Mutations in STAT3 and TTN Associated with Clinical Outcomes in Large Granular Lymphocyte Leukemia

Large granular lymphocyte (LGL) leukemia is a rare, chronic leukemia associated with clinical manifestations of anemia (RBC < 4.5 million/mcL in males, < 4 million/mcL in females), neutropenia (ANC < 1500/mm(3)), and autoimmune disease. Progress has been made in identifying a significant and frequent mutation in the STAT3 gene among LGL patients; however, STAT signaling is still largely unexplained in about 60% of those LGL leukemia patients lacking STAT3 mutations. This paper sought to confirm previous studies regarding the association of a STAT3 mutation with clinical manifestations, as well as search for other significant mutations across the rest of the genome in order to determine whether the specific clinical features of autoimmune disease, anemia, and neutropenia present in LGL leukemia patients are associated with additional genomic mutations in LGL cells. As LGL leukemia is rare, presents heterogeneous conditions, and does not have a high mutation burden, our approach is distinct from standard approaches in cancer research where mutation rates are much higher. Methods of dimension reduction are employed in tandem with association analysis and decision trees to search for signals between significant genetic mutations and clinical manifestations of anemia, neutropenia, and autoimmune disease within the LGL patient sample. Results indicate an association exists between anemia and concurrent mutations in STAT3 and TTN (p = 0.03) in T-LGLL patients. Additionally, an association was identified between neutropenia and a mutation in either TTN (p = 0.049) or STAT3 (p = 0.03) in T-LGLL patients as well. These findings imply that TTN may be responsible for STAT activation in combination with a STAT3 mutation or independently in T-LGLL patients. Through XGBoost, 66% accuracy was achieved in predicting neutropenia and 55% accuracy in predicting anemia using gene mutations as the predictor variables. However, the relatively small sample size (N=116 patients), presents concerns of limited statistical power, and the expectation on the number of times these findings might be repeated in independent samples. The ideal sample size needed for an association test to have adequate statistical power was examined. Additionally, a review of past LGL leukemia publications was undertaken to compare the statistical power of their reported analyses. To obtain satisfactory statistical power in analyzing the association between a STAT3 gene mutation and neutropenia in the T-LGLL population (e.g. p <= 0.01, power = 0.9), the T-LGLL sample size must be at least 312 patients. This sample size exceeds not only that of the present study but also that in the majority of sample sizes in the LGLL literature. The pooling of extant LGLL datasets as well as the undertaking of new, major multi-site trials is, therefore, warranted.