Biomedical subjects

Sayan Mukherjee

Publications and source records attributed to Sayan Mukherjee.

9 recordsLinked to original sources

PubMed2006

A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer.

BACKGROUND: Clinical trials have indicated a benefit of adjuvant chemotherapy for patients with stage IB, II, or IIIA--but not stage IA--non-small-cell lung cancer (NSCLC). This classification scheme is probably an imprecise predictor of the prognosis of an individual patient. Indeed, approximately 25 percent of patients with stage IA disease have a recurrence after surgery, suggesting the need to identify patients in this subgroup for more effective therapy. METHODS: We identified gene-expression profiles that predicted the risk of recurrence in a cohort of 89 patients with early-stage NSCLC (the lung metagene model). We evaluated the predictor in two independent groups of 25 patients from the American College of Surgeons Oncology Group (ACOSOG) Z0030 study and 84 patients from the Cancer and Leukemia Group B (CALGB) 9761 study. RESULTS: The lung metagene model predicted recurrence for individual patients significantly better than did clinical prognostic factors and was consistent across all early stages of NSCLC. Applied to the cohorts from the ACOSOG Z0030 trial and the CALGB 9761 trial, the lung metagene model had an overall predictive accuracy of 72 percent and 79 percent, respectively. The predictor also identified a subgroup of patients with stage IA disease who were at high risk for recurrence and who might be best treated by adjuvant chemotherapy. CONCLUSIONS: The lung metagene model provides a potential mechanism to refine the estimation of a patient's risk of disease recurrence and, in principle, to alter decisions regarding the use of adjuvant chemotherapy in early-stage NSCLC.

Adult↗

PubMed2006

Evidence of influence of genomic DNA sequence on human X chromosome inactivation.

A significant number of human X-linked genes escape X chromosome inactivation and are thus expressed from both the active and inactive X chromosomes. The basis for escape from inactivation and the potential role of the X chromosome primary DNA sequence in determining a gene's X inactivation status is unclear. Using a combination of the X chromosome sequence and a comprehensive X inactivation profile of more than 600 genes, two independent yet complementary approaches were used to systematically investigate the relationship between X inactivation and DNA sequence features. First, statistical analyses revealed that a number of repeat features, including long interspersed nuclear element (LINE) and mammalian-wide interspersed repeat repetitive elements, are significantly enriched in regions surrounding transcription start sites of genes that are subject to inactivation, while Alu repetitive elements and short motifs containing ACG/CGT are significantly enriched in those that escape inactivation. Second, linear support vector machine classifiers constructed using primary DNA sequence features were used to correctly predict the X inactivation status for >80% of all X-linked genes. We further identified a small set of features that are important for accurate classification, among which LINE-1 and LINE-2 content show the greatest individual discriminatory power. Finally, as few as 12 features can be used for accurate support vector machine classification. Taken together, these results suggest that features of the underlying primary DNA sequence of the human X chromosome may influence the spreading and/or maintenance of X inactivation.

Base Sequence↗

PubMed2006

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles.

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.

Algorithms↗

PubMed2006

Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex.

Two key models for examining activity-dependent development of primary visual cortex (V1) involve either reduction of activity in both eyes via dark-rearing (DR) or imbalance of activity between the two eyes via monocular deprivation (MD). Combining DNA microarray analysis with computational approaches, RT-PCR, immunohistochemistry and physiological imaging, we find that DR leads to (i) upregulation of genes subserving synaptic transmission and electrical activity, consistent with a coordinated response of cortical neurons to reduction of visual drive, and (ii) downregulation of parvalbumin expression, implicating parvalbumin-expressing interneurons as underlying the delay in cortical maturation after DR. MD partially activates homeostatic mechanisms but differentially upregulates molecular pathways related to growth factors and neuronal degeneration, consistent with reorganization of connections after MD. Expression of a binding protein of insulin-like growth factor-1 (IGF1) is highly upregulated after MD, and exogenous application of IGF1 prevents the physiological effects of MD on ocular dominance plasticity examined in vivo.

Animals↗

PubMed2005

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Cell Line, Tumor↗

PubMed2004

An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis.

Using advanced gene targeting methods, generating mouse models of cancer that accurately reproduce the genetic alterations present in human tumors is now relatively straightforward. The challenge is to determine to what extent such models faithfully mimic human disease with respect to the underlying molecular mechanisms that accompany tumor progression. Here we describe a method for comparing mouse models of cancer with human tumors using gene-expression profiling. We applied this method to the analysis of a model of Kras2-mediated lung cancer and found a good relationship to human lung adenocarcinoma, thereby validating the model. Furthermore, we found that whereas a gene-expression signature of KRAS2 activation was not identifiable when analyzing human tumors with known KRAS2 mutation status alone, integrating mouse and human data uncovered a gene-expression signature of KRAS2 mutation in human lung cancer. We confirmed the importance of this signature by gene-expression analysis of short hairpin RNA-mediated inhibition of oncogenic Kras2. These experiments identified both a pattern of gene expression indicative of KRAS2 mutation and potential effectors of oncogenic KRAS2 activity in human cancer. This approach provides a strategy for using genomic analysis of animal models to probe human disease.

Adenocarcinoma↗

PubMed2004

General conditions for predictivity in learning theory.

Developing theoretical foundations for learning is a key step towards understanding intelligence. 'Learning from examples' is a paradigm in which systems (natural or artificial) learn a functional relationship from a training set of examples. Within this paradigm, a learning algorithm is a map from the space of training sets to the hypothesis space of possible functional solutions. A central question for the theory is to determine conditions under which a learning algorithm will generalize from its finite training set to novel examples. A milestone in learning theory was a characterization of conditions on the hypothesis space that ensure generalization for the natural class of empirical risk minimization (ERM) learning algorithms that are based on minimizing the error on the training set. Here we provide conditions for generalization in terms of a precise stability property of the learning process: when the training set is perturbed by deleting one example, the learned hypothesis does not change much. This stability property stipulates conditions on the learning map rather than on the hypothesis space, subsumes the classical theory for ERM algorithms, and is applicable to more general algorithms. The surprising connection between stability and predictivity has implications for the foundations of learning theory and for the design of novel algorithms, and provides insights into problems as diverse as language learning and inverse problems in physics and engineering.

Algorithms↗

PubMed2003

Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.

Algorithms↗

PubMed2002

Optimal gene expression analysis by microarrays.

DNA microarrays make possible the rapid and comprehensive assessment of the transcriptional activity of a cell, and as such have proven valuable in assessing the molecular contributors to biological processes and in the classification of human cancers. The major challenge in using this technology is the analysis of its massive data output, which requires computational means for interpretation and a heightened need for quality data. The optimal analysis requires an accounting and control of the many sources of variance within the system, an understanding of the limitations of the statistical approaches, and the ability to make sense of the results through intelligent database interrogation.

Algorithms↗