LAUSR

T-cell lymphoblastic lymphoma (T-LBL) is an aggressive malignancy arising from precursor T cells that occurs mostly in adolescents and young adults, comprising ~2% of all non-Hodgkin’s lymphomas (NHL) patients [1, 2]. Due to the scarcity of this disease, genomic and proteomic biomarkers definitely associated with prognosis and treatment response or drug resistance have not been well recognized. The commonly used prognostic factors, such as typical clinical factors, radiographic findings, and specific molecular biomarkers, have not shown consistent prognostic performance across various studies in the world [3, 4]. Though the prognosis of childhood T-LBL has been significantly improved during the past 10 years due to few emerging new regimens [4], adults with T-LBL have fared poorly in response and outcome, partially due to greater genetic heterogeneity and vunerable tolerance of intensive chemotherapy. Acute lymphoblastic leukemia (ALL)-like chemotherapies, including BFM protocol and hyperCVAD regimen, have been widely used, with the latter one less intensive and slightly better tolerated by adult patients. However, great challenge exists concerning who deem to have good prognosis and should receive less intensive treatment. We read with interest the impressive paper by Xiaopeng Tian et al, published in Leukemia in the recent 2020 February issue [5]. In this study, an eleven-gene-based signature was established in a large cohort of 613 patients with T-LBL/ALL from 28 medical centers across China academic centers. This mRNA signature had excellent performance in predicting the survival outcomes of adults with TLBL. Of more interest to clinical physicians, a nomogram based on this gene signature with other clinicopathological variables was constructed, which could identify those who really benefited from the intensive BFM protocol, thus maximizing the efficacy and minimizing the toxicities in the specific patient populations. With rapid development of Genome-Wide Association Studies (GWAS) and Data mining techniques [6, 7], it becomes possible to explore the story underlying T-LBL at the genetic level. This study by Xiaopeng Tian et al used a clinically applicable NanoString platform that can reliably quantify gene expression level from formalin-fixed paraffin embedded (FFPE) tissues, which are more accessible than fresh lymphoma tissues in our daily practice. These eleven genes (namely BRD2, CSNK1E, HIST2H2B, IGHD, PAK2, MRPL30, RP11-74K11.2, SMN1, VTRNA1-3, ZNF383, YTHDC1) have distinct potential roles involving in various biological pathways and regulatory functions, that could contribute to lymphomagenesis and treatment efficacy or resistance. Thus, this gene signature was able to distinguish patients with different prognosis more precisely via deepen elucidating the heterogeneity of T-LBL. Notwithstanding the importance of this study, several emerging works need to be performed further to facilitate the use of this 11-gene signature-based nomogram, because it is not easily used in calculating the risk score for each individual patient, unless a ready online software is available in the clinical laboratory with integrated bioinformatics system. In addition, this signature should be validated worldwide, specially in Caucasian T-LBL/ALL patient population, before implementation in clinical practice. Despite these weaknesses, we congratulate the authors for this impressive work. To answer the question posed in the title, we expect this gene signature may render clinical values for adults with T-LBL. Comparison of such results with adolescents and young Caucasian T-LBL/ALL patients should also be performed. Correlation of such signature with the underlying genomic and epigenomic alterations will be essential and will likely shed new light for better risk * Ken H. Young [email protected]