Diogo F.C. Patrão

Gene Expression Profile Associated with Response to Doxorubicin-Based Therapy in Breast Cancer

Purpose: This study was designed to identify genes that could predict response to doxorubicin-based primary chemotherapy in breast cancer patients. Experimental Design: Biopsy samples were obtained before primary treatment with doxorubicin and cyclophosphamide. RNA was extracted and amplified and gene expression was analyzed using cDNA microarrays. Results: Response to chemotherapy was evaluated in 51 patients, and based on Response Evaluation Criteria in Solid Tumors guidelines, 42 patients, who presented at least a partial response (≥30% reduction in tumor dimension), were classified as responsive. Gene profile of samples, divided into training set (n = 38) and independent validation set (n = 13), were at first analyzed against a cDNA microarray platform containing 692 genes. Unsupervised clustering could not separate responders from nonresponders. A classifier was identified comprising EMILIN1, FAM14B, and PBEF, which however could not correctly classify samples included in the validation set. Our next step was to analyze gene profile in a more comprehensive cDNA microarray platform, containing 4,608 open reading frame expressed sequence tags. Seven samples of the initial training set (all responder patients) could not be analyzed. Unsupervised clustering could correctly group all the resistant samples as well as at least 85% of the sensitive samples. Additionally, a classifier, including PRSS11, MTSS1, and CLPTM1, could correctly distinguish 95.4% of the 44 samples analyzed, with only two misclassifications, one sensitive sample and one resistant tumor. The robustness of this classifier is 2.5 greater than the first one. Conclusion: A trio of genes might potentially distinguish doxorubicin-responsive from nonresponsive tumors, but further validation by a larger number of samples is still needed.

Molecular profiling of isolated histological components of Wilms tumor implicates a common role for the Wnt signaling pathway in kidney and tumor development

Wilms tumor (WT), a tumor composed of three histological components – blastema (BL), epithelia and stroma – is considered an appropriate model system to study the biological relationship between differentiation and tumorigenesis. To investigate molecular associations between nephrogenesis and WT, the gene expression pattern of individual cellular components was analyzed, using a customized platform containing 4,608 genes. WT gene expression patterns were compared to genes regulated during kidney differentiation. BL had a closer gene expression pattern to the earliest stage of normal renal development. The BL gene expression pattern was compared to that of fetal kidney (FK) and also between FK and mature kidney, identifying 25 common deregulated genes supposedly involved in the earliest events of WT onset. Quantitative RT-PCR was performed, confirming the difference in expression levels for 13 of 16 genes (81.2%) in the initial set and 8 of 13 (61.5%) in an independent set of samples. An overrepresentation of genes belonging to the Wnt signaling pathway was identified, namely PLCG2, ROCK2 and adenomatous polyposis coli (APC). Activation of the Wnt pathway was confirmed in WT, using APC at protein level and PLCG2 at mRNA and protein level. APC showed positive nuclear immunostaining for an independent set of WT samples, similarly to the FK in week 11. Lack of PLCG2 expression was confirmed in WT and in FK until week 18. Taken together, these results provided molecular evidence of the recapitulation of the embryonic kidney by WT as well as involvement of the Wnt pathway in the earliest events of WT onset.

Gene expression profiling of clinical stages II and III breast cancer

Clinical stage (CS) is an established indicator of breast cancer outcome. In the present study, a cDNA microarray platform containing 692 genes was used to identify molecular differences between CSII and CSIII disease. Tumor samples were collected from patients with CSII or CSIII breast cancer, and normal breast tissue was collected from women without invasive cancer. Seventy-eight genes were deregulated in CSIII tumors and 22 in CSII tumors when compared to normal tissue, and 20 of them were differentially expressed in both CSII and CSIII tumors. In addition, 58 genes were specifically altered in CSIII and expression of 6 of them was tested by real time RT-PCR in another cohort of patients with CSII or CSIII breast cancer and in women without cancer. Among these genes, MAX, KRT15 and S100A14, but not APOBEC3G or KRT19, were differentially expressed on both CSIII and CSII tumors as compared to normal tissue. Increased HMOX1 levels were detected only in CSIII tumors and may represent a molecular marker of this stage. A clear difference in gene expression pattern occurs at the normal-to-cancer transition; however, most of the differentially expressed genes are deregulated in tumors of both CS (II and III) compared to normal breast tissue.

The Value of a Tumor Bank in the Development of Cancer Research in Brazil: 13 Years of Experience at the A C Camargo Hospital

This article discusses the importance of biobanking to health research advancement in developing countries by analyzing the impact of the establishment of a tumor bank at the A C Camargo Hospital, a cancer care and research center located in Sao Paulo, Brazil. For the past 13 years, the human biological samples provided by the tumor bank have been used by investigators to study various types of cancer. We analyze the impact of biobanking in the overall quality of research projects performed at our institution. We also summarize the main findings of these investigations focusing on breast, prostate, head-neck, and gastroesophageal tumors, as well as the lessons learned over these years. We conclude that biobanking should be part of the strategy employed by scientists and research institutions dedicated to the study of human diseases.

Tumor slices as a model to evaluate doxorubicin in vitro treatment and expression of trios of genes PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2 in canine mammary gland cancer

BackgroundIn women with breast cancer submitted to neoadjuvant chemotherapy based in doxorubicin, tumor expression of groups of three genes (PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2) have classified them as responsive or resistant. We have investigated whether expression of these trios of genes could predict mammary carcinoma response in dogs and whether tumor slices, which maintain epithelial-mesenchymal interactions, could be used to evaluate drug response in vitro.MethodsTumors from 38 dogs were sliced and cultured with or without doxorubicin 1 μM for 24 h. Tumor cells were counted by two observers to establish a percentage variation in cell number, between slices. Based on these results, a reduction in cell number between treated and control samples ≥ 21.7%, arbitrarily classified samples, as drug responsive. Tumor expression of PRSS11, MTSS1, CLPTM1 and SMYD2, was evaluated by real time PCR. Relative expression results were then transformed to their natural logarithm values, which were spatially disposed according to the expression of trios of genes, comprising PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2. Fisher linear discrimination test was used to generate a separation plane between responsive and non-responsive tumors.ResultsCulture of tumor slices for 24 h was feasible. Nine samples were considered responsive and 29 non-responsive to doxorubicin, considering the pre-established cut-off value of cell number reduction ≥ 21.7%, between doxorubicin treated and control samples. Relative gene expression was evaluated and tumor samples were then spatially distributed according to the expression of the trios of genes: PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2. A separation plane was generated. However, no clear separation between responsive and non-responsive samples could be observed.ConclusionThree-dimensional distribution of samples according to the expression of the trios of genes PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2 could not predict doxorubicin in vitro responsiveness. Short term culture of mammary gland cancer slices may be an interesting model to evaluate chemotherapy activity.

A feature selection approach for identification of signature genes from SAGE data

BackgroundOne goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements.ResultsA new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology.ConclusionThe model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.