Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction.

MicroRNA(miRNA)-mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA-mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA-mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.

Pan-cancer and single-cell analyses identify CD44 as an immunotherapy response predictor and regulating macrophage polarization and tumor progression in colorectal cancer.

Introduction: Cluster of differentiation (CD) 44 is a non-kinase cell surface transmembrane glycoprotein critical for tumor maintenance and progression. Methods: We conducted a systematic analysis of the expression profile and genomic alteration profile of CD44 in 33 types of cancer. The immune characteristics of CD44 were comprehensively explored by TIMER2.0 and CIBERSORT. In addition, the CD44 transcriptional landscape was examined at the single-cell level. Then, Pseudotime trajectory analysis of CD44 gene expression was performed using Monocle 2, and CellChat was utilized to compare the crosstalk differences between CD44+monocytes and CD44- monocytes. Tumor immune dysfunction and exclusion (TIDE) was used to evaluate the predictive ability of CD44 for immune checkpoint blockade (ICB) responses. The effects of CD44 on colorectal cancer (CRC) and macrophage polarization were investigated by knocking down the expression of CD44 in HCT-116 cell and macrophages in vitro. Results: The expression of CD44 elevated in most cancers, predicting unfavorable prognosis. In addditon, CD44 was correlation with immune cell infiltration and key immune regulators. CD44+ monocytes had a higher information flow intensity than CD44- monocytes. CD44 had good predictive ability for immune checkpoint blockade responses. Knockdown of CD44 inhibited the proliferation, migration, and invasion of HCT-116 cell in vitro. Knockdown of CD44 inhibited M2 macrophage polarization. Discussion: These findings suggest that CD44 is involved in regulating tumor development, macrophage polarization, and has certain predictive value for patient clinical prognosis and response to immunotherapy.

3D bioprinted CRC model brings to light the replication necessity of an oncolytic vaccinia virus encoding FCU1 gene to exert an efficient anti-tumoral activity.

The oncolytic virus represents a promising therapeutic strategy involving the targeted replication of viruses to eliminate cancer cells, while preserving healthy ones. Despite ongoing clinical trials, this approach encounters significant challenges. This study delves into the interaction between an oncolytic virus and extracellular matrix mimics (ECM mimics). A three-dimensional colorectal cancer model, enriched with ECM mimics through bioprinting, was subjected to infection by an oncolytic virus derived from the vaccinia virus (oVV). The investigation revealed prolonged expression and sustained oVV production. However, the absence of a significant antitumor effect suggested that the virus's progression toward non-infected tumoral clusters was hindered by the ECM mimics. Effective elimination of tumoral cells was achieved by introducing an oVV expressing FCU1 (an enzyme converting the prodrug 5-FC into the chemotherapeutic compound 5-FU) alongside 5-FC. Notably, this efficacy was absent when using a non-replicative vaccinia virus expressing FCU1. Our findings underscore then the crucial role of oVV proliferation in a complex ECM mimics. Its proliferation facilitates payload expression and generates a bystander effect to eradicate tumors. Additionally, this study emphasizes the utility of 3D bioprinting for assessing ECM mimics impact on oVV and demonstrates how enhancing oVV capabilities allows overcoming these barriers. This showcases the potential of 3D bioprinting technology in designing purpose-fit models for such investigations.

Digital spatial profiling identifies molecular changes involved in development of colitis-associated colorectal cancer.

