Who's Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy

Vibrational microspectroscopy as a tool to unveil new chemotherapeutic strategies against osteosarcoma

Over the years, osteosarcoma therapy has had a significative improvement with the use of a multidrug regime strategy, increasing the survival rates from less than 20 % to circa 70 %. Different types of development of new antineoplastic agents are critical to achieve irreversible damage to cancer cells, while preserving the integrity of their healthy counterparts. In the present study, complexes with two and three Pd(II) centres linked by the biogenic polyamines: spermine (Pd2SpmCl4) and spermidine (Pd3Spd2Cl6) were tested against non-malignant (osteoblasts, HOb) and cancer (osteosarcoma, MG-63) human cell lines. Either alone or in combination according to the EURAMOS-1 protocol, they were used versus cisplatin as a drug reference. By evaluating the cytotoxic effects of both therapeutic approaches (single and drug combination) in HOb and MG-63 cell lines, the selective anti-tumoral potential is assessed. To understand the different treatments at a molecular level, Synchrotron Radiation Fourier Transform Infrared and Raman microspectroscopies were applied. Principal component analysis and hierarchical cluster analysis are applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug-to-cell impact. The main changes were observed for the B-DNA native conformation to either Z-DNA (higher in the presence of polynuclear complexes) or A-DNA (preferably after cisplatin exposure). Additionally, a higher effect upon variation in proteins content was detected in drug combination when compared to single drug administration proving the efficacy of the EURAMOS-1 protocol with the new drugs tested.

Combined SERS-Raman screening of HER2-overexpressing or silenced breast cancer cell lines

BACKGROUND

Breast cancer (BC) is a heterogeneous neoplasm characterized by several subtypes. One of the most aggressive with high metastasis rates presents overexpression of the human epidermal growth factor receptor 2 (HER2). A quantitative evaluation of HER2 levels is essential for a correct diagnosis, selection of the most appropriate therapeutic strategy and monitoring the response to therapy.

RESULTS

In this paper, we propose the synergistic use of SERS and Raman technologies for the identification of HER2 expressing cells and its accurate assessment. To this end, we selected SKBR3 and MDA-MB-468 breast cancer cell lines, which have the highest and lowest HER2 expression, respectively, and MCF10A, a non-tumorigenic cell line from normal breast epithelium for comparison. The combined approach provides a quantitative estimate of HER2 expression and visualization of its distribution on the membrane at single cell level, clearly identifying cancer cells. Moreover, it provides a more comprehensive picture of the investigated cells disclosing a metabolic signature represented by an elevated content of proteins and aromatic amino acids. We further support these data by silencing the HER2 gene in SKBR3 cells, using the RNA interference technology, generating stable clones further analysed with the same combined methodology. Significant changes in HER2 expression are detected at single cell level before and after HER2 silencing and the HER2 status correlates with variations of fatty acids and downstream signalling molecule contents in the context of the general metabolic rewiring occurring in cancer cells. Specifically, HER2 silencing does reduce the growth ability but not the lipid metabolism that, instead, increases, suggesting that higher fatty acids biosynthesis and metabolism can occur independently of the proliferating potential tied to HER2 overexpression.

CONCLUSIONS

Our results clearly demonstrate the efficacy of the combined SERS and Raman approach to definitely pose a correct diagnosis, further supported by the data obtained by the HER2 gene silencing. Furthermore, they pave the way to a new approach to monitor the efficacy of pharmacologic treatments with the aim to tailor personalized therapies and optimize patients' outcome.

Metastasis diagnosis using attenuated total reflection-Fourier transform infra-red (ATR-FTIR) spectroscopy

