Pre-treatment levels of circulating free IGF-1 identify NSCLC patients who derive clinical benefit from figitumumab.

BACKGROUND: Phase III trials of the anti-insulin-like growth factor type 1 receptor (IGF-IR) antibody figitumumab (F) in unselected non-small-cell lung cancer (NSCLC) patients were recently discontinued owing to futility. Here, we investigated a role of free IGF-1 (fIGF-1) as a potential predictive biomarker of clinical benefit from F treatment. MATERIALS AND METHOD: Pre-treatment circulating levels of fIGF-1 were tested in 110 advanced NSCLC patients enrolled in a phase II study of paclitaxel and carboplatin given alone (PC) or in combination with F at doses of 10 or 20 mg kg(-1) (PCF10, PCF20). RESULTS: Cox proportional hazards model interactions were between 2.5 and 3.5 for fIGF-1 criteria in the 0.5-0.9 ng ml(-1) range. Patients above each criterion had a substantial improvement in progression-free survival on PCF20 related to PC alone. Free IGF-1 correlated inversely with IGF binding protein 1 (IGFBP-1, ρ=-0.295, P=0.005), and the pre-treatment ratio of insulin to IGFBP-1 was also predictive of F clinical benefit. In addition, fIGF-1 levels correlated with tumour vimentin expression (ρ=0.594, P=0.021) and inversely with E-cadherin (ρ=-0.389, P=0.152), suggesting a role for fIGF-1 in tumour de-differentiation. CONCLUSION: Free IGF-1 may contribute to the identification of a subset of NSCLC patients who benefit from F therapy.

Human epidermal growth factor receptor-2 and estrogen receptor expression, a demonstration project using the residual tissue repository of the Surveillance, Epidemiology, and End Results (SEER) program.

BACKGROUND: In 2001, the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program established Residual Tissue Repositories (RTR) in the Hawaii, Iowa, and Los Angeles Tumor Registries to collect discarded tissue blocks from pathologic laboratories within their catchment areas. To validate the utility of the RTR for supplementing SEER's central database, we assessed human epidermal growth factor receptor-2 (HER2) and estrogen receptor expression (ER) in a demonstration project. MATERIALS: Using a prepared set of tissue microarrays (TMAs) residing in the Hawaii Tumor Registry (HTR), we performed standard immunohistochemistry. Breast cancers in the TMA were diagnosed in 1995, followed through 2006, and linked to SEER's main database. RESULTS: The TMA included 354 cases, representing 51% of 687 breast cancers in the HTR (1995). The HTR and TMA cases were similar with respect to patient demographics and tumor characteristics. Seventy-six percent (76%, 268 of 354) of TMA cases were HER2+ and/or ER+, i.e., 28 HER2+ER-, 12 HER2+ER+, and 228 HER2-ER+. There were 67 HER2-ER- cases and 19 were unclassified. Age distributions at diagnosis were bimodal with dominant early-onset modes for HER2+ER- tumors and dominant late-onset modes for HER2-ER+ breast cancers. Epidemiologic patterns for concordant HER2+ER+ (double-positive) and HER2-ER- (double-negative) were intermediate to discordant HER2+ER- and HER2-ER+. CONCLUSION: Results showed contrasting incidence patterns for HER2+ (HER2+ER-) and ER+ (HER2-ER+) breast cancers, diagnosed in 1995. Though sample sizes were small, this demonstration project validates the potential utility of the RTR for supplementing the SEER program.

Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions.

The complexity of biological systems provides for a great diversity of relationships between genes. The current analysis of whole-genome expression data focuses on relationships based on global correlation over a whole time-course, identifying clusters of genes whose expression levels simultaneously rise and fall. There are, of course, other potential relationships between genes, which are missed by such global clustering. These include activation, where one expects a time-delay between related expression profiles, and inhibition, where one expects an inverted relationship. Here, we propose a new method, which we call local clustering, for identifying these time-delayed and inverted relationships. It is related to conventional gene-expression clustering in a fashion analogous to the way local sequence alignment (the Smith-Waterman algorithm) is derived from global alignment (Needleman-Wunsch). An integral part of our method is the use of random score distributions to assess the statistical significance of each cluster. We applied our method to the yeast cell-cycle expression dataset and were able to detect a considerable number of additional biological relationships between genes, beyond those resulting from conventional correlation. We related these new relationships between genes to their similarity in function (as determined from the MIPS scheme) or their having known protein-protein interactions (as determined from the large-scale two-hybrid experiment); we found that genes strongly related by local clustering were considerably more likely than random to have a known interaction or a similar cellular role. This suggests that local clustering may be useful in functional annotation of uncharacterized genes. We examined many of the new relationships in detail. Some of them were already well-documented examples of inhibition or activation, which provide corroboration for our results. For instance, we found an inverted expression profile relationship between genes YME1 and YNT20, where the latter has been experimentally documented as a bypass suppressor of the former. We also found new relationships involving uncharacterized yeast genes and were able to suggest functions for many of them. In particular, we found a time-delayed expression relationship between J0544 (which has not yet been functionally characterized) and four genes associated with the mitochondria. This suggests that J0544 may be involved in the control or activation of mitochondrial genes. We have also looked at other, less extensive datasets than the yeast cell-cycle and found further interesting relationships. Our clustering program and a detailed website of clustering results is available at http://www.bioinfo.mbb.yale.edu/expression/cluster (or http://www.genecensus.org/expression/cluster).