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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.

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 r2 = 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 r2 = 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. Cancer Epidemiol Biomarkers Prev; 19(4); 982–91. ©2010 AACR.

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Background Computational pathology-based methods, eg, Quantitative Continuous Scoring (QCS) [Gustavson, SABCS 2020], are built to provide objective and quantitative methods to assess HER2 expression in breast cancer (BC). For accurate HER2 quantification, it is important to exclude non-invasive epithelium from analysis since HER2 overexpression could be more frequent in ductal carcinoma in situ (DCIS) and pleomorphic lobular carcinoma in situ (PLCIS) than in invasive BC [Lari, J Cancer 2011]. Generally, computational pathology-based approaches require experts to delineate the invasive BC regions of interest for analysis and exclude all benign/non-invasive epithelium. Developing a tool that delineates the invasive BC regions automatically without human intervention to avoid subjectivity of manual annotation by pathologists is ideal. We developed a novel, deep-learning–based system, called DualScaleNet, to perform Automated Region segmentation of Tumor (ART) by automatically identifying the invasive BC regions and excluding benign/non-invasive epithelium on HER2-stained digitized images. Identification and diagnosis of these regions, especially the in situ tumors are a challenge as they can mimic benign and invasive lesions causing wrong HER2 evaluation. Additional stains (eg, p63 for myoepithelial cells or laminin for basement membrane) are often required for diagnosis [Pinder, Mod Pathol 2010] but were not available for this study. Methods DualScaleNet works simultaneously on HER2-stained immunohistochemistry (IHC) image patches at 2 different resolutions. The target branch uses a higher resolution RGB image (0.5 μm/pixel) to learn accurate local details; the context branch uses a lower resolution image (4.0 μm/pixel) to incorporate more context in visual learning. The algorithm generates 4 output image layers representing probabilities of 4 classes: invasive tumor, ductal/lobular carcinoma in situ, benign epithelium, and other tissue. The final segmentation result is generated by assigning each image pixel the class with the largest probability value. The algorithm was trained using ground truth (GT) annotations generated by 5 pathologists using 6157 square field of views (FOVs), 200-500 μm in size. These FOVs were collected from 850 whole slide images (WSI), spanning 9 commercial BC sample cohorts stained with different HER2 assays and scanned by several versions of the Aperio AT2 scanner. The samples included a mixture of biopsies and resections and covered different BC histologies and HER2 staining intensities. To evaluate the reproducibility of tumor area detection by human pathologists, an interpathologist comparison in detection of invasive tumor regions was performed using 225 FOVs annotated by multiple pathologists. Results Analysis generated an average Dice/F1 Score of 81.6% among different pathologists for invasive cancer. On the same sample set (independent of the ART training set), the invasive cancer detection by the ART algorithm was on par with human pathologists, achieving a similar average Dice/F1 score of 80.7%. Conclusions Novel deep learning-based ART algorithm provides accurate segmentation of invasive cancer on HER2-stained IHC images. The performance was verified against the GT annotations provided by multiple pathologists. Since the algorithm is trained using annotations from multiple pathologists, it is not possible to generate higher accuracy with computational pathology than is achievable between independent pathologists. Importantly, the same WSI read by the ART will consistently output the exact same tumor region identification result thus removing the inherent human subjectivity and variability, while improving the turnaround time for analysis. This development serves as the necessary foundation upon which a computational pathology-based diagnostic can be built. Citation Format: Ansh Kapil, Anatoliy Shumilov, Philipp Wortmann, Sihem Khelifa, Jessica Chan, Michel Vandenberghe, Craig Barker, Mark Gustavson, Danielle Carroll, Hadassah Sade, Günter Schmidt. ART: Automated Region segmentation of Tumor on HER2-stained breast cancer tissue [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-16.

