Precision Medicine of Cancer

癌症的精准医学是多方面的，该实验室的研究已经取得了重大发现，这些发现将促进生物标志物的诊断和预后利用，包括阐明微生物组在肺癌发生中的作用和机制。癌症微生物组：为了确定精准医学的额外诊断靶点，我们之前证明了肺癌中的微生物组发生了改变，并确定了Acidovorax属在肺癌中富集，并与TP53突变和吸烟相关。全长16S测序鉴定出了Acidovorax temperans，这使我们不禁要问，由a . temperans灌注模拟的微生物生态失调是否在肿瘤发展中发挥了辅助或驱动作用。在肺腺癌小鼠模型中，我们发现反复暴露于a . temperans可显著加速肿瘤发展并降低生存率，表明其具有驱动作用。相比之下，肺共生菌发酵乳杆菌的注入对肿瘤的发展没有影响。通过FACS和scRNA-seq分析，我们发现了一条促炎通路，在a . temperans小鼠中特异性增加，其中TLR4/NF-kB信号在巨噬细胞中被激活，从而上调MHC II激活效应CD4+ T细胞，使其极化到TH17状态。这些TH17细胞将中性粒细胞招募到肺部，从而获得组织驻留和促肿瘤表型。然后中性粒细胞增加IL-23信号以募集更多的TH17细胞。相反，L. gasseri处理小鼠上调抗炎细胞因子抑制中性粒细胞和阻止CD4+ T活化。这项工作的scRNA-seq部分目前正在Oncogenesis进行审查。我们现在正在努力确定策略，以改善由A. temperans加速的肿瘤发展，包括添加益生菌菌株。随着食管癌（ESCA）的发病率在全球范围内不断上升，特别是在西方，我们也想知道食管癌的微生物群是否也能揭示这种疾病的精准医学靶点。我们与Leigh Greathouse合作，对来自NCI-MD病例对照研究（126 NT, 98 T）的229个组织样本进行了16S测序，并对TCGA ESCA的RNA-seq （11 NT, 162 T）和WGS数据集（61 NT, 62 T）中的非人类序列进行了宏基因组分析。我们确定了四个在ESCA肿瘤组织中共同富集的属：弯曲杆菌、梭杆菌、普雷沃菌和链球菌，这是对这四个分类群的首次描述。我们进一步确定了大肠直肠癌（梭杆菌，普雷沃氏菌，细毛菌，细毛菌）中富集的细菌共现，这是另一种肠道恶性肿瘤。预测免疫细胞浸润鉴定出这些类群血小板浸润增加，这发生在ESCA转移之前。这项工作目前正在《科学报告》上进行审查。与Eytan Ruppin合作，我们继续对肠道微生物组进行研究，开发了一种用于识别scRNA-seq数据中的微生物读数的算法。我们首先展示了更新的、基于液滴的测序方式比基于平板的测序方式返回更少的微生物读数，但在体外感染模型中更具特异性。结肠直肠癌和ESCA患者数据集显示，大多数细菌读取存在于肿瘤微环境中的骨髓细胞中，而不是之前认为的肿瘤细胞中。细菌阳性的骨髓细胞上调促炎细胞因子，而细菌阳性的肿瘤细胞上调抗原递呈途径，这表明肿瘤内细菌负荷和定位在免疫治疗中的潜在作用。这项工作目前正在《科学进展》杂志上进行审查。癌症代谢组：已鉴定的代谢物与特定癌症的相关性创建了生物标志物谱，可用于许多类型的人类癌症的非侵入性诊断和预后评估，并可为靶向治疗铺平道路。尿液、血清和血浆液体活检用于质谱测定肺癌的四种生物标志物（肌酸核糖苷（CR）、n -乙酰氨基酸（NANA）、硫酸皮质醇（CS）和27 α -不-5 β -胆甾醇-3 α、7 α、12 α、24 α、25 α戊糖醛酸（NCPG）） （Haznadar， M. et al., cancer epidemiology）。生物标志物，2016,25:978- 86,2016)。CR与其他已鉴定的尿液代谢物生物标志物如（NANA）配对可提高诊断能力和可靠性（Mathe， Ewy A等，Cancer research vol. 74,12(2014): 3259-70）。我们已经证明，肌酸核糖体（CR）是一种癌细胞衍生的代谢物，在高水平时，与线粒体尿素循环失调有关，是癌症患者预后不良的一个指标（Parker, a . et al., JNCI 132(14),2022）。这些基础研究验证了尿液代谢物筛选的使用，从而进一步研究生物标志物与人类癌症的关联，以及使用液相色谱-串联质谱分析方法（Patel， DP）。et al。[J] .中国生物医学工程学报，2016,31(2):357 - 357。正如2020年和2021年年度报告中提到的，尿代谢物生物标志物分析可以提供肝内胆管癌（ICC）的诊断和预后评估。采用UPLC-MS/MS，用于定量代谢物CR、n -乙酰神经氨酸（NANA）、硫酸皮质醇和葡萄糖醛酸碎片离子561+的四种代谢物在HCC和ICC中显著增加，并且与临床使用的标记物CA19-9联合用于ICC分类。研究了NCI-MD队列，并通过TIGER-LC队列验证了观察结果。通过进行严格的分析验证和机制研究，我们的目标是全面了解这些新型代谢物在人类癌症发展中的意义和潜在影响。这项研究为预后工具铺平了道路，例如利用所描述的生物标志物和其他临床因素在实验室开发的准确风险评分计算器。该计算器是使用经过NCI-MD肺癌队列训练的复杂机器学习算法构建的。使用该工具，可以为特定患者生成R_score作为癌症风险预测因子和复发风险预测因子，并且具有很高的准确性。通过这项研究，实验室已经确定了生物标志物的重要特性，这些特性将用于液体活检中基于CLIA实验室的生物标志物分析，最终将有利于患者的预后。

Precision medicine in cancer is multifaceted and the lab's research has led to significant discoveries that will advance the diagnostic and prognostic utilization of biomarkers including elucidating the role and mechanism the microbiome plays in lung carcinogenesis. Cancer Microbiome: To identify additional diagnostic targets for precision medicine, we previously demonstrated that the microbiome is altered in lung cancer and identified the Acidovorax genus as enriched in lung cancer and associated with TP53 mutations and smoking. Full-length 16S sequencing identified the species Acidovorax temperans, which lead us to ask if microbial dysbiosis, as