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Record W4362459260 · doi:10.2217/fon-2022-1035

Plain language summary of the development of tepotinib: a treatment for a subtype of non-small cell lung cancer called <i>MET</i> exon 14 skipping

2023· article· en· W4362459260 on OpenAlexaff
John Hallick, Anne‐Marie Baird, Gerald S. Falchook, Xiuning Le, David S. Hong, Santiago Viteri, Jo Raskin, Niels Reinmuth, Soetkin Vlassak, Mihaela Militaru, Paul K. Paik

Bibliographic record

VenueFuture Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsTrinity College
Fundersnot available
KeywordsMedicineLung cancerExonCancerTargeted therapyOncologyCancer researchInternal medicineMutationExon skippingGeneticsGeneBiology

Abstract

fetched live from OpenAlex

Plain Language SummaryWhat is this summary about? This plain language summary provides an overview of two of the main clinical studies that led to tepotinib’s approval, the phase I first-in-human study and the phase II VISION study.What is tepotinib? Tepotinib is a targeted anti-cancer treatment taken orally (by mouth). It is available in many countries for people with advanced or metastatic non-small cell lung cancer (NSCLC), where the tumor contains a genetic mutation (alteration) called ‘MET exon 14 skipping’. Tumor cells rely on this mutation to grow and survive, so targeted blocking of the effect of this mutation is an important treatment approach. MET exon 14 skipping occurs in approximately 3–4% of people with NSCLC. These people are usually of older age. This subtype of NSCLC is associated with poor outcomes. Before treatments that specifically target this MET mutation were developed, only general treatments such as chemotherapy were available for this type of cancer. Because chemotherapy attacks all rapidly dividing cells in a person’s body and is administered intravenously (through a vein), it can often cause unwanted side effects. Cancer cells grow and divide rapidly because of defects, often involving proteins called ‘tyrosine kinases’. Specific tyrosine kinase inhibitors (TKIs) were therefore developed to slow or stop cancer growth by targeting these proteins. Tepotinib is a MET TKI. This means that it blocks the activity of the MET pathway that is overactive in MET exon 14 skipping NSCLC. Doing this, may slow down cancer growth.What were the results from the clinical studies of tepotinib? In the studies summarized here, people with MET exon 14 skipping NSCLC who took tepotinib had their tumor growth stopped or their tumor shrunk for a period of time, and they mostly experienced side effects that they could tolerate. Clinical Trial Registration: NCT01014936 (tepotinib first-in-human), NCT02864992 (VISION), NCT03940703 (INSIGHT 2) (ClinicalTrials.gov)To read the full Plain Language Summary of this article, click here to view the PDF.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.017
GPT teacher head0.352
Teacher spread0.335 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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