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Record W2908417681 · doi:10.1182/blood-2018-99-115126

Factors Impacting Progression-Free Survival after CD19-Specific CAR-T Cell Therapy for Relapsed/Refractory Aggressive B-Cell Non-Hodgkin Lymphoma

2018· article· en· W2908417681 on OpenAlexaff
Alexandre V. Hirayama, Jordan Gauthier, Kevin A. Hay, Jenna Voutsinas, Qian Wu, Ted Gooley, Daniel Li, Alyssa Sheih, Janaki Purushe, Sindhu Cherian, Xueyan Chen, Barbara S. Pender, Reed M. Hawkins, Aesha Vakil, Rachel N. Steinmetz, Tinh-Doan Phi, Ryan C. Lynch, Aude G. Chapuis, Brian G. Till, Hans‐Peter Kiem, Jorge Ramos, Mazyar Shadman, Ryan D. Cassaday, Utkarsh Acharya, Stanley R. Riddell, David G. Maloney, Cameron J. Turtle

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFludarabineInternal medicineCyclophosphamideOncologyNon-Hodgkin's lymphomaLymphomaAggressive lymphomaImmunologyChemotherapyGastroenterologyRituximab

Abstract

fetched live from OpenAlex

Abstract Introduction Lymphodepletion chemotherapy followed by infusion of T cells engineered to express a CD19-specific chimeric antigen receptor (CAR) has shown remarkable efficacy in patients (pts) with relapsed/refractory (R/R) CD19+ B-cell malignancies, with high response rates reported in non-Hodgkin lymphoma (NHL). Durable responses have been observed in a subset of pts, but the factors associated with these long-term remissions have not been identified. We studied adults with R/R CD19+ B-cell NHL treated with cyclophosphamide and fludarabine lymphodepletion followed by infusion of 2 x 106 CD19 CAR-T cells/kg, and identified factors before and after CAR-T cell infusion that are associated with progression-free survival (PFS). Methods We conducted a phase 1/2 open-label clinical trial (NCT01865617) with the primary objective of evaluating the feasibility and safety of infusing a defined composition of CD4+ and CD8+ CD19 CAR-T cells after lymphodepletion chemotherapy in pts with R/R CD19+ B-cell malignancies. Best responses are reported according to the Lugano criteria (Cheson, JCO 2014). PFS was defined as the time from CAR-T cell infusion until disease progression or death, without censoring for new therapy. Logistic regression and penalized Cox regression multivariable modeling using elastic net were performed for analysis of response and PFS, respectively. Results Characteristics of the 57 pts in the study are shown in Table 1. One patient with incomplete response assessment was excluded. For the 56 remaining pts, the best overall response rate (ORR) without additional therapy was 57% (95% confidence interval [CI], 43-70%), with 48% achieving complete remission (CR; 95% CI, 35-62%). Most pts with partial response (PR) or stable disease (SD) after initial restaging at 4 weeks after CAR-T cell infusion received new therapy (11 of 15, 73%). All pts with PR/SD on initial restaging who did not receive additional therapy after CAR-T cells (n = 4) subsequently achieved CR. The duration of persistence of CAR-T cells was longer in pts who did not receive new therapy (15.7 vs. 5.3 months; P = .06). Eight of 9 pts with indolent histology achieved CR (89%; 95% CI, 51-99%). For the 47 pts with aggressive NHL, the best ORR was 51% (95% CI, 36-66%), with 40% (95% CI, 27-56%) achieving CR. Among aggressive NHL subtypes, pts with DLBCL (n = 28) had best ORR and CR rates of 50% (95% CI, 33-67%) and 43% (95% CI, 25-63%), respectively. In pts with aggressive lymphoma, multivariable analysis showed that the probability of achieving CR was independently associated with a lower pre-lymphodepletion serum LDH concentration (P = .003) and greater increase in serum MCP-1 concentration from a pre-lymphodepletion timepoint to immediately before CAR-T cell infusion (P = .01). Analysis of pts with all histologic subtypes showed that those achieving CR had better PFS and overall survival (OS) compared to those who did not achieve CR (median PFS: CR, not reached; non-CR, 