Biology subjects

Pawitan, Y.

Publications and source records attributed to Pawitan, Y..

3 recordsLinked to original sources

bioRxiv2026

SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data

Spatial transcriptomics data are largely available with RNA expression alone, limiting the detection of cell states defined by surface protein abundance. The lack of multi-omics spatial data limits the ability to identify immune cells and their signaling in the tumor microenvironment, as most solid tumors are immunologically poor and exhibit protein-RNA abundance discordance in critical immune cell surface markers. Although emerging technologies enable spatial multi-omics profiling, technical and cost constraints remain a hurdle. We introduce SR2P, a stacking-based machine-learning framework for predicting spatial protein abundance from RNA expression. SR2P integrates 11 complementary predictive models and consistently outperforms existing methods across multiple spatial multi-omics benchmark. We showcased an application of SR2P recovered macrophage-enriched regions and identified potential immune markers associated with therapeutic response from head-and-neck squamous cell carcinoma patients. SR2P enables protein-abundance inference from RNA-only spatial data, extending the analytical capabilities of current spatial platforms for studies of tumor immunology.

bioinformatics↗

bioRxiv2024

Pathway activation model for personalized prediction of drug synergy

Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce DIPx, an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the models ability to handle novel combinations. The Spearman correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

bioinformatics↗

bioRxiv2018

Levels of histidine-rich glycoprotein in human blood predict accelerated aging

1.Despite recognizing aging as risk factor of human diseases, little is still known about the molecular traits of biological age and mortality risk. To identify age-associated proteins circulating human blood, we screened 156 subjects aged 50-92 years using an exploratory and multiplexed affinity proteomics approach. We corroborated the top age-associated protein profile (adjusted P < 0.001) in eight additional study sets (N = 4,044 individuals), and confirmed a consistent age-associated increase (P = 6.61 x 10-6) by meta-analysis. Applying antibody validation determined circulating histidine-rich glycoprotein (HRG) as the target, and we observed that sequence variants influenced the antibodies ability to bind to the protein. Profiles of circulating HRG were associated to several clinical traits and predicted the risk of mortality during a follow-up period of 8.5 years (IQR = 7.7-9.3 years) after blood sampling (HR = 1.25 per SD; 95% CI = 1.12-1.39; P = 7.41 x 10-5). In conclusion, our affinity proteomics analysis found associations between the molecular traits of circulating HRG with age and all-cause mortality. This suggests that the profiles of multi-purpose protein HRG could serve as an accessible indicator of physiological processes related to aging.

epidemiology↗