“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, and many others, offer tremendous promise for the personalized treatment of cancer. These technologies measure the amount of specific proteins, RNA, DNA or other molecular targets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumor specimens are heterogeneous; an individual specimen typically contains unknown amounts of multiple tissues types. Thus, the measured molecular concentrations result from an unknown mixture of tissue types, and must be normalized to account for the composition of the mixture. For example, a breast tumor biopsy may contain normal, dysplastic and cancerous epithelial cells, as well as stromal components (fatty and connective tissue) and blood and lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic and cancerous epithelial cells. The remaining tissue components serve to “contaminate” the signal of interest. The proportion of each of the tissue components changes as a function of patient characteristics (e.g., age), and varies spatially across the tumor region. Because each of the tissue components produces a different molecular signature, and the amount of each tissue type is specimen dependent, we must estimate the tissue composition of the specimen, and adjust the molecular signal for this composition. Using the idea of a chemical mass balance, we consider the total measured concentrations to be a weighted sum of the individual tissue signatures, where weights are determined by the relative amounts of the different tissue types. We develop a compositional source apportionment model to estimate the relative amounts of tissue components in a tumor specimen. We then use these estimates to infer the tissuespecific concentrations of key molecular targets for sub-typing individual tumors. We anticipate these specific measurements will greatly improve our ability to discriminate between different classes of tumors, and allow more precise matching of each patient to the appropriate treatment

Biological Basis and Clinical Application of Serum Tumor Markers

Cancer is the result of the accumulation of changes in molecules with important functions in processes such as cell proliferation, apoptosis, cell death and gene repair. Molecules, substances or altered pathways constitute tumor markers or biomarkers useful in clinical monitoring of cancer patients, because they have demonstrated to be suitable for the valuation of the patient’s treatment and it efficiency. Determination of tumor markers has not been very successful due to the low sensitivity and specificity of the techniques used and the requirement of large volumes of biological samples or the use of invasive methods for collecting them. The serum tumor markers arise, as a useful tool to obtain information about the disease progress and constitute as a scientific challenge to improve its applicability in early diagnosis, prognosis, monitoring of the disease and evaluation of therapeutic efficacy.

Brain Tumor, Mood Disorder and Encephalopathy

Brain Tumor, Mood Disorder and EncephalopathyWe report a case of a patient was 65 years old, who was admitted with neurologic symptoms ill-defined by imaging findings that initial meningioma wing of the sphenoid, tumor resection, is performed. She presented torpid evolution, progressive neurological deterioration, until her death.

Tumor de células granulares en lengua (tumor de Abrikossoff): reporte de caso

El tumor de células granulares (tumor de Abrikossoff) es una neoplasia benigna en un 98% de casos. Su presentación es escasa y la estirpe celular del tumor es controvertida; sin embargo, la detección positiva de proteína S-100 sugiere un origen neural. Se reporta el caso de una paciente de 16 años de edad con diagnóstico histopatológico de tumor de células granulares en el estudio de espécimen de resección quirúrgica de nódulo lingual.

DNA damage in canine transmissible venereal tumor cells

O Tumor Venéreo Transmissível (TVT) tem sido classificado de acordo com o tipo celular predominante da seguinte forma: linfocitóide, plasmocitóide e misto. Vários graus de agressividade com grande variedade de comportamento biológico, têm sido descritos de acordo com as morfologias das células do TVT. O presente estudo teve como objectivo investigar o nível de danos no DNA nos três tipos de células do TVT, visando uma melhor compreensão dos mecanismos relacionados com a agressividade dessa neoplasia. Um total de 35 cães, sem restrição quanto à idade, sexo ou raça, e com diagnóstico clínico e citológico de TVT foram avaliados. Amostras de células foram obtidas a partir de 35 tumores, por aspiração com agulha fina, e realizada citologia e análise dos danos no DNA, pelo Ensaio Cometa. Dos 35 casos de TVT, 12 (34,3%) foram plasmocitóide, 11 (31,4%) linfocitóide, e 12 (34,3%) misto. Estatísticamente (p <0,05) o TVT linfocitóide apresentou maior nível de danos no DNA quando comparado com os outros dois tipos.

Peptides generated ex vivo from serum proteins by tumor-specific exopeptidases are not useful biomarkers in ovarian cancer

