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Characterization of liver function tests (LFTs) following tebentafusp (tebe) in previously treated (2L+) metastatic uveal melanoma (mUM) patients (pts).

2021· article· en· W3168870529 on OpenAlexaff
Takami Sato, Richard D. Carvajal, Joseph J. Sacco, Alexander N. Shoushtari, Jessica C. Hassel, Alexandra P. Ikeguchi, Leonel F. Hernandez‐Aya, Paul Nathan, Matthew J. Rioth, Omid Hamid, Josep M. Piulats, Jason J. Luke, Douglas B. Johnson, Serge Leyvraz, Enrique Espinosa, Shaad E. Abdullah, Daniel Ching-Wah Sum, Sarah Lockwood, Patricia Méndez, Marcus O. Butler

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineMetastasisGastroenterologyInternal medicineLiver functionMelanomaOncologyCancer researchCancer

Abstract

fetched live from OpenAlex

e21513 Background: Tebe is a bispecific gp100-targeted T cell receptor fusion protein that can redirect polyclonal T cells to target gp100+ cells leading to T cell activation and release of inflammatory mediators. Hepatocytes do not express gp100 and tebe did not redirect T cells against normal hepatocytes in preclinical in vitro studies. However, since most mUM pts have liver metastases, tebe may result in secondary effects from localized tumor-related inflammation. Here we describe LFT kinetics and outcomes for pts in the IMCgp100-102 study (NCT02570308). Methods: 127 HLA-A*02:01+ pts with 2L+ mUM received tebe, administered weekly at the RP2D following intra-patient dose escalation (C1D1: 20μg; C1D8: 30μg; C1D15+: 68μg). Pts were eligible if ALT/AST ≤ 3 x ULN and bilirubin ≤ 1.5 x ULN. LFTs were measured at baseline (BL) and weekly prior to each dose using local laboratories. AE grading was based on CTCAE v4.03. This analysis was conducted on the primary analysis snapshot dated 04Jun20. Results: At BL,125/127 (98%) pts had ALT/AST ≤ grade(G)1 and 122/127 (96%) had liver metastasis. 68/127 (54%) had an increase in post-BL grade for ALT, AST or both. Of these 48/68 (71%) increased to G1, 9/68 (13%) to G2, 7/68 (10%) to G3, and 4/68 (6%) to G4. 67 of these 68 pts (99%) had liver metastasis and most had largest liver metastasis > 3cm (38 pts > 3 cm, 29 pts < 3cm and 1 pt without). ALT/AST increases occurred early in treatment in 36/68 (53%) including at Dose 1 (12/68; 18%), Dose 2 (10/68; 15%), or Dose 3 (14/68; 21%). In the other 32/68 pts (47%), ALT/AST increases occurred at or after Dose 4 (4-65), and most of these events (21/32; 66%) were associated temporally with increase in size of liver metastases. Among the 11 pts with G3/4 ALT/AST increases post-BL, most pts experienced these events early (Doses 1-3) (8/11; 73%) and in the context of either increase in size of liver metastases / disease progression or biliary obstruction (9/11; 82%). Most pts, 60/68 (88%), continued treatment despite an increase in ALT/AST grade. Among 8 pts who discontinued treatment, 3 were due to disease progression and 3 were due to adverse events. Median time for ALT/AST to return to BL was 9 days and there were no temporal increases in albumin or INR. Conclusions: Approximately 1/2 of tebe treated pts experienced an increase in post-BL CTCAE grade for ALT/AST. Among these pts, most ALT/AST increases were mild. Most of the pts with G3/4 ALT/AST increase (9/11) were explained by increase in size of liver metastases or biliary obstruction. Increase in LFT are not unusual at time of disease progression in mUM given high frequency of liver metastases. In summary, ALT/AST increases occurred early in about half of the pts, were generally self-limiting, permitted treatment to continue, and did not appear to impact liver synthetic function (INR/Albumin). Clinical trial information: NCT02570308.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.067
GPT teacher head0.400
Teacher spread0.333 · 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
Published2021
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