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Laura Mezquita

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    YI02 - Clinical Trials (ID 108)

    • Event: WCLC 2019
    • Type: Young Investigator Session
    • Track: Young Investigators
    • Presentations: 5
    • Now Available
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      YI02.01 - Academic Versus Industrial Clinical Trials (Now Available) (ID 3696)

      09:00 - 10:30  |  Presenting Author(s): Tony Mok

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      YI02.02 - Basket and Umbrella Trial Designs in Oncology (Now Available) (ID 3697)

      09:00 - 10:30  |  Presenting Author(s): Jessica Menis

      • Abstract
      • Presentation
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      Abstract

      Cancer treatment has made gigantic improvements in patients’ prognosis in multiple cancer types, by virtue of major steps forward in the concept of personalized medicine. This involves scientific progress in tumour biology, genomics technology, computational analysis and drug discovery that has propelled advances in both translational and clinical cancer research.

      In particular, rapid development, decreased cost, and increased availability of next-generation genomic sequencing and other methods for both molecular and, more recently, immunological tumor classification have changed the paradigm for understanding and treating cancer.

      Until recently, drug development has been conducted separately for different histological tumor types since the histological type was the primary known determinant of drug efficacy. However, this histology focus has been integrated by new knowledge on genomic alterations and immunological profile. Therefore, clinical trials have been evolving in parallel, from the traditional two-arm comparison of an experimental treatment vs. a control, to accelerate identification of promising therapies, to increase throughput and to allow for the increasing use of molecular and immunological classification of patients into smaller sub-groups.

      Also cost-efficiency need to be considered: classical phase I, II and III models for drug development require large resources, limiting the number of experimental agents that can be tested and making the evaluation of targeted agents inefficient.

      On the other hand, methodology and quality assurance need to be preserved since the validation of biomarkers is generally affected by several challenges, such as the multitude of assessment methods (i.e. immunohistochemistry, fluorescence in situ hybridisation, next-generation sequencing, etc.), reliability in terms of sensitivity and specificity, reproducibility of the test, feasibility of obtaining an adequate and representative tumour sample and, finally, the overall related costs.

      All these considerations, added to the strong collaboration with the regulatory agencies, approving novel agents based on data obtained from phase 1/2 trials, have led to an evolution in the design of early-stage clinical trials.

      The enrichment design can require many fewer patients, i.e. only those patients hypothesized to benefit, to be randomized relative to the “all comers” randomized design. The choice between an unselected versus enriched design should always be made also based on the existing level of evidence for the predictive biomarker.

      Two main enrichment strategies can be used to avoid over-treatment and save valuable resources, by matching the right drug to the right subgroup of patients. They can be defined as: basket trials and umbrella trials.

      Basket trials allow patients with multiple diseases and one or more target to be enrolled in cohorts or groups in one trial (the basket). They are often viewed as parallel phase II trials within the same entity, designed on the basis of a common denominator. Researchers are therefore allowed to separately analyse the patients’ responses as each tumour type can be put in one cohort, and assess the impact of the drug on all of the patients as one group. If one group shows a good response, this group will be expanded to immediately assess whether others could benefit from the new therapy. If another group does not show evidence of effectiveness, this group may be closed and the other cohort can continue the recruitment. Basket trials can be further sub-classified in three groups: basket trials on one drug in several tumour types (1), basket trials on one drug for one molecular alteration in several tumour types (2), and basket trials on one drug in several molecular alterations in several tumour types.

      Umbrella trials are built on a centrally performed molecular portrait and molecularly selected cohorts with matched drugs, and can include patients’ randomisation and strategy validation. In the umbrella design, a separate enrichment trial is conducted for each bio- marker stratum. The enrichment design for a given stratum uses as the test regimen a drug expected to be active for the alteration defining that stratum.

      Beyond new designs, new end-points and new evaluation techniques are also warranted to finally achieve methodology and clinical improvements, in particular within immunotherapy trials.

      As clinicians continuously learn from their patients, applying knowledge gained from one set of patients to their forthcoming patients, in adaptive designs, modifications of some aspects of the trial can be prospectively planned so that changes (‘‘adaptations’’) may take place while the study is ongoing (for example: a treatment arm or a subgroup of patients could be dropped; the trial size could be increased, etc). Planning for such types of studies would allow to overcome the challenge related to the limited available information in the literature describing the targeted sub-populations.

      Alongside the growing complexity of these clinical trials, new frameworks for stronger and faster collaboration between all stakeholders in drug development, including academic institutions and frameworks, clinicians, pharma companies and regulatory agencies, has to be further encouraged.

      In the current era, the main goal should be to identify large and meaningful differences in small, molecularly and immunologically selected groups of patients and to develop rapidly new compounds. Basket and umbrella trials respond to the need of ‘‘trials designed to learn’’, that can evolve into ‘‘trials designed to conclude’’.

      Menis J, Hasan B, Besse B. New clinical research strategies in thoracic oncology: clinical trial design, adaptive, basket and umbrella trials, new end-points and new evaluations of response. Eur Respir Rev 2014; 23: 367–78

      Simon R. Critical Review of Umbrella, Basket, and Platform Designs for OncologyClinical Trials. Clin Pharmacol Ther. 2017; 102(6):934-41

      Renfro LA, Sargent DJ. Statistical controversies in clinical research: basket trials, umbrella trials, and other master protocols: a review and examples. Ann Oncol. 2017;28(1):34-43

      Garralda E, Dienstmann R, Piris-Giménez A, Braña I, Rodon J, Tabernero J. New clinical trial designs in the era of precision medicine. Mol Oncol. 2019;13(3):549-57

      Renfro LA, Mandrekar SJ. Definitions and statistical properties of master protocols for personalized medicine in oncology. J Biopharm Stat. 2018;28(2):217-228

      Rashdan S, Gerber DE. Going into BATTLE: umbrella and basket clinical trials to accelerate the study of biomarker-based therapies. Ann Transl Med. 2016;4(24):529

      Morrell L, Hordern J, Brown L, et al. Mind the gap? The platform trial as a working environment. Trials. 2019; 20(1):297

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      YI02.03 - Statistical Pitfalls in Clinical Trial Design (Now Available) (ID 3698)

      09:00 - 10:30  |  Presenting Author(s): Urania Dafni

      • Abstract
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      Abstract

      This topic will be discussed in the context of the following principles that clinical trials should adhere to.

