Propensity score models are better when post-calibrated

Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, especially when outputted by flexible statistical estimators, do not necessarily behave like probabilities. We perform a simulation study to assess the error in estimating the average treatment effect before and after applying a simple and well-established post-processing method to calibrate the propensity scores. We find that post-calibration reduces the error in effect estimation for expressive uncalibrated statistical estimators, and that this improvement is not mediated by better balancing. The larger the initial lack of calibration, the larger the improvement in effect estimation, with the effect on already-calibrated estimators being very small. Given the improvement in effect estimation and that post-calibration is computationally cheap, we recommend it will be adopted when modelling propensity scores with expressive models.Comment: 15 pages, 6 figure

Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation

Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in the absence of ground truth labels to evaluate the accuracy of such models. To date, attempts at transparent causal effect estimation consist of applying post hoc explanation methods to black-box models, which are not interpretable. Here, we present BICauseTree: an interpretable balancing method that identifies clusters where natural experiments occur locally. Our approach builds on decision trees with a customized objective function to improve balancing and reduce treatment allocation bias. Consequently, it can additionally detect subgroups presenting positivity violations, exclude them, and provide a covariate-based definition of the target population we can infer from and generalize to. We evaluate the method's performance using synthetic and realistic datasets, explore its bias-interpretability tradeoff, and show that it is comparable with existing approaches

A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates

Presentation Mode of Glycans Affect Recognition of Human Serum anti-Neu5Gc IgG Antibodies

Recognition of carbohydrates by antibodies can be affected by antigen composition and density. This had been investigated in a variety of controllable multivalent systems using synthetic carbohydrate antigens, yet such effects on anticarbohydrate antibodies in circulating human serum have not been fully addressed thus far. All humans develop a polyclonal and diverse response against carbohydrates containing a nonhuman sialic acid form, N-glycolylneuraminic acid (Neu5Gc). This red meat-derived monosaccharide is incorporated into a diverse collection of human glycans resulting in circulating anti-Neu5Gc antibodies in human sera. Such antibodies can cause exacerbation of diseases mediated by chronic inflammation such as cancer and atherosclerosis. We aimed to evaluate how different presentation modes of Neu5Gc-glycans can affect the detection of anti-Neu5Gc IgGs in human serum. Here, we compare serum IgG recognition of Neu5Gc-containing glycoproteins, glycopeptides, and synthetic glycans. First, Neu5Gc-positive or Neu5Gc-deficient mouse strains were used to generate glycopeptides from serum glycoproteins. Then we developed a reproducible ELISA to screen human sera against Neu5Gc-positive glycopeptides for detection of human serum anti-Neu5Gc IgGs. Finally, we evaluated ELISA screens against glycopeptides in comparison with glycoproteins, as well as against elaborated arrays displaying synthetic Neu5Gc-glycans. Our results demonstrate that the presentation mode and diversity of Neu5Gc-glycans are critical for detection of the full collection of human serum anti-Neu5Gc IgGs

Sensitive and Specific Detection of the Non-Human Sialic Acid N-Glycolylneuraminic Acid In Human Tissues and Biotherapeutic Products

Humans are genetically defective in synthesizing the common mammalian sialic acid N-glycolylneuraminic acid (Neu5Gc), but can metabolically incorporate it from dietary sources (particularly red meat and milk) into glycoproteins and glycolipids of human tumors, fetuses and some normal tissues. Metabolic incorporation of Neu5Gc from animal-derived cells and medium components also results in variable contamination of molecules and cells intended for human therapies. These Neu5Gc-incorporation phenomena are practically significant, because normal humans can have high levels of circulating anti-Neu5Gc antibodies. Thus, there is need for the sensitive and specific detection of Neu5Gc in human tissues and biotherapeutic products. Unlike monoclonal antibodies that recognize Neu5Gc only in the context of underlying structures, chicken immunoglobulin Y (IgY) polyclonal antibodies can recognize Neu5Gc in broader contexts. However, prior preparations of such antibodies (including our own) suffered from some non-specificity, as well as some cross-reactivity with the human sialic acid N-acetylneuraminic acid (Neu5Ac).We have developed a novel affinity method utilizing sequential columns of immobilized human and chimpanzee serum sialoglycoproteins, followed by specific elution from the latter column by free Neu5Gc. The resulting mono-specific antibody shows no staining in tissues or cells from mice with a human-like defect in Neu5Gc production. It allows sensitive and specific detection of Neu5Gc in all underlying glycan structural contexts studied, and is applicable to immunohistochemical, enzyme-linked immunosorbent assay (ELISA), Western blot and flow cytometry analyses. Non-immune chicken IgY is used as a reliable negative control. We show that these approaches allow sensitive detection of Neu5Gc in human tissue samples and in some biotherapeutic products, and finally show an example of how Neu5Gc might be eliminated from such products, by using a human cell line grown under defined conditions.We report a reliable antibody-based method for highly sensitive and specific detection of the non-human sialic acid Neu5Gc in human tissues and biotherapeutic products that has not been previously described