High-grade B-cell lymphoma with a quadruple-hit genetic profile including concurrent MYC, BCL2, BCL6, and CCND1 gene rearrangements

Several reports of concurrent MYC, BCL2, BCL6, and CCND1 rearrangements in high-grade B-cell lymphoma (HGBL) have been recently described. Herein, we aimed to delineate the scope of this entity through a review of HGBL with a "quadruple-hit" genetic profile identified at our institution. We performed a retrospective review (2015-2023) at our institution of B-cell lymphoma (BCL) cases that were evaluated with concurrent MYC, BCL2, and BCL6 break-apart and IGH::MYC and IGH::CCND1 dual-color dual-fusion fluorescence in situ hybridization studies. Of 203 cases meeting inclusion criteria, 2 (1%) with a quadruple-hit genetic profile were identified. Case 1 represented a 59-year-old female with widespread lymphadenopathy and a diagnosis of HGBL who exhibited primary refractoriness to dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab (DA-EPOCH-R) chemotherapy. Case 2 represented a 58-year-old male with mediastinal and abdominal lymphadenopathy and a diagnosis of large BCL who died from disease after 1 cycle of DA-EPOCH-R chemotherapy. Similarly, a literature review of 7 previously reported cases of HGBL with a quadruple-hit profile also demonstrated aggressive disease behavior. Our study adds 2 new cases to the rarely encountered quadruple-hit HGBL, and a brief meta-analysis of the 9 available cases indicates aggressive disease behavior conferred by this constellation of genetic events.

Image-Based Deep Learning Detection of High-Grade B-Cell Lymphomas Directly from Hematoxylin and Eosin Images

Deep learning applications are emerging as promising new tools that can support the diagnosis and classification of different cancer types. While such solutions hold great potential for hematological malignancies, there have been limited studies describing the use of such applications in this field. The rapid diagnosis of double/triple-hit lymphomas (DHLs/THLs) involving MYC, BCL2 and/or BCL6 rearrangements is obligatory for optimal patient care. Here, we present a novel deep learning tool for diagnosing DHLs/THLs directly from scanned images of biopsy slides. A total of 57 biopsies, including 32 in a training set (including five DH lymphoma cases) and 25 in a validation set (including 10 DH/TH cases), were included. The DHL-classifier demonstrated a sensitivity of 100%, a specificity of 87% and an AUC of 0.95, with only two false positive cases, compared to FISH. The DHL-classifier showed a 92% predictive value as a screening tool for performing conventional FISH analysis, over-performing currently used criteria. The work presented here provides the proof of concept for the potential use of an AI tool for the identification of DH/TH events. However, more extensive follow-up studies are required to assess the robustness of this tool and achieve high performances in a diverse population.

Pitfalls in mononucleotide microsatellite repeats instability assessing (MSI) in the patients with B-cell lymphomas

Analysis of microsatellite instability (MSI) is a routine study in the diagnostics of solid malignancies. The standard for determining MSI is a pentaplex PCR panel of mononucleotide repeats: NR-21, NR-24, NR-27, BAT-25, BAT-26. The presence of MSI is established based on differences in the length of markers in the tumor tissue and in the control, but due to the quasimonomorphic nature of standard mononucleotide loci the use of a control sample is not necessary in the diagnosis of MSI-positive solid tumors. The significance of the MSI phenomenon in oncohematology has not been established. This paper presents the results of a study of MSI in B-cell lymphomas: follicular lymphoma (FL), diffuse large B-cell lymphoma (DLBCL), high-grade B-cell lymphoma (HGBL). We have shown that aberrations of mononucleotide markers occur in these diseases, but the nature of the changes does not correspond to the classical MSI in solid neoplasms. This fact requires further study of the pathogenesis of such genetic disorders. Due to the possibility of ambiguous interpretation of the results of the MSI study for previously uncharacterized diseases, strict compliance with the methodology of parallel analysis of the tumor tissue and the control sample is mandatory.