      A clinical trial is an experiment testing medical treatments on human subjects.

      The process of evaluating medical treatment in humans starts with a Phase I clinical trial, followed by a Phase II and a Phase III trial towards regulatory approval.

      Phase III

      Phase III clinical trials are the gold standard for the evaluation of therapeutic interventions’ efficacy. The goals of a Phase III clinical trial include minimization of random error, elimination of systematic error (bias) and ensuring the generalizability of study results. The clinical trial design is the methodology for achieving these goals.

      Randomization, always present in phase III trials, provides a treatment assignment that is independent of outcome and patient/disease features, thus balancing treatment groups on known and unknown factors associated with outcome. Further, the intention-to-treat (ITT) analysis approach is the gold standard for all phase III randomized, controlled clinical trials. It analyzes all patients in the treatment groups as randomized without regard to treatment actually received. The systematic error (Bias), which is any effect rendering the observed results not representative of the treatment effect, is addressed through randomization and corresponding ITT analysis. Minimization of random error is addressed through the use of adequately large sample size.

      Phase II

      Phase II clinical trials can be either randomized (screening, selection, randomized discontinuation designs) or non-randomized. The latter may be seriously misleading since the impact of prognostic factors is usually far larger than that of treatment, while known prognostic factors may explain little variance.

      Randomized phase II trials allow control of selection bias and simultaneous testing of several new treatments, combinations, doses etc. They are preferable than non-randomized trials (some degree of control is better than none!) but they could be misleading due to the small sample sizes. They cannot replace phase III trials. In Phase II trials, the objective is to select active drugs for further testing and document toxicity, but not to provide definite estimate of new drugs’ efficacy, wh8ch is achieved only though a well-powered Phase III clinical trial.

      Hypothesis Testing

      In any experiment, two hypotheses covering all possible outcomes are pitted against each other. Only one of them can be true. The alternative hypothesis is the statement we would like to prove, and the null hypothesis, the statement we would like to reject. The only possible conclusions in hypothesis testing are: 1. Reject the Null Hypothesis, and thus prove the desired alternative hypothesis (positive trial), or 2. Not able to reject the null hypothesis (negative trial).

      Any trial needs to be designed in such a way that it is known a-priori what the errors relative with each of the two possible conclusions will be. Rejecting the null wrongly (false positive result), is subject to Type-I error (alpha), or significance level, while not rejecting the null hypothesis wrongly (false negative result), is subject to Type-II error (beta). The sample size of the trial is decided at the design stage to guarantee that these two errors remain below pre-defined bounds. These bounds are usually 5% for Type-I error, and 20% for type II error.

      One more important design characteristic is power, which is equal to (1-beta) and it is the probability of correctly rejecting the null hypothesis (values usually set above 80%).

      Superiority vs Equivalence/Non-inferiority

      Clear distinction should be made between superiority and equivalence/non-inferiority Phase III trials, with each testing a different type of null hypothesis. In a superiority trial we aim to reject a null hypothesis of “no effect” or “no difference”, while in an equivalence trial we aim to reject a null hypothesis of “different effect”.

      More particularly, in a superiority trial we aim to demonstrate the superiority of a new therapy compared to an established therapy or placebo. In this case, the determination of the sample size takes into account the clinical significance (by how much the new therapy should be better than the established one), the power and the significance level of the test, as well as the magnitude of the variation of the corresponding measure of interest.

      In equivalence trials, the objective is to demonstrate that a new treatment is equivalent to a standard therapy with regards to a specific clinical end point, while it has an intrinsic benefit for other clinical end points, while in non-inferiority trials, to evaluate whether the new treatment is not inferior to or as effective as the standard therapy for a particular end point. In this case, a tolerance and a non-inferiority margin must be predefined, along with the power and the significance level of the test.

      Failure to reject the Null hypothesis should not be confused with acceptance of the Null hypothesis.

      Subgroup Analysis

      Another issue that should be taken into account in the design of a trial is subgroup analysis, involving multiplicity issues. It is common practice to perform multiple subgroup analyses, but the probability of a false positive finding (type-I error) increases as the number of subgroup analyses increases (curse of multiplicity).

      Prognostic vs Predictive Marker

      The correlation between a biomarker and a true clinical endpoint corresponds to a prognostic marker, but not to a predictive one. It is the statistically significant difference in treatment effect between the levels of the biomarker (treatment by group interaction), that characterizes a predictive biomarker.

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      YI02.04 - Bioinformatics: The Basics (Now Available) (ID 3699)

      09:00 - 10:30  |  Presenting Author(s): Yu Shyr

      • Abstract
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      Abstract

      For physicians, advanced data science methods such as artificial intelligence (AI), machine learning and novel statistical methods are no longer just a headline in the news. Use of advanced data science methods are gaining acceptance in the field of medicine, as researchers push the limits of rapidly progressing technologies to assist in delivering excellence in health care.

      With the growth in use of advanced data science methods, however, we must bear in mind the limitations of these technologies. AI—specifically, machine learning (ML) of the deep-learning type—is only as good as the training dataset from which the algorithm learns. Deep learning in the clinical setting is most highly developed for image recognition; a training dataset for this purpose consists of a large number of case and control images. The algorithm then uses these images to teach itself how to differentiate, for example, malignant lesions from benign tumors or normal tissue. If the training dataset consists only of unambiguous cases and controls—rather than representing real-world variation and ambiguity—the algorithm will fail to function as desired with real-world patients. Thus, well-considered experimental design remains essential to realize the promise of deep learning.

