vivo and in vitro heterotypic recombinant models of human bronchial epithelial cells (hBECs) and fibroblasts. We demonstrate that injury-associated TGF-{beta} signaling creates a fibroblast state dependent upon HSP47 upregulation. These fibroblasts accumulated collagen, thus elevating tissue stiffness and activating mechanosignaling that sustained YAP-dependent embryonic-like, pro-malignant activities in adjacent hBECs. This Stress/Tension-Instructive Fibroblast (STIF) state, exhibited by stressed fibroblasts in premalignant and malignant lesions across multiple cancer types, was sufficient to reprogram disease-free hBECs to metaplasia and to drive hBECs with compromised tumor suppressor function to dysplasia, yet could be inhibited and reversed. STIFs suffice to activate epithelial phenotypes reminiscent of oncogene-mediated cell transformation and induce (pre)malignancy via increased force transmission, providing novel targets for prevention. Statement of significanceTissue injury creates a regenerative pro-tumorigenic Stress/Tension-Instructive Fibroblast (STIF) state which is sufficient to activate a YAP-dependent, pre-malignant program to induce or unmask pre-cancerous phenotypes in epithelial cells through mechanotransduction. Inhibition of STIF activity or mechanosignaling prevents metaplasia and progression to dysplasia. HighlightsO_LITissue injury creates a pro-tumorigenic Stress/Tension-Instructive Fibroblast (STIF) state in multiple organs that precedes and persists through cancer C_LIO_LISTIF signaling alone, working through fibroblasts and not epithelial cells, is sufficient to activate embryonic-like plasticity and induce epithelial pre-cancerous metaplastic lesions C_LIO_LISTIFs program (pre)malignant phenotypes in adjacent epithelial cells through mechanosignaling by activating YAP prior to tumor formation C_LIO_LIInhibiting STIFs or mechanosignaling prevents/reverts metaplasia and prevents progression to dysplasia C_LI

cancer biology↗

bioRxiv2022

Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq (scRNA-seq)

Gene set scoring (or enrichment) is a common dimension reduction task in bioinformatics that can be focused on differences between groups or at the single sample level. Gene sets can represent biological functions, molecular pathways, cell identities, and more. Gene set scores are context dependent values that are useful for interpreting biological changes following experiments or perturbations. Single sample scoring produces a set of scores, one for each member of a group, which can be analyzed with statistical models that can include additional clinically important factors such as gender or age. However, the sparsity and technical noise of single cell expression measures create difficulties for these methods, which were originally designed for bulk expression profiling (microarrays, RNAseq). This can be greatly remedied by first applying a smoothing transformation that shares gene measure information within transcriptomic neighborhoods. In this work, we use the nearest neighbor graph of cells for matrix smoothing to produce high quality gene set scores on a per-cell, per-group, level which is useful for visualization and statistical analysis. Availability and implementationThe gssnng software is available using the python package index (PyPI) and works with Scanpy AnnData objects. It can be installed using pip install gssnng. More information and demo notebooks: See https://github.com/IlyaLab/gssnng

bioinformatics↗

bioRxiv2019

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

Models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations from exponential growth only at higher densities due to limited resources such as space and nutrients. However, recent pre-clinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models however, tumor growth data is limited by the lower limit of detection (i.e. a measurable lesion) and confounding variables, such as tumor microenvironment and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to consider the small number of cells in this low-density regime and use the moment approach for stochastic parameter estimation to calibrate the stochastic growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells depends on cell number. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging tumors and relapse.\n\nAuthor SummaryThe growth kinetics of cancer cells at very low cell densities are of utmost clinical importance as the ability of a small number of newly transformed or surviving cells to grow exponentially and thus, to \"take off\" underlies tumor formation and relapse after treatment. Mathematical models of stochastic tumor cell growth typically assume a stochastic birth-death process of cells impacted by limited nutrients and space when cells reach high density, resulting in the widely accepted logistic growth model. Here we present an in-depth investigation of alternate growth models adopted from ecology to describe potential deviations from a simple cell autonomous birth-death model at low cell densities. We show that our stochastic modeling framework is robust and can be used to identify the underlying structure of stochastic growth trajectories from both simulated and experimental data taken from a controlled in vitro setting in which we can capture data from the relevant low cell density regime. This work suggests that the assumption of cell autonomous proliferation via a constant exponential growth rate at low cell densities may not be appropriate for all cancer cell growth dynamics. Consideration of cooperative behavior amongst tumor cells in this regime is critical for elucidating strategies for controlling tumor cell growth.

cancer biology↗