Objective: Chronic colonic inflammation seen in inflammatory bowel disease (IBD) is a risk factor for colorectal cancer (CRC). Colitis-associated cancers (CAC) are molecularly different from sporadic CRC. This study aimed to evaluate spatially defined molecular changes associated with neoplastic progression to identify mechanisms of action and potential biomarkers for prognostication. Design: IBD patients who had undergone colectomy for treatment of their IBD or dysplasia were identified from an institutional database. Formalin-fixed paraffin embedded samples from areas of normal, inflamed, dysplastic and adenocarcinoma tissue were identified for digital spatial profiling using the Nanostring GeoMx™ Cancer Transcriptome Atlas. RNA expression and quantification of 1812 genes was measured and analysed in a spatial context to compare differences in gene expression. Results: Sixteen patients were included, nine patients had CAC, two had dysplasia only and five had colitis only. Significant, step-wise differences in gene expression were seen between tissue types, mainly involving progressive over-expression of collagen genes associated with stromal remodelling. Similarly, MYC over-expression was associated with neoplastic progression. Comparison of normal and inflamed tissue from patients who progressed to those who did not also showed significant differences in immune-related genes, including under-expression of thte chemokines CCL18, CCL25 and IL-R7, as well as CD3, CD6 and lysozyme. The known oncogene CD24 was significantly overexpressed. Conclusion: Both tissue types and patient groups are molecularly distinguishable on the basis of their gene expression patterns. Further prospective work is necessary to confirm these differences and establish their clinical significance and potential utility as biomarkers.

Innovation in gastrointestinal surgery: the evolution of minimally invasive surgery-a narrative review.

Background: Minimally invasive (MI) surgery has revolutionised surgery, becoming the standard of care in many countries around the globe. Observed benefits over traditional open surgery include reduced pain, shorter hospital stay, and decreased recovery time. Gastrointestinal surgery in particular was an early adaptor to both laparoscopic and robotic surgery. Within this review, we provide a comprehensive overview of the evolution of minimally invasive gastrointestinal surgery and a critical outlook on the evidence surrounding its effectiveness and safety. Methods: A literature review was conducted to identify relevant articles for the topic of this review. The literature search was performed using Medical Subject Heading terms on PubMed. The methodology for evidence synthesis was in line with the four steps for narrative reviews outlined in current literature. The key words used were minimally invasive, robotic, laparoscopic colorectal, colon, rectal surgery. Conclusion: The introduction of minimally surgery has revolutionised patient care. Despite the evidence supporting this technique in gastrointestinal surgery, several controversies remain. Here we discuss some of them; the lack of high level evidence regarding the oncological outcomes of TaTME and lack of supporting evidence for robotic colorectalrectal surgery and upper GI surgery. These controversies open pathways for future research opportunities with RCTs focusing on comparing robotic to laparoscopic with different primary outcomes including ergonomics and surgeon comfort.

A perfused multi-well bioreactor platform to assess tumor organoid response to a chemotherapeutic gradient.

There is an urgent need to develop new therapies for colorectal cancer that has metastasized to the liver and, more fundamentally, to develop improved preclinical platforms of colorectal cancer liver metastases (CRCLM) to screen therapies for efficacy. To this end, we developed a multi-well perfusable bioreactor capable of monitoring CRCLM patient-derived organoid response to a chemotherapeutic gradient. CRCLM patient-derived organoids were cultured in the multi-well bioreactor for 7 days and the subsequently established gradient in 5-fluorouracil (5-FU) concentration resulted in a lower IC50 in the region near the perfusion channel versus the region far from the channel. We compared behaviour of organoids in this platform to two commonly used PDO culture models: organoids in media and organoids in a static (no perfusion) hydrogel. The bioreactor IC50 values were significantly higher than IC50 values for organoids cultured in media whereas only the IC50 for organoids far from the channel were significantly different than organoids cultured in the static hydrogel condition. Using finite element simulations, we showed that the total dose delivered, calculated using area under the curve (AUC) was similar between platforms, however normalized viability was lower for the organoid in media condition than in the static gel and bioreactor. Our results highlight the utility of our multi-well bioreactor for studying organoid response to chemical gradients and demonstrate that comparing drug response across these different platforms is nontrivial.

Cell-free circulating tumor RNAs in plasma as the potential prognostic biomarkers in colorectal cancer.

