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Response monitoring during neoadjuvant targeted treatment in early stage non‐
small cell lung cancer
van Gool, M.H.
Publication date
2019
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Citation for published version (APA):
van Gool, M. H. (2019). Response monitoring during neoadjuvant targeted treatment in early
stage non‐small cell lung cancer.
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Chapter
9
general discussion and future perspectives
9
General discussion and future discussion
Technical developments such as genomic sequencing have proceeded rapidly and have refined the classification of non‐small cell lung cancer (NSCLC) from histologic into oncogenic subsets.1,2 With the discovery of relevant mutations and deregulated signaling pathways, a better understanding of tumor types within NSCLC has been evolving, leading to new targets and more individualized patient treatment. Recent clinical studies are encouraging, and show that targeted therapy and immunotherapy alone or in combination with conventional treatments can significantly improve patient outcomes.3‐6
In patients with advanced NSCLC harboring epidermal growth factor receptor (EGFR) mutations, EGFR‐TKI targeted therapy showed an improved survival compared to conventional treatment.5,6 Furthermore, targeted therapy was superior to conventional treatment as first line therapy in patients with advanced ALK‐positive NSCLC.7 In addition, immunotherapy has now found a place in the first‐line treatment of patients with advanced NSCLC as well. In patients with metastatic NSCLC without TKI‐sensitizing mutations, immunotherapy is recommended for all patients with PD‐L1 expression with chemotherapy or as single agent in patients with PD‐L1 expression of ≥ 50%.8 These advances are promising and help to identify subsets of patients with (advanced) NSCLC for whom better treatment options are available than chemotherapy.
There is an ongoing discussion on the prediction of response and survival in NSCLC patients receiving new treatment options.9,10 In our study we administered erlotinib in a neoadjuvant setting. A practical benefit of neoadjuvant therapy is that it will provide an early in vivo assessment of tumor response to a drug regimen. Furthermore, after a favorable response to neoadjuvant therapy, the drug may be administered as adjuvant treatment. Occasionally, downstaging of tumors by systemic therapy can make patients eligible for surgical resection.
On the other hand, therapy response monitoring can be of value to avoid unnecessary toxicity and utilization of health‐care resources in the case of ineffective treatment, especially if monitoring is feasible and informative early during treatment.
The objective of the M06NEL trial, one of the studies described in this thesis, was to investigate whether erlotinib was able to contribute to the curative treatment of early stage NSCLC. If erlotinib is able to induce responses in patients with clinical stage I or II NSCLC, this agent might find a place in the (neo)adjuvant setting. This thesis reflected on response monitoring during neoadjuvant erlotinib as new treatment option for patients
with early stage resectable NSCLC and how early response was related to outcome and long‐term survival.
Conventional techniques such as computed tomography (CT) imaging have been used to monitor (neoadjuvant) treatment effects in oncology.11 These methods monitor the change in tumor size to evaluate the effect of (chemo)therapy (according to criteria such as RECIST 1.1).12 However, response evaluation based on morphologic changes has limitations in reliable differentiation of residual tumor tissue from necrotic tumor or fibrotic scar formation.13 Furthermore, it is expected that functional changes will precede morphologic changes and, therefore, techniques such as fluorodeoxyglucose (FDG) tumor uptake as measured by positron emission tomography acquired together with low dose computed tomography (PET/CT) that evaluate the function of the tumor rather than anatomy might provide better accuracy to monitor response and overall treatment outcome.12,14‐17 EGFR‐TKI therapy is expected to induce (early) response via inhibition of cell growth and multiplication rather than morphologic shrinkage. However, there has been ongoing search for optimal parameters to measure response with FDG‐ PET/CT.18
In this thesis we studied the potential value of FDG‐PET/CT and CT as markers for response and survival. For NSCLC, response evaluation based on the morphological tumor changes as measured by CT has been the golden standard (RECIST 1.1). We show that FDG‐PET/CT is a better early predictor for response and survival than CT for monitoring (preoperative) EGFR‐TKI therapy in early stage NSCLC patients (Chapter 3, 8). In this setting (of early response monitoring), CT has severe limitations. According to RECIST 1.1, the best radiologic response evaluation can be claimed at least 4 weeks after initiation of therapy. This could explain the relatively small number of patients with radiologic response by the time of monitoring CT scan in our study. RECIST 1.1 has further limitations due to delineation of structural abnormalities, before and after treatment, which may not actually contain tumor. Although metabolic response was significantly better than radiologic response in predicting histopathologic response and survival, the metabolic response did not exactly correspond to histopathologic regression of the tumor in our study. FDG uptake on PET may reflect various tissue reactions, such as tumor progression or regression, as well as senescence, fibrosis formation, and inflammatory reactions such as macrophage infiltration.
