Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5th, 10th, 15th & 20th, were excited with 281 nm pulsed laser light and recorded the corresponding photoacoustic spectra in time domain. The spectra were then pre-processed, augmented for a 10-fold increase in the data strength, and subjected to wavelet packet transformation for feature extraction and selection using MATLAB software. In the present study, the top 10 features from all the time point groups under study were selected based on their prediction ranking values using the mRMR algorithm. The chosen features of all the time-point groups were then subjected to multi-class Support Vector Machine (SVM) algorithms for learning and classifying into respective time point groups under study. The analysis demonstrated accuracy values of 95.2%, 99.5%, and 80.3% with SVM- Radial Basis Function (SVM-RBF), SVM-Polynomial & SVM-Linear, respectively. The serum metabolomic levels during tumor progression complemented photoacoustic patterns of tumor progression, depicting breast cancer pathophysiology.

Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study.

The current study reports an integrated approach of machine learning and tryptophan fluorescence and photoacoustic spectral properties to assess the mitochondrial status under oral pathological conditions. The mitochondria in the study were isolated from oral cancer tissues and adjacent normal counterparts, and the corresponding fluorescence and photoacoustic spectra of tryptophan were recorded at 281 nm pulsed laser excitations. A set of features were selected from the pre-processed spectra and were used to classify the data using support vector machine (SVM) learning in the MATLAB platform. SVM analysis demonstrated clear differentiation between mitochondria isolated from normal and cancer tissues for fluorescence (sensitivity, 86.6%; specificity, 90%) and photoacoustic (sensitivity, 86.6%; specificity, 96.6%) measurements. Further investigation into the influence of change in protein conformation on the nature of tryptophan spectral properties was evaluated by 8-anilino-1-naphthalene sulfonic acid (ANS) fluorescence assay. The impact of protein structural changes on the mitochondrial functions was also estimated by mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and cytochrome c oxidase (COX) assays, suggesting an altered mitochondrial function. The findings indicate that tryptophan fluorescence and photoacoustic spectral properties together with machine learning algorithms may delineate the mitochondrial functional status in vitro, indicating its translational potential.

Relevance and actionable mutational spectrum in oral squamous cell carcinoma.

BACKGROUND: Screening for lesions in the oral cavity is critical for early diagnosis of oral squamous cell carcinoma (OSCC). Targeted next generation sequencing-based (NGS) mutation analysis of cancer driver genes becomes a reality for personalized medicine and cancer therapeutics. MATERIALS AND METHODS: In the present study, we have performed a targeted NGS-based mutation analysis of 50 known oncogenes and tumor suppressor genes in clinically diagnosed potentially malignant lesions and tissues of OSCC. NGS-based analysis of DNA obtained from biopsies of histopathologically confirmed cases of potentially malignant lesions and OSCC specimens were performed using Ion AmpliSeq™ Cancer Hotspot Panel V2 using the Ion Proton™ Sequencer System, followed by data analysis using Ion Reporter™ and Torrent Suite™ software. RESULTS: NGS analysis indicated a total of 69 mutations present in 25 genes in potentially malignant lesions and OSCC specimens. We identified recurrent mutations in known OSCC driver genes ATM (11%), TP53 (55%), HRAS (16%), SMAD4 (13%), PIK3CA (16%), and ERBB4 (11%) in potentially malignant lesions and OSCC specimens. Driver mutation analysis identified recurrent TP53 and HRAS driver mutations in our OSCC specimens. CONCLUSION: Data generated from our study may enable an application of targeted NGS analysis of driver mutations for better therapeutic choice and improved outcomes for OSCC subjects when combined with clinical diagnosis.

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo: A Preclinical Study.

Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filter-based feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.

Promoter methylation of MGMT in oral carcinoma: A population-based study and meta-analysis.

