Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial.

CONTEXT: Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. OBJECTIVE: To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. DESIGN: The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. RESULTS: The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. CONCLUSIONS: The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

Influence of tumor microenvironment on prognosis in colorectal cancer: Tissue architecture-dependent signature of endosialin (TEM-1) and associated proteins.

Tumor survival is influenced by interactions between tumor cells and the stromal microenvironment. One example is Endosialin (Tumor Endothelial Marker-1 (TEM-1) or CD248), which is expressed primarily by cells of mesenchymal origin and some tumor cells. The expression, as a function of architectural masking, of TEM-1 and its pathway-associated proteins was quantified and examined for association with five-year disease-specific survival on a colorectal cancer (CRC) cohort divided into training (n=330) and validation (n=164) sets. Although stromal expression of TEM-1 had prognostic value, a more significant prognostic signature was obtained through linear combination of five compartment-specific expression scores (TEM-1 Stroma, TEM-1 Tumor Vessel, HIF2α Stromal Vessel, Collagen IV Tumor, and Fibronectin Stroma). This resulted in a single continuous risk score (TAPPS: TEM-1 Associated Pathway Prognostic Signature) which was significantly associated with decreased survival on both the training set [HR=1.76 (95%CI: 1.44-2.15); p<0.001] and validation set [HR=1.38 (95%CI: 1.02-1.88); p=0.04]. Importantly, since prognosis is a critical clinical question in Stage II patients, the TAPPS score also significantly predicted survival in the Stage II patient (n=126) cohort [HR=1.75 (95%CI: 1.22-2.52); p=0.002] suggesting the potential of using the TAPPS score to assess overall risk in CRC patients, and specifically in Stage II patients.

Prognostic significance of FRA expression in epithelial cancers using AQUA(®) technology.

AIM: Although agents that target FRA have advanced through clinical trials, comprehensive analyses of FRA expression in epithelial cancers compared with clinical variables and prognosis are limited. MATERIALS & METHODS: FRA expression was examined in non-small-cell lung cancer (NSCLC), ovarian cancer and endometrial cancer cohorts using AQUA(®) technology. RESULTS: For the NSCLC cohort, FRA expression was significantly higher in adenocarcinoma samples (p < 0.001) than other histologies, and in females (p = 0.003) versus males. High FRA expression was significantly associated with better survival in NSCLC cases (p = 0.01) while significantly and independently associated with worse prognosis in endometrial (p < 0.001) and ovarian cancers (p < 0.001). CONCLUSION: These studies confirm the prognostic value of FRA in multiple indications. The opposing prognostic effects observed may suggest differential biology.

Tissue microarrays: leaping the gap between research and clinical adoption.

The use of tissue microarrays (TMAs) in the preclinical and translational research settings has become ubiquitous as they allow for high-throughput in situ biomarker analysis of hundreds of patient samples, with time and cost efficiency. Coupled with advanced imaging and image-analysis technologies that allow for objective and standardized biomarker expression assessment, TMAs have become critical tools for the development and validation of clinically meaningful biomarker diagnostic assays. However, their diagnostic use in the clinical laboratory setting is limited due to the need for conventional whole-section tissue assessment used for routine diagnostic purposes. In this article, after reviewing TMA basics and their translational and clinical research applications, we will focus on the use of TMAs for robust assay development and quality control in the clinical laboratory setting, as well as provide insights into how TMAs may serve well in the clinical setting as assay performance and quantification controls.

RTOG 0211: a phase 1/2 study of radiation therapy with concurrent gefitinib for newly diagnosed glioblastoma patients.

PURPOSE: To determine the safety and efficacy of gefitinib, an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, in combination with radiation for newly diagnosed glioblastoma (GBM) patients. METHODS AND MATERIALS: Between March 21, 2002, and May 3, 2004, Radiation Therapy Oncology Group (RTOG) 0211 enrolled 31 and 147 GBM patients in the phase 1 and 2 arms, respectively. Treatment consisted of daily oral gefinitnib started at the time of conventional cranial radiation therapy (RT) and continued post RT for 18 months or until progression. Tissue microarrays from 68 cases were analyzed for EGFR expression. RESULTS: The maximum tolerated dose (MTD) of gefitinib was determined to be 500 mg in patients on non-enzyme-inducing anticonvulsant drugs (non-EIAEDs). All patients in the phase 2 component were treated at a gefitinib dose of 500 mg; patients receiving EIADSs could be escalated to 750 mg. The most common side effects of gefitinib in combination with radiation were dermatologic and gastrointestinal. Median survival was 11.5 months for patients treated per protocol. There was no overall survival benefit for patients treated with gefitinib + RT when compared with a historical cohort of patients treated with RT alone, matched by RTOG recursive partitioning analysis (RPA) class distribution. Younger age was significantly associated with better outcome. Per protocol stratification, EGFR expression was not found to be of prognostic value for gefitinib + RT-treated patients. CONCLUSIONS: The addition of gefitinib to RT is well tolerated. Median survival of RTOG 0211 patients treated with RT with concurrent and adjuvant gefitinib was similar to that in a historical control cohort treated with radiation alone.