Background: Cell free RNA (cfRNA) contains transcript fragments from multiple cell types, making it useful for cancer detection in clinical settings. However, the pathophysiological origins of cfRNAs in plasma from colorectal cancer (CRC) patients remain unclear. Methods: To identify the tissue-specific contributions of cfRNAs transcriptomic profile, we used a published single-cell transcriptomics profile to deconvolute cell type abundance among paired plasma samples from CRC patients who underwent tumor-ablative surgery. We further validated the differentially expressed cfRNAs in 5 pairs of CRC tumor samples and adjacent tissue samples as well as 3 additional CRC tumor samples using RNA-sequencing. Results: The transcriptomic component from intestinal secretory cells was significantly decreased in the in-house post-surgical cfRNA. The HPGD, PACS1, and TDP2 expression was consistent across cfRNA and tissue samples. Using the Cancer Genome Atlas (TCGA) CRC datasets, we were able to classify the patients into two groups with significantly different survival outcomes. Conclusions: The three-gene signature holds promise in applying minimal residual disease (MRD) testing, which involves profiling remnants of cancer cells after or during treatment. Biomarkers identified in the present study need to be validated in a larger cohort of samples in order to ascertain their possible use in early diagnosis of CRC.

Study Protocol for a Randomised Controlled Trial on Pulmonary Metastasectomy vs. Standard of Care in Colorectal Cancer Patients With ≥ 3 Lung Metastases (PUCC-Trial).

This is a multicentre prospective randomised controlled trial for patients with 3 or more resectable pulmonary metastases from colorectal carcinoma. The study investigates the effects of pulmonary metastasectomy in addition to standard medical treatment in comparison to standard medical treatment plus possible local ablative measures such as SBRT. This trial is intended to demonstrate an overall survival difference in the group undergoing pulmonary metastasectomy. Further secondary and exploratory endpoints include quality of life (EORTC QLQ-C30, QLQ-CR29 and QLQ-LC29 questionnaires), progression-free survival and impact of mutational status. Due to the heterogeneity and complexity of the disease and treatment trajectories in metastasised colorectal cancer, well powered trials have been very challenging to design and execute. The goal of this study is to create a setting which allows treatment as close to the real life conditions as possible but under well standardised conditions. Based on previous trials, in which patient recruitment in the given setting hindered successful study completion, we decided to (1) restrict inclusion to patients with 3 or more metastases (since in case of lesser, surgery will probably be the preferred option) and (2) allow for real world standard of care (SOC) treatment options before and after randomisation including watchful waiting (as opposed to a predefined treatment protocol) and (3) possibility that patient can receive SOC externally (to reduce patient burden). Moreover, we chose to stipulate 12 weeks of systemic treatment prior to possible resection to further standardize treatment response and disease course over a certain period of time. Hence, included patients will be in the disease state of oligopersistence rather than primary oligometastatic. The trial was registered in the German Clinical Trials Register (DRKS-No.: DRKS00024727).

The role of artificial intelligence based systems for cost optimization in colorectal cancer prevention programs.