Background HER2 directed therapies for breast cancer (BC) rely on accurate estimation of HER2 expression by pathologist scoring of immunohistochemically (IHC) stained tissue according to ASCO/CAP guidelines Emerging HER2-targeted antibody drug conjugates (ADCs) like trastuzumab deruxtecan (T-DXd), have demonstrated efficacy in the HER2-low (IHC 1+ or IHC 2+/ISH-) population (Modi, NEJM 2022). A deeper understanding of the spectrum of HER2 expression and its spatial distribution could provide insights about the mode of action of ADCs, including potential bystander activity. Computational pathology-based methods like Quantitative Continuous Scoring (QCS) can help here by objectively quantifying HER2 expression levels on a per cell basis from digitized HER2 IHC slides (Gustavson, SABCS 2020). We applied QCS to a cohort of HER2-negative (HER2-neg) patients (pts) from a retrospective study (NCT04807595) to quantify the prevalence of HER2 expression in this population and investigate the relationship with manual scoring. Methods To analyze the prevalence of HER2 expression in the HER2-neg population, we used available digital images (N=207 pts) from retrospectively rescored HER2 slides from tumors categorized as HER2-low or IHC 0 (IHC 0 or >0< 1+). QCS algorithm was applied to perform an instance segmentation of each tumor cell into the membrane, cytoplasmic and nuclear sub-compartments. HER2 expression levels on the membrane were estimated from a Hue-Saturation-Density model (Van der Laak, JQCS 2000) in terms of optical density (OD). Descriptive statistics and spatial modelling were used to aggregate cell-level information to a slide level score using the membrane OD values and tumor cell locations. A novel Spatial Proximity Score (SPS) was used to mathematically model the proportion of tumor cells that could potentially be targeted either directly or via bystander activity of ADCs. The analysis is ongoing, complete results with additional patient data to be presented. Analytical validation of the QCS algorithm demonstrated high correlation between OD values as measured on the automatically detected membranes from QCS and those measured on consolidated manual membrane annotations (N=2157 cells) from three pathologists (R = 0.993). This is very similar to the correlation observed between individual pathologists (R = 0.995). Results In the analyzed cohort (N=207), median OD of HER2-low tumors was significantly higher compared to IHC 0 tumors (one-sided Wilcoxon p-value < 0.001). A significant increase of OD values was observed for increasing IHC categories from 0 through >0 < 1+ and 1+ to 2+/ISH- (one-sided Jonckheere-Terpstra p-value < 0.001). OD values within each IHC category showed considerable variability, particularly in IHC 1+ and IHC 2+. In 49% of pts (N=101), greater than 88% of tumor cells expressed HER2 at any intensity (OD≥10). Among the remaining 106 pts, the number of potentially ADC-susceptible cells (within 25μm radius of HER2 expressing cells) as estimated by SPS was at least double the amount estimated by single cell-based scores alone in 45 cases (42%) and increased by at least 50% in another 12 cases (11%). Conclusions Computational approaches such as QCS can help us to objectively characterize the spectrum and spatial distribution of HER2 expression. These mathematical models contribute to our understanding of potential mechanisms of action of ADCs. While this study confirmed a general association of QCS-based scores with manual IHC categories, we also saw considerable variation, as some IHC 1+ or 2+ samples had low OD. Building on these and other promising initial results (Gustavson et al, SABCS 2020), we will further explore clinical relevance of QCS-based scoring. Eventually, digital scoring may be able to define data-driven signatures to select HER2-low pts that might benefit from HER2 targeted therapies. Citation Format: Andreas Spitzmüller, Ansh Kapil, Anatoliy Shumilov, Jessica Chan, Lemonia Konstantinidou, Zonera Hassan, Mark Gustavson, Danielle Carroll, Della Varghese, Gareth D. James, Akira Moh, Andrew Livingston, Victoria de Giorgio-Miller. Computational pathology based HER2 expression quantification in HER2-low breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-03.

