Microimaging FT-IR of oral cavity tumours. Part III: Cells, inoculated tissues and human tissues

Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

Does the molecular and metabolic profile of human granulosa cells correlate with oocyte fate? New insights by Fourier transform infrared microspectroscopy analysis

STUDY QUESTION

Does the molecular and metabolic profile of human mural granulosa cells (GCs) correlate with oocyte fate?

SUMMARY ANSWER

A close relation between the metabolic profile of mural GCs and the fate of the corresponding oocyte was revealed by the analysis of selected biomarkers defined by GC Fourier transform infrared microspectroscopy (FTIRM) analysis.

WHAT IS KNOWN ALREADY

In ART, oocyte selection is mainly based on the subjective observation of its morphological features; despite recent efforts, the success rate of this practice is still unsatisfactory. FTIRM is a well-established vibrational technique recently applied to evaluate oocytes quality in several experimental models, including human.

STUDY DESIGN, SIZE, DURATION

GCs retrieved from single-follicle aspirates were obtained with informed consent from 55 women undergoing controlled ovarian stimulation for IVF treatment. GCs were analysed by FTIRM to retrospectively correlate their spectral features with the fate of the companion oocytes. The study has been conducted between March 2016 and September 2017.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Patients were selected according to the following inclusion criteria: age <40 years; non-smokers; no ovarian infertility diagnosis (only tubal, idiopathic and male infertility); regular ovulatory menstrual cycles (25-30 days) with FSH < 10 IU/I on Day 3 of the menstrual cycle; sperm sample with a total motility count after treatment ≥300.000; number of retrieved oocytes ≥8. Based on the clinical outcome of the corresponding oocyte, GCs were retrospectively classified into the following experimental groups: clinical pregnancy (CP), fertilization failure (FF), embryo development failure (EDF) and implantation failure (IF). All samples were analysed by the FTIRM technique. The spectral biomarker signature of different oocyte fates was derived by several feature selection procedures ('Leave-one-out' method on factorial discriminant analysis (FDA), variable characterization method and logistic regression method with the multinomial Logit model). ANOVA, permutational multivariate ANOVA, FDA and canonical analysis of principal co-ordinates statistical tools were also applied to validate the identified spectral biomarkers.

MAIN RESULTS AND THE ROLE OF CHANCE

In total, 284 GCs samples were retrieved and retrospectively classified as FF: (N = 92), EDF (N = 113), IF (N = 56) and CP (N = 23). From the spectral profiles of GCs belonging to CP, FF, EDF and IF experimental groups, 17 spectral biomarkers, were identified by several feature selection procedures (P < 0.0001). These biomarkers were then validated by applying multivariate tools, to evaluate their ability to segregate GCs samples into the four experimental groups. FDA showed a clear separation along the F1-axis (62.75% of discrimination) between GCs from oocytes able (CP, IF groups) or not (FF, EDF groups) to develop into embryos; the F2-axis (24.14% of discrimination) segregated the embryos that gave pregnancy (CP) from those that failed implantation (IF). The confusion matrix (total percentage of correctness = 80.25%) obtained from this analysis pinpointed that GCs from oocytes unable to develop into embryos (FF, EDF) were better characterized than those from oocytes able to give viable embryos (CP, IF). ANOVA (P < 0.05) analysis pinpointed that: each experimental group showed specific macromolecular traits, ascribable to different biological and metabolic characteristics of GCs; these metabolic features were likely associated with different oocytes fates, but not to patient characteristics, since from the same patient we obtained GCs with different metabolic profiles.

LIMITATIONS, REASONS FOR CAUTION

The study is based on a small sample size but provides proof of concept that the GCs' metabolic profile is associated with the companion oocyte fate. The generated model should be further tested on a larger cohort of patients, classified in a similar manner, to assess the potential predictive value of this approach. Ultimately, validity of the proposed approach should be tested in a RCT.

WIDER IMPLICATIONS OF THE FINDINGS

For the first time, the FTIRM analysis of human GCs has demonstrated an approach to better understand the molecular crosstalk between follicular cells and oocytes and has identified potential spectral biomarkers for improving human IVF success rate.

STUDY FUNDING/COMPETING INTEREST(S)

The study was funded by GFI 2014 grant. The authors declare that there is no conflict of interest.

A comparative profiling of oral cancer patients and high risk niswar users using FT-IR and chemometric analysis

Oral cancer is one of the major cancer types, which has increased sustainably in Southeast Asian countries due to the extensive use of a variety of tobacco and betel nut products. The current study is focused on developing an easy, efficient and cost-effective method for plasma profiling of oral cancer patients and tobacco users in order to have a progressive picture towards oral cancer. For this purpose, the profiling of 147 plasma samples including 67 oral cancer patients' samples, 60 "niswar" (a dipping tobacco product) user samples, and 20 healthy controls using attenuated total reflectance-fourier transform infrared spectroscopy (ATR-FTIR), and chemometric analysis was carried out. Fingerprint region (500-1500 cm^-1) of all three groups showed interesting variations in peaks pattern. From these observations, height ratios of two bands H1646/H1550 and H1080/H1024 with p value of 2.01 × 10^-6 and 8.39 × 10^-7, respectively, showed a pattern between healthy to oral cancer and "niswar" user samples. Chemometric analysis of the data showed a clean separation among the groups. PLS-DA and OPLS-DA models provided 87.7% and 89.5% classification rate, respectively. Area under the curve (AUC) for healthy control, oral cancer and "niswar" users were found to be 0.97, 0.95 and 0.92%, respectively. The results of the present study indicate that FT-IR spectroscopy, in conjunction with chemometric data, can be effectively used for the preliminary differentiation of plasma samples of oral cancer patients, "niswar" users and control samples of healthy persons.

