Integrated spatial and multimodal single-cell transcriptomics reveal patient-dependent cell heterogeneity in splenic marginal zone lymphoma.

Biological hallmarks of splenic marginal zone lymphoma (SMZL) remain poorly described. Herein, we performed in-depth SMZL characterization through multimodal single-cell analyses of paired blood/spleen samples. The 3'-single-cell RNA-sequencing, Cellular Indexing of Transcriptomes and Epitopes by sequencing, and 5'-V(D)J single-cell RNA-sequencing datasets were integrated to characterize SMZL transcriptome profiles, including B-cell receptor and T-cell receptor repertoires. Hyperexpanded B-cell clones in the spleen were at a memory-like stage, whereas recirculating tumor B-cells in blood encompassed multiple differentiation stages, indicating an unexpected desynchronization of the B-cell maturation program in SMZL cells. Spatial transcriptomics showed the enrichment of T-effector and T-follicular helper (TFH) signatures in the nodular subtype of SMZL. This latter also exhibited gene-based cell-cell interactions suggestive of dynamic crosstalk between TFH and cancer cells in transcriptomics, further substantiated by using imaging mass cytometry. Our findings provide a comprehensive high-resolution description of SMZL biological hallmarks and characterize, for the first time in situ, inter- and intra-patient heterogeneity at both transcriptomic and protein levels. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models.

Large B-cell lymphoma (LBCL) is a heterogeneous lymphoid malignancy in which MYC gene rearrangement (MYC-R) is associated with a poor prognosis, prompting the recommendation for more intensive treatment. MYC-R detection relies on fluorescence in situ hybridization method which is time consuming, expensive, and not available in all laboratories. Automating MYC-R detection on hematoxylin-and-eosin-stained whole slide images of LBCL would decrease the need for costly molecular testing and improve pathologists' productivity. We developed an interpretable deep learning algorithm to detect MYC-R considering recent advances in self-supervised learning and providing an extensive comparison of 7 feature extractors and 6 multiple instance learning models, themselves. Four different multicentric cohorts, including 1247 patients with LBCL, were used for training and validation. The best deep learning model reached an average area under the receiver operating characteristic curve score of 81.9% during crossvalidation on the largest LBCL cohort, and area under the receiver operating characteristic curve scores ranging from 62.2% to 74.5% when evaluated on other unseen cohorts. In addition, we demonstrated that using this model as a prescreening tool (with a false-negative rate of 0%), fluorescence in situ hybridization testing would be avoided in 35% of cases. This work demonstrates the feasibility of developing a medical device to efficiently detect MYC gene rearrangement on hematoxylin-and-eosin-stained whole slide images in daily practice.

Lymph node excisions provide more precise lymphoma diagnoses than core biopsies: a French Lymphopath network survey.

According to expert guidelines, lymph node surgical excision is the standard of care for lymphoma diagnosis. However, core needle biopsy (CNB) has become widely accepted as part of the lymphoma diagnostic workup over the past decades. The aim of this study was to present the largest multicenter inventory of lymph nodes sampled either by CNB or surgical excision in patients with suspected lymphoma and to compare their diagnostic performance in routine pathologic practice. We reviewed 32 285 cases registered in the French Lymphopath network, which provides a systematic expert review of all lymphoma diagnoses in France, and evaluated the percentage of CNB and surgical excision cases accurately diagnosed according to the World Health Organization classification. Although CNB provided a definitive diagnosis in 92.3% and seemed to be a reliable method of investigation for most patients with suspected lymphoma, it remained less conclusive than surgical excision, which provided a definitive diagnosis in 98.1%. Discordance rates between referral and expert diagnoses were higher on CNB (23.1%) than on surgical excision (21.2%; P = .004), and referral pathologists provided more cases with unclassified lymphoma or equivocal lesion through CNB. In such cases, expert review improved the diagnostic workup by classifying ∼90% of cases, with higher efficacy on surgical excision (93.3%) than CNB (81.4%; P < 10-6). Moreover, diagnostic concordance for reactive lesions was higher on surgical excision than CNB (P = .009). Overall, although CNB accurately diagnoses lymphoma in most instances, it increases the risk of erroneous or nondefinitive conclusions. This large-scale survey also emphasizes the need for systematic expert review in cases of lymphoma suspicion, especially in those sampled by using CNB.

Role of artificial intelligence in haematolymphoid diagnostics.

