Mahteme Bekele Muleta

Introduction: The prevalence of end-stage renal disease has raised the need for renal replacement therapy over recent decades. Even though a kidney transplant offers an improved quality of life and lower cost of care than dialysis, graft failure is possible after transplantation. Hence, this study aimed to predict the risk of graft failure among post-transplant recipients in Ethiopia using the selected machine learning prediction models. Methodology: The data was extracted from the retrospective cohort of kidney transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. In response to the imbalanced nature of the data, we performed hyperparameter tuning, probability threshold moving, tree-based ensemble learning, stacking ensemble learning, and probability calibrations to improve the prediction results. Merit-based selected probabilistic (logistic regression, naive Bayes, and artificial neural network) and tree-based ensemble (random forest, bagged tree, and stochastic gradient boosting) models were applied. Model comparison was performed in terms of discrimination and calibration performance. The best-performing model was then used to predict the risk of graft failure. Results: A total of 278 completed cases were analyzed, with 21 graft failures and 3 events per predictor. Of these, 74.8% are male, and 25.2% are female, with a median age of 37. From the comparison of models at the individual level, the bagged tree and random forest have top and equal discrimination performance (AUC-ROC = 0.84). In contrast, the random forest has the best calibration performance (brier score = 0.045). Under testing the individual model as a meta-learner for stacking ensemble learning, the result of stochastic gradient boosting as a meta-learner has the top discrimination (AUC-ROC = 0.88) and calibration (brier score = 0.048) performance. Regarding feature importance, chronic rejection, blood urea nitrogen, number of post-transplant admissions, phosphorus level, acute rejection, and urological complications are the top predictors of graft failure. Conclusions: Bagging, boosting, and stacking, with probability calibration, are good choices for clinical risk predictions working on imbalanced data. The data-driven probability threshold is more beneficial than the natural threshold of 0.5 to improve the prediction result from imbalanced data. Integrating various techniques in a systematic framework is a smart strategy to improve prediction results from imbalanced data. It is recommended for clinical experts in kidney transplantation to use the final calibrated model as a decision support system to predict the risk of graft failure for individual patients.

Supplementary Table from African Ancestry–Associated Gene Expression Profiles in Triple-Negative Breast Cancer Underlie Altered Tumor Biology and Clinical Outcome in Women of African Descent

Supplementary Table from African Ancestry–Associated Gene Expression Profiles in Triple-Negative Breast Cancer Underlie Altered Tumor Biology and Clinical Outcome in Women of African Descent

Supplementary Data from African Ancestry–Associated Gene Expression Profiles in Triple-Negative Breast Cancer Underlie Altered Tumor Biology and Clinical Outcome in Women of African Descent

Supplementary Data from African Ancestry–Associated Gene Expression Profiles in Triple-Negative Breast Cancer Underlie Altered Tumor Biology and Clinical Outcome in Women of African Descent

Supplementary Table from African Ancestry–Associated Gene Expression Profiles in Triple-Negative Breast Cancer Underlie Altered Tumor Biology and Clinical Outcome in Women of African Descent

Women of sub-Saharan African descent have disproportionately higher incidence of triple-negative breast cancer (TNBC) and TNBC-specific mortality across all populations. Population studies show racial differences in TNBC biology, including higher prevalence of basal-like and quadruple-negative subtypes in African Americans (AA). However, previous investigations relied on self-reported race (SRR) of primarily U.S. populations. Due to heterogeneous genetic admixture and biological consequences of social determinants, the true association of African ancestry with TNBC biology is unclear. To address this, we conducted RNA sequencing on an international cohort of AAs, as well as West and East Africans with TNBC. Using comprehensive genetic ancestry estimation in this African-enriched cohort, we found expression of 613 genes associated with African ancestry and 2,000+ associated with regional African ancestry. A subset of African-associated genes also showed differences in normal breast tissue. Pathway enrichment and deconvolution of tumor cellular composition revealed that tumor-associated immunologic profiles are distinct in patients of African descent.Significance:Our comprehensive ancestry quantification process revealed that ancestry-associated gene expression profiles in TNBC include population-level distinctions in immunologic landscapes. These differences may explain some differences in race–group clinical outcomes. This study shows the first definitive link between African ancestry and the TNBC immunologic landscape, from an African-enriched international multiethnic cohort.See related commentary by Hamilton et al., p. 2496.This article is highlighted in the In This Issue feature, p. 2483

Supplementary Figure from African Ancestry–Associated Gene Expression Profiles in Triple-Negative Breast Cancer Underlie Altered Tumor Biology and Clinical Outcome in Women of African Descent

Supplementary Table from African Ancestry–Associated Gene Expression Profiles in Triple-Negative Breast Cancer Underlie Altered Tumor Biology and Clinical Outcome in Women of African Descent

Women of sub-Saharan African descent have disproportionately higher incidence of triple-negative breast cancer (TNBC) and TNBC-specific mortality across all populations. Population studies show racial differences in TNBC biology, including higher prevalence of basal-like and quadruple-negative subtypes in African Americans (AA). However, previous investigations relied on self-reported race (SRR) of primarily U.S. populations. Due to heterogeneous genetic admixture and biological consequences of social determinants, the true association of African ancestry with TNBC biology is unclear. To address this, we conducted RNA sequencing on an international cohort of AAs, as well as West and East Africans with TNBC. Using comprehensive genetic ancestry estimation in this African-enriched cohort, we found expression of 613 genes associated with African ancestry and 2,000+ associated with regional African ancestry. A subset of African-associated genes also showed differences in normal breast tissue. Pathway enrichment and deconvolution of tumor cellular composition revealed that tumor-associated immunologic profiles are distinct in patients of African descent.Significance:Our comprehensive ancestry quantification process revealed that ancestry-associated gene expression profiles in TNBC include population-level distinctions in immunologic landscapes. These differences may explain some differences in race–group clinical outcomes. This study shows the first definitive link between African ancestry and the TNBC immunologic landscape, from an African-enriched international multiethnic cohort.See related commentary by Hamilton et al., p. 2496.This article is highlighted in the In This Issue feature, p. 2483