Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.

Recursive partitioning for tumor classification with gene expression microarray data

Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate that it is significantly more accurate for discriminating among distinct colon cancer tissues than other statistical approaches used heretofore. In addition, competing classification trees are displayed, which suggest that different genes may coregulate colon cancers.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Multiclass Classification of SRBCTs

A novel approach to multiclass tumor classification using Artificial Neural Networks (ANNs) was introduced in a recent paper cite{Khan2001}. The method successfully classified and diagnosed small, round blue cell tumors (SRBCTs) of childhood into four distinct categories, neuroblastoma (NB), rhabdomyosarcoma (RMS), non-Hodgkin lymphoma (NHL) and the Ewing family of tumors (EWS), using cDNA gene expression profiles of samples that included both tumor biopsy material and cell lines. We report that using an approach similar to the one reported by Yeang et al cite{Yeang2001}, i.e. multiclass classification by combining outputs of binary classifiers, we achieved equal accuracy with much fewer features. We report the performances of 3 binary classifiers (k-nearest neighbors (kNN), weighted-voting (WV), and support vector machines (SVM)) with 3 feature selection techniques (Golub's Signal to Noise (SN) ratios cite{Golub99}, Fisher scores (FSc) and Mukherjee's SVM feature selection (SVMFS))cite{Sayan98}.

Lymph Node Involvement and Not the Histophatologic Subtype Is Correlated with Outcome After Resection of Adenocarcinoma of the Ampulla of Vater

Background Intestinal and pancreaticobiliary types of Vater`s ampulla adenocarcinoma have been considered as having different biologic behavior and prognosis. The aim of the present study was to determine the best immunohistochemical panel for tumor classification and to analyze the survival of patients having these histological types of adenocarcinoma. Method Ninety-seven resected ampullary adenocarcinomas were histologically classified, and the prognosis factors were analyzed. The expression of MUC1, MUC2, MUC5AC, MUC6, CK7, CK17, CK20, CD10, and CDX2 was evaluated by using immunohistochemistry. Results Forty-three Vater`s ampulla carcinomas were histologically classified as intestinal type, 47 as pancreaticobiliary, and seven as other types. The intestinal type had a significantly higher expression of MUC2 (74.4% vs. 23.4%), CK20 (76.7% vs. 29.8%), CDX2 (86% vs. 21.3%), and CD10 (81.4% vs. 51.1%), while MUC1 (53.5% vs. 82.9%) and CK7 (79.1% vs. 95.7%) were higher in pancreatobiliary adenocarcinomas. The most accurate markers for immunohistochemical classification were CDX2, MUC1, and MUC2. Survival was significantly affected by pancreaticobiliary type (p=0.021), but only lymph node metastasis, lymphatic invasion, and stage were independent risk factors for survival in a multivariate analysis. Conclusion The immunohistochemical expression of CDX2, MUC1, and MUC2 allows a reproducible classification of ampullary carcinomas. Although carcinomas of the intestinal type showed better survival in the univariate analysis, neither histological classification nor immunohistochemistry were independent predictors of poor prognosis.

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.

A combined molecular clinical predictor of survival validated with the rtog-0525 cohort

For glioblastoma (GBM), survival classification has primarily relied on clinical criteria, exemplified by the Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis (RPA). We sought to improve tumor classification by combining tumor biomarkers with the clinical RPA data. To accomplish this, we first developed 4 molecular biomarkers derived from gene expression profiling, a glioma CpG island methylator phenotype, a novel MGMT promoter methylation assay, and IDH1 mutations. A molecular predictor (MP) model was created with these 4 biomarkers on a training set of 220 retrospectively collected archival GBMtumors. ThisMPwas further combined with RPA classification to develop a molecular-clinical predictor (MCP). The median survivals for the combined, 4-class MCP were 65 months, 31 months, 13 months, and 9 months, which was significantly improved when compared with the RPA alone. The MCP was then applied to 725 samples from the RTOG-0525 cohort, showing median survival for each risk group of NR, 26 months, 16 months, and 11 months. The MCP was significantly improved over the RPA at outcome prediction in the RTOG 0525 cohort with a 33%increase in explained variation with respect to survival, validating the result obtained in the training set. To illustrate the benefit of the MCP for patient stratification, we examined progression-free survival (PFS) for patients receiving standard-dose temozolomide (SD-TMZ) vs. dose-dense TMZ (DD-TMZ) in RPA and MCP risk groups. A significant difference between DD-TMZ and SD-TMZ was observed in the poorest surviving MCP risk group with a median PFS of 6 months vs. 3 months (p ¼ 0.048, log-rank test). This difference was not seen using the RPA classification alone. In summary, we have developed a combined molecular-clinical predictor that appears to improve outcome prediction when compared with clinical variables alone. This MCP may serve to better identify patients requiring intensive treatments beyond the standard of care.