The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures. From diagnostic assistance to powerful research tools, the potential fields of application of machine learning techniques in pathology are vast and constitute the subject of considerable research work. The aim of this article is to provide an overview of the potential applications of AI in the field of haematopathology and to define the role that these emerging technologies could play in our laboratories in the short to medium term.

BCL3 rearrangements in B-cell lymphoid neoplasms occur in two breakpoint clusters associated with different diseases.

The t(14;19)(q32;q13) often juxtaposes BCL3 with immunoglobulin heavy chain (IGH) resulting in overexpression of the gene. In contrast to other oncogenic translocations, BCL3 rearrangement (BCL3-R) has been associated with a broad spectrum of lymphoid neoplasms. Here we report an integrative whole-genome sequence, transcriptomic, and DNA methylation analysis of 13 lymphoid neoplasms with BCL3-R. The resolution of the breakpoints at single base-pair revealed that they occur in two clusters at 5' (n=9) and 3' (n=4) regions of BCL3 associated with two different biological and clinical entities. Both breakpoints were mediated by aberrant class switch recombination of the IGH locus. However, the 5' breakpoints (upstream) juxtaposed BCL3 next to an IGH enhancer leading to overexpression of the gene whereas the 3' breakpoints (downstream) positioned BCL3 outside the influence of the IGH and were not associated with its expression. Upstream BCL3-R tumors had unmutated IGHV, trisomy 12, and mutated genes frequently seen in chronic lymphocytic leukemia (CLL) but had an atypical CLL morphology, immunophenotype, DNA methylome, and expression profile that differ from conventional CLL. In contrast, downstream BCL3-R neoplasms were atypical splenic or nodal marginal zone lymphomas (MZL) with mutated IGHV, complex karyotypes and mutated genes typical of MZL. Two of the latter four tumors transformed to a large B-cell lymphoma. We designed a novel fluorescence in situ hybridization assay that recognizes the two different breakpoints and validated these findings in 17 independent tumors. Overall, upstream or downstream breakpoints of BCL3-R are mainly associated with two subtypes of lymphoid neoplasms with different (epi)genomic, expression, and clinicopathological features resembling atypical CLL and MZL, respectively.

[Dasatinib-induced follicular lymphoid hyperplasia, an entity to know]. / Hyperplasie lymphoïde folliculaire induite par la prise de dasatinib, une entité à connaître.

Follicular lymphoid hyperplasia induced by dasatinib is an entity recently described. It is sometimes difficult to rule out the diagnostic of small B-cell lymphoma. Usually, the node is swollen, with follicular architecture conserved, composed by germinal centers with variable size and shape, with a hight number of mitoses and tingible bodies macrophages inside. Follicular lymphoid hyperplasia is isolated or associated with multiple reactive patterns. The immunohistochemical profil of germinal centers is CD20+, CD10+, BCL6+, BCL2-. Swollen node disappears in a short time after dasatinib discontinuation. Clinicians and pathologists need to be aware of this entity, so as not to avoid mistakenly suspect lymphoma when lymphadenopathy occurs in a patient with chronic myeloid leukemia treated with dasatinib.

Baseline SUVmax is related to tumor cell proliferation and patient outcome in follicular lymphoma.

Follicular lymphoma (FL) is the most common indolent lymphoma. Despite the clear benefit of CD20-based therapy, a subset of FL patients still progress to aggressive lymphoma. Thus, identifying early biomarkers that incorporate PET metrics could be helpful to identify patients with a high risk of treatment failure with Rituximab. We retrospectively included a total of 132 untreated FL patients separated into training and validation cohorts. Optimal threshold of baseline SUVmax was first determined in the training cohort (n=48) to predict progression-free survival (PFS). The PET results were investigated along with the tumor and immune microenvironment, which were determined by immunochemistry and transcriptome studies involving gene set enrichment analyses and immune cell deconvolution, together with the tumor mutation profile. We report that baseline SUVmax >14.5 was associated with poorer PFS than baseline SUVmax ≤14.5 (HR=0.28; p=0.00046). Neither immune T-cell infiltration nor immune checkpoint expression were associated with baseline PET metrics. By contrast, FL samples with Ki-67 staining ≥10% showed enrichment of cell cycle/DNA genes (p=0.013) and significantly higher SUVmax values (p=0.007). Despite similar oncogenic pathway alterations in both SUVmax groups of FL samples, 4 out of 5 cases harboring the infrequent FOXO1 transcription factor mutation were seen in FL patients with SUVmax >14.5. Thus, high baseline SUVmax reflects FL tumor proliferation and, together with Ki-67 proliferative index, can be used to identify patients at risk of early relapse with R-chemotherapy.