INTRODUCTION: The relevance of DNA methylation of O6-methylguanine-DNA methyltransferase (MGMT) in relation to several cancers and other disorders has been extensively explored in several cancer types. AIMS: To ascertain the significance of DNA methylation of MGMT promoter in oral squamous cell carcinoma (OSCC), we undertook a study to a) analyse the methylation patterns of MGMT gene promoter in afflicted and normal population of coastal Karnataka, b) determine the expression status of MGMT in oral cancer cell lines (CAL-27 and SCC-4) and its relationship to DNA methylation and c) performed a meta-analysis of the published data. METHODS: Bisulfite sequencing of MGMT promoter region was performed on non-malignant/malignant oral samples, and oral cancer cell lines, followed by gene expression studies. Further, using a systematic search, 1024 publications were retrieved from PubMed, Scopus, Google Scholar and Web of Science and 23 relevant articles were reviewed. RESULTS: Significant association of MGMT promoter methylation with OSCC (p<0.0001) was observed in the case-control study. The studies chosen for meta-analysis showed predictive significance of MGMT gene promoter. Overall, we obtained a statistically significant (p<0.0001) association for both sensitivity and specificity of MGMT DNA promoter methylation in oral cancer cases without publication bias. Gene expression was significantly elevated in both oral cancer cell lines (p<0.03) after treatment with a demethylating agent (5-Aza-2'-deoxycytidine). CONCLUSION: DNA promoter hypermethylation and gene expression of MGMT may associate with recursive mutagenesis and is a promising biomarker for OSCC prediction. Studies suggest further validation in large distinct cohorts to facilitate translation to clinics.

Serum butyrylcholinesterase and zinc in breast cancer.

CONTEXT: Even though, a large number of serological, molecular markers have been proposed for breast cancer screening, most of them lack specificity, sensitivity, prognostic value, and cost effectiveness. Butyrylcholinesterase (BChE) and its genes are aberrantly expressed in a variety of human cancers. It has-been linked to tumorigenesis, cell proliferation, and cell differentiation. Zinc (Zn) is a cofactor for superoxide dismutase, an enzyme that protects cellular components against free radical-induced damage and carcinogenesis. Therefore, the aim of present study was to estimate and compare serum BChE and serum Zn levels in healthy controls and biopsy proven breast cancer patients before definitive therapy. AIMS: To estimate and compare serum BChE and serum Zn levels in healthy controls and biopsy proven breast cancer patients before definitive therapy. SETTINGS AND DESIGN: Serum BChE and Zn were estimated in 46 newly diagnosed (preoperative) female patients with breast cancer and 50 healthy female volunteers. SUBJECTS AND METHODS: Serum BChE and Zn were estimated by spectrophotometric method. STATISTICAL ANALYSIS USED: Data was expressed as median and inter quartile range. Comparisons between different stages of cancer were done using Kruskal-Wallis test. RESULTS: There was a significant increase in serum BChE and Zn in breast cancer patients compared to controls (P < 0.001). Serum BChE showed a significant increase and Zn was significantly decreased in different stages of breast cancer. CONCLUSIONS: Both BChE and Zn are inexpensive and can easily be analyzed and may play a role in the management of breast cancer.

Gene promoter-associated CpG island hypermethylation in squamous cell carcinoma of the tongue.

The present study was undertaken to explore and validate novel hypermethylated DNA regions in squamous cell carcinoma of the tongue (SCCT). Genome-wide methylation changes were identified by differential methylation hybridization (DMH) microarray and validated by bisulfite genome sequencing (BGS). The results were compared against datasets from The Cancer Genome Atlas head and neck squamous cell carcinoma (TCGA-HNSCC), Gene Expression Omnibus (GSE26549), and ArrayExpress (E-MTAB-1328). DMH identified 116 hypomethylated and 241 hypermethylated regions. Of the latter, 24 were localized to promoter or 5'-UTR regions. By BGS, promoter sequences of DAPK1, LRPPRC, RAB6C, and ZNF471 were significantly hypermethylated in tumors when compared with matched normal tissues (P < 0.0001). A TCGA-HNSCC dataset (516 cases of cancer and 50 normal tissue samples) further confirmed hypermethylation of DAPK1, RAB6C, and ZNF471. Sensitivity and specificity of methylation markers for a diagnosis of cancer were in the range of 70-100% in our study and from TCGA-HNSCC datasets, with an area under curve (AUC) of 0.83 and above. Kaplan-Meier survival analysis of TCGA-HNSCC expression data revealed that patients with low expressions of DAPK1, RAB6C, and ZNF471 showed poorer survival than patients with high expression (P = 0.02). Human papillomavirus (HPV) was found in 55% of cases, HPV16 being the predominant genotype. DAPK1 immunohistochemical staining was lower in SCCT than in normal buccal epithelial cells. This is the first study to report hypermethylation of LRPPRC, RAB6C, and ZNF471 in SCCT and its diagnostic and prognostic potentials in a specific head and neck squamous cell carcinoma.

