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Predictive value of EGFR mutation in non-small-cell lung cancer patients treated with platinum doublet postoperative chemotherapy.

ABSTRACT: The mutation status of tumor tissue DNA (n = 389) of resected stage II-III non-squamous non-small-cell lung cancer (Ns-NSCLC) was analyzed using targeted deep sequencing as an exploratory biomarker study (JIPANG-TR) for the JIPANG study, a randomized phase III study of pemetrexed/cisplatin (Pem/Cis) vs vinorelbine/cisplatin (Vnr/Cis). The TP53 mutation, common EGFR mutations (exon 19 deletion and L858R), and KRAS mutations were frequently detected. The frequency of the EGFR mutation was significant among female patients. Patients with an EGFR mutation-positive status had a significantly shorter recurrence-free survival (RFS) time (24 mo vs not reached) (HR, 1.64; 95% CI, 1.22-2.21; P = .0011 for EGFR mutation status). Multivariable analysis identified both the pathological stage and EGFR mutation status as independent prognostic factors for RFS (HR, 1.78; 95% CI, 1.30-2.44; P = .0003 for disease stage; and HR, 1.57; 95% CI, 1.15-2.16; P = .0050 for EGFR mutation status). This study demonstrated that the EGFR mutation has either a poor prognostic or predictive impact on a poor response to postoperative chemotherapy with platinum doublet chemotherapy for stage II-III Ns-NSCLC patients. This result supports a role for mandatory molecular diagnosis of early-stage Ns-NSCLC for precision oncology and signifies the importance of adjuvant for the 3rd generation tyrosine kinase inhibitor rather than platinum-based chemotherapy. This study is registered with the UMIN Clinical Trial Registry (UMIN 000012237).

SUBMITTER: Takahashi T

PROVIDER: S-EPMC8748214 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Predictive value of EGFR mutation in non-small-cell lung cancer patients treated with platinum doublet postoperative chemotherapy.

Takahashi Toshiaki T Sakai Kazuko K Kenmotsu Hirotsugu H Yoh Kiyotaka K Daga Haruko H Ohira Tatsuo T Ueno Tsuyoshi T Aoki Tadashi T Hayashi Hidetoshi H Yamazaki Koji K Hosomi Yukio Y Chen-Yoshikawa Toyofumi F TF Okumura Norihito N Takiguchi Yuichi Y Sekine Akimasa A Haruki Tomohiro T Yamamoto Hiromasa H Sato Yuki Y Akamatsu Hiroaki H Seto Takashi T Saeki Sho S Sugio Kenji K Nishio Makoto M Inokawa Hidetoshi H Yamamoto Nobuyuki N Nishio Kazuto K Tsuboi Masahiro M

Cancer science 20211107 1

The mutation status of tumor tissue DNA (n = 389) of resected stage II-III non-squamous non-small-cell lung cancer (Ns-NSCLC) was analyzed using targeted deep sequencing as an exploratory biomarker study (JIPANG-TR) for the JIPANG study, a randomized phase III study of pemetrexed/cisplatin (Pem/Cis) vs vinorelbine/cisplatin (Vnr/Cis). The TP53 mutation, common EGFR mutations (exon 19 deletion and L858R), and KRAS mutations were frequently detected. The frequency of the EGFR mutation was signific ...[more]

PMID: 34689382

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Project description:We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004-2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.

Project description:AimAccurate causality assessment (CA) of adverse events (AEs) is important in clinical research and routine clinical practice. The Naranjo scale (NS) used for CA lacks specificity, leading to a high rate of false positive causal associations. NS is a simple scale for CA; however, its limitations have reduced its popularity in favour of other scales. We therefore attempted to improvise the algorithm by addressing specific lacunae in NS.MethodsWe attempted to modify the existing NS by (a) changing the weightage given to certain responses, (b) achieving higher resolution to certain responses for delineating drug related and unrelated AEs and (c) modifying the slabs for classification of association as 'likely' and 'unlikely'. The new scale, named as the Sharma-Nookala-Gota (SNG) algorithm, was evaluated in a training set of 19 AEs in a tertiary care cancer hospital in western India, and further validated in a set of 104 AEs. Consensus of four physician opinion was taken as gold standard for comparison.ResultsOf the 19 AEs in the training set, 6 were described by the treating physician as 'not related' and 13 as related to the drug. The SNG algorithm had 100% concordance with physician opinion, whereas the NS had only 73.7% concordance. NS showed a tendency to misclassify AEs as 'related' when they were indeed 'not related'. In the validation set of 104 AEs, NS and SNG algorithms misclassified 30 and 2 AEs, respectively, leading to a concordance of 70.2% and 98.1%, respectively, with physician opinion.ConclusionDecisive modifications of the NS resulted in the SNG scale, with superior specificity while retaining sensitivity against the gold standard.Plain language summarySNG algorithm - A novel tool for causality assessment of adverse drug reactions Adverse events (AEs) can cause increased morbidity, hospitalisation, and even death. Hence it is essential to recognise AEs and to establish their correct causal relationship to a drug. Many causality assessment methods, scales and algorithms are available to assess the relationship between an AE and a drug. The Naranjo algorithm is most commonly employed in spite of its many drawbacks as it is simple to use. Concerns have been raised regarding the performance of the scale, and researchers have tried to answer them, but none of them could address all issues satisfactorily. We too experienced many problems while using it in our routine clinical practice and in clinical trials. For instance, the Naranjo scale is non-specific and shows a bias toward implicating the drug as the causal factor for AEs. This improper assessment has often led to drug discontinuation, thereby compromising the efficacy of treatment. Hence, we modified the existing Naranjo scale to a new one (the Sharma-Nookala-Gota - SNG algorithm) to address these shortcomings. We piloted the SNG causality assessment algorithm in patients suffering from AEs due to various drugs. The SNG algorithm was found to have good concordance with the physicians' assessment of causality. As a next step, we validated the SNG algorithm in patients receiving a standard drug combination of pemetrexed and carboplatin for lung cancer combination. Out of the 104 AEs observed in 65 patients, the SNG causality assessment algorithm showed good concordance (except in two cases) with the physicians' decision of causality assessment, while the Naranjo algorithm was not so successful. Hence, the SNG algorithm can be a better guide for causality assessment of AEs.

