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Associations Between Molecular Classifications of Colorectal Cancer and Patient Survival: A Systematic Review
Accepted Manuscript Associations Between Molecular Classifications of Colorectal Cancer and Patient Survival: a Systematic Review Elizabeth Alwers, MSc, Min Jia, MSc, Matthias Kloor, PhD, Hendrik Bläker, PhD, Hermann Brenner, MD, MPH, Michael Hoffmeister, PhD
PII: DOI: Reference:
S1542-3565(17)31536-7 10.1016/j.cgh.2017.12.038 YJCGH 55622
To appear in: Clinical Gastroenterology and Hepatology Accepted Date: 21 December 2017 Please cite this article as: Alwers E, Jia M, Kloor M, Bläker H, Brenner H, Hoffmeister M, Associations Between Molecular Classifications of Colorectal Cancer and Patient Survival: a Systematic Review, Clinical Gastroenterology and Hepatology (2018), doi: 10.1016/j.cgh.2017.12.038. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Associations Between Molecular Classifications of Colorectal Cancer and Patient Survival: a Systematic Review Short title: Molecular classifications of CRC
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Authors: Elizabeth Alwers, MSc a, Min Jia, MSc a, Matthias Kloor, PhD b, Hendrik Bläker, PhD c, Hermann Brenner, MD, MPH a,d,e, Michael Hoffmeister, PhD a.
Affiliations Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, Germany
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a
b
Department of Applied Tumor Biology, Institute of Pathology, University of Heidelberg, Im Neuenheimer Feld 220, Heidelberg, Germany
c
d
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Department of General Pathology, Institute of Pathology, Charité University Medicine Hospital, Charitéplatz 1, Berlin, Germany Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
e
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
Grant support: Not applicable
for Cancer Control.
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Abbreviations used: CIMP: CpG island methylator phenotype; CIN: chromosomal instability; CMS: Consensus molecular subtypes; CRC: colorectal cancer; DSS: disease-specific survival; HR: hazard Ratio; IHC: immunohistochemical; MMR: Mismatch repair; MSI: microsatellite instability; MSS: microsatellite stable; NOS: Newcastle-Ottawa Quality Assessment Scale; OS: overall survival; RFS: recurrence-free survival; SIGN: Scottish Intercollegiate Guidelines Network; UICC: International Union
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Correspondence Dr. Michael Hoffmeister Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, 69120, Germany E-mail address: [email protected] Tel: +49 6221 42-1303 Disclosures: The authors have no conflict of interest to declare. Author contributions: Study concept and design: EA, MJ, MK, HBl, HBr, MH; acquisition of data: EA, MJ, MH; analysis and interpretation of data: EA, MJ, MK, HBl, HBr, MH; drafting of manuscript: EA, MH; critical addition of important intellectual content to manuscript: EA, MJ, MK, HBl, HBr, MH; all authors approved final version of the manuscript.
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Abstract Background & Aims: Colorectal cancer (CRC) is a heterogeneous disease with different mechanisms of pathogenesis. Classification systems have been proposed based on molecular
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features of tumors, but none are used in clinical practice. We performed a systematic review of studies on the associations between molecular classifications of CRC and patient survival. Methods: We searched the PubMed, Embase, Cochrane, and Web of Science databases for
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combinations of terms related to CRC, molecular markers, subtype classifications, and survival (overall survival, disease-specific survival, disease-free survival). We included only studies that
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used at least 3 molecular markers to classify tumors and provided an estimate of survival associated with each subtype. Data extraction and quality assessment were performed independently by 2 reviewers.
Results: We identified 6 studies that fulfilled the inclusion criteria. In these studies, molecular
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subtypes were assigned based on pathways associated with tumor development or findings from gene expression clustering analyses. Most studies proposed classification systems with 5 subtypes, including information on microsatellite instability, mutations in BRAF, and mutations in
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KRAS. None of the studies included TNM stage in their classification system. Three classification systems used similar definitions. Only 3 studies provided internal or external
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validation of the proposed classification schemes. Tumors with microsatellite stability and mutations in KRAS or BRAF were associated with decreased survival times, compared to tumors with microsatellite stability and no mutations. Conclusion: In a systematic review of studies of molecular classifications of CRC and patient survival, we found that most subtypes were not significantly or not differentially associated with survival. None of the systems integrated TNM staging. Further research and validation are needed to develop molecular subtype classification systems for clinical practice.
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KEY WORDS: MSI; colon cancer; genetics; biomarker Summary: In a systematic review, we found that systems for classifying colorectal tumors based on at least 3 molecular features do not accurately predict patient survival. Improvements
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to classification systems and validation are needed for them to have prognostic value and
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relevance to clinical practice.
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Introduction Colorectal cancer (CRC) is one of the leading causes of cancer incidence among men and women worldwide 1. Even with decreasing mortality rates observed in high income countries
cause of cancer mortality in the world
1,2
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due to early detection and more effective treatment, CRC represents the fourth most common . The strongest prognostic factor and determinant of
treatment for CRC patients used in clinical routine is the stage of disease at diagnosis. Based
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on the size of the tumor, number of compromised lymph nodes, and presence of distant metastatic lesions, patients can generally be classified into one of four stages of disease 3.
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CRC can arise from different pathological mechanisms that include defective DNA mismatchrepair (MMR) -presenting with microsatellite instability (MSI) phenotype-, chromosomal instability (CIN) 4, or epigenetic alterations, such as the CpG island methylator phenotype (CIMP) 5. There is an increasing amount of studies that address the topic of a molecular classification of CRC that could complement the current staging system, improve therapy
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decisions, and better predict survival after treatment. Several studies have reported associations of one or two molecular markers with CRC clinical
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characteristics, treatment response and survival. For example, patients with MSI-high tumors have shown to have better survival than patients with microsatellite stable (MSS) tumors
6-8
.
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Stage IV patients with KRAS mutations are known to not respond to anti-EGFR treatment 9. Several studies have described that patients with MSS/BRAF mutated tumors have worse overall and relapse-free survival compared to those with MSS/BRAF wild-type tumors and MSIhigh tumors 10,11.
Based on the complex associations between the molecular and clinical features of CRC, some attempts towards a more comprehensive classification have been made
12
. Jass suggested five
possible subtypes based on genetic instability and DNA methylation in CRC tumors, considering
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MSI and CIMP status as the main determinants of different subtypes
13
. Later on, the Colon
Cancer Subtype (CCS) and the Colorectal Cancer Assigner (CRCA) systems used unsupervised clustering analyses based on gene expression profiles
14,15
. Recently, a
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classification from the Colorectal Cancer Subtyping Consortium presented four CRC consensus molecular subtypes (CMS) based on analysis among six working groups 16. Yet, a definitive and comprehensive subtype classification is still not routinely implemented in the clinical setting. The
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aim of this systematic review was to identify and summarize the available literature addressing different molecular subtype classifications of CRC and evaluate their associations with patient
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survival.
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Methods Information sources and search strategy
on PRISMA-P recommendations
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A systematic review of the literature was conducted according to a pre-specified protocol based 17
. Four databases (PubMed, Web of Science, Embase and
Cochrane Library) were searched by one author (EA) from inception until December 2016 using MeSH terms and free-text words to build a search strategy based on a combination of terms
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related to colorectal cancer, molecular classifications, molecular markers, and survival (see Supplementary material for search terms). No language restrictions were applied, and limits
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were used to exclude editorials and general reviews. The reference lists of relevant studies were scanned for additional potentially eligible papers. Eligibility criteria and study selection
After removal of duplicate records, titles and abstracts of identified references were screened for
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eligibility criteria and potentially relevant full-text articles were assessed for inclusion independently by two reviewers (EA, MJ); all discrepancies regarding the inclusion or exclusion of an article were discussed and evaluated with a third reviewer (MH).
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Original articles including patients with a diagnosis of CRC at any stage were considered relevant for the review. Studies were included if they provided a measure of the association
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between the different subtypes and survival of the patients. Only studies that included at least three molecular markers in the definition of a subtype were eligible; this criterion was specified to take into account several molecular markers related to CRC and to arrive at a comprehensive classification that could complement the TNM staging, since multiple two by two combinations of several markers have been already extensively described. Data extraction
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Data extraction was performed by one reviewer (EA) and independently checked by a second reviewer (MJ) on an extraction sheet including information on authors; year of publication; title of the article; type of study; time frame; patient population; size of the study; stage of disease;
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technique used to determine the molecular markers; proposed subgroups; molecular markers included in each subgroup; type of outcome measure; adjustment of statistical analysis; and survival estimates for each molecular subtype.
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Quality evaluation
Quality assessment was performed by two independent reviewers (EA, MJ) using a scale
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constructed specifically for this review based on the Newcastle-Ottawa Quality Assessment Scale (NOS) and the Scottish Intercollegiate Guidelines Network (SIGN) methodology checklists to evaluate quality of non-randomized studies. Studies were awarded one point for each of the following items: presenting an adequate selection of patients; sample size or calculation of power; use of a reliable method to measure molecular markers; having based the definition of
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the classification on a previously studied classification, or having used a valid method to arrive at it; use of appropriate methods for outcome assessment; adjustment for at least age, sex and stage in the statistical analysis; and performing any type of validation of the predictive model
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according to the TRIPOD statement
18
. The TRIPOD statement describes four types of
prediction model validity, depending on whether these were developmental only or performed
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validation using either an internal or external data set. Synthesis of findings
The results were summarized in a qualitative manner only (i.e. no meta-analyses were performed), since the included studies presented heterogeneous CRC classifications based on different molecular markers, used different methodologies to arrive at the various subtypes and had different baseline categories for the survival analyses.
