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      Neoadjuvant sintilimab plus chemotherapy in EGFR-mutant NSCLC: Phase 2 trial interim results (NEOTIDE/CTONG2104)

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          Summary

          The clinical efficacy of neoadjuvant immunotherapy plus chemotherapy remains elusive in localized epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC). Here, we report interim results of a Simon’s two-stage design, phase 2 trial using neoadjuvant sintilimab with carboplatin and nab-paclitaxel in resectable EGFR-mutant NSCLC. All 18 patients undergo radical surgery, with one patient experiencing surgery delay. Fourteen patients exhibit confirmed radiological response, with 44% achieving major pathological response (MPR) and no pathological complete response (pCR). Similar genomic alterations are observed before and after treatment without influencing the efficacy of subsequent EGFR-tyrosine kinase inhibitors (TKIs) in vitro. Infiltration and T cell receptor (TCR) clonal expansion of CCR8 + regulatory T (Treg) hi/CXCL13 + exhausted T (Tex) lo cells define a subtype of EGFR-mutant NSCLC highly resistant to immunotherapy, with the phenotype potentially serving as a promising signature to predict immunotherapy efficacy. Informed circulating tumor DNA (ctDNA) detection in EGFR-mutant NSCLC could help identify patients nonresponsive to neoadjuvant immunochemotherapy. These findings provide supportive data for the utilization of neoadjuvant immunochemotherapy and insight into immune resistance in EGFR-mutant NSCLC.

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          Highlights

          • Neoadjuvant sintilimab and chemotherapy are clinically feasible in EGFR-mutant NSCLC

          • The treatment regimen is well tolerated, with comparable surgical outcome

          • Baseline genomic features and dynamic MRD help identify potential beneficiaries

          • CXCL13 +Tex/CCR8 +Treg cell phenotype predicts efficacy of pre- and post-immunotherapy

          Abstract

          Zhang et al. demonstrate the acceptable clinical feasibility and safety of neoadjuvant sintilimab and chemotherapy in patients with EGFR-mutant localized NSCLC. They highlight that the baseline genomic features and dynamic MRD detection help identify responders and illustrate that the CXCL13 + Tex/CCR8 + Treg cell infiltrating phenotype is correlated with diverse immune statuses and clinical efficacy of immunotherapy.

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          Most cited references67

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          fastp: an ultra-fast all-in-one FASTQ preprocessor

          Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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                Author and article information

                Contributors
                Journal
                Cell Rep Med
                Cell Rep Med
                Cell Reports Medicine
                Elsevier
                2666-3791
                18 June 2024
                16 July 2024
                18 June 2024
                : 5
                : 7
                : 101615
                Affiliations
                [1 ]Department of Pulmonary Surgery, Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
                [2 ]Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
                [3 ]School of Life Sciences, Peking University, Beijing, China
                [4 ]Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
                [5 ]Department of Radiation Therapy, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
                [6 ]Burning Rock Biotech, Guangzhou, China
                [7 ]Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
                [8 ]Institute of Biomedical Research, Yunnan University, Kunming, China
                [9 ]PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
                [10 ]BIOPIC, Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
                [11 ]School of Medicine, South China University of Technology, Guangzhou, China
                Author notes
                []Corresponding author syzhongwenzhao@ 123456scut.edu.cn
                [12]

                These authors contributed equally

                [13]

                Lead contact

                Article
                S2666-3791(24)00319-7 101615
                10.1016/j.xcrm.2024.101615
                11293361
                38897205
                e40caea8-9bd0-4ce6-b1fd-14ddba227381
                © 2024 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 2 November 2023
                : 31 January 2024
                : 23 May 2024
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