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Record W3213746728 · doi:10.1182/blood-2021-147568

Novel Agents May be Preferable to Chemotherapy for Large B-Cell Lymphoma Progressing after CD19-CAR-T: A Multicenter Observational Study

2021· article· en· W3213746728 on OpenAlexaff
Ana Alarcón Tomás, Joshua Fein, Shalev Fried, Warren Fingrut, Theodora Anagnostou, Anna Alperovich, Nishi Shah, Ellen Fraint, Richard J. Lin, Michael Scordo, Aishat Olaide Afuye, Connie Lee Batlevi, Michal J. Besser, Parastoo B. Dahi, Ivetta Danylesko, Sergio Giralt, Brandon S. Imber, Arnon Nagler, M. Lia Palomba, Gilles Salles, Craig S. Sauter, Gunjan L. Shah, Noga Shem‐Tov, Avichai Shimoni, Joachim Yahalom, Ronit Yerushalmi, Abraham Avigdor, Miguel‐Angel Perales, Roni Shouval

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsStem Cell Network
Fundersnot available
KeywordsMedicineInterquartile rangeInternal medicineCAR T-cell therapyCumulative incidencePopulationCohortOncologyCytokine release syndromeMinimal residual diseaseLymphomaCancerChimeric antigen receptorImmunotherapyLeukemia

