G. Parmigiani | Dana-Farber Cancer Institute (original) (raw)

Papers by G. Parmigiani

... MISC{Müller_numericalevaluation, author = {Peter Müller and Giovanni Parmigiani}, title = {Nu... more ... MISC{Müller_numericalevaluation, author = {Peter Müller and Giovanni Parmigiani}, title = {Numerical Evaluation of Information Theoretic Measures}, year = {} }. ... 34, Modelling complexity: applications of Gibbs sampling in medicine Gilks, Clayton, et al. - 1993. ...

Contributions to Statistics, 1995

Breast disease, 1998

Tests for the presence of mutations of genes BRCA1 and BRCA2 are increasingly available. Genetic ... more Tests for the presence of mutations of genes BRCA1 and BRCA2 are increasingly available. Genetic testing creates dilemmas for women and men who regard themselves to be at high risk for breast cancer. Who will benefit from genetic testing? What is the benefit? Does testing improve quality of life? An important consideration in addressing these questions is the woman's chance of carrying a mutation at BRCA1 or BRCA2. Also important are the effectiveness and cost of the testing procedure, the availability of prophylactic interventions, the effectiveness and negative aspects of interventions, the impact of testing on other family members, and the impact of testing on the woman's ability to obtain insurance coverage. In this article we review the development of a statistical model for predicting whether a woman is a carrier of a BRCA1 or a BRCA2 mutation. We also show how this calculation can be used to assess the benefit of testing, and we quantify the size of the benefit in ter...

Genome Biology, 2011

We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple... more We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually.

Magnetic resonance imaging (MRI) is recommended for women at high risk for breast cancer. We eval... more Magnetic resonance imaging (MRI) is recommended for women at high risk for breast cancer. We evaluated the cost-effectiveness of alternative screening strategies involving MRI. Using a microsimulation model, we generated life histories under different risk profiles, and assessed the impact of screening on quality-adjusted life-years, and lifetime costs, both discounted at 3%. We compared 12 screening strategies combining annual or biennial MRI with mammography and clinical breast examination (CBE) in intervals of 0.5, 1, or 2 years vs without, and reported incremental cost-effectiveness ratios (ICERs). Based on an ICER threshold of 100,000/QALY,themostcost−effectivestrategyforwomenat25100,000/QALY, the most cost-effective strategy for women at 25% lifetime risk was to stagger MRI and mammography plus CBE every year from age 30 to 74, yielding ICER 100,000/QALY,themostcost−effectivestrategyforwomenat2558,400 (compared to biennial MRI alone). At 50% lifetime risk and with 70% reduction in MRI cost, the recommended strategy was to stagger MRI and mammography plus CBE every 6 months (ICER=$84,400). At 75% lifetime risk, the recommended strategy is biennial MRI combined with mammography plus CBE every 6 months (ICER=$62,800). The high costs of MRI and its lower specificity are limiting factors for annual screening schedule of MRI, except for women at sufficiently high risk.

BioTechniques, 2003

Current cancer classifications using morphological criteria produce heterogeneous classes with va... more Current cancer classifications using morphological criteria produce heterogeneous classes with variable prognosis and clinical course. By measuring gene expression for thousands of genes in a single hybridization experiment, microarrays have the potential to contribute to more effective classifications based on molecular information. This gives hope to improve both prognosis and treatment. Statistical methods for molecular classification have focused on using high dimensional representations of molecular profiles to identify subclasses. These can be noisy, unstable, and highly platform-specific. In this article, we emphasize the notion of molecular profiles based on latent categories signifying under-, over-, and baseline expression. Following this approach, we can generate results that are more easily interpretable, more easily translated into clinical tools, more robust to noise, and less platform-dependent. We illustrate both the methods and the associated software for molecular ...

Lecture Notes in Statistics, 1999

Recent advances in the understanding of genetic susceptibility to breastcancer, notably identific... more Recent advances in the understanding of genetic susceptibility to breastcancer, notably identification of the BRCA1 and BRCA2 genes, and theadvent of genetic testing, raise important questions for clinicians, patientsand policy makers. Answers to many of these questions hinge on accurateassessment of the risk of breast cancer. In particular, it is important topredict genetic susceptibility based on easy-to-collect data about familyhistory

Frontiers in Genetics, 2014

In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation ... more In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest. Our focus is the systematic evaluation of the trade-off between network size and network search efficiency in the construction of these networks.

