Abstract PS03-08: Spatial biomechanics determines fate in breast cancer
Authors: Sara Nizzero, Maria Pelaez Soni, Gregory Zaugg, Mariam Gachechiladze, Yitian Xu, Licheng Zhang, Junjun Zheng, Brian Menegaz, Lee B Jordan, Colin A Purdie, Philip R Quinlan, Chandandeep Nagi, Karla A Sepulveda, Philipp Oertle, Tobias A Appenzeller, Marko Loparic, Zhihui Wang, Shu-Hsia Chen, Vittorio Cristini, Marija Plodinec, Alastair Thompson
Journal: Cancer Research (2024)

Abstract Background: With the recent advancements of spatial omics, it has become increasingly recognized that spatial analysis the of tumor architecture is key to decipher the heterogenous intratumoral relationships between different tumor components and provide better understanding of cancer therapy resistance, as well as help to identify potential targets for personalized therapy. The challenge of the discovery and implementation of such biomarkers still lays in the technological and methodological difficulties, and their translation into clinical practice. In parallel a major advancement in cancer research has been the recognition of the importance of cancer biomechanical properties in cancer progression, metastasis, and therapy response. Methods: We developed a new computational platform to identify biomechanical drivers of cancer outcome from spatial omics data. We used it to identify recurring spatial patterns of these markers within the tumor microenvironment, and define the spatial scale of heterogeneity of such patterns. Our dataset consists in 700 primary breast cancer baseline samples analyzed with two 30-marker imaging mass cytometry panels to identify cancer, immune, and stromal cells and structures and their biomechanical states. Our patient cohort includes 20+ years of follow up, with up to 6 samples per patient from 64 patients alive 12 years post diagnosis, and 49 patients lost to breast cancer deaths. Patients were treated with surgery, radiation, chemotherapy, endocrine therapy, or combinations of these post-surgery. Based on this approach, we derived a new three-parameter quantitative metric of preferential spatial co-localization between different cells or structures, and we use this metric to stratify patients into responders and non-responders with the aid of single and multivariate survival analysis. Finally, we include a correlation with Atomic Force Microscopy to identify the mechanical signature of these driving patterns in breast cancer. Results: Our work enabled the identification of tumor-immune-stromal spatial patterns that drive breast cancer outcome and their spatial scale. Among our results, we were able to further define spatial regions of hypoxia-driven EMT. We found these regions to be usually on the scale of 50 mm radius, and enriched in presence of unspecified immune cells. They are also more frequent in ER+ areas, with high NaKATPase activity. Our analysis also shed light on the controversy on the role of fibroblasts. We have found that the presence of a strong, spatially structured stroma, in fibroblast-rich regions is a strong predictor of positive outcome. Similarly, in our analysis collagen cross-linking emerges as a positive control factor in Vimentin-rich microenvironments, offering new interpretation to the role of the ECM structure. Importantly, our first principle approach allows for the identification of driving structures beyond heterogeneity, and thus enables easy extension to additional datasets. Finally, when correlating these patterns with Atomic force microscopy measurements, we can clearly see that patterns predictive of poor survival present a clear heterogeneous stiffness signature, as opposed to a very homogenous good survival pattern signature. Conclusion: These results confirm the importance of spatial distribution of biomechanical drivers in cancer, offering new avenues of physics-based therapeutic targets. Our results also offer a solid base to inform machine learning algorithms on how to significantly parametrize spatial patterns in breast cancer for clinical significance. Furthermore, we demonstrate for the first time the use of Atomic Force Microscopy as a single biomarker for these patterns in breast cancer with a direct correlation based on pathology matching. Citation Format: Sara Nizzero, Maria Pelaez Soni, Gregory Zaugg, Mariam Gachechiladze, Yitian Xu, Licheng Zhang, Junjun Zheng, Brian Menegaz, Lee B Jordan, Colin A Purdie, Philip R Quinlan, Chandandeep Nagi, Karla A Sepulveda, Philipp Oertle, Tobias A Appenzeller, Marko Loparic, Zhihui Wang, Shu-Hsia Chen, Vittorio Cristini, Marija Plodinec, Alastair Thompson. Spatial biomechanics determines fate in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-08.