Quantifying Epithelial-Mesenchymal Tumor Heterogeneity for Prediction of Patient Prognosis Based on EMT State

Title:

Quantifying Epithelial-Mesenchymal Tumor Heterogeneity for Prediction of Patient Prognosis Based on EMT State

Link:

https://aacrjournals.org/cancerres/article/82/4_Supplement/P4-07-19/681138

Abstract:

Background: Triple Negative Breast Cancer (TNBC) is an aggressive and heterogeneous subtype characterized by ER/PR/HER2 negative status. Much of the disease potential and aggressive nature of this subtype derives from inter- and intra-tumoral heterogeneity, which makes developing targeted therapies challenging. A key contributor to both heterogeneity in TNBC and later stage chemo-resistance and metastasis is the Epithelial-to-Mesenchymal transition (EMT). This developmental program is frequently exploited in the context of cancer to increase migratory abilities, invasiveness, metastatic potential, and resistance to chemotherapy. Indeed, EMT has been demonstrated and linked to poor prognosis and decreased survival in many solid cancer types. Cells have been found to reside in multiple stable intermediate states along the EMT spectrum, which confer increased aggressive, metastatic, and chemoresistance attributes to a heterogeneous tumor through increased stem-like characteristics. Identifying and targeting this disease-potentiating population in patient tumors is a major hurdle in overcoming metastatic disease. Knowledge gap: Despite major advances in our understanding, the contributions of EMT research to improvements in diagnostic pathology or cancer therapy have been minimal. One reason for this gap stems from our inability to accurately detect and quantify epithelial-mesenchymal heterogeneity in primary tumor specimens. Secondly, the significance of recently identified intermediate or partial EMT states to predicting tumor prognosis and therapy response are unclear. Approach & Results: To study the role of various states within the EMT spectrum and their regulatory networks, the heterogeneous breast cancer cell line, SUM149PT, was used to derive six single cell clones encompassing the spectrum of EMT states, from epithelial to mesenchymal. Interrogation of this model system in vivo has revealed increased tumor growth and metastatic potential in the intermediate EMT states when compared to the extreme epithelial and mesenchymal states. To further elucidate EMT states in vivo, we employ a 6-marker multi-round immunofluorescence-based staining approach to identify cells that reside in various states along the EMT spectrum. We subsequently used an entropy-based approach and nearest-neighbor analysis on these tumors with the purpose of scoring heterogeneity and overall EMT state. Notably, this analysis segregated stromal infiltrates and their contributions to aggregate EMT scoring, which has been a major hurdle in using EMT as a scoring metric in patient samples. Overall, SUM149 clone-derived tumors held true to the relative EMT states of the starting cell populations; intermediate-derived tumors displayed high heterogeneity while epithelial and mesenchymal clone-derived tumors had lower levels of heterogeneity, despite retaining different EMT scores. Decoupling of heterogeneity and EMT state in this way provides two metrics to assess potential metastatic ability of a tumor. This staining method and analysis has been successfully applied in a preliminary set of patient tumors, showing promise for these two factors, E-M Heterogeneity and EMT score, as a tumor prognostic indicator to inform therapeutic decision-making. Conclusions: EMT tumor states and EMT-derived intra-tumoral heterogeneity play an important role in tumor metastasis and disease progression. Here, we have validated a multiplexed staining approach to quantify these metrics within a tumor, while segregating out stromal infiltrating cells. In the future, this staining and quantification shows promise as a means of predicting patient prognosis and informing potential treatment options based on targeting EMT states

Citation:

Meredith S. Brown, Behnaz Abdollahi, Nevena Ognjenovic, Kristen E. Muller, Saeed Hassanpour, Diwakar R, Pattabiraman, “Quantifying Epithelial-Mesenchymal Tumor Heterogeneity for Prediction of Patient Prognosis Based on EMT State”, American Association for Cancer Research Annual Meeting (AACR), Cancer Research, 82:P4-07-19, 2022.