Clinical performance study of a fecal bacterial signature test for colorectal cancer screening.

The fecal immunochemical test (FIT) is the most widely used test for colorectal cancer (CRC) screening. RAID-CRC Screen is a new non-invasive test based on fecal bacterial markers, developed to complement FIT by increasing its specificity. The test was previously clinically evaluated in FIT-positive patients (>20 μg of hemoglobin/g of feces, "FIT20"), in which it reduced the proportion of false positive results by 16.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies.

Background: DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

Bovine meat and milk factor protein expression in tumor-free mucosa of colorectal cancer patients coincides with macrophages and might interfere with patient survival.

Bovine milk and meat factors (BMMFs) are plasmid-like DNA molecules isolated from bovine milk and serum, as well as the peritumor of colorectal cancer (CRC) patients. BMMFs have been proposed as zoonotic infectious agents and drivers of indirect carcinogenesis of CRC, inducing chronic tissue inflammation, radical formation and increased levels of DNA damage. Data on expression of BMMFs in large clinical cohorts to test an association with co-markers and clinical parameters were not previously available and were therefore assessed in this study.

Validation of the prognostic value of CD3 and CD8 cell densities analogous to the Immunoscore® by stage and location of colorectal cancer: an independent patient cohort study.

In addition to the traditional staging system in colorectal cancer (CRC), the Immunoscore® has been proposed to characterize the level of immune infiltration in tumor tissue and as a potential prognostic marker. The aim of this study was to examine and validate associations of an immune cell score analogous to the Immunoscore® with established molecular tumor markers and with CRC patient survival in a routine setting. Patients from a population-based cohort study with available CRC tumor tissue blocks were included in this analysis.

Reproductive Factors and Colorectal Cancer Risk: A Population-Based Case-Control Study.

Background: Hormone-replacement therapy (HRT) is associated with lower colorectal cancer (CRC) risk among postmenopausal women. However, little is known about the effects of lifetime exposure of women to varying levels of estrogen and progesterone through reproductive factors such as parity, use of oral contraceptives (OC), breastfeeding, and menstruation on CRC risk.

Methods: We assessed associations between reproductive factors and CRC risk among 2650 female CRC patients aged 30+ years and 2175 matched controls in a population-based study in Germany, adjusting for potential confounders by multiple logistic regression.

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.

Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised: the ground truth is only known for the slide, not for every single tile. In classical weakly-supervised analysis pipelines, all tiles inherit the slide label while in multiple-instance learning (MIL), only bags of tiles inherit the label.

Swarm learning for decentralized artificial intelligence in cancer histopathology.

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance.

Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application.

Background: Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly in routine pathology slides, but the test performance has not been systematically investigated with predefined test thresholds.

Method: We trained and validated AI-based MSI/dMMR detectors and evaluated predefined performance metrics using nine patient cohorts of 8343 patients across different countries and ethnicities.

Variation of Positive Predictive Values of Fecal Immunochemical Tests by Polygenic Risk Score in a Large Screening Cohort.

Introduction: Prevalence of colorectal neoplasms varies by polygenic risk scores (PRS). We aimed to assess to what extent a PRS might be relevant for defining personalized cutoff values for fecal immunochemical tests (FITs) in colorectal cancer screening.

Methods: Among 5,306 participants of screening colonoscopy who provided a stool sample for a quantitative FIT (Ridascreen Hemoglobin or FOB Gold) before colonoscopy, a PRS was determined, based on the number of risk alleles in 140 single nucleotide polymorphisms.

Validation of Genetic Markers Associated with Survival in Colorectal Cancer Patients Treated with Oxaliplatin-Based Chemotherapy.

Background: Associations between candidate genetic variants and treatment outcomes of oxaliplatin, a drug commonly used for colorectal cancer patients, have been reported but not robustly established. This study aimed to validate previously reported prognostic and predictive genetic markers for oxaliplatin treatment outcomes and evaluate additional putative functional variants.

Methods: Fifty-three SNPs were selected based on previous reports (40 SNPs) or putative function in candidate genes (13 SNPs).

