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Francesca Demichelis

Publications and source records attributed to Francesca Demichelis.

12 recordsLinked to original sources
PubMed2006

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.

BACKGROUND: Uncertainty often affects molecular biology experiments and data for different reasons. Heterogeneity of gene or protein expression within the same tumor tissue is an example of biological uncertainty which should be taken into account when molecular markers are used in decision making. Tissue Microarray (TMA) experiments allow for large scale profiling of tissue biopsies, investigating protein patterns characterizing specific disease states. TMA studies deal with multiple sampling of the same patient, and therefore with multiple measurements of same protein target, to account for possible biological heterogeneity. The aim of this paper is to provide and validate a classification model taking into consideration the uncertainty associated with measuring replicate samples. RESULTS: We propose an extension of the well-known Naïve Bayes classifier, which accounts for biological heterogeneity in a probabilistic framework, relying on Bayesian hierarchical models. The model, which can be efficiently learned from the training dataset, exploits a closed-form of classification equation, thus providing no additional computational cost with respect to the standard Naïve Bayes classifier. We validated the approach on several simulated datasets comparing its performances with the Naïve Bayes classifier. Moreover, we demonstrated that explicitly dealing with heterogeneity can improve classification accuracy on a TMA prostate cancer dataset. CONCLUSION: The proposed Hierarchical Naïve Bayes classifier can be conveniently applied in problems where within sample heterogeneity must be taken into account, such as TMA experiments and biological contexts where several measurements (replicates) are available for the same biological sample. The performance of the new approach is better than the standard Naïve Bayes model, in particular when the within sample heterogeneity is different in the different classes.

Algorithms↗
PubMed2006

M-CAM expression as marker of poor prognosis in epithelial ovarian cancer.

Currently available clinico-pathologic criteria provide an imperfect assessment of outcome for patients with advanced epithelial ovarian cancer (EOC). Identification of prognostic factors related to tumor biology might improve this assessment. We investigated the prognostic significance of the melanoma cell adhesion molecule (M-CAM) in EOC. Using the same antibody, M-CAM expression was tested by Western blotting in protein extracts and by immunohistochemestry in tissue microarrays generated from 133 consecutively resected, well characterized EOC samples. Fisher test, Kaplan-Meier method and Cox proportional hazards analysis were used to relate M-CAM expression to clinico-pathological variables and to time to progression (TTP) and overall survival (OS). In vitro biochemical analysis showed a progressively increased M-CAM expression from normal to malignant cells. M-CAM protein, detected immunohistochemically, was significantly associated with advanced tumor stage, serous and undifferentiated histotype, extent of residual disease and p53 accumulation. Presence or absence of M-CAM significantly divided patients according to their TTP (median, 22 vs. 79 months, respectively; log-rank p = 0.001) and OS (median, 42 vs. 131 months, respectively; log-rank p = 0.0003). In the subgroup of advanced stage patients who achieved complete response after front-line treatment, M-CAM expression and absence of residual disease were significantly associated with shorter TTP (p = 0.003, HR 5.25, 95% Cl 1.79-15.41 and p = 0.011, HR 3.77, 95% Cl 1.36-10.49 respectively) at the multivariate level. In the same sub-group of patients, M-CAM expression remained the only parameter significantly associated with OS (p = 0.005, HR 3.35, 95% Cl 1.42-6.88). M-CAM is a marker of early relapse and poorer outcome in EOC. In particular, M-CAM expression identifies a subgroup of front-line therapy-responding patients who undergo dramatic relapses, thus helping to better select patients who might benefit from new/alternative therapeutic modalities.

Adult↗
PubMed2006

TMPRSS2:ERG fusion-associated deletions provide insight into the heterogeneity of prostate cancer.