1.35 month; Figure 1). In pts achieving CR, after a median follow-up of 20.2 months (range 2.5-32.4 months), the 24-month probabilities of PFS and OS were 59% (95% CI, 41-84%) and 79% (95% CI, 64-97%), respectively. No pts with indolent NHL who achieved CR (n = 8) have relapsed with a median follow-up of 14.5 months (range, 10.7-30.1 months). For pts with aggressive lymphoma who achieved CR, after a median follow-up of 26.9 months (range, 2.5-32.4 months), the median PFS was 20.0 months (95% CI, 9.2-not reached), and 24-month probabilities of PFS and OS were 46% (95% CI, 28-76%) and 72% (95% CI, 54-96%), respectively. In aggressive NHL, multivariable analysis suggested that, in addition to being associated with the probability of achieving CR, serum LDH and MCP-1 concentration also impacted the probability of longer PFS. The model found that lower pre-lymphodepletion serum LDH (P = .0004) and higher serum MCP-1 peak after CAR-T cell infusion (P = .05), along with higher serum IL-7 (P = .02) and lower serum IL-18 (P = .02) concentrations before lymphodepletion were independently associated with better PFS. Similar findings were obtained after multivariable analysis was performed only in those who had achieved CR. Conclusion CR after CD19 CAR-T cell therapy appears to be a strong predictor of PFS in adult pts with B-cell NHL. Identification of additional factors associated with better PFS might guide future management strategies for pts achieving CR after CD19 CAR-T cell therapy. Disclosures Hirayama: DAVA Oncology: Honoraria. Hay:DAVA Oncology: Honoraria. Li:Juno Therapeutics: Employment, Equity Ownership. Lynch:Incyte: Research Funding; Johnson Graffe Keay Moniz and Wick LLP: Consultancy; Juno Therapeutics: Research Funding; Rhizen Pharmaceuticals: Research Funding; Takeda: Research Funding. Till:Mustang Bio: Patents & Royalties, Research Funding. Kiem:Homology Medicine: Consultancy; Rocket Pharmaceuticals: Consultancy; Magenta: Consultancy. Ramos:Seattle Genetics: Employment, Equity Ownership. Shadman:Gilead Sciences: Research Funding; Genentech: Consultancy; Pharmacyclics: Research Funding; Celgene: Research Funding; Mustang Biopharma: Research Funding; Genentech: Research Funding; TG Therapeutics: Research Funding; Acerta Pharma: Research Funding; AbbVie: Consultancy; Verastem: Consultancy; Beigene: Research Funding; AstraZeneca: Consultancy; Qilu Puget Sound Biotherapeutics: Consultancy. Cassaday:Jazz Pharmaceuticals: Consultancy; Amgen: Consultancy, Research Funding; Kite Pharma: Research Funding; Adaptive Biotechnologies: Consultancy; Merck: Research Funding; Pfizer: Consultancy, Research Funding; Seattle Genetics: Other: Spouse Employment, Research Funding; Incyte: Research Funding. Acharya:Juno Therapeutics: Research Funding; Teva: Honoraria. Riddell:Cell Medica: Membership on an entity's Board of Directors or advisory committees; Juno Therapeutics: Equity Ownership, Patents & Royalties, Research Funding; NOHLA: Consultancy; Adaptive Biotechnologies: Consultancy. Maloney:GlaxoSmithKline: Research Funding; Juno Therapeutics: Research Funding; Roche/Genentech: Honoraria; Seattle Genetics: Honoraria; Janssen Scientific Affairs: Honoraria. Turtle:Bluebird Bio: Consultancy; Juno Therapeutics / Celgene: Consultancy, Patents & Royalties, Research Funding; Nektar Therapeutics: Consultancy, Research Funding; Eureka Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Precision Biosciences: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Caribou Biosciences: Consultancy; Gilead: Consultancy; Adaptive Biotechnologies: Consultancy; Aptevo: Consultancy.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.316
Teacher spread0.283 · 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 designObservational
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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Citations1
Published2018
Admission routes1
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