Incorporation of Digital Gene Expression Profiling for Cell-of-Origin Determination (Lymph2Cx Testing) into the Routine Work-Up of Diffuse Large B-Cell Lymphoma.
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ABSTRACT:
SUBMITTER: Robetorye RS
PROVIDER: S-EPMC8386504 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We examined the utility of post-therapy surveillance imaging in a large, prospectively enrolled cohort of patients with diffuse large B-cell lymphoma (DLBCL) from the United States and confirmed our results in an independent cohort of patients from France.Patients with newly diagnosed DLBCL and treated with anthracycline-based immunochemotherapy were identified from the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence and the Léon Bérard Cancer Center, Lyon, France. In those with relapse, details at relapse and outcomes were abstracted from records.680 individuals with DLBCL were identified from the MER, 552 (81%) of whom achieved remission after induction. 112 of the 552 patients (20%) suffered a relapse. The majority (64%) of relapses were identified before a scheduled follow-up visit. Surveillance imaging detected DLBCL relapse before clinical manifestations in nine out of 552 patients (1.6%) observed after therapy. In the Lyon cohort, imaging identified asymptomatic DLBCL relapse in four out of 222 patients (1.8%). There was no difference in survival after DLBCL relapse in patients detected at scheduled follow-up versus before scheduled follow-up in both the MER (P = .56) and Lyon cohorts (P = .25).The majority of DLBCL relapses are detected outside of planned follow-up, with no difference in outcome in patients with DLBCL detected at a scheduled visit compared with patients with relapse detected outside of planned follow-up. These data do not support the use of routine surveillance imaging for follow-up of DLBCL.
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Project description:Diffuse large B cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma worldwide. We describe the establishment and molecular characteristics of the DLBCL cell line U-2946. This cell line was derived from a 52-year-old male with DLBCL. U-2946 cells carried the chromosomal translocation t(8;14) and strongly expressed MYC, but not the mature B-cell lymphoma associated oncogenes BCL2 and BCL6. Instead, U-2946 cells expressed the antiapoptotic BCL2 family member MCL1 which was highly amplified genomically (14n). MCL1 amplification is recurrent in DLBCL, especially in the activated B cell (ABC) variant. Results of microarray expression cluster analysis placed U-2946 together with ABC-, but apart from germinal center (GC)-type DLBCL cell lines. The 1q21.3 region including MCL1 was focally coamplified with a short region of 17p11.2 (also present at 14n). The MCL1 inhibitor A-1210477 triggered apoptosis in U-2946 (MCL1pos/BCL2neg) cells. In contrast to BCL2pos DLBCL cell lines, U-2946 did not respond to the BCL2 inhibitor ABT-263. In conclusion, the novel characteristics of cell line U-2946 renders it a unique model system to test the function of small molecule inhibitors, especially when constructing a panel of DLBCL cell lines expressing broad combinations of antiapoptotic BCL2-family members.
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Project description:Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.
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Project description:To assess the prevalence and prognostic value of myeloid differentiation factor 88 (MYD88) expression and mutational status in diffuse large B cell lymphoma (DLBCL), a total cohort of 100 patients with DLBCL were studied using immunohistochemistry (IHC) and droplet digital polymerase chain reaction (DDPCR), and the association between MYD88 expression and clinicopathological parameters was analyzed. Overall, the positive expression rate of MYD88 protein was 38% and the gene mutation rate was 29%. The positive expression and mutation rates were the highest in the primary central nervous system lymphomas (58.33 and 66.67%, respectively). The coincidence rate of the results of MYD88 expression between IHC and DDPCR results was 73% (73/100). Univariate survival analysis showed that age (≥60 years old), high neutrophil/lymphocyte count ratio, low lymphocyte count, c‑Myc ≥40%, positive MYD88 protein expression, and gene mutation were associated with poorer prognosis rates. Multivariate survival analysis revealed that MYD88 expression was an independent prognostic factor affecting overall survival. In conclusion, the results of this study demonstrated that MYD88 mutation was a valuable index to evaluate the prognosis of DLBCL. DDPCR can be used as a method for detecting MYD88 mutations, although it was not completely consistent with the results of IHC.
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Project description:Precision medicine is modernizing strategies for clinical study design to help improve diagnoses guiding individualized treatment based on genetic or phenotypic characteristics that discriminate between patients with similar clinical presentations. Methodology to personalize treatment choices is being increasingly employed in clinical trials, yielding favorable correlations with improved response rates and survival. In patients with diffuse large B-cell lymphoma (DLBCL), disease characteristics and outcomes may vary widely, underscoring the importance of patient classification through identification of sensitive prognostic features. The discovery of distinct DLBCL molecular subtypes based on cell of origin (COO) is redefining the prognosis and treatment of this heterogeneous cancer. Owing to significant molecular and clinical differences between activated B-cell-like (ABC)- and germinal center B-cell-like (GCB)-DLBCL subtypes, COO identification offers opportunities to optimize treatment selection. Widespread adoption of COO classification would greatly improve treatment and prognosis; however, limitations in interlaboratory concordance between immunohistochemistry techniques, cost, and availability of gene expression profiling tools undermine universal integration in the clinical setting. With advanced methodology to determine COO in a real-world clinical setting, therapies targeted to specific subtypes are under development. The focus here is to review applications of precision medicine exemplified by COO determination in DLBCL patients.