Prostate cancer is a common and clinically heterogeneous disease with marked variability in progression. The recent identification of gene fusions of the 5'-untranslated region of TMPRSS2 (21q22.3) with the ETS transcription factor family members, either ERG (21q22.2), ETV1 (7p21.2), or ETV4 (17q21), suggests a mechanism for overexpression of the ETS genes in the majority of prostate cancers. In the current study using fluorescence in situ hybridization (FISH), we identified the TMPRSS2:ERG rearrangements in 49.2% of 118 primary prostate cancers and 41.2% of 18 hormone-naive lymph node metastases. The FISH assay detected intronic deletions between ERG and TMPRSS2 resulting in TMPRSS2:ERG fusion in 60.3% (35 of 58) of the primary TMPRSS2:ERG prostate cancers and 42.9% (3 of 7) of the TMPRSS2:ERG hormone-naive lymph node metastases. A significant association was observed between TMPRSS2:ERG rearranged tumors through deletions and higher tumor stage and the presence of metastatic disease involving pelvic lymph nodes. Using 100K oligonucleotide single nucleotide polymorphism arrays, a homogeneous deletion site between ERG and TMPRSS2 on chromosome 21q22.2-3 was identified with two distinct subclasses distinguished by the start point of the deletion at either 38.765 or 38.911 Mb. This study confirms that TMPRSS2:ERG is fused in approximately half of the prostate cancers through deletion of genomic DNA between ERG and TMPRSS2. The deletion as cause of TMPRSS2:ERG fusion is associated with clinical features for prostate cancer progression compared with tumors that lack the TMPRSS2:ERG rearrangement.

5' Untranslated Regions↗
PubMed2006

User attitudes in analyzing digital slides in a quality control test bed: a preliminary study.

The pathologist examines suitably stained glass slides through a bright field microscope in order to render histopathological or cytological diagnosis by looking at tissues and cells. Glass slides serve as a permanent record of the patient disease. Over the course of a patient's treatment slides may need to be reviewed at other institutions before treatment can commence. Due to their fragile nature a transportable permanent digital facsimile of the glass slide would be ideal. A digital slide is a set of digital images representing the whole slide normally used by the pathologist, or a significant part of it; it is usually made by a large amount of images, up to thousands, which makes its management difficult. The present paper provides a description of the requirements needed to reproduce glass slides and of the available technological equipment, then the features of the two systems we implemented on different hardware are described, together with those of the digital slide viewer. The viewer was evaluated in two experimental test phases, during which user behaviour and diagnostic reports were measured. Digital slides used in the two experiments were acquired with either system. Possible applications of digital slides are then discussed, including undergraduate and professional education, quality control, and image analysis on full samples as well as on tissue microarrays.

Attitude↗
PubMed2006

TMABoost: an integrated system for comprehensive management of tissue microarray data.

In the last decade, high-throughput technologies such as DNA and tissue microarrays (TMAs) have become a means of large-scale investigation of gene expression, providing a plethora of new biomedical data in a relatively short time. Data collection and organization are critical aspects in this process to ensure the quality and reliability of future data interpretation. In this work, we propose a comprehensive approach to handle TMA data with the aim of supporting and promoting biomarker development. We describe a web-based system for the complete management of tissue microarray data in the field of pathology. The system has been in use since June, 2003. Our approach includes automatic localization and identification of tissue microarray samples, and quantitative image analysis that allows high-throughput screening of TMAs by ensuring nonsubjective measures and novel prognosis associations. In this paper, we present the architecture and the components of this system.

Algorithms↗
PubMed2006

Defining aggressive prostate cancer using a 12-gene model.

The critical clinical question in prostate cancer research is: How do we develop means of distinguishing aggressive disease from indolent disease? Using a combination of proteomic and expression array data, we identified a set of 36 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Another five prostate cancer biomarkers were included using linear discriminant analysis, we determined that the optimal model used to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, transcriptional levels of the 12 genes encoding for these proteins predicted prostate-specific antigen failure in 79 men following surgery for clinically localized prostate cancer (P = .0015). This study demonstrates that cross-platform models can lead to predictive models with the possible advantage of being more robust through this selection process.

Biomarkers, Tumor↗
PubMed2005

Internet-based Profiler system as integrative framework to support translational research.

