Molecular landscape of osimertinib resistance in patients and patient-derived preclinical models

Introduction: Osimertinib is a third-generation EGFR tyrosine kinase inhibitor (TKI) that is approved for the use of EGFR-mutant non-small cell lung cancer (NSCLC) patients. In this study, we investigated the acquired resistance mechanisms in NSCLC patients and patient-derived preclinical models. Methods: Formalin-fixed paraffin-embedded tumor samples and plasma samples from 55 NSCLC patients who were treated with osimertinib were collected at baseline and at progressive disease (PD). Next-generation sequencing was performed in tumor and plasma samples using a 600-gene hybrid capture panel designed by AstraZeneca. Osimertinib-resistant cell lines and patient-derived xenografts and cells were generated and whole exome sequencing and RNA sequencing were performed. In vitro experiments were performed to functionally study the acquired mutations identified. Results: A total of 55 patients and a total of 149 samples (57 tumor samples and 92 plasma samples) were analyzed, and among them 36 patients had matched pre- and post-treatment samples. EGFR C797S (14%) mutation was the most frequent EGFR-dependent mechanism identified in all available progression samples, followed by EGFR G824D (6%), V726M (3%), and V843I (3%). Matched pre- and post-treatment sample analysis revealed in-depth acquired mechanisms of resistance. EGFR C797S was still most frequent (11%) among EGFR-dependent mechanism, while among EGFR-independent mechanisms, PIK3CA, ALK, BRAF, EP300, KRAS, and RAF1 mutations were detected. Among Osimertinib-resistant cell lines and patient-derived models, we noted acquired mutations which were potentially targetable such as NRAS p.Q61K, in which resistance could be overcome with combination of osimertinib and trametinib. A patient-derived xenograft established from osimertinib-resistant patient revealed KRAS p.G12D mutation which could be overcome with combination of osimertinib, trametinib, and buparlisib. Conclusion: In this study, we explored the genetic profiles of osimertinib-resistant NSCLC patient samples using targeted deep sequencing. In vitro and in vivo models harboring osimertinib resistance revealed potential novel treatment strategies after osimertinib failure.ope

Repotrectinib Exhibits Potent Antitumor Activity in Treatment-Naïve and Solvent-Front-Mutant ROS1-Rearranged Non-Small Cell Lung Cancer

Purpose: Although first-line crizotinib treatment leads to clinical benefit in ROS1+ lung cancer, high prevalence of crizotinib-resistant ROS1-G2032R (ROS1G2032R) mutation and progression in the central nervous system (CNS) represents a therapeutic challenge. Here, we investigated the antitumor activity of repotrectinib, a novel next-generation ROS1/TRK/ALK-tyrosine kinase inhibitor (TKI) in ROS1+ patient-derived preclinical models. Experimental design: Antitumor activity of repotrectinib was evaluated in ROS1+ patient-derived preclinical models including treatment-naïve and ROS1G2032R models and was further demonstrated in patients enrolled in an on-going phase I/II clinical trial (NCT03093116). Intracranial antitumor activity of repotrectinib was evaluated in a brain-metastasis mouse model. Results: Repotrectinib potently inhibited in vitro and in vivo tumor growth and ROS1 downstream signal in treatment-naïve YU1078 compared with clinically available crizotinib, ceritinib, and entrectinib. Despite comparable tumor regression between repotrectinib and lorlatinib in YU1078-derived xenograft model, repotrectinib markedly delayed the onset of tumor recurrence following drug withdrawal. Moreover, repotrectinib induced profound antitumor activity in the CNS with efficient blood-brain barrier penetrating properties. Notably, repotrectinib showed selective and potent in vitro and in vivo activity against ROS1G2032R. These findings were supported by systemic and intracranial activity of repotrectinib observed in patients enrolled in the on-going clinical trial. Conclusions: Repotrectinib is a novel next-generation ROS1-TKI with improved potency and selectivity against treatment-naïve and ROS1G2032R with efficient CNS penetration. Our findings suggest that repotrectinib can be effective both as first-line and after progression to prior ROS1-TKI.ope

Modeling Clinical Responses to Targeted Therapies by Patient-Derived Organoids of Advanced Lung Adenocarcinoma

