No abstract

Circulating tumor DNA (ctDNA), a tumor-derived fraction of cell-free DNA (cfDNA), has emerged as a promising marker in targeted therapy, immunotherapy, and minimal residual disease (MRD) monitoring in postsurgical patients. However, ctDNA level in early-stage cancers and postsurgical patients is very low, which posed many technical challenges to improve the detection rate and sensitivity, especially in the clinical practice of MRD detection. These challenges usually include insufficient DNA input amount, limit of detection (LOD), and high experimental costs. To resolve these challenges, we developed an ultrasensitive ctDNA MRD detection system in this study, namely PErsonalized Analysis of Cancer (PEAC), to simultaneously detect up to 37 mutations, which account for 70–80% non-small cell lung cancer (NSCLC) driver mutations from low plasma sample volume and enables LOD of 0.01% at a single-site level. We demonstrated the high performance achieved by PEAC on both cfDNA reference standards and clinical plasma samples from three NSCLC patient cohorts. For cfDNA reference standards, PEAC achieved a specificity of 99% and a sensitivity of 87% for the mutations at 0.01% allele fraction. In the second cohort, PEAC showed 100% concordance rate between ddPCR and Next-generation sequencing (NGS) among 29 samples. In the third cohort, 22 of 59 patients received EGFR TKI treatment. Among them, three in four patients identified low level actionable gene mutations only by PEAC had partial responses after targeted therapy, demonstrating high ctDNA detection ability of PEAC. Overall, the developed PEAC system can detect the majority of NSCLC driver mutations using 8–10 ml plasma samples, and has the advantages of high detection sensitivity and lower costs compared with the existing technologies such as ddPCR and NGS. These advantages make the PEAC system quite appropriate for ctDNA and MRD detection in early-stage NSCLC and postsurgical recurrence monitoring.

e21001 Background: Single-cell RNA sequencing (scRNA-seq) is a powerful tool to investigate the tumor microenvironment including infiltration of different types of immune cells. However, there is limited study utilizing scRNA-seq technology to investigate patient response to immunotherapy using a comprehensive set of samples. In this study, we used scRNA-seq approach to study tumor infiltrating immune cells in lung cancer patients receiving PD-1 inhibitor neoadjuvant immunotherapy. Methods: CD45 cells were collected from the tumor tissues, the adjacent normal tissues, peripheral blood, lymph nodes, and normal lung tissues for three lung cancer patients receiving PD-1 inhibitor neoadjuvant immunotherapy and four treatment naive lung cancer patients, followed by single cell library construction using 10x Genomics technology and sequenced on Illumina HiSeq X-Ten platform. CellRanger was used to generate expression matrices, and Seurat was used to identify cell subpopulations. Results: From a total of 62,970 single cells, we identified 23 clusters that belonged to different types of immune cells including myeloid cells, T cells, NK cells, mast cells, and B cells. Compared with the adjacent normal tissues, the tumor tissues had lower proportion of NK cells and higher percentage of B cells in all but one patient, and higher percentage of Treg cells for all the seven patients. We observed that patients treated with neoadjuvant immunotherapy had higher proportion of tumor-infiltrating T cells and lower percentage of myeloid cells compared with the treatment naive patients. Patients in the two groups showed differential gene expression in categories including leukocyte cell-cell adhesion, and cell-cycle arrest. Furthermore, the three patients undergoing immunotherapy had different response; the gene expression pattern for the patients differed in functional groups including regulation of inflammatory response, leukocyte migration, and regulation of immune effector process. These results support the potential application for scRNA-Seq in monitoring patient response to neoadjuvant immunotherapy. Conclusions: scRNA-Seq technology can detect the composition of tumor-infiltrating immune cells after neoadjuvant immunotherapy, which provides new tool for predicting the efficacy of treatment. We will next focus on the gene expression of tumor-infiltrating Treg cells and its effect on the therapeutic response.

e21028 Background: Familial lung cancer is rare, let alone inherited lung cancer with multiple synchronous primary lung cancer lesions. Study of inherited lung cancer may facilitate the understanding of the molecular mechanism of the tumorigenesis. Methods: We identified an extremely rare family of five siblings, four with multiple primary lung adenocarcinomas by pathological examination. DNA samples were extracted from the patient tumor tissues and libraries were prepared for next-generation sequencing (NGS) analysis using a custom 500-gene cancer panel. Results: EGFR L858R or 19DEL mutations were identified in at least one lesion in all of the four lung cancer patients. Interestingly, lung cancer patient L1 had EGFR 19DEL in one lesion and L858R in the other, lung cancer patient L2 had EGFR L858R and KRAS G12C mutation in lesion 1 and lesion 2, respectively. The mutation statuses of EGFR and KRAS were confirmed by ARMS-PCR. The mutational spectrum of the two lesions from the same patient distinguished significantly based on the 500-gene NGS panel analysis, further demonstrating the heterogeneity of cancer mutations. Taken together, these results indicate that patients L1 and L2 both had synchronous primary lung cancers. Due to the small lesions, the two lesions for patient L3 were mixed together; the concurrent EGFR L858R and 19DEL mutations in the tissue sample implicated different drivers in the two different lesions since co-occurring of these two mutations was very rare. For patient L4, EGFR L858R was identified in one lesion and uncommon mutations with low frequencies were observed in the other lesion, consistent with different driving mechanisms for the two lesions. Conclusions: To our knowledge, this is the first study identifying a family of multiple siblings with multiple synchronous primary lung cancers and using NGS to reveal the genetics. Together with germline mutation analysis, this study may shed light on the tumorigenesis of familial lung cancer.