Infrared Spectroscopic Imaging Visualizes a Prognostic Extracellular Matrix-Related Signature in Breast Cancer

Abstract

Molecular analysis techniques such as gene expression analysis and proteomics have contributed greatly to our understanding of cancer heterogeneity. In prior studies, gene expression analysis was shown to stratify patient outcome on the basis of tumor-microenvironment associated genes. A specific gene expression profile, referred to as ECM3 (Extracellular Matrix Cluster 3), indicated poorer survival in patients with grade III tumors. In this work, we aimed to visualize the downstream effects of this gene expression profile onto the tissue, thus providing a spatial context to altered gene expression profiles. Using infrared spectroscopic imaging, we identified spectral patterns specific to the ECM3 gene expression profile, achieving a high spectral classification performance of 0.87 as measured by the area under the curve of the receiver operating characteristic curve. On a patient level, we correctly identified 20 out of 22 ECM3 group patients and 19 out of 20 non-ECM3 group patients by using this spectroscopic imaging-based classifier. By comparing pixels that were identified as ECM3 or non-ECM3 with H&E and IHC images, we were also able to observe an association between tissue morphology and the gene expression clusters, showing the ability of our method to capture broad outcome associated features from infrared images. Breast cancer is the most prevalent cancer in women 1 and is estimated to cause over 40,000 deaths annually in the United States. Although cancer has been thought as a consequence of genetic mutations in an aberrant tissue mass, tumor progression is increasingly viewed as functionally interconnected with the surrounding microenvironment 2. Indeed, crosstalk between cancer cells and the tumor microenvironment components [immune cells, stromal cells, and the extracellular matrix (ECM)] plays a major role in regulating several biological processes 3. Differences in ECM-associated gene expression can describe the biological and clinical heterogeneity of breast cancer, especially in terms of prognosis and response to therapy 2. Recent works have identified a subgroup of breast cancers characterized by overexpression of a robust cluster of genes mainly encoding structural ECM proteins, originally referred to as the ECM3 group 4,5. These features indicate an epithelial to mesenchymal transition (EMT) but with accelerated metastatic potential only in the undifferentiated (grade III) phenotype 4,5. Notably, ECM3 classification provides additional information to assess tumor progression 5. It also identifies tumors in which the interplay between myeloid-derived suppressor cells (MDSCs) and the ECM drives the induction of EMT in tumor cells, and suggests a strategy for inhibition of MDSC by aminobisphosphonates that could revert EMT and lead to less aggressive tumor phenotype 6. Even though such molecular classification is of great importance in stratifying patients, it requires multi-step tissue processing and still requires the validation of an algorithm for classification at the single tumor level to be

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

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Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis

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Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues

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