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A systematic review and meta-analysis of intrathecal versus alternate routes of delivery for HER2-targeted therapies in patients with HER2+ breast cancer leptomeningeal metastases.

2023· review· en· W4379283972 on OpenAlexaff
Anna-Maria Lazaratos, Sarah M. Maritan, Amélie Darlix, Ivica Ratoša, Emanuela Ferraro, Gaia Griguolo, Alessia Pellerino, Silvia Höfer, William Jacot, Hans‐Joachim Stemmler, Marcel van den Broek, François Panet, Zubin Lahijanian, Aki Morikawa, Andrew D. Seidman, Riccardo Soffietti, Kevin Petrecca, April A. N. Rose, Nathaniel Bouganim, Matthew Dankner

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsMedicineLapatinibTrastuzumabTrastuzumab emtansinePertuzumabInternal medicineOncologyClinical endpointHazard ratioMetastatic breast cancerBreast cancerNeratinibTargeted therapyPalbociclibConfidence intervalCancerRandomized controlled trial

Abstract

fetched live from OpenAlex

2034 Background: Patients with HER2+ breast cancer (BC) frequently develop leptomeningeal metastases (LM). While HER2-targeted therapies have demonstrated efficacy in the neoadjuvant, adjuvant, and metastatic settings, including for parenchymal brain metastases, their efficacy for patients with LM has not been studied in a randomized controlled trial. However, several single-armed prospective studies, case series and case reports have studied oral, intravenous (IV), or intrathecally (IT) administered HER2-targeted therapy regimens for patients with HER2+ BCLM. Methods: We conducted a systematic review and meta-analysis of individual patient data to evaluate the efficacy of HER2-targeted therapies in HER2+ BCLM in accordance with PRISMA guidelines. Targeted therapies evaluated were trastuzumab (IT or IV), pertuzumab, lapatinib, neratinib, tucatinib, trastuzumab-emtansine (T-DM1) and trastuzumab-deruxtecan (TDXd). The primary endpoint was overall survival (OS), with progression-free survival (PFS) as a secondary endpoint. To assess differences between groups, shared frailty Cox regression models were used to estimate the hazard ratio (HR), 95% confidence interval (CI) and p-value. Results: 7780 abstracts were screened, identifying 44 publications with 200 patients, corresponding to 257 lines of HER2-targeted therapy for BC LM which met inclusion criteria. In univariable (OS: HR=0.9, 95% CI: 0.64-1.4, P=0.76; PFS: HR=0.8, 95% CI: 0.57-1.2, P=0.35) and multivariable (OS: HR=1.4, 95% CI: 0.68-3.1, P=0.4; PFS: HR=0.8, 95% CI: 0.41-1.7, P=0.6) analyses, we observed no significant difference between IT, oral or IV administration of HER2-targeted therapy. Meanwhile, ECOG performance status remained independently associated with prolonged OS (HR=2.2, 95% CI: 1.5-3.2, P<0.001) and PFS (HR=2.2, 95% CI: 1.5-3.2, P<0.001 and HR=1.9, 95% CI: 1.4-2.8, P<0.001) in the final multivariable model. ECOG status was not associated with route of trastuzumab delivery (P>0.40). Anti-HER2 monoclonal antibody-based regimens did not demonstrate superiority over HER2 tyrosine kinase inhibitors (OS: P=0.647; PFS: P=0.983). In a cohort of 7 patients, TDXd demonstrated improved OS compared to other HER2-targeted therapies and compared to T-DM1 (P<0.05). Conclusions: The results of this meta-analysis suggest that IT administration of HER2-targeted therapy for patients with HER2+ BCLM confers no additional benefit over oral and/or IV treatment regimens. We also present the first evidence supporting the efficacy of TDXd compared to alternative strategies for this patient population. Although the number of patients receiving TDXd in this cohort is small, this novel agent offers promise for this patient population and requires further investigation in prospective studies.

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)0.011
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0180.032
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.230
GPT teacher head0.498
Teacher spread0.268 · 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 designMeta-analysis
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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