Factores asociados a recaída en pacientes con cáncer de mama de una institución de Bogotá

Introducción: El cáncer de seno es la primera causa de cáncer entre las mujeres, además es la primera causa de muerte por cáncer entre las hispanas y la segunda entre otras razas, sin contar con el gran impacto social y económico que conlleva esta patología. Esto motiva la realización de estudios propios, que permitan ampliar nuestro conocimiento y aportar a la literatura colombiana, una publicación que refleje los factores asociados a la recaída en el cáncer de mama. Métodos: Estudio observacional analítico retrospectivo de casos y controles en el que se tomaron 267 historias clínicas de pacientes con diagnóstico de cáncer de seno, clasificadas según estadio clínico y expresión molecular del tumor, se analizaron los factores más fuertemente asociados a la recaída. Resultados: La población total consistió en 267 mujeres de las cuales 58 presentaron recaída, con un relación caso – control, 1:3. Al evaluar los grupos se evidencia homogeneidad en cuanto a edad, tipo de neoplasia, paridad e histología con lo que concluimos que estos grupos son comparables. Se presentó una tasa de mortalidad de 13,8 % en las pacientes que presentaron recaída tumoral vs un 0% de mortalidad en aquellas pacientes sin recaída. Adicionalmente se evidencia una relación entre la presencia del receptor HER 2 y recaída tumoral, que aunque no es estadísticamente significativa (p = 0.112) es importante tener en cuenta por su significancia clínica. Por su parte la presencia de receptor de estrógenos y progestágenos no es un predictor de recaída. La realización de cirugía se muestra como un factor de protección (OAR: 0.046 p = 0.008). Finalmente se encontró una asociación estadísticamente significativa como variables de asociación a recaída tumoral: la edad (p=0.009), el estadio clínico en el momento del diagnóstico (p= <0.001) y la clasificación molecular del tumor (p= 0.016). Conclusiones: Se identificaron como factores asociados a recaída tumoral en pacientes con cáncer de mama de una institución de Bogotá, Colombia a: la edad, el estadio clínico en el momento del diagnóstico y la clasificación molecular del tumor, confirmando la agresividad de los tumores triple negativos. Todos los hallazgos son compatibles a lo descrito en la literatura mundial. Esto permite definir la necesidad de generar en nuestro país estrategias de salud pública, que permitan la educación a todos los grupos etarios para el tamizaje en población joven que está siendo afectada, la detección en estadios tempranos del cáncer de mama, asociados a priorización del manejo y mejoras en la ruta de atención de las pacientes que permitan impactar positivamente en el desenlace y calidad de vida de las mujeres con esta patología. Adicionalmente estos resultados impulsan a la continua investigación de nuevas tecnologías y medicamentos que permitan combatir los tumores más agresivos molecularmente hablando.

Expressão dos filamentos intermediários no diagnóstico dos tumores mamários de cadelas

Immunohistochemical evaluation was performed to study the histogenesis of canine mammary tumors and to contribute to a better understanding of their classification. Monoclonal antibodies specific for different types of intermediate filaments (cytokeratins, vimentin, α-actin) were used. Epithelial cells stained positively for cytokeratins and their expression was lost as the malignant transformation occurs. Myoepithelial cells stained positively for vimentin and α-actin. In contrast to vimentin, α-actin lost the expression as the cartilaginous or osseous metaplasia occurs. Immunohistochemical evaluation with monoclonal antibodies proved to be efficient for identification of tumor histogenesis.