Colorectal Cancer (CRC) has seen a dramatic increase in incidence globally. In 2019, colorectal cancer accounted for 1.15 million deaths and 24.28 million disability-adjusted life-years (DALYs) worldwide. In India, the annual incidence rates (AARs) for colon cancer was 4.4 per 100,000. There has been a steady rise in the prevalence of CRC in India which may be attributed to urbanization, mass migration of population, westernization of diet and lifestyle practices and a rise of obesity and metabolic risk factors that place the population at a higher risk of CRC. Moreoever, CRC in India differs from that described in the Western countries, with a higher proportion of young patients and more patients presenting with an advanced stage. This may be due to poor access to specialized healthcare and socio-economic factors. Early identification of adenomatous colonic polyps, which are well-recognized pre-cancerous lesions, at the time of screening colonoscopy has been shown to be the most effective measure used for CRC prevention. However, colonic polyps are frequently missed during colonoscopy and moreover, these screening programs necessitate man-power, time and resources for processing resected polyps, that may hamper penetration and efficacy in mid- to low-income countries. In the last decade, there has been significant progress made in the automatic detection of colonic polyps by multiple AI-based systems. With the advent of better AI methodology, the focus has shifted from mere detection to accurate discrimination and diagnosis of colonic polyps. These systems, once validated, could usher in a new era in Colorectal Cancer (CRC) prevention programs which would center around "Leave in-situ" and "Resect and discard" strategies. These new strategies hinge around the specificity and accuracy of AI based systems in correctly identifying the pathological diagnosis of the polyps, thereby providing the endoscopist with real-time information in order to make a clinical decision of either leaving the lesion in-situ (mucosal polyps) or resecting and discarding the polyp (hyperplastic polyps). The major advantage of employing these strategies would be in cost optimization of CRC prevention programs while ensuring good clinical outcomes. The adoption of these AI-based systems in the national cancer prevention program of India in accordance with the mandate to increase technology integration could prove to be cost-effective and enable implementation of CRC prevention programs at the population level. This level of penetration could potentially reduce the incidence of CRC and improve patient survival by enabling early diagnosis and treatment. In this review, we will highlight key advancements made in the field of AI in the identification of polyps during colonoscopy and explore the role of AI based systems in cost optimization during the universal implementation of CRC prevention programs in the context of mid-income countries like India.

Bevacizumab increases the risk of anastomosis site leakage in metastatic colorectal cancer.

Background: Bevacizumab is a humanized monoclonal antibody against vascular endothelial growth factor and is used in combination with first-line chemotherapy in the treatment of metastatic colorectal cancer. One of the side effects of bevacizumab is gastrointestinal perforation. This study was designed to identify the effect of bevacizumab in intestinal anastomosis site healing. Methods: From January 2010 to December 2020, patients diagnosed with stage IV colorectal cancer treated with palliative chemotherapy or chemoradiotherapy followed by radical surgery were retrospectively reviewed. Clinical signs or symptoms and computed tomography were tools used for diagnosing anastomosis site leakage. The patients were divided into two groups, the bevacizumab group (n = 136) and the non-bevacizumab group (n = 124). Results: Among the 260 patients 14 (5.4%) patients were diagnosed with anastomosis site leakage. In the bevacizumab group, 13 (9.6%) patients were diagnosed with anastomotic leakage. In the non-bevacizumab group, 1 (0.8%) patient was diagnosed with anastomotic leakage. Anastomosis site leakage was significantly higher in the bevacizumab treatment group (P < 0.001). In the bevacizumab group, period of drug discontinuation before surgery was factor associated with anastomosis site leakage in multivariable analysis (P = 0.031). Conclusion: Stage IV colorectal patients treated with bevacizumab before radical surgery for primary cancer should be carefully observed of anastomosis site leakage after surgery, and the period of drug discontinuation before surgery should be longer than 5 weeks to avoid anastomosis site leakage.

Update for Advance CAR-T Therapy in Solid Tumors, Clinical Application in Peritoneal Carcinomatosis From Colorectal Cancer and Future Prospects.

Latest advances in the field of cancer immunotherapy have developed the (Chimeric Antigen Receptor) CAR-T cell therapy. This therapy was first used in hematological malignancies which obtained promising results; therefore, the use of CAR-T cells has become a popular approach for treating non-solid tumors. CAR-T cells consist of T-lymphocytes that are engineered to express an artificial receptor against any surface antigen of our choice giving us the capacity of offering precise and personalized treatment. This leaded to the development of CAR-T cells for treating solid tumors with the hope of obtaining the same result; however, their use in solid tumor and their efficacy have not achieved the expected results. The reason of these results is because solid tumors have some peculiarities that are not present in hematological malignancies. In this review we explain how CAR-T cells are made, their mechanism of action, adverse effect and how solid tumors can evade their action, and also we summarize their use in colorectal cancer and peritoneal carcinomatosis.

