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Alan Nelson



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    P2.11 - Screening and Early Detection (Not CME Accredited Session) (ID 960)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall
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      P2.11-14 - Malignancy Associated Change and The LuCED® Test for Detection of Early Stage Lung Cancer (ID 14044)

      16:45 - 18:00  |  Author(s): Alan Nelson

      • Abstract
      • Slides

      Background

      Early detection remains the most reliable and effective strategy for curing lung cancer. Many approaches, however, are limited by poor sensitivity or specificity that increase health care costs and potentially risk patient health through unneeded procedures.

      The association between cell morphology and cancer has been established in the pathology literature. However, through the so-called field effect, cancer can introduce subtle morphological changes into non-cancerous cells that are proximal to the tumor site.

      The Cell-CT platform and LuCED® test represent a promising new method for detecting lung cancer with high (92%) sensitivity and (95%) specificity based on cytologically abnormal cells. In this research we investigate use of the Cell-CT to detect malignancy associated changes in normal cells near the cancer with a view towards enhancing LuCED test performance.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      We present a study of 3D morphological alterations in non-cancer cells obtained from sputum of healthy subjects and biopsy confirmed lung cancer patients. Three major cell types were analyzed from 235 patients: bronchial epithelial columnar, squamous intermediate, and mature macrophages. We used the Cell-CT™ platform to measure over 700 different structural biomarkers for each cell. The measurements were used to define prominent clusters of cells through a hierarchical process that were then used under supervised learning, with case status as ground truth, to create classifiers that optimally separated cells from cancer vs. normal cases.

      4c3880bb027f159e801041b1021e88e8 Result

      The table gives classifier development and performance characteristics:

      Cell Type

      Number of cells from cancer patients

      Number of cells from normal patients

      Cluster-based Supervised Learning – aROC

      Squamous Intermediate

      2316

      684

      0.94

      Macrophages

      4960

      5040

      0.94

      Columnar cells

      3227

      3234

      0.92

      8eea62084ca7e541d918e823422bd82e Conclusion

      Our results indicate that the Cell-CT can discriminate cell features that are too subtle to distinguish by a human. The study suggests that detection of cells with Malignancy Associated Changes may be used to further enhance the LuCED test’s performance beyond published levels.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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    P3.03 - Biology (Not CME Accredited Session) (ID 969)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
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      P3.03-16 - 3D Morphometric Detection of Mismatch Repair Deficiency in Human Lung Adenocarcinoma Cell Lines using the Cell-CT® Platform (ID 14118)

      12:00 - 13:30  |  Author(s): Alan Nelson

      • Abstract
      • Slides

      Background

      Mismatch repair protein deficiency (MMR-D) is proving to be a predictive biomarker for the efficacy of immune checkpoint inhibitor therapy for many malignant neoplasms. MMR-D leads to microsatellite instability and high tumor mutational burden (TMB) and ultimately results in the generation of neoantigens. While multiple molecular tests are available today for the detection of MMR-D, including immunohistochemistry staining for mismatch repair proteins (MLH1, MSH2, MSH6, and PMS2), PCR to evaluate MSI, MLH1 promoter methylation analysis, and targeted next-generation sequencing, they require invasive biopsy and are often not applicable for early stage disease.

      Studies have demonstrated that MMR-D results in histological and morphological changes. The automated Cell-CT® platform produces isometric, high-resolution 3D images of cells in liquid biopsies, such as sputum, where published studies have demonstrated 92% sensitivity to biopsy-confirmed lung cancer with 95% specificity. This new study reports the development of morphology-based classifiers for lung cancer cells that have been engineered to exhibit MMR-D.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Two human lung cancer adenocarcinoma cell lines, NCI-H23 and NCI-H1650, were transduced with lentiviral particles expressing either scrambled or MLH1 shRNAs and selected for puromycin resistance. Individual clones were isolated and screened for MLH1 expression. Several scrambled shRNA clones, with parental levels of MLH1, and several shMLH1 clones, with a range of 86 to 97% suppression of MLH1, were expanded, fixed, and analyzed using the Cell-CT® platform. Over 1,000 cells from several harvests of each cell line were used to measure 845 different 3D structural biomarkers for each cell.

      4c3880bb027f159e801041b1021e88e8 Result

      A classifier to test the degree to which the features discriminate between control scrambled shRNA and shMLH1 cell lines was developed and its performance was characterized by the area under the ROC curve (aROC). MLH1-knockdown specific differences for both the H23 and H1650 line clones as compared to pooled shRNA clone data were observed. The aROC’s for 4 different H23 shMLH1 clones were 0.86, 0.81, 0.81, and 0.84, while those for 3 H1650 shMLH1 clones were 0.83, 0.80, and 0.90.

      8eea62084ca7e541d918e823422bd82e Conclusion

      This study demonstrates the feasibility of using the Cell-CT® platform for morphometric detection of MMR-D. Discriminatory features assessed from both cell lines will be presented as well as possible correlation to TMB level.

      6f8b794f3246b0c1e1780bb4d4d5dc53

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.