      This talk will provide an overview of the state of the art in AI, machine learning, novel statistical methods, and deep learning algorithm for clinical application, including examples from the research literature as well as FDA-approved devices that use advanced data science methods. We will then address the pitfalls of deep learning, including the need for clear and rigorous standards for regulatory approval of devices and software. We close with thoughts on the future of advanced data science methods in medicine.

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      YI02.05 - Principles to Get Your Paper Published (Now Available) (ID 3700)

      09:00 - 10:30  |  Presenting Author(s): Alex A Adjei

      • Abstract
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      Abstract

      How to get your Manuscript Published

      Alex A. Adjei, MD;PhD

      Mayo Clinic, Rochester, MN, USA

      Scientific publication is the backbone of academic research. Findings by investigators need to be disseminated to the community so that results can influence human health and well-being as well lay groundwork for future research.

      In spite of the central role of publishing in academic life described above, there are very few formal courses or seminars teaching academics, particularly physician scientists on how to publish their work. The table below outlines reasons for rejection of manuscripts, coming out of a survey of a number of journal editors

      Using information from this survey as a starting point, we will discuss the “fatal flaws” that lead to outright rejection of manuscripts, and outline strategies on how to write a manuscript of high impact, which is likely to be accepted for publication.

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Author of

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    MA05 - Update on Clinical Trials and Treatments (ID 123)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Mesothelioma
    • Presentations: 1
    • Now Available
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      MA05.11 - Safety and Efficacy of Nintedanib in Combination with Pembrolizumab in Patients with Refractory/Relapsing Malignant Pleural Mesothelioma (Now Available) (ID 2170)

      13:30 - 15:00  |  Author(s): Laura Mezquita

      • Abstract
      • Presentation
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      Background

      Malignant pleural mesothelioma (MPM) is an aggressive disease with no standard of care after progression to first line pemetrexed and platinum-based chemotherapy. Combinations between anti-angiogenic agents and immunotherapy are being developed as angiogenesis and immunosuppression influence each other leading to a more powerful anti-tumor response. Both Nintedanib and Pembrolizumab have been investigated as single agents or in different treatment combinations in MPM patients with interesting activity.

      Method

      The PEMBIB trial is a multi-centric open-label non-randomized basket phase 1 trial evaluating the combination of nintedanib with pembrolizumab in multiple tumor types. The safety and activity of the dose escalation part of the study were reported at AACR & ASCO meetings in 2018 with an established DLT defined as grade 3 alanine and/or aspartate aminotransferase elevation (ALT/AST). The recommended phase 2 dose is set at 150 mg BID of nintedanib with 200 mg flat dose of pembrolizumab. We would like to report the safety and activity of one of the expansion cohorts of patients with relapsing/refractory MPM which has now been completed. Eligible MPM patients were 18 years or older with an ECOG performance status of 0 or 1, histologically proven MPM that relapsed after at least one line of pemetrexed and platinum-based combination, specific anti-angiogenic eligibility criteria such as no radiographic evidence of cavitary/necrotic or tumors with local invasion of major blood vessels.

      Updated results on the safety profile and efficacy of this anti-angiogenic and anti-PD-1 combination therapy including overall response rate as per RECIST, irRC and mRECIST criteria, disease control rate will be presented at the meeting.

      Result

      The first patient from the MPM cohort was enrolled in July 2017 and the last one in April 2019. Thirty-one eligible MPM patients have been evaluable at the data cut off onJuly 2019, one of them had been enrolled since the dose-escalation part at dose level of 200mg. The age at inclusion was 68 (ranging from 38 to 85), 68% of the patients having an ECOG of 1 and 58% of the histological type was epithelioid. The most frequent adverse events (grades 1, 2 and 3) related to any of the combination drugs were liver enzymes increase, fatigue, decreased appetite, nausea, diarrhea and hypothyroidism. There were two cases of myocarditis, one of grade 3 (pembrolizumab related) and one of grade 5(pembrolizumab and nintedanib related). At the time of the data analysis the efficacy data shows six partial responses (overall response rate of 21%) and seventeen stable disease (disease control rate at 61%.).

      Conclusion

      The combination of Nintedanib with Pembrolizumab shows promising activity in relapsed MPM patients .The toxicity profile appear consistent with previous reports of anti-angiogenic agents and immunotherapy combination.

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    MA07 - Clinical Questions and Potential Blood Markers for Immunotherapy (ID 125)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Immuno-oncology
    • Presentations: 3
    • Now Available
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      MA07.01 - Circulating Immature Neutrophils, Tumor-Associated Neutrophils and dNLR for Identification of Fast Progressors to Immunotherapy in NSCLC (Now Available) (ID 1618)

      13:30 - 15:00  |  Presenting Author(s): Laura Mezquita

      • Abstract
      • Presentation
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      Background

      Neutrophils are active regulators of the antitumor immune response, with pro- and antitumor- properties, but generally are associated with progression (PD) and poor outcomes. We reported that pretreatment dNLR ((neutrophils/[leucocytes-neutrophils]; high>3) correlated with immune checkpoint inhibitor (ICI) outcomes in advanced (a) NSCLC pts. Although neutrophil population is heterogeneous, the immature neutrophils (i.e. CD15+CD244-CD16low, among others) seem to be a key subpopulation linked to PD. Tumor-associated neutrophils (TAN) can be also modulator on the microenvironment. We aimed to assess the role of pretreatment circulating immature-neutrophils and tissue-TAN, combined with dNLR, on ICI outcomes in aNSCLC pts.

