Diagnosing molecular subtypes of breast cancer by means of Raman spectroscopy

Abstract

Objectives Raman spectroscopy has been used to discriminate human breast cancer and its different tumor molecular subtypes (luminal A, luminal B, HER2, and triple‐negative) from normal tissue in surgical specimens. Materials and Methods Breast cancer and normal tissue samples from 31 patients were obtained by surgical resection and submitted for histopathology. Before anatomopathological processing, the samples had been submitted to Raman spectroscopy (830 nm, 25 mW excitation laser parameters). In total, 424 Raman spectra were obtained. Principal component analysis (PCA) was used in an exploratory analysis to unveil the compositional differences between the tumors and normal tissues. Discriminant models were developed to distinguish the different cancer subtypes by means of partial least squares (PLS) regression. Results PCA vectors showed spectral features referred to the biochemical constitution of breast tissues, such as lipids, proteins, amino acids, and carotenoids, where lipids were decreased and proteins were increased in breast tumors. Despite the small spectral differences between the different subtypes of tumor and normal tissues, the discriminant model based on PLS was able to discriminate the spectra of the breast tumors from normal tissues with an accuracy of 97.3%, between luminal and nonluminal subtypes with an accuracy of 89.9%, between nontriple‐negative and triple‐negative with an accuracy of 94.7%, and each molecular subtype with an accuracy of 73.0%. Conclusion PCA could reveal the compositional difference between tumors and normal tissues, and PLS could discriminate the Raman spectra of breast tissues regarding the molecular subtypes of cancer, being a useful tool for cancer diagnosis.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

“…The goal is to classify samples based on the calculated covariance between the predictor variables (the spectra) and the responses (the group each sample belongs) using a training dataset [37,38], meaning that PLS-DA finds the variation in the information present in the Raman spectra, which is correlated with the group and variation used for classification. PLS-DA has been used to discriminate leukemias in blood serum [30,34], basal cell carcinoma and melanoma [12], as well as to diagnose molecular subtypes of breast cancer in humans using biopsy fragments [17].…”

“…Raman spectroscopy stands out for being minimally invasive, label-free without specific sample preparation, and using only a small amount of biological material [10], facilitating its potential use in veterinary medicine [13,31,70], especially in serum analysis. An advantage of Raman spectroscopy applied to diagnose neoplastic diseases is that it may detect early changes in biochemistry tissue related to the neoplastic changes [12,16,17,70,71], discriminating the group to which the patient belongs to through pre-cancerous biomolecular alterations [29], and assisting in therapeutic follow-up [29,30,72]. However, it still could not substitute the histopathological biopsy (gold standard), which provides an accuracy greater than 90% [8].…”

“…The advantages of Raman spectroscopy compared to other optical techniques, such as absorption spectroscopy, include obtaining information on the vibrational energy of molecules [10] without the need for specific sample preparation [14]. Raman spectrum can identify the biochemical composition of a sample and thereby suggest a diagnosis [10,[12][13][14][15][16][17] by using biomarkers to differentiate a patient with cancer from a healthy individual.…”

High-content stimulated Raman histology of human breast cancer

“…Our fingerprint results showcased a clear contrast for nucleic acid and solid-state ester as well as representative peaks for different amino acids. These contrasts are beyond the reach of C-H based SRH and show the potential of fingerprint SRH for discovering new biomarkers for cancer diagnosis 22 .…”

“…Recent studies show that spectroscopic SRS is capable of targeting multiple biomolecules of interest 19 , 20 . As cancer development involves a variety of biomolecules, providing such rich chemical information is important for accurate subtyping of breast cancer 21 , 22 and enables precise treatment.…”