Background: HER2 overexpression is an established molecular target in breast and gastric cancer therapy, but no HER2-targeted therapies are currently available for NSCLC. In studies of NSCLC and lung cancer overall, the prevalence of HER2 overexpression has been reported to be anywhere from Methods: DESTINY-Lung03 (NCT04686305) is a phase 1b, open-label, multicenter, dose-escalation (part 1) and dose-expansion (part 2) study in pts with HER2-overexpressing (≥25% immunohistochemistry 2+/3+ tumor cells), unresectable, locally advanced or metastatic, nonsquamous NSCLC. In part 1, pts with progression after 1 or 2 lines of systemic therapy in the recurrent/metastatic setting are enrolled to receive T-DXd and durvalumab in combination with cisplatin (arm 1A), carboplatin (arm 1B), or pemetrexed (arm 1C), or T-DXd monotherapy (arm 1D). In part 2, pts who have not received prior curative treatment in the locally advanced setting or systemic treatment in the metastatic setting are enrolled to receive T-DXd and durvalumab in combination with cisplatin (arm 2A), carboplatin (arm 2B), or pemetrexed (arm 2C), or T-DXd plus durvalumab (arm 2D); pts in part 2 must not have activating EGFR mutations, an EML4-ALK fusion, or other targetable alterations. The primary endpoint is the frequency of adverse events (AEs) and serious AEs (SAEs) with T-DXd plus durvalumab plus chemotherapy combinations to assess safety and tolerability and determine the recommended phase 2 dose. Secondary endpoints include confirmed ORR, duration of response, disease control rate, progression-free survival, OS, frequency of AEs and SAEs with T-DXd monotherapy (part 1) and T-DXd plus durvalumab (part 2), pharmacokinetics, and immunogenicity. Citation Format: David Planchard, Julie R. Brahmer, James C. Yang, Yuh-Min Chen, Kang-Yun Lee, Thatthan Suksombooncharoen, Natasha Viglianti, Mark Gustavson, Alejandra Ragone, Amaya Gasco Hernandez. Phase 1b dose-escalation and dose-expansion study evaluating trastuzumab deruxtecan (T-DXd) in combination with durvalumab and cisplatin, carboplatin, or pemetrexed in advanced or metastatic, HER2-overexpressing, nonsquamous non-small cell lung cancer (NSCLC): DESTINY-Lung03 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT572.

TPS623 Background: Human epidermal growth factor receptor 2 (HER2) is an established therapeutic target in both breast and gastric cancer. However, HER2-targeting therapies are not approved beyond these malignancies despite a high prevalence of HER2 expression across cancers of epithelial origin. Trastuzumab deruxtecan (T-DXd) is an antibody-drug conjugate consisting of an anti-HER2 antibody, a cleavable tetrapeptide-based linker, and a topoisomerase I inhibitor payload. In a cohort of pretreated patients with multiple HER2-expressing tumors, T-DXd demonstrated promising antitumor activity, with an investigator-assessed confirmed objective response rate of 40.9% (9 of 22 patients) and median progression-free survival of 11.1 months (95% CI, 5.4-20.5 months; Tsurutani J, et al. Cancer Discov. 2020;10:688-701). Here we describe the phase 2 DESTINY-PanTumor02 trial evaluating T-DXd in patients with select HER2-expressing solid tumors. Methods: DESTINY-PanTumor02 (NCT04482309) is an open-label, multicenter, multicohort, phase 2 study evaluating T-DXd for the treatment of patients with select HER2-expressing locally advanced, unresectable, or metastatic tumors. The study will consist of 7 cohorts of patients (n≈40 each) with urothelial bladder, biliary tract, cervical, endometrial, ovarian, pancreatic, or rare tumors (tumors excluding those in the other cohorts and breast, gastric, colorectal, and non-small cell lung cancer). Patients are required to have disease progression following ≥1 prior systemic treatment for advanced or metastatic disease or have no satisfactory alternative treatment options. Prior HER2-targeting therapy is allowed. The primary endpoint is investigator-assessed confirmed objective response rate per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Secondary endpoints include duration of response, disease control rate, progression-free survival (all per investigator assessment according to RECIST 1.1), overall survival, safety, pharmacokinetics, and immunogenicity. Previously presented at the ESMO Congress 2021, FPN: 1869 TiP, Meric-Bernstam et al. Reused with permission. Clinical trial information: NCT04482309.

