Experimental and modeling work, described in this article, is focused on the metabolic pathway of Chinese hamster ovary (CHO) cells, which are the preferred expression system for monoclonal antibody protein production. CHO cells are one of the primary hosts for monoclonal antibodies production, which have extensive applications in multiple fields like biochemistry, biology and medicine. Here, an approach to explain cellular metabolism with in silico modeling of a microkinetic reaction network is presented and validated with unique experimental results. Experimental data of 25 different fed-batch bioprocesses included the variation of multiple process parameters, such as pH, agitation speed, oxygen and CO 2 content, and dissolved oxygen. A total of 151 metabolites were involved in our proposed metabolic network, which consisted of 132 chemical reactions that describe the reaction pathways, and include 25 reactions describing N-glycosylation and additional reactions for the accumulation of the produced glycoforms. Additional eight reactions are considered for accumulation of the N-glycosylation products in the extracellular environment and one reaction to correlate cell degradation. The following pathways were considered: glycolysis, pentose phosphate pathway, nucleotide synthesis, tricarboxylic acid cycle, lipid synthesis, protein synthesis, biomass production, anaplerotic reactions, and membrane transport. With the applied modeling procedure, different operational scenarios and fed-batch techniques can be tested.

In this work, the kinetic model based on the previously developed metabolic and glycan reaction networks of the ovarian cells of the Chinese hamster ovary (CHO) cell line was improved by the inclusion of transcriptomic data that took into account the values of the RPKM gene (Reads per Kilobase of Exon per Million Reads Mapped). The transcriptomic (RNASeq) data were obtained together with metabolic and glycan data from the literature, and the concentrations with RPKM values were collected at several points in time from two fed‐batch processes. First, the fluxes were determined by regression analysis of the metabolic data, then these fluxes were corrected by using the fold change in gene expression as a measure of enzyme concentrations. Next, the corrected fluxes in the kinetic model were used to calculate the concentration profiles of the metabolites, and literature data were used to evaluate the predicted results of the model. Compared to other studies where the concentration profiles of CHO cell metabolites were described using a kinetic model without consideration of RNA‐Seq data to correct the fluxes, this model is unique. The additional integration of transcriptomic data led to better predictions of metabolic concentrations in the fed‐batch process, which is a significant improvement of the modelling technique used.

Background: DNA methylation has been previously shown to have diagnostic and predictive potential for colorectal cancer (CRC). Aim of this study was to evaluate putative methylation markers in the context of early cancer development and diagnostics as well as further investigate the biological significance of these regions. Methods: Biomarker discovery was done by whole genome bisulfite sequencing (WGBS) of 88 CRC, 48 advanced adenoma (AA) and corresponding adjacent normal tissue (NAT) samples. Short-list of significantly hypermethylated regions (DMRs) was correlated to transcriptomics data from 512 CRC patients in The Cancer Genome Atlas (TCGA) cohort. Pathway enrichment for biological pathway analysis of the DMRs was done by using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. Survival analysis was performed using Kaplan–Meier method on sub-groups of patients divided by the methylation status of individual markers. Finally, individual marker significance of selected regions was evaluated by analyzing 26 plasma samples from early stage (stage I-IIA) CRC samples and 42 colonoscopy verified controls (CNT) with targeted methylation sequencing assay. Results: 4167 putative marker regions were identified from biomarker discovery with WGBS. Differential signal could be observed both between AA and NAT and CRC and NAT, while several of these regions were differentially methylated also between AA and CRC samples, indicating biological signal change with adenoma progression to cancer. 84 hypermethylated DMRs from several verification studies were further evaluated against transcriptome data from TCGA, where overlap for 69 genes was found. 19 of these genes showed a significant down- regulation (p< 0.05), indicating a link between hypermethylation and gene expression. 2 genes showed significant up-regulation (p< 0.05), which could indicate other epigenic processes to be in place. KEGG pathway analysis revealed that the top pathways involved were axonal guidance, ephrin receptor signaling, epithelial-mesenchymal transition and FGF signaling, which all play significant role in the context of cancer development and progression. Kaplan-Meier analysis showed significant correlation to patients 5- year survival prediction linked to 3 genes: FGF14 (p=0.025, HR = 1.75) DPY19L2P1 (p=0.012, HR = 1.86), PTPRO (p=0.046, HR = 1.63). Targeted sequencing analysis on plasma samples of patients with early stage (I-IIA) colorectal cancer and age and gender matching colonoscopy-verified controls, showed high individual marker accuracy with AUC= 0.78 for FGF14, AUC= 0.81 for DPY19L2P1 and AUC= 0.73 for PTPRO. Conclusions: Methylation markers have distinct signals in early development of CRC, with high individual accuracy for separating early-stage cancers from matching controls. These regions have impact on gene expression and can be linked to relevant biological pathways. Extending early detection potential of the markers to further prognostics and stratification, could lead to better outcomes and improved survival of the patients. Citation Format: Pol Canal Noguer, Alejandro Requena Bermejo, Francesco Mattia Mancuso, Juan Carlos Higareda, Marina Manrique López, Marko Chersicola, Pablo Pérez Martínez, Pablo Antonio Camino, Primoz Knap, Vivian Erklavec Zajec, Kristi Kruusmaa. Methylation biomarkers for colorectal cancer early detection and survival prognostics impact gene expression and link to cancer-related biological pathways. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P028.

