Multiparameter imaging reveals clinically relevant cancer cell – stroma interaction dynamics in head and neck cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE252265 | GEO | 2024/10/28
REPOSITORIES: GEO
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Project description:Epithelial tumors are characterized by abundant inter- and intra-tumor heterogeneity, which complicates diagnostics and treatment. The contribution of cancer-stroma interactions to this heterogeneity is poorly understood. Here we report a paradigm to quantify phenotypic diversity in head and neck squamous cell carcinoma (HNSCC) with single cell resolution. By combining cell state markers with morphological features, we identify phenotypic signatures that correlate with clinical features, including metastasis and recurrence. Integration of tumor and stromal signatures reveals that partial epithelial-mesenchymal transition (pEMT) renders disease outcome highly sensitive to stromal composition, generating a strong prognostic and predictive signature. Spatial transcriptomics and subsequent analyses of cancer spheroid dynamics identifies cancer-associated fibroblast–pEMT axis as a nexus for intercompartment signaling that reprograms pEMT cells into an invasive phenotype. Taken together, we establish a paradigm to identify clinically relevant tumor phenotypes, and discover a cell state-dependent interplay between stromal and epithelial compartments that drives cancer aggression.
2024-10-28 | GSE251902 | GEO
Project description:Tissue of the breast is heterogeneous, consisting of a variety of cell types and connective tissue. This heterogeneity is also present in breast tumors and will complicate proteomic analysis, as it is not always clear whether a signal originates from the stromal environment, from normal epithelial or tumor cells. Here we microdissected a variety of cell types and stroma from benign and malignant breast tissues. We compared proteomic differences between these tissues, both from cells of epithelial origin and the stromal environment. Differences in protein abundances corresponded with several hallmarks of cancer, including loss of cell adhesion, transformation to a migratory phenotype, and enhanced energy metabolism. Furthermore, despite enriching for (tumor) epithelial cells, many changes to the extracellular matrix were detected in microdissected cells of epithelial origin. The stromal compartment was heterogeneous and richer in the number of fibroblast and immune cells in malignant sections, compared to benign tissue sections. Although this heterogeneity complicated detection of differentially abundant proteins, several markers were exclusively detected in stroma. However, as heterogeneity in the stroma is more difficult to be reduced through microdissection, comparative analysis was most informative in microdissected cells of epithelial origin, and provided a relatively complete picture of malignant transformations.
2017-01-11 | PXD003632 | Pride
Project description:Cancer stem cells (CSCs) drive tumour spread and therapeutic resistance, and can undergo epithelial-to-mesenchymal transition (EMT) and mesenchymal-to-epithelial transition (MET) to switch between epithelial and post-EMT sub-populations. Examining oral squamous cell carcinoma (OSCC), we now show that increased phenotypic plasticity, the ability to undergo EMT/MET, underlies increased CSC therapeutic resistance within both the epithelial and post-EMT sub-populations. The post-EMT CSCs that possess plasticity exhibit particularly enhanced therapeutic resistance and are defined by a CD44highEpCAMlow/-CD24+ cell surface marker profile. Treatment with TGFβ and retinoic acid (RA) enabled enrichment of this sub-population for therapeutic testing, through which the endoplasmic reticulum (ER) stressor and autophagy inhibitor Thapsigargin was shown to selectively target these cells. Demonstration of the link between phenotypic plasticity and therapeutic resistance, and development of an in vitro method for enrichment of a highly resistant CSC sub-population, provides an opportunity for the development of improved chemotherapeutic agents that can eliminate CSCs. The CA1 OSCC cell line was sub-cloned to derive 4 clonal sub-lines, termed pEMT-P, pEMT-S, Epi-S and Epi-P (here 18, 23, 7 and 4 respectively).
2016-01-12 | E-GEOD-74578 | biostudies-arrayexpress
Project description:Personalized biological insights into heterogeneous tumors, such as breast cancer could improve clinical management. While genomic analysis has contributed significantly towards dissecting breast cancer heterogeneity, limitations in clinical application are partly rooted in the inter-tumor variability arising from a largely uncharacterized interactive exchange between diverse cell types in the tumor microenvironment. Here we first identified a common response signature to stromal coculture across breast cancer of varying clinicopathologic phenotypes. Proximity to fibroblasts resulted in gene transcript alterations of >2-fold for 107 probe sets, collectively designated as Fibroblast Triggered Gene Expression in Tumor (FTExT). Prominent features of tumor cell response included transcript repression related to biofunctions encompassing inflammatory signaling, cell movement, cell death, and cell growth and proliferation. In an evaluation of intertumor heterogeneity, the FTExT classifier stratified moderate and high histopathologic grade breast cancer according to clinical outcome (dataset 1, n=401, p=0.031; dataset 2, n=200, p=0.013), delineating a novel phenotype of stromal crosstalk underlying the prognostic potential of tumor grade. Extending correlative data through functional analysis of stromal-epithelial cocultures of both malignant and nonmalignant derivation, significant differences in cell cycle regulation, rate of proliferation, resistance to therapy-induced apoptosis, and growth arrest were observed in FTExT-based subgroups. Instead of a stromal impact that is uniformly cancer promoting, our data demonstrate striking variability in tumor cell response that directly contributes to contrasting functional aggressiveness of malignant breast tissue. Our findings uniquely reveal dynamically interacting paracrine components underlying the molecular and functional heterogeneity of breast cancer, thus presenting novel opportunities for tumor targeting. 10 tumor samples cocultured with fibroblast were profiled in their gene expression with microarrays, and compared with 7 tumor samples cultured without fibroblast. Immortalized tumor cell lines of varying histologic grade were developed and maintained in a growth median as described in Dairkee, et al., Oncogene, 2007. In the coculture set up, epithelial cells were seeded in 6-well plates, and fibroblasts in 0.4 μm inserts with hanging geometry (BD Biosciences, Franklin Lakes, NJ) at a 3:1 ratio in a common pool of growth medium for 3-day harvests. Controls were comprised of each epithelial sample maintained in the absence of fibroblast-seeded inserts under the same culture conditions.
