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Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

5372 - Augmenting TNM Staging with Machine Learning-based Immune Profiling for Improved Prognosis Prediction in Muscle-Invasive Bladder Cancer Patients

Date

20 Oct 2018

Session

Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

Topics

Translational Research

Tumour Site

Urothelial Cancer

Presenters

Nicolas Brieu

Citation

Annals of Oncology (2018) 29 (suppl_8): viii14-viii57. 10.1093/annonc/mdy269

Authors

N. Brieu1, C.G. Gavriel2, D.J. Harrison2, G. Schmidt1, P.D. Caie2

Author affiliations

  • 1 Research, Definiens AG, 80636 - Munich/DE
  • 2 School Of Medicine, University of St Andrews, KY16 9TF - St Andrews/GB

Resources

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Abstract 5372

Background

Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease whose clinical reporting is based on TNM staging. A more accurate and personalized prognosis could be achieved by profiling the immune contexture alongside clinical TNM staging. This study reports on features captured from the labelling of cells for CD3, CD8 and PD-L1 expression, within both the tumor and stroma of MIBC patients. This data is distilled to identify a novel immune-based prognostic signature which augments TNM staging.

Methods

An image analysis solution was developed to quantify the density of cell populations across immunofluorescence (IF) labelled whole slide images from 105 MIBC patients with known survival data. A regression random forest was used for the detection of nuclei using the Hoechst channel [Brieu et al., ISBI2017] and a convolutional neural network employed for the segmentation of the tumor from the stroma using the pan-cytokeratin channel [Brieu et al., SPIE2018]. The CD3, CD8 and PD-L1 channels were used to classify the cells and calculate their proportion within either the tumor or stroma regions. A decision tree of depth two was trained on these proportions as well as cross-validated. More explicitly, the patients were recursively partitioned during training to maximize at each node their survival difference, leaves showing low survival difference (p-value>0.5) being finally merged.

Results

The method yielded a decision tree which stratified patients into three groups utilizing only two parameters: the proportion of CD8(+) cells in the stroma and the proportion of PD-L1(+) cells across the whole tissue, which were negatively and positively correlated with cancer-specific death respectively. This method was used to replace TNM stages 1 to 3 while retaining the original stage 4 stratification. Testing for survival curve differences showed that this combined system yielded a higher prognostic value (Chisq=41.5, p-value=5.0x10-9) than the standalone TNM staging system (Chisq=34.9, p-value=1.25x10-7).

Conclusions

Our results suggest that immune profiling derived from image analysis provides additional prognostic value to TNM scoring for MIBC.

Clinical trial identification

Legal entity responsible for the study

University of St Andrews.

Funding

Definiens AG.

Editorial Acknowledgement

Disclosure

N. Brieu, G. Schmidt: Fully employed: Definiens AG, a subsidary of Medimmune and Astrazeneca. C.G. Gavriel: PhD student at the University of St. Andrews; PhD work is funded by Definiens AG. D.J. Harrison: Professor of pathology at the University of St Andrews; Secondary PhD supervisor of C. Gavriel, funded by Definiens AG. P.D. Caie: Senior research fellow at the University of St Andrews; Primary PhD supervisor of C. Gavriel, funded by Definiens AG.

Resources from the same session

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