Genetic heterogeneity highlighted by differential FDG-PET response in diffuse large B-cell lymphoma.
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ABSTRACT:
SUBMITTER: Araf S
PROVIDER: S-EPMC7271579 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
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Genetic heterogeneity highlighted by differential FDG-PET response in diffuse large B-cell lymphoma.
Araf Shamzah S Korfi Koorosh K Bewicke-Copley Findlay F Wang Jun J Cogliatti Sergio S Kumar Emil E Forrer Flavio F Barrington Sally F SF Graham Trevor A TA Scott David W DW Rimsza Lisa M LM Davies Andrew A Johnson Peter P Okosun Jessica J Fitzgibbon Jude J Fehr Martin M
Haematologica 20200409 6
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Project description:Diffuse large B-cell lymphoma (DLBCL) is the most common form of lymphoma in adults. The disease exhibits a striking heterogeneity in gene expression profiles and clinical outcomes, but its genetic causes remain to be fully defined. Through whole genome and exome sequencing, we characterized the genetic diversity of DLBCL. In all, we sequenced 73 DLBCL primary tumors (34 with matched normal DNA). Separately, we sequenced the exomes of 21 DLBCL cell lines. We identified 322 DLBCL cancer genes that were recurrently mutated in primary DLBCLs. We identified recurrent mutations implicating a number of known and not previously identified genes and pathways in DLBCL including those related to chromatin modification (ARID1A and MEF2B), NF-?B (CARD11 and TNFAIP3), PI3 kinase (PIK3CD, PIK3R1, and MTOR), B-cell lineage (IRF8, POU2F2, and GNA13), and WNT signaling (WIF1). We also experimentally validated a mutation in PIK3CD, a gene not previously implicated in lymphomas. The patterns of mutation demonstrated a classic long tail distribution with substantial variation of mutated genes from patient to patient and also between published studies. Thus, our study reveals the tremendous genetic heterogeneity that underlies lymphomas and highlights the need for personalized medicine approaches to treating these patients.
| S-EPMC3557051 | biostudies-literature
Project description:Fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) after one cycle of standard chemotherapy in patients with diffuse large B cell lymphoma (DLBCL) was assessed. Prospectively enrolled 51 patients had four PET/CT studies using the same protocol and system: at baseline and after one, three, and six cycles of chemotherapy (PET0, PET1, PET3, PET6). The PET1 and PET6 Deauville five-point score (D5PS) agreed in 60.8%, while PET3 and PET6 D5PS agreed in 90.2%. The absolute and percent changes of peak standard uptake value corrected for lean body mass (SULpeak) compared to baseline were significantly different between PET1 and PET3 (p = 0.001, p < 0.001) and PET1 and PET6 (p = 0.002, p = 0.001), but not between PET3 and PET6 (p = 0.276, p = 0.181). The absolute SULpeak from PET1 predicted treatment failure with accuracy of 78.4% (area under the curve 0.73, p = 0.023). D5PS, SULpeak, and metabolic tumor volume (MTV) were not statistically different between responders versus non-responders, or the one year disease-free versus relapse groups. D5PS and PERCIST responses showed 100% agreement at end-of-therapy. In conclusion, the responses after three and six cycles of therapy showed high degree of agreement. D5PS or MTV after one cycle of chemotherapy could not predict response or one-year disease-free status, but the SULpeak from PET1 was associated with response to first line therapy in DLBCL. Deauville and PERCIST criteria show high concordance.
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Project description:The current standard front-line therapy for patients with diffuse large-B cell lymphoma (DLBCL)-rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP)-is found to be ineffective in up to one-third of them. Thus, their early identification is an important step towards testing alternative treatment options. In this retrospective study, we assessed the ability of 18F-FDG PET/CT imaging features (radiomic + PET conventional parameters) plus clinical data, alone or in combination with genomic parameters to predict complete response to first-line treatment. Imaging features were extracted from images prior treatment. Lesions were segmented as a whole to reflect tumor burden. Multivariate logistic regression predictive models for response to first-line treatment trained with clinical and imaging features, or with clinical, imaging, and genomic features were developed. For imaging feature selection, a manual selection approach or a linear discriminant analysis (LDA) for dimensionality reduction were applied. Confusion matrices and performance metrics were obtained to assess model performance. Thirty-three patients (median [range] age, 58 [49-69] years) were included, of whom 23 (69.69%) achieved long-term complete response. Overall, the inclusion of genomic features improved prediction ability. The best performance metrics were obtained with the combined model including genomic data and built applying the LDA method (AUC of 0.904, and 90% of balanced accuracy). The amplification of BCL6 was found to significantly contribute to explain response to first-line treatment in both manual and LDA models. Among imaging features, radiomic features reflecting lesion distribution heterogeneity (GLSZM_GrayLevelVariance, Sphericity and GLCM_Correlation) were predictors of response in manual models. Interestingly, when the dimensionality reduction was applied, the whole set of imaging features-mostly composed of radiomic features-significantly contributed to explain response to front-line therapy. A nomogram predictive for response to first-line treatment was constructed. In summary, a combination of imaging features, clinical variables and genomic data was able to successfully predict complete response to first-line treatment in DLBCL patients, with the amplification of BCL6 as the genetic marker retaining the highest predictive value. Additionally, a panel of imaging features may provide important information when predicting treatment response, with lesion dissemination-related radiomic features deserving especial attention.
