Non-small cell lung cancer (NSCLC), which is comprised mainly of adenocarcinoma and squamous cell carcinoma (SCC), is the cause of 80% of all lung cancer deaths in the United States. NSCLC is also associated with a high rate of relapse after clinical treatment and, therefore, requires robust prognostic markers to better manage therapy options. The aim of this study was to identify microRNA (miRNA) expression profiles in SCC of the lung that would better predict prognosis. Total RNA from 61 SCC samples and 10 matched normal lung samples was processed for small RNA species and profiled on MirVana miRNA Bioarrays (version 2, Ambion). We identified 15 miRNAs that were differentially expressed between normal lung and SCC, including members of the miR-17-92 cluster and its paralogues. We also identified miRNAs, including miR-155 and let-7, which had previously been shown to have prognostic value in adenocarcinoma. Based on cross-fold validation analyses, miR-146b alone was found to have the strongest prediction accuracy for stratifying prognostic groups at f78%. The miRNA signatures were superior in predicting overall survival than a previously described 50-gene prognostic signature. Whereas there was no overlap between the mRNAs targeted by the prognostic miRNAs and the 50-gene expression signature, there was a significant overlap in the corresponding biological pathways, including fibroblast growth factor and interleukin-6 signaling. Our data indicate that miRNAs may have greater clinical utility in predicting the prognosis of patients with squamous cell lung carcinomas than mRNA-based signatures.

BackgroundMicroRNAs (MiRNAs) are short non-coding RNAs that control protein expression through various mechanisms. Their altered expression has been shown to be associated with various cancers. The aim of this study was to profile miRNA expression in colorectal cancer (CRC) and to analyze the function of specific miRNAs in CRC cells. MirVana miRNA Bioarrays were used to determine the miRNA expression profile in eight CRC cell line models, 45 human CRC samples of different stages, and four matched normal colon tissue samples. SW620 CRC cells were stably transduced with miR-143 or miR-145 expression vectors and analyzed in vitro for cell proliferation, cell differentiation and anchorage-independent growth. Signalling pathways associated with differentially expressed miRNAs were identified using a gene set enrichment analysis.ResultsThe expression analysis of clinical CRC samples identified 37 miRNAs that were differentially expressed between CRC and normal tissue. Furthermore, several of these miRNAs were associated with CRC tumor progression including loss of miR-133a and gain of miR-224. We identified 11 common miRNAs that were differentially expressed between normal colon and CRC in both the cell line models and clinical samples. In vitro functional studies indicated that miR-143 and miR-145 appear to function in opposing manners to either inhibit or augment cell proliferation in a metastatic CRC model. The pathways targeted by miR-143 and miR-145 showed no significant overlap. Furthermore, gene expression analysis of metastatic versus non-metastatic isogenic cell lines indicated that miR-145 targets involved in cell cycle and neuregulin pathways were significantly down-regulated in the metastatic context.ConclusionMiRNAs showing altered expression at different stages of CRC could be targets for CRC therapies and be further developed as potential diagnostic and prognostic analytes. The identified biological processes and signalling pathways collectively targeted by co-expressed miRNAs in CRC provide a basis for understanding the functional role of miRNAs in cancer.

