Abstract
Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higherlevel modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.
| Original language | American English |
|---|---|
| Pages (from-to) | S38-S45 |
| Journal | Nature Genetics |
| Volume | 37 |
| Issue number | 6S |
| DOIs | |
| State | Published - Jun 2005 |
Bibliographical note
Funding Information:All authors contributed equally to this work. We thank M. Scott and T. Raveh for making available to us their mouse brain microarrays for the multispecies module network analysis. E.S. was supported by a Fellowship from the Center for Studies in Physics and Biology at Rockefeller University. N.F. was supported by the Harry & Abe Sherman Senior Lectureship in Computer Science, by the United States-Israel Bi-National Science Foundation grant and by grants from the US National Institutes of Health. N.K. was partly supported by grants from the US National Institutes of Health, by the Tel-Aviv Chapter of the Israeli Lung Association and by a donation from the Simmons family. A.R. was supported by a grant from the US National Institutes of Health and by the Bauer Center. D.K. was supported by a grant from the US National Science Foundation and by a BioX Center grant.
