Pretreatment Epidermal Growth Factor Receptor (<i>EGFR</i>) T790M Mutation Predicts Shorter EGFR Tyrosine Kinase Inhibitor Response Duration in Patients With Non–Small-Cell Lung Cancer

Su, Kang‐Yi; Chen, Hsuan‐Yu; Li, Ker-Chau; Kuo, Min-Liang; Yang, James Chih‐Hsin; Chan, Wing‐Kai; Ho, Bing-Ching; Chang, Gee‐Chen; Shih, Jin‐Yuan; Yu, Shan‐Fu; Yang, Pan‐Chyr

doi:10.1200/jco.2011.38.3224

Cited by 471 publications

(412 citation statements)

References 40 publications

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“…Next, 5 × 10 6 cells/100 μL in PBS mixed with equal volume of Matrigel (BD Biosciences) were subcutaneously injected into the back of mice. When tumor volumes reached ∼100 mm 3 , as determined by measuring tumor length and width using calipers and calculating volume through the equation [1/2 (length × width 2 )], mice were randomly allocated into groups of six mice to receive gefitinib or vehicle by oral gavage at 100 mg/kg every 2 d and tumor volumes were measured every 2 d.…”

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confidence: 99%

NF-κB–driven suppression of FOXO3a contributes to EGFR mutation-independent gefitinib resistance

Chiu

Chang

Kuo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Therapy with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (EGFR-TKIs, such as gefitinib or erlotinib) significantly prolongs survival time for patients with tumors harboring an activated mutation on EGFR; however, up to 40% of lung cancer patients exhibit acquired resistance to EGFR-TKIs with an unknown mechanism. FOXO3a, a transcription factor of the forkhead family, triggers apoptosis, but the mechanistic details involved in EGFR-TKI resistance and cancer stemness remain largely unclear. Here, we observed that a high level of FOXO3a was correlated with EGFR mutation-independent EGFR-TKI sensitivity, the suppression of cancer stemness, and better progression-free survival in lung cancer patients. The suppression of FOXO3a obviously increased gefitinib resistance and enhanced the stem-like properties of lung cancer cells; consistent overexpression of FOXO3a in gefitinib-resistant lung cancer cells reduced these effects. Moreover, we identified that miR-155 targeted the 3′UTR of FOXO3a and was transcriptionally regulated by NF-κB, leading to repressed FOXO3a expression and increased gefitinib resistance, as well as enhanced cancer stemness of lung cancer in vitro and in vivo. Our findings indicate that FOXO3a is a significant factor in EGFR mutation-independent gefitinib resistance and the stemness of lung cancer, and suggest that targeting the NF-κB/miR-155/FOXO3a pathway has potential therapeutic value in lung cancer with the acquisition of resistance to EGFR-TKIs.

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confidence: 99%

NF-κB–driven suppression of FOXO3a contributes to EGFR mutation-independent gefitinib resistance

Chiu

Chang

Kuo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Currently, the prevalent clinical practice for diagnosing tumor type and drug regimen is based on histological and/or biomarker identification and generally is biased toward the predominant subpopulation as a result of its greater representation in the population (2,27). However, minor subpopulations are important in determining how a tumor responds to treatments and may be responsible for resistance and relapse (28). An immediate critical question is, how should consideration of heterogeneity, instead of simply the predominant subpopulation, affect drug combination design (Fig.…”

Section: Resultsmentioning

confidence: 99%

Intratumor heterogeneity alters most effective drugs in designed combinations

Zhao

Hemann

Lauffenburger

2014

Proc. Natl. Acad. Sci. U.S.A.

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The substantial spatial and temporal heterogeneity observed in patient tumors poses considerable challenges for the design of effective drug combinations with predictable outcomes. Currently, the implications of tissue heterogeneity and sampling bias during diagnosis are unclear for selection and subsequent performance of potential combination therapies. Here, we apply a multiobjective computational optimization approach integrated with empirical information on efficacy and toxicity for individual drugs with respect to a spectrum of genetic perturbations, enabling derivation of optimal drug combinations for heterogeneous tumors comprising distributions of subpopulations possessing these perturbations. Analysis across probabilistic samplings from the spectrum of various possible distributions reveals that the most beneficial (considering both efficacy and toxicity) set of drugs changes as the complexity of genetic heterogeneity increases. Importantly, a significant likelihood arises that a drug selected as the most beneficial single agent with respect to the predominant subpopulation in fact does not reside within the most broadly useful drug combinations for heterogeneous tumors. The underlying explanation appears to be that heterogeneity essentially homogenizes the benefit of drug combinations, reducing the special advantage of a particular drug on a specific subpopulation. Thus, this study underscores the importance of considering heterogeneity in choosing drug combinations and offers a principled approach toward designing the most likely beneficial set, even if the subpopulation distribution is not precisely known.systems biology | cancer | combination therapy G enetic intratumor heterogeneity has long been appreciated as present in cancer patients (1). Recent sequencing studies further revealed the extent of this tumor diversity, arising from highly complex clonal evolutionary processes (2). This phenomenon has been observed in many solid (3-5) and hematopoietic cancers (6-8). Moreover, treatments also may have dramatic effects on tumor composition-with a preexisting subclone at diagnosis often becoming dominant at relapse (9-12). To meet this challenge, rational drug combination treatments must be designed so as to account for intratumor heterogeneity to better predict reoccurrence.The history of theoretical studies aimed at drug optimization in cancer therapy is long. Some studies used differential equation models formulated as deterministic optimal control problems (13-16), whereas others used stochastic birth-death process models (17-21), to examine treatment regimens for tumors comprising a sensitive population along with a few resistant subpopulations. However, these studies, many of which dealt only with generic scenarios, focused primarily on drug scheduling, investigating the effects of frequency and dose intensity of drugs on resistance potential. Practical applications of such results have been limited, although some of these strategies recently were combined with experimental validation to examin...

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“…In the present study, mutations in these oncogenes were reported in NPC for the first time. This could be explained by the use of a more sensitive MALDI-TOF technology which is as sensitive as next generation sequencing, and more sensitive than Sanger sequencing, as Su et al demonstrated (36). Secondly, according to the COSMIC database, most of the previous studies were based on cohorts <100 samples (Table V).…”

Section: Discussionmentioning

confidence: 99%

Hotspot mutations in common oncogenes are infrequent in nasopharyngeal carcinoma

et al. 2014

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Pretreatment Epidermal Growth Factor Receptor (EGFR) T790M Mutation Predicts Shorter EGFR Tyrosine Kinase Inhibitor Response Duration in Patients With Non–Small-Cell Lung Cancer

Abstract: T790M may not be a rare event before or after TKI therapy in patients with NSCLC with EGFR-activating mutations. The pretreatment T790M mutation was associated with shorter PFS with EGFR TKI therapy in patients with NSCLC.

Cited by 471 publications

References 40 publications

NF-κB–driven suppression of FOXO3a contributes to EGFR mutation-independent gefitinib resistance

NF-κB–driven suppression of FOXO3a contributes to EGFR mutation-independent gefitinib resistance

Intratumor heterogeneity alters most effective drugs in designed combinations

Hotspot mutations in common oncogenes are infrequent in nasopharyngeal carcinoma

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