Author of

• 10548

  +

  P1.03 - Poster Session 1 - Technology and Novel Development (ID 150)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Biology
  • Presentations: 2
  • Moderators:
  • Coordinates: 10/28/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 10549

    +

    P1.03-001 - Multiplexing technology for in situ biomarker profiling of Non-Small Cell Lung Cancer (NSCLC) (ID 1467)

    09:30 - 09:30  |  Author(s): A. Santamaria-Pang
    • Abstract

    Background
    NSCLC is a heterogeneous neoplasm comprising several histologic types, etiology, genetics, survival and response to therapy. Accurate analysis of these subtypes has increased sample requirements, which is challenging in the era of minimally invasive procedures. A recent survey of 90 US pathologists presented at ASCO 2013 meeting, concluded insufficient sample availability in 6% of all NSCLC samples recently handled by these pathologists. Moreover, subcellular localization of marker expression linked to tumor pathobiology necessitates methodological advancement. With the development of a new platform that allows in situ, multiplexed sub-cellular analysis of over 60 proteins, this project aims to demonstrate the feasibility of detailed in situ molecular profiling and perform comparative analysis of known cancer pathways and prognostic markers on the same serial section.

    Methods
    Multiplex immunofluorescence staining and imaging of over 30 biomarkers, including several RTKs, cell adhesion molecules, select members of PI3K, MAPK/ERK, JAK/STAT pathways, angiogenesis, hypoxia, proliferation and chemotherapy resistance markers were performed on replicate FFPE tissue microarrays (TMA) from 382 samples. Cell-level and subcellular-level marker expressions were quantified using image analysis algorithms and compared between serial sections. Associations between marker expressions and histological subtypes and survival were investigated in European male smokers. Multivariate analysis was performed using logistic regression and Cox proportional hazard models on over 300 quantitated features of marker expression. All models controlled for age. Serial sections were modeled separately and combined to improve confidence in associations. EGFR and cMET positivity was evaluated using whole cohort median expression values to define positive cells and the summary statistics are reported using 10% positive cells as cutoff for characterizing positive samples.

    Results
    In concordance with previous reports, differential expression of RRM1, CK5 and CK7 was observed in SCC vs AD in the high grade, early stage male smokers (N=86). With a 10% cell positivity threshold, 72.7% (76.3%, serial section (SS)) of all male smokers (N=183 (190, SS)) were positive for EGFR. EGFR positivity was higher in SCC, 83.9% (86.0%, SS) compared to AD 54.9% (61.8%, SS). Opposite was observed for cMET with 81.7% (78.9%, SS) of AD characterized as positive compared to only 58.0% (57.0%, SS) SCC. Among the several previously reported prognostic markers evaluated in this study, only CA9 expression was associated with overall patient survival with a hazard ratio of 1.47, p-value 0.0005 (N=278). Again, analysis of serial section produced a similar result confirming the robustness of the platform.

    Conclusion
    The study demonstrates the capabilities of multiplexing technology (MultiOmyx[TM]) for assessment of limited lung samples, encompassing topographic expression features and the ability to observe relationships between markers through in situ pathway profiling. Additionally, by evaluating markers on exactly the same sample set (same section), a direct comparison of their relative significance in predicting course of disease is now feasible.

  • 10550

    +

    P1.03-002 - Multiplexed analysis of lung cancer for distinguishing adenocarcinoma from squamous cell carcinoma (ID 2874)

    09:30 - 09:30  |  Author(s): A. Santamaria-Pang
    • Abstract

    Background
    Lung cancer is a leading cause of cancer related deaths, 80% of which are classified as non-small cell carcinomas (NSCLC). Differentiating the two main sub-types of NSCLC, adenocarcinoma (AD) from squamous cell carcinoma (SCC) is crucial for therapeutic decision-making. Current methods for characterizing subtypes may involve DAB stains on up to 7 tissue sections, depending on complexity of diagnosis. This process may deplete precious tissue required for molecular studies sequencing and other predictive markers. The goal of the current study was to measure 11 proteins on a single section using a novel multiplexed immunofluorescence (IF) technology (MultiOmyx[TM]) and evaluate performance of analytical workflows in automatic biomarker scoring and in AD, SCC discrimination, with reference to the Pulmotype® test of 5markers

    Methods
    The protein markers included in the study were comprised of the five antibodies from the Pulmotype® test - Muc1, CK5/6, TRIM29, CEACAM5 and SLC7A5. Six additional markers TTF1, p40, CK7, CK20, p63, NapsinA were selected based on literature reports. These markers were applied to two separate cohorts of NSCLC cases. The entire set of 11 markers was multiplexed on a 378 core tissue microarray (TMA) containing 213 cases of AD or SCC diagnosis (cohort 1). A second 74 core TMA with 50 cases of AD or SCC was stained with the Pulmotype® markers. Manual scores were generated for the immunofluorescence protein images and DAB stained serial tissue sections were used to generate manual ground truth protein expression scores. The first cohort was used to model diagnosis of AD or SCC using an implementation of Breiman and Cutler’s Random Forest and compared to the performance of a previously published lung classifier using manual DAB scores. Image and data analysis algorithms were developed to aid automated biomarker scoring. These algorithms segment the immunofluorescence images into tumor and stromal areas and compute a large number of biomarker-related metrics. Linear regression modeling was used on a down-selected set of metrics to generate automated protein expression scores per biomarker for the second cohort.

    Results
    Manual scoring of all 11 targets demonstrated excellent concordance between fluorescence and DAB. Concordance was also demonstrated between manual DAB scores and automated IF metrics for the five Pulmotype® markers with an overall sensitivity and specificity of 95% and 87%, respectively. Statistical modeling indicated that 9 (of the 11) multiplexed markers provided 97% specificity and 90% sensitivity in classifying AD versus SCC. The observed 7% indeterminate rate measures well against the existing published indeterminate rates for the Pulmotype® test (11%) and the classic IHC marker combination TTF-1/p63 (29%).

    Conclusion
    Multiplexed analysis of a single tissue section allows maximum use of limited sample and enhanced protein profiling in context of tissue histology. We have shown that differential diagnosis of AD and SCC may be achieved using a multiplexed panel of markers in a single tissue section, when compared to the Pulmotype® test panel. Concordance between fluorescence and DAB shows transferability of the two detection methods. Furthermore, we demonstrated that image and data analysis tools can be applied for consistent automatic biomarker scoring.