PD-L1, as assessed by immunohistochemistry, is a predictive biomarker for immuno-oncology treatment in lung cancer. Different scoring methods have been used to assess its status, resulting in a... Show morePD-L1, as assessed by immunohistochemistry, is a predictive biomarker for immuno-oncology treatment in lung cancer. Different scoring methods have been used to assess its status, resulting in a wide range of positivity rates. We use the European Thoracic Oncology Platform Lungscape non-small cell lung carcinoma cohort to explore this issue. PD-L1 expression was assessed via immunohistochemistry on tissue microarrays (up to four cores per case), using the DAKO 28-8 immunohistochemistry assay, following a two-round external quality assessment procedure. All samples were analyzed under the same protocol. Cross-validation of scoring between tissue microarray and whole sections was performed in 10% randomly selected samples. Cutoff points considered: >= 1, 50 (primarily), and 25%. At the two external quality assessment rounds, tissue microarray scoring agreement rates between pathologists were: 73% and 81%. There were 2008 cases with valid immunohistochemistry tissue microarray results (50% all cores evaluable). Concordant cases at 1, 25, and 50% were: 85, 91, and 93%. Tissue microarray core results were identical for 70% of cases. Sensitivity of the tissue microarray method for 1, 25, and 50% was: 80, 78, and 79% (specificity: 90, 95, 98%). Complete agreement between tissue microarrays and whole sections was achieved for 60% of the cases. Highest sensitivity rates for 1% and 50% cutoffs were detected for higher number of cores. Underestimation of PD-L1 expression on small samples is more common than overestimation. We demonstrated that classification of PD-L1 on small biopsy samples does not represent the overall expression of PD-L1 in all non-small cell cancer carcinoma cases, although the majority of cases are 'correctly' classified. In future studies, sampling more and larger biopsies, recording the biopsy size and tumor load may permit further refinement, increasing predictive accuracy. Show less
Zhang, Y.; Wester, L.; He, J.; Geiger, T.; Moerkens, M.; Siddappa, R.; ... ; Water, B. van de 2018
Antiestrogen resistance in estrogen receptor positive (ER+) breast cancer is associated with increased expression and activity of insulin-like growth factor 1 receptor (IGF1R). Here, a kinome siRNA... Show moreAntiestrogen resistance in estrogen receptor positive (ER+) breast cancer is associated with increased expression and activity of insulin-like growth factor 1 receptor (IGF1R). Here, a kinome siRNA screen has identified 10 regulators of IGF1R-mediated antiestrogen with clinical significance. These include the tamoxifen resistance suppressors BMPR1B, CDK10, CDK5, EIF2AK1, and MAP2K5, and the tamoxifen resistance inducers CHEK1, PAK2, RPS6KC1, TTK, and TXK. The p21-activated kinase 2, PAK2, is the strongest resistance inducer. Silencing of the tamoxifen resistance inducing genes, particularly PAK2, attenuates IGF1R-mediated resistance to tamoxifen and fulvestrant. High expression of PAK2 in ER+ metastatic breast cancer patients is correlated with unfavorable outcome after first-line tamoxifen monotherapy. Phospho-proteomics has defined PAK2 and the PAK-interacting exchange factors PIXα/β as downstream targets of IGF1R signaling, which are independent from PI3K/ATK and MAPK/ERK pathways. PAK2 and PIXα/β modulate IGF1R signaling-driven cell scattering. Targeting PIXα/β entirely mimics the effect of PAK2 silencing on antiestrogen re-sensitization. These data indicate PAK2/PIX as an effector pathway in IGF1R-mediated antiestrogen resistance. Show less
Rudolph, J.D.; Graauw, M. de; Water, B. van de; Geiger, T.; Sharan, R. 2016
Phosphoproteomic experiments typically identify sites within a protein that are differentially phosphorylated between two or more cell states. However, the interpretation of these data is hampered... Show morePhosphoproteomic experiments typically identify sites within a protein that are differentially phosphorylated between two or more cell states. However, the interpretation of these data is hampered by the lack of methods that can translate site-specific information into global maps of active proteins and signaling networks, especially as the phosphoproteome is often undersampled. Here, we describe PHOTON, a method for interpreting phosphorylation data within their signaling context, as captured by protein-protein interaction networks, to identify active proteins and pathways and pinpoint functional phosphosites. We apply PHOTON to interpret existing and novel phosphoproteomic datasets related to epidermal growth factor and insulin responses. PHOTON substantially outperforms the widely used cutoff approach, providing highly reproducible predictions that are more in line with current biological knowledge. Altogether, PHOTON overcomes the fundamental challenge of delineating signaling pathways from large-scale phosphoproteomic data, thereby enabling translation of environmental cues to downstream cellular responses. Show less