BACKGROUND: The serum peptidome may be a valuable source of diagnostic cancer biomarkers. Previous mass spectrometry (MS) studies have suggested that groups of related peptides discriminatory for different cancer types are generated ex vivo from abundant serum proteins by tumor-specific exopeptidases. We tested 2 complementary serum profiling strategies to see if similar peptides could be found that discriminate ovarian cancer from benign cases and healthy controls. METHODS: We subjected identically collected and processed serum samples from healthy volunteers and patients to automated polypeptide extraction on octadecylsilane-coated magnetic beads and separately on ZipTips before MALDI-TOF MS profiling at 2 centers. The 2 platforms were compared and case control profiling data analyzed to find altered MS peak intensities. We tested models built from training datasets for both methods for their ability to classify a blinded test set. RESULTS: Both profiling platforms had CVs of approximately 15% and could be applied for high-throughput analysis of clinical samples. The 2 methods generated overlapping peptide profiles, with some differences in peak intensity in different mass regions. In cross-validation, models from training data gave diagnostic accuracies up to 87% for discriminating malignant ovarian cancer from healthy controls and up to 81% for discriminating malignant from benign samples. Diagnostic accuracies up to 71% (malignant vs healthy) and up to 65% (malignant vs benign) were obtained when the models were validated on the blinded test set. CONCLUSIONS: For ovarian cancer, altered MALDI-TOF MS peptide profiles alone cannot be used for accurate diagnoses.

Antiproliferative activity of Titanocene Y against tumor colony-forming units

Bis-[(p-methoxybenzyl)cyclopentadienyl] titanium dichloride, better known as Titanocene Y, is a newly synthesized titanium-based anticancer drug. We studied the antitumor activity of Titanocene Y with concentrations of 2.1, 21 and 210 μmol/l against a range of freshly explanted human tumors, using an in-vitro soft agar cloning system. The sensitivity against Titanocene Y was highly remarkable in the case of renal cell, ovarian, nonsmall cell lung and colon cancer. In particular the surprisingly good response of nonsmall cell lung cancer and colon cancer against Titanocene Y at its lowest concentration of 2.1 μmol/l was well comparable or better with respect to cisplatin, given at a concentration of 1.0 μmol/l. Further clinical development of Titanocene Y appears to be warranted because of the broad cytotoxic activity shown and the specific activity of Titanocene Y against renal cell cancer.

Anti-tumor activity of Titanocene Y in xenografted Caki-1 tumors in mice

The benzyl-substituted unbridged titanocene bis-[(p-methoxybenzyl)cyclopentadienyl] titanium(IV) dichloride (Titanocene Y) was tested in vitro against human renal cancer cells (Caki-1), in which it showed an IC50 value of 36 x 10(-6) mol/l. Titanocene Y was then given in vivo in doses of 10, 20, 30, 40 and 50 mg/kg on 5 consecutive days to Caki-1-bearing mice, and it showed concentration-dependent and statistically significant tumor growth reduction with respect to a solvent-treated control cohort. The maximum tolerable dose of Titanocene Y was determined to be 40 mg/kg and it showed significantly better tumor volume growth reduction than cisplatin given at a dose of 2 mg/kg. This superior activity of Titanocene Y with respect to cisplatin will hopefully lead to clinical tests against metastatic renal cell cancer in the near future.

Antitumor activity of Titanocene Y against freshly explanted human breast tumor cells and in xenografted MCF-7 tumors in mice

Bis-[(p-methoxybenzyl)cyclopentadienyl] titanium dichloride, better known as Titanocene Y, is a newly synthesized transition metal-based anticancer drug. We studied the antitumor activity of Titanocene Y with concentrations of 2.1, 21 and 210 mu mol/l against a freshly explanted human breast cancer, using an in-vitro soft agar cloning system. The sensitivity against Titanocene Y was highly remarkable in the breast cancer tumor in the full concentration range. Titanocene Y showed cell death induction at 2.1 mu mol/l, well comparable to cisplatin, given at a concentration of 1.0 mu mol/l. A further preclinical development of Titanocene Y was warranted and therefore an MCF-7 human breast cancer xenograft nonobese diabetic/severe combined immunodeficient mouse model was used. Titanocene Y was given for 21 days at 30 mg/kg/ day (75% of the maximum tolerable dose of Titanocene Y), which resulted in the reduction of the tumor volume to around one-third, whereas no mouse was lost because of the surprisingly low toxicity of Titanocene Y.

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.

Intra-tumor heterogeneity: lessons from microbial evolution and clinical implications

Multiple subclonal populations of tumor cells can coexist within the same tumor. This intra-tumor heterogeneity will have clinical implications and it is therefore important to identify factors that drive or suppress such heterogeneous tumor progression. Evolutionary biology can provide important insights into this process. In particular, experimental evolution studies of microbial populations, which exist as clonal populations that can diversify into multiple subclones, have revealed important evolutionary processes driving heterogeneity within a population. There are transferrable lessons that can be learnt from these studies that will help us to understand the process of intra-tumor heterogeneity in the clinical setting. In this review, we summarize drivers of microbial diversity that have been identified, such as mutation rate and environmental influences, and discuss how knowledge gained from microbial experimental evolution studies may guide us to identify and understand important selective factors that promote intra-tumor heterogeneity. Furthermore, we discuss how these factors could be used to direct and optimize research efforts to improve patient care, focusing on therapeutic resistance. Finally, we emphasize the need for longitudinal studies to address the impact of these potential tumor heterogeneity-promoting factors on drug resistance, metastatic potential and clinical outcome.