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Results Search results
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After removal of duplicates, 514 records retrieved from the databases were screened for eligibility (Figure 1). Twenty-six studies were identified as potentially relevant during this process and twenty were excluded after full-text review: four studies were not original articles eleven did not include three or more markers in the definition of a subtype
,
22-32
, and five did not 33-37
. Thus, six
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provide a measure of association between the subtypes and patient survival studies were included in the final review and synthesis.
10,19-21
All included studies had a quality assessment score above four points from seven possible (online Supplementary table). The main quality criterion that was not fulfilled by most studies was the validation of the model in an independent external cohort. Other criteria not fulfilled were not providing information on a sample size calculation or power estimation.
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Study characteristics and methods
Of the six studies included in the review, three performed analyses using tumor samples in previously established cohort studies 41,42
, two studies included patient cohorts from clinical
and one study consisted of a consensus analysis of six working groups that had
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trials
38-40
previously published CRC classifications based on gene expression analysis
16
. Most studies
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classified CRC into five subtypes based on several molecular markers. The included studies are summarized in table 1. Marisa et al.
38
and Domingo et al.
42
obtained their classification by performing hierarchical
clustering analysis of gene expression data. Their subtypes include information on BRAF, KRAS and MSI, as well as gene expression information. The consensus study group by Guinney et al.16 performed a network-based analysis in which subtyping algorithms were applied to an aggregated gene expression data set, developing a classification with four subtypes. This 8
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analysis included information from retrospective and prospective studies, publicly available gene expression profile data sets and a clinical trial. Three studies: Samadder et al. 39, Phipps et al. 40 41
, defined the subtypes based on adaptations from previously suggested
biological pathways described by Jass
13
and Leggett et al.
43
, presenting a five subtype
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and Sinicrope et al.
classification. Molecular subtype classifications
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Table 2 presents each of the identified molecular subtype classifications with the markers used
Domingo et al.
42
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for the definition of each subgroup and the hazard ratios provided for survival in each group. only found a significant association with disease-free survival for the KRAS
mutated, CIN positive and PIK3CA mutated subgroup in their analysis, showing a poorer survival compared to all other subgroups together. The authors did not provide survival estimates for the other subtypes in their publication. Marisa et al.
38
provided unadjusted HRs
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from a univariate analysis for 6 subtypes; however, for the multivariable analysis they combined subtypes C4 and C6 (MSS, KRAS mutated) into one category and compared it to all other subtypes, obtaining a HR of 1.8 (95%CI 1.3-2.5) for relapse-free survival. 16
used different biological and genetic characteristics for the definition of four
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Guinney et al.
subtypes, with only two – CMS1 and CMS3 – considering MSI, BRAF, KRAS or CIMP. CMS2
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and CMS4 included information on SCNA, WNT and MYC activation and other biological characteristics, such as immune infiltration and metabolic deregulation. They found a significant association for poorer relapse-free and overall survival only for CMS4 compared to CMS2. The CMS4 subtype comprised 23% of the consensus sample and consisted of SCNA-high tumors with stromal infiltration, TGF-ß activation and angiogenesis. Samadder et al.
39
, Phipps et al.
40
and Sinicrope et al.
BRAF and KRAS. Samadder et al.
39
41
, presented subtypes including MSI,
provided HRs for CRC-specific mortality and found 9
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significantly increased mortality for one of the unassigned clusters (KRAS mutated); however, in a similar group with KRAS mutations, no statistically significant association with survival was found. Phipps et al.
40
described a better CRC-specific survival for two subtypes with MSI-high
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and KRAS wild-type tumors, and a poorer survival for MSS subtypes with BRAF or KRAS mutations. The subtype showing the strongest association with CRC-specific mortality was characterized by BRAF mutation and CIMP. Sinicrope et al.
41
presented pairwise comparisons
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of other groups versus a non-mutated BRAF/KRAS group for DFS. They found significantly poorer DFS for MSS tumors with KRAS or BRAF mutations. Subtypes with MSI-high showed no
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statistically significant association with survival. Synthesis of results
From the six included studies, three used population-based cohorts and described similar although not identical classifications. Their molecular subtypes were based on Jass’ pathways and used tumors with all four markers negative as a baseline for analyses. Table 3 presents a
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synthesis of similar subtypes for only these three studies, since conducting of a meta-analysis was not considered appropriate. Other studies were not included in this summary because of the different methods used to generate the classifications
16
, the different composition of the
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subtypes, the combination of different subtypes into one baseline category
38,42
and the
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estimates generated only from univariate analyses 38. From the three studies included in the synthesis, only Samadder et al provided information on CIMP status. Samadder et al
39
39
and Phipps et al
40
classified one of the subtypes
irrespective of the status of MSI (either MSI or MSS), making that subtype inadequate for inclusion in the synthesis and preventing a meta-analysis of the results. The MSS, KRAS mutated, BRAF wild-type, and CIMP-negative subtype was associated with worse survival, with HRs between 1.25 and 1.48. The MSI-high, BRAF mutated, and CIMP-positive subtype showed
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dissimilar and non-significant HRs. Finally, the MSS, BRAF mutated, CIMP-positive subtype
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was associated with a statistically significant worse survival, with HRs of 1.43 and 1.84.
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Discussion In this review we have summarized available evidence regarding different molecular subtype classifications of CRC and their associations with patient survival. We found six studies that
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proposed classifications based on different molecular markers and gene expression analyses; however, only three studies with similar subtypes and methods of analysis could be
than those without any mutation. Contrary to what previous reports have found
8,44,45
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synthesized. We found that subtypes with either KRAS or BRAF mutations had poorer survival
, subtypes with MSI showed no clear
review
40
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association with survival in the synthesized classifications. Only one study included in this found better survival for two subtypes with MSI-high, both of which were negative for
KRAS mutations. MSI has been consistently associated with better outcomes; however, it appears to have less prognostic value in stage III colon cancer patients. In a recent report of patients receiving FOLFOX with or without cetuximab, no association was found between MMR 46
. Other analyses of large cohorts and clinical trials
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status and disease-free or overall survival
in stage III patients have found conflicting results regarding disease-free and overall survival, and no clear benefit from adjuvant 5FU chemotherapy has been described in these patients
47
.
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This suggests that the association requires further investigation, since the MSI phenotype has also been associated with other features, such as laterality of the tumor, immune infiltration and
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CIMP and BRAF mutations, which might explain some conflicting reports. Several studies have described the association between MMR status in combination with BRAF mutations and survival, observing that patients with MSS/BRAF mutated tumors have poorer survival in comparison to those with MSS/BRAF wild-type tumors
10,11,48
. Significantly poorer
survival of the MSS/BRAF mutated subtype was also found in two studies in this review
40,41
.
Also, somewhat poorer survival was found for the MSS/KRAS mutated subtype compared with the negative baseline (MSS, BRAFwt, and KRASwt) 40,41. 12
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There was no association of CIMP with survival in any of the included studies in concordance with existing literature, where this association has been either negative or controversial
28,49,50
.
Subtypes integrating TP53, PIK3CA or PTEN expression showed no clear association with
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survival, and studies including these genetic markers did not present multivariable regression results for individual subtypes. PIK3CA mutations are present in 10 – 20% of CRC, and it has been suggested that in stage IV patients this mutation could be associated with resistance to
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anti-EGFR monoclonal antibodies. A recent meta-analysis based on data from over 12000 patients reported no association of PIK3CA with overall or progression-free survival in CRC
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patients 51.
The Colorectal Cancer Subtyping Consortium integrated data from six studies to generate a unified classification from a large-scale network analysis. Only one of the proposed subtypes (CMS4) showed an association with worse patient survival, and one other (CMS1) with survival after relapse. It is important to consider that 13% of patients could not be classified into any
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subtype and that CMS4 tumors were diagnosed at later stages and were heterogeneous regarding MSI status, making comparisons between the CMS groups and other pathological classifications difficult. Even though this network analysis presented a complex methodology
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from several expert working groups, molecular pathological features- such as MSI, BRAF and KRAS- were not considered in all subtypes. The value and applicability of the CMS subtypes
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needs to be elaborated in future studies, taking into account that its implementation might not be feasible for all institutions or clinicians, also with respect to costs and technical difficulties. There have been other approaches to classify CRC, such as the immunoscore
52
in which a
score is assigned depending on the density of in-situ T lymphocytes in the core and invasive margins of the tumor
53
. Tumors presenting with a higher count of T lymphocytes have been
associated with less nodal and metastatic involvement
54
. The immunoscore has shown an
association with survival in CRC, where tumors with a higher immune infiltrate had a better 13
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survival, and were more likely to be MSI-high
55
. Other reports have also described that MSI
tumors are characterized by a high density of infiltrating lymphocytes, which has been associated with better prognosis in CRC patients 55,56.
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Currently, the TNM staging is the sole classification routinely used in clinical practice, with MSI status also being considered in the risk assessment of stage II patients 57. Stage II patients with low risk do not receive adjuvant chemotherapy after surgery, whereas for those with high risk
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features chemotherapy is considered as adjuvant treatment. Testing for BRAF and KRAS mutations is only recommended for patients with metastatic disease when considering treatment 57
. While some studies have found poorer survival for stage III patients
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with anti-EGFR agents
with KRAS mutations 46,58,59, the role of KRAS as a prognostic marker is still controversial 60, and its use in current clinical practice is so far restricted to predicting the response to treatment with anti-EGFR monoclonal antibodies for stage IV patients 9.
