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. 2025 Sep 1;6(5):412-424.
doi: 10.1158/2643-3230.BCD-25-0049. Epub 2025 May 28.

Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia

Haley Newman  1 , Shawn H R Lee  2 , Petri Pölönen  3 , Rawan Shraim  1 , Yimei Li  4 , Hongyan Liu  4 , Richard Aplenc  1 , Shovik Bandyopadhyay  5 , Changya Chen  4 , Meenakshi Devidas  6 , Caroline Diorio  1 , Kimberly Dunsmore  7 , Omar Elghawy  5 , Amira Elhachimi  4 , Tori Fuller  4 , Sumit Gupta  8 , Junior Hall  4 , Andrew D Hughes  4 , Stephen P Hunger  1 , Mignon L Loh  9 , Zachary Martinez  4 , Michael F McCoy  10 , Cassidy G Mullen  4 , Stanley B Pounds  11 , Elizabeth Raetz  12 , Anna Eames Seffernick  6 , Gongping Shi  1 , Jonathan Sussman  5 , Kai Tan  1 , Lahari Uppuluri  4 , Tiffaney L Vincent  10 , Ruth Wang'ondu  6 , Lena E Winestone  13 , Stuart S Winter  14 , Brent L Wood  15 , Gang Wu  3 , Jason Xu  16 , Wenjian Yang  11 , Charles G Mullighan  3 , Jun J Yang  3 , Kira Bona  17 , David T Teachey  1
Affiliations

Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia

Haley Newman et al. Blood Cancer Discov. .

Abstract

The influence of genetic ancestry on genomics in T-cell Acute Lymphoblastic Leukemia (T-ALL) has not been fully explored. We examined the impact of genetic ancestry on multi-omic alterations, survival outcomes, and risk stratification. Among 1309 children and young adults with T-ALL treated on the Children's Oncology Group trial AALL0434, the prognostic value of five commonly altered T-ALL genes varied by ancestry-including NOTCH1, which was associated with superior overall survival for patients of European ancestry but non-prognostic among patients of African ancestry. Integrating genetic ancestry with published T-ALL risk classifiers, we identified that a X01 Penalized Cox Regression classifier stratified patients regardless of ancestry, whereas a European multi-gene classifier misclassified patients of certain ancestries. Overall, 80% of patients harbored a genomic alteration in at least one gene with differential prognostic impact in an ancestry-specific manner. These data demonstrate the importance of incorporating genetic ancestry into genomic risk classification.

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Conflict of interest statement

S. Bandyopadhyay reports grants from the NIH during the conduct of the study. C. Diorio reports personal fees from Merck outside the submitted work. S.P. Hunger reports other support from Amgen, Jazz Pharmaceuticals, and Servier outside the submitted work. M.L. Loh reports personal fees from Jazz Pharmaceuticals outside the submitted work. S.B. Pounds reports grants from the NIH during the conduct of the study. E. Raetz reports other support from Bristol Myers Squibb and grants from Pfizer outside the submitted work. A.E. Seffernick reports other support from Eli Lilly and Company outside the submitted work; employment with Lilly and ownership of Lilly stock; and that this work was completed prior to her employment at Lilly, she is acting entirely on her own, and these endeavors are not in any manner affiliated with Lilly. B.L. Wood reports grants from the COG during the conduct of the study as well as personal fees from Amgen outside the submitted work. C.G. Mullighan reports personal fees from Illumina during the conduct of the study as well as grants from Pfizer and AbbVie and personal fees from Amgen outside the submitted work and that he has a patent for Cyrus with royalties paid. J.J. Yang reports grants from Takeda Pharmaceutical Company and AstraZeneca outside the submitted work. D.T. Teachey reports grants from the NIH, Hyundai Hope on Wheels, Alex’s Lemonade Stand Foundation, Pennsylvania Department of Health, St. Baldrick’s Foundation, Harris Willing Memorial Research Fund, The Invisible Prince Foundation, and the Aiden Everett Davies Innovation Fund during the conduct of the study as well as nonfinancial support from Amgen, Johnson & Johnson Innovation, Sobi, Servier, Jazz Pharmaceuticals, Novartis, and Pfizer; grants and nonfinancial support from BEAM Therapeutics; and grants from NeoImmuneTech outside the submitted work. No disclosures were reported by the other authors.

Figures

Figure 1.
Figure 1.
T-ALL subtype and Uniform Manifold Approximation and Projection for Dimension Reduction by genetic ancestry. A, Patients with T-ALL categorized by genetic ancestry (see “Methods” section, “Exposure: Genetic Ancestry”) and T-ALL subtype (Supplementary Table S1). P values are calculated using the χ2 test with European ancestry as the reference group (*, P < 0.001). B, Uniform Manifold Approximation and Projection for Dimension Reduction scatterplot of T-ALL subtypes with individuals of predominantly African ancestry shown as black dots with red outlines. Modified from Pölönen and colleagues (16) with permission. TCR, T-cell receptor; TME, tumor microenvironment.
Figure 2.
Figure 2.
Survival outcomes of patients with T-ALL by genetic ancestry. See the “Methods” section, “Exposure: Genetic Ancestry,” for details on genetic ancestry determination. A, Kaplan–Meier EFS for all patients, patients of predominantly admixed American ancestry versus predominantly European ancestry, and patients of predominantly African ancestry versus predominantly European ancestry. B, Kaplan–Meier OS for all patients, patients of predominantly admixed American ancestry vs. European ancestry, and patients of predominantly African ancestry vs. predominantly European ancestry. P values are calculated using the log-rank test.
Figure 3.
Figure 3.
EFS by NOTCH pathway alteration and risk classifiers by genetic ancestry. See the “Methods” section, “Exposure: Genetic Ancestry” for details on genetic ancestry determination. A, EFS by NOTCH pathway alteration (NOTCH, pathogenic variant in NOTCH1, FBXW7, and/or ZMIZ1; no NOTCH, no pathogenic variant in NOTCH1, FBXW7, and/or ZMIZ1) among all patients and patients of predominantly European, admixed American, and African ancestry. P values are calculated using the log-rank test. B, EFS by the European NGS classifier: low risk, low-risk NGS (NOTCH1/FBXW7, PHF6, or EP300 mutations in the absence of DNMT3A, IDH1/2, IKZF1, N/K-RAS, PIK3CA, PIK3R1, PTEN, and TP53 mutations), low WBC (<200 × 109/L), and negative MRD status (<10−4); high risk, high-risk NGS (not meeting low-risk mutational criteria) and high WBC (>200 × 109/L) or positive MRD (≥10−4); and intermediate risk, all others not classified as low-risk or high-risk. Data are presented for all patients and among patients of predominantly European, admixed American, and African ancestry. P values are calculated from the log-rank test. C, EFS by the X01 penalized Cox regression model in risk score quartiles for all patients and among patients of predominantly European, admixed American, and African ancestry. P values are calculated using the log-rank test.
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