Exclusive B-cell phenotype of primary prostatic lymphomas: a potential role of chronic prostatitis.

AIMS: Primary prostatic lymphomas (PPL) is exceedingly rare. The aim of this study was to investigate the largest series of PPL obtained from a nationwide expert pathologist network, and thus try to understand the pathophysiology of these tumours. METHODS AND RESULTS: Up to 66 000 lymphoma cases have been collected and submitted for central expert review by the French Lymphopath network. We confirm the low frequency of PPL (n = 77; 0.12%), all cases being of B-cell origin. Diffuse large B-cell lymphoma and small lymphocytic lymphoma were the most frequent subtypes, comprising 31% and 26% of cases respectively, followed by mucosa-associated lymphoid tissue (MALT)/lymphoplasmacytic lymphoma (19%), follicular lymphoma (12%), mantle cell lymphoma (6%), Burkitt lymphoma (4%), and unclassified lymphoma (1%). Clinical data obtained in 25 cases suggests that PPLs are rather indolent tumours. Our hypothesis for B-cell recruitment in the prostatic tissue was derived from the observation in chronic inflammation (prostatitis) of frequent heterotopic proliferation of high endothelial venules (HEVs). The latter are dedicated to lymphocyte entry into secondary lymphoid organs, here putatively driving circulating clonal B-lymphocytes from the blood into the inflamed prostate. This may account for the relatively high incidence of small lymphocytic lymphoma consistently reported in series of primary or secondary prostatic lymphoma. As in other organs or glands, chronic inflammation may promote antigen-dependent intraprostatic MALT lymphoma and diffuse large B-cell lymphoma development. CONCLUSIONS: PPLs are exclusively of B-cell origin, and chronic inflammation resulting from the proliferation of high endothelial venules could play some role in their development.

Using a contextualized sensemaking model for interaction design: A case study of tumor contouring.

Sensemaking theories help designers understand the cognitive processes of a user when he/she performs a complicated task. This paper introduces a two-step approach of incorporating sensemaking support within the design of health information systems by: (1) modeling the sensemaking process of physicians while performing a task, and (2) identifying software interaction design requirements that support sensemaking based on this model. The two-step approach is presented based on a case study of the tumor contouring clinical task for radiotherapy planning. In the first step of the approach, a contextualized sensemaking model was developed to describe the sensemaking process based on the goal, the workflow and the context of the task. In the second step, based on a research software prototype, an experiment was conducted where three contouring tasks were performed by eight physicians respectively. Four types of navigation interactions and five types of interaction sequence patterns were identified by analyzing the gathered interaction log data from those twenty-four cases. Further in-depth study on each of the navigation interactions and interaction sequence patterns in relation to the contextualized sensemaking model revealed five main areas for design improvements to increase sensemaking support. Outcomes of the case study indicate that the proposed two-step approach was beneficial for gaining a deeper understanding of the sensemaking process during the task, as well as for identifying design requirements for better sensemaking support.

Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images.

Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However, a frequent drawback of AI models is their propension to make decisions based rather on bias in training dataset than on concrete biological features, thus weakening pathologists' trust in these tools. Technically, it is well known that microscopic images are altered by tissue processing and staining procedures, being one of the main sources of bias in machine learning for digital pathology. So as to deal with it, many teams have written about color normalization and augmentation methods. However, only a few of them have monitored their effects on bias reduction and model generalizability. In our study, two methods for stain augmentation (AugmentHE) and fast normalization (HEnorm) have been created and their effect on bias reduction has been monitored. Actually, they have also been compared to previously described strategies. To that end, a multicenter dataset created for breast cancer histological grading has been used. Thanks to it, classification models have been trained in a single center before assessing its performance in other centers images. This setting led to extensively monitor bias reduction while providing accurate insight of both augmentation and normalization methods. AugmentHE provided an 81% increase in color dispersion compared to geometric augmentations only. In addition, every classification model that involved AugmentHE presented a significant increase in the area under receiving operator characteristic curve (AUC) over the widely used RGB shift. More precisely, AugmentHE-based models showed at least 0.14 AUC increase over RGB shift-based models. Regarding normalization, HEnorm appeared to be up to 78x faster than conventional methods. It also provided satisfying results in terms of bias reduction. Altogether, our pipeline composed of AugmentHE and HEnorm improved AUC on biased data by up to 21.7% compared to usual augmentations. Conventional normalization methods coupled with AugmentHE yielded similar results while being much slower. In conclusion, we have validated an open-source tool that can be used in any deep learning-based digital pathology project on H&E whole slide images (WSI) that efficiently reduces stain-induced bias and later on might help increase pathologists' confidence when using AI-based products.

