Search

The multiple sclerosis (MS) patient population is highly heterogeneous in terms of disease course and treatment response. We used a transcriptional profile generated from peripheral blood mononuclear cells to define the structure of an MS patient population. Two subsets of MS subjects (MS(A) and MS(B)) were found among 141 untreated subjects. We replicated this structure in two additional groups of MS subjects treated with one of the two first-line disease-modifying treatments in MS: glatiramer acetate (GA) (n = 94) and interferon-β (IFN-β) (n = 128). One of the two subsets of subjects (MS(A)) was distinguished by higher expression of molecules involved in lymphocyte signaling pathways. Further, subjects in this MS(A) subset were more likely to have a new inflammatory event while on treatment with either GA or IFN-β (P = 0.0077). We thus report a transcriptional signature that differentiates subjects with MS into two classes with different levels of disease activity.

The comprehensive characterization of a large number of cancer genomes will eventually lead to a compendium of genetic alterations in specific cancers. Unfortunately, the number and complexity of identified alterations complicate endeavors to identify biologically relevant mutations critical for tumor maintenance because many of these targets are not amenable to manipulation by small molecules or antibodies. RNA interference provides a direct way to study putative cancer targets; however, specific delivery of therapeutics to the tumor parenchyma remains an intractable problem. We describe a platform for the discovery and initial validation of cancer targets, composed of a systematic effort to identify amplified and essential genes in human cancer cell lines and tumors partnered with a novel modular delivery technology. We developed a tumor-penetrating nanocomplex (TPN) that comprised small interfering RNA (siRNA) complexed with a tandem tumor-penetrating and membrane-translocating peptide, which enabled the specific delivery of siRNA deep into the tumor parenchyma. We used TPN in vivo to evaluate inhibitor of DNA binding 4 (ID4) as a novel oncogene. Treatment of ovarian tumor-bearing mice with ID4-specific TPN suppressed growth of established tumors and significantly improved survival. These observations not only credential ID4 as an oncogene in 32% of high-grade ovarian cancers but also provide a framework for the identification, validation, and understanding of potential therapeutic cancer targets.

Cell microarrays are a promising tool for performing large-scale functional genomic screening in mammalian cells at reasonable cost, but owing to technical limitations they have been restricted for use with a narrow range of cell lines and short-term assays. Here, we describe MicroSCALE (Microarrays of Spatially Confined Adhesive Lentiviral Features), a cell microarray-based platform that enables application of this technology to a wide range of cell types and longer-term assays. We used MicroSCALE to uncover kinases that when overexpressed partially desensitized B-RAFV600E-mutant melanoma cells to inhibitors of the mitogen-activated protein kinase kinase kinase (MAPKKK) RAF, the MAPKKs MEK1 and 2 (MEK1/2, mitogen-activated protein kinase kinase 1 and 2), mTOR (mammalian target of rapamycin), or PI3K (phosphatidylinositol 3-kinase). These screens indicated that cells treated with inhibitors acting through common mechanisms were affected by a similar profile of overexpressed proteins. In contrast, screens involving inhibitors acting through distinct mechanisms yielded unique profiles, a finding that has potential relevance for small-molecule target identification and combination drugging studies. Further, by integrating large-scale functional screening results with cancer cell line gene expression and pharmacological sensitivity data, we validated the nuclear factor κB pathway as a potential mediator of resistance to MAPK pathway inhibitors. The MicroSCALE platform described here may enable new classes of large-scale, resource-efficient screens that were not previously feasible, including those involving combinations of cell lines, perturbations, and assay outputs or those involving limited numbers of cells and limited or expensive reagents.

Sjöblom et al. (Research Article, 13 October 2006, p. 268) reported nearly 200 novel cancer genes said to have a 90% probability of being involved in colon or breast cancer. However, their analysis raises two statistical concerns. When these concerns are addressed, few genes with significantly elevated mutation rates remain. Although the biological methodology in Sjöblom et al. is sound, more samples are needed to achieve sufficient power.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.