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17IN VIVO CLINICAL RAMAN SPECTROSCOPY: NON-INVASIVE APPLICATIONS IN CANCERS
DR. C. MURALI KRISHNA
Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, India *mchilakapati@actrec.gov.in. pittu1043@gmail.com
ABSTRACT
Conventionally, diseases are diagnosed by clinical examination followed by relevant biochemical/microbiological/pathological examinations. Such approaches, rely on symptoms, are considered to diagnose an existing disease, and could be a late diagnosis which often leads to poor prognosis. Therefore early stage diagnosis can provide better prognosis. Since morphological changes could be rather late signs, diagnostics should be sensitive to biochemical changes, thus can pave early diagnosis. This is particularly applicable to diseases like cancers which are multi-step process and usually pass through multiple stages, i.e., hyperplasia/metaplasia, dysplasia, carcinoma in situ, and eventually to invasive cancers. During this process several biomolecules (tumour markers/biomarkers) are expressed which can be exploited to diagnose the pathological conditions. Though tumour markers are also been employed for diagnosis, it is been limited by paucity of reliable markers, hence proteomics/genomics based methods are also explored to get holistic information. Such global biochemical information also can be obtained using Raman spectroscopy, an analytical chemistry tool. Raman spectroscopy, named after its discoverer Sir C.V. Raman is a better tool as it provides 'molecular fingerprint' of a sample and less interference due to water spectrum. Raman scattered photon is generated when incident photon cause change in the vibrational state of a molecule. Since this frequency shift is unique to specific molecular vibration of the molecule, either by direct comparison of the spectra of known and unknown materials recorded consecutively or by comparison of the spectrum of the unknown compound with catalogues of reference spectra, identification of 'chemical moieties' provides 'molecular fingerprint' of the sample. Hence Raman spectroscopy is been pursued as potential alternatives/adjuncts due to attributes, in addition to sensitivity to biochemical composition, such as- less time consuming, no external labelling or sample processing, more objective, and most importantly feasible in vivo/in situ on line diagnosis. Objective disease diagnosis by Raman spectral molecular signatures of tissue or body fluids is achieved using multivariate analysis. The aim of multivariate analysis is to train models that distinguish seemingly similar patterns in pathological conditions. Multivariate analysis can be unsupervised where no prior information is given and the method tries to establish relationships or trends in classification de novo. In the absence of preliminary information, non-supervised methods provide a first-hand estimate about the nature of data. Supervised learning methods are used to predict the association of new variables to one of the pre-existing groups.
Cancer is one of the leading causes of death and high mortality rate, mainly due to late detection and recurrences, ascribed to limitations of conventional diagnostic methodologies, which involve invasive procedures and are prone to subjective errors. Screening and early detection are thus main stay for the overall management of cancer and to improve prognosis. The routine cancer diagnosis involves a clinical examination followed by biopsy. The biopsied sample is subjected to histopathology, the gold standard. This procedure is shown to be prone to subjective errors, time consuming and depends on visible morphological alterations which are late signs of an existing disease. In this context extensive studies have been carried out on applications of Raman spectroscopy in cancer theranostics, i.e., diagnostic/screening and therapeutic monitoring. The present paper would provide an update of our explorations in theranostic applications. The non-invasive (in vivo - on subjects) approaches included screening/diagnosis, recurrence prediction, and disease-free survival, in oral and cervical cancers.
Before undertaking in vivo studies, it is prerequisite to demonstrate efficacy in ex vivo biopsied specimen. Hence studies were carried out on several cancers such as, oral, uterine, cervix, breast, stomach, colon and ovarian cancers [1-16]. The ex vivo studies could distinctly stratify the inflammatory, premalignant, and malignant oral tissues [1], normal and malignant oral and cervix tissues [2,3,6], and normal, malignant and 2-fractions after radiotherapy formalin-fixed cervix tissues [5]. Multiparametric approach unambiguously classified normal and malignant cervix tissue with 99.5% sensitivity and specificity [7]. Studies further demonstrated the feasibility of stratifying radiotherapy responders, partial responders and non-responders [8,9]. Rubina et al [10] showed utility of fibre-optic Raman probe for early prediction of CCRT response in locally advanced cervical cancers. The study also showed distinct stratification of normal tissue from pre- and post-treatment biopsies. Kumar et al. [11] analysed 69 breast tissue samples, of which 61 were unambiguously diagnosed as 29 normal, 15 benign, and 17 malignant while the remaining 8 samples were diagnosed as pathological. Chowdary et al. [13] has shown classification of normal, benign and malignant breast tissues and reported excess of lipids in malignant tissues and excess of proteins in benign tissues. Studies were subsequently extended to stomach, colon and ovarian cancers, and showed distinct classification between normal and malignant stomach mucosal tissues with 93% and 84% sensitivity and specificity [14], normal and malignant colon tissues with 95% sensitivity and specificity [15] and normal and malignant ovarian tissues with 95% sensitivity and specificity [16].