At present, there is no method available to predict response to farnesyltransferase inhibitors (FTIs). We analyzed gene expression profiles from the bone marrow of patients from a phase 2 study of the FTI tipifarnib in older adults with previously untreated acute myeloid leukemia (AML). The RASGRP1/APTX gene expression ratio was found to predict response to tipifarnib with the greatest accuracy using a "leave one out" cross validation (LOOCV; 96%). RASGRP1 is a guanine nucleotide exchange factor that activates RAS, while APTX (aprataxin) is involved in DNA excision repair. The utility of this classifier for predicting response to tipifarnib was validated in an independent set of 58 samples from relapsed or refractory AML, with a negative predictive value (NPV) and positive predictive value (PPV) of 92% and 28%, respectively (odds ratio of 4.4). The classifier also predicted for improved overall survival (154 vs 56 days; P < .001), which was independent of other covariates, including a previously described prognostic gene expression classifier. Therefore, these data indicate that a 2-gene expression assay may have utility in categorizing a population of patients with AML who are more likely to respond to tipifarnib. IntroductionTipifarnib (R115777; ZARNESTRA, Janssen Ortho, Gurabo, Puerto Rico) was one of the first farnesyltransferase inhibitors (FTIs) to be tested in the clinic. 1 It has demonstrated significant activity in hematologic disorders, including acute myeloid leukemia (AML), multiple myeloma (MM), myelodysplastic syndrome (MDS), and chronic myelogenous leukemia (CML), with complete response rates in AML and MDS of up to approximately 15%. [2][3][4][5][6] FTIs function by competitively inhibiting the addition of a farnesyl moiety to a number of important signaling molecules, including RAS. 1,7 Historically, the mutation status of the RAS gene was considered to be a candidate biomarker for patient response to FTIs. This rationale was based on the finding that specific point mutations within RAS genes cause constitutive activation of the RAS pathway in many cancers, 8 and on preclinical evidence that FTIs could block RAS-transformed cells. 9,10 Since it is generally accepted that tumors are heavily reliant on the activation of 1 or 2 pathways ("oncogene addiction" hypothesis), it follows that patients whose tumors are promoted by aberrant expression of one or more components of those pathways should respond to drugs that inhibit those components. 11 However, pathways can be activated by multiple events, and it has been found that RAS can be up-regulated in the absence of activating RAS mutations. 12 Furthermore, no correlation between RAS mutations and response to FTIs has been demonstrated in clinical studies. 2,13 Indeed, while several early clinical studies focused on cancers that exhibited high frequencies of RAS mutations, the response rates to FTIs were disappointingly low in those trials. 3,14,15 Microarray technology has been used to identify gene expression profiles that are predictive of respo...

Currently there is no method available to predict response to farnesyltransferase inhibitors (FTI). We analyzed gene expression profiles from the bone marrow of patients from a phase 2 study of the FTI tipifarnib (R115777, ZARNESTRA), in older adults with previously untreated acute myeloid leukemia (AML). The RASGRP1:APTX gene expression ratio was found to predict response to tipifarnib with the greatest accuracy. This two-gene ratio was validated by quantitative PCR in the newly diagnosed AML cohort. We further demonstrated that this classifier could predict response to tipifarnib in an independent set of 54 samples from relapsed or refractory AML, with a negative predictive value and positive predictive value of 92% and 28%, respectively (odds ratio of 4.4). The classifier also predicted for improved overall survival (154 vs 56 days, p = 0.0001), which was shown to be independent of other prognostic factors including a previously described gene expression classifier predictive of prognosis. Therefore, these data indicate that a two-gene expression assay may have utility in categorizing a population of AML patients who are more likely to respond to tipifarnib.

<div>Abstract<p>Non–small cell lung cancer (NSCLC), which is comprised mainly of adenocarcinoma and squamous cell carcinoma (SCC), is the cause of 80% of all lung cancer deaths in the United States. NSCLC is also associated with a high rate of relapse after clinical treatment and, therefore, requires robust prognostic markers to better manage therapy options. The aim of this study was to identify microRNA (miRNA) expression profiles in SCC of the lung that would better predict prognosis. Total RNA from 61 SCC samples and 10 matched normal lung samples was processed for small RNA species and profiled on MirVana miRNA Bioarrays (version 2, Ambion). We identified 15 miRNAs that were differentially expressed between normal lung and SCC, including members of the miR-17-92 cluster and its paralogues. We also identified miRNAs, including miR-155 and <i>let-7,</i> which had previously been shown to have prognostic value in adenocarcinoma. Based on cross-fold validation analyses, miR-146b alone was found to have the strongest prediction accuracy for stratifying prognostic groups at ∼78%. The miRNA signatures were superior in predicting overall survival than a previously described 50-gene prognostic signature. Whereas there was no overlap between the mRNAs targeted by the prognostic miRNAs and the 50-gene expression signature, there was a significant overlap in the corresponding biological pathways, including fibroblast growth factor and interleukin-6 signaling. Our data indicate that miRNAs may have greater clinical utility in predicting the prognosis of patients with squamous cell lung carcinomas than mRNA-based signatures. [Cancer Res 2009;69(14):5776–83]</p></div>