From 2016–2019, we enrolled 11 cis-gendered women with newly diagnosed CRC prior to their initiation of treatment (case) and 178 cis-gendered women from the same geographic area who were cancer free (control). Cases were recruited collaboratively with the multidisciplinary cancer treatment clinic at the University of Alabama at Birmingham. Research staff and clinic staff worked together to identify potential participants with upcoming gastrointestinal oncology confirmatory appointments. After completing the clinic appointment, research staff met with eligible patients to inform them of the study opportunity and invited them to participate. Interested patients were then enrolled in the study and completed the first study visit the same day to limit participant burden. Controls were cancer-free volunteers from the local area that would be served by the same hospital as cases. Controls were recruited using flyers, word of mouth, and small media. Participants self-identified as non-Hispanic Black or White. Exclusion criteria were the following: (1) current tobacco use, (2) current pregnancy, (3) previous cancer diagnosis, or (4) use of antibiotics or other medications known to alter the gut in the previous 90 days. All study-related protocols and questionnaires received approval from The University of Alabama at Birmingham Institutional Review Board for human subjects. Participants provided written informed consent and were compensated US $50 for their time.
For this case-control analysis, CRC patients were matched to cancer-free controls using age, BMI, and race. Cases were matched 1:2 with controls leading to a sample of 33 participants. All participants completed two study visits during which demographics, anthropometrics, survey data, and biospecimens were collected. During visit 1, participants completed the following:
Demographics survey –Age; race and ethnicity; education (less than high school, high school/general equivalency diploma, some college, Associates, Bachelors, Masters, Doctoral); household income (US <$10,000, $10,000–$19,999, $20,000–$29,999, $30,000–$39,999, $40,000–$49,999, or ≥$50,000); and number of individuals in the household were collected using a standardized demographics data collection survey.
Anthropometric measures – Weight and height were measured in light, indoor clothing, without shoes, using a calibrated digital measuring station (Seca 284 measuring station, Hanover, MD). Body mass index (BMI) was calculated as weight (kilogram)/height (square meter). Waist circumference was measured to the nearest 0.10 centimeter using the Gulick II tension spring measuring tape (model 67020).
Perceived Stress Scale-10 (PSS-10) – Participants’ global perception of perceived stress in the previous month was assessed using the PSS-10, a validated 10-item scale used to assess the degree to which situations in one's life are appraised as stressful.26 Participants provided a response (0 = never, 1 = almost never, 2 = sometimes, 3 = fairly often, and 4 = very often) to a series of 10 statements about the occurrence of stressful events. Higher scores on the PSS-10 indicate greater perceived stress (possible range 0–40). The PSS has good internal consistency (Cronbach's α = 0.86) and is positively correlated with other indices of stress among adults.27
At the end of the first study visit, participants were given a stool collection kit along with verbal and written instructions for proper sample collection at home. Participants were asked to collect one fecal sample at home and return at the next study visit which was scheduled within the following 5 to 7 days. During the second visit, participants returned stool samples and provided dietary information using the National Cancer Institute Automated Self-administered 24-hour Dietary Recall, which includes multilevel food probes and cues to assess food types and amounts.28 Total calories, macronutrient, sugar, and fiber intakes were retrieved from ASA-24 for analyses.
Sample Collection
Participants were asked to self-collect stool samples using ParaPak vials (Meridian Biosciences, Inc; Cincinnati, OH) no more than 48 hours before their second clinic visit. Participants were advised to store vials with collected samples in a biohazard bag provided by the study in their home freezer until samples were returned to research staff at the second study visit. Once received by research staff, each sample was diluted to 0.1 mg/ml in Cary-Blair medium for total volume of 20 mL with 10% glycerol by volume. Samples were aliquoted into cryovials and stored at -80°C until time for DNA extraction and processing.
DNA Extraction and Illumina MiSeq DNA Sequencing
Microbial genomic DNA was isolated using the Fecal DNA isolation kit from Zymo Research following the manufacturer's instructions. Once the sample DNA was prepared, polymerase chain reaction was used with unique barcoded primers to amplify the variable region 4 (V4) region of the 16S rDNA gene to create an amplicon library from individual samples. The polymerase chain reaction product was approximately 255 bases from the V4 segment of the 16S rDNA gene, and we sequenced 251 bases single-end reads using Illumina MiSeq.
Bioinformatics
FASTQ conversion of the raw data files was performed following demultiplexing using MiSeq reporter. Quality control of sequence reads was performed with DADA2 29 and low-quality data filtered out using the function fastqPairedFilter (truncLen = c(240,240), maxN = 0, maxEE = c(2,2), truncQ = 2). Filtering, denoising, and clustering of reads into Amplicon Sequence Variants (ASVs) was done using DADA2. Taxon assignment was performed with Mothur30 and the SILVA 16S rDNA database (SILVA_132_QIIME_release).31–33 These tools were incorporated into an update of our automated analysis pipeline, QWRAP.34 Alpha (Shannon, Simpson, and observed species) and beta diversity (Bray Curtis and weighted and unweighted Unifrac) were calculated using QIIME.35
Statistical Methods:
We chose 26 genera a priori based on their previously observed relevance to CRC, anxiety symptoms, and diet and are listed by phylum as follows: Euryarchaeota - Methanobrevibacter; Actinobacteria- Bifidobacterium; Bacteroidetes - Bacteroides, Porphyromonas, Prevotella; Clostridia - Parvimonas, Peptostreptococcus; Firmicutes- Eubacterium, [Ruminococcus], Blautia, Clostridium, Coprococcus, Lachnospira, Roseburia, Butyricicoccus, Faecalibacterium, Ruminococcus, [Eubacterium], Clostridium, Dialister, Gemella, Lactobacillus; Fusobacteria - Fusobacterium; Proteobacteria -Sutterella, Succinivibrio; Verrucomicrobia - Akkermansia.
Statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0. (IBM Corp., Armonk, NY), R version (1.3.1), and figures were generated using GraphPad Prism version 8.0.0 (GraphPad Software, San Diego, CA). Complete data were available for matching from four non-Hispanic Black and seven non-Hispanic White women with CRC and 92 non-Hispanic Black and 86 non-Hispanic White cancer-free women. Controls were frequency-matched 2:1 to cases using the FUZZY extension in SPSS with the following parameters: race- exact; age- +/- 5 years; BMI- +/- 5kg/m2.
We conducted descriptive analyses among each of the four race-cancer status combinations for participant characteristics, diet variables, perceived stress, alpha diversity metrics, and relative abundance of genera. Normality was determined using the Shapiro-Wilk test. Overall differences in normally distributed variables were assessed using one-way analysis of variance (ANOVA). Between-group differences for variables with non-normal distribution (genera and food groups) were analyzed using Kruskal-Wallis ANOVA. We used unconditional multivariable logistic regression to estimate the associations of the microbiome metrics with CRC. We included age, race, and body mass index (BMI) in the regression models based on previous literature and biological plausibility. Post-hoc exploratory analyses between stress variables of interest and genera were conducted using partial Spearman correlations. We accounted for multiple testing in all analyses using Bonferroni correction.