Moreover, histopathologic evaluation of (residual) tumor tissue after targeted treatment proved to be challenging. Since EGFR‐TKIs have been used primarily to treat patients with advanced NSCLC, studies about histopathologic changes in tumors associated with
9
EGFR TKI therapy are limited. Histopathologic features of response after EGFR‐TKI therapy may be different from those reported for chemotherapy and/or radiotherapy due to the different mechanisms of action between these modalities and targeted therapy.19,20 Criteria for histopathologic response to neoadjuvant treatment have been described based on necrosis, fibrosis, and morphological signs of therapy‐induced regression, but a gold standard for classifying regression in response to EGFR‐TKI therapy is absent.21 At the time of our study the best available criteria were the Junker criteria.19 One of these criteria is the amount of vital tumor tissue. Because some spontaneous necrosis exists in most NSCLCs, a cautious cutoff of more than 50% necrosis within the area with macroscopically (most) viable tumor tissue was used for a (partial) pathologic response.22
Although metabolic response monitoring has been reported extensively as a tool to predict tumor aggressiveness and predict disease recurrence and survival, several methods for measuring metabolic activity have been described. PET scanners are designed to measure the in vivo radioactivity concentration [kBq/ml], which is directly linked to the FDG‐concentration. However, it is the relative tissue uptake of FDG that is of interest. The two most significant sources of variation that occur in practice are the amount of injected FDG and the patient size. To compensate for these variations, at least to first order, the maximum standardized uptake value (SUVmax) is commonly used as a relative measure of FDG‐uptake. Criteria according to EORTC or PRECIST based on measuring the rate and/or total amount of FDG‐accumulation in tumor are used to classify metabolic response.23,24 These were summarized in chapter 2. An additional window for predicting response is the measured SUV distribution inside the tumor.25 These textural features (although blurred) contain information about the heterogeneity of the tumor and are promising in predicting response (Chapter 5). An advantage is that these parameters are not solely dependent on the absolute uptake but also take into account changes within the tumor. However, such measurements are highly dependent on the resolution of PET scanners and statistical modelling which limits generalizability. Another disadvantage of this approach is that it is not clear what type of heterogeneity is correlating with the tumor response. This information would be vital for future development towards prospective use.
Another question involves the optimal timing in performing FDG‐PET/CT after initiation of treatment (with EGFR‐TKI’s). Chapter 4 shows that a change in metabolic activity within 1 week after the initiation of erlotinib treatment is informative for a histopathologic response after 3 weeks of treatment. Correspondingly, a decrease in metabolic activity within 1 week likely will continue after 3 weeks of therapy (94%).
Furthermore, an increase in the SUVmax during the first week will persist in most patients, suggesting ineffective treatment. In our series, early screening for no change or an increase in the SUVmax on FDG‐PET/CT would result in a discontinuation of TKI therapy for 53% of the patients. The results of our study show that response monitoring with FDG‐PET/CT has great potential for targeted treatment and can be performed as early as 1 week after the initiation of treatment.
Since FDG is a glucose analogue, blood glucose levels can influence accuracy of SUV measurements. Increased blood glucose levels result in decreased FDG tumor uptake through competitive inhibition. In Chapter 7 we show that, in a separate cohort of patients with resectable NSCLC, pre‐operative FDG tumor uptake defined by glucose corrected (GC)‐SUVmax was predictive for overall survival after complete surgical resection. Patients with lower FDG tumor uptake were more likely to be alive at 5 years after surgery. However, in this series, GC‐SUVmax did not show improved predictive ability/power compared to SUVmax alone.
