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Fecal immunochemical tests (FITs) detect the majority of colorectal cancers (CRCs), but evidence for variation in sensitivity according to the CRC stage is sparse and has not yet been systematically synthesized. Thus, our objective was to systematically review and summarize evidence on the stage-specific sensitivity of FITs. We screened PubMed, Web of Science, Embase, and the Cochrane Library from inception to June 14, 2019, for English-language articles reporting on the stage-specific sensitivity of FIT for CRC detection using colonoscopy as a reference standard. Studies reporting stage-specific sensitivities and the specificity of FIT for CRC detection were included. Summary estimates of sensitivity according to the CRC stage and study setting (screening cohorts, symptomatic/diagnostic cohorts, and case-control studies) were derived from bivariate meta-analysis. Forty-four studies (92,447 participants including 3,034 CRC cases) were included. Pooled stage-specific sensitivities were overall very similar but suffered from high levels of imprecision because of small case numbers when calculated separately for screening cohorts, symptomatic/diagnostic cohorts, and case-control studies. Pooled sensitivities (95% confidence intervals) for all studies combined were 73% (65%-79%) for stage-I-CRCs and 80% (74%-84%), 82% (77%-87%), and 79% (70%-86%) for the detection of CRC stages II, III, and IV, respectively. Even substantially larger variation was seen in sensitivity by T-stage, with summary estimates ranging from 40% (21%-64%) for T1 to 83% (68%-91%) for T3-CRC. Although FITs detect 4 of 5 CRCs at stages II-IV, the substantially lower sensitivity for stage-I-CRC and, in particular, T1 CRC indicates both need and potential for further improvement in performance for the early detection of CRC.

More than 1·2 million patients are diagnosed with colorectal cancer every year, and more than 600 000 die from the disease. Incidence strongly varies globally and is closely linked to elements of a so-called western lifestyle. Incidence is higher in men than women and strongly increases with age; median age at diagnosis is about 70 years in developed countries. Despite strong hereditary components, most cases of colorectal cancer are sporadic and develop slowly over several years through the adenoma–carcinoma sequence. The cornerstones of therapy are surgery, neoadjuvant radiotherapy (for patients with rectal cancer), and adjuvant chemotherapy (for patients with stage III/IV and high-risk stage II colon cancer). 5-year relative survival ranges from greater than 90% in patients with stage I disease to slightly greater than 10% in patients with stage IV disease. Screening has been shown to reduce colorectal cancer incidence and mortality, but organised screening programmes are still to be implemented in most countries.

Long-term survival rates for many types of cancer have substantially improved in past decades because of advances in early detection and treatment. However, much of this improvement is only seen many years later with traditional cohort-based methods of survival analysis. I aimed to assess achievements in cancer patients' survival by an alternative method of survival analysis, known as period analysis, which provides more up-to-date estimates of long-term survival rates than do conventional methods. The 1973–98 database of the Surveillance, Epidemiology, and End Results (SEER) programme of the US National Cancer Institute was analysed by period analysis. Estimates of 5-year, 10-year, 15-year, and 20-year relative survival rates for all types of cancer were 63%, 57%, 53%, and 51%, respectively, by period analysis. These estimates were 1%, 7%, 11%, and 11% higher, respectively, than corresponding estimates by cohort-based survival analysis. By period analysis, 20-year relative survival rates were close to 90% for thyroid and testis cancer, exceeded 80% for melanomas and prostate cancer, were about 80% for endometrial cancer, and almost 70% for bladder cancer and Hodgkin's disease. A 20-year relative survival rate of 65% was estimated for breast cancer, of 60% for cervical cancer, and of about 50% for colorectal, ovarian, and renal cancer. Timely detection of improvements in long-term survival rates might help to prevent clinicians and their patients from undue discouragement or depression by outdated and often overly pessimistic survival expectations. It also adds to the value of cancer surveillance as a basis for appropriate public-health decisions.

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.A deep residual learning framework identifies microsatellite instability in histology slides from patients with cancer and can be used to guide immunotherapy.

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers. Circulating microRNAs (miRNAs) have been suggested as potentially promising markers for early detection of CRC. We aimed to identify and evaluate a panel of miRNAs that might be suitable for CRC early detection. MiRNAs were profiled by TaqMan MicroRNA Array and screened for differential expression in 5 pools of plasma samples of CRC patients (N = 50) and 5 pools of neoplasm-free controls (N = 50). Additional miRNAs were selected from a literature review. Identified candidates were evaluated in independent validation samples with respect to discrimination of CRC patients (N = 80) or advanced adenoma patients (N = 50) and neoplasm-free controls (N = 194). Diagnostic performance of the panel of miRNAs was assessed by multiple logistic regression, using bootstrap analysis to correct for over-optimism. Five miRNAs identified to be differentially expressed from TaqMan MicroRNA Array (miR-29a, -106b, -133a, -342-3p, -532-3p), and seven miRNAs reported to be differentially expressed in the literature (miR-18a, -20a, -21, -92a, -143, -145, -181b) were selected for validation. Nine of the twelve miRNAs (miR-18a, -20a, -21, -29a, -92a, -106b, -133a, -143, -145) were found to be differentially expressed in CRC patients and controls in the validation samples. The optimism-corrected area under the curve was 0.745 (95% confidence interval: 0.708-0.846). None of the selected miRNAs showed significant differential expression between advanced adenoma patients and neoplasm-free controls. The identified panel of miRNAs could be of potential use in the development of a multi-marker blood based test for early detection of CRC. The study underscores the high potential of plasma miRNAs for the improvement of current offers of non-invasive CRC screening.