Purpose: Patient-derived organoids (PDO) of lung cancer has been recently introduced, reflecting the genomic landscape of lung cancer. However, clinical relevance of advanced lung adenocarcinoma organoids remains unknown. Here, we examined the ability of PDOs to predict clinical responses to targeted therapies in individual patients and to identify effective anticancer therapies for novel molecular targets. Experimental design: Eighty-four organoids were established from patients with advanced lung adenocarcinoma. Formalin-fixed, paraffin-embedded tumor specimens from corresponding patients were analyzed by whole-exome sequencing (n = 12). Organoids were analyzed by whole-exome sequencing (n = 61) and RNA sequencing (n = 55). Responses to mono or combination targeted therapies were examined in organoids and organoid-derived xenografts. Results: PDOs largely retained somatic alterations including driver mutations of matching patient tumors. PDOs were able to recapitulate progression-free survival and objective responses of patients with non-small cell lung cancer receiving clinically approved tyrosine kinase inhibitors. PDOs recapitulated activity of therapeutic strategies under clinical investigation. YUO-071 harboring an EGFR exon 19 deletion and a BRAF G464A mutation and the matching patient responded to dabrafenib/trametinib combination therapy. YUO-004 and YUO-050 harboring an EGFR L747P mutation was sensitive to afatinib, consistent with the response in the matching patient of YUO-050. Furthermore, we utilized organoids to identify effective therapies for novel molecular targets by demonstrating the efficacy of poziotinib against ERBB2 exon 20 insertions and pralsetinib against RET fusions. Conclusions: We demonstrated translational relevance of PDOs in advanced lung adenocarcinoma. PDOs are an important diagnostic tool, which can assist clinical decision making and accelerate development of therapeutic strategies.ope

A phase II study of poziotinib in patients with recurrent and/or metastatic head and neck squamous cell carcinoma

Background: In phase I studies, poziotinib has shown meaningful efficacy against various types of cancers. This phase 2 study aimed to investigate the efficacy and safety of poziotinib in recurrent and/or metastatic head and neck squamous cell carcinoma (R/M-HNSCC). Methods: Overall, 49 patients were enrolled (median age, 62 years; age range, 21-78 years). Patients received a median of two prior treatments including chemotherapy and others and received 12 mg poziotinib orally once daily as part of a 28-day cycle. The primary endpoint was objective response rate (ORR), and the secondary endpoints were progression-free survival (PFS) and overall survival (OS). Targeted capture sequencing was performed using available tissues to identify translational biomarkers related to clinical response. Results: ORR was 22.4%, median PFS was 4.0 months (95% confidence interval [CI], 1.8-6.2 months), and median OS was 7.6 months (95% CI, 4.4-10.8 months). The most common treatment-related adverse events were acneiform rash (85%) and mucositis (77%). A grade 3 or higher adverse event was acneiform rash (3%). Targeted capture sequencing was performed in 30 tissue samples. TP53 and PIK3CA were the most frequently mutated genes (43%), followed by CCND1 (33%) and EGFR (30%). Mutations in ERBB2, ERBB3, and ERBB4, which are HER family genes, were observed in 17%, 13%, and 10% samples, respectively. There was no difference in the frequency of somatic mutations in the HER family genes between the clinically benefitted and non-benefitted groups. Conclusion: Compared to other pan-HER inhibitors, poziotinib showed clinically meaningful efficacy in heavily treated R/M-HNSCC. Clinical trial registration number: NCT02216916.ope

The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.ope

Design and Optimization of Pilot-Scale Bunsen Process in Sulfur-Iodine (SI) Cycle for Hydrogen Production

