Critical research gaps and recommendations to inform research prioritisation for more effective prevention and improved outcomes in colorectal cancer

ObjectiveColorectal cancer (CRC) leads to significant morbidity/mortality worldwide. Defining critical research gaps (RG), their prioritisation and resolution, could improve patient outcomes.DesignRG analysis was conducted by a multidisciplinary panel of patients, clinicians and researchers (n=71). Eight working groups (WG) were constituted: discovery science; risk; prevention; early diagnosis and screening; pathology; curative treatment; stage IV disease; and living with and beyond CRC. A series of discussions led to development of draft papers by each WG, which were evaluated by a 20-strong patient panel. A final list of RGs and research recommendations (RR) was endorsed by all participants.ResultsFifteen critical RGs are summarised below:RG1: Lack of realistic models that recapitulate tumour/tumour micro/macroenvironment;RG2: Insufficient evidence on precise contributions of genetic/environmental/lifestyle factors to CRC risk;RG3: Pressing need for prevention trials;RG4: Lack of integration of different prevention approaches;RG5: Lack of optimal strategies for CRC screening;RG6: Lack of effective triage systems for invasive investigations;RG7: Imprecise pathological assessment of CRC;RG8: Lack of qualified personnel in genomics, data sciences and digital pathology;RG9: Inadequate assessment/communication of risk, benefit and uncertainty of treatment choices;RG10: Need for novel technologies/interventions to improve curative outcomes;RG11: Lack of approaches that recognise molecular interplay between metastasising tumours and their microenvironment;RG12: Lack of reliable biomarkers to guide stage IV treatment;RG13: Need to increase understanding of health related quality of life (HRQOL) and promote residual symptom resolution;RG14: Lack of coordination of CRC research/funding;RG15: Lack of effective communication between relevant stakeholders.ConclusionPrioritising research activity and funding could have a significant impact on reducing CRC disease burden over the next 5 years.

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Study of Folfiri/Cetuximab in FcGammaRIIIa V/V Stage IV Colorectal Cancer Patients

Case Medical Research ◽

10.31525/ct1-nct03874026 ◽

2019 ◽

Author(s):

Keyword(s):

Colorectal Cancer ◽

Cancer Patients ◽

Stage Iv ◽

Stage Iv Colorectal Cancer ◽

Colorectal Cancer Patients

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INVESTIGATION KRAS MUTATION IN CIRCULATION TUMOR DNA IN DIFFERENT STAGES OF COLORECTAL CANCER

Problems in oncology ◽

10.37469/0507-3758-2019-65-5-701-707 ◽

2019 ◽

Vol 65 (5) ◽

pp. 701-707

Author(s):

Vitaliy Shubin ◽

Yuriy Shelygin ◽

Sergey Achkasov ◽

Yevgeniy Rybakov ◽

Aleksey Ponomarenko ◽

...

Keyword(s):

Colorectal Cancer ◽

Plasma Volume ◽

Kras Mutation ◽

Stage Iv ◽

Circulating Tumor Dna ◽

Stage Ii ◽

Kras Gene ◽

Kras Mutations ◽

Droplet Pcr ◽

Tumor Dna

To determine mutations in the plasma KRAS gene in patients with colorectal cancer was the aim of this study. The material was obtained from 44 patients with colorectal cancer of different stages (T1-4N0-2bM0-1c). Plasma for the presence of KRAS gene mutation in circulating tumor DNA was investigated using digital droplet polymerase chain reaction (PCR). KRAS mutations in circulating tumor DNA isolated from 1 ml of plasma were detected in 13 (30%) patients with cancer of different stages. Of these, with stage II, there were 3 patients, with III - 5 and with IV - 5. Patients who did not have mutations in 1 ml of plasma were analyzed for mutations of KRAS in circulating tumor DNA isolated from 3 ml of plasma. Five more patients with KRAS mutations were found with II and III stages. The highest concentrations of circulating tumor DNA with KRAS mutation were found in patients with stage IV. The increase in plasma volume to 3 ml did not lead to the identification of mutations in I stage. This study showed that digital droplet PCR allows identification of circulating tumor DNA with the KRAS mutations in patients with stage II-IV of colon cancer. The results can be used to determine the degree of aggressiveness of the tumor at different stages of the disease, but not the 1st, and it is recommended to use a plasma volume of at least 3 ml.