Molecular classification of nonsmall cell lung cancer using a 4-protein quantitative assay.

BACKGROUND: The importance of definitive histological subclassification has increased as drug trials have shown benefit associated with histology in nonsmall-cell lung cancer (NSCLC). The acuity of this problem is further exacerbated by the use of minimally invasive cytology samples. Here we describe the development and validation of a 4-protein classifier that differentiates primary lung adenocarcinomas (AC) from squamous cell carcinomas (SCC). METHODS: Quantitative immunofluorescence (AQUA) was employed to measure proteins differentially expressed between AC and SCC followed by logistic regression analysis. An objective 4-protein classifier was generated to define likelihood of AC in a training set of 343 patients followed by validation in 2 independent cohorts (n = 197 and n = 235). The assay was then tested on 11 cytology specimens. RESULTS: Statistical modeling selected thyroid transcription factor 1 (TTF1), CK5, CK13, and epidermal growth factor receptor (EGFR) to generate a weighted classifier and to identify the optimal cutpoint for differentiating AC from SCC. Using the pathologist's final diagnosis as the criterion standard, the molecular test showed a sensitivity of 96% and specificity of 93%. Blinded analysis of the validation sets yielded sensitivity and specificity of 96% and 97%, respectively. Our assay classified the cytology specimens with a specificity of 100% and sensitivity of 87.5%. CONCLUSIONS: Molecular classification of NSCLC using an objective quantitative test can be highly accurate and could be translated into a diagnostic platform for broad clinical application.

Company Profile: HistoRx: tissue-based diagnostic solutions for personalized medicine.

The mission of HistoRx (CT, USA) is to develop and commercialize clinically actionable immunohistochemical (IHC) biomarker assays with the distinctive characteristic of being as quantitative, objective and standardized as the measurement of glucose in blood. In clinical laboratory practice, serum glucose measurement is expected to give the same result regardless of the laboratory or operator performing the test, and the same should be true for tissue-based IHC tests in anatomical pathology. By offering such improvements to standard IHC analysis using reproducible AQUA® assays, patients will benefit from standardized results for determination of appropriate targeted therapeutic drug treatment.

Tissue microarrays and quantitative tissue-based image analysis as a tool for oncology biomarker and diagnostic development.

INTRODUCTION: Translational oncology has been improved by using tissue microarrays (TMAs), which facilitate biomarker analysis of large cohorts on a single slide. This has allowed for rapid analysis and validation of potential biomarkers for prognostic and predictive value, as well as for evaluation of biomarker prevalence. Coupled with quantitative analysis of immunohistochemical (IHC) staining, objective and standardized biomarker data from tumor samples can further advance companion diagnostic approaches for the identification of drug-responsive or resistant patient subpopulations. AREAS COVERED: This review covers the advantages, disadvantages and applications of TMAs for biomarker research. Research literature and reviews of TMAs and quantitative image analysis methodology have been surveyed for this review (with an AQUA® analysis focus). Applications such as multi-marker diagnostic development and pathway-based biomarker subpopulation analyses are described. EXPERT OPINION: Tissue microarrays are a useful tool for biomarker analyses including prevalence surveys, disease progression assessment and addressing potential prognostic or predictive value. By combining quantitative image analysis with TMAs, analyses will be more objective and reproducible, allowing for more robust IHC-based diagnostic test development. Quantitative multi-biomarker IHC diagnostic tests that can predict drug response will allow for greater success of clinical trials for targeted therapies and provide more personalized clinical decision making.

Heterogeneity mapping of protein expression in tumors using quantitative immunofluorescence.

