Distinguishing metastatic triple‐negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

Abstract

Triple‐negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African‐American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time‐consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label‐free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole‐tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.

Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

“…NFL has the potential to be an alternative optical tool for medical armamentarium 59 . Compared to other techniques for detection of metastatic potential of cancer cells such as those by Paidi et al 60 and Bendau et al 61 , the NFL technique can not only be used for measurements with cells in vitro and fresh tissue specimens ex vivo, but also for in vivo measurements. Since it does not involve spatial scanning (mapping), it works faster than those that require mapping 60 , 61 , and can potentially be implemented for real-time diagnosis during surgery.…”

Multiphoton microscopy for monitoring the occurrence, metastasis and therapy of breast cancer

“…The SHG image analysis confirmed that literalized and stiffened collagen morphology in 4T1 tumors could be used as a marker for metastatic potential. And the studies also indicated that in vivo SHG imaging and in vitro RRS could distinguish the collagen, molecular and metabolic characteristics between metastatic 4T1 tumors and non‐metastatic 67NR tumors 109 …”

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

“…By using the different fit coefficients for fat and collagen, the authors demonstrated a Raman diagnostic algorithm illustrating 94% sensitivity, 96% specificity, and 86% overall accuracy for detecting infiltrating carcinoma. A range of other studies also demonstrated the potential of Raman spectroscopy in diagnosing breast cancer in human and mouse breast tissues [ 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ]. By exploiting the fingerprint region of the Raman spectrum and various machine learning techniques, Raman spectroscopy showed diagnostic potential for distinguishing cancerous tissues from normal tissues in brain cancer [ 56 , 97 , 98 , 99 , 100 ], skin cancer [ 42 , 101 , 102 , 103 , 104 , 105 ], gastrointestinal cancer [ 106 , 107 , 108 , 109 ], and lung cancer [ 110 , 111 ].…”