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Drug Sensitivity and Allele‐specificity of First‐line Osimertinib Resistance <i>EGFR</i> Mutations

2020· article· en· W3018637389 on OpenAlexaboutno aff
Jacqueline H. Starrett, Alexis Guernet, Maria Emanuela Cuomo, Kamrine E. Poels, Iris K. van Alderwerelt van Rosenburgh, Amy Nagelberg, Dylan Farnsworth, Kristin S. Price, Hina Khan, Kumar Dilip Ashtekar, Mmaserame Gaefele, Deborah Ayeni, Tyler F. Stewart, Alexandra Kuhlmann, Susan M. Kaech, Arun M. Unni, Robert Homer, William W. Lockwood, Franziska Michor, Sarah B. Goldberg, Mark A. Lemmon, Paul D. Smith, Darren A.E. Cross, Katerina Politi

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
Fundersnot available
KeywordsOsimertinibAfatinibErlotinibLung cancerContext (archaeology)MedicineDrug resistanceCancer researchMutationCancerOncologyEpidermal growth factor receptorBiologyGeneticsInternal medicineGene

Abstract

fetched live from OpenAlex

Osimertinib, a mutant‐specific third generation EGFR TKI, is emerging as the preferred first‐line therapy for EGFR mutant lung cancer. Despite initial responses in patients, however, resistance inevitably develops over time. In order to investigate mechanisms of resistance to first‐line osimertinib, we modeled acquired resistance to this drug in transgenic mouse models of EGFR L858R ‐induced lung adenocarcinoma and found that it is mediated largely through secondary mutations in EGFR – either C797S or L718V/Q (Figure and 1B). Analysis of circulating free DNA data from patients with EGFR mutant lung cancer revealed that L718Q/V mutations almost always arise in the context of an L858R driver mutation. Therapeutic testing in mice revealed that both erlotinib and afatinib caused regression of osimertinib‐resistant C797S‐containing tumors, whereas only afatinib was effective in L718Q mutant tumors (Figure ). Combination first‐line osimertinib plus erlotinib treatment prevented the emergence of secondary mutations in EGFR . Our data identify specific secondary EGFR mutations as a major mechanism of acquired resistance to first‐line osimertinib treatment and highlight potential strategies to overcome or prevent osimertinib resistance in vivo . Furthermore, these findings emphasize how knowledge of the specific characteristics of resistance mutations are important for determining potential subsequent treatment approaches. Support or Funding Information This work was supported by ‐‐‐‐‐Yale’s Specialized Program of Research Excellence in Lung Cancer grant (to K. Politi, S.B. Goldberg and M.A. Lemmon) and funding from AstraZeneca (to K. Politi). Additional support came from the NIH/NCI‐funded Yale Cancer Biology Training Program T32 CA193200‐01A1 and F31 CA228268‐01A1 (to J.H. Starrett), R01 CA198164 (M.A. Lemmon), the Ginny and Kenneth Grunley Fund for Lung Cancer Research, and the Canadian Institutes of Health Research Project Grant PJT‐148725 (to W.W. Lockwood). W.W. Lockwood is supported by a Michael Smith Foundation for Health Research Scholar and NIHR New Investigator Awards, A. Guernet is a fellow funded by the IMED AstraZeneca postdoc program, A. Nagelberg is supported by a scholarship from the CIHR, and K.D. Ashtekar is an Arnold and Mabel Beckman Foundation Postdoctoral Fellow. Yale Cancer Center Shared Resources used for this work were in part supported by NIH/NCI Cancer Center Support Grant P30 CA016359. Acquired resistance to first‐line osimertinib arises partially due to the emergence of secondary mutations in EGFR , which are differentially sensitive to other EGFR TKIs. A . Schema of the experiment. CCSP‐rtTA;TetO‐EGFR L858R mice were administered doxycycline (dox) for the duration of the experiment and developed tumors after ~6 weeks on dox. When tumors were detected by MRI ( see pre‐treatment image ), osimertinib treatment was initiated (25 mg/kg QD M‐F) which elicited a response ( see representative response MRI ) and treated until the emergence of resistant tumors by MRI. Coronal MR images are shown, in which ‘H’ indicates heart and red arrows indicate tumor. The osimertinib‐resistant tumors were then collected and analyzed to determine the resistance mechanisms present. B . Pie‐chart illustrating the resistance mechanisms found in osimertinib‐resistant tumors. C and D . Average tumor volume changes for the osimertinib‐resistant tumors switched to 25 mg/kg erlotinib for 3 weeks ( C ) or 25 mg/kg afatinib for 10 days ( D ), as determined by MRI tumor volume measurements. Tumor volume is normalized to the point of TKI switch. Error bars represent SEM. For C , curves are the average of n=11 total tumors (C797S n=5; L718V n=3; L718Q n=3). For D , curves are the average of n=12 total tumors (C797S n=7; L718Q n=5). Figure 1

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.021
GPT teacher head0.285
Teacher spread0.263 · 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 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".

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Citations7
Published2020
Admission routes1
Has abstractyes

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