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of areas of distinct differentiation into recognized histological subtypes, or different pathological grade, often there are more subtle differences in phenotype which defy accurate classification (Figure 1). Ultimately, since morphology is dictated by the underlying molecular phenotype, areas with visible differences are likely to be accompanied by differences in the expression of proteins which orchestrate cellular function and behavior, and therefore, appearance. The significance of visible and invisible (molecular) heterogeneity for prognosis is unknown, but recent evidence suggests that, at least at the genetic level, heterogeneity exists in the primary tumor(1,2), and some of these sub-clones give rise to metastatic (and therefore lethal) disease. Moreover, some proteins are measured as biomarkers because they are the targets of therapy (for instance ER and HER2 for tamoxifen and trastuzumab (Herceptin), respectively). If these proteins show variable expression within a tumor then therapeutic responses may also be variable. The widely used histopathologic scoring schemes for immunohistochemistry either ignore, or numerically homogenize the quantification of protein expression. Similarly, in destructive techniques, where the tumor samples are homogenized (such as gene expression profiling), quantitative information can be elucidated, but spatial information is lost. Genetic heterogeneity mapping approaches in pancreatic cancer have relied either on generation of a single cell suspension(3), or on macrodissection(4). A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue(5), providing proof of principle that morphology and molecular mapping is feasible, but falling short of quantifying the heterogeneity. Since immunohistochemistry is, at best, only semi-quantitative and subject to intra- and inter-observer bias, more sensitive and quantitative methodologies are required in order to accurately map and quantify tissue heterogeneity in situ. We have developed and applied an experimental and statistical methodology in order to systematically quantify the heterogeneity of protein expression in whole tissue sections of tumors, based on the Automated QUantitative Analysis (AQUA) system(6). Tissue sections are labeled with specific antibodies directed against cytokeratins and targets of interest, coupled to fluorophore-labeled secondary antibodies. Slides are imaged using a whole-slide fluorescence scanner. Images are subdivided into hundreds to thousands of tiles, and each tile is then assigned an AQUA score which is a measure of protein concentration within the epithelial (tumor) component of the tissue. Heatmaps are generated to represent tissue expression of the proteins and a heterogeneity score assigned, using a statistical measure of heterogeneity originally used in ecology, based on the Simpson's biodiversity index(7). To date there have been no attempts to systematically map and quantify this variability in tandem with protein expression, in histological preparations. Here, we illustrate the first use of the method applied to ER and HER2 biomarker expression in ovarian cancer. Using this method paves the way for analyzing heterogeneity as an independent variable in studies of biomarker expression in translational studies, in order to establish the significance of heterogeneity in prognosis and prediction of responses to therapy.

EGFR protein expression in non-small cell lung cancer predicts response to an EGFR tyrosine kinase inhibitor--a novel antibody for immunohistochemistry or AQUA technology.

INTRODUCTION: Epidermal growth factor receptor (EGFR) protein expression in non-small cell lung cancer (NSCLC) is not recommended for predicting response to EGFR tyrosine kinase inhibitors (TKI) due to conflicting results, all using antibodies detecting EGFR external domain (ED). We tested the predictive value of EGFR protein expression for response to an EGFR TKI with an antibody that detects the intracellular domain (ID) and compared fluorescence-based Automated QUantitative Analysis (AQUA) technology to immunohistochemistry (IHC). METHODS: Specimens from 98 gefitinib-treated NSCLC Japanese patients were evaluated by IHC (n = 98 of 98) and AQUA technology (n = 70 of 98). EGFR ID (5B7)- and ED-specific antibodies (3C6 and 31G7) were compared. RESULTS: EGFR expression evaluated with 5B7 was significantly higher in responders versus nonresponders to gefitinib both with IHC and with AQUA. ED-specific antibodies did not significantly predict response. Using AQUA and ID-specific antibody resulted in the best prediction performance with a positive and negative predictive value (PPV/NPV) for responders of 50% and 87%, respectively. EGFR expression with ID-specific antibody and AQUA also predicted responders in EGFR-mutated patients. Increased EGFR expression with the ID antibody is associated with increased median progression free survival (PFS; 11.7 months vs. 5.0, log rank, P = 0.034) and overall survival (OS; 38.6 vs. 14.9, P = 0.040) from gefitinib therapy. CONCLUSIONS: EGFR protein expression using an ID-specific antibody specifically predicts response to gefitinib in NSCLC patients, including in EGFR-mutated patients, and increased PFS/OS from gefitinib. These data suggest that the choice of diagnostic antibody and methodology matters to predict response and outcome to specific therapies. The potential clinical application needs further validation. Clin Cancer Res; 17(24); 7796-807. ©2011 AACR.