Wiley Series in Probability and Statistics, 2009

Tissue microarray technology promises to enhance tissue-based molecular research by allowing impr... more Tissue microarray technology promises to enhance tissue-based molecular research by allowing improved conservation of tissue resources and experimental reagents, improved internal experimental control, and increased sample numbers per experiment. Organized, well-validated collection and analysis of the voluminous image data produced by tissue microarray technology is critical to maximize its value. Web-based technology for visual analysis and searchable storage of microarray image data could provide optimal flexibility for research groups in meeting this goal, but this approach has not been examined scientifically. Toward this goal, a prostate tissue microarray block containing 432 tissue cores (0.6 mm diameter) was constructed. Moderately compressed (200 kb).jpg images of each tissue spot were acquired and were saved using a naming convention developed by the SPORE Prostate Tissue Microarray Collaborative Group. Four hundred three tissue array spot images were uploaded into a database developed for this study and were converted to.fpx format to decrease Internet transmission times for high-resolution image data. In phase I of the image analysis portion of the study, testing and preliminary analysis of the Web technology was performed by 2 pathologists (M.A.R. and G.S.B.). In phase II, 2 pathologists (J.I.E. and T.M.W.) with no previous exposure to this technology and no knowledge of the structure of the study were presented a set of 130 sequential tissue spot images via the Web on their office computers. In phase III, the same pathologists were presented a set of 193 images, including all 130 from phase II and 63 others, with image presentation order randomized. With each zoomable tissue spot image, each pathologist was presented with a nested set of questions regarding overall interpretability of the image, presence or absence of cancer, and predominant and second most frequent Gleason grade. In phases II and III of the study, 319 of 323 (99%) image presentations using this Web technology were rated interpretable. Comparing the 2 pathologists&amp;#39; readings in phases II and III, Gleason grade determinations by each pathologist were identical in 179 of 221 (81%) determinations and were within 1 point of each other in 221 of 221 (100%) determinations, a performance rate similar to if not better than that previously reported for direct microscopic Gleason grading. Interobserver comparison of Gleason score determinations and intraobserver comparisons for Gleason grade and score also showed a pattern of uniformity similar to those reported in direct microscope-based Gleason grading studies. Interobserver (7.5%) and intraobserver (5% and 3%) variability in determining whether diagnosable cancer was present point out the existence of a &amp;quot;threshold effect&amp;quot; that has rarely been studied but may provide a basis for identification of features that are most amenable to improved diagnostic standardization. In summary, storage and analysis of tissue microarray spot images using Web-based technology is feasible and practical, and the quality of images obtained using the techniques described here appears adequate for most tissue-based pathology research applications. HUM PATHOL 32:417-427.

Hosmer/Applied Logistic Regression, 2000

... BERNARDO and SMITH · Bayesian Theory BERRY, CHALONER and GEWEKE · Bayesian Analysis in Statis... more ... BERNARDO and SMITH · Bayesian Theory BERRY, CHALONER and GEWEKE · Bayesian Analysis in Statistics and Econometrics ... Sampling HEDEKER and GIBBONS · Longitudinal Data Analysis HELLER · MACSYMA for Statisticians HINKELMANN and KEMPTHORNE ...

Methods in Molecular Biology, 2013

This chapter provides a description and illustration of CancerMutationAnalysis and CancerMutation... more This chapter provides a description and illustration of CancerMutationAnalysis and CancerMutationMCMC, two open source R packages specifically designed for the analysis of somatic mutations in cancer genome studies, at both the gene and gene-set levels.

Statistics in Medicine, 2008

Mendelian models can predict who carries an inherited deleterious mutation of known disease genes... more Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 familes from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPRO's concordance index from 0.758 to 0.762 (p = 0.046), improves its positive predictive value from 35% to 39% (p < 10 −6 ) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability < 10%.