Search

The insulin-like growth factor-I receptor (IGF-IR) pathway is required for the maintenance of the transformed phenotype in neoplastic cells and hence has been the subject of intensive drug discovery efforts. A key aspect of successful clinical development of targeted therapies directed against IGF-IR will be identification of responsive patient populations. Toward that end, we have endeavored to identify predictive biomarkers of response to an anti-IGF-IR-targeting monoclonal antibody in preclinical models of breast and colorectal cancer. We find that levels of the IGF-IR itself may have predictive value in these tumor types and identify other gene expression predictors of in vitro response. Studies in breast cancer models suggest that IGF-IR expression is both correlated and functionally linked with estrogen receptor signaling and provide a basis for both patient stratification and rational combination therapy with antiestrogen-targeting agents. In addition, we find that levels of other components of the signaling pathway such as the adaptor proteins IRS1 and IRS2, as well as the ligand IGF-II, have predictive value and report on the development of a pathway-focused panel of diagnostic biomarkers that could be used to test these hypotheses during clinical development of IGF-IR-targeting therapies.

Purpose: The pathways underlying basal-like breast cancer are poorly understood, and as yet, there is no approved targeted therapy for this disease. We investigated the role of mitogen-activated protein kinase kinase (MEK) and phosphatidylinositol 3-kinase (PI3K) inhibitors as targeted therapies for basal-like breast cancer., Experimental Design: We used pharmacogenomic analysis of a large panel of breast cancer cell lines with detailed accompanying molecular information to identify molecular predictors of response to a potent and selective inhibitor of MEK and also to define molecular mechanisms underlying combined MEK and PI3K targeting in basal-like breast cancer. Hypotheses were confirmed by testing in multiple tumor xenograft models., Results: We found that basal-like breast cancer models have an activated RAS-like transcriptional program and show greater sensitivity to a selective inhibitor of MEK compared with models representative of other breast cancer subtypes. We also showed that loss of PTEN is a negative predictor of response to MEK inhibition, that treatment with a selective MEK inhibitor caused up-regulation of PI3K pathway signaling, and that dual blockade of both PI3K and MEK/extracellular signal-regulated kinase signaling synergized to potently impair the growth of basal-like breast cancer models in vitro and in vivo., Conclusions: Our studies suggest that single-agent MEK inhibition is a promising therapeutic modality for basal-like breast cancers with intact PTEN, and also provide a basis for rational combination of MEK and PI3K inhibitors in basal-like cancers with both intact and deleted PTEN.

Breast cancers can be divided into subtypes with important implications for prognosis and treatment. We set out to characterize the genetic alterations observed in different breast cancer subtypes and to identify specific candidate genes and pathways associated with subtype biology. mRNA expression levels of estrogen receptor, progesterone receptor, and HER2 were shown to predict marker status determined by immunohistochemistry and to be effective at assigning samples to subtypes. HER2(+) cancers were shown to have the greatest frequency of high-level amplification (independent of the ERBB2 amplicon itself), but triple-negative cancers had the highest overall frequencies of copy gain. Triple-negative cancers also were shown to have more frequent loss of phosphatase and tensin homologue and mutation of RB1, which may contribute to genomic instability. We identified and validated seven regions of copy number alteration associated with different subtypes, and used integrative bioinformatics analysis to identify candidate oncogenes and tumor suppressors, including ERBB2, GRB7, MYST2, PPM1D, CCND1, HDAC2, FOXA1, and RASA1. We tested the candidate oncogene MYST2 and showed that it enhances the anchorage-independent growth of breast cancer cells. The genome-wide and region-specific differences between subtypes suggest the differential activation of oncogenic pathways.

Breast cancer is a heterogeneous disease with distinct molecular subtypes characterized by differential response to targeted and chemotherapeutic agents. Enhanced understanding of the genetic alterations characteristic of different subtypes is needed to pave the way for more personalized administration of therapeutic agents. We have taken a functional genomics approach using a well-characterized panel of breast cancer cell lines to identify putative biomarkers of resistance to antimitotic agents such as paclitaxel and monomethyl-auristatin-E (MMAE). In vitro studies revealed a striking difference in sensitivity to these agents between cell lines from different subtypes, with basal-like cell lines being significantly more sensitive to both agents than luminal or HER2-amplified cell lines. Genome-wide association studies using copy number data from Affymetrix single nucleotide polymorphism arrays identified amplification of the chromosome 17q21 region as being highly associated with resistance to both paclitaxel and MMAE. An unbiased approach consisting of RNA interference and high content analysis was used to show that amplification and concomitant overexpression of the gene encoding the ABCC3 drug transporter is responsible for conferring in vitro resistance to paclitaxel and MMAE. We also show that amplification of ABCC3 is present in primary breast tumors and that it occurs predominantly in HER2-amplified and luminal tumors, and we report on development of a specific fluorescence in situ hybridization assay that may have utility as a predictive biomarker of taxane resistance in breast cancer.

Analysis of recurrent DNA amplification can lead to the identification of cancer driver genes, but this process is often hampered by the low resolution of existing copy number analysis platforms. Fifty-one breast tumors were profiled for copy number alterations (CNAs) with the high-resolution Affymetrix 500K SNP array. These tumors were also expression-profiled and surveyed for mutations in selected genes commonly mutated in breast cancer (TP53, CDKN2A, ERBB2, KRAS, PIK3CA, PTEN). Combined analysis of common CNAs and mutations revealed putative associations between features. Analysis of both the prevalence and amplitude of CNAs defined regions of recurrent alteration. Compared with previous array comparative genomic hybridization studies, our analysis provided boundaries for frequently altered regions that were approximately one-fourth the size, greatly reducing the number of potential alteration-driving genes. Expression data from matched tumor samples were used to further interrogate the functional relevance of genes located in recurrent amplicons. Although our data support the importance of some known driver genes such as ERBB2, refined amplicon boundaries at other locations, such as 8p11-12 and 11q13.5-q14.2, greatly reduce the number of potential driver genes and indicate alternatives to commonly suggested driver genes in some cases. For example, the previously reported recurrent amplification at 17q23.2 is reduced to a 249 kb minimal region containing the putative driver RPS6KB1 as well as the putative oncogenic microRNA mir-21. High-resolution copy number analysis provides refined insight into many breast cancer amplicons and their relationships to gene expression, point mutations and breast cancer subtype classifications. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

Sjöblom et al. (Research Articles, 13 October 2006, p. 268) used data from cancer genome resequencing to identify genes with elevated mutation rates. Their analysis used point probabilities when it should have used P values for the hypotheses they intended to test. Reimplementing their analysis method with exact P values results in far fewer genes with mutation rates that achieve statistical significance.