Autologous hematopoietic stem cell transplantation (auto-HSCT) is the standard of care for patients with diffuse large B-cell lymphoma (DLBCL) who relapse/progress after first line chemoimmunotherapy. Long-term outcome of those who relapse after transplant is poor. We present the results of a retrospective study of 256 adult patients reported to the EBMT registry with DLBCL who relapsed after auto-HSCT performed between 2003 and 2013, and who received active salvage strategies. One hundred and fifty-four (60%) were male; median age was 53 years. Median time to relapse was 7 months, 65% relapsed during the first year. Overall response rate after salvage therapy was 46%. Median follow-up after first salvage therapy was 40 months (IQR 23-63 months). Overall survival (OS) at 3 years was 27% (95% CI 22-33). OS at 3 years of patients relapsing longer than 1 year after auto-HSCT was 41% (95% CI 31-53) compared with 20% (95% CI 14-24) in those who relapsed in less than 1 year. Eighty-two patients (32%) had a second HSCT, an allogeneic HSCT (allo-HSCT) in 69 cases, at a median time of 6.5 months after relapse. OS at 3 years after allo-HSCT was 36% (95% CI 25-51). In conclusion, the prognosis of patients with DLBCL that relapse after auto-HSCT is dismal. Patients who relapse in less than 1 year remain an unmet need, and should be considered for CAR T cell therapy or clinical trials. Patients who relapse after 1 year can be rescued with salvage therapies and a second HSCT. These results provide a benchmark to compare data of new prospective studies.
- MeSH
- Transplantation, Autologous MeSH
- Lymphoma, Large B-Cell, Diffuse * therapy MeSH
- Adult MeSH
- Bone Marrow MeSH
- Middle Aged MeSH
- Humans MeSH
- Neoplasm Recurrence, Local MeSH
- Disease-Free Survival MeSH
- Prospective Studies MeSH
- Retrospective Studies MeSH
- Hematopoietic Stem Cell Transplantation * MeSH
- Treatment Outcome MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. RESULTS: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. CONCLUSIONS: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.
