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Background: Physical activity is associated not only with a decreased risk of developing colorectal cancer but also with improved survival. One putative mechanism is the infiltration of immune cells in the tumor microenvironment. Experimental findings suggest that physical activity may mobilize immune cells to the tumor. We hypothesized that higher levels of physical activity prior to colorectal cancer diagnosis are associated with higher densities of tumor-infiltrating T-lymphocytes in colorectal cancer patients. Methods: The study setting was a northern Swedish population-based cohort, including 109,792 participants with prospectively collected health- and lifestyle-related data. For 592 participants who later developed colorectal cancer, archival tumor tissue samples were used to assess the density of CD3+ and CD8+ cytotoxic T cells by IHC. Odds ratios for associations between self-reported, prediagnostic recreational physical activity and immune cell infiltration were estimated by ordinal logistic regression. Results: Recreational physical activity >3 times per week was associated with a higher density of CD8+ T cells in the tumor front and center compared with participants reporting no recreational physical activity. Odds ratios were 2.77 (95% CI, 1.21–6.35) and 2.85 (95% CI, 1.28–6.33) for the tumor front and center, respectively, after adjustment for sex, age at diagnosis, and tumor stage. The risk estimates were consistent after additional adjustment for several potential confounders. For CD3, no clear associations were found. Conclusions: Physical activity may promote the infiltration of CD8+ immune cells in the tumor microenvironment of colorectal cancer. Impact: The study provides some evidence on how physical activity may alter the prognosis in colorectal cancer.

Overweight and obesity have been linked to increased risk of several diseases, including colorectal cancer, but the underlying mechanisms are not fully known. An earlier study analyzed metabolically defined body size phenotypes in relation to colorectal cancer risk, using combinations of C-peptide (a marker of insulin resistance) and body mass index (BMI). Higher serum C-peptide concentrations were associated with higher colorectal cancer risk in both overweight and normal weight individuals compared to individuals with low levels of C-peptide and normal weight. The aim of the present study was to see if these results could be replicated and to further explore the role of metabolism and body size phenotypes in colorectal cancer subtypes. We conducted a nested case-control study of 1010 individuals with colorectal cancer and 1010 individually matched (age, sex, cohort, year of blood sampling and data collection, number of freeze-thaw cycles of plasma samples and fasting status at blood sampling) control participants from the population-based Northern Sweden Health and Disease Study. The blood samples and data used in our analyses were collected at a mean of 9.7 years prior to case diagnosis. Participants were categorized as (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), or (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2), where metabolically healthy was defined as C-peptide in the lowest tertile, as in the previous study. We used multivariable conditional logistic regression to calculate odds ratios and confidence intervals and adjusted for tobacco smoking, recreational physical activity and alcohol consumption as potential confounders. In our preliminary results, metabolically unhealthy/overweight individuals had a significantly higher risk of developing colorectal cancer compared to metabolically healthy/normal weight individuals (adjusted odds ratio =1.44, 95% CI 1.13-1.85). The odds ratio for metabolically unhealthy/normal weight individuals was 1.23 (95% CI 0.92-1.65) and for metabolically healthy/overweight individuals 1.23 (95% CI 0.89-1.70). These results support the potential of a more nuanced analysis of metabolism and body size to better understand their etiological contribution to colorectal cancer development. To gain more knowledge about how the role of metabolism and body size might differ between tumor subtypes, we will also analyze body size phenotypes in relation to subtypes of colorectal cancer based on anatomical tumor site, KRAS and BRAF mutations and microsatellite instability status of the tumor. Citation Format: Linda Vidman, Simon Knekta, Björn Gylling, Carl Zingmark, Anna Löfgren-Burström, Richard Palmqvist, Sophia Harlid, Bethany Van Guelpen. Metabolically defined body size phenotypes in relation to subsequent colorectal cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB142.