Hypermethylation of Death-Associated Protein Kinase (DAPK1) and its association with oral carcinogenesis - An experimental and meta-analysis study.

OBJECTIVES: The value of abnormal DNA methylation of DAPK1 promoter and its association with various cancers have been suggested in the literature. To establish the significance of DNA methylation of DAPK1 promoter in oral squamous cell carcinoma (OSCC), we a) performed a case-control study, b) evaluated published data for its utility in the diagnosis and prognosis of OSCC and c) identified the association of DAPK1 gene expression with promoter DNA methylation status. DESIGN: Bisulfite gene sequencing of DAPK1 promoter region was performed on non-malignant and malignant oral samples. Further, using a systematic search, 330 publications were retrieved from PubMed, Scopus, and Google Scholar and 11 relevant articles were identified. RESULTS: Significant association of DAPK1 promoter methylation with OSCC (p<0.0001) was observed in the case-control study. The studies chosen for meta-analysis showed prognostic and predictive significance of DAPK1 gene promoter, despite defined inconsistencies in few studies. Overall, we obtained a statistically significant (p-value<0.001) association for both sensitivity and specificity of DAPK1 DNA promoter methylation in oral cancer cases, without publication bias. CONCLUSION: DNA hypermethylation of DAPK1 gene promoter is a promising biomarker for OSCC prediction/prognostics and suggests further validation in large distinct cohorts to facilitate translation to clinics.

Primary Paratesticular Lymphoma with Testicular Sparing: Account of an Unusual Scrotal Mass.

Tumours of the testicular adnexa include a heterogeous group of mesothelial, mesenchymal and germ cell tumours. Adenomatoid tumour, pseudosarcomatous myofibroblastic proliferations and rhabdomyosarcoma are the more frequently encountered neoplasms. Lymphoma/leukemic infiltration secondary to testicular involvement or primary tumour elsewhere is not unusual. However, Primary Para-Testicular Lymphoma (PPTL) involving spermatic cord and/or epididymis with sparing of the testicular parenchyma is extremely rare. Accurate staging and typing is crucial for effective management. We present a rare case of Diffuse Large B Cell Lymphoma (DLBCL) involving the left paratesticular tissue with testicular sparing in a young immunocompetant male patient.

Metastatic renal cell carcinoma of the buccal mucosa masquerading as a salivary gland neoplasm.

Metastasis to the oral cavity is a rare occurrence with renal cell carcinoma (RCC) being the third most common tumor to metastasize to this location. Buccal mucosa is rarely involved and in the absence of a known primary, such lesions pose a diagnostic challenge to the pathologist. The histomorphological features may mimic a primary salivary gland neoplasm adding to the dilemma. We present one such case of metastatic RCC of the buccal mucosa.

A screening model for oral cancer using risk scores: development and validation.

OBJECTIVE: A study was conducted to develop and validate a screening model using risk scores to identify individuals at high risk for developing oral cancer in an Indian population. METHODS: Life-course data collected from a multicentre case-control study in India were used. Interview was conducted to collect information on predictors limited to the time before the onset of symptoms or cancer diagnosis. Predictors included statistically significant risk factors in the multivariable model. A risk score for each predictor was derived from respective odds ratios (OR). Discrimination of the final model, risk scores and various risk score cut-offs was examined using the c statistic. The optimal cut-off was determined as the one with good area under curve (AUC) and high sensitivity. Predictive ability of the regression model and cut-off risk score was determined by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Models were validated from a bootstrap sample. RESULTS: Smoking, chewing quid and/or tobacco, alcohol, a family history of upper aero-digestive tract cancer, diet and oral hygiene behaviour were the predictors. Risk scores ranged from 0 to 28. Area under the receiver operating characteristic (ROC) curve for risk scores was good (0.866). The sensitivity (0.928) and negative predictive value (0.927) were high, while specificity (0.603) and positive predictive value (0.607) were low for a risk score cut-off of 6. CONCLUSION: A risk score model to screen for individuals with high risk of oral cancer with satisfactory predictive ability was developed in the Indian population. Validation of the model in other populations is necessary before it can be recommended to identify subgroups of the population to be directed towards more extensive clinical evaluation.

