PROGNOSTIC ROLE OF MOLECULAR SUBTYPES OF COLON CANCER. A CURRENT VIEW ON THE PROBLEM

Purpose of the study: to review the available data on the heterogeneity of colon cancer and to assess the prognostic significance of colon cancer subtypes.Material and Methods. Medline, PubMed, Cochrane library, elibrary databases were used to identify studies that characterized the current view on the problem of choosing the optimal postoperative treatment for colon cancer.Results. the review showed the results of international studies of colon cancer subtypes based on complex multomics characteristics. Particular attention was paid to the description of modern studies on the search for prognostic markers for colon cancer. The relevance of the study of immunohistochemical markers was confirmed by the analysis of the world literature. the outcomes will make it possible to classify colon cancer into molecular subtypes in real clinical practice and, as a consequence, significantly improve the effectiveness of adjuvant therapy.

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Tumor sidedness and intrinsic subtypes in patients with stage II/III colon cancer: Analysis of NSABP C-07 (NRG Oncology).

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.3514 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 3514-3514 ◽

Cited By ~ 1

Author(s):

S. Rim Kim ◽

Nan Song ◽

Greg Yothers ◽

Patrick Gavin ◽

Carmen Joseph Allegra ◽

...

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Therapy Response ◽

Stage Ii ◽

Intrinsic Subtypes ◽

Treatment Benefit ◽

Cancer Subtypes ◽

Metastatic Setting

3514 Background: The predictive value of tumor sidedness in colorectal cancer is currently of interest especially in metastatic setting for anti-EGFR therapy response. We tested whether intrinsic molecular subtype classification predictive of treatment benefit in stage II/III colon cancer is an independent novel marker in association with tumor sidedness. Methods: All available cases included in the NSABP/NRG C-07 trial for which we had both gene expression data and anatomical data (n=1603) were used to determine the molecular subtypes using the following classifiers; the Colorectal Cancer Assigner (CRCA), the Colon Cancer Subtypes (CCS) and the Consensus Molecular Subtypes (CMS). Frequency of tumor sidedness in each subtype and recurrence-free survival were analyzed. Results: Intrinsic subtypes were differentially distributed in right- and left-colon tumors with the exception of the stem-like or CMS4 (mesenchymal) subtype (Table 1). Sidedness was not associated with prognosis (p=0.82, HR: 1.022 [CI: 0.851-1.227]) or prediction of patients with greater benefit from oxaliplatin when combined with 5-Fu+LV (interaction p=0.484). Conclusions: Although tumor sidedness is associated with distribution of intrinsic subtypes in stage II/III colon cancer, it is not predictive of survival benefit from oxaliplatin in C-07. Support: -180868, -180822, U24-CA196067; HI13C2162; PA DOH; Sanofi-Synthelabo Clinical trial information: NCT00004931. [Table: see text]

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ColotypeR: A tool to classify colon cancers by consensus molecular subtype and subtype-specific risk of recurrence.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.4_suppl.632 ◽

2018 ◽

Vol 36 (4_suppl) ◽

pp. 632-632

Author(s):

Steven Allen Buechler ◽

Sunil S. Badve ◽

Yesim Gokmen-Polar ◽

Emily Herring ◽

Katelyn Ludwig ◽

...

Keyword(s):

Colon Cancer ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Prognostic Significance ◽

Response To Treatment ◽

Training Set ◽

Risk Of Recurrence ◽

Molecular Features ◽

Colon Cancers ◽

Specific Risk

632 Background: Colon cancer is highly heterogeneous in prognosis and response to treatment. The consensus molecular subtypes (CMS1-4, and mixed) partition colon cancers into distinct groups. CMS4 tumors, a mesenchymal subtype, have the worst prognosis and poor response to standard chemotherapies. There is a critical need for accurate molecular subtyping, and subtype-specific management. Methods: Affymetrix microarrays colon cancer datasets (N = 813; GSE39582, GSE14333) were partitioned into training (AT; N = 370) and validation sets (AV; N = 443) balanced for clinical traits. A novel multistate gene methodology was used to predict CMS, and prognosticate subtype-specific relapse-free survival (RFS) in the training set. Accuracy of CMS prediction and prognostic significance was validated in the AV and TCGA colon cancer (COAD; N = 458) sets. Results: In the training set, a 20-gene panel (ColotypeR-CMS) predicts CMS subtype. Mean accuracy for CMS1-4 prediction was 0.87 in AV and 0.81 in COAD. In AV, 5-year RFS is 0.52 (95%CI 0.43 – 0.63) in the predicted CMS4, and 0.70 (95%CI 0.65 – 0.77) in the non-CMS4 samples. The risk of relapse for non-CMS4 samples was refined by a genomic score (ColotypeR-Risk) computed using expression of 25 genes in the training set. ColotypeR-Risk was prognostic of RFS (p = 0.0004) among the AV non-CMS4 samples, and also prognostic (p = 0.0001) among the stage II non-CMS4 AV samples not treated with chemotherapy. The prognostic significance of ColotypeR-Risk among non-CMS4 samples is independent of tumor location (right or left) and CMS subtype (CMS1-3 or mixed). ColotypeR-Risk was also prognostic of RFS in non-CMS4 samples in COAD (p = 0.005). Conclusions: ColotypeR identifies the consensus molecular subtypes (CMS1, CMS2, CMS3, CMS4, or mixed), and assesses subtype-specific risk of recurrence of colon cancer. ColotypeR identifies prognostic risk and molecular features that could help guide the management of colon cancer patients.