      Method

      aNSCLC pts treated with ICI at our institution between 11/2012 and 08/2018 were eligible. Pretreatment immunophenotyping of monocytes, monocytic MDSC (mMDSC) and granulocytes (CD15, CD11b, CD33, CD244, CD16, CD14, CD32, CD64, HLA-DR) was prospectively performed by flow cytometry in fresh whole blood in 58 pts; we defined immature-neutrophils as CD15+CD244-CD16low. TAN in the stroma were assessed using H&E staining from archival specimen, available from 80 pts. dNLR was retrospectively collected; available from 343 pts. Correlation between baseline circulating neutrophils phenotype, TAN and dNLR was evaluated as well as their impact on outcomes: progression-free survival (PFS), overall (OS), including death before 12 weeks (12wk-death) (fast-PD)

      Result

      366 pts included; 320 (90%) smokers, median age 63; 280 (77%) nonsquamous, 117 (64%) ≥1%PDL1 and 183 missing. Median PFS (mPFS) was 1.93 months (m) [95%CI, 1.8-2.3] and mOS 8.8m [6.5-11.6]. Overall, 12wk-death rate was 31% [25.9-35.6].

      Pretreatment high-dNLR (143/343; 42%) was correlated with poor PFS (P=0.002), OS P=0.0003) and a 12wk-death rate of 43% [34.5-50.9]. Pretreatment high immature-neutrophils (30/58; 53%), defined by logrank maximization method (>0.22%), were also associated with poor PFS (P=0.04), OS (P=0.0007) and a 12wk-death rate of 48.7% [26.7-64.1]. TAN (9/80; 11%) were not correlated with outcomes. There was not a correlation between immature-neutrophils, tissue-TAN and dNLR.

      When evaluating pretreatment immature-neutrophils and dNLR together, we identified a fast-PD phenotype (high immature-neutrophils/high-dNLR, 10/58; 17%), with a mOS of 1.3m [0.73- not reached (NR)] and 12wk-death rate of 60% [14.5-81.3] compared to a responder-phenotype (low immature-neutrophils/low-dNLR, 12/58; 21%), associated with good outcomes: mOS NR [18.23-NR] (P=0.002).

      Conclusion

      Pretreatment high circulating immature-neutrophils (CD15+CD244-CD16low) correlate with early failure to ICI and fast-PD phenotype. The combination of circulating immature-neutrophils and dNLR could improve the identification of this population. The impact of immature-neutrophils on ICI should be more deeply explored.

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      MA07.02 - Early Change of dNLR Is Correlated with Outcomes in Advanced NSCLC Patients Treated with Immunotherapy (Now Available) (ID 2676)

      13:30 - 15:00  |  Presenting Author(s): Laura Mezquita

      • Abstract
      • Presentation
      • Slides

      Background

      The [neutrophils/[leucocytes-neutrophils] ratio (dNLR) correlates with immune checkpoint inhibitors (ICI) outcomes in advanced non-small cell lung cancer (aNSCLC) patients. Significance of early dNLR change after the first course of ICI is unknown.

      Method

      Patients with NSCLC treated with ICI (PD(L)1+/-CTLA4) between Nov. 2012 and Jun. 2018 at 16 EU/US centers were included. A control group treated with chemotherapy (CT) only was also evaluated (NCT02105168). dNLR was collected at baseline (B) and at cycle 2 (C2). Patients were categorized as low vs high dNLR at each timepoint (defined as < vs > 3, as previously done), and the change between B and C2 (good = low at both timepoints, poor = high at both timepoints, mixed = different at each timepoint).

      Result

      1485 patients treated with ICI were analyzed. PDL1 was negative in 162 (11%), 1-49% in 178 (12%), ≥50% in 201 (14%), and missing in 944 (64%). dNLR at B and C2 did not associate with PD-L1 status.

      At baseline, dNLR was high in 509 (34%) patients and associated with worse PFS compared to those patients with low dNLR at baseline (HR 1.56, P<0.0001) and OS (HR 2.02, P<0.0001). At C2, dNLR was high in 484 (34%) and similarly associated with worse outcomes compared to patients with low dNLR at C2 (PFS HR 1.64, P<0.0001; OS HR 2.13, P<0.0001).

      Between B and C2, dNLR remained low in 804 (56%, « good ») or high in 327 (23%, « poor ») or changed in 310 pts (22%, « intermediate »). Those with a good dNLR demonstrated mPFS 5.3, mOS 18.6 mo), followed by those intermediate with mixed dNLR (mPFS 3, mOS 9.2 mo), and finally poor dNLR (mPFS 2, mOS 5mo). Outcomes were independant of PD-L1 expression (adjusted HR for PFS 1.94 for intermediate and 3.16 for poor groups, compared to good dNLR group, P<.001; adjusted HR for OS was 2.08 for intermediate and 3.67 for poor groups, P<0.001).A bootstrap tested the stability of OS/PFS prediction (P<0.001).

      In the chemo-cohort (n=173), high C1-dNLR (n=81, 47%) was not associated with OS (P=0.84).

      Conclusion

      dNLR at baseline, at cycle 2, and the change between these two timepoints associated with outcomes in patients treated with immunotherapy independent of PD-L1, but not in patients treated with chemotherapy alone. dNLR is specifically prognostic in the context of immunotherapy.

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      MA07.05 - Immune Checkpoint Inhibitor (ICPi) Re-Challenge: Outcomes Analysis in a French National Cohort of Non-Small-Cell Lung Cancer (NSCLC) Patients (Now Available) (ID 1903)

      13:30 - 15:00  |  Author(s): Laura Mezquita

      • Abstract
      • Presentation
      • Slides

      Background

      Anti-PD1/PDL1 deeply changed the NSCLC therapeutic algorithm in the past few years. Unfortunately, a majority of patients experiences disease progression. ICPis re-challenge could be an attractive option but no data supporting this strategy are available. Here we report outcomes of a large cohort of NSCLC patients treated with anti-PD1/PDL1 re-challenge.

      Method

      We retrospectively collected data about 144 advanced NSCLC patients (diagnosis between 2010 and 2018) from 26 French centers. Patients were re-challenged with ICPis after at least 12 weeks of discontinuation for toxicity, disease progression or clinical decision. Progression Free Survival (PFS) and Overall Survival (OS) were calculated from the start of first or second ICPi to disease progression (PFS1;PFSR) and death or last follow-up (OS1;OS2) respectively.

