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Martial Guillaud



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    P3.09 - Pathology (Not CME Accredited Session) (ID 975)

    • 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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      P3.09-11 - Genomic Organization at Large Scales (GOALS) within Nuclei and Cell Sociology for Predicting Lung Cancer Outcomes (ID 14160)

      12:00 - 13:30  |  Author(s): Martial Guillaud

      • Abstract
      • Slides

      Background

      Accurate prediction of the biological aggressiveness of lung cancers in patients from limited material could have utility with respect to patient treatment planning. We examined the hypothesis that the quantification of large scale DNA organization or GOALS within the nucleus combined with tumour microenvironment as quantified by cell sociology (which cells types are adjacent which cell types) could predict patient outcomes.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Tissue (TMA cores) from patients with poor outcomes (lung cancer mortality within 24 months) and good outcomes (5+ year survivors) was stained using a stochiometric DNA stain (Feulgen-Thionin). High resolution brightfield imaging of 29 cores was performed using multiple wavelengths and spectral unmixing used to retrieve the DNA concentration at every pixel. In house software was used to automatically segment all the nuclei within each core, calculate 100+ features for each nucleus and classify each nucleus as epithelial, stromal or immune in origin. Further each nucleus was scored as coming from a patient with poor or good outcome.

      For each TMA core the percentage of these cell categories was tabulated as well as cell-cell association frequencies (for example the frequently an epithelial cell predicted to have come from a patient with good outcome was found next to a stromal cell predicted to come from a patient with poor outcome). Cell percentages and cell-cell interaction frequencies were used to predict patient outcome.

      4c3880bb027f159e801041b1021e88e8 Result

      abstract2-1.jpgMany of the individually calculated features had a statistically significant association with patient outcome. Four+ features could predict outcome with an 79% accuracy, 15+ different pairs of features could predict patient outcome with greater than 85% accuracy. Epithelial- stromal cell interactions and stromal cell – immune cell interactions were particularly predictive of outcome suggesting that microenvironment cell - tumour cell interactions predict future biological activity.

      8eea62084ca7e541d918e823422bd82e Conclusion

      This pilot study suggests that GOALS and cell sociology could predict patient outcomes.

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