Missing association between telomerase activity and clinicopathological features in patients with early stage carcinoma of the cervix

Objectives: This study was undertaken to evaluate the association between the telomerase activity in the tumor and clinicopathological findings in patients with stage IB-IIA (FIGO) carcinoma of the cervix. Methods: Thirty-eight patients with carcinoma of the cervix submitted to radical hysterectomy were prospectively from January 1998 to November 2001. Samples from the tumor were taken and analyzed by the telomerase PCR-TRAP-ELISA kit. Clinicopathological characteristics such as age, stage, tumor size, grade of differentiation, lymphatic vascular space invasion (LVSI), parametrial involvement and status of pelvic lymph nodes were also recorded. Results: Patient's mean age was 49.3 ± 1.99 years (29-76 years). The clinical stage (FIGO) was IB in 35 patients (92.1%) and IIA in 3 patients (7.9%). The histological classification identified squamous cell carcinoma in 33 patients (86.8%) and adenocarcinoma in 5 patients (13.2%). There was no association between age, clinical stage, histological classification, tumor size, grade of differentiation and presence of LVSI with tumoral telomerase activity. The telomerase activity was not associated with the presence of vaginal involvement (P = 0.349), parametrium involvement (P = 0.916), pelvic lymph node metastasis (P = 0.988) or tumoral recurrence (P = 0.328) in patients with carcinoma of the cervix. Conclusions: Telomerase activity in the tumor is not associated with clinicopathological findings or tumor recurrence in patients with early stage cervical carcinoma. © 2006 Springer-Verlag.

Overexpression of ANXA1 in Penile Carcinomas Positive for High-Risk HPVs

The incidence of penile cancer varies between populations but is rare in developed nations. Penile cancer is associated with a number of established risk factors and associated diseases including phimosis with chronic inflammation, human papillomavirus (HPV) infection, poor hygiene and smoking. The objective of this study was to identify genes related to this type of cancer. The detection of HPV was analyzed in 47 penile squamous cell carcinoma samples. HPV DNA was detected in 48.9% of penile squamous cell carcinoma cases. High-risk HPV were present in 42.5% of cases and low-risk HPV were detected in 10.6% of penile squamous cell carcinomas. The RaSH approach identified differential expression of Annexin A1 (ANXA1), p16, RPL6, PBEF1 and KIAA1033 in high-risk HPV positive penile carcinoma; ANXA1 and p16 were overexpressed in penile squamous cells positive for high-risk HPVs compared to normal penile samples by qPCR. ANXA1 and p16 proteins were significantly more expressed in the cells from high-risk HPV-positive penile carcinoma as compared to HPV-negative tumors (p<0.0001) independently of the subtype of the carcinoma. Overexpression of ANXA1 might be mediated by HPV E6 in penile squamous cell carcinoma of patients with high-risk HPVs, suggesting that this gene plays an important role in penile cancer. © 2013 Calmon et al.

Evaluation of reference-based two-color methods for measurement of gene expression ratios using spotted cDNA microarrays