      Result

      Median age was 63 year [39 –83], most of patients were male (67%), smokers (87%), adenocarcinomas (62%) and stage IV at diagnosis (66%). Most of patients received the first ICPi round in first or second line (66%) and the second ICPi round in third line or later (79%). In both settings patients received preferentially an anti-PD1 (87%) and no differences were detected regarding brain metastasis or ECOG PS (P = 1.10-1 and P = 1.10-1 respectively). The Best Response during the re-challenge was not associated to that one achieved to the first ICPi (P = 1.10-1). The median PFS1 and PFSR were 13 months [95% CI 10-16.5] and 4.4 months [95% CI 3-6.5] respectively. PFSR was longer in patients discontinued because of clinical decision (6.5 months [95% CI 2.5-11.9]) or toxicity (5.8 months [95%CI 3.5-18]) compared to disease progression (2.9 months [95% CI 2.0-4.4]) (P = 2.10-2) and in those not receiving chemotherapy between the two ICPis (5.8 months [95%CI 4.1-10.5]) compared to those who did (3.0 months [95% CI 2.0-4.4])(P = 2.10-3). Median OS1 was 3.3 years [95% CI 2.9-3.9] without differences according to the discontinuation reason (P =2.10-1). Median OS2 was 1.5 y [95%CI 1.0-2.1] and was longer in patients discontinuing the first ICPi due to toxicity (2.1y [95%CI 1.4-NR]) compared to disease progression (1.0y [95%CI 0.4-1.5]) or clinical decision (1.5y [95%CI 0.4-NR]) (P = 3.10-2). Neither OS1 nor OS2 were affected by treatments received between the two ICPis (P = 3.10-1 and P = 1.10-1 respectively).

      Conclusion

      ICPis re-challenge might be a useful option mainly in patients discontinuing the first ICPi because of toxicity or clinical decision and in those able to keep a treatment-free period between the two ICPis.

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    MA11 - Immunotherapy in Special Populations and Predictive Markers (ID 135)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Immuno-oncology
    • Presentations: 1
    • Now Available
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      MA11.11 - STK11/LKB1 Genomic Alterations Are Associated with Inferior Clinical Outcomes with Chemo-Immunotherapy in Non-Squamous NSCLC (Now Available) (ID 2898)

      14:00 - 15:30  |  Author(s): Laura Mezquita

      • Abstract
      • Presentation
      • Slides

      Background

      Addition of pembrolizumab (P) to platinum-doublet chemotherapy [carboplatin (or cisplatin) and pemetrexed (CP)] prolongs overall survival and is a standard of care (SOC) for the 1st line treatment of metastatic EGFR/ALK wild-type (wt) non-squamous non-small cell lung cancer (mnsNSCLC). Despite widespread use of the CPP regimen, molecular determinants of clinical benefit from the addition of P to CP remain poorly defined. We previously identified genomic alterations in STK11/LKB1 as a major driver of primary resistance to PD-1/PD-L1 blockade in mnsNSCLC. Here, we present updated data on the impact of STK11/LKB1 alterations on clinical outcomes with CPP chemo-immunotherapy from a large retrospective multi-institution international study.

      Method

      620 pts with mnsNSCLC and tumor genomic profiling encompassing STK11/LKB1 from 21 academic institutions in the US and Europe were included in this study. Clinical outcomes were collected for two distinct patient cohorts: a) 468 pts treated with first-line CPP (or >1st line following FDA-approved TKIs) that were alive for 14 days thereafter and b) 152 STK11/LKB1-mt pts that received CP prior to regulatory approval of CPP.

      Result

      Among 468 CPP-treated pts, STK11/LKB1 genomic alterations (N=118) were associated with significantly shorter PFS (mPFS 5.0m vs 6.8m, HR 1.45, 95% CI 1.11 to 1.91; P=0.007) and shorter OS (mOS 10.6m vs 16.7m, HR 1.46, 95% CI 1.04 to 2.07; P=0.031) compared with STK11/LKB1-wt tumors (N=350). The likelihood of disease progression as BOR to CPP differed significantly between the two groups (29.5% vs 17%, P= 0.006). Similar results were obtained when limiting the analysis to EGFR and ALK-wt tumors (N=435) (mPFS 5.0m vs 6.9m, HR 1.48, 95% CI 1.12-1.95, P=0.006 and mOS 10.6m vs 16.7m, HR 1.45, 95% CI 1.02-2.05, P=0.036). Importantly, in pts with STK11/LKB1-mt mnsNSCLC, addition of pembrolizumab to CP did not result in significant improvement of PFS (mPFS 5.0m vs 3.9m, HR 0.82, 95% CI 0.63 to 1.07, P=0.14) or OS (mOS 10.6m vs 9.1m, HR 0.93, 95% CI 0.67 to 1.30, P=0.69) compared to CP alone.

      Conclusion

      In mnsNSCLC, STK11/LKB1 alterations define a subgroup of pts with inferior clinical outcomes with CPP and lack of benefit from the addition of pembrolizumab to CP chemotherapy. Novel therapeutic strategies are required to establish effective antitumor immunity in STK11/LKB1-mutant NSCLC.

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    MA21 - Non EGFR/MET Targeted Therapies (ID 153)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Targeted Therapy
    • Presentations: 2
    • Now Available
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      MA21.07 - Circulating Tumor DNA Analysis Depicts Potential Mechanisms of Resistance to BRAF-Targeted Therapies in BRAF+ Non-Small Cell Lung Cancer (Now Available) (ID 1365)

      14:30 - 16:00  |  Author(s): Laura Mezquita

      • Abstract
      • Presentation
      • Slides

      Background

      Oncogenic BRAF-V600 mutations are observed in 1-2% of non-small cell lung cancer (NSCLC). Targeted therapies including vemurafenib (V), dabrafenib (D) or combination of dabrafenib plus trametinib (D+T) are associated with favorable outcomes in these patients (pts). The mechanisms of resistance to BRAF-targeted therapies (BRAF-TT) in NSCLC are largely unknown.