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Laparoscopic surgery for palliative resection of the primary tumor in incurable stage IV colorectal cancer

Surgical Endoscopy ◽

10.1007/s00464-012-2323-3 ◽

2012 ◽

Vol 26 (11) ◽

pp. 3201-3206 ◽

Cited By ~ 6

Author(s):

Hideaki Nishigori ◽

Masaaki Ito ◽

Yuji Nishizawa ◽

Atsushi Kohyama ◽

Takamaru Koda ◽

...

Keyword(s):

Colorectal Cancer ◽

Laparoscopic Surgery ◽

Primary Tumor ◽

Stage Iv ◽

Palliative Resection ◽

Stage Iv Colorectal Cancer

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Critical Research Gaps for Understanding Environmental Impacts of Discharging Treated Municipal Wastewater into Assimilation Wetlands

Wetlands ◽

10.1007/s13157-021-01396-8 ◽

2021 ◽

Vol 41 (1) ◽

Author(s):

Taylor M. Sloey ◽

Brian J. Roberts ◽

Skyler R. Flaska ◽

James A. Nelson

Keyword(s):

Environmental Impacts ◽

Municipal Wastewater ◽

Critical Research ◽

Research Gaps ◽

Treated Municipal Wastewater

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Blood-Based Detection of Colorectal Cancer Using Cancer-Specific DNA Methylation Markers

Diagnostics ◽

10.3390/diagnostics11010051 ◽

2020 ◽

Vol 11 (1) ◽

pp. 51

Author(s):

Nam-Yun Cho ◽

Ji-Won Park ◽

Xianyu Wen ◽

Yun-Joo Shin ◽

Jun-Kyu Kang ◽

...

Keyword(s):

Colorectal Cancer ◽

Dna Methylation ◽

Sensitivity And Specificity ◽

Stage Iv ◽

High Sensitivity ◽

Liquid Biopsies ◽

Blood Leukocytes ◽

Marker Panel ◽

A Genome ◽

Methylight Assay

Cancer tissues have characteristic DNA methylation profiles compared with their corresponding normal tissues that can be utilized for cancer diagnosis with liquid biopsy. Using a genome-scale DNA methylation approach, we sought to identify a panel of DNA methylation markers specific for cell-free DNA (cfDNA) from patients with colorectal cancer (CRC). By comparing DNA methylomes between CRC and normal mucosal tissues or blood leukocytes, we identified eight cancer-specific methylated loci (ADGRB1, ANKRD13, FAM123A, GLI3, PCDHG, PPP1R16B, SLIT3, and TMEM90B) and developed a five-marker panel (FAM123A, GLI3, PPP1R16B, SLIT3, and TMEM90B) that detected CRC in liquid biopsies with a high sensitivity and specificity with a droplet digital MethyLight assay. In a set of cfDNA samples from CRC patients (n = 117) and healthy volunteers (n = 60), a panel of five markers on the platform of the droplet digital MethyLight assay detected stages I–III and stage IV CRCs with sensitivities of 45.9% and 95.7%, respectively, and a specificity of 95.0%. The number of detected markers was correlated with the cancer stage, perineural invasion, lymphatic emboli, and venous invasion. Our five-marker panel with the droplet digital MethyLight assay showed a high sensitivity and specificity for the detection of CRC with cfDNA samples from patients with metastatic CRC.

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Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Journal of Imaging ◽

10.3390/jimaging7030051 ◽

2021 ◽

Vol 7 (3) ◽

pp. 51

Author(s):

Emanuela Paladini ◽

Edoardo Vantaggiato ◽

Fares Bougourzi ◽

Cosimo Distante ◽

Abdenour Hadid ◽

...

Keyword(s):

Colorectal Cancer ◽

Deep Learning ◽

Digital Pathology ◽

Texture Features ◽

Tissue Type ◽

Cancer Tissue ◽

Learning Methods ◽

Feature Based ◽

Type Classification ◽

Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

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