Large-scale transcriptomics analysis reveals a novel stress biomarker in CHO cells producing difficult to express mAbs

Monoclonal antibodies (mAbs) are considered one of the most game-changing products of the biopharmaceutical industry. The introduction of several diverse and complex formats consisting of several polypeptide chains and engineered with multiple antigen-binding domains has made the manufacturability process particularly challenging, especially in the context of assessing expression levels and yields of the formats. Here we present the largest and most diversified CHO transcriptomics analysis consisting of data derived from 892 different monoclonal cell lines, producing 11 different mAbs with various non-standard, highly complex formats. We apply three robust feature selection methods, one traditional differential expression analysis and two machine learning approaches to identify genes correlated to high product titer and quality. Cnpy3 gene is identified as a novel gene biomarker, showing a very strong negative correlation (Pearson r² = 0.94) to the overall format productivity. These results were validated by a hold-out data set from cell lines expressing two different antibody formats. Additionally, the expression of Cnpy3 gene is positively correlated to the structural complexity of the examined mAbs. As complexity increases, cellular stress escalates leading to reduced productivity, implicating Cnpy3 as a strong CHO cell lines stress indicator. Thus, we conclude that Cnpy3 gene has the potential to be used as a screening biomarker for assessing format manufacturability and selecting formats and pools with a high potential to deliver subsequent higher productivity rates, resulting in a substantially smarter cell line and process development.