A recent study showed that adding MSI, BRAF and KRAS information to TNM staging in
patients
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survival models improved their capacity to predict overall survival in stage II and III colon cancer . The authors recommended that more efforts should be made to achieve an ideal
stratification. Before endorsing a new classification system to be applied in clinical practice, the
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UICC suggests external validation needs to be conducted in several studies on different populations and time periods
62
. Among the studies included in this review, only two performed
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external validation of their suggested classifications, which underlines the need of further validation to arrive at a classification that provides independent prognostic value, is both molecularly and clinically feasible, and practical and easy to implement in the clinical setting. This review was restricted to articles including three or more molecular markers in the definition of a CRC subtype classification. This could be perceived as a limitation of the study, since a large amount of literature has investigated the relationship between one or two markers and CRC survival. For the development of a comprehensive molecular pathological classification of 14
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CRC subtypes, we considered that at least MSI, KRAS, and BRAF should be incorporated, which have repeatedly been associated with survival in previous studies. Some two-feature subtypes have shown rather conflicting results, such as the combination of MSI and CIMP
63
.
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On the other hand, two marker combinations, as shown for the combination of MSI and BRAF, may sometimes be sufficient for the definition of a clinically relevant subtype. Due to the limited number of studies and the differences in the subtype classifications between the studies, a
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meta-analysis was not considered feasible. However, we identified and discussed comparable subtypes wherever possible.
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In conclusion, we found that only few molecular subtype definitions were similar among the different molecular pathological classifications currently proposed for CRC. Molecular subtypes showing MSS and BRAF mutation had an unfavorable prognosis, compared to MSS CRC without BRAF mutation. The combination of MSS and KRAS mutation also showed a moderate association with poorer survival compared to MSS cancers without KRAS mutation. Subtypes
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with MSI-high were not associated with improved CRC survival in the studies included in this review when tumors showed BRAF mutations. None of the proposed subtype classifications was optimal in predicting survival of CRC patients, since significant associations with survival
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were only seen for one or two subtypes. Additionally, the CMS methodology focused on several genetic factors and was not easily comparable to other subtypes that are more commonly used
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by clinicians.
Further research closely related to clinical outcomes is required, before any recommendations can be implemented in clinical practice. For example, the association of different combinations of molecular markers by stage with the patient’s response to different treatment schemes is a topic that needs further investigation. Also, current molecular subtype classifications do not incorporate molecular markers into the traditional TNM staging of CRC. Further studies and external validation will be essential to consolidate a system that integrates the most relevant 15
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biological, molecular, clinical, and pathological factors for a comprehensive classification of
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clinically meaningful subtypes.
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References 1. Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012. v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No.11. Lyon, France: International Agency for Research on Cancer; 2013.
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2. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet. 2014;383(9927):1490-502.
3. American Joint Commitee on Cancer. Colon and Rectum Cancer Staging. 2009;7th Edition.
4. Ogino S, Goel A. Molecular Classification and Correlates in Colorectal Cancer. J Mol Diagn.
SC
2008;10(1):13-27.
5. Markowitz SD, Bertagnolli MM. Molecular Basis of Colorectal Cancer. N Engl J Med.
M AN U
2009;361(25):2449-60.
6. Boland CR, Goel A. Microsatellite Instability in Colorectal Cancer. Gastroenterology. 2010;138(6):207387.
7. Popat S, Hubner RA, Houlston RS. Systematic Review of Microsatellite Instability and Colorectal Cancer Prognosis. J Clin Oncol. 2005;23(3):609-18.
8. Guastadisegni C, Colafranceschi M, Ottini L, Dogliotti E. Microsatellite instability as a marker of
2010;46(15):2788-98.
TE D
prognosis and response to therapy: A meta-analysis of colorectal cancer survival data. Eur J Cancer.
9. Karapetis CS, Khambata S, Jonker DJ, et al. K-ras Mutations and Benefit from Cetuximab in Advanced
EP
Colorectal Cancer. N Engl J Med. 2008;359(17):1757-65. 10. Lochhead P, Kuchiba A, Imamura Y, et al. Microsatellite Instability and BRAF Mutation Testing in
AC C
Colorectal Cancer Prognostication. J Natl Cancer Inst. 2013;105(15):1151-6. 11. Seppälä TT, Böhm JP, Friman M, et al. Combination of microsatellite instability and BRAF mutation status for subtyping colorectal cancer. Br J Cancer. 2015;112(12):1966-75. 12. Rodriguez-Salas N, Dominguez G, Barderas R, et al. Clinical relevance of colorectal cancer molecular subtypes. Crit Rev Oncol/Hematol. 2017;109:9-19. 13. Jass JR. Classification of colorectal cancer based on correlation of clinical, morphological and molecular features. Histopathology. 2007;50(1):113-30.
17
ACCEPTED MANUSCRIPT
14. De Sousa E Melo F, Wang X, Jansen M, et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat Med. 2013;19(5):614-8. 15. Sadanandam A, Lyssiotis CA, Homicsko K, et al. A colorectal cancer classification system that
RI PT
associates cellular phenotype and responses to therapy. Nat Med. 2013;19(5):619-25. 16. Guinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350-6.
17. Shamseer L, Moher D, Clarke M, et al. PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review
SC
protocol. BMJ. 2015;349:7647-.
18. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction
M AN U
model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med. 2015;162(1):55-63.
19. Bae JM, Kim JH, Kang GH. Molecular Subtypes of Colorectal Cancer and Their Clinicopathologic Features, With an Emphasis on the Serrated Neoplasia Pathway. Arch Pathol Lab Med. 2016;140(5):40612.
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20. Sadanandam A, Wang X, de Sousa E Melo F, et al. Reconciliation of classification systems defining molecular subtypes of colorectal cancer: interrelationships and clinical implications. Cell Cycle. 2014;13(3):353-7.
21. Sinicrope FA, Okamoto K, Kasi PM, Kawakami H. Molecular Biomarkers in the Personalized
EP
Treatment of Colorectal Cancer. Clin Gastroenterol Hepatol. 2016;14(5):651-8. 22. Berg M, Nordgaard O, Korner H, et al. Molecular subtypes in stage II-III colon cancer defined by genomic instability: early recurrence-risk associated with a high copy-number variation and loss of
AC C
RUNX3 and CDKN2A. PLoS One. 2015;10(4):e0122391. 23. Braxton DR, Zhang R, Morrissette JD, Loaiza-Bonilla A, Furth EE. Clinicopathogenomic analysis of mismatch repair proficient colorectal adenocarcinoma uncovers novel prognostic subgroups with differing patterns of genetic evolution. Int J Cancer. 2016;139(7):1546-56. 24. Eklöf V, Wikberg ML, Edin S, et al. The prognostic role of KRAS, BRAF, PIK3CA and PTEN in colorectal cancer. Br J Cancer. 2013;108(10):2153-63. 25. Foltran L, De Maglio G, Pella N, et al. Prognostic role of KRAS, NRAS, BRAF and PIK3CA mutations in advanced colorectal cancer. Future Oncol. 2015;11(4):629-40.
18
ACCEPTED MANUSCRIPT
26. Fu T, Liu Y, Li K, et al. Tumors with unmethylated MLH1 and the CpG island methylator phenotype are associated with a poor prognosis in stage II colorectal cancer patients. Oncotarget. 2016;7(52). 27. Kim JH, Rhee Y-Y, Bae JM, Cho N-Y, Kang GH. Loss of CDX2/CK20 Expression Is Associated With Poorly Differentiated Carcinoma, the CpG Island Methylator Phenotype, and Adverse Prognosis in
RI PT
Microsatellite-unstable Colorectal Cancer. Am J Surg Pathol. 2013;37(10):1532-41.
28. Ogino S, Nosho K, Kirkner GJ, et al. CpG island methylator phenotype, microsatellite instability, BRAF mutation and clinical outcome in colon cancer. Gut. 2009;58(1):90-6.
29. Roepman P, Schlicker A, Tabernero J, et al. Colorectal cancer intrinsic subtypes predict
SC
chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition. Int J Cancer. 2013;134(3):552-62.
M AN U
30. Sanchez JA, Krumroy L, Plummer S, et al. Genetic and epigenetic classifications define clinical phenotypes and determine patient outcomes in colorectal cancer. Br J Surg. 2009;96(10):1196-204. 31. Shiovitz S, Bertagnolli MM, Renfro LA, et al. CpG island methylator phenotype is associated with response to adjuvant irinotecan-based therapy for stage III colon cancer. Gastroenterology. 2014;147(3):637-45.
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32. Song N, Pogue-Geile KL, Gavin PG, et al. Clinical Outcome From Oxaliplatin Treatment in Stage II/III Colon Cancer According to Intrinsic Subtypes Secondary Analysis of NSABP C-O7/NRG Oncology Randomized Clinical Trial. JAMA Oncol. 2016;2(9):1162-9. 33. Farchoukh L, Kuan S-F, Dudley B, Brand R, Nikiforova M, Pai RK. MLH1-deficient Colorectal
EP
Carcinoma With Wild-type BRAF and MLH1 Promoter Hypermethylation Harbor KRAS Mutations and Arise From Conventional Adenomas. Am J Surg Pathol. 2016;40(10):1390-99. 34. Furlan D, Carnevali IW, Bernasconi B, et al. Hierarchical clustering analysis of pathologic and
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molecular data identifies prognostically and biologically distinct groups of colorectal carcinomas. Mod Pathol. 2011;24(1):126-37.
35. Giampieri R, Mandolesi A, Abouelkhair KM, et al. Prospective study of a molecular selection profile for RAS wild type colorectal cancer patients receiving irinotecan-cetuximab. J Transl Med. 2015;13(1):140. 36. Inoue M, Takahashi S, Soeda H, et al. Gene-expression profiles correlate with the efficacy of antiEGFR therapy and chemotherapy for colorectal cancer. Int J Clin Oncol. 2015;20(6):1147-55.
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37. Perez-Villamil B, Romera-Lopez A, Hernandez-Prieto S, et al. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior. BMC cancer. 2012;12:260. 38. Marisa L, de Reynies A, Duval A, et al. Gene Expression Classification of Colon Cancer into
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Molecular Subtypes: Characterization, Validation, and Prognostic Value. PLoS Med. 2013;10(5).
