Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models

Abstract

Purpose Interaction of the programmed death-1 (PD-1) co-receptor on T-cells with the programmed death-ligand 1 (PD-L1) on tumor cells can lead to immunosuppression, a key event in the pathogenesis of many tumors. Thus, determining the amount of PD-L1 in tumors by immunohistochemistry (IHC) is important as both a diagnostic aid and as a clinical predictor of immunotherapy treatment success. Because IHC reactivity can vary, we developed computational simulation models to accurately predict PD-L1 expression as a complementary assay to affirm IHC reactivity. Methods Multiple myeloma (MM) and oral squamous cell carcinoma (SCC) cell lines were modeled as examples of our approach. Non-transformed cell models were first simulated to establish non-tumorigenic control baselines. Cell line genomic aberration profiles, from next generation sequencing (NGS) information for MM.1S, U266B1, SCC4, SCC15, and SCC25 cell lines were introduced into the workflow to create cancer cell line-specific simulation models. Percentage changes of PD-L1 expression with respect to control baselines were determined and verified against observed PD-L1 expression by ELISA, IHC, and flow cytometry on the same cells grown in culture. Results The observed PD-L1 expression matched the predicted PD-L1 expression for MM. 1S, U266B1, SCC4, SCC15, and SCC25 cell lines and clearly demonstrated that cell-genomics play an integral role by influencing cell signaling and downstream effects on PD-L1 expression. Conclusion This concept can easily be extended to cancer patient cells where an accurate method to predict PD-L1 expression would affirm IHC results and improve its potential as a biomarker and a clinical predictor of treatment success.

Matrix metalloproteinase (MMP) and immunosuppressive biomarker profiles of seven head and neck squamous cell carcinoma (HNSCC) cell lines

“…These cell lines were each grown and maintained in a humidified atmosphere of 5% CO 2 at 37 °C in T75 flasks. SCC4 cells were grown in complete Dulbecco’s Modified Eagle’s Medium: F-12 (DMEM:F-12) containing 2 mM L-glutamine, 1% nonessential amino acids (ATCC), 400ng/mL hydrocortisone (Sigma-Aldrich Corp., St. Louis, MO, USA), 100 units/mL penicillin (Life Technologies, Madison, WI, USA), 100 units/mL streptomycin (Life Technologies), and 10% fetal bovine serum (ATCC) (18). SCC15, SCC25, and SCC84 cells were grown in complete Lymphocyte Growth Media-3 (LGM- 3) (Lonza, Walkersville, MD, USA), 100 units/ mL penicillin (Life Technologies), 100 units/mL streptomycin (Life Technologies), and 10% fetal bovine serum (ATCC) (18).…”

“…SCC4 cells were grown in complete Dulbecco’s Modified Eagle’s Medium: F-12 (DMEM:F-12) containing 2 mM L-glutamine, 1% nonessential amino acids (ATCC), 400ng/mL hydrocortisone (Sigma-Aldrich Corp., St. Louis, MO, USA), 100 units/mL penicillin (Life Technologies, Madison, WI, USA), 100 units/mL streptomycin (Life Technologies), and 10% fetal bovine serum (ATCC) (18). SCC15, SCC25, and SCC84 cells were grown in complete Lymphocyte Growth Media-3 (LGM- 3) (Lonza, Walkersville, MD, USA), 100 units/ mL penicillin (Life Technologies), 100 units/mL streptomycin (Life Technologies), and 10% fetal bovine serum (ATCC) (18). SCC19, SCC92, and SCC99 were grown in Dulbecco’s Modified Eagle’s Medium (DMEM) containing 2 mM L-glutamine, 1% nonessential amino acids (ATCC), 100 units/mL penicillin (Life Technologies), 100 units/mL streptomycin (Life Technologies), and 10% fetal bovine serum (ATCC).…”

Cell genomics and immunosuppressive biomarker expression influence PD-L1 immunotherapy treatment responses in HNSCC—a computational study

“…11 Next generation sequencing (NGS) information containing mutations and copy number variations were taken from the cBioPortal for Cancer Genomics database 12,13 and the Sanger sites for SCC4 (http://www.cbioportal.org/case.do?sample_id=SCC4_UPPER_AERODIGESTIVE_TRACT&cancer_study_id=cellline_ccle_broad, http://cancer.sanger.ac.uk/cell_lines/sample/overview?id=910904); SCC15 (http://www.cbioportal.org/case.do?sample_id=SCC15_UPPER_AERODIGESTIVE_TRACT&cancer_study_id=cellline_ccle_broad, http://cancer.sanger.ac.uk/cell_lines/sample/overview?id=910911); and SCC25 (http://www.cbioportal.org/case.do?sample_id=SCC25_UPPER_AERODIGESTIVE_TRACT&cancer_study_id=cellline_ccle_broad, http://cancer.sanger.ac.uk/cell_lines/sample/overview?id=910701). …”

“…Exomes from each cell line were examined for deleterious gene mutations as recently described 11 using cancer mutation effect prediction algorithms including FannsDB 14 , SIFT 15 , Polyphen 16 , FATHMM 14 , Mutation Assessor (MA) 17 , and PROVEAN 18 . Final results after sifting the gene mutations through these algorithms were recorded as an effect of unknown significance, of neutral significance, or deleterious to gene function.…”

“…Final results after sifting the gene mutations through these algorithms were recorded as an effect of unknown significance, of neutral significance, or deleterious to gene function. 11 …”