Salicylic Acid and Risk of Colorectal Cancer: A Two-Sample Mendelian Randomization Study.

Salicylic acid (SA) has observationally been shown to decrease colorectal cancer (CRC) risk. Aspirin (acetylsalicylic acid, that rapidly deacetylates to SA) is an effective primary and secondary chemopreventive agent. Through a Mendelian randomization (MR) approach, we aimed to address whether levels of SA affected CRC risk, stratifying by aspirin use.

Smoking Behavior and Prognosis After Colorectal Cancer Diagnosis: A Pooled Analysis of 11 Studies.

Background: Smoking has been associated with colorectal cancer (CRC) incidence and mortality in previous studies, but current evidence on smoking in association with survival after CRC diagnosis is limited.

Methods: We pooled data from 12 345 patients with stage I-IV CRC from 11 epidemiologic studies in the International Survival Analysis in Colorectal Cancer Consortium. Cox proportional hazards regression models were used to evaluate the associations of prediagnostic smoking behavior with overall, CRC-specific, and non-CRC-specific survival.

Deep learning can predict lymph node status directly from histology in colorectal cancer.

Background: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC).

Objectives: The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM).

Methods: Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM.

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness.

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.

Background: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision.

Consistent Major Differences in Sex- and Age-Specific Diagnostic Performance among Nine Faecal Immunochemical Tests Used for Colorectal Cancer Screening.

Evidence on diagnostic performance of faecal immunochemical tests (FITs) by sex and age is scarce. We aimed to evaluate FIT performance for detection of advanced colorectal neoplasia (AN) by sex and age across nine different FIT brands in a colonoscopy-controlled setting. The faecal samples were obtained from 2042 participants of colonoscopy screening.

DNA Methylation-Based Estimates of Circulating Leukocyte Composition for Predicting Colorectal Cancer Survival: A Prospective Cohort Study.

Leukocytes are involved in the progression of colorectal cancer (CRC). The proportion of six major leukocyte subtypes can be estimated using epigenome-wide DNA methylation (DNAm) data from stored blood samples. Whether the composition of circulating leukocytes can be used as a prognostic factor is unclear.

Genetically Predicted Circulating C-Reactive Protein Concentration and Colorectal Cancer Survival: A Mendelian Randomization Consortium Study.

Background: A positive association between circulating C-reactive protein (CRP) and colorectal cancer survival was reported in observational studies, which are susceptible to unmeasured confounding and reverse causality. We used a Mendelian randomization approach to evaluate the association between genetically predicted CRP concentrations and colorectal cancer-specific survival.

Methods: We used individual-level data for 16,918 eligible colorectal cancer cases of European ancestry from 15 studies within the International Survival Analysis of Colorectal Cancer Consortium.

Uptake Rates of Novel Therapies and Survival Among Privately Insured Versus Publicly Insured Patients With Colorectal Cancer in Germany.

Background: In the era of personalized medicine, cancer care is subject to major changes and innovations. It is unclear, however, to what extent implementation of such innovations and their impact on patient outcomes differ by health insurance type. This study compared provision of treatment and survival outcomes among patients with colorectal cancer (CRC) who had statutory health insurance (SHI) versus private health insurance (PHI) in Germany.

Response to neoadjuvant treatment among rectal cancer patients in a population-based cohort.

Background: In rectal cancer, prediction of tumor response and pathological complete response (pCR) to neoadjuvant treatment could contribute to refine selection of patients who might benefit from a delayed- or no-surgery approach. The aim of this study was to explore the association of clinical and molecular characteristics of rectal cancer with response to neoadjuvant treatment and to compare patient survival according to level of response.

Methods: Resected rectal cancer patients were selected from a population-based cohort study.

Age-dependent performance of BRAF mutation testing in Lynch syndrome diagnostics.

BRAF V600E mutations have been reported as a marker of sporadic microsatellite instability (MSI) colorectal cancer (CRC). Current international diagnostic guidelines recommend BRAF mutation testing in MSI CRC patients to predict low risk of Lynch syndrome (LS). We evaluated the age-specific performance of BRAF testing in LS diagnostics.