Raman and infra-red microspectroscopy: towards quantitative evaluation for clinical research by ratiometric analysis

We demonstrate how ratioing spectral bands can circumvent experimental artefacts, and present a library of ratios from the biomedical literature.

Screening of chemopreventive effect of naringenin-loaded nanoparticles in DMBA-induced hamster buccal pouch carcinogenesis by FT-IR spectroscopy

The aim of the present study is to investigate the chemopreventive effects of the prepared naringenin-loaded nanoparticles (NARNPs) relative to efficacy of free naringenin (NAR) in modifying the functional, structural, and compositional changes at the molecular level during 7, 12-dimethylbenz[a]anthracene (DMBA)-induced hamster buccal pouch (HBP) carcinogenesis by Fourier transform infrared (FT-IR) spectroscopy. The results revealed that a significant increase in the amount of proteins and nucleic acid contents and a decrease in the amount of lipids and glycogen contents are observed in DMBA-induced tumor tissues. In addition, in tumor tissues a decrease in lipid order and a significant increase in membrane dynamics were noticed. Further, the composition and secondary structure of proteins were found to be altered, which indicates some important structural alterations in the existing proteins and/or the expression of new types of proteins occurring under the tumor transformation. Furthermore, oral administration of free NAR and NARNPs significantly increased lipids and their order as well as increased the glycogen contents and decreased the levels of proteins and nucleic acid contents. On a comparative basis, NARNPs were found to have a more potent antitumor effect than free NAR in completely preventing the formation of squamous cell carcinoma and in improving the biochemical constituents to a normal range in DMBA-induced HBP carcinogenesis. The present study further shows a great potential of FT-IR spectroscopy as a complimentary tool for the screening of various anticancer drugs and follow-up, which may allow faster response to critical problems arising during treatment.

Oral cancer diagnostics based on infrared spectral markers and wax physisorption kinetics

Infrared microspectroscopy is an emerging approach for disease analysis owing to its capability for in situ chemical characterization of pathological processes. Synchrotron-based infrared microspectroscopy (SR-IMS) provides ultra-high spatial resolution for profiling biochemical events associated with disease progression. Spectral alterations were observed in cultured oral cells derived from healthy, precancerous, primary, and metastatic cancers. An innovative wax-physisorption-based kinetic FTIR imaging method for the detection of oral precancer and cancer was demonstrated successfully. The approach is based on determining the residual amount of paraffin wax (C(25)H(52)) or beeswax (C(46)H(92)O(2)) on a sample surface after xylene washing. This amount is used as a signpost of the degree of physisorption that altered during malignant transformation. The results of linear discriminant analysis (LDA) of oral cell lines indicated that the methylene (CH(2)) and methyl group (CH(3)) stretching vibrations in the range of 3,000-2,800 cm(-1) have the highest accuracy rate (89.6 %) to discriminate the healthy keratinocytes (NHOK) from cancer cells. The results of wax-physisorption-based FTIR imaging showed a stronger physisorption with beeswax in oral precancerous and cancer cells as compared with that of NHOK, which showed a strong capability with paraffin wax. The infrared kinetic study of oral cavity tissue showed a consistency in the wax physisorption of the cell lines. On the basis of our findings, these results show the potential use of wax-physisorption-based kinetic FTIR imaging for the early screening of oral cancer lesions and the chemical changes during oral carcinogenesis.

FTIR microspectroscopy of melanocytic skin lesions: a preliminary study

Fourier transform infrared (FTIR) microspectroscopy has been employed to investigate benign (ordinary dermal and Reed nevi), dysplastic and malignant (invasive melanoma) skin lesions through the analysis of spectral changes of melanocytes as well as in the evaluation of the presence of melanin. Hierarchical cluster analysis and principal component analysis led to a satisfactory separation of malignant from dysplastic and normal melanocytes. Also, on enlarging the clustering with spectra from Reed and dermal nevi, the multivariate analysis segregated well the spectral data into discrete clusters, allowing the obtaining of reliable average spectra for analysis at the molecular level of the main groups or components responsible for the biological and biochemical changes. The most significant spectral characteristics appear to be related to differences in secondary protein structures, in nucleic acid conformation, in intra- and intermolecular bonding. In all cases, supervised and unsupervised spectral analyses resulted in satisfactory agreement with histopathological findings.

Combining metabolic fingerprinting and footprinting to understand the phenotypic response of HPV16 E6 expressing cervical carcinoma cells exposed to the HIV anti-viral drug lopinavir

Recently, it has been reported that the anti-viral drug, lopinavir, which is currently used as a human immunodeficiency virus (HIV) protease inhibitor, could also inhibit E6-mediated proteasomal degradation of mutant p53 in E6-transfected C33A cells. In this study, C33A parent control cells and HPV16 E6-transfected cells were exposed to lopinavir at concentrations ranging from 0 to 30 microM. The phenotypic response was assessed by Fourier transform infrared (FT-IR) spectroscopy directly on cells (the metabolic fingerprint) and on the cell growth medium (the metabolic footprint). Multivariate analysis of the data using both principal components analysis (PCA) and canonical variates analysis (PC-CVA) showed trends in scores plots that were related to the concentration of the drug. Inspection of the PC-CVA loadings vector revealed that the effect was not due to the drug alone and that several IR spectral regions including proteins, nucleotides and carbohydrates contributed to the separation in PC-CVA space. Finally, partial least squares regression (PLSR) could be used to predict the concentration of the drug accurately from the metabolic fingerprints and footprints, indicating a dose related phenotypic response. This study shows that the combination of metabolic fingerprinting and footprinting with appropriate chemometric analysis is a valuable approach for studying cellular responses to anti-viral drugs.