Metabolite driven mechanisms by which gut microbes impact checkpoint inhibitor success in non-small cell lung cancer patients

摘要 免疫检查点抑制剂(ICIS)使癌症治疗发生了革命性的变化，但临床反应仍然有限 由于原发和/或获得性ICI抵抗。初步研究表明，肠道微生物群是一个独立的， 对ICIS的全身和肿瘤内反应的新调节剂。已发表的研究，主要是在黑色素瘤和 非小细胞肺癌(NSCLC)，报告了一个多样化的肠道微生物群和一些相关的细菌物种 肿瘤对ICIS的反应。然而，现有的研究受到过于简单的分类的限制 应答者状况和缺乏纵向分析。此外，推测特定的细菌或细菌群落 对ICI反应有帮助或有害的反应在研究人群和肿瘤类型之间基本上是不一致的 仅与免疫或突变生物标记物的相关性有限。为了解决这些矛盾，我们重新分析了 来自五项已发表研究的原始16S rRNA扩增和全基因组测序(WGS)排泄物数据(n=303， 仅限治疗前大便)使用高维计算方法，发现我们的预测指数 与黑色素瘤相比，非小细胞肺癌的表现不佳，非小细胞肺癌是癌症死亡的主要原因。因此，要进一步提炼 这一指标和微生物群在非小细胞肺癌ICI治疗中的作用机制，在这里，我们建议 我们NSCLC队列的详细代谢物(血浆、粪便)和元基因组(粪便、WGS)纵向研究 (n=108)。我们将专注于对ICIS有持久反应的患者进行研究，将他们与那些对ICIS有持久反应的患者进行比较 原始抵抗力(6个月内疾病进展)或获得性抵抗力(疾病进展后 初步答复&>6个月)。微生物组代谢产物的产生，具有影响局部和系统的能力 免疫反应，被认为是促进抗肿瘤反应的“共同途径”机制。为 例如，最近的文献和我们在可移植肿瘤小鼠模型中的初步数据都表明肌苷 短链脂肪酸(SCFA)是肿瘤微环境的微生物调节剂。此外，初步的 研究发现，在对ICIS有持久反应的非小细胞肺癌患者的粪便样本中富含SCFA(n=11)。我们 假设特定的细菌物种和/或群落促进了单链脂肪酸和/或肌苷的产生 对ICI治疗有持久反应的非小细胞肺癌患者的肠道和血浆 在表现出原发和/或最终发展为获得性耐药性的患者中。为了检验我们的假设，我们建议 确定与持久反应相关的粪便和/或血浆代谢物(SA1)和/或微生物分类群(SA2) 非小细胞肺癌患者存在ICI耐药。我们将通过定义肿瘤内免疫细胞来进一步检验我们的假设 使用单细胞RNAseq和配对T细胞在同基因小鼠肿瘤模型中对患者队列的反应 受体测序以确定T细胞谱系(SA3)。我们的多维方法，既有横截面 以及纵向翻译分析，将有助于确定影响ICI耐药性的关键代谢物和微生物 在NSCLC。这些研究将是我作为一名内科科学家过渡到独立的基础，并将 为考虑对非小细胞肺癌患者进行介入性研究奠定基础。

ABSTRACT Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but clinical responses remain limited due to primary and/or acquired ICI resistance. Preliminary studies suggest the gut microbiome is an independent, novel modulator of systemic and intra-tumoral responses to ICIs. Published studies, primarily in melanoma and non-small cell lung cancer (NSCLC), reported a diverse gut microbiome and a few bacterial species associated with tumor responses to ICIs. However, existing studies are limited by an oversimplified classification of responder status and lack of longitudinal analysis. Moreover, specific bacteria or bacterial communities putatively helpful or harmful to ICI responses have largely been inconsistent across study populations and tumor types with only limited correlations with immune or mutational biomarkers. To address these inconsistencies, we reanalyzed raw 16S rRNA amplicon and whole genome sequencing (WGS) fecal data from five published studies (n=303, pre-treatment stools only) using a high dimensional computational approach and found that our predictive index underperformed in NSCLC, the leading cause of cancer death, compared to melanoma. Thus, to further refine this index and identify mechanisms of action for the microbiome in NSCLC ICI therapy, herein, we propose detailed metabolite (plasma, fecal) and metagenomic (fecal, WGS) longitudinal studies of our NSCLC cohort (n=108). We will focus our studies on patients with a durable response to ICIs, comparing them to those with primary resistance (disease progression within 6 months) or acquired resistance (disease progression after an initial response >6 months). Microbiota metabolite production, with the capacity to impact local and systemic immune responses, is proposed as a ‘common pathway’ mechanism promoting anti-tumor responses. For example, both recent literature and our preliminary data in transplantable tumor murine models suggest inosine and short chain fatty acids (SCFA) are microbial modifiers of the tumor microenvironment. Further, a preliminary study found SCFAs enriched in fecal sample from NSCLC patients with durable response to ICIs (n=11). We hypothesize that specific bacterial species and/or communities promote production of SCFAs and/or inosine in the gut and plasma of NSCLC patients who exhibit durable responses to ICI therapy whereas these are absent in patients who exhibit primary and/or ultimately develop acquired resistance. To test our hypothesis, we propose to identify fecal and/or plasma metabolites (SA1) and/or microbial taxa (SA2) that correlate with durable response or ICI resistance in NSCLC patients. We will further test our hypothesis by defining the intra-tumoral immune cell response to our patient cohorts in syngeneic murine tumor models using single cell RNAseq and paired T cell receptor sequencing to define T cell repertoires (SA3). Our multidimensional approach, with both cross-sectional and longitudinal translational analysis, will help define key metabolites and microbes that impact ICI resistance in NSCLC. These studies will be foundational to my transition to independence as a physician-scientist and will set the stage for consideration of interventional studies in NSCLC patients.