Evaluation of prognostic and predictive value of microtubule associated protein tau in two independent cohorts.

INTRODUCTION: Microtubule associated proteins (MAPs) endogenously regulate microtubule stabilization and have been reported as prognostic and predictive markers for taxane response. The microtubule stabilizer, MAP-tau, has shown conflicting results. We quantitatively assessed MAP-tau expression in two independent breast cancer cohorts to determine prognostic and predictive value of this biomarker. METHODS: MAP-tau expression was evaluated in the retrospective Yale University breast cancer cohort (n = 651) using tissue microarrays and also in the TAX 307 cohort, a clinical trial randomized for TAC versus FAC chemotherapy (n = 140), using conventional whole tissue sections. Expression was measured using the AQUA method for quantitative immunofluorescence. Scores were correlated with clinicopathologic variables, survival, and response to therapy. RESULTS: Assessment of the Yale cohort using Cox univariate analysis indicated an improved overall survival (OS) in tumors with a positive correlation between high MAP-tau expression and overall survival (OS) (HR = 0.691, 95% CI = 0.489-0.974; P = 0.004). Kaplan Meier analysis showed 10-year survival for 65% of patients with high MAP-tau expression compared to 52% with low expression (P = .006). In TAX 307, high expression was associated with significantly longer median time to tumor progression (TTP) regardless of treatment arm (33.0 versus 23.4 months, P = 0.010) with mean TTP of 31.2 months. Response rates did not differ by MAP-tau expression (P = 0.518) or by treatment arm (P = 0.584). CONCLUSIONS: Quantitative measurement of MAP-tau expression has prognostic value in both cohorts, with high expression associated with longer TTP and OS. Differences by treatment arm or response rate in low versus high MAP-tau groups were not observed, indicating that MAP-tau is not associated with response to taxanes and is not a useful predictive marker for taxane-based chemotherapy.

Automated analysis of tissue microarrays.

The analysis of protein expression in tissue by immunohistochemistry (IHC) presents three significant challenges. They are (1) the time-consuming nature of pathologist-based scoring of slides; (2) the need for objective quantification and localization of protein expression; and (3) the need for a highly reproducible measurement to limit intra- and inter-observer variability. While there are a variety of commercially available platforms for automated chromagen-based and fluorescence-based image acquisition of tissue microarrays, this chapter is focused on the analysis of fluorescent images by AQUA(R) analysis (Automated QUantitative Analysis) and the solutions offered by such a method for research and diagnostics. AQUA analysis is a method for molecularly defining regions of interest or "compartments" within a tissue section. The methodology can be utilized with tissue microarrays to provide rapid, quantitative, localized, and reproducible protein expression data that can then be used to identify statistically relevant correlations in populations. Ultimately this allows for a multiplexed, objective and standardized quantitative approach for biomarker research and diagnostic assay development for protein expression in tissue.

Molecular analysis of non-small cell lung cancer identifies subsets with different sensitivity to insulin-like growth factor I receptor inhibition.

PURPOSE: This study aimed to identify molecular determinants of sensitivity of non-small cell lung cancer (NSCLC) to anti-insulin-like growth factor receptor (IGF-IR) therapy. EXPERIMENTAL DESIGN: A total of 216 tumor samples were investigated, of which 165 consisted of retrospective analyses of banked tissue and an additional 51 were from patients enrolled in a phase II study of figitumumab, a monoclonal antibody against IGF-IR, in stage IIIb/IV NSCLC. Biomarkers assessed included IGF-IR, epidermal growth factor receptor, IGF-II, IGF-IIR, insulin receptor substrate 1 (IRS-1), IRS-2, vimentin, and E-cadherin. Subcellular localization of IRS-1 and phosphorylation levels of mitogen-activated protein kinase and Akt1 were also analyzed. RESULTS: IGF-IR was differentially expressed across histologic subtypes (P = 0.04), with highest levels observed in squamous cell tumors. Elevated IGF-IR expression was also observed in a small number of squamous cell tumors responding to chemotherapy combined with figitumumab (P = 0.008). Because no other biomarker/response interaction was observed using classical histologic subtyping, a molecular approach was undertaken to segment NSCLC into mechanism-based subpopulations. Principal component analysis and unsupervised Bayesian clustering identified three NSCLC subsets that resembled the steps of the epithelial to mesenchymal transition: E-cadherin high/IRS-1 low (epithelial-like), E-cadherin intermediate/IRS-1 high (transitional), and E-cadherin low/IRS-1 low (mesenchymal-like). Several markers of the IGF-IR pathway were overexpressed in the transitional subset. Furthermore, a higher response rate to the combination of chemotherapy and figitumumab was observed in transitional tumors (71%) compared with those in the mesenchymal-like subset (32%; P = 0.03). Only one epithelial-like tumor was identified in the phase II study, suggesting that advanced NSCLC has undergone significant dedifferentiation at diagnosis. CONCLUSION: NSCLC comprises molecular subsets with differential sensitivity to IGF-IR inhibition.

