Women have a lower incidence of colorectal cancer (CRC) than men, however, they have a higher incidence of right-sided colon cancer (RCC). This is of concern as patients with RCC have the poorest clinical outcomes among all CRC patients. Aberrant metabolism is a known hallmark and therapeutic target for cancer. We propose that metabolic subphenotypes exist between CRCs due to intertumoral molecular and genomic variation, and differences in environmental milieu of the colon which vary between the sexes. Metabolomics analysis of patient colon tumors (n = 197) and normal tissues (n = 39) revealed sex-specific metabolic subphenotypes dependent on anatomic location. Tumors from women with RCC were nutrient-deplete, showing enhanced energy production to fuel asparagine synthesis and amino acid uptake. The clinical importance of our findings were further investigated in an independent data set from The Cancer Genomic Atlas, and demonstrated that high asparagine synthetase (ASNS) expression correlated with poorer survival for women. This is the first study to show a unique, nutrientdeplete metabolic subphenotype in women with RCC, with implications for tumor progression and outcomes in CRC patients. Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the United States, and the second leading cause of cancer-related deaths for cancers that affect both men and women 1. In 2019, there will be an estimated 145,600 new cases and 51,020 deaths attributed to the disease 2. Cancers of the colorectum can be categorized by anatomic location. Right-sided colon cancers (RCCs) occur in the cecum, ascending colon and hepatic flexure, and left-sided colon cancers (LCCs) occur in the splenic flexure, descending, sigmoid and rectosigmoid colon. Mid and lower rectal cancers are sometimes grouped as LCCs 3. Tumors in different anatomic locations have differing clinical outcomes, and recent epidemiologic studies have shown that patients with RCC have the poorest survival, even when adjusted for confounding factors, including clinical stage 4-6. This suggests that differences exist in the underlying tumor biology based on anatomic location. Distinguishing molecular features of RCCs include high microsatellite instability (MSI-H), valine to glutamate BRAF mutations at codon 600, cytosine-guanosine (CpG) island methylation phenotype (CIMP) 7,8 , and diploid cells 9. In contradistinction, patients with LCCs more frequently have chromosomal instability, mutations in the TP53 and APC genes, and aneuploidy 10. Recently, expression arrays have revealed four molecular subtypes of CRCs, called "consensus molecular subtypes" which tend to occur more frequently in either the right-or left-sides of the colon. Consensus molecular subtype 1, for example, has a 77% frequency in RCCs and is enriched for high MSI, CIMP, and BRAF mutations. In addition, gene set enrichment analysis shows molecular pathways related to immune infiltration and PD-1 activation for this subtype 10,11. In addition to genetic variance in colorectal subsites, RCCs ...

No abstract

Metabolomics studies of the early-life exposome often use maternal urine specimens to investigate critical developmental windows, including the periconceptional period and early pregnancy. During these windows changes in kidney function can impact urine concentration. This makes accounting for differential urinary dilution across samples challenging. Because there is no consensus on the ideal normalization approach for urinary metabolomics data, this study’s objective was to determine the optimal post-analytical normalization approach for untargeted metabolomics analysis from a periconceptional cohort of 45 women. Urine samples consisted of 90 paired pre- and post-implantation samples. After untargeted mass spectrometry-based metabolomics analysis, we systematically compared the performance of three common approaches to adjust for urinary dilution—creatinine adjustment, specific gravity adjustment, and probabilistic quotient normalization (PQN)—using unsupervised principal components analysis, relative standard deviation (RSD) of pooled quality control samples, and orthogonal partial least-squares discriminant analysis (OPLS-DA). Results showed that creatinine adjustment is not a reliable approach to normalize urinary periconceptional metabolomics data. Either specific gravity or PQN are more reliable methods to adjust for urinary concentration, with tighter quality control sample clustering, lower RSD, and better OPLS-DA performance compared to creatinine adjustment. These findings have implications for metabolomics analyses on urine samples taken around the time of conception and in contexts where kidney function may be altered.