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Comprehensive Analysis of Subtype-Specific Molecular Characteristics of Colon Cancer: Specific Genes, Driver Genes, Signaling Pathways, and Immunotherapy Responses

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.758776 ◽

2021 ◽

Vol 9 ◽

Author(s):

Fangjie Hu ◽

Jianyi Wang ◽

Minghui Zhang ◽

Shuoshuo Wang ◽

Lingyu Zhao ◽

...

Keyword(s):

Colon Cancer ◽

Signaling Pathways ◽

Classification System ◽

Immune Cell ◽

Molecular Subtypes ◽

Molecular Classification ◽

The Cancer Genome Atlas ◽

Molecular Characteristics ◽

Driver Genes ◽

Cancer Subtypes

Colon cancer is a complex, heterogeneous disease. The Colorectal Cancer Subtyping Consortium reported a novel classification system for colon cancer in 2015 to better understand its heterogeneity. This molecular classification system divided colon cancer into four distinct consensus molecular subtypes (CMS 1, 2, 3, and 4). However, the characteristics of different colon cancer molecular subtypes have not been fully elucidated. This study comprehensively analyzed the molecular characteristics of varying colon cancer subtypes using multiple databases and algorithms, including The Cancer Genome Atlas (TCGA) database, DriverDBv3 database, CIBERSORT, and MCP-counter algorithms. We analyzed the alterations in the subtype-specific genes of different colon cancer subtypes, such as the RNA levels and DNA alterations, and showed that specific subtype-specific genes significantly affected prognosis. We also explored the changes in colon cancer driver genes and representative genes of 10 signaling pathways in different subtypes. We identified genes that were altered in specific subtypes. We further detected the infiltration of 22 immune cell types in four colon cancer subtypes and the infiltration level of primary immune cells among these subtypes. Additionally, we explored changes in immune checkpoint genes (ICGs) and immunotherapy responses among different colon cancer subtypes. This study may provide clues for the molecular mechanism of tumorigenesis and progression in colon cancer. It also offers potential biomarkers and targets for the clinical diagnosis and treatment of different colon cancer subtypes.

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polyCluster: Defining Communities of Reconciled Cancer Subtypes with Biological and Prognostic Significance

10.1101/228551 ◽

2017 ◽

Author(s):

Katherine Eason ◽

Gift Nyamundanda ◽

Anguraj Sadanandam

Keyword(s):

Molecular Subtypes ◽

Relative Proportion ◽

Prognostic Significance ◽

Optimal Solution ◽

Breast Cancer Dataset ◽

Hypergeometric Test ◽

Clustering Methods ◽

Cancer Dataset ◽

Cancer Subtypes ◽

Context Specific

AbstractTo stratify cancer patients for most beneficial therapies, it is a priority to define robust molecular subtypes using clustering methods and “big data”. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce “polyCluster”, a tool that reconciles clusters identified by different methods into context-specific subtype “communities” using a hypergeometric test or a measure of relative proportion of common samples. The polycluster was tested using a breast cancer dataset, and latter using uveal melanoma datasets to identify novel subtype communities with significant metastasis-free prognostic differences. Available at: https://github.com/syspremed/polyClustR

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Prognostic Significance of Tumor Location in T2 Gallbladder Cancer: A Systematic Review and Meta-Analysis

Journal of Clinical Medicine ◽

10.3390/jcm10153317 ◽

2021 ◽

Vol 10 (15) ◽

pp. 3317

Author(s):

Hyun Kang ◽

Yoo Shin Choi ◽

Suk-Won Suh ◽

Geunjoo Choi ◽

Jae Hyuk Do ◽

...

