ABSTRACT
BACKGROUND: Despite high expression of PD-L1, around half of advanced non-small cell lung cancer (NSCLC) will not experience tumor response with pembrolizumab. There is an need for a better understanding of the resistance mechanisms in this setting. METHODS: This bi-centric retrospective study included all consecutive patients with PDL1 ≥ 50% advanced NSCLC treated with pembrolizumab in first-line treatment between 2016 and 2020. We compared the clinical characteristics of patients with early progression (refractory) vs others. We performed a comprehensive gene expression profile screening by RNAseq capture on tumor samples. RESULTS: We included 46 patients. Twenty-two patients were refractory to pembrolizumab, mainly women, with poor performance status and lower albumin concentration. RNAseq analysis was performed on 19 samples. Hierarchical clustering allowed the identification of 3 clusters with various proportion of refractory tumors: intermediate (C1: 57%), high (C2: 71%) and low proportion (C3: 40%). Comparative analysis between C2 and C3 allowed the identification of overexpressed (n = 137) and underexpressed (n = 40) genes. Among the genes of interest, C2 exhibits higher activation of pathways associated with stemness phenotype (Hedgehog, Notch and Hippo pathways) and pathways associated with loss of PTEN and JAK2. In C2, genes associated with PD-1, toll-like receptor-9 (TLR-9), major histocompatibility complex (MHC) and interferon-γ pathways were underexpressed. CONCLUSION: This study gives an overview of activated and downregulated pathways in high PD-L1 NSCLC refractory to pembrolizumab. These tumors showed activation of pathways associated with cancer stem cells, loss of PTEN and JAK2, and inhibition of both priming and effector phases of the immune response.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Albumins/therapeutic use , Antibodies, Monoclonal, Humanized , B7-H1 Antigen/metabolism , Carcinoma, Non-Small-Cell Lung/drug therapy , Carcinoma, Non-Small-Cell Lung/genetics , Carcinoma, Non-Small-Cell Lung/metabolism , Drug Resistance, Neoplasm , Female , Humans , Interferon-gamma/therapeutic use , Lung Neoplasms/drug therapy , Lung Neoplasms/genetics , Lung Neoplasms/metabolism , Male , Programmed Cell Death 1 Receptor , Retrospective Studies , Toll-Like Receptor 9/metabolism , TranscriptomeABSTRACT
The french society of pathology (SFP) organized in 2020 its first data challenge with the help of Health Data Hub (HDH). The organisation of this event first consisted in recruiting almost 5000 slides of uterus cervical biopsies obtained in 20 pathology centers. After having made sure that patients did not refuse to include their slides in the project, the slides were anonymised, digitized and annotated by expert pathologists, and were finally uploaded on a data challenge platform for competitors all around the world. Competitors teams had to develop algorithms that could distinguish among four diagnostic classes in epithelial lesions of uterine cervix. Among many submissions by competitors, the best algorithms obtained an overall score close to 95%. The best 3 teams shared 25k prizes during a special session organised during the national congress of the SFP. The final part of the competition lasted only 6 weeks and the goal of SFP and HDH is now to allow for the collection to be published in open access. This final step will allow data scientists and pathologists to further develop artificial intelligence algorithms in this medical area.
Subject(s)
Algorithms , Artificial Intelligence , Biopsy , Cervix Uteri , Female , Humans , PathologistsABSTRACT
The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world. Competing teams had to develop algorithms that could distinguish 4 diagnostic classes in cervical epithelial lesions. Among the many submissions from competitors, the best algorithms achieved an overall score close to 95%. The final part of the competition lasted only 6 weeks, and the goal of SFP and HDH is now to allow for the collection to be published in open access for the scientific community. In this report, we have performed a "post-competition analysis" of the results. We first described the algorithmic pipelines of 3 top competitors. We then analyzed several difficult cases that even the top competitors could not predict correctly. A medical committee of several expert pathologists looked for possible explanations for these erroneous results by reviewing the images, and we present their findings here targeted for a large audience of pathologists and data scientists in the field of digital pathology.