Skin rash during EGFR‐TKI treatment for NSCLC has been linked to better survival, and we investigated whether skin rash could be used as an alternative (and cheap) marker for response.26 Three weeks of EGFR‐TKI treatment in patients with early stage NSCLC resulted in skin rash in the majority (66%), mostly mild to moderate in severity. Although a meta‐analysis showed that the presence of skin rash in advanced stage NSCLC was significantly associated with a favorable survival, in our study skin rash did not predict response or survival.27 This may be due to the small number of patients in this study, the neoadjuvant setting or earlier NSCLC stage (Chapter 6).
Over time, it has become clear that patients with advanced NSCLC (adenocarcinoma) and an EGFR‐sensitizing mutation show improved survival when treated with EGFR‐TKI therapy as compared to chemotherapy.28‐30 However, despite initial marked responses to EGFR‐TKI’s in the majority of EGFR‐driven NSCLC patients, almost every patient will eventually relapse. Accordingly, it is not surprising that the trials with adjuvant EGFR TKIs in NSCLC patients after radical resection eventually failed to show a clear survival benefit.31‐33 In these adjuvant trials, survival curves showed a similar pattern, separating after 12 months with an apparent benefit in DFS for patients receiving TKIs, and convergence after 36 months. Based on these data, the use of EGFR‐TKI’s in the adjuvant setting could not be recommended. As EGFR‐TKI’s are generally rather well tolerated, the “preoperative window” offered a good opportunity to explore the feasibility of TKIs in NSCLC patients, who are candidates for surgery, and to estimate the chances of (metabolic) tumor response after short‐term prescription of TKIs.15,34
9
In the current era of clinical trials for biomarker defined patient populations, surrogate endpoints of clinical efficacy are increasingly relevant.35 Although a surrogate endpoint might be associated with overall survival, a surrogate endpoint should also manifest the treatment effect.36 As described in Chapter 8, NSCLC patients who showed metabolic response to EGFR‐TKI treatment in the preoperative period had a significantly better five‐year survival rate (81%) than non‐responders (50%). Metabolic response to short‐ term (neoadjuvant) EGFR blockade may be seen as a surrogate endpoint. However, this seems puzzling after treatment period of only three weeks in the preoperative period. The M06NEL study was designed at a time when patients were not yet routinely selected for TKI therapy on the basis of EGFR mutation status. Consequently, only around 12% of our study population was EGFR mutation positive, which is consistent with the mutation incidence in a Caucasian population,37 and we are unable to explain the survival differences observed in our study by making a correction for EGFR mutation status. In chapter 8, we show in a multivariable model that survival differences were associated with metabolic response. Inversely, no‐response after neoadjuvant erlotinib did not correlate with excess death, suggesting that a 3‐weeks delay of surgery was not harmful.
As EGFR mutation status does not seem to provide sufficient explanation for the remarkable survival differences observed in our study, we have searched for alternative explanations. Several experimental studies (in murine and human NSCLC cell line models) suggest that EGFR‐TKIs apart from inhibiting cell signaling may also enhance anti‐tumor immunity though downregulation of PD‐L1.38‐40 For instance, erlotinib seems to be able to increase both basal and IFN‐induced MHC class‐I presentation, thereby enhancing recognition and tumor cell lysis by cytotoxic T lymphocytes.40 These immune‐ modulatory effects were seen in EGFR mutated cell lines as well as in so called ‘wild type’ cell lines.39
Tumor cells seem to be able to escape from immune recognition by undergoing profound phenotypic changes via epithelial‐mesenchymal transition (EMT).41 Signaling through the EGFR axis is able to drive EMT, while blockade by EGFR‐TKI’s is able reverse this process.42 Short‐term exposure of tumor cells to erlotinib led to remarkable enhancement of cytotoxicity mediated by NK cells and antigen‐specific T cells. This effect was lost when erlotinib was utilized for longer periods, resulting in gain of mesenchymal features and decreased lysis.43 In an EGFR‐driven NSCLC model in transgenic mice, accumulation of suppressor cells (MDSCs) was noted, as soon as CD8+ cytotoxic T cells were declining. In summary, experimental data have emerged that seem to confirm the existence of a therapeutic window during which EGFR inhibition might enhance anti‐
tumor immunity. Surgical procedures have been shown to diminish immune defense and increase the risk of metastatic disease.44 The preoperative window may be an excellent time to boost immune defense.45 The long‐term results of the M06NEL study may be seen as hypothesis generating in the sense that short term EGFR‐TKI treatment may stimulate tumor cell recognition, T cell activation and killing, in addition to inhibition of EGFR signaling. Looking back at our metabolic monitoring (timing) data in chapter 4 and the above‐mentioned literature, an even shorter treatment period than 3 weeks can be considered in future studies.