Prediction of absorption coefficients by pulsed laser induced photoacoustic measurements.

In the current study, a pulsed laser induced photoacoustic spectroscopy setup was designed and developed, aiming its application in clinical diagnostics. The setup was optimized with carbon black samples in water and with various tryptophan concentrations at 281nm excitations. The sensitivity of the setup was estimated by determining minimum detectable concentration of tryptophan in water at the same excitation, and was found to be 0.035mM. The photoacoustic experiments were also performed with various tryptophan concentrations at 281nm excitation for predicting optical absorption coefficients in them and for comparing the outcomes with the spectrophotometrically-determined absorption coefficients for the same samples. Absorption coefficients for a few serum samples, obtained from some healthy female volunteers, were also determined through photoacoustic and spectrophotometric measurements at the same excitations, which showed good agreement between them, indicating its clinical implications.

Second primary malignant neoplasms: a clinicopathological analysis from a cancer centre in India.

CONTEXT: Patients diagnosed with a cancer have a life time risk of developing another de novo malignancy depending on various inherited, environmental and iatrogenic risk factors. Of late the detection of new primary has increased mainly due to refinement in both diagnostic and treatment modalities. Cancer victims are surviving longer and thus are more likely to develop a new metachronous malignancy. AIMS: To report our observed trend of increase in prevalence of both synchronous and metachronous second malignant neoplasms among cancer victims and to review the relevant literature. SETTINGS AND DESIGN: A hospital based retrospective collection of prospective data of patients diagnosed with second denovo malignancy. MATERIALS AND METHOD: The study was conducted over a 5 year period from July 2008 to June 2012. All patients diagnosed with a histologically proven second malignancy as per Warren Gate's criteria were included. Various details regarding sex, age at presentation, synchronous or metachronous, treatment and outcome were recorded. CONCLUSIONS: The occurrence of multiple primary malignancies is not rare. Awareness of the possibility alerts the clinician in evaluation of patients with a known malignancy presenting with unusual sites of metastasis. Individualizing the treatment according to the stages of the primaries will result in durable cancer control particularly in synchronous double malignancy.

Photoacoustic spectroscopy of ovarian normal, benign, and malignant tissues: a pilot study.

Photoacoustic spectra of normal, benign, and malignant ovarian tissues are recorded using 325-nm pulsed laser excitation in vitro. A total of 102 (34 normal, 38 benign, and 30 malignant) spectra are obtained from 22 samples belonging to normal, benign, and malignant subjects. Applying multi-algorithm approach, comprised of methods such as, principal component analysis (PCA) based k-nearest neighbor (k-NN) analysis, artificial neural network (ANN) analysis, and support vector machine (SVM) analysis, classification of the data has been carried out. For PCA, first the calibration set is formed by pooling 45 spectra, 15 belonging to each of pathologically certified normal, benign, and malignant samples. PCA is then performed on the data matrix, comprised of the six spectral features extracted from each of 45 calibration samples, and three principal components (PCs) containing maximum diagnostic information are selected. The scores of the selected PCs are used to train the k-NN, ANN, and SVM classifiers. The ANN used is a classical multilayer feed forward network with back propagation algorithm for its training. For k-NN, the Euclidean distance based algorithm is used and for SVM, one-versus-rest multiclass kernel-radial basis function is used. The performance evaluation of the classification results are obtained by calculating statistical parameters like specificity and sensitivity. ANN and k-NN techniques showed identical performance with specificity and sensitivity values of 100 and 86.76%, whereas SVM had these values at 100 and 80.18%, respectively. In order to determine the relative diagnostic performance of the techniques, receiver operating characteristics analysis is also performed.

Malignant extrarenal rhabdoid tumor of the vulva in an adult.