      Method

      We performed genomic profiling of serial circulating-tumor DNA (ctDNA) in a cohort of 79 metastatic BRAF-mutant NSCLC pts (96% V600E, 4% non-V600). BRAFmutational status was ascertained based on local testing. Plasma samples were collected, from 2014-2018 in 27 Hospitals, from pts treated with V (n=34), D (n=2) or D+T (n=23). We collected 41 plasma samples at baseline to BRAF-TT, 40 at progressive disease (PD) and ~200 samples during treatment follow-up, concomitant to routine radiological evaluation. Inivata InVisionSeq™ assay was used to detect the presence of SNVs, indels and CNAs in 36-cancer related genes.

      Result

      At baseline, 72,5% of BRAF mutations (V600E and non-V600E) were detected in plasma. BRAF-V600E detection in plasma was associated with the presence of liver metastasis, versus BRAF-V600E-negative cases (22% vs. 7%, respectively). Co-occurring molecular alterations at baseline, besides BRAF-V600E, were observed in 18/26 (70%) cases: FGFR2 (1pt), PIK3CA (2pts), ERBB2 (1pt), CTNNB1 (2pts) and IDH1 (2pts). FGFR2, PIK3CA or CTNNB1 alterations were associated with PD as the best response to the subsequent BRAF-TT. TP53 and STK11 mutations were observed in 54% (14/26) and 8% (2/26) of pts, respectively. Complete clearance of BRAF-V600E in plasma at baseline was observed at the first CT-scan evaluation in 42% (3/7) and 82% (9/11) pts treated with V or D+T, respectively. These pts were in complete or partial response, suggesting that monitoring BRAF-V600E levels in plasma on treatment may be a clinically useful marker of tumor response. At PD, a consistent rebound in BRAF-V600E plasma levels was observed in 60% (24/40) pts. Resistance to V was associated with alterations in the MAPK pathway: 1pt (KRAS), 1pt (GNA11), 1pt (NRAS and GNAS) and 1pt (MAP2K1 and NFE2L2). Activating PI3KCA mutations were observed in 4 pts who progressed in <6 months on V treatment. ctDNA analyses at PD under D+T revealed that, similar to what we observed in patients who progressed on V, alterations in KRAS, NRAS, PIK3CA and CTNNB1 are associated with D+T resistance. Prediction of the impact of these alterations, at the protein level, was assessed using in silico structure modeling and will be presented.

      Conclusion

      ctDNA monitoring might be an informative tool for assessing disease response and resistance in NSCLC pts treated with BRAF-TT. MAPK reactivation remains an important resistance mechanism to BRAFi-monotherapy or to BRAFi and MEKi combination therapy.

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      MA21.09 - Tyrosine Kinase Inhibitors' Plasma Concentration and Oncogene-Addicted Advanced Non-Small Lung Cancer (aNSCLC) Resistance (Now Available) (ID 830)

      14:30 - 16:00  |  Author(s): Laura Mezquita

      • Abstract
      • Presentation
      • Slides

      Background

      The development of TKIs against driver molecular alteration has changed treatment paradigm in aNSCLC patients (pts). All tumors eventually progress and a resistance mechanism is identified in only a fraction of pts. Plasma concentration of TKI can decrease after chronic exposition but limited data are available. Our hypothesis is that an insufficient plasma exposure could contribute to tumor progression (PD).

      Method

      We assessed the plasma concentration of TKI in pts with aNSCLC harboring ALK rearrangement, EGFR or BRAF V600E mutation. We defined chronic exposure as a treatment administered > 3 months. Patients’ characteristics and co-medications were collected. Residual plasma concentrations were measured using Ultra Performance Liquid Chromatography coupled with tandem mass spectrometry validated methods. We compared results to currently recommended therapeutic targets and correlated exposure levels to treatment benefit.

      Result

      Between Apr. 2014 and Feb. 2019, 51 samples were prospectively collected (gefitinib n=11, osimertinib n=10, erlotinib n=13, crizotinib n=7, dabrafenib + trametinib n=5) in 41 pts. Median time of exposure was 20.3 months (range 2.18 - 67.813). Low plasma concentration was observed in 31 (61%) samples. Out of 14 samples collected in pts with ongoing benefit, 10 (71%) had low plasma exposure. Smoking status was associated with low plasma TKI concentration (P=0.01) whatever the TKI used. A total of 37 samples were collected at PD, 21 (57%) had low plasma exposure. The median time to treatment failure (TTF) in the ‘low exposure group' (n=31) was 14.9 months (95% CI 12.48 – 33.2) vs. 24.6 months (95% CI 8.65 -not reached (NR) in the ‘normal exposure group’ (P=0.55). No significant impact of protons pump inhibitors on TTF was found (p=0.12), including with gefitinib and erlotinib (p=0.76; n=24). In case of isolated brain PD (n=4), 3 pts (75%) had low plasma exposure. TKI dose was reduced in 14 pts because of toxicity, median TTF was 17.0 months (95% CI 10.4-NR) vs. 20.1 months (95% CI 10.4-59.8, P=0.45 in pts treated with standard dose. In the EGFR mutated aNSCLC population at PD (n=19), T790M resistance mutation was more frequent in the ‘normal exposure group’ (37.5%, n= 3/8,) than in the ‘low exposure group’ (9.1%, n=1/11), OR=0.13 95%CI (0.01-1.29), p=0.08.

      Conclusion

      TKI is underdose in the majority of aNSCLC patients at PD. Low TKI concentration were more frequent in pts without tumor resitance mechanism. Altogether, it suggests that low TKI exposure might contribute to PD.

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    MA25 - Precision Medicine in Advanced NSCLC (ID 352)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Advanced NSCLC
    • Presentations: 1
    • Now Available
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      MA25.03 - Tumor-Infiltrating Lymphocytes (TIL) and Outcomes with Immunotherapy (ICI) or Chemotherapy in Advanced NSCLC (aNSCLC) Patients (Now Available) (ID 1374)

      14:30 - 16:00  |  Author(s): Laura Mezquita

      • Abstract
      • Presentation
      • Slides

      Background

      Tumor infiltrating lymphocytes (TIL) morphologically assessed is prognostic in early stages in several tumors. We previously reported the correlation of TIL with immune checkpoint inhibitors (ICI) outcomes in 98 advanced (a) NSCLC patients treated with ICI. We aimed to assess the role of TIL in a larger cohort treated with ICI, and in patients exclusively treated with chemotherapy (CT).

