Jason Christiansen

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. Clin Cancer Res; 16(18); 4654–65. ©2010 AACR.

Standardization of HER2 Immunohistochemistry in Breast Cancer by Automated Quantitative Analysis

CONTEXTnThere 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.nnnOBJECTIVEnAs 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.nnnDESIGNnWe 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.nnnRESULTSnThe 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.nnnCONCLUSIONSnThese 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.

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.

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 tumor1,2, 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 suspension3, or on macrodissection4. A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue5, 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) system6. 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 Simpsons biodiversity index7. 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.

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

CONTEXTnHormone 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.nnnOBJECTIVEnTo 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.nnnDESIGNnThe 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.nnnRESULTSnThe 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.nnnCONCLUSIONSnThe 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.

Genomic profiling of ER+ breast cancers after short-term estrogen suppression reveals alterations associated with endocrine resistance

Genomic profiling of ER+/HER2− breast tumors after short-term estrogen deprivation revealed alterations associated with intrinsic resistance and provided mechanistic insights. A patient look at cancer resistance A variety of drugs that inhibit estrogen signaling are in use for breast cancer, but patients often develop resistance to these treatments. To understand how this resistance develops, Giltnane et al. evaluated 143 patients who were receiving the aromatase inhibitor letrozole to block estrogen signaling before undergoing surgery for breast cancer. By performing genomic analysis on these patients’ tumors, the authors were able to identify not only changes in gene expression and estrogen receptor gene fusions that correlated with resistance to therapy but also potential leads for future treatments that could help overcome this resistance. Inhibition of proliferation in estrogen receptor–positive (ER+) breast cancers after short-term antiestrogen therapy correlates with long-term patient outcome. We profiled 155 ER+/human epidermal growth factor receptor 2–negative (HER2−) early breast cancers from 143 patients treated with the aromatase inhibitor letrozole for 10 to 21 days before surgery. Twenty-one percent of tumors remained highly proliferative, suggesting that these tumors harbor alterations associated with intrinsic endocrine therapy resistance. Whole-exome sequencing revealed a correlation between 8p11-12 and 11q13 gene amplifications, including FGFR1 and CCND1, respectively, and high Ki67. We corroborated these findings in a separate cohort of serial pretreatment, postneoadjuvant chemotherapy, and recurrent ER+ tumors. Combined inhibition of FGFR1 and CDK4/6 reversed antiestrogen resistance in ER+ FGFR1/CCND1 coamplified CAMA1 breast cancer cells. RNA sequencing of letrozole-treated tumors revealed the existence of intrachromosomal ESR1 fusion transcripts and increased expression of gene signatures indicative of enhanced E2F-mediated transcription and cell cycle processes in cancers with high Ki67. These data suggest that short-term preoperative estrogen deprivation followed by genomic profiling can be used to identify druggable alterations that may cause intrinsic endocrine therapy resistance.

Abstract P4-08-02: A Comparison between AQUA Quantitative Fluorescent Immunohistochemistry and Conventional Immunohistochemistry for Hormone Receptors

Background: We have previous data showing that quantitation of hormone receptors can be highly informative in determining risk of early relapse in ER positive early breast cancer treated with tamoxifen or exemestane. Both quantitative immunohistochemistry (QIHC) and flouresecent immunohistochemistry (F-IHC as measured by AQUA technology) are highly prognostic over a wide expression range. We have explored the results of both assays to determine if current assays provide maximum information using current approaches. Patients & Methods: Pathology blocks from 4598 TEAM patients were collected and tissue microarrays constructed. Quantitative AQUA and IHC analysis (using image quantitation) of ER and PgR was performed centrally (Edinburgh & HistoRx). Results from both assays were compared and their prognostic impact on DFS at 2.75 years examined. Results: Both AQUA and QIHC demonstrated linear relationships between intensity of staining for either ER or PgR and DFS at 2.75 years. For both PgR and ER AQUA provided significantly greater prognostic information that QIHC. However AQUA staining explained only 29% and 68% of the variability in ER and PgR QIHC results by logistic regression. Using both AQUA and QIHC data in a forward stepwise selection survival model demonstrated that AQUA and QIHC provided similar prognostic information over 70% and 50% of the range for ER and PgR respectively. High ER QIHC and low ER AQUA scores, and low PgR IHC and high PgR AQUA scores provided prognostic information unique to either platform. Conclusion: Both QIHC and AQUA analysis of HR expression provides significant and highly important information on DFS risk in early breast cancer. It appears that these two platforms provide overlapping prognostic information and that the range of ER and PgR expression which impacts patient outcome is wider than measured by either system alone. Further investigation of the clinical significance of this broader range of hormone receptor expression in treatment decisions is warranted. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P4-08-02.

Preliminary Comparison between AQUA and Centralised ER/PgR Analysis within the TEAM Pathology Study.

Background : The Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial identified quantitative relationships between ER/PgR (Can Res 69:83S) and patient prognosis, and suggested that high ER expression might be predictive for benefit from aromatase inhibitors. Conventional immunohistochemical assays, even when performed centrally, appear to be more variable than fluorescent immunohistochemical assays analysed by automated image analysis such as the AQUA system. However, output from the two systems may provide subtly different results for biomarkers. We have performed analysis of ER/PgR using AQUA technology in the TEAM pathology cohort and will present data on the full anlaysis of ER/PgR by central IHC and AQUA in 4600 cases. Preliminary data on ER (using a single TMA core) are included in the current analysis.Patients & Methods: TMAs from 4598 TEAM patients were analysed by AQUA and image analysis using established algorithms performed. Comparisons between IHC and AQUA were performed and outcome data will be analysed and presented.Results: Interim AQUA results from a single core were available from 2319 TEAM cases. Analysis of results suggested incremental increases in AQUA scores across conventional Allred categories (2-8). Regression analysis suggests that multiple cores may be required adequate representation of ER expression. PgR data will be presented.Conclusion: Initial comparison between AQUA and ER expression by central IHC suggests these methods are comparable. Further data is required to assess the potential value of AQUA scores and will be presented. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3045.