TPS3162 Background: There are substantial data suggesting that a subset of human epidermal growth factor receptor 2 (HER2)–activating mutations induce ligand-independent constitutive HER2 signaling and promote oncogenesis. Direct therapeutic targeting of HER2 has transformed the treatment of patients with HER2-overexpressing breast and gastric cancers. However, currently no targeted treatments are approved for patients with tumors harboring HER2-activating mutations. T-DXd is an antibody-drug conjugate consisting of an anti-HER2 antibody, a cleavable tetrapeptide-based linker, and a membrane-permeable topoisomerase I inhibitor payload that may be selectively internalized in tumors with HER2 mutations (Li BT, et al. Cancer Discov. 2020;10:674-687). In a phase 1 study (DS8201-A-J101), T-DXd demonstrated preliminary antitumor activity in patients with tumors harboring HER2 mutations, with confirmed responses observed in 9 of 19 patients (47.4%) (Tsurutani J, et al. Cancer Discov. 2020;10:688-701). Here we describe the DESTINY-PanTumor01 trial (NCT04639219). Methods: DESTINY-PanTumor01 is an open-label, multicenter, single-arm, phase 2 study evaluating T-DXd for the treatment of patients with unresectable and/or metastatic solid tumors (excluding non-small cell lung cancer) harboring prespecified HER2-activating mutations. Patients (N≈100) are required to have progressed following prior treatment for advanced or metastatic disease or have no satisfactory alternative treatment options. Prior HER2-targeting therapy is allowed. HER2 mutation status will be determined locally using next-generation sequencing or a validated nucleic acid–based methodology. The primary endpoint is confirmed objective response rate according to independent central review per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Secondary endpoints include duration of response, disease control rate, progression-free survival, investigator-assessed confirmed objective response rate, overall survival, safety, pharmacokinetics, and immunogenicity. Clinical trial information: NCT04639219.

Background T-DXd (Enhertu®) is an FDA-approved antibody-drug conjugate (ADC) targeting HER2. T-DXd has shown anti-tumor activity, not only in patients with HER2-overexpressing (IHC3+/2+ ISH+) breast cancer (BC) but also in patients with BC with low HER2 expression (IHC1+/2+ ISH−). Current HER2 protein expression assessment is based on manual pathologist scoring that classifies tumors by the percentage of tumor cells with highest intensity and completeness of staining. A critical need exists for more objective and quantitative methods to assess HER2 expression, specifically to better identify patients with low-level expression if T-DXd proves to be efficacious in this patient population. Methods We used deep learning (DL)-based image analysis (IA) to generate a novel HER2 Quantitative Continuous Score (QCS). Data analytic techniques determined optimal HER2 QCS for the J101 trial (NCT02564900) of 151 patients with varying HER2 expression levels (1+, 2+, 3+). HER2 QCS consists of DL models to detect membrane, cytoplasm, and nuclei of all tumor cells. QCS was extensively trained using pathologists’ annotations, and the performance was validated on unseen data to ensure its generalization and robustness. QCS was blindly applied to J101 data. The optical density (OD; level of brown stain intensity) was computed on detected membrane to derive features that could be linked to survival prediction. QCS features were selected to maximize ORR in positive group, minimize ORR in negative group maintaining while high prevalence in the positive group. Results Analytical validation showed high correlation between QCS from automatically detected membranes and QCS from those annotated by pathologists (R=0.993). This is in the same range as correlation between three pathologists (R=0.995). HER2 QCS was largely consistent with pathologist HER2 scoring as well but showed broad quantitative overlap between IHC and ISH categories. HER2 QCS showed a direct linear relationship between ORR and increased HER2 expression across the entire assay range. In the HER2-low population (n = 65), for whom HER2-targeting therapies are not currently approved, 42% of patients responded to T-DXd, with a median PFS (mPFS) of 11 mo. Using HER2 QCS, we were able to further stratify this population into a subgroup of QCS-high patients (above a staining intensity cut-off determined by IA), with response and mPFS increased to 53% (95% CI: 36%-68%) and 14.5 mo (95% CI: 10.9 mo-NR) respectively, while the QCS-low group only showed ORR of 24% (95% CI: 9%-45%) and mPFS of 8.6 mo (95% CI: 4.2 mo-NR). Generally, best-performing QCS cutoffs were driven by most tumor cells expressing a minimal amount of HER2, in contrast to current clinical guidelines that are driven by a minority of cells expressing higher levels of HER2. We also examined spatial heterogeneity by characterizing cells as either bearing membrane stain above a determined OD threshold (positive cell) or lying within certain distances from a positive cell. We observed similar efficacy with best performing-cutoffs, again, being found when a minimal level of HER2 expression (OD) was examined. Conclusions Taken together, these data establish a first proof-of-concept demonstrating that use of HER2 QCS can potentially enhance prediction of patient outcome with T-DXd by increasing sensitivity and specificity of response, especially in the HER2-low population. The ability to identify patients in the HER2-low group who could benefit from T-DXd is critical for its use in a patient population with a high unmet need that would otherwise not be treated with anti-HER2 therapy. Further clinical verification and validation is ongoing. Citation Format: Mark Gustavson, Susanne Haneder, Andreas Spitzmueller, Ansh Kapil, Katrin Schneider, Fabiola Cecchi, Sriram Sridhar, Guenter Schmidt, Sotirios Lakis, Regina Teichert, Anatoliy Shumilov, Ana Hidalgo-Sastre, Magdalena Wienken, Hadassah Sade, J. Carl Barrett, Danielle Carroll. Novel approach to HER2 quantification: Digital pathology coupled with AI-based image and data analysis delivers objective and quantitative HER2 expression analysis for enrichment of responders to trastuzumab deruxtecan (T-DXd; DS-8201), specifically in HER2-low patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-01.

