The Lymphoma Epidemiology of Outcomes cohort study: Design, baseline characteristics, and early outcomes.

To address the current and long-term unmet health needs of the growing population of non-Hodgkin lymphoma (NHL) patients, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/). A total of 7735 newly diagnosed patients aged 18 years and older with NHL were prospectively enrolled from 7/1/2015 to 5/31/2020 at 8 academic centers in the United States. The median age at diagnosis was 62 years (range, 18-99). Participants came from 49 US states and included 538 Black/African-Americans (AA), 822 Hispanics (regardless of race), 3386 women, 716 age <40 years, and 1513 rural residents. At study baseline, we abstracted clinical, pathology, and treatment data; banked serum/plasma (N = 5883, 76.0%) and germline DNA (N = 5465, 70.7%); constructed tissue microarrays for four major NHL subtypes (N = 1189); and collected quality of life (N = 5281, 68.3%) and epidemiologic risk factor (N = 4489, 58.0%) data. Through August 2022, there were 1492 deaths. Compared to population-based SEER data (2015-2019), LEO participants had a similar distribution of gender, AA race, Hispanic ethnicity, and NHL subtype, while LEO was underrepresented for patients who were Asian and aged 80 years and above. Observed overall survival rates for LEO at 1 and 2 years were similar to population-based SEER rates for indolent B-cell (follicular and marginal zone) and T-cell lymphomas, but were 10%-15% higher than SEER rates for aggressive B-cell subtypes (diffuse large B-cell and mantle cell). The LEO cohort is a robust and comprehensive national resource to address the role of clinical, tumor, host genetic, epidemiologic, and other biologic factors in NHL prognosis and survivorship.

The unnecessary use of short tandem repeat testing on bone marrow samples in patients after 1 year following allogeneic hematopoietic stem cell transplant.

OBJECTIVES: To determine whether the information provided by short tandem repeat (STR) testing and bone marrow (BM) biopsy specimens following hematopoietic stem cell transplant (HSCT) provides redundant information, leading to test overutilization, without additional clinical benefit. METHODS: Cases with synchronous STR and flow cytometric immunophenotyping (FCI) testing, as part of the BM evaluation, were assessed for STR/FCI concordance. RESULTS: Of 1199 cases (410 patients), we found the overall concordance between STR and FCI was 93%, with most cases (1063) classified as STR-/FCI-. Of all discordant cases, 75 (6%) were STR+/FCI-, with only 5 (6.7%) cases best explained as identification of disease relapse. Eight cases were STR-/FCI+, representing relapsed/residual disease. Analysis of cases 1 year or more from transplant (54% of all cases) indicated only 9 (1.5%) were STR+/FCI-, and none uniquely identified relapse. CONCLUSIONS: These data suggest that STR analysis performed 1 year or more post-HSCT does not identify unknown cases of relapse. Furthermore, while STR testing is critical for identifying graft failure/rejection within the first year posttransplant, FCI appears superior to STR at detecting late relapses with low-level disease. Therefore, STR testing from patients 1 year or more post-HSCT may be unnecessary, as BM biopsy evaluation is sufficient to identify disease relapse.

Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.

BACKGROUND: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images. METHODS: Our method applies a technique called attention-based multiple instance learning to regress the proportion of c-MYC-positive and BCL2-positive tumor cells from pathologist-scored tissue microarray cores. This technique does not require annotation of individual cell nuclei and is trained instead on core-level annotations of percent tumor positivity. We translate this model to scoring of whole-slide images by tessellating the slide into smaller core-sized tissue regions and calculating an aggregate score. Our method was trained on a public tissue microarray dataset from Stanford and applied to whole-slide images from a geographically diverse multi-center cohort produced by the Lymphoma Epidemiology of Outcomes study. RESULTS: In tissue microarrays, the automated method had Pearson correlations of 0.843 and 0.919 with pathologist scores for c-MYC and BCL2, respectively. When utilizing standard clinical thresholds, the sensitivity/specificity of our method was 0.743 / 0.963 for c-MYC and 0.938 / 0.951 for BCL2. For double-expressors, sensitivity and specificity were 0.720 and 0.974. When translated to the external WSI dataset scored by two pathologists, Pearson correlation was 0.753 & 0.883 for c-MYC and 0.749 & 0.765 for BCL2, and sensitivity/specificity was 0.857/0.991 & 0.706/0.930 for c-MYC, 0.856/0.719 & 0.855/0.690 for BCL2, and 0.890/1.00 & 0.598/0.952 for double-expressors. Survival analysis demonstrates that for progression-free survival, model-predicted TMA scores significantly stratify double-expressors and non double-expressors (p = 0.0345), whereas pathologist scores do not (p = 0.128). CONCLUSIONS: We conclude that proportion of positive stains can be regressed using attention-based multiple instance learning, that these models generalize well to whole slide images, and that our models can provide non-inferior stratification of progression-free survival outcomes.

SAMHD1 expression contributes to doxorubicin resistance and predicts survival outcomes in diffuse large B-cell lymphoma patients.

Diffuse large B-cell lymphoma (DLBCL) is a commonly diagnosed, aggressive non-Hodgkin's lymphoma. While R-CHOP chemoimmunotherapy is potentially curative, about 40% of DLBCL patients will fail, highlighting the need to identify biomarkers to optimize management. SAMHD1 has a dNTPase-independent role in promoting resection to facilitate DNA double-strand break (DSB) repair by homologous recombination. We evaluated the relationship of SAMHD1 levels with sensitivity to DSB-sensitizing agents in DLBCL cells and the association of SAMHD1 expression with clinical outcomes in 79 DLBCL patients treated with definitive therapy and an independent cohort dataset of 234 DLBCL patients. Low SAMHD1 expression, Vpx-mediated, or siRNA-mediated degradation/depletion in DLBCL cells was associated with greater sensitivity to doxorubicin and PARP inhibitors. On Kaplan-Meier log-rank survival analysis, low SAMHD1 expression was associated with improved overall survival (OS), which on subset analysis remained significant only in patients with advanced stage (III-IV) and moderate to high risk (2-5 International Prognostic Index (IPI)). The association of low SAMHD1 expression with improved OS remained significant on multivariate analysis independent of other adverse factors, including IPI, and was validated in an independent cohort. Our findings suggest that SAMHD1 expression mediates doxorubicin resistance and may be an important prognostic biomarker in advanced, higher-risk DLBCL patients.