Identification of osimertinib-resistant EGFR L792 mutations by cfDNA sequencing: oncogenic activity assessment and prevalence in large cfDNA cohort

Fairclough, Stephen R.; Kiedrowski, Lesli A.; Lin, Jessica J.; Zelichov, Ori; Tarcic, Gabi; Stinchcombe, Thomas E.; Odegaard, Justin I.; Lanman, Richard B.; Shaw, Alice T.; Nagy, Rebecca J.

doi:10.1186/s40164-019-0148-7

Cited by 19 publications

(12 citation statements)

References 23 publications

(24 reference statements)

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“…The L792 mutation usually co-exists with other EGFR mutations and exists in the cis -form with T790M and in the trans -form with G796/C797. , In silico modeling disclosed that the mutations in the L792 residue sterically clash with a methoxy group of the phenyl ring present in Osimertinib, buckling its attachment to the kinase domain . In addition, in vitro L792 mutant variants remained susceptible to Gefitinib …”

Section: Mechanisms Of Osimertinib Resistancementioning

confidence: 98%

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Emerging Approaches to Overcome Acquired Drug Resistance Obstacles to Osimertinib in Non-Small-Cell Lung Cancer

et al. 2021

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The pyrimidine core-containing compound Osimertinib is the only epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) from the third generation that has been approved by the U.S. Food and Drug Administration to target threonine 790 methionine (T790M) resistance while sparing the wild-type epidermal growth factor receptor (WT EGFR). It is nearly 200-fold more selective toward the mutant EGFR as compared to the WT EGFR. A tertiary cystein 797 to serine 797 (C797S) mutation in the EGFR kinase domain has hampered Osimertinib treatment in patients with advanced EGFR-mutated non-small-cell lung cancer (NSCLC). This C797S mutation is presumed to induce a tertiary-acquired resistance to all current reversible and irreversible EGFR TKIs. This review summarizes the molecular mechanisms of resistance to Osimertinib as well as different strategies for overcoming the EGFR-dependent and EGFR-independent mechanisms of resistance, new challenges, and a future direction.

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Section: Mechanisms Of Osimertinib Resistancementioning

confidence: 98%

“…64 In addition, in vitro L792 mutant variants remained susceptible to Gefitinib. 66 3.1.4. Mutations in G796.…”

Section: Mechanisms Of Osimertinib Resistancementioning

confidence: 99%

Emerging Approaches to Overcome Acquired Drug Resistance Obstacles to Osimertinib in Non-Small-Cell Lung Cancer

et al. 2021

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“…There is no effective care for heavily-treated and osimertinib-resistance metastatic LUAD patients yet, especially multiple resistance mutations ( 1 ). Multiple resistance mutations have been reported previously in very few cases similar to our patient, but no therapeutic regimen was reported, except for the idea of combination therapy ( 3 ). Here, we report an advanced LUAD patient with osimertinib-resistance who received subsequently combination targeted therapy and obtained a long-term clinical benefit.…”

Section: Discussionmentioning

confidence: 58%

Advanced lung adenocarcinoma (LUAD) patient with EGFR mutations benefited from multiline combination targeted therapies after osimertinib (AZD9291) resistance: a case report

Chen¹,

Zhao²,

Wang³

et al. 2022

Transl Cancer Res TCR

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Background: The resistance mechanisms to osimertinib encompass on-target molecular alterations, such as the well-known epidermal growth factor receptor (EGFR) C797S resistance mutation, and offtarget molecular alterations, such as the high-frequency MET amplification, but there's no further clearcut therapeutic option to date for these individuals yet. Here we reported a lung adenocarcinoma (LUAD) patient who progressed on osimertinib benefited from multiline combination target-therapy and obtained a long-term progression-free survival (PFS). Case Description: A 70-year-old Chinese woman without a smoking history presented with stage IV advanced LUAD harboring EGFR 19del and then developed EGFR T790M mutation after 6-month treatment of gefitinib [a first-generation EGFR tyrosine kinase inhibitor (TKI)]. Osimertinib (a thirdgeneration EGFR TKI) was immediately initiated, and the PFS was 11 months. After disease progression, next-generation sequencing (NGS) identified MET amplification, in addition to EGFR 19del. Combination therapy of osimertinib and cabozantinib (a small molecule inhibitor of the tyrosine kinases c-Met and VEGFR2)/capmatinib (a MET inhibitor) was administrated to the patient and the best overall response (OR) was stable disease (SD) with the PFS of 10 months. NGS detected the emergence of novel mutations EGFR S784Y and EGFR L799Q, together with EGFR C797S and all in cis with EGFR T790M, and retention of EGFR 19 del. The patient received pemetrexed (a chemotherapy drug) and bevacizumab (a VEGFR inhibitor) and achieved a partial response (PR). After 6 months of PFS, combination therapy of brigatinib (an inhibitor of ALK and EGFR) and cetuximab (an EGFR inhibitor) was initiated and the patient achieved a long-term PFS of 18 months and SD. Her overall survival (OS) was 51 months.Conclusions: This case highlights the importance of NGS on repeated biopsy which could offer better treatment options.