About 15% of breast cancers are HER2 over-expressing (HER2 IHC 3+ or IHC 2+/ISH+)), but another 45% have low levels of HER2 (HER2 IHC 2+/ISH- or IHC 1+), and these patients are not currently approved for treatment with trastuzumab. Recently, a new HER2 ADC, DS-8201 showed anti-tumor activity, not only in patients with HER2 over-expressing breast cancer but also in HER2 low expressing tumors in whom to date, there are no effective anti-HER2 therapies indicated. FDA-approved HER2 in vitro diagnostic tests have recognized several limitations including effects of pre-analytical variable (fixation affects antibody sensitivity), limited dynamic range of chromogen-based IHC, and subjectivity in interpretation of the HER2 score. Additionally, the cut-off values (percentage of cells to be positive) defining HER2 positive have been changing over time. Therefore, more accurate, sensitive, precise and objective assays to better identify patients who may benefit from anti-Her2 treatment therapies (e.g DS-8201) are needed. To address this gap, we evaluated upcoming technologies targeted MS and QRT-PCR and aim to compare expression with current diagnostic HER2 tests in FFPE samples Using selected reaction monitoring mass spectrometry (SRM-MS), we quantified proteins from formalin-fixed, paraffin-embedded tissue samples that had been classified as HER2 0, 1+, 2+ or 3+ by IHC (n=107). HER2 protein concentration measured by SRM-MS was compared between patients in different HER2 IHC classifications using an ANOVA, adjusting for multiple comparisons. HER2 concentration (measured by SRM-MS) was progressively increased according to HER2 IHC grouping (i.e. lowest concentration in HER2 0 samples, highest in HER2 3+ samples). HER2 levels were significantly elevated in 2+ vs. 0 (2.2-fold increase, p < 0.05) samples, and trended higher in 2+ vs. 1+ (1.6-fold increase, p = 0.07) and in 1+ vs. 0 (1.4-fold increase, p = 0.17) samples. About 73% of samples scored as IHC0 had detectable Her2 by SRM-MS (from 168 to 623 amol/µg). Among HER2 IHC 0 samples, ~15% (7/47) had HER2 concentrations above the median levels for the 1+ group. Similarly, 19% (3/16) 1+ samples had HER2 levels above the median for the 2+ group. About 20% of samples co-expressed either ERBB1 and/or ERBB3. Simultaneously from FFPE sections we quantified protein level of payload response and resistance markers (MDR, MRP1, Topo1 and SLFN11). We used an objective multiplex non-antibody-based method to quantify multiple targets from FFPE tissue. SRM-MS revealed a range of HER2 expression over 100 orders of magnitude and identify markers of payload response or resistance in the same assay. The differences seen in payload markers expression could affect therapeutic efficacy and may suggest differing responses to Her2-targeted ADC, depending on tumor biology. Multiplexed quantitative MS could be used to accurately predict which patients will derive the most benefit from Her2-ADC therapy based on the specific biology of their tumor. These studies are ongoing. Citation Format: Fabiola Cecchi, Mark Gustavson, Danielle Carroll, Sriram Sridhar, Steven Coats, Anuja Bhalkikar, Sheeno Thyparambil, Wei-Li Liao, Todd Hembrough. Quantitative mass spectrometry of HER2 protein levels reveals high variability within HER2 IHC grades [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5128.