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Project description:We aimed to determine the added value of baseline metabolic tumor volume (MTV) and interim PET (I-PET) to the age-adjusted international prognostic index (aaIPI) to predict 2-y progression-free survival (PFS) in diffuse large B-cell lymphoma. Secondary objectives were to investigate optimal I-PET response criteria (using Deauville score [DS] or quantitative change in SUVmax [ΔSUVmax] between baseline and I-PET4 [observational I-PET scans after 4 cycles of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone administered in 2-wk intervals with intensified rituximab in the first 4 cycles [R(R)-CHOP14]). Methods: I-PET4 scans in the HOVON-84 (Hemato-Oncologie voor Volwassenen Nederland [Haemato Oncology Foundation for Adults in the Netherlands]) randomized clinical trial (EudraCT 2006-005174-42) were centrally reviewed using DS (cutoff, 4-5). Additionally, ΔSUVmax (prespecified cutoff, 70%) and baseline MTV were measured. Multivariable hazard ratio (HR), positive predictive value (PPV), and negative predictive value (NPV) were obtained for 2-y PFS. Results: In total, 513 I-PET4 scans were reviewed according to DS, and ΔSUVmax and baseline MTV were available for 367 and 296 patients. The NPV of I-PET ranged between 82% and 86% for all PET response criteria. Univariate HR and PPV were better for ΔSUVmax (4.8% and 53%, respectively) than for DS (3.1% and 38%, respectively). aaIPI and ΔSUVmax independently predicted 2-y PFS (HR, 3.2 and 5.0, respectively); adding MTV brought about a slight improvement. Low or low-intermediate aaIPI combined with a ΔSUVmax of more than 70% (37% of patients) yielded an NPV of 93%, and the combination of high-intermediate or high aaIPI and a ΔSUVmax of 70% or less yielded a PPV of 65%. Conclusion: In this study on diffuse large B-cell lymphoma, I-PET after 4 cycles of R(R)-CHOP14 added predictive value to aaIPI for 2-y PFS, and both were independent response biomarkers in a multivariable Cox model. We externally validated that ΔSUVmax outperformed DS in 2-y PFS prediction.
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Project description: Not available
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Project description:Genetic Heterogeneity of Diffuse Large B-Cell Lymphoma
| PRJNA184911 | ENA
Project description:PURPOSE In studies of diffuse large B-cell lymphoma, positron emission tomography with [(18)F]fluorodeoxyglucose (FDG-PET) performed after two to four cycles of chemotherapy has demonstrated prognostic significance. However, some patients treated with immunochemotherapy experience a favorable long-term outcome despite a positive interim FDG-PET scan. To clarify the significance of interim FDG-PET scans, we prospectively studied interim FDG-positive disease within a risk-adapted sequential immunochemotherapy program. PATIENTS AND METHODS From March 2002 to November 2006, 98 patients at Memorial Sloan-Kettering Cancer Center received induction therapy with four cycles of accelerated R-CHOP (rituximab + cyclophosphamide, doxorubicin, vincristine, and prednisone) followed by an interim FDG-PET scan. If the FDG-PET scan was negative, patients received three cycles of ICE (ifosfamide, carboplatin, and etoposide) consolidation therapy. If residual FDG-positive disease was seen, patients underwent biopsy; if the biopsy was negative, they also received three cycles of ICE. Patients with a positive biopsy received ICE followed by autologous stem-cell transplantation. RESULTS At a median follow-up of 44 months, overall and progression-free survival were 90% and 79%, respectively. Ninety-seven patients underwent interim FDG-PET scans; 59 had a negative scan, 51 of whom are progression free. Thirty-eight patients with FDG-PET-positive disease underwent repeat biopsy; 33 were negative, and 26 remain progression free after ICE consolidation therapy. Progression-free survival of interim FDG-PET-positive/biopsy-negative patients was identical to that in patients with a negative interim FDG-PET scan (P = .27). CONCLUSION Interim or post-treatment FDG-PET evaluation did not predict outcome with this dose-dense, sequential immunochemotherapy program. Outside of a clinical trial, we recommend biopsy confirmation of an abnormal interim FDG-PET scan before changing therapy.
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Project description:This study investigates whether baseline 18F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent 18F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinkage and selection operator regression were used to reduce the dimensionality within radiomic features. Cox proportional hazards model was used to determine the prognostic factors for progression-free survival (PFS) and overall survival (OS). A prognostic stratification model was built in the training cohort and validated in the validation cohort using Kaplan-Meier survival analysis. In the training cohort, run length non-uniformity (RLN), extracted from a gray level run length matrix (GLRLM), was independently associated with PFS (hazard ratio (HR) = 15.7, p = 0.007) and OS (HR = 8.64, p = 0.040). The International Prognostic Index was an independent prognostic factor for OS (HR = 2.63, p = 0.049). A prognostic stratification model was devised based on both risk factors, which allowed identification of three risk groups for PFS and OS in the training (p < 0.001 and p < 0.001) and validation (p < 0.001 and p = 0.020) cohorts. Our results indicate that the baseline 18F-FDG PET radiomic feature, RLNGLRLM, is an independent prognostic factor for survival outcomes. Furthermore, we propose a prognostic stratification model that may enable tailored therapeutic strategies for patients with DLBCL.
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Project description:Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.
| S-EPMC10423266 | biostudies-literature
Project description:BackgroundDiffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL.ResultsA total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403).ConclusionsRadiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.
| S-EPMC10603012 | biostudies-literature