Sarcomas of the vulva account for only 1-3% of all vulvar malignancies. Most common vulvar sarcomas are leiomyosarcomas, malignant fibrohistiocytomas, and aggressive angiomyxomas. Malignant rhabdoid tumor (MRT) of the kidney is a distinctive clinicopathological entity that is recognized as a highly aggressive renal tumor of childhood. Extrarenal malignant rhabdoid tumors have been proposed to exist at several sites, including soft parts. MRT of the vulva is a rare and very aggressive neoplasm. Median survival reported in other studies is 9 months. Only 10 cases have been reported thus far in the English literature. We are reporting the 11 th case who remains disease free 30 months following surgery and radiotherapy till the time of reporting.

Numb chin syndrome as a manifestation of metastatic squamous cell carcinoma of esophagus.

Numb chin syndrome (NCS) is a sensory neuropathy presenting with numbness of the chin in the distribution of the mental nerve and the branches of the mandibular division of the trigeminal nerve. Though it can be caused by a benign process, NCS should be regarded as being due to malignancy until proven otherwise. Among the malignancies that cause NCS the most common are breast cancer, prostate cancer, and lymphoreticular malignancy. In squamous cell carcinoma (SCC) of the esophagus, spread to the mandible is a rare and often late event. An often overlooked clinical sign in mandibular metastases is hypoesthesia or paresthesia over the peripheral distribution of the inferior alveolar nerve/mental nerve; this sign has been referred to in the literature as NCS or numb lip syndrome or mental nerve neuropathy. Rarely, this may be the first presentation of a disseminated malignancy. Prognosis is usually poor. The discovery of this symptom should alert the clinician to the possibility of disseminated disease. In this article we report a rare case of metastatic SCC of the esophagus in a 40-year-old male patient who presented with NCS. We also review the mechanism, causes, and evaluation of NCS.

Autofluorescence of normal, benign, and malignant ovarian tissues: a pilot study.

OBJECTIVE: The objective of this study is to evaluate the efficacy of laser-induced fluorescence (LIF) data obtained at 325-nm pulsed laser excitation for the discrimination of normal, benign, and malignant ovarian tissues. BACKGROUND DATA: Several studies have reported that the autofluorescence technique has a high specificity and sensitivity for discrimination between diseased and non-diseased tissues of various cancers, and also has the advantages of being non-invasive and producing a real-time diagnosis. When using this technique on ovarian tissues in most of the previously reported studies, multivariate statistical tools were used and classification analyses were carried out. MATERIALS AND METHODS: Autofluorescence spectra of normal, benign, and malignant ovarian tissues were recorded with 325-nm pulsed laser excitation in the spectral region from 350-600 nm in vitro. The spectral analysis for discrimination between the different types of tissues was carried out using principal component analysis (PCA)-based non-parametric k-nearest neighbor (k-NN) analysis. RESULTS: A total of 97 (34 normal, 33 benign, and 30 malignant) spectra were obtained from 22 subjects with normal, benign, and malignant tissues. The discrimination analysis of data using a PCA-based k-NN algorithm showed very good discrimination. The performance of the analysis was evaluated by calculating statistical parameters, specificity, sensitivity, and accuracy and were found to be 100%, 90.90%, and 94.2%, respectively. CONCLUSION: The results show that the discrimination of normal, benign, and malignant ovarian conditions can be achieved quite successfully using LIF.

Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: classification of normal premalignant and malignant pathological conditions.

Pulsed laser-induced autofluorescence spectroscopic studies of pathologically certified normal, premalignant, and malignant oral tissues were carried out at 325 nm excitation. The spectral analysis and classification for discrimination among normal, premalignant, and malignant conditions were performed using principal component analysis (PCA) and artificial neural network (ANN) separately on the same set of spectral data. In case of PCA, spectral residuals, Mahalanobis distance, and scores of factors were used for discrimination among normal, premalignant, and malignant cases. In ANN, parameters like mean, spectral residual, standard deviation, and total energy were used to train the network. The ANN used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm for the training of the network. The specificity and sensitivity were determined in both classification schemes. In the case of PCA, they are 100 and 92.9%, respectively, whereas for ANN they are 100 and 96.5% for the data set considered.