Abstract

fetched live from OpenAlex

Abstract Up to 70% of large B-cell lymphoma (LBCL) patients will eventually experience relapse or progress following CD19-CAR-T therapy. Data guiding management of this challenging population are lacking. Therefore, we aimed to study the relationship between treatment strategies and outcomes following CD19-CAR-T failure. We included 273 adults, from two centers, treated with CD19-CAR-T (axicabtagene-ciloleucel [98, 36%], tisagenlecleucel [76, 28%], lisocabtagene-maraleucel [28, 10%], and an academic CD28-product [71, 26%]) for relapsed/refractory LBCL Cumulative incidence of relapse or progression was 40% (95% CI: 34%, 46%). Of 176 patients with residual or relapsed disease post-CAR-T (Fig. A), 133 received subsequent first-line anti-cancer therapy for active or residual disease (primary cohort) at a median of 79 days (interquartile range 49-124) after CAR-T infusion. Within the primary cohort, 65% of patients had stage III-IV disease at time of subsequent therapy. Most lymphomas remained CD19-positive after CAR-T therapy (45 biopsies, 91% positive by flow cytometry [58% normal, 33% dim expression]). At time of first treatment post-CAR-T, nearly all patients had either relapsed disease or stable/progressive disease (SD/PD), with eight patients in ongoing partial response (PR). With a median follow-up of 14.5 months (95% CI: 11.5-21.4), the median overall survival (OS) from time of first subsequent therapy was 8.6 months (IQR 6.9-12.0). We sought to identify determinants of survival among patients receiving initial post-CAR-T treatment. Variables measured pre- and post-CAR-T therapy and significantly associated (p < 0.1) with OS in univariable Cox regression were introduced into a multivariable model. Age ≥ 65y (HR 2.01 [95% CI: 1.23-3.29], p 0.005), bulky disease at apheresis (HR 2.05 [1.07-3.95], p 0.031), and disease refractory to CAR-T therapy (HR 1.89 [1.19-2.98], p 0.007) were associated with inferior OS in the multivariable analysis. Based on the cumulative burden of OS determinants, we propose a prognostic tool allowing risk stratification of patients receiving treatment post-CAR-T. Increasing number of these three risk factors was associated with greater mortality (HR 1.86 [1.32-2.62], p<0.001; Fig. B). Six-month OS ranged from 30% (95% CI: [16-57]) with ≥ 2 factors to 76% (64-91) with none. Therapy strategies post-CAR-T varied. Polatuzumab (n=25), anthracycline or platinum ("chemotherapy"; n=17), BTK inhibitors (n=13) and lenalidomide (n=12) based treatment were most frequently administered for non-localized disease (stage ≥2). Involved site radiation therapy (ISRT; n=20) was primarily given for stage I disease (Fig. C). Overall response rate (ORR) in the entire cohort was 47% (25% CR; 22% PR). Fig. D shows response rates by treatment. Remarkably, novel agents, including polatuzumab and lenalidomide-based therapies, had ORR of 52% (CR 35%) and 33% (CR 33%), respectively. In contrast, traditional chemotherapy-based approaches did not result in CR, and only 50% achieved PR. Survival was poorest with chemotherapy (6 month OS: 25% [95% CI: [11-59]), while rates with lenalidomide and polatuzumab-based therapies were 65% (42-100) and 67% (50-89). Patients and disease characteristics across treatment groups were unbalanced. However, the three prognostic factors comprising the OS prognostic tool: age ≥ 65y, bulky disease at apheresis and disease refractoriness to CAR-T, were similar across lenalidomide, polatuzumab, checkpoint inhibitors, and chemotherapy-based treatment groups. Patients who underwent alloHCT were significantly younger but achieved high rates of response. In conclusion, we present the most extensive and detailed experience of treatment outcomes post-CAR-T therapy. Our data suggest that novel agents may be preferable to traditional chemotherapies as the first post-CAR-T treatment. However, survival is still poor, and investigation of curative approaches is needed. We provide a tool to inform mortality risk in this difficult-to-treat population. Figure 1 Figure 1. Disclosures Scordo: i3 Health: Other: Speaker; Kite - A Gilead Company: Membership on an entity's Board of Directors or advisory committees; McKinsey & Company: Consultancy; Omeros Corporation: Consultancy; Angiocrine Bioscience: Consultancy, Research Funding. Batlevi: ADC Therapeutics: Consultancy; Juno/Celgene: Consultancy; Life Sciences: Consultancy; Regeneron: Current holder of individual stocks in a privately-held company; Karyopharm: Consultancy; Viatris: Current holder of individual stocks in a privately-held company; GLG Pharma: Consultancy; Xynomic: Research Funding; Seattle Genetics: Consultancy; Kite Pharma: Consultancy; TG Therapeutics: Consultancy; TouchIME: Honoraria; Memorial Sloan Kettering Cancer Center: Current Employment; Bayer: Research Funding; BMS: Current holder of individual stocks in a privately-held company; Medscape: Honoraria; Pfizer: Current holder of individual stocks in a privately-held company; Moderna: Current holder of individual stocks in a privately-held company; Dava Oncology: Honoraria; Roche/Genentech: Research Funding; Novartis: Research Funding; Epizyme: Research Funding; Janssen: Research Funding; Autolus: Research Funding. Dahi: Gilead sciences: Membership on an entity's Board of Directors or advisory committees; Kite pharma: Membership on an entity's Board of Directors or advisory committees. Giralt: PFIZER: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Actinnum: Membership on an entity's Board of Directors or advisory committees; SANOFI: Membership on an entity's Board of Directors or advisory committees; CELGENE: Membership on an entity's Board of Directors or advisory committees; AMGEN: Membership on an entity's Board of Directors or advisory committees; JANSENN: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; JAZZ: Membership on an entity's Board of Directors or advisory committees. Palomba: Pluto: Honoraria; Lygenesis: Honoraria; Magenta: Honoraria; Juno: Patents & Royalties; Wolters Kluwer: Patents & Royalties; WindMIL: Honoraria; Priothera: Honoraria; Nektar: Honoraria; Rheos: Honoraria; BeiGene: Consultancy; Seres: Honoraria, Other: Stock, Patents & Royalties, Research Funding; Kite: Consultancy; Ceramedix: Honoraria; Notch: Honoraria, Other: Stock; Novartis: Consultancy; PCYC: Consultancy. Salles: Ipsen: Consultancy; Regeneron: Consultancy, Honoraria; Genentech/Roche: Consultancy; Genmab: Consultancy; Takeda: Consultancy; Novartis: Consultancy; Incyte: Consultancy; Morphosys: Consultancy, Honoraria; Janssen: Consultancy; Epizyme: Consultancy, Honoraria; Allogene: Consultancy; Kite/Gilead: Consultancy; Loxo: Consultancy; Miltneiy: Consultancy; Debiopharm: Consultancy; Velosbio: Consultancy; Rapt: Consultancy; BMS/Celgene: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Honoraria; Bayer: Honoraria. Sauter: Genmab: Consultancy; Celgene: Consultancy, Research Funding; Gamida Cell: Consultancy; GSK: Consultancy; Bristol-Myers Squibb: Research Funding; Kite/Gilead: Consultancy; Precision Biosciences: Consultancy; Novartis: Consultancy; Spectrum Pharmaceuticals: Consultancy; Juno Therapeutics: Consultancy, Research Funding; Sanofi-Genzyme: Consultancy, Research Funding. Shah: Amgen: Research Funding; Janssen Pharmaceutica: Research Funding. Avigdor: Gilead: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; BMS: Research Funding; Janssen: Research Funding; Takeda: Consultancy, Honoraria. Perales: Bristol-Myers Squibb: Honoraria; Takeda: Honoraria; Equilium: Honoraria; Cidara: Honoraria; MorphoSys: Honoraria; Incyte: Honoraria, Other; Servier: Honoraria; Celgene: Honoraria; Medigene: Honoraria; Kite/Gilead: Honoraria, Other; Karyopharm: Honoraria; Nektar Therapeutics: Honoraria, Other; Merck: Honoraria; Novartis: Honoraria, Other; NexImmune: Honoraria; Miltenyi Biotec: Honoraria, Other; Omeros: Honoraria; Sellas Life Sciences: Honoraria. Shouval: Medexus: 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)0.004
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.367
Teacher spread0.269 · 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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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