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“…Pareto optimization placed 16 sequences on the frontier, 2 sequences in rank 2, 8 sequences in rank 3, 1 sequence in rank 4, and 5 sequences in rank 5. RESISTOR correctly identified five clinically significant resistance mutations to osimertinib: L792H, G796R, G796S, G796D, and G796C [50][51][52][53][54][55], and while L792H was in the 2 nd Pareto rank, all of the other correctly predicted resistance mutations are on the Pareto frontier.…”

Section: Egfr and Osimertinibmentioning

confidence: 99%

“…For gefitinib, T790M was also on the Pareto frontier. Previous studies have documented all of these resistance mutations as occurring in the clinic [50][51][52][53][54][55][56][57]. † indicates that RESISTOR predicted the mechanism of resistance to be improved binding of the endogenous ligand to the mutant.…”

Section: Resistor Identifies Clinically-relevant Resistance Mutations...mentioning

confidence: 99%

Resistor: an algorithm for predicting resistance mutations using Pareto optimization over multistate protein design and mutational signatures

Guerin

Kaserer

Donald

2022

Preprint

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Resistance to pharmacological treatments, such as to antibiotics or cancer therapeutics, ablates the efficacy of drugs and is a major public health challenge. Accurate, prospective prediction of resistance mutations would allow for design of drugs less susceptible to conferred resistance. Here we report RESISTOR--a novel structure- and sequence-based algorithm for predicting resistance mutations. RESISTOR optimizes over four objectives to find the Pareto frontier of these resistance-causing criteria. The first two axes of optimization are provable approximations to the relative change in binding affinity (ΔKa) of a drug and endogenous ligand upon a protein's mutation. These ΔKa predictions are made by the provable thermodynamic- and ensemble-based multistate computational protein design algorithm K* after an initial sequence filter using the multistate design algorithm COMETS from the computational protein design software OSPREY. By virtue of pruning using COMETS, RESISTOR inherits the empirical sublinearlity characteristics of the COMETS sequence search, rendering RESISTOR, to our knowledge, the first provable structure-based resistance prediction algorithm that is sublinear in the size of the sequence space. This is important because the size of sequence space is exponential in the number of residue positions that can mutate to confer resistance. On the third axis RESISTOR uses empirically derived mutational signatures to determine the probability that a particular single- or double-point mutation will occur in a given context. The fourth axis of optimization is over "mutational hotspots", or those locations in the protein where multiple amino acids are predicted to confer resistance. As validation of the algorithm, we applied RESISTOR to a number of tyrosine kinase inhibitors used to treat lung adenocarcinoma and melanoma through inhibition of EGFR or B-Raf kinase activity. In so doing, we searched over a set of 1257 sequences in EGFR and 1214 sequences in B-Raf with an average conformation space size of ~5.9×1010, using experimental structures when available and docked complexes when not. This search generated a set of predicted resistance mutations that we compared to known resistance mutations in EGFR and report herein that RESISTOR correctly identified eight clinically significant resistance mutations, including the "gatekeeper" T790M mutation to erlotinib and gefitinib and five known resistance mutations to osimertinib. This demonstrates that by exploiting the wealth of structural and sequence data available in the form of molecular structures and mutational signatures, RESISTOR is a general method for predicting resistance mutations that can be applied to a wide variety of cancer, antimicrobial, antiviral and antifungal drug targets. RESISTOR is available as part of OSPREY on GitHub and is free and open source software.

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Identification of osimertinib-resistant EGFR L792 mutations by cfDNA sequencing: oncogenic activity assessment and prevalence in large cfDNA cohort

Cited by 19 publications

References 23 publications

Emerging Approaches to Overcome Acquired Drug Resistance Obstacles to Osimertinib in Non-Small-Cell Lung Cancer

Emerging Approaches to Overcome Acquired Drug Resistance Obstacles to Osimertinib in Non-Small-Cell Lung Cancer

Advanced lung adenocarcinoma (LUAD) patient with EGFR mutations benefited from multiline combination targeted therapies after osimertinib (AZD9291) resistance: a case report

Resistor: an algorithm for predicting resistance mutations using Pareto optimization over multistate protein design and mutational signatures

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