Relative Influence of Socioeconomic and Biological Determinants of Outcomes in Adult Acute Lymphoblastic Leukemia

Background

Outcomes in adult patients with acute lymphoblastic leukemia (ALL) have improved in recent decades with the application of pediatric-inspired regimens, the incorporation of novel agents, and more sophisticated utilization of measurable residual disease tracking, yet there is a wide range of outcomes among various subpopulations with ALL. Disease biology, individual patient comorbidities, as well as socioeconomic factors can influence survival among patients with ALL, and recent studies differ in whether treatment at high-volume academic cancer centers can overcome non-biological determinants (Johnston, Blood Advances, 2024; Dykes, Blood, 2022). The objective of this study is to evaluate the relative influence of socioeconomic determinants and biologic determinants of survival in a uniquely diverse cohort of ALL patients treated at a large, urban academic cancer center.

Methods

We retrospectively reviewed all adult patients ≥18 years of age diagnosed with and/or treated for ALL or mixed phenotype acute leukemia (MPAL) at Mount Sinai Hospital between 2011 and 2024. Zip code was used as a surrogate measure for socioeconomic status by extracting publicly available 2022 US Census median household income data for each zip code. Based on this data, we categorized income as high (>$100,000), median ($50,000 - 100,000), low (<$50,000). Race and ethnic classification were based on patient-reported self-identification at hospital presentation. Univariable and multivariable Cox regression analyses of overall survival (OS) by socioeconomic and prognostic values were performed. Odds ratios and hazard ratios were presented with their 95% confidence intervals. Analysis was conducted with R software 4.0.3 and Python version 3.12.4.

Results

We identified 194 adult patients with ALL treated at Mount Sinai during the study's time period. Median age was 46 years (range: 18 - 79 years). The cohort included 89 (46%) non-Hispanic white patients, 77 (40%) Hispanic patients, and 28 (14%) non-Hispanic African American patients. Twenty-three percent (n=45) of patients were from high-income zip codes, 61% (n=118) were from medium-income areas and 16% (n=31) lived in low-income zip codes. ALL disease subtype included 84% (n=163) B-cell ALL, 2.1% (n=4) MPAL and 14% (n=27) T-cell ALL. Two-thirds of patients had Ph-negative disease (n=114), including 7.7% Ph-like, and 34% of patients were Ph-positive. Additional cytogenetic abnormalities included hyperdiploidy (4.1%), hypodiploidy (2.1%), KMT2A rearrangements (2.6%), complex karyotype (7.2%), and TP53 mutations (3.6%). There was no significant difference in the rates of clinical trial enrollment between Hispanic and non-Hispanic patients (X² = 0.55, p = 0.46). In a multivariable analysis, age (HR, 1.02; 95% CI, 1.00-1.04; p = 0.01) and higher BMI at diagnosis (HR, 1.04; 95% CI, 1.00-1.09; p = 0.02) were independent predictors of poor OS, whereas Hispanic ethnicity (HR, 1.45; 95% CI, 0.80-2.63; P = 0.21) and low median household income (HR, 0.89; 95% CI, 0.39-1.98; P = 0.77) were not statistically significant predictors of poor OS.

Discussion

In this highly diverse patient population, age and BMI were significantly correlated with poorer OS, while race/ethnicity and income were not. Despite the barriers to healthcare access and healthcare discrimination frequently faced by non-white patients and those with fewer economic resources, these factors did not appear to influence survival in this study. Though the retrospective, single-institution nature of this study limits interpretation, the study suggests that some types of traditional socioeconomic disadvantage in ALL care may be overcome in high-volume academic centers.

Mascarenhas:Incyte Corporation: Consultancy, Speakers Bureau; CTI BioPharma/SOBI: Consultancy, Research Funding; Celgene: Consultancy, Other: Travel Support, Speakers Bureau; Novartis: Consultancy, Other: Travel Support , Research Funding, Speakers Bureau; Geron: Consultancy, Research Funding; Kartos: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; PharmaEssentia: Consultancy, Research Funding; GSK: Consultancy; Karyopharm: Consultancy; Sumitomo: Consultancy; Merck: Consultancy; Pfizer: Research Funding; MorphoSys: Consultancy; Blueprint Medicines: Consultancy; Keros: Consultancy; Disc: Consultancy; Ajax: Research Funding; Icahn School of Medicine at Mount Sinai: Current Employment; Roche: Consultancy; Bristol Myers Squibb: Research Funding; NS Pharma: Research Funding; Ariad: Speakers Bureau; Astellas: Research Funding. Tremblay:Sobi: Consultancy, Research Funding; Sumitomo: Research Funding; Cogent Biosciences: Consultancy, Research Funding; Gilead: Research Funding; Novartis: Consultancy; Abbvie: Consultancy; Pharmaessentia: Consultancy; Sierra Oncology: Consultancy; GSK: Consultancy. Feld:Syros Pharmaceuticals: Research Funding; Oryzon Genomics: Research Funding; Taiho Pharmaceutical: Research Funding. Kremyanskaya:Protagonist Therapeutics: Consultancy; Incyte: Consultancy; Constellation/MorphoSys: Consultancy; AbbVie: Consultancy; Agios: Consultancy; Silence Therapeutics: Consultancy; Disc Medicine: Consultancy. Bar-Natan:Amgen: Research Funding; BMS: Research Funding; Incyte: Research Funding. Levavi:Sobi: Consultancy, Other: Advisory Board.