Homologous recombination deficiency and molecular subtype are associated with immunogenicity in ovarian cancer.

Aim: There is an unmet need for predictive biomarkers for immune checkpoint blockade in ovarian cancer. Homologous recombination deficiency (HRD) and immunoreactive molecular subtype may be associated with determinants of immunogenicity. Materials & methods: Neoantigen load, tumor inflammation signature (TIS), immune cell infiltrates and individual immune checkpoints were assessed based on HRD status and molecular subtype. Results: Tumors with HRD demonstrated significantly higher expression of neoantigens and multiple immune check points, but not higher TIS scores or increased immune cell infiltrates. Immunoreactive tumors had significantly higher neoantigen expression, TIS scores, immune cell infiltrate and immune checkpoint expression compared with other subtypes. Conclusion: HRD and the immunoreactive molecular subtype signature were associated with multiple determinants of immunogenicity and deserve further exploration as predictive biomarkers.

Low Concordance of Patient-Reported Outcomes With Clinical and Clinical Trial Documentation.

PURPOSE: Health care research increasingly relies on assessment of data extracted from electronic medical records (EMRs). Clinical trial adverse event (AE) logs and patient-reported outcomes (PROs) are sources of data often available in the context of specific research projects. The aim of this study was to evaluate the extent of data concordance from these sources. PATIENTS AND METHODS: Patients enrolled in clinical trials or receiving standard treatment for lung cancer (n = 62) completed validated questionnaires on physical and psychological symptoms at up to three assessment points. Temporally matched documentation was extracted from EMR notes and, for clinical trial participants (n = 41), AE logs. Evaluated data included symptom assessment, vital signs, medication logs, and laboratory values. Agreement (positive, negative) and Cohen's κ coefficients were calculated to assess concordance of symptoms among sources, with PROs considered the gold standard. RESULTS: Patient-reported weight loss correlated significantly with clinical measurements ( t = 2.90; P = .02), and average number of PROs correlated negatively with albumin concentration, supporting PROs as the gold standard. Comparisons of PROs versus EMR yielded poor concordance across 11 physical symptoms, anxiety, and depressive symptoms (all κ < 0.40). Providers under-reported the presence of each symptom in the EMR compared with PROs. AE logs showed similarly poor concordance with PROs (all κ < 0.40, except shortness of breath). Negative agreement among sources was higher than positive agreement for all symptoms except pain. CONCLUSION: There was poor concordance between EMR notes and AE logs with PROs. Findings suggest that EMR notes and AE logs may not be reliable sources for capturing physical and psychological symptoms experienced by patients with lung cancer, supporting use of PRO assessments in oncology practices.

Metabolic systems modeling for cell factories improvement.

Techniques for modeling microbial bioproduction systems have evolved over many decades. Here, we survey recent literature and focus on modeling approaches for improving bioproduction. These techniques from systems biology are based on different methodologies, starting from stoichiometry only to various stoichiometry with kinetics approaches that address different issues in metabolic systems. Techniques to overcome unknown kinetic parameters using random sampling have emerged to address meaningful questions. Among those questions, pathway robustness seems to be an important issue for metabolic engineering. We also discuss the increasing significance of databases in biology and their potential impact for biotechnology.

Kinetically accessible yield (KAY) for redirection of metabolism to produce exo-metabolites.

The product formation yield (product formed per unit substrate consumed) is often the most important performance indicator in metabolic engineering. Until now, the actual yield cannot be predicted, but it can be bounded by its maximum theoretical value. The maximum theoretical yield is calculated by considering the stoichiometry of the pathways and cofactor regeneration involved. Here we found that in many cases, dynamic stability becomes an issue when excessive pathway flux is drawn to a product. This constraint reduces the yield and renders the maximal theoretical yield too loose to be predictive. We propose a more realistic quantity, defined as the kinetically accessible yield (KAY) to predict the maximum accessible yield for a given flux alteration. KAY is either determined by the point of instability, beyond which steady states become unstable and disappear, or a local maximum before becoming unstable. Thus, KAY is the maximum flux that can be redirected for a given metabolic engineering strategy without losing stability. Strictly speaking, calculation of KAY requires complete kinetic information. With limited or no kinetic information, an Ensemble Modeling strategy can be used to determine a range of likely values for KAY, including an average prediction. We first apply the KAY concept with a toy model to demonstrate the principle of kinetic limitations on yield. We then used a full-scale E. coli model (193 reactions, 153 metabolites) and this approach was successful in E. coli for predicting production of isobutanol: the calculated KAY values are consistent with experimental data for three genotypes previously published.

Stability of Ensemble Models Predicts Productivity of Enzymatic Systems.

Stability in a metabolic system may not be obtained if incorrect amounts of enzymes are used. Without stability, some metabolites may accumulate or deplete leading to the irreversible loss of the desired operating point. Even if initial enzyme amounts achieve a stable steady state, changes in enzyme amount due to stochastic variations or environmental changes may move the system to the unstable region and lose the steady-state or quasi-steady-state flux. This situation is distinct from the phenomenon characterized by typical sensitivity analysis, which focuses on the smooth change before loss of stability. Here we show that metabolic networks differ significantly in their intrinsic ability to attain stability due to the network structure and kinetic forms, and that after achieving stability, some enzymes are prone to cause instability upon changes in enzyme amounts. We use Ensemble Modelling for Robustness Analysis (EMRA) to analyze stability in four cell-free enzymatic systems when enzyme amounts are changed. Loss of stability in continuous systems can lead to lower production even when the system is tested experimentally in batch experiments. The predictions of instability by EMRA are supported by the lower productivity in batch experimental tests. The EMRA method incorporates properties of network structure, including stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.

Building carbon-carbon bonds using a biocatalytic methanol condensation cycle.

Methanol is an important intermediate in the utilization of natural gas for synthesizing other feedstock chemicals. Typically, chemical approaches for building C-C bonds from methanol require high temperature and pressure. Biological conversion of methanol to longer carbon chain compounds is feasible; however, the natural biological pathways for methanol utilization involve carbon dioxide loss or ATP expenditure. Here we demonstrated a biocatalytic pathway, termed the methanol condensation cycle (MCC), by combining the nonoxidative glycolysis with the ribulose monophosphate pathway to convert methanol to higher-chain alcohols or other acetyl-CoA derivatives using enzymatic reactions in a carbon-conserved and ATP-independent system. We investigated the robustness of MCC and identified operational regions. We confirmed that the pathway forms a catalytic cycle through (13)C-carbon labeling. With a cell-free system, we demonstrated the conversion of methanol to ethanol or n-butanol. The high carbon efficiency and low operating temperature are attractive for transforming natural gas-derived methanol to longer-chain liquid fuels and other chemical derivatives.