      Method

      aNSCLC patients with treated with single-agent ICI, with H&E stained sample available, were included between 11/2012 and 02/2017 in 3 cancer centers (immuno-cohort). Patient’s characteristics, biological data were retrospectively collected. The CT-cohort was extracted from the prospective MSN study (NCT02105168), between 06/2009 and 10/2016, enrolling aNSCLC patients treated with platinum-based CT, and tissue available. TIL in the stroma was evaluated in archival samples. High-TIL was defined as ≥10% density. Multivariate Cox model was used to study its prognostic values on overall and progression-free survival (OS, PFS).

      Result

      A total of 221 patients were included in the immuno-cohort: 142 (64%) male, with median (m) age of 63, 182 (84%) smokers, 161 (77%) PS≤1, 162 (63%) adenocarcinoma; 125 (57%) received ICI as second-line. High-TIL was observed in 49/221 (28%), non-assessable in 46. High-TIL had independent impact on OS and PFS (HR 0.40; 95% CI 0.25-0.63, P<0.0001). The mPFS and OS were 3.1months (mo.) (2.5-4.9) and 11mo. (7.0-13.2) respectively. The high-TIL group had mPFS of 13mo. (5.0-NR) vs. 2.2mo. (1.7-3.0) in low-TIL group (P<0.0001). High-TIL group had mOS not reached (NR) (12.2-NR) vs. 8.4 mo. (5.0-11.6) in low-TIL (P=0.007). The CT-cohort (N=189) had high-TIL in 103/189 (54%). The mPFS and mOS were 5.7mo. (4.9-6.7) and 11.7mo. (9.3-13.0) respectively, with no association with TIL.

      OS, Immuno-cohort (n=221) OS, Chemo-cohort (n=188)

      Hazard ratio (HR)
      95% confidence interval (CI)

      P-value

      HR
      95% CI

      P-value

      TIL
      ≥10% (high)

      0.46 (0.28-0.81) 0.006 1.03 (0.76-1.41) 0.84
      Age
      ≥65 y
      0.86 (0.50-1.46) 0.57 0.99 (0.72-1.38) 0.99
      Line of treatment*
      second line
      0.69 (0.44-1.09) 0.11 0.84 (0.60-1.16) 0.29

      N# metastatic sites
      >2

      1.40 (0.88-2.20) 0.16 1.50 (1.07-2.12) 0.02
      Performance status
      ≥2
      2.75 (1.73-4.37) <0.0001 1.94 (1.23-3.04) 0.004
      Histology
      Squamous
      1.13 (0.70-1.81) 0.62 1.09 (0.65-1.83) 0.75
      *Line of treatment: lines of immunotherapy for the Immuno-cohort; lines of chemotherapy for the Chemo-cohort.

      Conclusion

      High-TIL (≥10%) is a simple and accessible marker associated with better ICI outcomes, but not with CT. This suggests a potential predictive value that must be validated in larger prospectively studies.

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    P1.01 - Advanced NSCLC (ID 158)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Advanced NSCLC
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.01-120 - Immune Checkpoint Inhibitors Versus Second Line Chemotherapy for Patients with Lung Cancer Refractory to First Line Chemotherapy (Now Available) (ID 2662)

      09:45 - 18:00  |  Author(s): Laura Mezquita

      • Abstract
      • Slides

      Background

      Anti Programmed Death-ligand (PD1/PD-L1) directed immune-checkpoint-inhibitors (ICI) are widely used to treat patients with advanced non-small cell lung cancer (NSCLC) who progress after first line chemotherapy. The best strategy after early progression under first line has not been specifically studied

      Method

      We conducted a multicenter, retrospective study including all consecutive NSCLC patients progressing within the first 3 months following introduction of first-line chemotherapy and being treated with second line ICI monotherapy or chemotherapy between March 2010 and November 2017. We analysed the clinicopathological data and outcome under second line chemotherapy vs. second line ICI: progression-free survival (PFS), overall survival (OS), and objective response rate (ORR).

      Result

      We identified 176 patients with refractory disease, 99 who received subsequent immunotherapy and 77 undergoing chemotherapy. The 2 populations were comparable regarding the main prognostic criteria, median age was 60, main histology was adenocarcimoma (68,2%). Compared to chemotherapy, ICI treated patients had a superior OS (logrank test, p=0.03) (Median [95% CI] OS 4.6 [2.8-6.7] versus 4.2 months [3.4-5.9] and a non-significant improvement in ORR (17.2% and 7.9%, respectively, p = 0.072). PFS was not significantly different (1.9 [1.8-2.1] versus 1.6 months [1.4- ; 2.0] (p=0.125). Poor performance status (ECOG PS≥2) and a higher number of metastatic sites (≥3) were associated with poorer prognosis. KRAS-mutated patients did not seem to benefit more from ICI than chemotherapy.