We need to be very cautious with conclusions, as many questions remain. There are considerable weaknesses of our study. It was a non‐comparative trial and second‐line treatment might have been capable to also affect outcomes. Our study did not provide EGFR mutation status for some patients and also the duration of erlotinib showed considerable variation. Patients included in our study represent a more heterogeneous population than the selection of patients eligible for EGFR‐TKI treatment today, as mutation analysis of the tumor has become paramount to consider EGFR‐TKI therapy (in advanced disease).46 Likewise, the recently published CTONG 1103 study enrolled stage IIIA‐N2 NSCLC patients with exon 19 or 21 EGFR mutations to compare neoadjuvant/adjuvant erlotinib treatment to gemcitabine‐cisplatin (showing prolonged PFS for erlotinib but similar OS).47 Although it may be difficult to advocate (future) studies with EGFR‐TKI therapy in unselected patients, our findings of biological activity of short‐term erlotinib in early stage NSCLC patients irrespective of mutation status are confirmed by a study with a similar neoadjuvant design.28 These data and those of the experimental studies mentioned suggest that carefully monitored neoadjuvant treatment strategies are promising and warrant further investigation.
While overall survival is the gold‐standard outcome measure in clinical trials, there are several limitations using this as an endpoint. It takes many years from the start of patient enrolment into a study until mature survival data are available, and this causes a long wait for results, which makes this research expensive and results in slow progress in improving treatment.48,49 Furthermore, due to developing classification of NSCLC into small oncogenic subsets large trials may become more and more problematic.1,2 Moreover, successive lines of therapy, patient crossover, and increased post‐progression survival can all mask or ‘dilute’ treatment effects. Therefore, phase II trials for specific drugs or interventions may increasingly become common practice.50,51 The pre‐operative window provides an opportunity for in vivo assessment of tumor response to a drug regimen. Combining the knowledge of today with the results of the M06NEL trial, it can be interesting to further study therapeutic interventions as short‐term EGFR‐TKI
9
treatment with low toxicity for their potential ability to enhance anti‐tumor immunity in NSCLC. Supportive preclinical data will be necessary to initiate any further clinical study in unselected patients.
References
1. Clinical Lung Cancer Genome P, Network Genomic M. A genomics‐based classification of human lung
tumors. Sci Transl Med. 2013;5(209):209ra153.
2. Pao W, Girard N. New driver mutations in non‐small‐cell lung cancer. Lancet Oncol. 2011;12(2):175‐80.
3. Gettinger SN, Horn L, Gandhi L, Spigel DR, Antonia SJ, Rizvi NA, et al. Overall Survival and Long‐Term Safety of Nivolumab (Anti‐Programmed Death 1 Antibody, BMS‐936558, ONO‐4538) in Patients With Previously Treated Advanced Non‐Small‐Cell Lung Cancer. J Clin Oncol. 2015;33(18):2004‐12.
4. Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Pembrolizumab for the treatment of
non‐small‐cell lung cancer. N Engl J Med. 2015;372(21):2018‐28.
5. Soria JC, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee KH, et al. Osimertinib in Untreated EGFR‐Mutated Advanced Non‐Small‐Cell Lung Cancer. N Engl J Med. 2018;378(2):113‐25. 6. Rosell R, Carcereny E, Gervais R, Vergnenegre A, Massuti B, Felip E, et al. Erlotinib versus standard
chemotherapy as first‐line treatment for European patients with advanced EGFR mutation‐positive non‐ small‐cell lung cancer (EURTAC): a multicentre, open‐label, randomised phase 3 trial. Lancet Oncol. 2012; 13(3):239‐46.
7. Solomon BJ, Mok T, Kim DW, Wu YL, Nakagawa K, Mekhail T, et al. First‐line crizotinib versus chemotherapy in ALK‐positive lung cancer. N Engl J Med. 2014;371(23):2167‐77.