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.

ContextHormone 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.ObjectiveTo 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.DesignThe 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.ResultsThe 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.ConclusionsThe 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.

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.

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.

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. Cancer 2011;. © 2011 American Cancer Society.

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.

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.

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 r2 = 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 r2 = 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. Cancer Epidemiol Biomarkers Prev; 19(4); 982–91. ©2010 AACR.

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.

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.

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.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.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.

Background: Tumor metastasis is responsible for the majority of solid tumor related deaths. Diagnostic assays that accurately predict risk of cancer dissemination provide useful information for optimizing personalized treatment strategies. As previously demonstrated, using manual methods, the number and distribution of MetaSites in the tumor microenvironment significantly predicts metastatic disease in patients with ER-positive early stage invasive breast cancer. A MetaSite is the juxtaposed multicellular structure comprised of a blood vessel, the macrophage immune cell, and an invasive tumor cell. To reduce pathologist counting variability, an objective and reproducible automated laboratory process workflow to identify and quantify MetaSites for a clinical grade assay was developed. Methods: Digital pathology imaging coupled with image analysis tools was employed to develop a fully-automated, objective method and workflow for quantification of MetaSites in formalin-fixed paraffin embedded tumor samples. Using this method, areas for analysis and quantification of MetaSites were automated by integrating high resolution automated microscopy with multiple image analysis algorithms. A pathologist ensured overall diagnostic quality of the sample in addition to approving individual images for MetaSite scoring. Results: In this analytical validation study, the platform was demonstrated to be greater than 97% reproducible with a mean coefficient of variation of 6.6% (n=35) for 3 independent measurements of the same slide. Further, MetaSite scores showed correlation coefficients (Pearson's R) greater than 0.98 between measurements with no significant difference in absolute values by repeated measures analysis. Importantly, MetaSite scores on independently stained tumor sections showed greater than 90% reproducibility, indicating minimal heterogeneity within the tumor with respect to MetaSite score, section to section. Additionally, day-to-day MetaSite scores showed correlation coefficients (Pearson's R) greater than 0.90 between staining runs with no significant difference in mean MetaSite scores. Conclusion: Taken together, these data demonstrate the successful development and analytical validation of a fully-automated, highly reproducible MetaSite quantification platform. With development and analytical validation of this test, it is now possible to provide physicians with information regarding the aggressiveness of patient tumors and accurate prediction of cancer metastasis. This method is being further validated in a large (n=481) case control clinical study. Citation Format: Michael Peterson, Lonnie Graves, Michael J. Donovan, Douglas Hamilton, Olga Poxdnyakova, Mark Gustavson. Development and analytical validation of a fully-automated platform for quantification of MetaSites to predict systemic metastasis. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Metastasis; 2015 Nov 30-Dec 3; Austin, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(7 Suppl):Abstract nr B64.

