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Cancer Technologies

Cancer Technologies - Poster Session D

Poster G10 - Lapatinib-treated HER2+ BT474 tumor cells exhibit higher pro-survival and antiapoptotic markers near fibroblasts in a mouse xenograft model.

Friday, October 25, 2024
3:30 PM – 4:30 PM EST
Location: Exhibit Hall E, F & G

Introduction:  Breast cancer accounts for 31% of all cancer cases. About 15% of these cases are classified as human epidermal growth factor receptor positive (HER2+), a subset that overexpresses the HER2 receptor. Targeted therapies, such as lapatinib, have been utilized to inhibit oncogenes, including the HER2 receptor; however, between 38%-75% of HER2+ breast cancer patients develop resistance to HER2-targeted therapies. More recent studies have drawn attention to fibroblasts as a regulator of therapeutic resistance. In our previous work, we showed that fibroblast-derived secreted factors sustain cancer cell proliferation via the PI3K/mTOR/pS6 pathway in the presence of lapatinib treatment. Resistance occurs through activation of antiapoptotic pathways. The physical layout of the tumor microenvironment has also been of interest regarding tumor-cell heterogeneity and how that may impact treatment responses. Spatial analysis of the distance between tumor cells and fibroblasts has shown that tumor cells closer to fibroblasts exhibit increased proliferative markers, Ki67 and BrdU, in both lapatinib treated and untreated in-vivo samples. Analyzing where fibroblasts are in relation to tumor cells, and more specifically what phenotypes tumor cells (e.g. proliferative, anti-apoptotic) are presenting in these environments, could offer insight into designing new combination therapies to restore drug sensitivity. Here, we analyze how tumor-fibroblast distances and number of neighboring fibroblasts impact the expression of phenotypic markers for proliferation, apoptosis, and proliferative pathways.

Materials and

Methods:  We employed the tissue-based cyclic immunofluorescence (t-CyCIF) assay to analyze multiple markers at the single-cell level. T-CyCIF allows multiple proteins to be imaged on the same tissue through repeated cycles of antibody staining and bleaching. Each cell can then be categorized based on protein expression for all proteins stained for. The tissue sample was taken from lapatinib-treated mouse xenograft models orthotopically injected with BT474 HER2+ breast cancer cells. The tissue was deparaffinized and antigen retrieval was performed using a citrate buffer heated in a vegetable steamer. The slide was washed in DI water, and antigens were blocked by incubating slides in the dark with Odyssey Blocking Buffer and then washed with PBS. Bleaching was performed using a 4.5% hydrogen peroxide, 24mM NaOH solution in PBS and stained with a 2.5ug/mL Hoechst solution for the first background image. Whole-slide images were acquired using a Nikon TI microscope. Coverslips were removed and the slide was incubated overnight with the following antibody cycles: e-cadherin, pRB and β-catenin (Cycle 1); BIM, pHER2, pAKT (Cycle 2); MCL1, panCK, pS6 (Cycle 3); PCNA, FAP, α-SMA (Cycle 4); Ki67, BCLXL, BCL2 (Cycle 5). Between each cycle, bleaching was performed with a 4.5% hydrogen peroxide, 24mM NaOH solution in PBS and background images were obtained, followed by Hoechst staining using 1.0ug/mL in the antibody staining step. Images were processed via MCMICRO for illumination correction, stitching, segmentation, and quantification. Spatial metrics were computed by first classifying cells as tumor (panCK+) and fibroblast (α-SMA+), then using coordinates to measure distance.

Results, Conclusions, and Discussions: We plotted seven spatial metrics measuring the tumor-fibroblast neighborhood and protein expression: the physical distance between a tumor cell and its nearest fibroblast, and the number of fibroblasts within a range of distances (0 to 300 um). The nearest fibroblast distance spatial metric resulted in the greatest Pearson correlation coefficient magnitude amongst all proteins stained compared to the other metrics (Fig. 1B). In our study, we found that pS6 and BCL2 had the strongest correlation coefficients (p-value < 0.05). Specifically, the survival signaling marker pS6 negatively correlated with distance to the nearest fibroblast (correlation coefficient -0.37 and p-value < 0.05). Thus, in the spatial maps pS6-high tumor cells were close to fibroblasts (Fig. 1C/D). Similarly, the antiapoptotic protein BCL2, had a negative correlation coefficient of -0.34 (p < 0.05), indicating that BCL2 expression was lower in tumor cells further away from fibroblasts. Cancer cells closer to fibroblasts exhibited higher amounts of BCL2 which indicates resistance to apoptosis (Fig. 1C/D).

We demonstrated that BT474 tumor cells closer to fibroblasts exhibit higher amounts of PI3k pathway activation via pS6 even with lapatinib treatment. Additionally, we were able to show that BT474 tumor cells display higher resistance to apoptosis the closer they are to fibroblasts.

Observing xenografts allows for in vivo measurements to be conducted which more accurately depicts the complex tumor microenvironment. By analyzing tumor-fibroblast neighborhoods with different phenotypic marker expressions, we can start identifying tumor cell subpopulations that may respond to anti-cancer treatments differently. Combination therapies can then be developed for the whole tumor through treatments that attack specific functions in multiple heterogenic tumor cells. In future studies, we are going to test an untreated tissue to measure any heterogeneity within that environment against spatial metrics to fibroblasts, as well as additional antibodies for paracrine signaling.

Acknowledgements (Optional):  I would like to thank the Swanson School of Engineering for selecting me to partake in the Student Undergraduate Research Internship (SURI) this summer. With this opportunity, I have grown my knowledge base for experimental setup, optimization, and problem solving that I will carry with me along my academic and post-academia careers.