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Record W2980223642 · doi:10.1182/blood.v126.23.817.817

Diffuse Large B-Cell Lymphoma Patient-Derived Xenograft Models Capture Molecular and Biologic Heterogeneity and Inform Therapy

2015· article· en· W2980223642 on OpenAlexaff
Bjoern Chapuy, Honwei Cheng, Akira Watahiki, Matthew D. Ducar, Daniel Gusenleitner, Lin‐Feng Chen, Margaretha GM Roemer, Jing Quyang, Amanda L. Christie, Liye Zhang, Yuxiang Tan, Ryan Abo, Frederike von Bonin, Aaron R. Thorner, Heather H. Sun, Geraldine S. Pinkus, Paul Van Hummelen, Gerald Wulf, Jon C. Aster, David M. Weinstock, Stefano Monti, Scott J. Rodig, Yuzhuo Wang, Margaret A. Shipp

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
Keywordsbreakpoint cluster regionBiologyCancer researchGerminal centerDiffuse large B-cell lymphomaB-cell receptorB cellLymphomaSomatic cellImmunologyGeneticsGeneAntibody

Abstract

fetched live from OpenAlex

Abstract Diffuse large B-cell lymphoma (DLBCL) is a clinically and biologically heterogeneous disease defined by different transcriptional classifications, associated signaling and survival pathways and additional recurrent genetic alterations. In the cell-of-origin (COO) scheme, DLBCLs subtypes share certain features with normal germinal center B-cells (GCB) and activated B-cells (ABC). In comparison to GCB DLBCLs, ABC tumors have increased baseline NFκB activity and more frequent genetic alterations of NFκB pathway components. DLBCLs with shared functional features are also defined by the consensus clustering classification (CCC) which delineates B-cell receptor (BCR), Oxidative Phosphorylation (OxPhos) and Host Response (HR) tumors. BCR DLBCLs have increased reliance on BCR-signaling and survival pathways and aerobic glycolysis. BCR-dependent DLBCLs with high or low baseline NFκB activity (which largely correspond to ABC or GCB tumors, respectively) have distinct SYK-PI3K-dependent survival pathways and shared sensitivity to proximal BCR pathway inhibitors. Although DLBCLs have infrequent inactivating somatic mutations of TP53, these tumors commonly have copy number alterations (CNAs) of TP53 and genes encoding cell cycle pathway components. Given the clinical and molecular heterogeneity of DLBCL, we sought to develop faithful subtype-specific model systems to assess targeted therapies. Fresh tumor biopsies from 27 primary LBCLs were implanted under the renal capsule of immune compromised NSG mice. Nine of 27 tumors were successfully expanded in vivo, serially propagated for > 5 generations and considered stable LBCL PDX models. All models were EBV- and had clonal IgH rearrangements. Morphological and immunohistochemical signatures defined 8 PDX models as DLBCL and 1 as EBV- plasmablastic lymphoma (PBL). All LBCL PDX models were subjected to RNA-Seq and classified with respect to COO and CCC subtypes. Models were also evaluated by whole exome sequencing with a modified bait set which captured coding mutations and selected chromosomal rearrangements. Six of 9 DLBCL PDX models were ABC type. These models exhibited mutations of MYD88 alone or in association with PIM1 or CD79B with other alterations, as reported in primary ABC DLBCLs. The remaining 2 DLBCL PDX models were GCB type, with characteristic alterations of GNA13 and EZH2, and chromosomal translocations involving IgH and either BCL2 or MYC. Of note, BCL2 and MYC translocations are known adverse prognostic features of primary GCB DLBCL. Certain PDX models had additional mutations including B2M, MLL2, TNFAIP3, MEF2B and TP53. Only 25% (2/8) of the DLBCL PDX models harbored inactivating TP53 mutations whereas 75% (6/8) of tumors exhibited CNAs of TP53 or its upstream modifier, CDKN2A. These data are consistent with the reported incidence and type of TP53 pathway alterations in primary DLBCLs and contrast sharply with the near-uniform presence of TP53 mutations in DLBCL cell lines. Using the CCC classification, 6/8 DLBCL PDX models (both GCBs and 4 of 6 ABCs) were defined as BCR-subtype and 2 models as non-BCR type. To assess the utility of the DLBCL PDX models for functional analysis of BCR signaling, we first assessed cell surface immunoglobulin (sIg) expression by flow cytometry. All 6 BCR-type DLBCLs expressed sIgM whereas the 2 non-BCR DLBCL models and the PBL model lacked sIg. Next, we treated viable tumor cell suspensions with a selective SYK inhibitor, entospletinib (GS-9973). SYK inhibition significantly decreased the proliferation of all 6 BCR-type DLBCLs, but had no effect on the non-BCR-type DLBCLs or the PBL PDX. Given the distinctive SYK/PI3K-dependent signaling and survival pathways in DLBCLs with low or high baseline NFκB, we also assessed selective apoptotic pathway readouts in entospletinib-treated PDX cell suspensions. SYK inhibition selectively upregulated the pro-apoptotic BH3 family member, HRK, in BCR-dependent GCB-type DLBCL PDX samples and significantly downregulated the anti-apoptotic BCL2 family member, BCL2A1, in BCR- dependent ABC-type DLBCL PDX tumors, effects consistent with those previously observed in primary DLBCL samples. In summary, we have established and molecularly characterized faithful PDX models of DLBCL and PBL and demonstrated their usefulness in evaluating novel BCR pathway inhibitors. Disclosures Rodig: Perkin Elmer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding. Shipp:Gilead: Consultancy; Sanofi: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.238
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2015
Admission routes1
Has abstractyes

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