modeled by A. temperans instillation, played a passenger or driver role in tumor development. In a lung adenocarcinoma mouse model, we found repeated exposure to A. temperans dramatically accelerated tumor development and reduced survival, indicating a driver role. In contrast, instillation of the lung commensal species Lactobacillus gasseri had no effect on tumor development. By FACS and scRNA-seq analyses we identified a pro-inflammatory pathway, specifically increased in A. temperans mice, where TLR4/NF-kB signaling was activated in macrophages, which upregulated MHC II to activate effector CD4+ T cells, polarizing them to TH17 states. These TH17 cells recruited neutrophils to the lungs which acquired tissue residency and pro-tumorigenic phenotypes. The neutrophils then increased IL-23 signaling to recruit additional TH17 cells. In contrast, L. gasseri-treated mice upregulated anti-inflammatory cytokines to inhibit neutrophils and prevent CD4+ T activation. The scRNA-seq portion of this work is currently Under Review at Oncogenesis. We are now working to identify strategies to ameliorate the tumor development accelerated by A. temperans, including addition of probiotic bacterial strains. As the rates of esophageal carcinoma (ESCA) are increasing globally, particularly adenocarcinoma in the West, we also asked if the esophageal microbiome could reveal targets for precision medicine in this disease, as well. In collaboration with Leigh Greathouse, we performed 16S sequencing on 229 tissue samples from the NCI-MD case control study (126 NT, 98 T) and also performed metagenomic analyses of the non-human aligned reads in the RNA-seq (11 NT, 162 T) and WGS datasets (61 NT, 62 T) for TCGA ESCA. We identified four genera co-enriched in ESCA tumor tissue across datasets: Campylobacter, Fusobacterium, Prevotella, and Streptococcus, the first such description of these four taxa. We identified further bacterial co-occurrences enriched in colorectal cancer (Fusobacterium, Prevotella, Leptotrichia, Veillonella), another gut malignancy. Predicted immune cell infiltration identified these taxa with an increase in platelet infiltration, which occurs prior to ESCA metastasis. This work is currently Under Review at Scientific Reports. In collaboration with Eytan Ruppin, we continued our investigations into the gut microbiome, developing an algorithm for identifying microbial reads within scRNA-seq data. We first showed newer, droplet-based sequencing modalities return fewer microbial reads than plate-based but are far more specific in in vitro infection models. Colorectal and ESCA patient datasets revealed most bacterial reads are present in myeloid cells within the tumor microenvironment and not tumor cells as previously thought. Bacterial-positive myeloid cells upregulated pro-inflammatory cytokines while bacterial-positive tumor cells upregulated antigen presentation pathways, which suggests potential roles for