On obtaining robust demonstration of classification of ex vivo tissues in single as well as multiple cancer scenario [17], extensive non-invasive in vivo studies on oral and cervical cancer subjects have been taken up, as to be described in succeeding sections.
Oral cancers: Cancers of the oral cavity predominantly occur with a higher frequency in South Central Asian countries and Melanesia. According to Globocan 2020, oral cancer has the highest mortality rate among Indian men [18]. Early
diagnosis / detection of the precancerous lesions of oral cancer would aid in improving patient prognosis. In this context, Singh et al. [19-21] showed stratification of premalignant, malignant and healthy controls with and without tobacco habits, in clinically implementable spectral acquisition time ( 5 sec) The same group later showed correlation between spectral and biochemical markers i.e., higher intensity of lipids in normal and proteins in tumor tissues by carrying out Raman and biochemical evaluations[22]. Singh et al. [23] conducted a clinical trial on 84 oral cancer subjects including, healthy, habitué, tobacco habitué with pre-cancerous lesions, contralateral, and subjects with cancerous lesions. The study stratified the healthy, habitué, premalignant and malignant conditions and also reported MAC / CFE in contralateral normal mucosa. Identification of these early events in oral carcinogenesis demonstrated the feasibility of Raman spectroscopy in detection of recurrence and thereby improving disease free survival [24-26]. The findings are also suggestive of the potential of Raman spectroscopy as an early detection tool.
Figure 1 Fiberoptic in vivo clinical Raman spectroscope for oral cancer applications
Cervical cancers: Cervical cancer is the fourth most commonly diagnosed cancer with high mortality rate in 36 countries. An in vivo Raman Spectroscopy clinical trial conducted on 93 subjects of cervical cancers showed 97% classification efficiency in stratification of normal and tumour tissues [27]. Due to the biochemical similarity between vagina and ecto-cervix, the study explored the feasibility of vagina as an internal control. Similar spectral features were observed in normal cervix and vaginal controls of healthy and tumour subjects [27]. Furthermore, a comparative study of Diffuse Reflectance Spectroscopy (DRS) and Raman Spectroscopy for detection of cervical cancer [28]. 20 cervical tumours and 6 normal cervix subjects were recruited in the study and the spectra were recorded from 67 tumour, 22 normal cervix and 57 normal vagina sites. The sensitivity and specificity of Raman spectroscopy was slightly higher than that of DRS [28].
Figure 2: Fiberoptic in vivo clinical Raman spectroscope for cervical cancers
Raman Spectroscopy has shown to detect and discriminate tumor stages across different cancers, such as brain, breast, colon, bladder and gynecological cancer [29]. Molckovsky et al. [30], demonstrated utility of RS in differentiating normal, hyperplastic and adenomatous polyps in colorectal cancer with high diagnostic accuracy. Kendall et al. [31] on comparing histopathology and Raman classification results for differentiating pathological subtypes in esophageal cancer observed 73% - 100% sensitivity and 90% - 100% specificity. Teh et al. [32] explored near-infrared (NIR) Raman spectroscopy for detecting malignant changes in gastric cancer and reported specific spectral differences in signals related to amide III and amide I proteins, CH3CH2 twisting of proteins/nucleic acids, and C=C stretching mode of phospholipids. Further, Bergholt et al. [33] evaluated clinical utility of image guided - Raman endoscopy and showed diagnostic sensitivity of 94.6% and specificity of 94.6% while discriminating normal tissue and gastric neoplasia. The study also reported reduction of collagen content, increased nucleic acid to lipid ratio and increased nucleic acid/cytoplasmic ratio in cancer tissues.
To summarize, the non-invasive in vivo clinical applications of Raman Spectroscopy in oral and cervical cancers
demonstrate the potential of the technique in early diagnosis, identification of MAC/ CFE and prediction of recurrence
and disease free survival. However, clinical translation of Raman Spectroscopy poses certain challenges, such as, need
for transportability of data collected across different platforms which is being pursued [34].
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