Statin Treatment as a Targeted Therapy for APC-Mutated Colorectal Cancer.

Background: Mutations in the tumor suppressor gene Adenomatous Polyposis Coli (APC) are found in 80% of sporadic colorectal cancer (CRC) tumors and are also responsible for the inherited form of CRC, Familial adenomatous polyposis (FAP). Methods: To identify novel therapeutic strategies for the treatment of APC mutated CRC, we generated a drug screening platform that incorporates a human cellular model of APC mutant CRC using CRISPR-cas9 gene editing and performed an FDA-approved drug screen targeting over 1000 compounds. Results: We have identified the group of HMG-CoA Reductase (HMGCR) inhibitors known as statins, which cause a significantly greater loss in cell viability in the APC mutated cell lines and in in vivo APC mutated patient derived xenograft (PDX) models, compared to wild-type APC cells. Mechanistically, our data reveals this new synthetic lethal relationship is a consequence of decreased Wnt signalling and, ultimately, a reduction in the level of expression of the anti-apoptotic protein Survivin, upon statin treatment in the APC-mutant cells only. This mechanism acts via a Rac1 mediated control of beta-catenin. Conclusion: Significantly, we have identified a novel synthetic lethal dependence between APC mutations and statin treatment, which could potentially be exploited for the treatment of APC mutated cancers.

Liquid Biopsy-Driven Cetuximab Rechallenge Strategy in Molecularly Selected Metastatic Colorectal Cancer Patients.

Background: Rechallenge with EGFR inhibitors represents a promising strategy for patients with RAS wild type (WT) colorectal cancer (CRC) but definitive selection criteria are lacking. Recently, the RAS WT status on circulating tumor DNA (ct-DNA) emerged as a potential watershed for this strategy. Our study explored the liquid biopsy-driven cetuximab rechallenge in a RAS and BRAF WT selected population. Methods: CRC patients with RAS and BRAF WT both on tumor tissue and on ct-DNA at baseline receiving rechallenge with cetuximab were eligible for our analysis. Ct-DNA was analyzed for RAS-BRAF mutations with pyro-sequencing and nucleotide sequencing assays. Real-time PCR and droplet digital PCR were performed to confirm the RAS-BRAF mutational status. Results: A total of 26 patients were included in our analysis. In the global population, RR was 25.0%, median overall survival (mOS) was 5.0 months, and median progression-free survival (mPFS) was 3.5 months. Previous response to anti-EGFR was associated with improved mPFS (5.0 vs. 2.0 months, HR: 0.26, p = 0.048); anti-EGFR free interval > 14 months and anti-EGFR free interval > 16 months were associated with improved mPFS (respectively 7.0 vs. 3.0 months, HR: 0.27, p = 0.013 and not reached vs. 3.0 months, HR: 0.20, p = 0.002) and with improved mOS (respectively 13.0 vs. 5.0 months, HR: 0.27, p = 0.013 and 13.0 vs. 5.0 months, HR: 0.20, p = 0.002). Previous lines >2 were correlated with improved mPFS (4.0 vs. 1.0 month, HR: 0.05, p = 0.041) and with improved mOS (7.0 vs. 1.0 month, HR: 0.045, p = 0.034). In a multiple logistic regression model, only the anti-EGFR free interval was confirmed to be a significant predictor for mOS and mPFS. Conclusions: Liquid biopsy-driven cetuximab rechallenge was confirmed to be effective. The clinical outcome was consistent with available results from phase II studies. In addition to the molecular selection through the analysis of ct-DNA for RAS, the long anti-EGFR free interval is confirmed as a prospective selection criterion for this therapeutic option.

Case Report Series: Aggressive HR Deficient Colorectal Cancers Related to BRCA1 Pathogenic Germline Variants.