8. Martinez P, Peters S, Stammers T, Soria JC. Immunotherapy for the first‐line treatment of patients with
metastatic non‐small cell lung cancer. Clin Cancer Res. 2019;25(9):2691‐2698.
9. Blumenthal GM, Karuri SW, Zhang H, Zhang L, Khozin S, Kazandjian D, et al. Overall response rate, progression‐free survival, and overall survival with targeted and standard therapies in advanced non‐ small‐cell lung cancer: US Food and Drug Administration trial‐level and patient‐level analyses. J Clin Oncol. 2015;33(9):1008‐14.
10. Hellmann MD, Chaft JE, William WN, Jr., Rusch V, Pisters KM, Kalhor N, et al. Pathological response after neoadjuvant chemotherapy in resectable non‐small‐cell lung cancers: proposal for the use of major pathological response as a surrogate endpoint. Lancet Oncol. 2014;15(1):e42‐50.
11. Suzuki C, Jacobsson H, Hatschek T, Torkzad MR, Boden K, Eriksson‐Alm Y, et al. Radiologic measurements of tumor response to treatment: practical approaches and limitations. Radiographics. 2008;28(2):329‐44. 12. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation
criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228‐47. 13. Tirkes T, Hollar MA, Tann M, Kohli MD, Akisik F, Sandrasegaran K. Response criteria in oncologic imaging:
review of traditional and new criteria. Radiographics. 2013;33(5):1323‐41.
14. Mileshkin L, Hicks RJ, Hughes BG, Mitchell PL, Charu V, Gitlitz BJ, et al. Changes in 18F‐fluorodeoxyglucose and 18F‐fluorodeoxythymidine positron emission tomography imaging in patients with non‐small cell lung cancer treated with erlotinib. Clin Cancer Res. 2011;17(10):3304‐15.
15. Zander T, Scheffler M, Nogova L, Kobe C, Engel‐Riedel W, Hellmich M, et al. Early prediction of nonprogression in advanced non‐small‐cell lung cancer treated with erlotinib by using [(18)F]fluorodeoxyglucose and [(18)F]fluorothymidine positron emission tomography. J Clin Oncol. 2011;29(13):1701‐8.
16. Gebhart G, Gamez C, Holmes E, Robles J, Garcia C, Cortes M, et al. 18F‐FDG PET/CT for early prediction of response to neoadjuvant lapatinib, trastuzumab, and their combination in HER2‐positive breast cancer: results from Neo‐ALTTO. J Nucl Med. 2013;54(11):1862‐8.
17. Pottgen C, Gauler T, Bellendorf A, Guberina M, Bockisch A, Schwenzer N, et al. Standardized uptake decrease on [18F]‐fluorodeoxyglucose positron emission tomography after neoadjuvant chemotherapy Is a prognostic classifier for long‐term outcome after multimodality treatment: secondary analysis of a randomized trial for resectable stage IIIA/B non‐small‐cell lung cancer. J Clin Oncol. 2016;4(21): 2526‐33. 18. Kahraman D, Holstein A, Scheffler M, Zander T, Nogova L, Lammertsma AA, et al. Tumor lesion glycolysis
and tumor lesion proliferation for response prediction and prognostic differentiation in patients with advanced non‐small cell lung cancer treated with erlotinib. Clin Nucl Med. 2012;37(11):1058‐64. 19. Junker K, Thomas M, Schulmann K, Klinke F, Bosse U, Muller KM. Tumour regression in non‐small‐cell
lung cancer following neoadjuvant therapy. Histological assessment. J Cancer Res Clin Oncol. 1997; 123(9):469‐77.
9
20. Pataer A, Kalhor N, Correa AM, Raso MG, Erasmus JJ, Kim ES, et al. Histopathologic response criteria predict survival of patients with resected lung cancer after neoadjuvant chemotherapy. J Thorac Oncol. 2012;7(5):825‐32.