Project description:BackgroundIrinotecan (CPT11) is an important drug for small cell lung cancer (SCLC) chemotherapy (CTx). UDP-glucuronosyltransferase 1A1 (UGT1A1) polymorphisms can influence CPT11-related toxicity. This study aimed to assess prevalence of UGT1A1 polymorphisms and their association with clinical outcomes in patients with SCLC on CPT11-CTx.MethodsAn observational cohort of treatment-naïve patients with SCLC was given CPT11-platinum doublet at a referral center in North India over 3 years. Clinical outcomes assessed were hematological and gastrointestinal toxicity (Common Terminology Criteria for Adverse Events, version 3.0), response rates (RECIST), and overall survival (OS). Peripheral blood was drawn from all enrolled patients just prior to CPT11-CTx initiation, and genomic DNA was isolated. Genotyping was done by polymerase chain reaction (PCR)-restriction fragment length polymorphism for UGT1A1*7, UGT1A1*6, and UGT1A1*27 and by PCR-DNA sequencing for UGT1A1*28. Patients were classified as homozygous wild-type (WT/WT), heterozygous mutant (WT/M), or homozygous mutant (M/M) for each polymorphism assessed.ResultsOf 140 patients enrolled, no mutant alleles were found for UGT1A1*27 or UGT1A1*7. Frequency of UGT1A1*6 polymorphisms (n = 111) was 89.2% for WT/WT, 8.1% for WT/M, and 2.7% for M/M. For UGT1A1*28 (n = 102), this was 41.2% for WT/WT, 43.1% for WT/M, and 15.7% for M/M. UGT1A1*6 WT/WT patients tolerated >95% predicted CPT11 dose more frequently (59.6% vs. 25.0% in WT/M-M/M combined group; p = .026). The UGT1A1*6 WT/M-M/M group also experienced severe (grade ≥3) diarrhea (41.7% vs. 17.2% in WT/WT; p = .044) and mucositis (41.7% vs. 8.1% in WT/WT; p = .005) more frequently. On multivariate logistic regression analysis, UGT1A1*6 WT/M-M/M status was the only variable associated with occurrence of both mucositis (odds ratio [OR], 10.4) and severe diarrhea (OR, 4.8). UGT1A1*28 WT/M-M/M patients had better OS (320 days [95% confidence interval, 203-437] vs. 216 days [95% confidence interval, 140-292] in WT/WT group; p = .047). On multivariate Cox proportional hazards analysis, UGT1A1*28 WT/M-M/M status was independently associated with better OS (hazard ratio, 0.35), whereas lack of objective radiological response, moderate/heavy smoking, and increasing age were associated with worse OS.ConclusionUGT1A1*6 and UGT1A1*28 polymorphisms were associated with increased gastrointestinal toxicity and improved OS, respectively, in North Indian patients with SCLC receiving CPT11-CTx.Implications for practiceUGT1A1 gene polymorphisms are known to influence irinotecan-related toxicity. In this prospective cohort study of patients with small cell lung cancer receiving first-line irinotecan-platinum chemotherapy, the prevalence of UGT1A1*6 polymorphisms was found to be 10.8% UGT1A1*6 and 58.8% UGT1A1*28 homo/heterozygous mutant, respectively. UGT1A1*6 homo/heterozygous mutant status was associated with severe diarrhea and mucositis. UGT1A1*28 homo/heterozygous mutant status was independently associated with improved overall survival.

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Predictive value of EGFR mutation in non-small-cell lung cancer patients treated with platinum doublet postoperative chemotherapy.

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Predictive value of EGFR mutation in non-small-cell lung cancer patients treated with platinum doublet postoperative chemotherapy.

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