Spatial Heterogeneity in Large Resected Diffuse Large B-Cell Lymphoma Bulks Analysed by Massively Parallel Sequencing of Multiple Synchronous Biopsies

Multi-site pre-therapeutic biopsies demonstrate genetic heterogeneity in patients with newly diagnosed diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, both regarding clinical presentation, response to treatment and outcome. Recently, subclassification of DLBCL based on mutational profile has been suggested, and next generation sequencing (NGS) analysis may be relevant as part of the diagnostic workflow. This will, however, often be based on analysis of one tumor biopsy. Here, we present a prospective study where multi-site sampling was performed prior to treatment in patients with newly diagnosed DLBCL. Two spatially different biopsies from 16 patients were analyzed using NGS with an in-house 59-gene lymphoma panel. In 8/16 (50%) patients, mutational differences were found between the two biopsy sites, including differences in TP53 mutational status. Our data indicate that a biopsy from the extra-nodal site may represent the most advanced clone, and an extra-nodal biopsy should be preferred for analysis, if safely accessible. This will help ensure a standardized stratification and treatment decision.

Molecular Monitoring of Lymphomas

Molecular monitoring of tumor-derived alterations has an established role in the surveillance of leukemias, and emerging nucleic acid sequencing technologies are likely to similarly transform the clinical management of lymphomas. Lymphomas are well suited for molecular surveillance due to relatively high cell-free DNA and circulating tumor DNA concentrations, high somatic mutational burden, and the existence of stereotyped variants enabling focused interrogation of recurrently altered regions. Here, we review the clinical scenarios and key technologies applicable for the molecular monitoring of lymphomas, summarizing current evidence in the literature regarding molecular subtyping and classification, evaluation of treatment response, the surveillance of active cellular therapies, and emerging clinical trial strategies.

In-depth characterization of intratumoral heterogeneity in refractory B-cell non-Hodgkin lymphoma through the lens of a Research Autopsy Program

Intratumoral heterogeneity (ITH) provides the substrate for tumor evolution and treatment resistance, yet is remarkably understudied in lymphoma, due to the often limited amount of tissue that gets sampled during the routine diagnostic process, generally from a single nodal or extranodal site. Furthermore, the trajectory of how lymphoma, and especially non-Hodgkin lymphoma, spreads throughout the human body remains poorly understood. Here, we present a detailed characterization of ITH by applying whole-genome sequencing to spatially separated tumor samples harvested at the time of autopsy (n=24) and/or diagnosis (n=3) in three patients presenting with refractory B-cell non-Hodgkin lymphoma. Through deconvolution of bulk samples into clonal mixtures and inference of phylogenetic trees, we found evidence that polyclonal seeding underlies tumor dissemination in lymphoma. We identify mutation signatures associated with ancestral and descendant clones. In our series of patients with highly refractory lymphoma, the determinants of resistance were often harbored by founding clones, although there was also evidence of positive selection of driver mutations, likely under the influence of therapy. Lastly, we show that circulating tumor DNA is suitable for the detection of ancestral mutations but may miss a significant proportion of private mutations that can be detected in tissue. Our study clearly shows the existence of intricate patterns of regional and anatomical evolution that can only be disentangled through multi-regional tumor tissue profiling.

CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

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CT-based radiomics with machine learning classifier is able to accurately predict primary refractory Diffuse Large B Cell Lymphomas (DLBCL).

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The radiomics model exhibits a better discrimination for refractory DLBCL identification compared to available standard clinical criteria.

Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.