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Project description:The opportunity to improve therapeutic choices on the basis of molecular features of the tumor cells is on the horizon in diffuse large B-cell lymphoma (DLBCL). Agents such as bortezomib exhibit selective activity against the poor outcome activated B-cell type (ABC) DLBCL. In order for targeted therapies to succeed in this disease, robust strategies that segregate patients into molecular groups with high reliability are needed. Although molecular studies are considered gold standard, several immunohistochemistry (IHC) algorithms have been published that claim to be able to stratify patients according to their cell-of-origin and to be relevant for patient outcome. However, results are poorly reproducible by independent groups.We investigated nine IHC algorithms for molecular classification in a dataset of DLBCL diagnostic biopsies, incorporating immunostaining for CD10, BCL6, BCL2, MUM1, FOXP1, GCET1, and LMO2. IHC profiles were assessed and agreed among three expert observers. A consensus matrix based on all scoring combinations and the number of subjects for each combination allowed us to assess reliability. The survival impact of individual markers and classifiers was evaluated using Kaplan-Meier curves and the log-rank test.The concordance in patient's classification across the different algorithms was low. Only 4% of the tumors have been classified as germinal center B-cell type (GCB) and 21% as ABC/non-GCB by all methods. None of the algorithms provided prognostic information in the R-CHOP (rituximab plus cyclophosphamide-adriamycin-vincristine-prednisone)-treated cohort.Further work is required to standardize IHC algorithms for DLBCL cell-of-origin classification for these to be considered reliable alternatives to molecular-based methods to be used for clinical decisions.
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Project description:The assignment of diffuse large B-cell lymphoma into cell-of-origin (COO) groups is becoming increasingly important with the emergence of novel therapies that have selective biological activity in germinal center B cell-like or activated B cell-like groups. The Lymphoma/Leukemia Molecular Profiling Project's Lymph2Cx assay is a parsimonious digital gene expression (NanoString)-based test for COO assignment in formalin-fixed paraffin-embedded tissue (FFPET). The 20-gene assay was trained using 51 FFPET biopsies; the locked assay was then validated using an independent cohort of 68 FFPET biopsies. Comparisons were made with COO assignment using the original COO model on matched frozen tissue. In the validation cohort, the assay was accurate, with only 1 case with definitive COO being incorrectly assigned, and robust, with >95% concordance of COO assignment between 2 independent laboratories. These qualities, along with the rapid turnaround time, make Lymph2Cx attractive for implementation in clinical trials and, ultimately, patient management.
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Project description:Diffuse large B-cell lymphoma (DLBCL) is commonly classified by gene expression profiling according to its cell of origin (COO) into activated B-cell (ABC)-like and germinal center B-cell (GCB)-like subgroups. Here we report the application of label-free nano-liquid chromatography - Sequential Window Acquisition of all THeoretical fragment-ion spectra - mass spectrometry (nanoLC-SWATH-MS) to the COO classification of DLBCL in formalin-fixed paraffin-embedded (FFPE) tissue. To generate a protein signature capable of predicting Affymetrix-based GCB scores, the summed log2-transformed fragment ion intensities of 780 proteins quantified in a training set of 42 DLBCL cases were used as independent variables in a penalized zero-sum elastic net regression model with variable selection. The eight-protein signature obtained showed an excellent correlation (r?=?0.873) between predicted and true GCB scores and yielded only 9 (21.4%) minor discrepancies between the three classifications: ABC, GCB, and unclassified. The robustness of the model was validated successfully in two independent cohorts of 42 and 31 DLBCL cases, the latter cohort comprising only patients aged >75 years, with Pearson correlation coefficients of 0.846 and 0.815, respectively, between predicted and NanoString nCounter based GCB scores. We further show that the 8-protein signature is directly transferable to both a triple quadrupole and a Q Exactive quadrupole-Orbitrap mass spectrometer, thus obviating the need for proprietary instrumentation and reagents. This method may therefore be used for robust and competitive classification of DLBCLs on the protein level.
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Project description:BackgroundSLE is associated with increased risk of diffuse large B-cell lymphoma (DLBCL). DLBCL is routinely classified by cell of origin (COO), with germinal centre B-cell (GCB) being more common and indicating better prognosis in the general population. We studied COO subtyping in patients with SLE diagnosed with DLBCL and their survival.Patients and methodsWe evaluated 20 cases of SLE with DLBCL. Immunohistochemistry analysis was performed (BCL2, MYC, BCL6, CD10, CD20, FOXP1, GCET1, MUM1) in tissue microarrays. We examined associations between molecular and clinical features, including overall survival.ResultsOf the 20 DLBCL SLE cases, 12/20 cases (60%) were classified as non-GCB using Hans or Choi algorithms. MYC and BCL2 protein expression was positive in 6/20 (30%) and 8/20 (40%) SLE cases, respectively, with 2/20 (10%) co-expressing both markers. Seven (7/20) had only extranodal involvement at DLBCL diagnosis. As expected, non-GCB cases had worse survival. Cases presenting exclusively with extranodal disease were associated with shorter SLE duration and better survival despite higher BCL2 protein expression.ConclusionsWe present novel data characterising DLBCL in SLE. Sixty per cent of the DLBCL in patients with SLE were non-GCB. The nodal and extranodal distribution in SLE was similar to what is known in the general population, but extranodal disease occurred more often with short SLE duration and was associated with longer overall survival. More research on cancer in SLE is the key to further understanding the complex interplay between cancer and the immune system.
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