Sulfur-Iodine cycle (SI cycle)은 요오드와 황을 첨가하여 최종적으로 물을 열화학적으로 분해하여 산소와 수소를 생산하는 공정으로 황산분해, 요오드화 수소 분해, 분젠반응 등 세가지 반응들로 이루어져 있다. 분젠 반응은 두가지 공정 중간에 존재하므로 두 반응에 필요한 화학물을 조달하는 역할로 이에 대한 상분리 및 반응기에 대한 분석이 중요하다. 본 연구에서는 50 L/hr 수소를 생산하는 pilot scale의 Sulfur-Iodine Cycle 중 분젠 공정에 대한 모사, 민감도 분석, 민감도 분석을 토대로한 각각 상분리기와 분젠 반응기에 대한 최적 조건을 제시하였다. 열역학 물성치의 계산을 위해 Electrolyte Non-Random Two Liquid (ELECNRTL) model 사용하였다. 모델에 대한 신뢰도 확보를 위해서 실제pilot scale의 공정 데이터와 검증을 수행하였다. 반응기의 종류를 선정하기 위해 Continuous Stirred Tank Reactor (CSTR)과 Plug Flow Reactor (PFR) 동일한 온도 및 부피 변화에서 SO2 전환율을 비교하였다. 상분리기 선정을 위해 3 상 분리 시스템(기체-액체-액체)과 액체-기체 분리 후 액체-액체 구조에서 H2SO4 상과 HIX 상에서의 불순물들을 비교하였다. PFR에서 온도, 지름, 길이를 결정 변수로 SO2 전환율을 최대화 하기 위한 최적화를 수행하였는데, 온도 121 oC와PFR의 지름이 0.20 m 및 길이 7.6 m 일 때 SO2 전환율이 98% 최적 결과임을 확인하였다. 기존 pilot scale과 동일한운전 조건 하에 PFR의 지름 3/8 inch, 길이 3.0 m, 120 oC 일 때 인입 몰량인 I2 및 H2O를 결정 변수로 SO2 전환율에대한 최적화를 수행하였을 때, SO2 전환율이 10% 일때 H2O 및 I2 의 인입 몰량은 각각 17%와 22%로 감소하였다. 앞선조업 조건 최적화 조건 (121 oC, 지름 0.20m, 길이: 7.6m) 경우에는 SO2 전환율이 98% 일 때 H2O가 1% 그리고 I2가7% 감소하였다. 상분리기에서 HIX 상내 H2SO4 최소화하는 목적함수에서 그에 상응하는 온도, I2와 H2O를 결정 변수로설정하였을 때, H2O 몰량이 기존공정보다 17% 감소하고 I2 몰량이 24% 감소하였을 때 최소 불순물이 생성하였다.11Nscopuskciothe

Preclinical Study of a Biparatopic METxMET Antibody-Drug Conjugate, REGN5093-M114, Overcomes MET-driven Acquired Resistance to EGFR TKIs in EGFR-mutant NSCLC

Purpose: MET amplification is a frequent mechanism of resistance to EGFR tyrosine kinase inhibitors (TKI) in patients with EGFR-mutated non-small cell lung cancer (NSCLC), and combined treatment with EGFR TKIs and MET TKIs has been explored as a strategy to overcome resistance. However, durable response is invariably limited by the emergence of acquired resistance. Here, we investigated the preclinical activity of REGN5093-M114, a novel antibody-drug conjugate targeting MET in MET-driven patient-derived models. Experimental Design: Patient-derived organoids, patient-derived cells, or ATCC cell lines were used to investigate the in vitro/in vivo activity of REGN5093-M114. Results: REGN5093-M114 exhibited significant antitumor efficacy compared with METTKI or unconjugated METxMET biparatopic antibody (REGN5093). Regardless of MET gene copy number, MET-overexpressed TKI-naive EGFR-mutant NSCLC cells responded to REGN5093-M114 treatment. Cell surface MET expression had the most predictive power in determining the efficacy of REGN5093-M114. REGN5093-M114 potently reduced tumor growth of EGFR-mutant NSCLC with PTEN loss or MET Y1230C mutation after progression on prior osimertinib and savolitinib treatment. Conclusions: Altogether, REGN5093-M114 is a promising candidate to overcome the challenges facing functional MET pathway blockade.,restrictio

Clinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer

Objective: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only some patients respond to the treatment. Various clinical characteristics, including the PD-ligand 1 (PD-L1) level, are related to the anti-PD-1 response; however, none of these can independently serve as predictive biomarkers. Herein, we established a machine learning (ML)-based clinical decision support algorithm to predict the anti-PD-1 response by comprehensively combining the clinical information. Materials and methods: We collected clinical data, including patient characteristics, mutations and laboratory findings, from the electronic medical records of 142 patients with NSCLC treated with anti-PD-1 therapy; these were analysed for the clinical outcome as the discovery set. Nineteen clinically meaningful features were used in supervised ML algorithms, including LightGBM, XGBoost, multilayer neural network, ridge regression and linear discriminant analysis, to predict anti-PD-1 responses. Based on each ML algorithm's prediction performance, the optimal ML was selected and validated in an independent validation set of PD-1 inhibitor-treated patients. Results: Several factors, including PD-L1 expression, tumour burden and neutrophil-to-lymphocyte ratio, could independently predict the anti-PD-1 response in the discovery set. ML platforms based on the LightGBM algorithm using 19 clinical features showed more significant prediction performance (area under the curve [AUC] 0.788) than on individual clinical features and traditional multivariate logistic regression (AUC 0.759). Conclusion: Collectively, our LightGBM algorithm offers a clinical decision support model to predict the anti-PD-1 response in patients with NSCLC.restrictio