T-cell lymphoblastic lymphoma (T-LBL) is a rare and highly aggressive form of non-Hodgkin lymphoma (NHL) with poor clinical outcomes and lacks of standard treatment approaches. It is generally recognized that T-LBL patients should be treated as acute lymphoblastic leukemia (ALL). However, adult patients of T-LBL can’t benefit from such intensive treatments so much as pediatric patients. “After these adult patients achieved first complete remission (CR1), concerning stem cell transplantation, to do or not to do, it is a question”, said Prof. Cai, the corresponding author of the study, recently published in Leukemia. “In the era of precision medicine, to solve this problem, we need to establish a reliable prognostic prediction model to foresee who will benefit those intensive treatment and transplantation. However, due to the low incidence rate of T-LBL, it is not an easy task”, said Dr. Tian, the first author of this study.
With the advance of molecular biology, the advent of “big data era” has become a reality. A series of molecular prediction models using high-throughput biochips, whole genome sequencing, machine-learning research and other high-dimensional data statistical models, have shown encouraging results. “It could be an opportunity for us to open the door of T –LBL world and let’s see how this goes inside the molecular biology”, said prof. Cai, “and our team has begun the exploration of developing T-LBL prognostic prediction models soon afterwards”.
In recent years, miRNA, an epigenetic marker, has attracted researchers' attention as a new molecular predictor. “The clinical significance of miRNAs in T-LBL has not been established, maybe we could start our preliminary attempt in this field”, said Dr.Tian. Finally, we identified five miRNAs (miR-513a, miR-21, miR-19b-3p, miR-638, and miR-26a-5p), which are closely related to the prognosis of adult T-LBL. A five-miRNA classifier was established to predict recurrence of adult T-LBL and assist in selecting patients who may benefit from HSCT, and the results have been published in Leukemia last year.1 “It could be a useful tool”, said prof. Cai, “to save the expensive cost of transplantation for low-risk patients, lighten the economic burden, and reduce treatment-related toxicity. However, this miRNA-based classifier didn’t have a commercial platform, which limits its general use in clinical practice”.
In the concurrent study, we further explored the link between the genome-wide gene spectrum and the prognosis of T-LBL. “The aim of the present study was to establish a prognostic prediction model by differentially expressed candidate genes, and conduct a robust nomogram that incorporates the newly established gene-based classifier, to stratify patients at risk”, said prof. Cai. Interestingly, when the former miRNA classifier is added to this nomogram, the predictive accuracy was significantly improved. In terms of genetic data analysis technology, Dr. Tian introduced that “the NanoString platform used in the current study was approved by the USA Food and Drug Administration, which ensures its general clinical applicability2. Moreover, the mRNA platform is certified by the Clinical Laboratory Improvement Amendments, and its authority is universally recognized”. As a result, the platform used for gene expression-based clinical testing in our study was proved to be reliable.
It is well known that methylation is another area of epigenetics that has received extensive attention. “One of our ongoing research is aimed at further exploring the association between DNA methylation and T-LBL prognosis, and we expect to establish a methylation classifier to assist in the diagnosis and treatment of T-LBL. We believe it will be an interesting research field”, said prof. Cai.
“For a long time, our team has been committed to exploring the prognostic factors of T-LBL, and we sincerely hope that this series of prognostic prediction models we have established can be widely applied to clinical practice and contribute to individual treatment decision-making”, which is the original and final goal of prof. Cai and her research team.
Written by Qingqing Cai,Xiaopeng Tian,Liang Wang,Shuyun Ma.
 Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China;
 Department of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
1. Tian XP, Huang WJ, Huang HQ, et al: Prognostic and predictive value of a microRNA signature in adults with T-cell lymphoblastic lymphoma. Leukemia 33:2454-2465, 2019
2. Veldman-Jones MH, Brant R, Rooney C, et al: Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples. Cancer Res 75:2587-93, 2015