39. Samadder NJ, Vierkant RA, Tillmans LS, et al. Associations between colorectal cancer molecular markers
and
pathways
with
clinicopathologic
features
in
older
2013;145(2):348-56.
women.
Gastroenterology.
and patient survival. Gastroenterology. 2015;148(1):77-87.
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40. Phipps AI, Limburg PJ, Baron JA, et al. Association between molecular subtypes of colorectal cancer
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41. Sinicrope FA, Shi Q, Smyrk TC, et al. Molecular markers identify subtypes of stage III colon cancer associated with patient outcomes. Gastroenterology. 2015;148(1):88-99.
42. Domingo E, Ramamoorthy R, Oukrif D, et al. Use of multivariate analysis to suggest a new molecular classification of colorectal cancer. J Pathol. 2013;229(3):441-8.
43. Leggett B, Whitehall V. Role of the Serrated Pathway in Colorectal Cancer Pathogenesis.
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Gastroenterology. 2010;138(6):2088-100.
44. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med. 2003;349(3):247-57. 45. Sinicrope FA, Sargent DJ. Molecular pathways: Microsatellite instability in colorectal cancer:
EP
Prognostic, predictive, and therapeutic implications. Clin Cancer Res. 2012;18(6):1506-12. 46. Taieb J, Zaanan A, Le Malicot K, et al. Prognostic Effect of BRAF and KRAS Mutations in Patients
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With Stage III Colon Cancer Treated With Leucovorin, Fluorouracil, and Oxaliplatin With or Without Cetuximab: A Post Hoc Analysis of the PETACC-8 Trial. JAMA Oncol. 2016;2(5):1-11. 47. Auclin E, Zaanan A, Vernerey D, et al. Subgroups and Prognostication in Stage III Colon Cancer: Future Perspectives for Adjuvant Therapy. Ann Oncol. 2017;28(5):958-68. 48. Taieb J, Le Malicot K, Shi Q, et al. Prognostic Value of BRAF and KRAS Mutations in MSI and MSS Stage III Colon Cancer. J Natl Cancer Inst. 2017;109(5). 49. Jia M, Jansen L, Walter V, et al. No association of CpG island methylator phenotype and colorectal cancer survival: population-based study. Br J Cancer. 2016;115(11):1359-66.
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ACCEPTED MANUSCRIPT
50. Juo YY, Johnston FM, Zhang DY, et al. Prognostic value of CpG island methylator phenotype among colorectal cancer patients: a systematic review and meta-analysis. Ann Oncol. 2014;25(12):2314-27. 51. Mei ZB, Duan CY, Li CB, Cui L, Ogino S. Prognostic role of tumor PIK3CA mutation in colorectal
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cancer: a systematic review and meta-analysis. Ann Oncol. 2016;27(10):1836-48. 52. Kirilovsky A, Marliot F, El Sissy C, Haicheur N, Galon J, Pages F. Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients. Int Immunol. 2016;28(8):373-82.
53. Galon J, Mlecnik B, Bindea G, et al. Towards the introduction of the 'Immunoscore' in the
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classification of malignant tumours. J Pathol. 2014;232(2):199-209.
54. Pagès F, Berger A, Camus M, et al. Effector Memory T Cells, Early Metastasis, and Survival in
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Colorectal Cancer. N Engl J Med. 2005;353(25):2654-66.
55. Mlecnik B, Bindea G, Angell HK, et al. Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability. Immunity. 2016;44:698-711. 56. Kloor M, von Knebel Doeberitz M. The Immune Biology of Microsatellite-Unstable Cancer. Trends Cancer. 2016;2(3):121-33.
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57. National Comprehensive Cancer Network. Clinical Practice Guidelines in Oncology (NCCN Guidelines)
Colon Cancer. Version 2.2016. 2016.
58. Imamura Y, Morikawa T, Liao X, et al. Specific mutations in KRAS codons 12 and 13, and patient
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prognosis in 1075 BRAF wild-type colorectal cancers. Clin Cancer Res. 2012;18(17):4753-63. 59. Phipps AI, Buchanan DD, Makar KW, et al. KRAS-mutation status in relation to colorectal cancer
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survival: the joint impact of correlated tumour markers. Br J Cancer. 2013;108(8):1757-64. 60. Roth AD, Tejpar S, Delorenzi M, et al. Prognostic role of KRAS and BRAF in stage II and III resected colon cancer: Results of the translational study on the PETACC-3, EORTC 40993, SAKK 60-00 trial. J Clin Oncol. 2010;28(3):466-74. 61. Dienstmann AR, Mason MJ, Sinicrope FA, et al. Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study. Ann Oncol. 2017;28(5):1023-31. 62. Kattan MW, Hess KR, Amin MB, Lu Y, Moons KGM. American Joint Committee on Cancer Acceptance Criteria for Inclusion of Risk Models for Individualized Prognosis in the Practice of Precision Medicine. CA Cancer J Clin. 2016;66:370-4.
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63. Jia M, Gao X, Zhang Y, Hoffmeister M, Brenner H. Different definitions of CpG island methylator
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phenotype and outcomes of colorectal cancer: a systematic review. Clin Epigenetics. 2016;8:25.
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Figure legends
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Figure 1. Literature search and selection process - adapted from PRISMA 2009 statement.
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Tables
Table 1. Characteristics of included studies
Marisa et al,
38
40
Time frame
N
CRC Stage
Treatment
UK
Cohort
2002 - 2004
906
II - III
Rofecoxib vs b placebo
France
Cohort
1987 - 2007
775
II - III
Any
c
US
Cohort
1986 - 2002
563
I - IV
Any
c
US
Cohort
1998 - 2007
2050
I - IV
42
2013
Samadder et al, 2013 Phipps et al,
Type of study a
39
2015 41
Subtypes
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Domingo et al, 2013
Country
Any
Outcomes
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Authors, year
c
Type of validation
5
DFS
Development
6
RFS
External
5
CSS, OS
Development
5
CSS, OS
Development
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Sinicrope et al, US Cohort a 2004 - 2009 2720 III FOLFOX 5 DFS External 2015 16 Guinney et al, c Various Consensus N/A 3104 I - IV Any 4 RFS, OS, SAR Internal 2015 DFS: Disease-free survival; CSS: Cancer-specific survival; RFS: Relapse-free survival; OS: Overall survival; RFS: Relapse-free survival; SAR: Survival after relapse a b c Cohort from a clinical trial setting; After primary treatment; Chemotherapy agents not specified
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Table 2. Subtype classifications and association with survival outcomes Authors, year
Group
MSI high + -
BRAF mut + -
KRAS mut
CIMP
CIN
TP53
Other
Outcome model DFS Unadj.
n
HR
95% CI
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169 1 Group 1 Group 2 + + 322 1 42 Domingo et al, a Group 3 + + PIK3CA+ 133 1.48 (1.1-2.1) 2013 + PIK3CA+ 80 1 Group 4 Group 5 NRAS+ 32 1 RFS 175 1 C1 + + + Unadj. C2 + + + + 167 1.10 (0.6-1.7) b Marisa et al , 96 0.94 (0.5-1.6) C3 + + 38 C4 + + + 72 2.30 (1.4-3.8) 2013 188 1.40 (0.9-2.1) C5 + + + Serrated C6 + + + Conventional 77 2.10 (1.3-3.4) CSS Traditional 1 170 Adj. Serrated ο + + 1.56 (0.9-2.8) 58 39 Samadder et al, Alternate + 1.26 (0.7-2.4) 142 2013 1.76 (1.1-2.9) Unassigned cA + 96 Unassigned cB low 1.46 (0.6-3.4) 25 CSS Type 1 + + + 0.54 (0.3-1.0) 100 Adj. 1.84 (1.2-2.8) Type 2 + + 55 Phipps et al, 40 Type 3 + 1.25 (1.0-1.5) 353 2015 1 Type 4 631 Type 5 + 0.42 (0.2-0.9) 50 DFS Non BRAF/KRAS 1 1331 Adj. Mutant KRAS + 1.48 (1.3-1.7) 945 41 Sinicrope et al, Mutant BRAF + 1.43 (1.1-1.8) 189 2015 1.10 (0.8-1.5) dMMR sporadic + + ο MLH1 methylation 184 dMMR familial + ο 0.77 (0.5-1.3) MLH1 unmethylated 71 RFS CMS 1 + + + 0.88 (0.5-1.5) 435 Guinney et al, Adj. CMS 2 1 SCNA high, WNT, MYC 1148 16 CMS 3 ο + 0.84 (0.6-1.2) SCNA low 404 2015 CMS 4 1.49 (1.2-1.9) SCNA high, TGF-ß 714 For each group, a plus or minus sign is presented depending on whether molecular markers are positive or negative for that category; blank spaces indicate that a specific marker was not measured or mentioned in the respective publication. DFS: Disease-free survival; CSS: Cancer-specific survival; RFS: Relapse-free a b survival. Shadowed cells indicate and/or; ο: Any; Unadj.: Unadjusted model; Adj.: Adjusted model. Group3 vs all other groups as reference; Marisa et al, adapted from original: -/+= - and ++/+++=+; results from univariate model.