Large B-cell lymphomas with CCND1 rearrangement have different immunoglobulin gene breakpoints and genomic profile than mantle cell lymphoma.

Mantle cell lymphoma (MCL) is genetically characterized by the IG::CCND1 translocation mediated by an aberrant V(D)J rearrangement. CCND1 translocations and overexpression have been identified in occasional aggressive B-cell lymphomas with unusual features for MCL. The mechanism generating CCND1 rearrangements in these tumors and their genomic profile are not known. We have reconstructed the IG::CCND1 translocations and the genomic profile of 13 SOX11-negative aggressive B-cell lymphomas using whole genome/exome and target sequencing. The mechanism behind the translocation was an aberrant V(D)J rearrangement in three tumors and by an anomalous IGH class-switch recombination (CSR) or somatic hypermutation (SHM) mechanism in ten. The tumors with a V(D)J-mediated translocation were two blastoid MCL and one high-grade B-cell lymphoma. None of them had a mutational profile suggestive of DLBCL. The ten tumors with CSR/SHM-mediated IGH::CCND1 were mainly large B-cell lymphomas, with mutated genes commonly seen in DLBCL and BCL6 rearrangements in 6. Two cases, which transformed from marginal zone lymphomas, carried mutations in KLF2, TNFAIP3 and KMT2D. These findings expand the spectrum of tumors carrying CCND1 rearrangement that may occur as a secondary event in DLBCL mediated by aberrant CSR/SHM and associated with a mutational profile different from that of MCL.

Single-cell RNA sequencing of human non-hematopoietic bone marrow cells reveals a unique set of inter-species conserved biomarkers for native mesenchymal stromal cells.

BACKGROUND: Native bone marrow (BM) mesenchymal stem/stromal cells (BM-MSCs) participate in generating and shaping the skeleton and BM throughout the lifespan. Moreover, BM-MSCs regulate hematopoiesis by contributing to the hematopoietic stem cell niche in providing critical cytokines, chemokines and extracellular matrix components. However, BM-MSCs contain a heterogeneous cell population that remains ill-defined. Although studies on the taxonomy of native BM-MSCs in mice have just started to emerge, the taxonomy of native human BM-MSCs remains unelucidated. METHODS: By using single-cell RNA sequencing (scRNA-seq), we aimed to define a proper taxonomy for native human BM non-hematopoietic subsets including endothelial cells (ECs) and mural cells (MCs) but with a focal point on MSCs. To this end, transcriptomic scRNA-seq data were generated from 5 distinct BM donors and were analyzed together with other transcriptomic data and with computational biology analyses at different levels to identify, characterize and classify distinct native cell subsets with relevant biomarkers. RESULTS: We could ascribe novel specific biomarkers to ECs, MCs and MSCs. Unlike ECs and MCs, MSCs exhibited an adipogenic transcriptomic pattern while co-expressing genes related to hematopoiesis support and multilineage commitment potential. Furthermore, by a comparative analysis of scRNA-seq of BM cells from humans and mice, we identified core genes conserved in both species. Notably, we identified MARCKS, CXCL12, PDGFRA, and LEPR together with adipogenic factors as archetypal biomarkers of native MSCs within BM. In addition, our data suggest some complex gene nodes regulating critical biological functions of native BM-MSCs together with a preferential commitment toward an adipocyte lineage. CONCLUSIONS: Overall, our taxonomy for native BM non-hematopoietic compartment provides an explicit depiction of gene expression in human ECs, MCs and MSCs at single-cell resolution. This analysis helps enhance our understanding of the phenotype and the complexity of biological functions of native human BM-MSCs.

Ultrafast Gene Fusion Assessment for Nonsquamous NSCLC.