21. Cottrell TR, Thompson ED, Forde PM, Stein JE, Duffield AS, Anagnostou V, et al. Pathologic features of response to neoadjuvant anti‐PD‐1 in resected non‐small cell lung carcinoma: a proposal for quantitative immune‐related pathologic response criteria (irPRC). Ann Oncol. 2018;29(8):1853‐60. 22. Schaake EE, Kappers I, Codrington HE, Valdes Olmos RA, Teertstra HJ, van Pel R, et al. Tumor response and toxicity of neoadjuvant erlotinib in patients with early‐stage non‐small‐cell lung cancer. J Clin Oncol. 2012;30(22):2731‐8. 23. Young H, Baum R, Cremerius U, Herholz K, Hoekstra O, Lammertsma AA, et al. Measurement of clinical and subclinical tumour response using [18F]‐fluorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. European Organization for Research and Treatment of Cancer (EORTC) PET Study Group. EurJCancer. 1999;35:1773‐82.
24. Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50 Suppl 1:122S‐50S.
25. Cook GJ, O'Brien ME, Siddique M, Chicklore S, Loi HY, Sharma B, et al. Non‐small cell lung cancer treated with erlotinib: heterogeneity of (18)F‐FDG uptake at PET‐association with treatment response and prognosis. Radiology. 2015;276(3):883‐93.
26. Lacouture ME. Mechanisms of cutaneous toxicities to EGFR inhibitors. Nat Rev Cancer. 2006;6(10): 803‐12.
27. Liu HB, Wu Y, Lv TF, Yao YW, Xiao YY, Yuan DM, et al. Skin rash could predict the response to EGFR tyrosine kinase inhibitor and the prognosis for patients with non‐small cell lung cancer: a systematic review and meta‐analysis. PLoS One. 2013;8(1):e55128.
28. Rosell R, Carcereny E, Gervais R, Vergnenegre A, Massuti B, Felip E, et al. Erlotinib versus standard chemotherapy as first‐line treatment for European patients with advanced EGFR mutation‐positive non‐ small‐cell lung cancer (EURTAC): a multicentre, open‐label, randomised phase 3 trial. Lancet Oncol. 2012; 13(3):239‐46.
29. Sequist LV, Yang JC, Yamamoto N, O'Byrne K, Hirsh V, Mok T, et al. Phase III study of afatinib or cisplatin plus pemetrexed in patients with metastatic lung adenocarcinoma with EGFR mutations. J Clin Oncol. 2013;31(27):3327‐34. 30. Wu YL, Zhou C, Hu CP, Feng J, Lu S, Huang Y, et al. Afatinib versus cisplatin plus gemcitabine for first‐line treatment of Asian patients with advanced non‐small‐cell lung cancer harbouring EGFR mutations (LUX‐ Lung 6): an open‐label, randomised phase 3 trial. Lancet Oncol. 2014;15(2):213‐22. 31. Kelly K, Altorki NK, Eberhardt WE, O'Brien ME, Spigel DR, Crino L, et al. Adjuvant erlotinib versus placebo in patients with stage IB‐IIIA non‐small‐cell lung cancer (RADIANT): a randomized, double‐blind, phase III trial. J Clin Oncol. 2015;33(34):4007‐14.
32. Greenhalgh J, Dwan K, Boland A, Bates V, Vecchio F, Dundar Y, et al. First‐line treatment of advanced epidermal growth factor receptor (EGFR) mutation positive non‐squamous non‐small cell lung cancer. Cochrane Database Syst Rev. 2016(5):CD010383.
33. Zhong WZ, Wang Q, Mao WM, Xu ST, Wu L, Shen Y, et al. Gefitinib versus vinorelbine plus cisplatin as adjuvant treatment for stage II‐IIIA (N1‐N2) EGFR‐mutant NSCLC (ADJUVANT/CTONG1104): a randomised, open‐label, phase 3 study. Lancet Oncol. 2018;19(1):139‐48.
34. Janne PA, Engelman JA, Johnson BE. Epidermal growth factor receptor mutations in non‐small‐cell lung cancer: implications for treatment and tumor biology. J Clin Oncol. 2005;23(14):3227‐34.
35. US Food and Drug Administration. 21 Code of Federal Regulations, H PS. [cited 2017 30‐3‐2017]. Available from: https://www.ecfr.gov/cgi‐bin/retrieveECFR?gp=&SID=4a7d18ee00ed49880444a85feb691343&mc= true&n=pt21.5.314&r=PART&ty=HTML#sp21.5.314.h.