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Table 3. Synthesis of similar molecular subtype classifications MSI BRAF KRAS CIMP HR 95% CI Authors, year high mut mut 1 Reference 40 + + + 0.54 (0.3-1.0) Phipps et al, 2015 41 a + + 1.10 (0.8-1.5) Sinicrope et al, 2015 40 + + 1.84 (1.2-2.8) Phipps et al, 2015 41 + 1.43 (1.1-1.8) Sinicrope et al, 2015 40 + 1.25 (1.0-1.5) Phipps et al, 2015 39 + 1.26 (0.7-2.4) Samadder et al, 2013 41 1.48 (1.3-1.7) Sinicrope et al, 2015 + a KRAS wild type or mutated; shadowed cells indicate no measurement
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PII: DOI: Reference:
S1542-3565(17)31536-7 10.1016/j.cgh.2017.12.038 YJCGH 55622
To appear in: Clinical Gastroenterology and Hepatology Accepted Date: 21 December 2017 Please cite this article as: Alwers E, Jia M, Kloor M, Bläker H, Brenner H, Hoffmeister M, Associations Between Molecular Classifications of Colorectal Cancer and Patient Survival: a Systematic Review, Clinical Gastroenterology and Hepatology (2018), doi: 10.1016/j.cgh.2017.12.038. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Associations Between Molecular Classifications of Colorectal Cancer and Patient Survival: a Systematic Review Short title: Molecular classifications of CRC
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Authors: Elizabeth Alwers, MSc a, Min Jia, MSc a, Matthias Kloor, PhD b, Hendrik Bläker, PhD c, Hermann Brenner, MD, MPH a,d,e, Michael Hoffmeister, PhD a.
Affiliations Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, Germany
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a
b
Department of Applied Tumor Biology, Institute of Pathology, University of Heidelberg, Im Neuenheimer Feld 220, Heidelberg, Germany
c
d
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Department of General Pathology, Institute of Pathology, Charité University Medicine Hospital, Charitéplatz 1, Berlin, Germany Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
e
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
Grant support: Not applicable
for Cancer Control.
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Abbreviations used: CIMP: CpG island methylator phenotype; CIN: chromosomal instability; CMS: Consensus molecular subtypes; CRC: colorectal cancer; DSS: disease-specific survival; HR: hazard Ratio; IHC: immunohistochemical; MMR: Mismatch repair; MSI: microsatellite instability; MSS: microsatellite stable; NOS: Newcastle-Ottawa Quality Assessment Scale; OS: overall survival; RFS: recurrence-free survival; SIGN: Scottish Intercollegiate Guidelines Network; UICC: International Union
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Correspondence Dr. Michael Hoffmeister Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, 69120, Germany E-mail address: [email protected] Tel: +49 6221 42-1303 Disclosures: The authors have no conflict of interest to declare. Author contributions: Study concept and design: EA, MJ, MK, HBl, HBr, MH; acquisition of data: EA, MJ, MH; analysis and interpretation of data: EA, MJ, MK, HBl, HBr, MH; drafting of manuscript: EA, MH; critical addition of important intellectual content to manuscript: EA, MJ, MK, HBl, HBr, MH; all authors approved final version of the manuscript.
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Abstract Background & Aims: Colorectal cancer (CRC) is a heterogeneous disease with different mechanisms of pathogenesis. Classification systems have been proposed based on molecular
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features of tumors, but none are used in clinical practice. We performed a systematic review of studies on the associations between molecular classifications of CRC and patient survival. Methods: We searched the PubMed, Embase, Cochrane, and Web of Science databases for
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combinations of terms related to CRC, molecular markers, subtype classifications, and survival (overall survival, disease-specific survival, disease-free survival). We included only studies that
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used at least 3 molecular markers to classify tumors and provided an estimate of survival associated with each subtype. Data extraction and quality assessment were performed independently by 2 reviewers.
Results: We identified 6 studies that fulfilled the inclusion criteria. In these studies, molecular
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subtypes were assigned based on pathways associated with tumor development or findings from gene expression clustering analyses. Most studies proposed classification systems with 5 subtypes, including information on microsatellite instability, mutations in BRAF, and mutations in
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KRAS. None of the studies included TNM stage in their classification system. Three classification systems used similar definitions. Only 3 studies provided internal or external
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validation of the proposed classification schemes. Tumors with microsatellite stability and mutations in KRAS or BRAF were associated with decreased survival times, compared to tumors with microsatellite stability and no mutations. Conclusion: In a systematic review of studies of molecular classifications of CRC and patient survival, we found that most subtypes were not significantly or not differentially associated with survival. None of the systems integrated TNM staging. Further research and validation are needed to develop molecular subtype classification systems for clinical practice.
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KEY WORDS: MSI; colon cancer; genetics; biomarker Summary: In a systematic review, we found that systems for classifying colorectal tumors based on at least 3 molecular features do not accurately predict patient survival. Improvements
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to classification systems and validation are needed for them to have prognostic value and
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relevance to clinical practice.
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Introduction Colorectal cancer (CRC) is one of the leading causes of cancer incidence among men and women worldwide 1. Even with decreasing mortality rates observed in high income countries
cause of cancer mortality in the world
1,2
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due to early detection and more effective treatment, CRC represents the fourth most common . The strongest prognostic factor and determinant of
treatment for CRC patients used in clinical routine is the stage of disease at diagnosis. Based
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on the size of the tumor, number of compromised lymph nodes, and presence of distant metastatic lesions, patients can generally be classified into one of four stages of disease 3.
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CRC can arise from different pathological mechanisms that include defective DNA mismatchrepair (MMR) -presenting with microsatellite instability (MSI) phenotype-, chromosomal instability (CIN) 4, or epigenetic alterations, such as the CpG island methylator phenotype (CIMP) 5. There is an increasing amount of studies that address the topic of a molecular classification of CRC that could complement the current staging system, improve therapy
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decisions, and better predict survival after treatment. Several studies have reported associations of one or two molecular markers with CRC clinical
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characteristics, treatment response and survival. For example, patients with MSI-high tumors have shown to have better survival than patients with microsatellite stable (MSS) tumors
6-8
.
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Stage IV patients with KRAS mutations are known to not respond to anti-EGFR treatment 9. Several studies have described that patients with MSS/BRAF mutated tumors have worse overall and relapse-free survival compared to those with MSS/BRAF wild-type tumors and MSIhigh tumors 10,11.
Based on the complex associations between the molecular and clinical features of CRC, some attempts towards a more comprehensive classification have been made
12
. Jass suggested five
possible subtypes based on genetic instability and DNA methylation in CRC tumors, considering
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MSI and CIMP status as the main determinants of different subtypes
13
. Later on, the Colon
Cancer Subtype (CCS) and the Colorectal Cancer Assigner (CRCA) systems used unsupervised clustering analyses based on gene expression profiles
14,15
. Recently, a
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classification from the Colorectal Cancer Subtyping Consortium presented four CRC consensus molecular subtypes (CMS) based on analysis among six working groups 16. Yet, a definitive and comprehensive subtype classification is still not routinely implemented in the clinical setting. The
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aim of this systematic review was to identify and summarize the available literature addressing different molecular subtype classifications of CRC and evaluate their associations with patient
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survival.
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Methods Information sources and search strategy
on PRISMA-P recommendations
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A systematic review of the literature was conducted according to a pre-specified protocol based 17
. Four databases (PubMed, Web of Science, Embase and
Cochrane Library) were searched by one author (EA) from inception until December 2016 using MeSH terms and free-text words to build a search strategy based on a combination of terms
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related to colorectal cancer, molecular classifications, molecular markers, and survival (see Supplementary material for search terms). No language restrictions were applied, and limits
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were used to exclude editorials and general reviews. The reference lists of relevant studies were scanned for additional potentially eligible papers. Eligibility criteria and study selection
After removal of duplicate records, titles and abstracts of identified references were screened for
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eligibility criteria and potentially relevant full-text articles were assessed for inclusion independently by two reviewers (EA, MJ); all discrepancies regarding the inclusion or exclusion of an article were discussed and evaluated with a third reviewer (MH).
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Original articles including patients with a diagnosis of CRC at any stage were considered relevant for the review. Studies were included if they provided a measure of the association
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between the different subtypes and survival of the patients. Only studies that included at least three molecular markers in the definition of a subtype were eligible; this criterion was specified to take into account several molecular markers related to CRC and to arrive at a comprehensive classification that could complement the TNM staging, since multiple two by two combinations of several markers have been already extensively described. Data extraction
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Data extraction was performed by one reviewer (EA) and independently checked by a second reviewer (MJ) on an extraction sheet including information on authors; year of publication; title of the article; type of study; time frame; patient population; size of the study; stage of disease;
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technique used to determine the molecular markers; proposed subgroups; molecular markers included in each subgroup; type of outcome measure; adjustment of statistical analysis; and survival estimates for each molecular subtype.
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Quality evaluation
Quality assessment was performed by two independent reviewers (EA, MJ) using a scale
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constructed specifically for this review based on the Newcastle-Ottawa Quality Assessment Scale (NOS) and the Scottish Intercollegiate Guidelines Network (SIGN) methodology checklists to evaluate quality of non-randomized studies. Studies were awarded one point for each of the following items: presenting an adequate selection of patients; sample size or calculation of power; use of a reliable method to measure molecular markers; having based the definition of
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the classification on a previously studied classification, or having used a valid method to arrive at it; use of appropriate methods for outcome assessment; adjustment for at least age, sex and stage in the statistical analysis; and performing any type of validation of the predictive model
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according to the TRIPOD statement
18
. The TRIPOD statement describes four types of
prediction model validity, depending on whether these were developmental only or performed
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validation using either an internal or external data set. Synthesis of findings
The results were summarized in a qualitative manner only (i.e. no meta-analyses were performed), since the included studies presented heterogeneous CRC classifications based on different molecular markers, used different methodologies to arrive at the various subtypes and had different baseline categories for the survival analyses.
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Results Search results
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After removal of duplicates, 514 records retrieved from the databases were screened for eligibility (Figure 1). Twenty-six studies were identified as potentially relevant during this process and twenty were excluded after full-text review: four studies were not original articles eleven did not include three or more markers in the definition of a subtype
,
22-32
, and five did not 33-37
. Thus, six
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provide a measure of association between the subtypes and patient survival studies were included in the final review and synthesis.