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through PC-PR2 analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Physical activity is associated not only with a decreased risk of developing colorectal cancer but also with improved survival. One putative mechanism is the infiltration of immune cells in the tumor microenvironment. Experimental findings suggest that physical activity may mobilize immune cells to the tumor. We hypothesized that higher levels of physical activity prior to colorectal cancer diagnosis are associated with higher densities of tumor-infiltrating T-lymphocytes in colorectal cancer patients.The study setting was a northern Swedish population-based cohort, including 109,792 participants with prospectively collected health- and lifestyle-related data. For 592 participants who later developed colorectal cancer, archival tumor tissue samples were used to assess the density of CD3Recreational physical activity3 times per week was associated with a higher density of CD8Physical activity may promote the infiltration of CD8The study provides some evidence on how physical activity may alter the prognosis in colorectal cancer.

Background: RNA-seq data from tumor samples can be used to identify novel cancer subtypes using cluster analysis. The number of features is often large compared to the number of samples and different clusters can appear in different subsets of the feature space. Feature selection techniques are therefore commonly used to reduce the dimension and remove redundant and irrelevant features before performing cluster analysis. An abundance of feature selection methods have been proposed in the literature, but it is unclear how the ability to identify novel subtypes of cancer using cluster analysis is affected by choice of feature selection method. Method: We evaluated 13 feature selection methods on four publicly available cancer data sets from The Cancer Genome Atlas. RNA-seq data and associated clinical data were retrieved from Broad institute GDAC Firehose. Overlap and characterization of the highest ranked features (i.e. genes) were studied for top 100, 1000 and 3000 genes. Performance was measured by comparing known cancer subtypes to partitions obtained using hierarchical clustering with Euclidean distance and Ward´s linkage, based on the selected features. The result was compared to both a random selection (negative control) and a supervised approach (positive control) using adjusted Rand index. Results: The relative performance of the feature selection methods varied heavily depending on number of included genes and the data set used. Based on all data, gene selection using the Dip-test statistic and the Bimodality index generated the overall highest clustering performance. The overlap of selected genes between the Bimodality index, the Dip-test and the supervised approach was relatively low. The Dip-test and Bimodality index tended to favor genes with relatively low expression values. Conclusions: The choice of feature selection method can have a huge impact on the ability to identify cancer subtypes using cluster analysis, and the relative performance is highly dependent on the data. Low overlap of selected genes between the highest ranked methods suggests that the methods identify genes that contain complementary information, and that it might be beneficial to combine two or more feature selection methods. Citation Format: Linda Vidman, David Källberg, Patrik Rydén. Evaluation of feature selection methods used for cluster analysis in identification of novel cancer subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5475.

The purpose is to review recent studies examining the role of gut microbiota in allergic diseases and asthma.Work in experimental models gives further evidence that a disturbed gut microbiota influences the propensity to develop allergic manifestations, and that changing the gut microbiota by dietary means (high fiber/acetate or prebiotics) in pregnancy may reduce the risk of allergic airways disease and food allergy in the offspring, respectively. The gut microbiome in established allergic disease and prior to disease onset has also been assessed in clinical trials. One study demonstrated a strong association between high abundance of Faecalibacterium prausnitzii and decreased levels of butyrate and propionate, and established eczema. Lower relative abundance of Ruminococcaceae appears to be implicated in food sensitization and to precede the development of atopic eczema. Decreased relative abundance of Lachnospira, Veillonella, Faecalibacterium, and Rothia in early infancy was reported to be associated with increased asthma risk. Inoculation of germ-free mice with these genera decreased airway inflammation in their offspring thereby proposing a causal role of bacteria in preventing allergic airways disease.Gut microbiome research is an actively developing field. Although candidate bacterial taxa have been reported it still remains unclear which bacteria (or other microbes), in which numbers and combinations, and when during the gut colonization process may prevent allergic diseases and asthma. There is still a call for standardized approaches that will enable direct comparison of different studies.