For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a \"deep stroma score,\" which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the \"Darmkrebs: Chancen der Verhütung durch Screening\" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

Colorectal cancer (CRC) incidence is increasing among young adults below screening age, despite the effectiveness of screening in older populations. Individuals with diabetes mellitus are at increased risk of early-onset CRC. We aimed to determine how many years earlier than the general population patients with diabetes with/without family history of CRC reach the threshold risk at which CRC screening is recommended to the general population. A nationwide cohort study (follow-up:1964-2015) involving all Swedish residents born after 1931 and their parents was carried out using record linkage of Swedish Population Register, Cancer Registry, National Patient Register, and Multi-Generation Register. Of 12,614,256 individuals who were followed between 1964 and 2015 (51% men; age range at baseline 0-107 years), 162,226 developed CRC, and 559,375 developed diabetes. Age-specific 10-year cumulative risk curves were used to draw conclusions about how many years earlier patients with diabetes reach the 10-year cumulative risks of CRC in 50-year-old men and women (most common age of first screening), which were 0.44% and 0.41%, respectively. Diabetic patients attained the screening level of CRC risk earlier than the general Swedish population. Men with diabetes reached 0.44% risk at age 45 (5 years earlier than the recommended age of screening). In women with diabetes, the risk advancement was 4 years. Risk was more pronounced for those with additional family history of CRC (12-21 years earlier depending on sex and benchmark starting age of screening). The study limitations include lack of detailed information on diabetes type, lifestyle factors, and colonoscopy data. Using high-quality registers, this study is, to our knowledge, the first one that provides novel evidence-based information for risk-adapted starting ages of CRC screening for patients with diabetes, who are at higher risk of early-onset CRC than the general population.

DNA methylation (DNAm) has been revealed to play a role in various diseases. Here we performed epigenome-wide screening and validation to identify mortality-related DNAm signatures in a general population-based cohort with up to 14 years follow-up. In the discovery panel in a case-cohort approach, 11,063 CpGs reach genome-wide significance (FDR<0.05). 58 CpGs, mapping to 38 well-known disease-related genes and 14 intergenic regions, are confirmed in a validation panel. A mortality risk score based on ten selected CpGs exhibits strong association with all-cause mortality, showing hazard ratios (95% CI) of 2.16 (1.10–4.24), 3.42 (1.81–6.46) and 7.36 (3.69–14.68), respectively, for participants with scores of 1, 2–5 and 5+ compared with a score of 0. These associations are confirmed in an independent cohort and are independent from the ‘epigenetic clock’. In conclusion, DNAm of multiple disease-related genes are strongly linked to mortality outcomes. The DNAm-based risk score might be informative for risk assessment and stratification. DNA methylation is modulated by environmental factors and has a role in many complex diseases. Here, the authors find that methylation at specific DNA sites is associated with all-cause mortality, and a methylation-based risk score may be informative for risk assessment and stratification.

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. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer. A decentralized, privacy-preserving machine learning framework used to train a clinically relevant AI system identifies actionable molecular alterations in patients with colorectal cancer by use of routine histopathology slides collected in real-world settings.

Active smoking is a major preventable public health problem and an established critical factor for epigenetic modification. In this systematic review, we identified 17 studies addressing the association of active smoking exposure with methylation modifications in blood DNA, including 14 recent epigenome-wide association studies (EWASs) and 3 gene-specific methylation studies (GSMSs) on the gene regions identified by EWASs. Overall, 1460 smoking-associated CpG sites were identified in the EWASs, of which 62 sites were detected in multiple (≥3) studies. The three most frequently reported CpG sites (genes) in whole blood samples were cg05575921 ( AHRR ), cg03636183 ( F2RL3 ), and cg19859270 ( GPR15 ), followed by other loci within intergenic regions 2q37.1 and 6p21.33 . These significant smoking-related genes were further assessed by specific methylation assays in three GSMSs and reflected not only current but also lifetime or long-term exposure to active smoking. In conclusion, this review summarizes the evidences for the use of blood DNA methylation patterns as biomarkers of smoking exposure for research and clinical practice. In particular, it provides a reservoir for constructing a smoking exposure index score which could be used to more precisely quantify long-term smoking exposure and evaluate the risks of smoking-induced diseases.