10,19-21
All included studies had a quality assessment score above four points from seven possible (online Supplementary table). The main quality criterion that was not fulfilled by most studies was the validation of the model in an independent external cohort. Other criteria not fulfilled were not providing information on a sample size calculation or power estimation.
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Study characteristics and methods
Of the six studies included in the review, three performed analyses using tumor samples in previously established cohort studies 41,42
, two studies included patient cohorts from clinical
and one study consisted of a consensus analysis of six working groups that had
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trials
38-40
previously published CRC classifications based on gene expression analysis
16
. Most studies
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classified CRC into five subtypes based on several molecular markers. The included studies are summarized in table 1. Marisa et al.
38
and Domingo et al.
42
obtained their classification by performing hierarchical
clustering analysis of gene expression data. Their subtypes include information on BRAF, KRAS and MSI, as well as gene expression information. The consensus study group by Guinney et al.16 performed a network-based analysis in which subtyping algorithms were applied to an aggregated gene expression data set, developing a classification with four subtypes. This 8
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analysis included information from retrospective and prospective studies, publicly available gene expression profile data sets and a clinical trial. Three studies: Samadder et al. 39, Phipps et al. 40 41
, defined the subtypes based on adaptations from previously suggested
biological pathways described by Jass
13
and Leggett et al.
43
, presenting a five subtype
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and Sinicrope et al.
classification. Molecular subtype classifications
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Table 2 presents each of the identified molecular subtype classifications with the markers used
Domingo et al.
42
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for the definition of each subgroup and the hazard ratios provided for survival in each group. only found a significant association with disease-free survival for the KRAS
mutated, CIN positive and PIK3CA mutated subgroup in their analysis, showing a poorer survival compared to all other subgroups together. The authors did not provide survival estimates for the other subtypes in their publication. Marisa et al.
38
provided unadjusted HRs
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from a univariate analysis for 6 subtypes; however, for the multivariable analysis they combined subtypes C4 and C6 (MSS, KRAS mutated) into one category and compared it to all other subtypes, obtaining a HR of 1.8 (95%CI 1.3-2.5) for relapse-free survival. 16
used different biological and genetic characteristics for the definition of four
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Guinney et al.
subtypes, with only two – CMS1 and CMS3 – considering MSI, BRAF, KRAS or CIMP. CMS2
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and CMS4 included information on SCNA, WNT and MYC activation and other biological characteristics, such as immune infiltration and metabolic deregulation. They found a significant association for poorer relapse-free and overall survival only for CMS4 compared to CMS2. The CMS4 subtype comprised 23% of the consensus sample and consisted of SCNA-high tumors with stromal infiltration, TGF-ß activation and angiogenesis. Samadder et al.
39
, Phipps et al.
40
and Sinicrope et al.
BRAF and KRAS. Samadder et al.
39
41
, presented subtypes including MSI,
provided HRs for CRC-specific mortality and found 9
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significantly increased mortality for one of the unassigned clusters (KRAS mutated); however, in a similar group with KRAS mutations, no statistically significant association with survival was found. Phipps et al.
40
described a better CRC-specific survival for two subtypes with MSI-high
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and KRAS wild-type tumors, and a poorer survival for MSS subtypes with BRAF or KRAS mutations. The subtype showing the strongest association with CRC-specific mortality was characterized by BRAF mutation and CIMP. Sinicrope et al.
41
presented pairwise comparisons
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of other groups versus a non-mutated BRAF/KRAS group for DFS. They found significantly poorer DFS for MSS tumors with KRAS or BRAF mutations. Subtypes with MSI-high showed no
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statistically significant association with survival. Synthesis of results
From the six included studies, three used population-based cohorts and described similar although not identical classifications. Their molecular subtypes were based on Jass’ pathways and used tumors with all four markers negative as a baseline for analyses. Table 3 presents a
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synthesis of similar subtypes for only these three studies, since conducting of a meta-analysis was not considered appropriate. Other studies were not included in this summary because of the different methods used to generate the classifications
16
, the different composition of the
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subtypes, the combination of different subtypes into one baseline category
38,42
and the
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estimates generated only from univariate analyses 38. From the three studies included in the synthesis, only Samadder et al provided information on CIMP status. Samadder et al
39
39
and Phipps et al
40
classified one of the subtypes
irrespective of the status of MSI (either MSI or MSS), making that subtype inadequate for inclusion in the synthesis and preventing a meta-analysis of the results. The MSS, KRAS mutated, BRAF wild-type, and CIMP-negative subtype was associated with worse survival, with HRs between 1.25 and 1.48. The MSI-high, BRAF mutated, and CIMP-positive subtype showed
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dissimilar and non-significant HRs. Finally, the MSS, BRAF mutated, CIMP-positive subtype
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was associated with a statistically significant worse survival, with HRs of 1.43 and 1.84.
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Discussion In this review we have summarized available evidence regarding different molecular subtype classifications of CRC and their associations with patient survival. We found six studies that
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proposed classifications based on different molecular markers and gene expression analyses; however, only three studies with similar subtypes and methods of analysis could be
than those without any mutation. Contrary to what previous reports have found
8,44,45
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synthesized. We found that subtypes with either KRAS or BRAF mutations had poorer survival
, subtypes with MSI showed no clear
review
40
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association with survival in the synthesized classifications. Only one study included in this found better survival for two subtypes with MSI-high, both of which were negative for
KRAS mutations. MSI has been consistently associated with better outcomes; however, it appears to have less prognostic value in stage III colon cancer patients. In a recent report of patients receiving FOLFOX with or without cetuximab, no association was found between MMR 46
. Other analyses of large cohorts and clinical trials
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status and disease-free or overall survival
in stage III patients have found conflicting results regarding disease-free and overall survival, and no clear benefit from adjuvant 5FU chemotherapy has been described in these patients
47
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This suggests that the association requires further investigation, since the MSI phenotype has also been associated with other features, such as laterality of the tumor, immune infiltration and
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CIMP and BRAF mutations, which might explain some conflicting reports. Several studies have described the association between MMR status in combination with BRAF mutations and survival, observing that patients with MSS/BRAF mutated tumors have poorer survival in comparison to those with MSS/BRAF wild-type tumors
10,11,48
. Significantly poorer
survival of the MSS/BRAF mutated subtype was also found in two studies in this review
40,41
.
Also, somewhat poorer survival was found for the MSS/KRAS mutated subtype compared with the negative baseline (MSS, BRAFwt, and KRASwt) 40,41. 12
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There was no association of CIMP with survival in any of the included studies in concordance with existing literature, where this association has been either negative or controversial
28,49,50
.
Subtypes integrating TP53, PIK3CA or PTEN expression showed no clear association with
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survival, and studies including these genetic markers did not present multivariable regression results for individual subtypes. PIK3CA mutations are present in 10 – 20% of CRC, and it has been suggested that in stage IV patients this mutation could be associated with resistance to
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anti-EGFR monoclonal antibodies. A recent meta-analysis based on data from over 12000 patients reported no association of PIK3CA with overall or progression-free survival in CRC
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patients 51.
The Colorectal Cancer Subtyping Consortium integrated data from six studies to generate a unified classification from a large-scale network analysis. Only one of the proposed subtypes (CMS4) showed an association with worse patient survival, and one other (CMS1) with survival after relapse. It is important to consider that 13% of patients could not be classified into any
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subtype and that CMS4 tumors were diagnosed at later stages and were heterogeneous regarding MSI status, making comparisons between the CMS groups and other pathological classifications difficult. Even though this network analysis presented a complex methodology
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from several expert working groups, molecular pathological features- such as MSI, BRAF and KRAS- were not considered in all subtypes. The value and applicability of the CMS subtypes
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needs to be elaborated in future studies, taking into account that its implementation might not be feasible for all institutions or clinicians, also with respect to costs and technical difficulties. There have been other approaches to classify CRC, such as the immunoscore
52
in which a
score is assigned depending on the density of in-situ T lymphocytes in the core and invasive margins of the tumor
53
. Tumors presenting with a higher count of T lymphocytes have been
associated with less nodal and metastatic involvement
54
. The immunoscore has shown an
association with survival in CRC, where tumors with a higher immune infiltrate had a better 13
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survival, and were more likely to be MSI-high
55
. Other reports have also described that MSI
tumors are characterized by a high density of infiltrating lymphocytes, which has been associated with better prognosis in CRC patients 55,56.
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Currently, the TNM staging is the sole classification routinely used in clinical practice, with MSI status also being considered in the risk assessment of stage II patients 57. Stage II patients with low risk do not receive adjuvant chemotherapy after surgery, whereas for those with high risk
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features chemotherapy is considered as adjuvant treatment. Testing for BRAF and KRAS mutations is only recommended for patients with metastatic disease when considering treatment 57
. While some studies have found poorer survival for stage III patients
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with anti-EGFR agents
with KRAS mutations 46,58,59, the role of KRAS as a prognostic marker is still controversial 60, and its use in current clinical practice is so far restricted to predicting the response to treatment with anti-EGFR monoclonal antibodies for stage IV patients 9.
A recent study showed that adding MSI, BRAF and KRAS information to TNM staging in
patients
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survival models improved their capacity to predict overall survival in stage II and III colon cancer . The authors recommended that more efforts should be made to achieve an ideal
stratification. Before endorsing a new classification system to be applied in clinical practice, the
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UICC suggests external validation needs to be conducted in several studies on different populations and time periods
62
. Among the studies included in this review, only two performed
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external validation of their suggested classifications, which underlines the need of further validation to arrive at a classification that provides independent prognostic value, is both molecularly and clinically feasible, and practical and easy to implement in the clinical setting. This review was restricted to articles including three or more molecular markers in the definition of a CRC subtype classification. This could be perceived as a limitation of the study, since a large amount of literature has investigated the relationship between one or two markers and CRC survival. For the development of a comprehensive molecular pathological classification of 14
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CRC subtypes, we considered that at least MSI, KRAS, and BRAF should be incorporated, which have repeatedly been associated with survival in previous studies. Some two-feature subtypes have shown rather conflicting results, such as the combination of MSI and CIMP
63
.
