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Predicting kidney graft survival with a machine learning model based on for-cause biopsy transcriptomics.

Valbert Oliveira Costa Filho1, Pedro Robson Costa Passos2, Luis Gustavo Modelli de Andrade3

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We developed a machine learning model using gene expression from kidney biopsies to predict long-term graft survival in kidney transplant recipients. This model accurately identifies high-risk patients, improving transplant care.

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Area of Science:

  • Nephrology
  • Genomics
  • Bioinformatics

Background:

  • Long-term kidney transplant graft survival remains a challenge despite improvements in end-stage renal disease management.
  • Traditional prognostic models have limited predictive accuracy for graft outcomes.
  • For-cause biopsies are routinely performed, offering a potential source of prognostic data.

Purpose of the Study:

  • To develop and compare machine learning (ML) models for predicting kidney graft survival.
  • To utilize gene expression profiles from indication biopsies for prognostic modeling.
  • To assess the diagnostic performance of developed models in identifying rejection.

Main Methods:

  • Collected gene expression data from six Gene Expression Omnibus (GEO) cohorts of for-cause renal biopsies.
  • Identified prognostic genes associated with graft loss using differential expression and Cox regression.
  • Trained and validated 117 ML models, focusing on Gradient Boosting Machine (GBM).
  • Performed external validation across four independent cohorts for diagnostic tasks.

Main Results:

  • Identified 11 key genes associated with graft loss.
  • The GBM model achieved a high C-index (>0.85) and accurately stratified patients by graft survival.
  • External validation demonstrated strong diagnostic performance for overall rejection (AUCs 0.760-0.826).
  • High-risk patients exhibited reduced graft survival and distinct immune pathway enrichment.

Conclusions:

  • A transcriptome-based GBM model accurately predicts kidney graft survival using for-cause biopsy samples.
  • The model shows robust prognostic and diagnostic capabilities across diverse cohorts.
  • The findings highlight the model's potential for integration into routine transplant care due to its practicality and biological relevance.