Analytic variability in immunohistochemistry biomarker studies.

BACKGROUND: Despite the widespread use of immunohistochemistry (IHC), there are no standardization guidelines that control for antibody probe variability. Here we describe the effect of variable antibody reagents in the assessment of cancer-related biomarkers by IHC. METHODS: Estrogen receptor (ER), epidermal growth factor receptor (EGFR) 1, and human epidermal growth factor receptor 3 (HER3) were evaluated by quantitative immunofluorescence. Correlations between ER clones 1D5, SP1, F10, and ER60c, and EGFR monoclonal 31G7, 2-18C9, H11, and 15F8, and polyclonal 2232 antibodies were assessed in 642 breast cancer patients. HER3 was measured by RTJ1, RTJ2, SGP1, M7297, RB-9211, and C-17 antibodies in 42 lung cancer patients. Survival analysis was done with the use of multiple cutoff points to reveal any prognostic classification. RESULTS: All ER antibodies were tightly correlated (Pearson's r(2) = 0.94-0.96; P < 0.0001) and western blotting confirmed their specificity in MCF-7 and BT474 cells. All EGFR antibodies but 2232 yielded specific results in western blotting; however, only 31G7 and 2-18C9 were strongly associated (Pearson's r(2) = 0.61; P < 0.0001). HER3 staining was nonspecific and nonreproducible. High EGFR-expressing patients had a worse prognosis when EGFR was measured with H11 or 31G7 (log rank P = 0.015 and P = 0.06). There was no statistically significant correlation between survival and EGFR detected by 2-18C9, 15F8, or polyclonal 2232 antibodies. CONCLUSIONS: Antibody validation is a critical analytic factor that regulates IHC readings in biomarker studies. Evaluation of IHC proficiency and quality control are key components toward IHC standardization. IMPACT: This work highlights the importance of IHC standardization and could result in the improvement of clinically relevant IHC protocols.

Standardization of HER2 immunohistochemistry in breast cancer by automated quantitative analysis.

CONTEXT: There is critical need for standardization of HER2 immunohistochemistry testing in the clinical laboratory setting. Recently, the American Society of Clinical Oncology and the College of American Pathologists have submitted guidelines recommending that laboratories achieve 95% concordance between assays and observers for HER2 testing. OBJECTIVE: As a potential aid to pathologists for achieving these new guidelines, we have conducted an examination using automated quantitative analysis (AQUA analysis) to provide a standardized HER2 immunohistochemistry expression score across instruments (sites), operators, and staining runs. DESIGN: We analyzed HER2 expression by immunohistochemistry in a cohort (n = 669) of invasive breast cancers in tissue microarray format across different instruments (n = 3), operators (n = 3), and staining runs (n = 3). Using light source, instrument calibration techniques, and a new generation of image analysis software, we produced normalized AQUA scores for each parameter and examined their reproducibility. RESULTS: The average percent coefficients of variation across instruments, operators, and staining runs were 1.8%, 2.0%, and 5.1%, respectively. For positive/negative classification between parameters, concordance rates ranged from 94.5% to 99.3% for all cases. Differentially classified cases only occurred around the determined cut point, not over the entire distribution. CONCLUSIONS: These data demonstrate that AQUA analysis can provide a standardized HER2 immunohistochemistry test that can meet current guidelines by the American Society of Clinical Oncology/College of American Pathologists. The use of AQUA analysis could allow for standardized and objective HER2 testing in clinical laboratories.

Development of an unsupervised pixel-based clustering algorithm for compartmentalization of immunohistochemical expression using Automated QUantitative Analysis.

