Mass Cytometry Based Classification of Inter- and Intra-Tumoral Heterogeneity in Diffuse Large B-Cell Lymphoma
Notice bibliographique
Résumé
Abstract Background: Recent work in both hematologic malignancies and solid tumors has supported the notion that human cancers exhibit marked intra-tumoral heterogeneity (ITH). Results from next generation sequencing (NGS) studies support that subclonal DNA mutations underlie genotypic ITH within a single tumor since the majority of sequence variants are present in only 5-50% of reads for a given tumor sample, and single cell analyses have shown that individual tumor subclones may be ancestrally related in a complex branching hierarchy, suggesting that therapy failures and progressive disease likely arise by Darwinian selection for more aggressive or therapy resistant clones. It has become increasingly clear that it will be important to understand the multi-clonal structure of tumors in order to treat them more effectively. In this study, we sought to use time-of-flight mass cytometry (CyTOF) to explore clonal phenotypic substructure in diffuse large B-cell lymphoma (DLBCL), a diagnostic entity notorious for clinical heterogeneity. Methods: We examined viably frozen single cell suspensions from diagnostic lymph node biopsy samples received for flow cytometric analysis at the BC Cancer Agency. We have thus far acquired CyTOF data from 25 cases of DLBCL using a two-tube, 40-parameter panel encompassing a total of 58 different markers including both surface and intracellular antigens that were selected to reveal heterogeneity within the malignant B-cell population. For each sample acquisition, we included "spiked-in" control cells from pooled reactive (non-malignant) lymph node samples to control for staining variation between antibody/reagent lots and also run-to-run CyTOF instrument drift, facilitated by a CD45 antibody "barcoding" approach. We analyzed the data using a combination of viSNE, Isomap, and PhenoGraph analysis packages. Results: Analysis of individual tumor samples readily distinguished between malignant and residual normal B-cell populations, and also revealed distinct subpopulations among malignant cells of varying degrees of relatedness to one another. These subpopulations were then sorted from one another by conventional FACS from parallel vials of cryopreserved cells using lower dimensional sorting strategies derived from the 40-parameter CyTOF data. Sorted subpopulations will be analyzed by targeted amplicon sequencing for single nucleotide variants identified from whole exome sequencing data obtained from unsorted material to explore the hypothesis that these may represent genotypic subclones. Analysis of multiple tumor samples at once yielded several observations. First, B-cells from reactive lymph nodes and non-malignant B-cells within patient lymphoma specimens reproducibly cluster atop one another, indicating highly similar if not identical phenotypic profiles. Second, the majority of patient DLBCL tumors form cohesive individual clusters, separate and distinct from one another, suggesting that the 40-dimensional panel defines cell populations with sufficient resolution such that each patient's tumor can be uniquely identified. Third, individual DLBCL tumors do not aggregate in tight proximity with one another to the extent that we observe among patient follicular lymphoma (FL) samples, suggesting DLBCL represents a broader diversity of phenotypic classes. Fourth, there is local, but loose aggregation of ABC versus GCB subtypes, but there are also clear outliers and areas of intermingling between ABC and GCB tumors, as defined by immunohistochemistry. Finally, a subset of DLBCL tumors exhibit minor subpopulations that map apart from their corresponding "parent" tumor populations, but yet overlap one another, raising the possibility of divergent evolution away from (or alternatively convergent evolution towards) a common tumor archetype. Conclusions: Taken together, these observations support that novel information can be derived from CyTOF data with important implications for our understanding of both intra- and inter-tumoral heterogeneity in DLBCL. Disclosures Scott: Celgene: Consultancy, Honoraria; NanoString: Patents & Royalties: Inventor on a patent that NanoString has licensed.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».