Objective: The link between BRCA1 and homologous recombination deficiency (HRD) in cancer has gained importance with the emergence of new targeted cancer treatments, while the available data on the role of the gene in colorectal cancer (CRC) remain contradictory. The aim of this case series was to elucidate the role of known pathogenic BRCA1 variants in the development of early-onset CRC. Design: Patients were evaluated using targeted next generation sequencing, exome sequencing and chromosomal microarray analysis of the paired germline and tumor samples. These results were used to calculate the HRD score and the frequency of mutational signatures in the tumors. Results: Three patients with metastatic CRC were heterozygous for a previously known BRCA1 nonsense variant. All tumors showed remarkably high HRD scores, and the HRD-related signature 3 had the second highest contribution to the somatic pattern of variant accumulation in the samples (23% in 1 and 2, and 13% in sample 3). Conclusions: A BRCA1 germline pathogenic variant can be involved in CRC development through HRD. Thus, BRCA1 testing should be considered in young patients with a personal history of microsatellite stable CRC as this could further allow a personalized treatment approach.

Comprehensive Imaging Characterization of Colorectal Liver Metastases.

Colorectal liver metastases (CRLM) have heterogenous histopathological and immunohistochemical phenotypes, which are associated with variable responses to treatment and outcomes. However, this information is usually only available after resection, and therefore of limited value in treatment planning. Improved techniques for in vivo disease assessment, which can characterise the variable tumour biology, would support further personalization of management strategies. Advanced imaging of CRLM including multiparametric MRI and functional imaging techniques have the potential to provide clinically-actionable phenotypic characterisation. This includes assessment of the tumour-liver interface, internal tumour components and treatment response. Advanced analysis techniques, including radiomics and machine learning now have a growing role in assessment of imaging, providing high-dimensional imaging feature extraction which can be linked to clinical relevant tumour phenotypes, such as a the Consensus Molecular Subtypes (CMS). In this review, we outline how imaging techniques could reproducibly characterize the histopathological features of CRLM, with several matched imaging and histology examples to illustrate these features, and discuss the oncological relevance of these features. Finally, we discuss the future challenges and opportunities of CRLM imaging, with a focus on the potential value of advanced analytics including radiomics and artificial intelligence, to help inform future research in this rapidly moving field.

A National Study of Colorectal Cancer Survivorship Disparities: A Latent Class Analysis Using SEER (Surveillance, Epidemiology, and End Results) Registries.

Introduction: Long-standing disparities in colorectal cancer (CRC) outcomes and survival between Whites and Blacks have been observed. A person-centered approach using latent class analysis (LCA) is a novel methodology to assess and address CRC health disparities. LCA can overcome statistical challenges from subgroup analyses that would normally impede variable-centered analyses like regression. Aim was to identify risk profiles and differences in malignant CRC survivorship outcomes. Methods: We conducted an LCA on the Surveillance, Epidemiology, and End Results data from 1975 to 2016 for adults ≥18 (N = 525,245). Sociodemographics used were age, sex/gender, marital status, race, and ethnicity (Hispanic/Latinos) and stage at diagnosis. To select the best fitting model, we employed a comparative approach comparing sample-size adjusted BIC and entropy; which indicates a good separation of classes. Results: A four-class solution with an entropy of 0.72 was identified as: lowest survivorship, medium-low, medium-high, and highest survivorship. The lowest survivorship class (26% of sample) with a mean survival rate of 53 months had the highest conditional probabilities of being 76-85 years-old at diagnosis, female, widowed, and non-Hispanic White, with a high likelihood with localized staging. The highest survivorship class (53% of sample) with a mean survival rate of 92 months had the highest likelihood of being married, male with localized staging, and a high likelihood of being non-Hispanic White. Conclusion: The use of a person-centered measure with population-based cancer registries data can help better detect cancer risk subgroups that may otherwise be overlooked.

Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping.