201 Background: Colorectal cancer (CRC) remains a leading cause of cancer related mortality worldwide. We utilized cell-free DNA (cfDNA) methylation and fragmentation characteristics of selected cancer-related biomarker regions and applied tumor-derived signal deduction and a machine learning algorithm to refine a blood test for the early detection of CRC. Methods: This was a prospective, international (Spain, Ukraine, Germany and USA [part of NCT04792684 study] population), observational cohort study. Plasma samples were collected either prior to a scheduled screening colonoscopy or prior to colonic surgery for primary CRC. 95 cfDNA samples from 48 early stage (I-II), 47 late-stage (III-IV) CRC patients (mean age 65 [48-83], female 45%, distal cancers 51%) and 204 age, gender and country of origin matched colonoscopy-checked controls were analyzed. 79 of the control patients had a negative colonoscopy finding (cNEG), 96 had benign findings of diverticulosis, hemorrhoids and/or hyperplastic polyps (BEN), 29 had non-advanced adenomas (NAA). Samples were analyzed utilizing previously described hybrid-capture based sequencing methodology. Panel of targeted biomarkers was previously identified through tissue- and plasma-based discovery and further narrowed down through cancer-related biological pathways analysis workflow. Individual cfDNA fragments belonging to each biomarker region were scored for cancer-specific signal. Finally, calculated scores were used in prediction model building and testing for establishing panel accuracy for CRC detection. Results: Prediction model utilizing a panel of methylation and fragmentation scores originating from cfDNA biomarkers belonging to relevant cancer development and progression related pathways, such as axonal guidance, ephrin receptor signaling, epithelial-mesenchymal transition and FGF signaling, correctly classified 92% (87/95) of CRC patients. Sensitivity per cancer stage ranged from 91% (21/23) for stage I, 92% (23/25) for stage II, 91% (30/33) for stage III and 93% (13/14) for stage IV. Fragmentation signals contributed most to early-stage cancers (I-II), while methylation signals were more significant for late stage (III-IV) detection. Specificity of the model was 94% (199/204), with 97% (28/29) NAA, 91% (87/96) BEN and 96% (76/79) cNEG patients correctly identified. Lesion location, gender, age and country of origin were not significantly correlated to prediction outcome. Conclusions: Use of methylation and fragmentation characteristics of cancer-related cfDNA regions, combined with a machine-learning algorithm is highly accurate for early-stage (I-II) CRCs (92% sensitivity at 94% specificity). The study is being further expanded on larger cohort for validation of a highly accurate and minimally invasive blood-based CRC screening test.

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