Keyword(s):

Gallbladder Cancer ◽

Large Scale ◽

Treatment Guidelines ◽

Prognostic Significance ◽

Tumor Location ◽

Cochrane Library ◽

Poor Prognostic Factor ◽

Simple Cholecystectomy ◽

Survival Difference ◽

Extended Cholecystectomy

(1) Background: The AJCC Cancer Staging Manual, Eighth Edition, subdivided T2 GBC into T2a and T2b. However, there still exist a lack of evidence on the prognostic significance of tumor location. The aim of the present study was to examine the existing evidence to determine the prognostic significance of tumor location of T2 gallbladder cancer (GBC) and to evaluate the optimal surgical extent according to tumor location. (2) Methods: We searched for relevant literature published in the electronic databases PubMed, MEDLINE, Web of Science, Cochrane Library, and Embase before September 2020 using search terms related to gallbladder, cancer, and stage. Data were weighted and pooled using random-effects modeling. (3) Results: Seven studies were deemed eligible for inclusion, representing a cohort of 1789 cases of resected T2 GBC. The overall survival for T2b tumor was significantly worse than that for T2a tumor (HR, 2.141; 95% confidence interval (CI), 1.140 to 4.023; I2 = 71.4%; Pchi2 = 0.007). The rate of lymph node metastasis was lower in the T2a group (26.6%) than in the T2b group (36.6%) (OR, 2.164; 95% CI, 1.309 to 3.575). There was no evidence of a survival difference between the patients who underwent extended cholecystectomy and simple cholecystectomy in T2a GBC (OR, 0.802; 95% CI, 0.618 to 1.042) and T2b GBC (OR, 0.820; 95% CI, 0.620 to 1.083). (4) Conclusions: Hepatic side tumor was a significant poor prognostic factor in T2 GBC. Extended cholecystectomy and simple cholecystectomy showed comparable survival outcomes in T2 GBC, and additional large-scale prospective studies are warranted to establish evidence-based treatment guidelines for T2 GBC.

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DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

Cancer Control ◽

10.1177/1073274820988519 ◽

2021 ◽

Vol 28 ◽

pp. 107327482098851

Author(s):

Zeng-Hong Wu ◽

Yun Tang ◽

Yan Zhou

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Molecular Subtypes ◽

Methylation Status ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Consensus Clustering ◽

Breast Cancer Patients ◽

Cancer Subtypes ◽

Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

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Relative infectiousness of asymptomatic SARS-CoV-2 infected persons compared with symptomatic individuals: a rapid scoping review

BMJ Open ◽

10.1136/bmjopen-2020-042354 ◽

2021 ◽

Vol 11 (5) ◽

pp. e042354

Author(s):

David McEvoy ◽

Conor McAloon ◽

Aine Collins ◽

Kevin Hunt ◽

Francis Butler ◽

...

Keyword(s):

Scoping Review ◽

Quantitative Measure ◽

Contact Tracing ◽

Cochrane Library ◽

International Studies ◽

Screening Process ◽

Modelling Studies ◽

Asymptomatic Individuals ◽

Definition Of ◽

The Cochrane Library

ObjectivesThe aim of this study was to determine the relative infectiousness of asymptomatic SARS-CoV-2 infected persons compared with symptomatic individuals based on a scoping review of available literature.DesignRapid scoping review of peer-reviewed literature from 1 January to 5 December 2020 using the LitCovid database and the Cochrane library.SettingInternational studies on the infectiousness of individuals infected with SARS-CoV-2.ParticipantsStudies were selected for inclusion if they defined asymptomatics as a separate cohort distinct from presymptomatics and if they provided a quantitative measure of the infectiousness of asymptomatics relative to symptomatics.Primary outcome measuresPCR result (PCR studies), the rate of infection (mathematical modelling studies) and secondary attack rate (contact tracing studies) - in each case from asymptomatic in comparison with symptomatic individuals.ResultsThere are only a limited number of published studies that report estimates of relative infectiousness of asymptomatic compared with symptomatic individuals. 12 studies were included after the screening process. Significant differences exist in the definition of infectiousness. PCR studies in general show no difference in shedding levels between symptomatic and asymptomatic individuals; however, the number of study subjects is generally limited. Two modelling studies estimate relative infectiousness to be 0.43 and 0.57, but both of these were more reflective of the infectiousness of undocumented rather than asymptomatic cases. The results from contact tracing studies include estimates of relative infectiousness of 0, but with insufficient evidence to conclude that it is significantly different from 1.ConclusionsThere is considerable heterogeneity in estimates of relative infectiousness highlighting the need for further investigation of this important parameter. It is not possible to provide any conclusive estimate of relative infectiousness, as the estimates from the reviewed studies varied between 0 and 1.

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Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

BMJ Open ◽

10.1136/bmjopen-2020-039652 ◽

2020 ◽

Vol 10 (8) ◽

pp. e039652 ◽

Cited By ~ 25

Author(s):

Conor McAloon ◽

Áine Collins ◽

Kevin Hunt ◽

Ann Barber ◽

Andrew W Byrne ◽

...

Keyword(s):

Systematic Review ◽

Incubation Period ◽

Lognormal Distribution ◽

Meta Analysis ◽

Observational Research ◽

Cochrane Library ◽

Parameter Estimates ◽

Sufficient Information ◽

International Studies ◽

Shiny App

ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19.DesignRapid systematic review and meta-analysis of observational research.SettingInternational studies on incubation period of COVID-19.ParticipantsSearches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies.Primary outcome measuresParameters of a lognormal distribution of incubation periods.ResultsThe incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days.ConclusionsThe choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.

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