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）彻底改变了癌症治疗，但临床反应仍然有限 由于原发性和/或获得性 ICI 耐药性。初步研究表明肠道微生物组是一个独立的、 ICI 的全身和肿瘤内反应的新型调节剂。已发表的研究，主要针对黑色素瘤和 非小细胞肺癌（NSCLC），报道了多样化的肠道微生物组和一些相关的细菌种类 肿瘤对 ICI 的反应。然而，现有的研究受到过于简单化的分类的限制。 应答者状态和缺乏纵向分析。此外，假定特定细菌或细菌群落 对 ICI 反应有帮助或有害的结果在不同研究人群和肿瘤类型之间基本上不一致 与免疫或突变生物标志物的相关性有限。为了解决这些不一致的问题，我们重新分析了 来自五项已发表研究的原始 16S rRNA 扩增子和全基因组测序 (WGS) 粪便数据 (n=303， 仅治疗前粪便）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（癌症死亡的主要原因）的表现不佳。因此，要进一步细化 这个索引并确定微生物组在 NSCLC ICI 治疗中的作用机制，在此，我们建议 我们的 NSCLC 队列的详细代谢物（血浆、粪便）和宏基因组（粪便、WGS）纵向研究 （n=108）。我们将把研究重点放在对 ICI 有持久反应的患者上，将他们与那些对 ICI 有持久反应的患者进行比较。 原发性耐药（6 个月内疾病进展）或获得性耐药（一次治疗后疾病进展） 初始响应 >6 个月）。微生物群代谢物的产生，能够影响局部和全身 免疫反应被认为是促进抗肿瘤反应的“共同途径”机制。对于 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 研究发现对 ICI 具有持久反应的 NSCLC 患者的粪便样本中富含 SCFA（n=11）。我们 假设特定细菌种类和/或群落促进 SCFA 和/或肌苷的产生 NSCLC 患者的肠道和血浆对 ICI 治疗表现出持久的反应，而这些却不存在 在表现出原发性和/或最终产生获得性耐药性的患者中。为了检验我们的假设，我们提出 识别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) 或 NSCLC 患者的 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 细胞对同基因小鼠肿瘤模型中的患者群体做出反应 受体测序以确定 T 细胞库 (SA3)。我们的多维方法，包括横截面 和纵向转化分析，将有助于确定影响 ICI 耐药性的关键代谢物和微生物 在非小细胞肺癌中。这些研究将为我作为一名医师科学家独立过渡奠定基础，并将 为考虑非小细胞肺癌患者的介入研究奠定了基础。

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.