36. Prentice RL. Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med. 1989;8(4):431‐40.
37. Zhang YL, Yuan JQ, Wang KF, Fu XH, Han XR, Threapleton D, et al. The prevalence of EGFR mutation in patients with non‐small cell lung cancer: a systematic review and meta‐analysis. Oncotarget. 2016;7(48):78985‐93.
38. Chen N, Fang W, Zhan J, Hong S, Tang Y, Kang S, et al. Upregulation of PD‐L1 by EGFR activation mediates the immune escape in EGFR‐driven NSCLC: implication for optional immune targeted therapy for NSCLC patients with EGFR mutation. J Thorac Oncol. 2015;10(6):910‐23.
39. Akbay EA, Koyama S, Carretero J, Altabef A, Tchaicha JH, Christensen CL, et al. Activation of the PD‐1 pathway contributes to immune escape in EGFR‐driven lung tumors. Cancer Discov. 2013;3(12):1355‐63. 40. Lizotte PH, Hong RL, Luster TA, Cavanaugh ME, Taus LJ, Wang S, et al. A high‐throughput immune‐
oncology screen identifies EGFR inhibitors as potent enhancers of antigen‐specific cytotoxic T‐ lymphocyte tumor cell killing. Cancer Immunol Res. 2018;6(12):1511‐23.
41. Terry S, Savagner P, Ortiz‐Cuaran S, Mahjoubi L, Saintigny P, Thiery JP, et al. New insights into the role of EMT in tumor immune escape. Mol Oncol. 2017;11(7):824‐46.
42. Lee AF, Chen MC, Chen CJ, Yang CJ, Huang MS, Liu YP. Reverse epithelial‐mesenchymal transition contributes to the regain of drug sensitivity in tyrosine kinase inhibitor‐resistant non‐small cell lung cancer cells. PLoS One. 2017;12(7):e0180383. 43. Dominguez C, Tsang KY, Palena C. Short‐term EGFR blockade enhances immune‐mediated cytotoxicity of EGFR mutant lung cancer cells: rationale for combination therapies. Cell Death Dis. 2016;7(9):e2380. 44. Shaked Y. Balancing efficacy of and host immune responses to cancer therapy: the yin and yang effects. Nat Rev Clin Oncol. 2016;13(10):611‐26. 45. Horowitz M, Neeman E, Sharon E, Ben‐Eliyahu S. Exploiting the critical perioperative period to improve long‐term cancer outcomes. Nat Rev Clin Oncol. 2015;12(4):213‐26.
46. Cheng L, Alexander RE, Maclennan GT, Cummings OW, Montironi R, Lopez‐Beltran A, et al. Molecular pathology of lung cancer: key to personalized medicine. Mod Pathol. 2012;25(3):347‐69.
47. Zhong WZ, Chen KN, Chen C, Gu CD, Wang J, Yang XN, et al. Erlotinib versus gemcitabine plus cisplatin as neoadjuvant treatment of stage IIIA‐N2 EGFR‐mutant non‐small‐cell lung cancer (EMERGING‐CTONG 1103): a randomized phase II study. J Clin Oncol. 2019:JCO1900075.
48. Douillard JY, Tribodet H, Aubert D, Shepherd FA, Rosell R, Ding K, et al. Adjuvant cisplatin and vinorelbine for completely resected non‐small cell lung cancer: subgroup analysis of the lung adjuvant cisplatin evaluation. J Thorac Oncol. 2010;5(2):220‐8.
49. Pisters KM, Vallieres E, Crowley JJ, Franklin WA, Bunn PA, Jr., Ginsberg RJ, et al. Surgery with or without preoperative paclitaxel and carboplatin in early‐stage non‐small‐cell lung cancer: Southwest Oncology Group Trial S9900, an intergroup, randomized, phase III trial. J Clin Oncol. 2010;28(11):1843‐9.
50. Wilson MK, Karakasis K, Oza AM. Outcomes and endpoints in trials of cancer treatment: the past, present, and future. Lancet Oncol. 2015;16(1):e32‐42.
51. Grayling MJ, Mander AP. Do single‐arm trials have a role in drug development plans incorporating randomised trials? Pharm Stat. 2016;15(2):143‐51.