517 Background: Hormone receptors, HER2 and Ki67 are residual risk markers in early breast cancer. Combining these markers into a unified algorithm (IHC4) provides information on residual recurrence risk of patients treated with hormone therapies. This study aimed to independently investigate the validity of the IHC4 algorithm for residual risk prediction using both conventional (DAB)-IHC and quantitative immunofluorescence (QIF-AQUA). Methods: The TEAM pathology study recruited >4500 samples from patients treated in the TEAM trial. TMAs were stained for ER, PgR, HER2 and Ki67 using QIF-AQUA technology or DAB-based immunohistochemistry (DAB-IHC). Central HER2 FISH was performed. Quantitative image analysis was used to generate expression scores that were normalized to produce “IHC4 algorithm” as well as novel algorithm scores. Algorithm scores were compared with disease recurrence in univariate and multivariate Cox Proportional Hazards models. Results: Both DAB-IHC and QIF-AQUA IHC4 continuous models were significant (P

Aaron Jay Kernis and Michael Torke, in a conversation moderated by the composer Mark Gustavson, discuss what is and is not tonality in contemporary music and how it does and does not operate. In addition, they discuss what inspires them to compose and some general problems in music today.

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. Publisher PDF

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.

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.

The Mena protein has been shown to be critical for breast cancer metastasis, and, in pre-clinical models two of its isoforms MenaINV and Mena11a are expressed in invasive and non-invasive tumor cell subpopulations, respectively. We have previously developed a quantitative immunofluorescence approach to assess the fraction of Mena protein lacking Mena11a called Menacalc as a method to infer presence of invasive tumor cells. Menacalc is determined by subtracting the population-based Z-score (quantifies the relationship to the mean) for Mena11a quantitative expression score from the population-based Z-score for total Mena quantitative expression score. The resulting Menacalc scores range from -4 to +4 with high scores reflecting tumors with relatively high total Mena (all isoforms) but low Mena11a. We showed that high MENAcalc scores associated significantly with poor disease-specific survival in breast cancer patients, including node-negative and positive cases. A recent study reported that a semi-quantitative approached to Mena assessment shows prognostic value in early Non-Small Cell Lung Cancer (NSCLC) (Bria et. al 2014). Here, we asked whether the quantitative Menacalc assay has prognostic value in NSCLC and if it can predict metastatic disease. We determined Menacalc on a cohort of 201 predominantly early-stage NSCLC cases using multiplexed quantitative immunofluorescence (MQIF) and then assessed Menacalc with respect to clinical and pathological features, as well as disease-specific survival. There was no significant difference in Menacalc by stage, sex, or age. However, Menacalc was significantly higher (P = 0.001) in squamous cell carcinoma (N = 32) as compared to other histological subtypes. High levels of Menacalc were associated (10% significance level) with decrease 5-year disease specific survival in all patients [HR = 1.78 (95%CI: 0.92-3.43); P = 0.08], and were significantly associated with survival when either corrected for histological subtype [HR = 2.10 (95%CI: 1.04-4.26); P = 0.04] or in the squamous-only population [HR = 6.60 (95%CI: 1.22-53.75); P = 0.04]. There was no difference in survival as a function of stage nor in adenocarcinomas. Furthermore, consistent with Mena isoform status being associated with the metastatic phenotype, Menacalc was 2-fold higher (P = 0.09) in metastatic patients, but the sample size with available clinical data was limited (N = 9). Taken together, these data demonstrate that Menacalc has potential prognostic value especially in squamous cell carcinoma patients. These data also preliminarily suggest that Menacalc can predict the metastatic phenotype. Further studies examining Mena isoform expression in squamous and later-stage metastatic NSCLC patients is thus warranted and currently ongoing. Citation Format: Mark Gustavson, Wendy Davis, Oscar Bronsther, Frank Gertler. Menacalc as an independent prognostic factor and predictor of metastasis in Non-Small Cell Lung Cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4331. doi:10.1158/1538-7445.AM2015-4331