      Table 1 Multivariable analysis of characteristics associated

      n= 175

      OS

      PFS

      Variable

      HR [CI 95%]

      p value

      HR [CI 95%]

      p value

      Treatment

      0.045

      0.040

      Chemotherapy (ref)

      1.00

      1.00

      Immunotherapy

      0.70 [0.49 ; 0.99]

      0.71 [0.51 ; 0.98]

      Number of metastatic location before 2nd line

      0.005

      0.011

      0-1-2 (ref)

      1.00

      1.00

      3 or +

      1.64 [1.16 ; 2.31]

      1.52 [1.10 ; 2.10]

      Performance Status

      0.038

      0 -1

      1.00

      2 - 3 - 4

      1.46 [1.02 ; 2.09]

      Figure 1 : Kaplan Meier curves for Overall Survival for ICI group and CT group

      figure.png

      Conclusion

      ICI appears to be the preferred second-line treatment for patients who are refractory to first line chemotherapy

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    P1.04 - Immuno-oncology (ID 164)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.04-31 - Immunosenescence Correlates with Poor Outcome from PD-(L)1 Blockade but Not Chemotherapy in Non-Small Cell Lung Cancer (NSCLC) (Now Available) (ID 2268)

      09:45 - 18:00  |  Author(s): Laura Mezquita

      • Abstract
      • Slides

      Background

      CD28, CD57 and KLRG1 on circulating T-lymphocytes have been identified as markers of immunosenescence. The characterization of a senescent immune phenotype (SIP) in advanced NSCLC (aNSCLC) and its impact on anti-PD(L)-1 (IO) or platinum-based chemotherapy (PCT) treatments are unknown.

      Method

      The percentage of circulating CD8+CD28-CD57+KLRG1+ T-lymphocytes (SIP) was assessed by flow cytometry on fresh blood from aNSCLC patients treated with IO or PCT. A SIP cut-off was identified by log-rank maximation method. Correlations with categorical or continuous variables were performed by logistic regression or t-test. Survival curves were estimated with Kaplan Meier and compared with log-rank.

      Result

      In the IO cohort, 43 patients were evaluated for SIP: 32% ≥ 65 years, 92% non-squamous, 51% with tumoral PD-L1 expression ≥1%, 93% chemotherapy pretreated. Disease control rate (DCR), median PFS and OS and FU were 57%, 4.6 (95% CI 0.5; 8.8) months, 13 (95% CI 2.8-23.2) months, and 14 (95% CI 8.8-19.8) months, respectively.

      SIP median value was 15.4% (min 1.6%, max 57.7%). 32% of patients had >21.72% CD28-CD57+KLRG1+CD8+ lymphocytes (SIP+). SIP was not significantly associated with clinical characteristics. SIP changed according to IO response by T-sne algorithm (Figure 1A). Compared to SIP-, SIP+ patients had significantly lower DCR (81% vs 28%, p=0.002), PFS [7.3 (95% CI 4.1; 10.4) vs 1.7 (95% CI 1.2; 2.3), p=0.02] and OS [NR (95% CI 6.04; NR) vs 2.4 (95% CI 1.7; 3.1), p=0.01].

      SIP was significantly associated with specific immune populations [higher peripheral activated (Ox40+ICOS+PD1+) T-regulatory (CD25highCD127low) cells, TEMRA (CCR7-CD45RA+) CD8+ and T-helper 1 (CXCR5-CXCR3+CCR4-CCR6-CCR10-) CD4+] (Figure 1B). The PCT cohort included 61 patients, 43% SIP+. No significant difference in DCR, PFS or OS were observed according to SIP.

      figure 1a-1b.jpg

      Conclusion

      Immunosenescence is observed in 32% of aNSCLC patients before IO and correlates with specific immune phenotypes. Immunosenescence predicts lower DCR, PFS and OS from IO but not from PCT.

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    P1.10 - Prevention and Tobacco Control (ID 175)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Prevention and Tobacco Control
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.10-06 - Pathological Characterization of Radon-Induced Lung Cancer in Rats  (ID 1616)

      09:45 - 18:00  |  Presenting Author(s): Laura Mezquita

      • Abstract

      Background

      Radon is a radioactive gas, considered the leading cause of lung cancer in non-smokers. Although the risk of lung cancer is linear, there is no safe level and even low dose can be associated with risk. In humans, no specific pathological subtypes of lung cancer have been clearly associated with radon. In animals, the French Atomic Energy Commission (CEA) exposed to low dose of radon (25 working level month, WLM) a large cohort of rats in a radon-exposure chamber, showing lung cancer induced by low exposure (Chameaud J, Radiation Prot Dosimetry 1984). We aimed to describe pathological features of radon-induced tumors in rats from the CEA’s cohort.

      Method

      Retrospective assessment of archival samples available of the rats exposed to low-dose radon in the Laboratoire de Pathologie Pulmonaire Experimentale, COGEMA (France), between 1989 and 1992. Autopsy reports were also reviewed. The pathological assessment was performed for a thoracic oncology pathologist (JA) in H&E staining slides according to the current WHO histological classification.

      Result

      Samples from 117 rats were collected. Among 104 tumors, to date the analysis has been performed in 94. Forty tumors (43%) were classified as malignant, 28 (30%) as uncertain malignant potential (UMP) and 26 (28%) benign. In 2 rats (2%) synchronous malignant and non-malignant tumors were observed.

      Among the malignant tumors, 23 (58%) were epithelial and 17 (42%) non-epithelial. Lung carcinoma was the most common primary epithelial tumor (n=10, 43%), followed by abdominal area tumors (n=5, 22%), and thyroid (n=3, 13%). In the UMP group, 7 (25%) were epithelial and 21 (75%) non-epithelial, with no lung tumors observed. In the benign group, most of them (n=24, 92%) were epithelial, with 4 cases with lung atypical adenomatous hyperplasia-like lesions; 2 synchronous with other malignant tumors (n=1 lymphoma, n=1 cutaneous squamous cell carcinoma).

      A total of 26 tumors (27%) had thoracic involvement: 4 (15%) primary lung non-malignant lesions, 11 primary lung malignancies (42%) and 11 with metastases from other tumors (42%). As primary malignant lung tumors, we observed: 7 (64%) adenocarcinoma in situ, one papillary adenocarcinoma, one undifferentiated large cell carcinoma with bilateral metastases, one metastatic squamous carcinoma and one metastatic undifferentiated tumor, compatible with sarcoma

      Conclusion

      In this cohort of radon-induced tumors in rats, we observed different tumor types, from non-malignant lesions to aggressive malignancies, with predominance of epithelial tumors. Lung carcinoma was the most common primary tumor and adenocarcinoma the histological subtype more observed, with histological similarities with humans.