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On the other hand, two marker combinations, as shown for the combination of MSI and BRAF, may sometimes be sufficient for the definition of a clinically relevant subtype. Due to the limited number of studies and the differences in the subtype classifications between the studies, a
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meta-analysis was not considered feasible. However, we identified and discussed comparable subtypes wherever possible.
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In conclusion, we found that only few molecular subtype definitions were similar among the different molecular pathological classifications currently proposed for CRC. Molecular subtypes showing MSS and BRAF mutation had an unfavorable prognosis, compared to MSS CRC without BRAF mutation. The combination of MSS and KRAS mutation also showed a moderate association with poorer survival compared to MSS cancers without KRAS mutation. Subtypes
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with MSI-high were not associated with improved CRC survival in the studies included in this review when tumors showed BRAF mutations. None of the proposed subtype classifications was optimal in predicting survival of CRC patients, since significant associations with survival
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were only seen for one or two subtypes. Additionally, the CMS methodology focused on several genetic factors and was not easily comparable to other subtypes that are more commonly used
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by clinicians.
Further research closely related to clinical outcomes is required, before any recommendations can be implemented in clinical practice. For example, the association of different combinations of molecular markers by stage with the patient’s response to different treatment schemes is a topic that needs further investigation. Also, current molecular subtype classifications do not incorporate molecular markers into the traditional TNM staging of CRC. Further studies and external validation will be essential to consolidate a system that integrates the most relevant 15
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biological, molecular, clinical, and pathological factors for a comprehensive classification of
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clinically meaningful subtypes.
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References 1. Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012. v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No.11. Lyon, France: International Agency for Research on Cancer; 2013.
RI PT
2. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet. 2014;383(9927):1490-502.
3. American Joint Commitee on Cancer. Colon and Rectum Cancer Staging. 2009;7th Edition.
4. Ogino S, Goel A. Molecular Classification and Correlates in Colorectal Cancer. J Mol Diagn.
SC
2008;10(1):13-27.
5. Markowitz SD, Bertagnolli MM. Molecular Basis of Colorectal Cancer. N Engl J Med.
M AN U
2009;361(25):2449-60.
6. Boland CR, Goel A. Microsatellite Instability in Colorectal Cancer. Gastroenterology. 2010;138(6):207387.
7. Popat S, Hubner RA, Houlston RS. Systematic Review of Microsatellite Instability and Colorectal Cancer Prognosis. J Clin Oncol. 2005;23(3):609-18.
8. Guastadisegni C, Colafranceschi M, Ottini L, Dogliotti E. Microsatellite instability as a marker of
2010;46(15):2788-98.
TE D
prognosis and response to therapy: A meta-analysis of colorectal cancer survival data. Eur J Cancer.
9. Karapetis CS, Khambata S, Jonker DJ, et al. K-ras Mutations and Benefit from Cetuximab in Advanced
EP
Colorectal Cancer. N Engl J Med. 2008;359(17):1757-65. 10. Lochhead P, Kuchiba A, Imamura Y, et al. Microsatellite Instability and BRAF Mutation Testing in
AC C
Colorectal Cancer Prognostication. J Natl Cancer Inst. 2013;105(15):1151-6. 11. Seppälä TT, Böhm JP, Friman M, et al. Combination of microsatellite instability and BRAF mutation status for subtyping colorectal cancer. Br J Cancer. 2015;112(12):1966-75. 12. Rodriguez-Salas N, Dominguez G, Barderas R, et al. Clinical relevance of colorectal cancer molecular subtypes. Crit Rev Oncol/Hematol. 2017;109:9-19. 13. Jass JR. Classification of colorectal cancer based on correlation of clinical, morphological and molecular features. Histopathology. 2007;50(1):113-30.
17
ACCEPTED MANUSCRIPT
14. De Sousa E Melo F, Wang X, Jansen M, et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat Med. 2013;19(5):614-8. 15. Sadanandam A, Lyssiotis CA, Homicsko K, et al. A colorectal cancer classification system that
RI PT
associates cellular phenotype and responses to therapy. Nat Med. 2013;19(5):619-25. 16. Guinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350-6.
17. Shamseer L, Moher D, Clarke M, et al. PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review
SC
protocol. BMJ. 2015;349:7647-.
18. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction
M AN U
model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med. 2015;162(1):55-63.
19. Bae JM, Kim JH, Kang GH. Molecular Subtypes of Colorectal Cancer and Their Clinicopathologic Features, With an Emphasis on the Serrated Neoplasia Pathway. Arch Pathol Lab Med. 2016;140(5):40612.
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20. Sadanandam A, Wang X, de Sousa E Melo F, et al. Reconciliation of classification systems defining molecular subtypes of colorectal cancer: interrelationships and clinical implications. Cell Cycle. 2014;13(3):353-7.
21. Sinicrope FA, Okamoto K, Kasi PM, Kawakami H. Molecular Biomarkers in the Personalized
EP
Treatment of Colorectal Cancer. Clin Gastroenterol Hepatol. 2016;14(5):651-8. 22. Berg M, Nordgaard O, Korner H, et al. Molecular subtypes in stage II-III colon cancer defined by genomic instability: early recurrence-risk associated with a high copy-number variation and loss of
AC C
RUNX3 and CDKN2A. PLoS One. 2015;10(4):e0122391. 23. Braxton DR, Zhang R, Morrissette JD, Loaiza-Bonilla A, Furth EE. Clinicopathogenomic analysis of mismatch repair proficient colorectal adenocarcinoma uncovers novel prognostic subgroups with differing patterns of genetic evolution. Int J Cancer. 2016;139(7):1546-56. 24. Eklöf V, Wikberg ML, Edin S, et al. The prognostic role of KRAS, BRAF, PIK3CA and PTEN in colorectal cancer. Br J Cancer. 2013;108(10):2153-63. 25. Foltran L, De Maglio G, Pella N, et al. Prognostic role of KRAS, NRAS, BRAF and PIK3CA mutations in advanced colorectal cancer. Future Oncol. 2015;11(4):629-40.
18
ACCEPTED MANUSCRIPT
26. Fu T, Liu Y, Li K, et al. Tumors with unmethylated MLH1 and the CpG island methylator phenotype are associated with a poor prognosis in stage II colorectal cancer patients. Oncotarget. 2016;7(52). 27. Kim JH, Rhee Y-Y, Bae JM, Cho N-Y, Kang GH. Loss of CDX2/CK20 Expression Is Associated With Poorly Differentiated Carcinoma, the CpG Island Methylator Phenotype, and Adverse Prognosis in
RI PT
Microsatellite-unstable Colorectal Cancer. Am J Surg Pathol. 2013;37(10):1532-41.
28. Ogino S, Nosho K, Kirkner GJ, et al. CpG island methylator phenotype, microsatellite instability, BRAF mutation and clinical outcome in colon cancer. Gut. 2009;58(1):90-6.
29. Roepman P, Schlicker A, Tabernero J, et al. Colorectal cancer intrinsic subtypes predict
SC
chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition. Int J Cancer. 2013;134(3):552-62.
M AN U
30. Sanchez JA, Krumroy L, Plummer S, et al. Genetic and epigenetic classifications define clinical phenotypes and determine patient outcomes in colorectal cancer. Br J Surg. 2009;96(10):1196-204. 31. Shiovitz S, Bertagnolli MM, Renfro LA, et al. CpG island methylator phenotype is associated with response to adjuvant irinotecan-based therapy for stage III colon cancer. Gastroenterology. 2014;147(3):637-45.
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32. Song N, Pogue-Geile KL, Gavin PG, et al. Clinical Outcome From Oxaliplatin Treatment in Stage II/III Colon Cancer According to Intrinsic Subtypes Secondary Analysis of NSABP C-O7/NRG Oncology Randomized Clinical Trial. JAMA Oncol. 2016;2(9):1162-9. 33. Farchoukh L, Kuan S-F, Dudley B, Brand R, Nikiforova M, Pai RK. MLH1-deficient Colorectal
EP
Carcinoma With Wild-type BRAF and MLH1 Promoter Hypermethylation Harbor KRAS Mutations and Arise From Conventional Adenomas. Am J Surg Pathol. 2016;40(10):1390-99. 34. Furlan D, Carnevali IW, Bernasconi B, et al. Hierarchical clustering analysis of pathologic and
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molecular data identifies prognostically and biologically distinct groups of colorectal carcinomas. Mod Pathol. 2011;24(1):126-37.
35. Giampieri R, Mandolesi A, Abouelkhair KM, et al. Prospective study of a molecular selection profile for RAS wild type colorectal cancer patients receiving irinotecan-cetuximab. J Transl Med. 2015;13(1):140. 36. Inoue M, Takahashi S, Soeda H, et al. Gene-expression profiles correlate with the efficacy of antiEGFR therapy and chemotherapy for colorectal cancer. Int J Clin Oncol. 2015;20(6):1147-55.
19
ACCEPTED MANUSCRIPT
37. Perez-Villamil B, Romera-Lopez A, Hernandez-Prieto S, et al. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior. BMC cancer. 2012;12:260. 38. Marisa L, de Reynies A, Duval A, et al. Gene Expression Classification of Colon Cancer into
RI PT
Molecular Subtypes: Characterization, Validation, and Prognostic Value. PLoS Med. 2013;10(5).
39. Samadder NJ, Vierkant RA, Tillmans LS, et al. Associations between colorectal cancer molecular markers
and
pathways
with
clinicopathologic
features
in
older
2013;145(2):348-56.
women.