intratumoral bacterial burden and location in immunotherapy. This work is currently Under Review at Science Advances. Cancer Metabolome: Correlation of identified metabolites with specific cancers created biomarker profiles that can be utilized for non-invasion diagnostic and prognostic evaluation of many types of human cancer and could pave the way for targeted therapies. Liquid biopsy of urine, serum and plasma are used to measure four biomarkers (creatine riboside (CR), N-acetylneuminic acid (NANA), cortisol sulfate (CS), and 27alpha-nor-5beta-cholestane-3alpha, 7alpha, 12alpha 24alpha, 25alpha Pentol glucuronide (NCPG) of lung cancer by mass spectrometry (Haznadar, M. et al., Cancer Epidemiol. Biomarker Prev. 25:978-86, 2016). CR paired with other identified urinary metabolite biomarkers such as (NANA) improve diagnostic capability and reliability (Mathe, Ewy A et al., Cancer research vol. 74,12 (2014): 3259-70). We have shown that creatine riboside (CR) is a cancer cell-derived metabolite that at high levels, is associated with mitochondrial urea cycle dysregulation and it an indicator of poor prognosis for cancer patients (Parker, A. et al., JNCI 132(14),2022). These foundational studies validated the use of urinary metabolite screening leading to further investigation into biomarker association with human cancer as well as the analytical method using liquid chromatography-tandem mass spectrometry (Patel, DP. et al. J Pharm Biomed Anal. 191: 113596, 2020). And as mentioned in the 2020 and 2021 annual report, urinary metabolite biomarker profiling could offer diagnostic and prognostic evaluation of intrahepatic cholangiocarcinoma (ICC). Employing UPLC-MS/MS, four metabolites, for the quantitation of metabolites CR, N-acetylneuraminic acid (NANA), cortisol sulfate, and a glucuronide fragmented ion designated as 561+, are significantly increased in HCC and ICC and are robust at classifying ICC in combination with a clinically utilized marker CA19-9. The NCI-MD cohort were studied, and observations verified by the TIGER-LC cohort. By conducting rigorous analytical validations and mechanistic studies, our aim is to gain a comprehensive understanding of the significance and potential implications of these novel metabolites in human cancer development. This research paved the way for prognostic tools such as an accurate risk score calculator developed in the lab utilizing the described biomarkers with additional clinical factors. The calculator is built using sophisticated machine learning algorithms trained on the NCI-MD lung cancer cohort. Using this tool, both a R_score as a cancer risk predicator, and predication of recurrence risk, can be generated for a specific patient with a high degree of accuracy. Through this research, the lab has determined properties that are significant for a biomarker to its use in CLIA lab-based assays of biomarkers in liquid biopsy which ultimately will benefit patients' outcomes.