Abstract Background Spotted cDNA microarrays generally employ co-hybridization of fluorescently-labeled RNA targets to produce gene expression ratios for subsequent analysis. Direct comparison of two RNA samples in the same microarray provides the highest level of accuracy; however, due to the number of combinatorial pair-wise comparisons, the direct method is impractical for studies including large number of individual samples (e.g., tumor classification studies). For such studies, indirect comparisons using a common reference standard have been the preferred method. Here we evaluated the precision and accuracy of reconstructed ratios from three indirect methods relative to ratios obtained from direct hybridizations, herein considered as the gold-standard. Results We performed hybridizations using a fixed amount of Cy3-labeled reference oligonucleotide (RefOligo) against distinct Cy5-labeled targets from prostate, breast and kidney tumor samples. Reconstructed ratios between all tissue pairs were derived from ratios between each tissue sample and RefOligo. Reconstructed ratios were compared to (i) ratios obtained in parallel from direct pair-wise hybridizations of tissue samples, and to (ii) reconstructed ratios derived from hybridization of each tissue against a reference RNA pool (RefPool). To evaluate the effect of the external references, reconstructed ratios were also calculated directly from intensity values of single-channel (One-Color) measurements derived from tissue sample data collected in the RefOligo experiments. We show that the average coefficient of variation of ratios between intra- and inter-slide replicates derived from RefOligo, RefPool and One-Color were similar and 2 to 4-fold higher than ratios obtained in direct hybridizations. Correlation coefficients calculated for all three tissue comparisons were also similar. In addition, the performance of all indirect methods in terms of their robustness to identify genes deemed as differentially expressed based on direct hybridizations, as well as false-positive and false-negative rates, were found to be comparable. Conclusion RefOligo produces ratios as precise and accurate as ratios reconstructed from a RNA pool, thus representing a reliable alternative in reference-based hybridization experiments. In addition, One-Color measurements alone can reconstruct expression ratios without loss in precision or accuracy. We conclude that both methods are adequate options in large-scale projects where the amount of a common reference RNA pool is usually restrictive.

Expression patterns of Bmi-1 and p16 significantly correlate with overall, disease-specific, and recurrence-free survival in oropharyngeal squamous cell carcinoma

BACKGROUND: The objective of this study was to link expression patterns of B-cell-specific Moloney murine leukemia virus integration site 1 (Bmi-1) and p16 to patient outcome (recurrence and survival) in a cohort of 252 patients with oral and oropharyngeal squamous cell cancer (OSCC). METHODS: Expression levels of Bmi-1 and p16 in samples from 252 patients with OSCC were evaluated immunohistochemically using the tissue microarray method. Staining intensity was determined by calculating an intensity reactivity score (IRS). Staining intensity and the localization of expression within tumor cells (nuclear or cytoplasmic) were correlated with overall, disease-specific, and recurrence-free survival. RESULTS: The majority of cancers were localized in the oropharynx (61.1%). In univariate analysis, patients who had OSCC and strong Bmi-1 expression (IRS >10) had worse outcomes compared with patients who had low and moderate Bmi-1 expression (P = .008; hazard ratio [HR], 1.82; 95% confidence interval [CI], 1.167-2.838); this correlation was also observed for atypical cytoplasmic Bmi-1 expression (P = .001; HR, 2.164; 95% CI, 1.389-3.371) and for negative p16 expression (P < .001; HR, 0.292; 95% CI, 0.178-0.477). The combination of both markers, as anticipated, had an even stronger correlation with overall survival (P < .001; HR, 8.485; 95% CI, 4.237-16.994). Multivariate analysis demonstrated significant results for patients with oropharyngeal cancers, but not for patients with oral cavity tumors: Tumor classification (P = .011; HR, 1.838; 95%CI, 1.146-2.947) and the combined marker expression patterns (P < .001; HR, 6.254; 95% CI, 2.869-13.635) were correlated with overall survival, disease-specific survival (tumor classification: P = .002; HR, 2.807; 95% CI, 1.477-5.334; combined markers: P = .002; HR, 5.386; 95% CI, 1.850-15.679), and the combined markers also were correlated with recurrence-free survival (P = .001; HR, 8.943; 95% CI, 2.562-31.220). CONCLUSIONS: Cytoplasmic Bmi-1 expression, an absence of p16 expression, and especially the combination of those 2 predictive markers were correlated negatively with disease-specific and recurrence-free survival in patients with oropharyngeal cancer. Therefore, the current results indicate that these may be applicable as predictive markers in combination with other factors to select patients for more aggressive treatment and follow-up. Cancer 2011;. © 2011 American Cancer Society.

MR imaging of parotid tumors: typical lesion characteristics in MR imaging improve discrimination between benign and malignant disease

The surgical approach to parotid tumors is different for benign and malignant neoplasms, but the clinical symptoms do not correlate well with histology. Difficulties in tumor classification also arise in imaging modalities, in which sonography has the lowest and MR imaging, the highest accuracy. The purpose of this study was to review our experience using conventional MR imaging of the neck in the evaluation of parotid tumors and to evaluate which MR imaging findings are best able to predict malignant histology.