The suitability of Fourier transform infrared spectroscopy as a metastasis prognostic tool has not been reported for some cancer types. Our main aim was to show spectroscopic differences between live un-preprocessed cancer cells of different metastatic levels. Spectra of four cancer cell pairs, including colon cancer (SW480, SW620); human melanoma (WM115, WM266.4); murine melanoma (B16F01, B16F10); and breast cancer (MCF7, MDA-MB-231); each pair having the same genetic background, but different metastatic level were analyzed in the regions 1400-1700 cm-1 and 3100-3500 cm-1 using Principal Component Analysis, curve fitting, multifractal dimension and receiver operating characteristic (ROC) curves. The results show spectral markers I1540/I1473, I1652/I1473, [Formula: see text], and multifractal dimension of the spectral images are significantly different for the cells based on their metastatic levels. ROC curve analysis showed good diagnostic performance of the spectral markers in separating cells based on metastatic degree, with areas under the ROC curves having 95% confidence interval lower limits greater than 0.5 for most instances. These spectral features can be important in predicting the probability of metastasis in primary tumors, providing useful guidance for treatment planning. Our markers are effective in differentiating metastatic levels without sample fixation or drying and therefore could be compactible for future use in in-vivo procedures involving spectroscopic cancer diagnosis.

Recognition of breast cancer subtypes using FTIR hyperspectral data

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy

Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.

Cellular dynamics as a marker of normal-to-cancer transition in human cells

Normal-to-cancer (NTC) transition is known to be closely associated to cell´s biomechanical properties which are dependent on the dynamics of the intracellular medium. This study probes different human cancer cells (breast, prostate and lung), concomitantly to their healthy counterparts, aiming at characterising the dynamical profile of water in distinct cellular locations, for each type of cell, and how it changes between normal and cancer states. An increased plasticity of the cytomatrix is observed upon normal-to-malignant transformation, the lung carcinoma cells displaying the highest flexibility followed by prostate and breast cancers. Also, lung cells show a distinct behaviour relative to breast and prostate, with a higher influence from hydration water motions and localised fast rotations upon NTC transformation. Quasielastic neutron scattering techniques allowed to accurately distinguish the different dynamical processes taking place within these highly heterogeneous cellular systems. The results thus obtained suggest that intracellular water dynamics may be regarded as a specific reporter of the cellular conditions-either healthy or malignant.

A Non-Conventional Platinum Drug against a Non-Small Cell Lung Cancer Line

A dinuclear Pt(II) complex with putrescine as bridging polyamine ligand ([Pt₂Put₂(NH₃)₄]Cl₄) was synthesized and assessed as to its potential anticancer activity against a human non-small cell lung cancer line (A549), as well as towards non-cancer cells (BEAS-2B). This effect was evaluated through in vitro cytotoxicity assays (MTT and SRB) coupled to microFTIR and microRaman spectroscopies, the former delivering information on growth-inhibiting and cytotoxic abilities while the latter provided very specific information on the metabolic impact of the metal agent (at the sub-cellular level). Regarding cancer cells, a major impact of [Pt₂Put₂(NH₃)₄]Cl₄ was evidenced on cellular proteins and lipids, as compared to DNA, particularly via the Amide I and Amide II signals. The effect of the chelate on non-malignant cells was lower than on malignant ones, evidencing a promising low toxicity towards healthy cells.

Evaluation of the Cytotoxic Effect of Pd2Spm against Prostate Cancer through Vibrational Microspectroscopies

Regarding the development of new antineoplastic agents, with a view to assess the selective antitumoral potential which aims at causing irreversible damage to cancer cells while preserving the integrity of their healthy counterparts, it is essential to evaluate the cytotoxic effects in both healthy and malignant human cell lines. In this study, a complex with two Pd(II) centers linked by the biogenic polyamine spermine (Pd₂Spm) was tested on healthy (PNT-2) and cancer (LNCaP and PC-3) prostate human cell lines, using cisplatin as a reference. To understand the mechanisms of action of both cisplatin and Pd₂Spm at a molecular level, Fourier Transform Infrared (FTIR) and Raman microspectroscopies were used. Principal component analysis was applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug impact. The main changes were observed between the B-DNA native conformation and either Z-DNA or A-DNA, with a higher effect on lipids having been detected in the presence of cisplatin as compared to Pd₂Spm. In turn, the Pd-agent showed a more significant impact on proteins.

Diagnosing molecular subtypes of breast cancer by means of Raman spectroscopy

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.

Breast cancer or surrounding normal tissue? A successful discrimination by FTIR or Raman microspectroscopy

Breast cancer is a type of cancer with the highest incidence worldwide in 2021, with early diagnosis and rapid treatment intervention being the reasons for the decreasing mortality rate associated with the disease.