Gastroenterology.
and patient survival. Gastroenterology. 2015;148(1):77-87.
SC
40. Phipps AI, Limburg PJ, Baron JA, et al. Association between molecular subtypes of colorectal cancer
M AN U
41. Sinicrope FA, Shi Q, Smyrk TC, et al. Molecular markers identify subtypes of stage III colon cancer associated with patient outcomes. Gastroenterology. 2015;148(1):88-99.
42. Domingo E, Ramamoorthy R, Oukrif D, et al. Use of multivariate analysis to suggest a new molecular classification of colorectal cancer. J Pathol. 2013;229(3):441-8.
43. Leggett B, Whitehall V. Role of the Serrated Pathway in Colorectal Cancer Pathogenesis.
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Gastroenterology. 2010;138(6):2088-100.
44. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med. 2003;349(3):247-57. 45. Sinicrope FA, Sargent DJ. Molecular pathways: Microsatellite instability in colorectal cancer:
EP
Prognostic, predictive, and therapeutic implications. Clin Cancer Res. 2012;18(6):1506-12. 46. Taieb J, Zaanan A, Le Malicot K, et al. Prognostic Effect of BRAF and KRAS Mutations in Patients
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With Stage III Colon Cancer Treated With Leucovorin, Fluorouracil, and Oxaliplatin With or Without Cetuximab: A Post Hoc Analysis of the PETACC-8 Trial. JAMA Oncol. 2016;2(5):1-11. 47. Auclin E, Zaanan A, Vernerey D, et al. Subgroups and Prognostication in Stage III Colon Cancer: Future Perspectives for Adjuvant Therapy. Ann Oncol. 2017;28(5):958-68. 48. Taieb J, Le Malicot K, Shi Q, et al. Prognostic Value of BRAF and KRAS Mutations in MSI and MSS Stage III Colon Cancer. J Natl Cancer Inst. 2017;109(5). 49. Jia M, Jansen L, Walter V, et al. No association of CpG island methylator phenotype and colorectal cancer survival: population-based study. Br J Cancer. 2016;115(11):1359-66.
20
ACCEPTED MANUSCRIPT
50. Juo YY, Johnston FM, Zhang DY, et al. Prognostic value of CpG island methylator phenotype among colorectal cancer patients: a systematic review and meta-analysis. Ann Oncol. 2014;25(12):2314-27. 51. Mei ZB, Duan CY, Li CB, Cui L, Ogino S. Prognostic role of tumor PIK3CA mutation in colorectal
RI PT
cancer: a systematic review and meta-analysis. Ann Oncol. 2016;27(10):1836-48. 52. Kirilovsky A, Marliot F, El Sissy C, Haicheur N, Galon J, Pages F. Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients. Int Immunol. 2016;28(8):373-82.
53. Galon J, Mlecnik B, Bindea G, et al. Towards the introduction of the 'Immunoscore' in the
SC
classification of malignant tumours. J Pathol. 2014;232(2):199-209.
54. Pagès F, Berger A, Camus M, et al. Effector Memory T Cells, Early Metastasis, and Survival in
M AN U
Colorectal Cancer. N Engl J Med. 2005;353(25):2654-66.
55. Mlecnik B, Bindea G, Angell HK, et al. Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability. Immunity. 2016;44:698-711. 56. Kloor M, von Knebel Doeberitz M. The Immune Biology of Microsatellite-Unstable Cancer. Trends Cancer. 2016;2(3):121-33.
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57. National Comprehensive Cancer Network. Clinical Practice Guidelines in Oncology (NCCN Guidelines)
Colon Cancer. Version 2.2016. 2016.
58. Imamura Y, Morikawa T, Liao X, et al. Specific mutations in KRAS codons 12 and 13, and patient
EP
prognosis in 1075 BRAF wild-type colorectal cancers. Clin Cancer Res. 2012;18(17):4753-63. 59. Phipps AI, Buchanan DD, Makar KW, et al. KRAS-mutation status in relation to colorectal cancer
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survival: the joint impact of correlated tumour markers. Br J Cancer. 2013;108(8):1757-64. 60. Roth AD, Tejpar S, Delorenzi M, et al. Prognostic role of KRAS and BRAF in stage II and III resected colon cancer: Results of the translational study on the PETACC-3, EORTC 40993, SAKK 60-00 trial. J Clin Oncol. 2010;28(3):466-74. 61. Dienstmann AR, Mason MJ, Sinicrope FA, et al. Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study. Ann Oncol. 2017;28(5):1023-31. 62. Kattan MW, Hess KR, Amin MB, Lu Y, Moons KGM. American Joint Committee on Cancer Acceptance Criteria for Inclusion of Risk Models for Individualized Prognosis in the Practice of Precision Medicine. CA Cancer J Clin. 2016;66:370-4.
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63. Jia M, Gao X, Zhang Y, Hoffmeister M, Brenner H. Different definitions of CpG island methylator
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phenotype and outcomes of colorectal cancer: a systematic review. Clin Epigenetics. 2016;8:25.
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Figure legends
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Figure 1. Literature search and selection process - adapted from PRISMA 2009 statement.
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Tables
Table 1. Characteristics of included studies
Marisa et al,
38
40
Time frame
N
CRC Stage
Treatment
UK
Cohort
2002 - 2004
906
II - III
Rofecoxib vs b placebo
France
Cohort
1987 - 2007
775
II - III
Any
c
US
Cohort
1986 - 2002
563
I - IV
Any
c
US
Cohort
1998 - 2007
2050
I - IV
42
2013
Samadder et al, 2013 Phipps et al,
Type of study a
39
2015 41
Subtypes
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Domingo et al, 2013
Country
Any
Outcomes
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Authors, year
c
Type of validation
5
DFS
Development
6
RFS
External
5
CSS, OS
Development
5
CSS, OS
Development
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Sinicrope et al, US Cohort a 2004 - 2009 2720 III FOLFOX 5 DFS External 2015 16 Guinney et al, c Various Consensus N/A 3104 I - IV Any 4 RFS, OS, SAR Internal 2015 DFS: Disease-free survival; CSS: Cancer-specific survival; RFS: Relapse-free survival; OS: Overall survival; RFS: Relapse-free survival; SAR: Survival after relapse a b c Cohort from a clinical trial setting; After primary treatment; Chemotherapy agents not specified
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Table 2. Subtype classifications and association with survival outcomes Authors, year
Group
MSI high + -
BRAF mut + -
KRAS mut
CIMP
CIN
TP53
Other
Outcome model DFS Unadj.
n
HR
95% CI
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169 1 Group 1 Group 2 + + 322 1 42 Domingo et al, a Group 3 + + PIK3CA+ 133 1.48 (1.1-2.1) 2013 + PIK3CA+ 80 1 Group 4 Group 5 NRAS+ 32 1 RFS 175 1 C1 + + + Unadj. C2 + + + + 167 1.10 (0.6-1.7) b Marisa et al , 96 0.94 (0.5-1.6) C3 + + 38 C4 + + + 72 2.30 (1.4-3.8) 2013 188 1.40 (0.9-2.1) C5 + + + Serrated C6 + + + Conventional 77 2.10 (1.3-3.4) CSS Traditional 1 170 Adj. Serrated ο + + 1.56 (0.9-2.8) 58 39 Samadder et al, Alternate + 1.26 (0.7-2.4) 142 2013 1.76 (1.1-2.9) Unassigned cA + 96 Unassigned cB low 1.46 (0.6-3.4) 25 CSS Type 1 + + + 0.54 (0.3-1.0) 100 Adj. 1.84 (1.2-2.8) Type 2 + + 55 Phipps et al, 40 Type 3 + 1.25 (1.0-1.5) 353 2015 1 Type 4 631 Type 5 + 0.42 (0.2-0.9) 50 DFS Non BRAF/KRAS 1 1331 Adj. Mutant KRAS + 1.48 (1.3-1.7) 945 41 Sinicrope et al, Mutant BRAF + 1.43 (1.1-1.8) 189 2015 1.10 (0.8-1.5) dMMR sporadic + + ο MLH1 methylation 184 dMMR familial + ο 0.77 (0.5-1.3) MLH1 unmethylated 71 RFS CMS 1 + + + 0.88 (0.5-1.5) 435 Guinney et al, Adj. CMS 2 1 SCNA high, WNT, MYC 1148 16 CMS 3 ο + 0.84 (0.6-1.2) SCNA low 404 2015 CMS 4 1.49 (1.2-1.9) SCNA high, TGF-ß 714 For each group, a plus or minus sign is presented depending on whether molecular markers are positive or negative for that category; blank spaces indicate that a specific marker was not measured or mentioned in the respective publication. DFS: Disease-free survival; CSS: Cancer-specific survival; RFS: Relapse-free a b survival. Shadowed cells indicate and/or; ο: Any; Unadj.: Unadjusted model; Adj.: Adjusted model. Group3 vs all other groups as reference; Marisa et al, adapted from original: -/+= - and ++/+++=+; results from univariate model.
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Table 3. Synthesis of similar molecular subtype classifications MSI BRAF KRAS CIMP HR 95% CI Authors, year high mut mut 1 Reference 40 + + + 0.54 (0.3-1.0) Phipps et al, 2015 41 a + + 1.10 (0.8-1.5) Sinicrope et al, 2015 40 + + 1.84 (1.2-2.8) Phipps et al, 2015 41 + 1.43 (1.1-1.8) Sinicrope et al, 2015 40 + 1.25 (1.0-1.5) Phipps et al, 2015 39 + 1.26 (0.7-2.4) Samadder et al, 2013 41 1.48 (1.3-1.7) Sinicrope et al, 2015 + a KRAS wild type or mutated; shadowed cells indicate no measurement
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