Inherent to most tissue image analysis routines are user-defined steps whereby specific pixel intensity thresholds must be set manually to differentiate background from signal-specific pixels within multiple images. To reduce operator time, remove operator-to-operator variability, and to obtain objective and optimal pixel separation for each image, we have developed an unsupervised pixel-based clustering algorithm allowing for the objective and unsupervised differentiation of signal from background, and differentiation of compartment-specific pixels on an image-by-image basis. We used the Automated QUantitative Analysis (AQUA) platform, a well-established automated fluorescence-based immunohistochemistry image analysis platform used for quantification of protein expression in specific cellular compartments to demonstrate utility of this methodology. As a metric for cellular compartmentalization, we examined correlation of percentage nuclear volume with histologic grade in 3 serial sections of a large cohort (n=669) of invasive breast cancer samples. We observed a significant (P=0.002, 0.006, and 0.08) difference in mean percentage nuclear volume between low and high-grade tumors. Reproducibility of percentage nuclear volume was also significant (P<0.001) across 3 serial sections. We then quantified compartment-specific expression of 5 biomarkers in 3 cancer types for association with outcome: estrogen receptor (nuclear), progesterone receptor (nuclear), HER2 (membrane/cytoplasm), ERCC1 (nuclear), and PTEN (cytoplasm). All 5 markers showed an expected and significant (P<0.05) association with survival. This new clustering algorithm thus produces accurate and precise compartmentalization for assessment of target gene expression, and will enhance the efficiency and objectivity of the current Automated QUantitative Analysis and other image analysis platform.

AQUA analysis of thymidylate synthase reveals localization to be a key prognostic biomarker in 2 large cohorts of colorectal carcinoma.

CONTEXT: Increased thymidylate synthase expression is a marker for decreased survival in colorectal cancer. OBJECTIVE: Thymidylate synthase localizes to both the nucleus and cytoplasm, but how the relationship of these expression levels affects colon cancer outcome has yet to be determined. DESIGN: Using AQUA, we assessed prognosis of thymidylate synthase expression as a function of subcellular localization in 2 retrospective cohorts of colorectal carcinoma. We used the first cohort (n = 599) as a training set, subsequently validating optimal expression cut points in the second cohort (n = 447). RESULTS: A significant association between decreased 5-year disease-specific survival and increased nuclear expression (16% decreased survival [72% to 56%] for the top 60% of nuclear-expressing tumors [P < .001]) and cytoplasmic expression (12% decreased survival [70% to 58%] for the top 54% of cytoplasmic-expressing tumors [P = .02]) was observed for the training set. A higher nuclear to cytoplasmic ratio also correlated significantly with decreased survival (15% decreased survival [66% to 51%] for the top 25% of tumors [P < .001]). Applying these findings to the validation set, as a function of time to recurrence, only the ratio (P = .03 [expression ratio]; P = .18 [nuclear]; P = .71 [cytoplasmic]) showed a significant association with decreased time to recurrence. Additionally, the expression ratio significantly added to the prognostic value given by the primary tumor pathologic classification and nodal status. CONCLUSIONS: These data suggest the relationship of nuclear to cytoplasmic thymidylate synthase expression, given as a ratio of continuous AQUA scores, to be a strong predictor of colon cancer survival.

Tcf binding sequence and position determines beta-catenin and Lef-1 responsiveness of MMP-7 promoters.

The matrix metalloproteinase-7 (MMP-7) gene is a target of beta-catenin transactivation. Expression of the T-cell factor, Lef-1, enhances transcriptional activation of the human MMP-7 promoter by beta-catenin, but represses activation of the mouse MMP-7 promoter, both activities through consensus Tcf binding sites. The mouse promoter has a single Tcf binding element (mTBE) located downstream of the transcriptional start site, while the human promoter has two Tcf binding elements (hTBE1, hTBE2), both located upstream of the transcriptional start. hTBE1 and hTBE2 also differ in sequence from mTBE. Here we demonstrate that positioning of mTBE, upstream or downstream of the transcriptional start site dictated whether Lef-1 functioned as an activator or repressor, respectively. Sequence differences between mTBE and hTBE sites determined the potency of these activities, with hTBE sites being weaker. Mutational analysis of mTBE showed that increased Lef-1 activity mapped to G . C base pairings at 5' and 3' ends, and correlated with a threefold increase in Lef-1 binding affinity in vitro. Heterologous promoters with high affinity binding sites were 115-fold more responsive to beta-catenin than those with low affinity sites. Converting low affinity Tcf binding sites to high affinity sites increased beta-catenin responsiveness of the mouse and human promoters by 2-3 fold, and ectopic expression of Lef-1 increased beta-catenin responsiveness for promoters with low affinity binding sequences. We concluded that sequence and position of Tcf binding sites can determine the extent of beta-catenin-Lef-1 responsiveness for beta-catenin target genes.