Introduction: Gene fusion testing of ALK, ROS1, RET, NTRK, and MET exon 14 skipping mutations is guideline recommended in nonsquamous NSCLC (NS-NSCLC). Nevertheless, assessment is often hindered by the limited availability of tissue and prolonged next-generation sequencing (NGS) testing, which can protract the initiation of a targeted therapy. Therefore, the development of faster gene fusion assessment is critical for optimal clinical decision-making. Here, we compared two ultrafast gene fusion assays (UFGFAs) using NGS (Genexus, Oncomine Precision Assay, Thermo Fisher Scientific) and a multiplex reverse-transcriptase polymerase chain reaction (Idylla, GeneFusion Assay, Biocartis) approach at diagnosis in a retrospective series of 195 NS-NSCLC cases and five extrapulmonary tumors with a known NTRK fusion. Methods: A total of 195 NS-NSCLC cases (113 known gene fusions and 82 wild-type tumors) were included retrospectively. To validate the detection of a NTRK fusion, we added five NTRK-positive extrathoracic tumors. The diagnostic performance of the two UFGFAs and standard procedures was compared. Results: The accuracy was 92.3% and 93.1% for Idylla and Genexus, respectively. Both systems improved the sensitivity for detection by including a 5'-3' imbalance analysis. Although detection of ROS1, MET exon 14 skipping, and RET was excellent with both systems, ALK fusion detection was reduced with sensitivities of 87% and 88%, respectively. Idylla had a limited sensitivity of 67% for NTRK fusions, in which only an imbalance assessment was used. Conclusions: UFGFA using NGS and reverse-transcriptase polymerase chain reaction approaches had an equal level of detection of gene fusion but with some technique-specific limitations. Nevertheless, UFGFA detection in routine clinical care is feasible with both systems allowing faster initiation of therapy and a broad degree of screening.

Phenotypic and Histological Distribution Analysis Identify Mast Cell Heterogeneity in Non-Small Cell Lung Cancer.

Mast cells (MCs) are multifaceted innate immune cells often present in the tumor microenvironment (TME). However, MCs have been only barely characterized in studies focusing on global immune infiltrate phenotyping. Consequently, their role in cancer is still poorly understood. Furthermore, their prognosis value is confusing since MCs have been associated with good and bad (or both) prognosis depending on the cancer type. In this pilot study performed on a surgical cohort of 48 patients with Non-Small Cell Lung Cancer (NSCLC), we characterized MC population within the TME and in matching non-lesional lung areas, by multicolor flow cytometry and confocal microscopy. Our results showed that tumor-associated MCs (TAMCs) harbor a distinct phenotype as compared with MCs present in non-lesional counterpart of the lung. Moreover, we found two TAMCs subsets based on the expression of CD103 (also named alphaE integrin). CD103+ TAMCs appeared more mature, more prone to interact with CD4+ T cells, and located closer to cancer cells than their CD103- counterpart. In spite of these characteristics, we did not observe a prognosis advantage of a high frequency of CD103+ TAMCs, while a high frequency of total TAMC correlated with better overall survival and progression free survival. Together, this study reveals that TAMCs constitute a heterogeneous population and indicates that MC subsets should be considered for patients' stratification and management in future research.

Histological Subtypes Drive Distinct Prognostic Immune Signatures in Classical Hodgkin Lymphoma.

Despite the success of standard front-line chemotherapy, 20% of classical Hodgkin lymphoma (cHL) patients still relapse or have refractory disease (r/r), and a subset of them die due to disease progression. There is a critical lack of predictive factors for early identification of those r/r patients who may benefit from new therapeutic strategies. This study aimed to evaluate the dynamic expression of 586 immune-related genes in a cohort of 42 cHL patients including 30 r/r cHL after first-line chemotherapy. Gene expression profiling (GEP) using NanoString technology identified a 19-gene immune signature at diagnosis predictive of cHL relapse, but dependent on histological subtypes. Genes related to tumor survival were found upregulated while genes related to B-lineage were downregulated at diagnosis in r/r nodular sclerosis cHL. In contrast to the mixed-cellularity subtype, comparative GEP analyses between paired diagnosis/relapse biopsies of nodular sclerosis cHL showed 118 differentially expressed genes, supporting an immune contexture switch at relapse with upregulation of immunosuppressive cytokines, such as LGALS1 and TGFB1, and downregulation of the T-cell co-stimulatory receptor ICOS. These results indicate that the predictive value of immune signature in cHL is strongly influenced by histological subtype which should be considered when assessing new immunotherapy target strategies.