Carboxylesterase II (CES2), a serine ester hydrolase, is the major carboxylesterase responsible for the conversion of the prodrug of gemcitabine into its active form, gemcitabine (LY2334737), an anticancer chemotherapeutic. In humans, the ratio of plasma levels of prodrug to active form is 10:1. Therefore, increased availability of LY2334737 can result in cleavage of the prodrug into its active form at the tumor site and has potential for greater tumor cytotoxicity. Relatively high levels of expression of CES2 within non-neoplastic human tissue occur in the liver, kidney and gastrointestinal tract as visualized by immunohistochemistry (IHC). CES2 is also reported to be expressed in human cancers such as non-small cell lung carcinoma and colon adenocarcinomas. In this study, we analyzed CES2- transfected in vitro cell lines by real-time PCR, chromogenic IHC, and automated quantitative analysis (AQUA) of immunofluorescent in situ protein expression and showed that high levels of CES2 correlate with higher cytotoxicity. qRT-PCR analysis revealed that the mRNA levels of CES2 measured in formalin-fixed paraffin-embedded tumors correlate with protein levels. CES2 transcriptional profiling was performed to identify additional tumor types with high levels of expression of CES2. CES2 overexpression was detectable by IHC and/or AQUA analysis in human colon carcinoma, mesothelioma, non-small cell lung cancer, and tumors of the breast and ovary. CES2 IHC labeling in colonic adenocarcinomas was diffuse. However, labeling in non-neoplastic mucosa showed low levels in proliferating cells at the crypt base and increasingly higher expression toward the terminally differentiated cells at the tips—cells that would not progress through the cell cycle and should not be affected by antiproliferative agents. In conclusion, we have developed robust IHC and AQUA-based assays for differential expression of CES protein levels in a variety of archival human tumor types and have shown high correlation with CES2 transcript levels by qRT-PCR. These methodologies can identify tumors with high levels of CES2, a biomarker that may further be investigated in biomarker-driven clinical trials to identify patients who will more likely respond to LY2334737. Citation Information: Clin Cancer Res 2010;16(14 Suppl):B31.

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.

567 Background: The discovery of an astoundingly high false negative rate for estrogen receptor (ER) testing in Canada has raised questions about the accuracy and reproducibility of ER testing. One solution would be the introduction of a standardized and reproducible diagnostic test that is easily adaptable in the clinical setting. Here, we have tested the AQUA method of quantitative immunofluorescence for the standardized and reproducible quantification of ER protein expression in tissue. Methods: Quantitative Western blotting was used in conjunction with AQUA analysis to create standard curves for assessment of absolute ER protein concentration in tissue (n = 118). We used standard scoring methods and AQUA analysis to quantify ER protein expression in a large cohort of breast cancer samples (n =669). Results: Using a series of standard curves, we determined that the range of the ER AQUA assay is between 100 pg/μg and 1500 pg/μg total protein. ER protein concentration in breast cancer samples showed an expected unimodal distribution for quantitative assessment of ER. Reproducibility studies of AQUA analysis demonstrated significant instrument-to-instrument (laboratory-to-laboratory) reproducibility for 3 instruments across the range of AQUA scores (average %CV = 1.34; R2>0.99; ANOVA p = 0.67). The same cases were then read and classified by 3 pathologists using the Allred scoring system. Although their concordance is similar to that seen in the literature (Kappa = 0.81, 0.88, and 0.89), pathologist concordance rate is lower than for that observed with AQUA analysis (Kappa = 0.95, 0.96, and 0.97). Importantly, 9.0% of cases showed a change of diagnosis (positive/negative) across 3 pathologists whereas only 2.8% of cases changed classification using AQUA analysis. Additionally, misclassified cases occurred across the entire range of Allred scores, but were restricted to a narrow region defined by the cut-point for AQUA scoring. Conclusions: We have demonstrated that AQUA technology can provide for the standardized and reproducible quantification of ER with a 3 fold reduction in misclassification. This approach has the potential to decrease the problem of false negative tests for ER. [Table: see text]

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.