Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

Momenzadeh, A.; Kreimer, S.; Guo, D.; Ayres, M.; Berman, D.; Chyu, K.-Y.; Shah, P. K.; Milewicz, D.; Azizzadeh, A.; Meyer, J. G.; Parker, S.

doi:10.1101/2023.04.26.538468

bioRxiv · 10.1101/2023.04.26.538468

Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

Momenzadeh, A.·Kreimer, S.·Guo, D.·Ayres, M.·Berman, D.·Chyu, K.-Y.·Shah, P. K.·Milewicz, D.·Azizzadeh, A.·Meyer, J. G.·Parker, S.

View original source ↗Export metadata ↓

Abstract

BackgroundDescending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. MethodsThis study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. ResultsOf the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. ConclusionsWe conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

Explore related subjects

systems biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Momenzadeh, A., Kreimer, S., Guo, D., Ayres, M., Berman, D., Chyu, K.-Y., Shah, P. K., Milewicz, D., Azizzadeh, A., Meyer, J. G., Parker, S.. 2023-04-28. Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease. https://doi.org/10.1101/2023.04.26.538468

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Parsimonious Creatures: Building Gene Regulatory Networks Robust to Somatic Evolution

For early multicellular life to have formed and maintained cellular organization, preventing loss of controlled cell replication due to somatic evolution was of critical importance, and remains important to this day for control of cancer. We study how gene regulatory networks (GRNs) can be designed to slow the divergence of somatic clonal lineages in the context of early multicellular life. We model the mutation of GRNs within simplified 12-celled organisms we term "parsimonious creatures", who must replicate from a single cell to 12 cells and then prevent replication past 12 cells to maximize fitness. We found two minimal motifs can achieve these goals, but both are prone to breakdown through somatic evolution: The feedforward motif, which is loosely regulating but relatively robust, and the simple step motif, which uses strong interactions and feedback to form memoryless ultrasensitivity, but is fragile. These simple motifs may be duplicated to form redundant networks via one of two structures: averaging redundancy, which averages multiple sources of information, and min redundancy, which adopts the most conservative source. Averaging redundancy uniformly slows somatic evolution, while min redundancy stalls somatic evolution until neutral mutations accumulate. Either may be superior dependent on life history. However, averaging redundancy slowly increases fitness as the network becomes larger, but large min redundancy networks eventually become "paralyzed", increasing the probability of senescence and reducing fitness. These GRN designs are idealized, yet provide imperfect solutions with fundamental tradeoffs, offering insight as to why somatic evolution remains an incompletely solved problem for multicellular life.

systems biology↗

bioRxiv2026

Homogeneous network inference for subtype-stratified master regulator analysis in cancer

Master-regulator analysis of 34 TCGA tumour cohorts (cancer x molecular subtype; n between 27 and 562 tumours) requires one regulatory network per cohort. Mutual-information (MI) networks inferred with ARACNe3 are the usual choice, but they are not available for the smallest cohorts; filling the gap with correlation networks would mix two methods inside the very comparison between subtypes that the analysis is meant to make. We therefore infer every network with a single procedure, fixed-size Spearman regulons (FSR): rank correlation between regulator and target, a fixed effect-size threshold rho >= 0.2, the per-target data-processing-inequality (DPI) pruning implemented in corto, bootstrap support, and truncation to the 100 best-supported targets per regulator. We quantify what this choice concedes and what it provides, labelling each claim as a standard result, a heuristic, or an empirical result. Rank correlation is more powerful than the MI estimators we simulated for n < 200 but cannot see non-monotone dependence, which in a synthetic worst case costs at most about 0.05-0.10 of the master regulators recovered; the per-target DPI is a heuristic with a measurable disagreement rate with the MI-based one; and pairwise dependence is not direct interaction, for MI and correlation alike. Where both methods are feasible (n >= 95) they overlap in 0.7 of the top-20 regulators, the same as two filterings of the same ARACNe3 network, although the rank correlation of the full score vector shows a measurable method effect in the tail. Subsampling large real cohorts indicates that networks inferred from fewer than 60 tumours do not reliably reproduce their own cohort's master regulators, that 60-100 is a grey zone, and that a cancer-wide network does not rescue small subtypes. We present FSR as an adequate and uniform choice for multi-cohort analysis, not as a statistically superior one.

systems biology↗

bioRxiv2026

Low-fidelity simulator selection for multi-fidelity inference of epithelial vertex models

Vertex models link cell division, neighbour exchange, and intercellular forces to epithelial tissue form through parameters such as junctional line tension and perimeter contractility. Bayesian inference of these parameters from data quantifies their uncertainty, but for stochastic models of proliferating tissues it requires many costly simulations. Multi-fidelity simulation-based inference reduces this cost by training on many runs of a cheaper, less accurate ('low-fidelity') simulator before refining with a few full ('high-fidelity') runs. Low-fidelity versions can be constructed in several ways, for example by stopping simulations early, coarsening the timestep, or averaging fewer replicates, but it is unclear which to choose before committing to costly training. To address this, we introduce PRISM (Paired Ranking Index for Simulator Matching), a screen that compares each candidate with the full simulator on paired runs, scoring agreement in cell-packing statistics against cost. For a vertex model of a proliferating tissue, PRISM flags stopping early as unreliable before training; once trained, this choice can produce confidently wrong parameter estimates. By contrast, a coarser timestep, within the range tested, is cheap and accurate; while fewer replicates are accurate but too costly. Using a screened low-fidelity simulator, multi-fidelity inference outperforms approximate Bayesian computation and neural-network inference trained on full simulations alone, at matched cost and with well-calibrated uncertainty. The same pattern recurs for a second vertex model fitted to published Drosophila pupal-wing imaging data, although there stopping halfway proves safe. Together, our findings suggest that which approximations are safe depends on the model, and that screening them before training makes multi-fidelity inference of tissue mechanics practical.

systems biology↗