Evidence-based review of genomic aberrations in diffuse large B cell lymphoma, not otherwise specified (DLBCL, NOS): Report from the cancer genomics consortium lymphoma working group.

Diffuse large B cell lymphoma, not otherwise specified (DLBCL, NOS) is the most common type of non-Hodgkin lymphoma (NHL). The 2016 World Health Organization (WHO) classification defined DLBCL, NOS and its subtypes based on clinical findings, morphology, immunophenotype, and genetics. However, even within the WHO subtypes, it is clear that additional clinical and genetic heterogeneity exists. Significant efforts have been focused on utilizing advanced genomic technologies to further subclassify DLBCL, NOS into clinically relevant subtypes. These efforts have led to the implementation of novel algorithms to support optimal risk-oriented therapy and improvement in the overall survival of DLBCL patients. We gathered an international group of experts to review the current literature on DLBCL, NOS, with respect to genomic aberrations and the role they may play in the diagnosis, prognosis and therapeutic decisions. We comprehensively surveyed clinical laboratory directors/professionals about their genetic testing practices for DLBCL, NOS. The survey results indicated that a variety of diagnostic approaches were being utilized and that there was an overwhelming interest in further standardization of routine genetic testing along with the incorporation of new genetic testing modalities to help guide a precision medicine approach. Additionally, we present a comprehensive literature summary on the most clinically relevant genomic aberrations in DLBCL, NOS. Based upon the survey results and literature review, we propose a standardized, tiered testing approach which will help laboratories optimize genomic testing in order to provide the maximum information to guide patient care.

Artificial Intelligence in Plasma Cell Myeloma: Neural Networks and Support Vector Machines in the Classification of Plasma Cell Myeloma Data at Diagnosis.

BACKGROUND: Plasma cell neoplasm and/or plasma cell myeloma (PCM) is a mature B-cell lymphoproliferative neoplasm of plasma cells that secrete a single homogeneous immunoglobulin called paraprotein or M-protein. Plasma cells accumulate in the bone marrow (BM) leading to bone destruction and BM failure. Diagnosis of PCM is based on clinical, radiologic, and pathological characteristics. The percent of plasma cells by manual differential (bone marrow morphology), the white blood cell (WBC) count, cytogenetics, fluorescence in situ hybridization (FISH), microarray, and next-generation sequencing of BM are used in the risk stratification of newly diagnosed PCM patients. The genetics of PCM is highly complex and heterogeneous with several genetic subtypes that have different clinical outcomes. National Comprehensive Cancer Network guidelines recommend targeted FISH analysis of plasma cells with specific DNA probes to detect genetic abnormalities for the staging of PCM (4.2021). Recognition of risk categories through training software for classification of high-risk PCM and a novel way of addressing the current approaches through bioinformatics will be a significant step toward automation of PCM analysis. METHODS: A new artificial neural network (ANN) classification model was developed and tested in Python programming language with a first data set of 301 cases and a second data set of 176 cases for a total of 477 cases of PCM at diagnosis. Classification model was also developed with support vector machines (SVM) algorithm in R studio and interactive data visuals using Tableau. RESULTS: The resulting ANN algorithm had 94% accuracy for the first and second data sets with a classification summary of precision (PPV): 0.97, recall (sensitivity): 0.76, f1 score: 0.83, and accuracy of logistic regression of 1.0. SVM of plasma cells versus TP53 revealed a 95% accuracy level. CONCLUSION: A novel classification model based only on specific morphological and genetic variables was developed using a machine learning algorithm, the ANN. ANN identified an association of WBC and BM plasma cell percentage with two of the high-risk genetic categories in the diagnostic cases of PCM. With further training and testing of additional data sets that include morphologic and additional genetic rearrangements, the newly developed ANN model has the potential to develop an accurate classification of high-risk categories of PCM.

BCR-ABL1-like B-Lymphoblastic Leukemia/Lymphoma with FOXP1-ABL1 Rearrangement: Comprehensive Laboratory Identification Allowing Tyrosine Kinase Inhibitor Use.

B-lymphoblastic leukemia/lymphoma (B-ALL) is the most common type of childhood cancer; it also occurs in teenagers and adults, in whom the prognosis is generally less favorable. Therapeutic and molecular advances have substantially improved the treatment for subtypes of B-ALL, such that subclassification by cytogenetic and molecular alterations is critical for risk stratification and management. Novel rearrangements involving ABL1, JAK2, EPO, and other kinases have been identified that may respond to inhibition akin to BCR-ABL1. This diverse group of leukemias has been recognized as a provisional entity in the 2016 revision of the World Health Organization (WHO) Classification of the Hematopoietic Neoplasms as B-lymphoblastic leukemia/lymphoma, BCR-ABL1-like (Ph-like B-ALL). Herein, we present cytogenetic and molecular analysis of a case of B-ALL in a 16-year-old Caucasian boy with t(3;9) FOXP1-ABL1 rearrangement and concurrent loss of IKZF1, CDKN2A, and RB1 gene loci, meeting WHO criteria for Ph-like ALL. This case highlights diagnostic, prognostic, and therapeutic considerations of this recently recognized entity.