New Approaches to Treating Challenging Subtypes of ALL in AYA Patients

Long-Term Outcomes among Adolescent and Young Adult Survivors of Acute Leukemia: A Surveillance, Epidemiology, and End Results Analysis

BACKGROUND

There is a growing population of adolescent and young adult (AYA, age 15-39 years) acute leukemia survivors in whom long-term mortality outcomes are largely unknown.

METHODS

The current study utilized the Surveillance, Epidemiology, and End Results (SEER) registry to assess long-term outcomes of AYA acute leukemia 5-year survivors. The impact of diagnosis age, sex, race/ethnicity, socioeconomic status, and decade of diagnosis on long-term survival were assessed utilizing an accelerated failure time model.

RESULTS

A total of 1,938 AYA acute lymphoblastic leukemia (ALL) and 2,350 AYA acute myeloid leukemia (AML) survivors diagnosed between 1980 and 2009 were included with a median follow-up of 12.3 and 12.7 years, respectively. Ten-year survival for ALL and AML survivors was 87% and 89%, respectively, and 99% for the general population. Survival for AYA leukemia survivors remained below that of the age-adjusted general population at up to 30 years of follow-up. Primary cancer mortality was the most common cause of death in early survivorship with noncancer causes of death becoming more prevalent in later decades of follow-up. Male AML survivors had significantly worse survival than females (survival time ratio: 0.61, 95% confidence interval: 0.45-0.82).

CONCLUSIONS

AYA leukemia survivors have higher mortality rates than the general population that persist for decades after diagnosis.

IMPACT

While there have been improvements in late mortality, long-term survival for AYA leukemia survivors remains below that of the general population. Studies investigating risk factors for mortality and disparities in late effects among long-term AYA leukemia survivors are needed.

Early T-Cell Precursor ALL and Beyond: Immature and Ambiguous Lineage T-ALL Subsets

Simple Summary

Immature T-cell acute lymphoblastic leukemias englobes a wide range of low prevalence subtypes, not well identified, that in some cases overlap with myeloid lineage subtypes. Globally, this “grey zone” of immature leukemias, are difficult to precisely diagnose using a classical immunophenotypic approach. Interesting, genomic data collected during last years has shown that these subtypes share several genomic alterations, raising the question of how their phenotypes reflect distinct AL entities. Here we provide a systematic overview of the genetic events associated with immature T-ALL and outline their relationship with treatment choices and outcomes. Our goal is to offer a basis for using the genetic information for new diagnostic algorithms. An immunogenetic classification of these immature subtypes will better stratify patients and improve their management with more efficient and personalized therapeutic options.

Abstract

A wide range of immature acute leukemias (AL), ranging from acute myeloid leukemias with minimal differentiation to acute leukemias with an ambiguous lineage, i.e., acute undifferentiated leukemias and mixed phenotype acute leukemia with T- or B-plus myeloid markers, cannot be definitely assigned to a single cell lineage. This somewhat “grey zone” of AL expresses partly overlapping features with the most immature forms of T-cell acute lymphoblastic leukemia (T-ALL), i.e., early T-cell precursor ALL (ETP-ALL), near-ETP-ALL, and pro-T ALL. These are troublesome cases in terms of precise diagnosis because of their similarities and overlapping phenotypic features. Moreover, it has become evident that they share several genomic alterations, raising the question of how their phenotypes reflect distinct AL entities. The aim of this review was to provide a systematic overview of the genetic events associated with immature T-ALL and outline their relationship with treatment choices and outcomes, especially looking at the most recent preclinical and clinical studies. We wish to offer a basis for using the genetic information for new diagnostic algorithms, in order to better stratify patients and improve their management with more efficient and personalized therapeutic options. Understanding the genetic profile of this high-risk T-ALL subset is a prerequisite for changing the current clinical scenario.