BACKGROUND: Translational research requires taking basic science observations and developing them into clinically useful tests and therapeutics. We have developed a process to develop molecular biomarkers for diagnosis and prognosis by integrating tissue microarray (TMA) technology and an internet-database tool, Profiler. TMA technology allows investigators to study hundreds of patient samples on a single glass slide resulting in the conservation of tissue and the reduction in inter-experimental variability. The Profiler system allows investigator to reliably track, store, and evaluate TMA experiments. Here within we describe the process that has evolved through an empirical basis over the past 5 years at two academic institutions. RESULTS: The generic design of this system makes it compatible with multiple organ system (e.g., prostate, breast, lung, renal, and hematopoietic system,). Studies and folders are restricted to authorized users as required. Over the past 5 years, investigators at 2 academic institutions have scanned 656 TMA experiments and collected 63,311 digital images of these tissue samples. 68 pathologists from 12 major user groups have accessed the system. Two groups directly link clinical data from over 500 patients for immediate access and the remaining groups choose to maintain clinical and pathology data on separate systems. Profiler currently has 170 K data points such as staining intensity, tumor grade, and nuclear size. Due to the relational database structure, analysis can be easily performed on single or multiple TMA experimental results. The TMA module of Profiler can maintain images acquired from multiple systems. CONCLUSION: We have developed a robust process to develop molecular biomarkers using TMA technology and an internet-based database system to track all steps of this process. This system is extendable to other types of molecular data as separate modules and is freely available to academic institutions for licensing.

Automation↗
PubMed2005

An automated procedure to properly handle digital images in large scale tissue microarray experiments.

Tissue Microarray (TMA) methodology has been recently developed to enable "genome-scale" molecular pathology studies. To enable high-throughput screening of TMAs automation is mandatory, both to speed up the process and to improve data quality. In particular, in acquiring digital images of single tissues (core sections) a crucial step is the correct recognition of each tissue position in the array. In fact, further reliable data analysis is based on the exact assignment of each tissue to the corresponding tumor. As most of the times tissue alignment in the microarray grid is far from being perfect, simple strategies to perform proper acquisition do not fit well. The present paper describes a new solution to automatically perform grid location assignment. We developed an ad hoc image processing procedure and a robust algorithm for object recognition. Algorithm accuracy tests and assessment of working constraints are discussed. Our approach speeds up TMA data collection and enables large scale investigation.

Algorithms↗
PubMed2004

JAGGED1 expression is associated with prostate cancer metastasis and recurrence.

Recent studies suggest that NOTCH signaling can promote epithelial-mesenchymal transitions and augment signaling through AKT, an important growth and survival pathway in epithelial cells and prostate cancer in particular. Here we show that JAGGED1, a NOTCH receptor ligand, is significantly more highly expressed in metastatic prostate cancer as compared with localized prostate cancer or benign prostatic tissues, based on immunohistochemical analysis of JAGGED1 expression in human tumor samples from 154 men. Furthermore, high JAGGED1 expression in a subset of clinically localized tumors was significantly associated with recurrence, independent of other clinical parameters. These findings support a model in which dysregulation of JAGGED1 protein levels plays a role in prostate cancer progression and metastasis and suggest that JAGGED1 may be a useful marker in distinguishing indolent and aggressive prostate cancers.

Adult↗
PubMed2002

Digital storage of glass slides for quality assurance in histopathology and cytopathology.

Proficiency testing programmes for measuring screening skills in pathology are mainly conducted using conventional glass microscope slides. However, the availability of robotic microscopes allows an entire conventional slide to be digitized. Our experiments have shown that, using a widely available robotized microscope and a PC, the image of a single field may be acquired in 2 s on average, including stage movements, autofocus and storage. Digitizing an entire slide, a fully automated procedure, takes up to 8 h. If the image of each field is compressed at an appropriate quality level (a compression ratio of, say, 35:1) it requires about 40 kByte to be stored, resulting in a total storage requirement of about 600 MByte per slide. Thus one CD-ROM can be used to store one virtual slide, as well as a self-installing program to provide a microscope simulator facility. This allows pathologists to examine the virtual case from their computer in a similar manner to looking at a glass slide on a conventional microscope. This permits a new, computer-based approach to proficiency testing in histopathology and cytopathology. Use of virtual slides should encourage the diffusion of national quality assurance programmes, which at present suffer from certain organizational and logistical limitations.

Analog-Digital Conversion↗