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BackgroundThe Affordable Care Act expanded Medicaid coverage for people with low income in the United States. Expanded insurance coverage could promote more timely access to cancer treatment, which could improve overall survival (OS), yet the long-term effects of Medicaid expansion (ME) remain unknown. We evaluated whether ME was associated with improved timely treatment initiation (TTI) and 3-year OS among patients with breast, cervical, colon, and lung cancers who were affected by the policy.MethodsMedicaid-insured or uninsured patients aged 40-64 with stage I-III breast, cervical, colon, or non-small cell lung cancer within the National Cancer Database (NCDB). A difference-in-differences (DID) approach was used to compare changes in TTI (within 60 days) and 3-year OS between patients in ME states versus nonexpansion (NE) states before (2010-2013) and after (2015-2018) ME. Adjusted DID estimates for TTI and 3-year OS were calculated using multivariable linear regression and Cox proportional hazards regression models, respectively.ResultsME was associated with a relative increase in TTI within 60 days for breast (DID = 4.6; p

PURPOSE: To examine the association of a traditional Mexican diet score with risk of total, breast, and colorectal cancer among women of Mexican ethnic descent in the Womens Health Initiative (WHI). METHODS: Participants were WHI enrollees who self-identified as being of Mexican descent. Data from food frequency questionnaires self-administered at study baseline were used to calculate the MexD score, with higher scores indicating greater adherence to an a priori-defined traditional Mexican diet (high in dietary fiber, vegetables, and legumes). Incident cancers were self-reported by participants from 1993 to 2020 and adjudicated by trained physicians. We used multivariable-adjusted Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Among 2,343 Mexican descent women (median baseline age: 59 years), a total of 270 cancers (88 breast, 37 colorectal) occurred during a mean follow-up of 14.4 years. The highest tertile of MexD score was associated with a lower risk of all-cancer incidence (HR: 0.67; 95% CI 0.49-0.91; p-trend: 0.01) and colorectal cancer (HR: 0.38; 95% CI 0.14-0.998; p-trend

BACKGROUND: Both defects in mismatch repair (dMMR) and high microsatellite instability (MSI-H) have been recognised as crucial biomarkers that guide treatment strategies and disease management in colorectal cancer (CRC). As MMR and MSI tests are being widely conducted, an increasing number of MSI-H tumours have been identified in CRCs with mismatch repair proficiency (pMMR). The objective of this study was to assess the clinical features of patients with pMMR/MSI-H CRC and elucidate the underlying molecular mechanism in these cases. METHODS: From January 2015 to December 2018, 1684 cases of pMMR and 401 dMMR CRCs were enrolled. Of those patients, 93 pMMR/MSI-H were identified. The clinical phenotypes and prognosis were analysed. Frozen and paraffin-embedded tissue were available in 35 patients with pMMR/MSI-H, for which comprehensive genomic and transcriptomic analyses were performed. FINDINGS: In comparison to pMMR/MSS CRCs, pMMR/MSI-H CRCs exhibited significantly less tumour progression and better long-term prognosis. The pMMR/MSI-H cohorts displayed a higher presence of CD8+ T cells and NK cells when compared to the pMMR/MSS group. Mutational signature analysis revealed that nearly all samples exhibited deficiencies in MMR genes, and we also identified deleterious mutations in MSH3-K383fs. INTERPRETATION: This study revealed pMMR/MSI-H CRC as a distinct subgroup within CRC, which manifests diverse clinicopathological features and long-term prognostic outcomes. Distinct features in the tumour immune-microenvironment were observed in pMMR/MSI-H CRCs. Pathogenic deleterious mutations in MSH3-K383fs were frequently detected, suggesting another potential biomarker for identifying MSI-H. FUNDING: This work was supported by the Science and Technology Commission of Shanghai Municipality (20DZ1100101).

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patients body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

BACKGROUND: Rural residents have a higher prevalence of colorectal cancer (CRC) mortality compared to urban individuals. Policies have been aimed at improving access to CRC screening to reduce these outcomes. However, little attention has been paid to other determinants of CRC-related outcomes, such as stage at diagnosis, treatment, or survivorship care. The main objective of this analysis was to evaluate literature describing differences in CRC screening, stage at diagnosis, treatment, and survivorship care between rural and urban individuals. MATERIALS AND METHODS: We conducted a systematic review of electronic databases using a combination of MeSH and free-text search terms related to CRC screening, stage at diagnosis, treatment, survivorship care, and rurality. We identified 921 studies, of which 39 were included. We assessed methodological quality using the ROBINS-E tool and summarized findings descriptively. A meta-analysis was performed of studies evaluating CRC screening using a random-effects model. RESULTS: Seventeen studies reported disparities between urban and rural populations in CRC screening, 12 on treatment disparities, and 8 on staging disparities. We found that rural individuals were significantly less likely to report any type of screening at any time period (pooled odds ratio = 0.81, 95% CI, 0.76-0.86). Results were inconclusive for disparities in staging at diagnosis and treatment. One study reported a lower likelihood of use of CRC survivorship care for rural individuals compared to urban individuals. CONCLUSION: There remains an urgent need to evaluate and address CRC disparities in rural areas. Investigators should focus future work on assessing the quality of staging at diagnosis, treatment, and survivorship care in rural areas.