2011-02-08 | E-GEOD-27018 | biostudies-arrayexpress
Project description:This is a non-linear mathematical model of cancer immunosurveillance that takes into account intratumoral phenotypic heterogeneity, such as differential expression of cell surface receptors and growth factors, according to cell-mediated immune responses. The model describes phenomena that have also been observed in vivo, such as tumor dormancy, cancer immunoediting, and a strong sensitivity to initial conditions.
2024-09-02 | BIOMD0000000790 | BioModels
Project description:Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMaps to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.
2023-04-30 | GSE179983 | GEO
Project description:Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten high-grade serous ovarian carcinomas (HGSOC) and analyzed by mass spectrometry. Unsupervised analyses of protein abundance data revealed independent clustering of enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering driven by overall tumor “purity”. Comparing these data to previously defined prognostic HGSOC molecular subtypes revealed protein expression from tumor epithelium correlated with the differentiated subtype, whereas stromal proteins correlated with the mesenchymal subtype. Protein abundance in tumor epithelium and stroma exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial tumor microenvironment protein heterogeneity that directly bears on prognostic signatures, biomarker discovery, and cancer pathophysiology, and underscore the need to enrich cellular subpopulations for expression profiling.
2021-07-01 | PXD026570 | Pride
Project description:Purpose: Multiple studies from last decades have shown that the microenvironment of carcinomas plays an important role in the initiation, progression and metastasis of cancer. Our group has previously identified novel cancer stroma gene expression signatures associated with outcome differences in breast cancer by gene expression profiling of two tumors of fibroblasts as surrogates for physiologic stromal expression patterns. The aim of this study is to find additional new types of tumor stroma gene expression patterns. Results: 53 tumors were sequenced by 3SEQ with an average of 29 million reads per sample. Both the elastofibroma (EF) and fibroma of tendon sheath (FOTS) gene signatures demonstrated robust outcome results for survival in the four breast cancer datasets. The EF signature positive breast cancers (20-33% of the cohort) demonstrated significantly better outcome for survival. In contrast, the FOTS signature positive breast cancers (11-35% of the cohort) had a worse outcome. The combined stromal signatures of EF, FOTS, and our previously identified DTF, and CSF1 signatures characterize, in part, the stromal expression profile for the tumor microenvironment for between 74%-90% of all breast cancers. Conclusions: We defined and validated two new stromal signatures in breast cancer (EF and FOTS), which are significantly associated with prognosis. Gene expression profiling by 3SEQ was performed on 8 additional types of fibrous tumors, to identify different fibrous tumor specific gene expression signatures. We then determined the significance of the fibrous tumor gene signatures in four publically available breast cancer datasets (GSE1456, GSE4922, GSE3494, NKI Dataset).
2013-11-21 | E-GEOD-42948 | biostudies-arrayexpress
Project description:This model is based on:
Computational Modeling of the Crosstalk Between Macrophage Polarization and Tumor Cell Plasticity in the Tumor Microenvironment.
Abstract:
Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M1 vs. M2) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.
2019-09-23 | MODEL1909230002 | BioModels
Project description:Background: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer (IBC) pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. Results: We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive re-wiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, ER-positive IBC, and ER-negative IBC, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in IBC mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. Conclusions: Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially-localized transcriptomic data. The analysis provides new biological insights into the re-wiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer. 36 flash-frozen human primary breast cancer samples were subjected to laser capture microdissection to separately isolate matched tumor epithelial and tumor-associated stromal components. RNA was isolated, subjected to 2 rounds of amplification, and hybridized on Agilent 4x44K microarrays along with a common reference (single round-amplified commercially obtained Universal Human Reference RNA) in a dyeswap design. For two samples of tumor-associated stroma, a second technical replicate was performed. Samples were labelled as ER-positive based on ESR1 gene expression levels in the tumor epithelium, using univariate Gaussian mixture model-based clustering via the mclust package in R.
2015-05-12 | E-GEOD-68744 | biostudies-arrayexpress