Rising cancer care costs impose financial burdens on health systems. Applying artificial intelligence to diagnostic algorithms may reduce testing costs and avoid wasteful therapy-related expenditures. To evaluate the financial and clinical impact of incorporating artificial intelligence-based determination of mismatch repair/microsatellite instability status into the first-line metastatic colorectal carcinoma setting, we developed a deterministic model to compare eight testing strategies: A) next-generation sequencing alone, B) high-sensitivity polymerase chain reaction or immunohistochemistry panel alone, C) high-specificity panel alone, D) high-specificity artificial intelligence alone, E) high-sensitivity artificial intelligence followed by next generation sequencing, F) high-specificity artificial intelligence followed by next-generation sequencing, G) high-sensitivity artificial intelligence and high-sensitivity panel, and H) high-sensitivity artificial intelligence and high-specificity panel. We used a hypothetical, nationally representative, population-based sample of individuals receiving first-line treatment for de novo metastatic colorectal cancer (N = 32,549) in the United States. Model inputs were derived from secondary research (peer-reviewed literature and Medicare data). We estimated the population-level diagnostic costs and clinical implications for each testing strategy. The testing strategy that resulted in the greatest project cost savings (including testing and first-line drug cost) compared to next-generation sequencing alone in newly-diagnosed metastatic colorectal cancer was using high-sensitivity artificial intelligence followed by confirmatory high-specificity polymerase chain reaction or immunohistochemistry panel for patients testing negative by artificial intelligence ($400 million, 12.9%). The high-specificity artificial intelligence-only strategy resulted in the most favorable clinical impact, with 97% diagnostic accuracy in guiding genotype-directed treatment and average time to treatment initiation of less than one day. Artificial intelligence has the potential to reduce both time to treatment initiation and costs in the metastatic colorectal cancer setting without meaningfully sacrificing diagnostic accuracy. We expect the artificial intelligence value proposition to improve in coming years, with increasing diagnostic accuracy and decreasing costs of processing power. To extract maximal value from the technology, health systems should evaluate integrating diagnostic histopathologic artificial intelligence into institutional protocols, perhaps in place of other genotyping methodologies.

Black Raspberries Suppress Colorectal Cancer by Enhancing Smad4 Expression in Colonic Epithelium and Natural Killer Cells.

Innate immune cells in the tumor microenvironment have been proposed to control the transition from benign to malignant stages. In many cancers, increased infiltration of natural killer (NK) cells associates with good prognosis. Although the mechanisms that enable NK cells to restrain colorectal cancer (CRC) are unclear, the current study suggests the involvement of Smad4. We found suppressed Smad4 expression in circulating NK cells of untreated metastatic CRC patients. Moreover, NK cell-specific Smad4 deletion promoted colon adenomas in DSS-treated ApcMin/+ mice and adenocarcinomas in AOM/DSS-treated mice. Other studies have shown that Smad4 loss or weak expression in colonic epithelium associates with poor survival in CRC patients. Therefore, targeting Smad4 in both colonic epithelium and NK cells could provide an excellent opportunity to manage CRC. Toward this end, we showed that dietary intervention with black raspberries (BRBs) increased Smad4 expression in colonic epithelium in patients with FAP or CRC and in the two CRC mouse models. Also, benzoate metabolites of BRBs, such as hippurate, upregulated Smad4 and Gzmb expression that might enhance the cytotoxicity of primary human NK cells. Of note, increased levels of hippurate is a metabolomic marker of a healthy gut microbiota in humans, and hippurate also has antitumor effects. In conclusion, our study suggests a new mechanism for the action of benzoate metabolites derived from plant-based foods. This mechanism could be exploited clinically to upregulate Smad4 in colonic epithelium and NK cells, thereby delaying CRC progression.

Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features.

Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on a selection of exemplar image patches from a convolution neural network (CNN) and patch classification by quantifying the divergence between the PHPs of exemplars and the input image patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for image patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperform competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet, and healthy cases. Collectively, the accurate tumor segmentation produces the highest average patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology image analysis.