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CRESTONE: Initial efficacy and safety of seribantumab in solid tumors harboring <i>NRG1</i> fusions.

2022· article· en· W4286297054 on OpenAlexaff
Daniel R. Carrizosa, Mark E. Burkard, Yasir Y. Elamin, Jayesh Desai, Shirish M. Gadgeel, Saiama N. Waqar, David R. Spigel, Young Kwang Chae, Parneet Cheema, Eric B. Haura, Stephen V. Liu, Danny Nguyen, Karen L. Reckamp, Frank Tsai, Valerie M. Jansen, Alexander Drilon, Sai‐Hong Ignatius Ou, D. Ross Camidge, Tejas Patil

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsWilliam Osler Health System
Fundersnot available
KeywordsMedicineCohortResponse Evaluation Criteria in Solid TumorsOncologyClinical endpointInternal medicineDiscontinuationCancerTargeted therapyPhases of clinical researchClinical trial

Abstract

fetched live from OpenAlex

3006 Background: NRG1 fusions are rare oncogenic drivers found in ̃0.2% of all solid tumors. These fusions elicit ERBB3/HER3 overactivation to drive tumor growth and cancer cell survival. Currently there are no approved targeted therapies for NRG1 fusion-positive tumors. Furthermore, patients (pts) with tumors harboring NRG1 fusions have poor outcomes with standard therapies. Seribantumab is a fully human anti-HER3 IgG2 monoclonal antibody that suppressed tumor growth in NRG1 fusion-driven preclinical models. Here, we present initial clinical data from the CRESTONE study (NCT04383210). Methods: CRESTONE is a Phase 2, global, multicenter, open-label study of seribantumab in adult pts with locally advanced or metastatic solid tumors harboring NRG1 fusions. A dose ranging phase established the RP2D as a 3g once weekly (QW) intravenous dose administered until treatment discontinuation criteria are met. In the expansion phase, cohort 1 will enroll at least 55 pts who had received at least one prior therapy and are naïve to ERBB-targeted therapy. Exploratory cohorts 2 or 3 will enroll pts previously treated with ERBB-targeted therapies and/or tumors harboring additional molecular alterations. The primary endpoint is objective response rate (ORR) by independent central review per RECIST v1.1. Initial data from cohort 1 pts who received seribantumab 3g QW with investigator (INV)-assessed response per RECIST v1.1 are reported. Results: By JAN-13-2022, 12 pts have received seribantumab 3g QW in cohort 1. Median age was 65 years (range 44–76), 67% were female, and median number of prior therapies was 1 (range 1–5). 92% (11/12) of pts had non-small cell lung cancer (NSCLC); 5 different NRG1 fusion partners ( ATP1B1, CD74, ITG B1, SDC4, SLC3 A2) were reported by local next-generation sequencing tests. Among 10 pts evaluable for INV-assessed response, the confirmed ORR was 30%, and the disease control rate was 90% (1 complete response, 2 partial responses, 6 stable disease, 1 progressive disease). 58% (7/12) of pts remain on study treatment, including 2 pts with NSCLC who achieved objective responses with an ongoing duration of response of 6 and 8.5 months. Seribantumab 3g QW was well tolerated with no drug discontinuations or dose reductions. Across all cohorts (n = 29), the most frequently (≥20%) reported treatment-related adverse events (TRAEs) were diarrhea (38%), fatigue (34%), and rash (24%), all were grade 1 or 2. One grade 3 TRAE of vomiting occurred; there were no Grade 4 or 5 TRAEs. Efficacy analysis is ongoing and updated efficacy data from evaluable pts in cohort 1 will be presented. Conclusions: Initial data indicate seribantumab induced durable responses in advanced solid tumors harboring NRG1 fusions and has a favorable safety profile. These data support the continued evaluation of seribantumab in NRG1 fusion-positive solid tumors in the ongoing CRESTONE study. Clinical trial information: NCT04383210.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.072
GPT teacher head0.502
Teacher spread0.430 · 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 designNon-randomized trial
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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Citations40
Published2022
Admission routes1
Has abstractyes

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