Human GRAP2 ELISA Matched Antibody Pair

To demonstrate the utility of phage display in generating highly specific antibodies, affinity selections were conducted on 20 related Src Homology 2 (SH2) domains (ABL1, ABL2, BTK, BCAR3, CRK, FYN, GRB2, GRAP2, LYN, LCK, NCK1, PTPN11 C, PIK3R1 C, PLC gamma 1 C, RASA1 C, SHC1, SH2D1A, SYK N, VAV1 and the tandem domains of ZAP70). The domains were expressed in Escherichia coli, purified and used in affinity selection experiments. In total, 1292/3800 of the resultant antibodies were shown to bind the target antigen. Of the 695 further evaluated in specificity ELISAs against all 20 SH2 domains, 379 antibodies were identified with unique specificity (i.e. monospecific). Sequence analysis revealed that there were at least 150 different clones with 1-19 different antibodies/antigen. This includes antibodies that distinguish between ABL1 and ABL2, despite their 89% sequence identity. Specificity was confirmed for many on protein arrays fabricated with 432 different proteins. Thus, even though the SH2 domains share a common three-dimensional structure and 20-89% identity at the primary structure level, we were able to isolate antibodies with exquisite specificity within this family of structurally related domains.

Aim: Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. Materials & methods: The DNA copy number variation, the simple nucleotide variation, methylation and mRNA data of pancreatic cancer patients were obtained from The Cancer Genome Atlas. Four molecular subgroups (iC1, iC2, iC3 and iC4) of pancreatic cancer were identified by integrating analysis. Results: The iC1 subgroup harbors better prognosis, higher immune score, lesser DNA copy number variation mutations and better genomic stability compared with iC2, iC3 and iC4 subgroups. Three new genes (GRAP2, ICAM3 and A2ML1) correlated with prognosis were identified. Conclusion: Integrated multi-omics analysis provides fresh insight into molecular classification of pancreatic cancer, which may help discover new prognostic biomarkers and reveal the underlying mechanism of pancreatic cancer.