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Record W4405047353 · doi:10.1182/blood-2024-205223

Treatment Outcomes of Patients Treated with Venetoclax-Obinutuzumab Therapy Vs Btki Therapies in 1L CLL: An International Real-World Study

2024· article· en· W4405047353 on OpenAlexaff
Nicole Lamanna, Jennifer R. Brown, Chaitra S. Ujjani, Toby A. Eyre, Beenish S. Manzoor, Nilanjan Ghosh, Lindsey E. Roeker, Matthew S. Davids, Catherine C. Coombs, Alan P Skarbnik, Brian T. Hill, Hande H. Tuncer, Lori A. Leslie, Joanna Rhodes, Isabelle Fleury, Paul M. Barr, Nnadozie Emechebe, Nicolás Martínez‐Calle, Christopher E. Jensen, Yun Young Choi, Dureshahwar Jawaid, Laurie Pearson, Meghan C. Thompson, Steven E. Marx, Wendy Sinai, Frederick Lansigan, Bita Fakhri, Deborah M. Stephens, Stephen J. Schuster, Michael Coyle, Irina Pivneva, Talissa Watson, Annie Guerin, Mazyar Shadman

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsGroup for Research in Decision AnalysisUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsVenetoclaxObinutuzumabMedicineInternal medicineOncologyIntensive care medicineChronic lymphocytic leukemiaLeukemia

Abstract

fetched live from OpenAlex

Introduction: Venetoclax + obinutuzumab (VO) and Bruton tyrosine kinase inhibitor (BTKi) therapies used in the first line (1L) setting are highly effective for patients (pts) with chronic/small lymphocytic leukemia (CLL/SLL) but there are no reported comparative studies. This study compared real-world clinical outcomes of pts treated with VO vs BTKi therapy in 1L. Methods: Data from the CLL Collaborative Study of Real-World Evidence (CORE), an international, retrospective, multi-center chart review study (25 centers) were used. Adult pts who initiated approved 1L treatments of VO (VO cohort) or covalent BTKi (cBTKi cohort) therapy in 1L between 2019-2024 were included. Cohorts were balanced using entropy balancing on sex, age at 1L, year of 1L start, ECOG performance status, Rai stage, del(17p)/TP53 mutation, and comorbidities. Among pts with known mutation status, proportion of pts with unmutated IGHV was similar across cohorts; hence it was not included (VO: 54.8%; cBTKi: 57.6%). Weighted outcomes included overall response rate (ORR: proportion of pts with physician-reported clinical complete/partial response [CR/PR] out of pts with available response data), progression-free-survival (PFS: time from initiation of therapy to disease progression/death [event] or last follow-up [censor]), and time to next treatment or death (TTNT-D: time from initiation of therapy to the change of therapy (including cBTKi to cBTKi)/death [event] or last follow-up [censor]). Time-to-event data were assessed using Kaplan-Meier methods and Cox proportional hazards model at 12 and 18 months (mos). Given >20% of pts were still at risk 18-mos after 1L initiation, results for 18-mos rates were reported. Results: Of 2,309 total pts, 110 initiated VO and 242 a cBTKi (ibrutinib: 111 [45.9%], acalabrutinib: 102 [42.1%], zanubrutinib: 15 [6.2%], cBTKi+anti-CD20: 14 [5.8%]) in 1L. Before weighting, the VO cohort was younger at 1L initiation (median years VO: 64.5; cBTKi: 67.9) and median time to 1L start post-diagnosis was longer (39.1 vs 25.1 mos). The VO cohort had higher proportion of pts with ECOG 0-2 (among those with known ECOG: 99.0% vs 98.9%), lower proportion with del(17p)/TP53 mutation (6.7% vs 17.1%), and lower median number of comorbidities (1 vs 2). Common comorbidities for both cohorts included cardiovascular (43.6% vs 52.1%) and endocrine/metabolic conditions (20.9% vs 36.8%). Pt and treatment characteristics were well-matched between cohorts after weighting. After weighting, with a median follow-up of 10.2 mos (IQR: 5.6, 17.9) for the VO cohort and 10.7 mos (IQR: 4.9, 25.5) for the cBTKi cohort, median duration of treatment was 9.5 [IQR: 5.4, 17.3] vs 12.1 [IQR: 4.9, 18.3] mos, respectively; majority of pts were still on therapy at the time of analyses (63.6% vs 74.7%) but fewer pts in the VO cohort initiated a subsequent line of therapy (2.7% vs 18.8%). Among the 58 pts in the cBTKi cohort who sequenced to another therapy, largely due to intolerance (66.7%), 41.9% switched to another cBTKi (36.2% to acalabrutinib) and 58.1% to a different regimen (i.e., venetoclax-based: 45.7%; other: 12.4%). Only 3 pts in the VO cohort started another therapy (2 due to intolerance). The ORR was descriptively higher for the VO cohort (ORR: 89.2% [CR: 58.1%; PR: 31.1%], available response data for 67.3%) relative to the cBTKi cohort (ORR: 80.7% [CR: 8.8%; PR: 71.9%], available response data for 63.0%). The median PFS was not reached, however the 18-mos rate was trending higher for the VO cohort (92.2% vs 84.3%). Based on the Cox model, there was no statistically significant difference in PFS at 18-mos for the cBTKi cohort compared to the VO cohort (hazard ratio [HR]: 2.37 (confidence interval [CI]: 0.76, 7.44; p-value: 0.14). The median TTNT-D was not reached, however the 18-mos rate was higher for the VO cohort (89.4% vs 70.2%). Based on the Cox model, the cBTKi cohort had a statistically significant greater risk of sequencing to the next treatment/death at 18-mos compared to the VO-based cohort (HR: 3.27 (CI: 1.35, 7.94; p-value: <0.01). Conclusions: This study is one of the first to demonstrate advantages in clinical outcomes of pts using VO vs cBTKi therapy in 1L for TTNT-D. Considering that 19% of BTKi pts switched therapy, largely due to intolerance, this highlights the need for future studies to assess VO vs 2nd-generation BTKis in the 1L setting with longer follow-up time and larger cohorts.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.342
Teacher spread0.313 · 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
Published2024
Admission routes1
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