Automated Tumour Recognition and Digital Pathology Scoring Unravels New Role for PD-L1 in Predicting Good Outcome in ER-/HER2+ Breast Cancer.
Matthew Phillip HumphriesSeán O HynesVictoria BinghamDelphine CougotJacqueline A JamesFarah Patel-SochaEileen E ParkesJaine K BlayneyMichael A O'RorkeGareth W IrwinDarragh G McArtRichard D KennedyPaul B MullanStephen McQuaidManuel Salto TellezNiamh E BuckleyPublished in: Journal of oncology (2018)
The role of PD-L1 as a prognostic and predictive biomarker is an area of great interest. However, there is a lack of consensus on how to deliver PD-L1 as a clinical biomarker. At the heart of this conundrum is the subjective scoring of PD-L1 IHC in most studies to date. Current standard scoring systems involve separation of epithelial and inflammatory cells and find clinical significance in different percentages of expression, e.g., above or below 1%. Clearly, an objective, reproducible and accurate approach to PD-L1 scoring would bring a degree of necessary consistency to this landscape. Using a systematic comparison of technologies and the application of QuPath, a digital pathology platform, we show that high PD-L1 expression is associated with improved clinical outcome in Triple Negative breast cancer in the context of standard of care (SoC) chemotherapy, consistent with previous findings. In addition, we demonstrate for the first time that high PD-L1 expression is also associated with better outcome in ER- disease as a whole including HER2+ breast cancer. We demonstrate the influence of antibody choice on quantification and clinical impact with the Ventana antibody (SP142) providing the most robust assay in our hands. Through sampling different regions of the tumour, we show that tumour rich regions display the greatest range of PD-L1 expression and this has the most clinical significance compared to stroma and lymphoid rich areas. Furthermore, we observe that both inflammatory and epithelial PD-L1 expression are associated with improved survival in the context of chemotherapy. Moreover, as seen with PD-L1 inhibitor studies, a low threshold of PD-L1 expression stratifies patient outcome. This emphasises the importance of using digital pathology and precise biomarker quantitation to achieve accurate and reproducible scores that can discriminate low PD-L1 expression.
Keyphrases
- high throughput
- oxidative stress
- healthcare
- induced apoptosis
- high resolution
- heart failure
- locally advanced
- palliative care
- ms ms
- mass spectrometry
- machine learning
- endoplasmic reticulum
- case report
- deep learning
- single cell
- liquid chromatography tandem mass spectrometry
- cell cycle arrest
- endoplasmic reticulum stress
- depressive symptoms
- cell proliferation
- pain management
- high performance liquid chromatography
- clinical practice
- tandem mass spectrometry