Precision Medicine of Cancer

Division Of Basic Sciences - Nci

Investigators

Linked publications & trials

Abstract

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.