Posts Tagged ‘dMMR’

Personalized Medicine and Colon Cancer

Author: Tilda Barliya, PhD

According to Dr. Neil Risch a leading expert in statistical genetics and the director of the UCSF Institute for Human Genetics,  “Personalized medicine, in which a suite of molecules measured in a patient’s lab tests can inform decisions about preventing or treating diseases, is becoming a reality” (7).

Colorectal cancer (CRC) is the third most common cancer and the fourth-leading cause of cancer death worldwide despite advances in screening, diagnosis, and treatment. Staging is the only prognostic classification used in clinical practice to select patients for adjuvant chemotherapy. However, pathological staging fails to predict recurrence accurately in many patients undergoing curative surgery for localized CRC (1,2). Most of the patients who are not eligible for surgery need adjuvant chemotherapy in order to avoid relapse or to increase survival. Unfortunately, only a small portion of them shows an objective response to chemotherapy, becoming problematic to correctly predict patients’ clinical outcome (3).

CRC patients are normally being tested for several known biomarkers which falls into 4 main categories (5):

  1. Chromosomal Instability (CIN)
  2. Microsatellite Instability (MSI)
  3. CpG Island methylator phynotype (CIMP)
  4. Global DNA hypomethylation

In the past few years many studies have exploited microarray technology to investigate gene expression profiles (GEPs) in CRC, but no established signature has been found that is useful for clinical practice, especially for predicting prognosis.  Only a subset of CRC patients with MSI tumors have been shown to have better prognosis and probably respond differently to adjuvant chemotherapy compared to microsatellite stable (MSS) cancer patients (6).

Pritchard & Grady have summarized the selected biomarkers that have been evaluated in colon cancer patients (10).

Table 1

Selected Biomarkers That Have Been Evaluated in Colorectal Cancer

Biomarker Molecular Lesion Frequency
in CRC
Prediction Prognosis Diagnosis
KRAS Codon 12/13 activating
mutations; rarely codon
61, 117,146
40% Yes Possible
BRAF V600E activating
10% Probable Probable Lynch
PIK3CA Helical and kinase
domain mutations
20% Possible Possible
PTEN Loss of protein by IHC 30% Possible
Microsatellite Instability (MSI) Defined as >30%
unstable loci in the NCI
consensus panel or
>40% unstable loci in a
panel of mononucleotide
microsatellite repeats9
15% Probable Yes Lynch
Chromosome Instability (CIN) Aneuploidy 70% Probable Yes
18qLOH Deletion of the long arm
of chromosome 18
50% Probable Probable
CpG Island Methylator
Phenotype (CIMP)
Methylation of at least
three loci from a selected
panel of five markers
15% +/− +/−
Vimentin (VIM) Methylation 75% Early
TGFBR2 Inactivating Mutations 30%
TP53 Mutations Inactivating Mutations 50%
APC Mutations Inactivating Mutations 70% FAP
CTNNB1 (β-Catenin) Activating Mutations 2%
Mismatch Repair Genes Loss of protein by IHC;
methylation; inactivating
1–15% Lynch

CRC- colorectal cancer; IHC- immunohistochemistry; FAP- Familial Adenomatous Polyposis

Examples for the great need of personalized medicine tailored according to the patients’ genetics is clearly seen with two specific drugs for CRC:  Cetuximab and panitumumab are two antibodies that were developed to treat colon cancer. However, at first it seemed as if they were a failure because they did not work in many patients. Then, it was discovered that if a cancer cell has a specific genetic mutation, known as K-ras, these drugs do not work.  This is an excellent example of using individual tumor genetics to predict whether or not treatment will work (8).

According to Marisa L et al, however, the molecular classification of CC currently used, which is based on a few common DNA markers as mentioned above (MSI, CpG island methylator phenotype [CIMP], chromosomal instability [CIN], and BRAF and KRAS mutations), needs to be refined.

Genetic Expression Profiles (GEP)

CRC is composed of distinct molecular entities that may develop through multiple pathways on the basis of different molecular features, as a consequence, there may be several prognostic signatures for CRC, each corresponding to a different entity. GEP studies have recently identified at least three distinct molecular subtypes of CC (4). Dr. Marisa Laetitia and her colleagues from the Boige’s lab however, have conducted a very thorough study and identifies 6 distinct clusters for CC patients. Herein, we’ll describe the majority of this study and their results.

Study  Design:

Marisa L et al (1) performed a consensus unsupervised analysis (using an Affymertix chip) of the GEP on tumor tissue sample from 750 patients with stage I to IV CC. Patients were staged according to the American Joint Committee on Cancer tumor node metastasis (TNM) staging system. Of the 750 tumor samples of the CIT cohort, 566 fulfilled RNA quality requirements for GEP analysis. The 566 samples were split into a discovery set (n = 443) and a validation set (n = 123).

Several known mutations were used as internal controls, including:

  • The seven most frequent mutations in codons 12 and 13 of KRAS .
  • The BRAF c.1799T>A (p.V600E)
  • TP53mutations (exons 4–9)
  • MSI was analyzed using a panel of five different microsatellite loci from the Bethesda reference panel
  • CIMP status was determined using a panel of five markers (CACNA1G, IGF2, NEUROG1, RUNX3, and SOCS1)


The results revealed six clusters of samples based on the most variant probe sets. The consensus matrix showed that C2, C3, C4, and C6 appeared as well-individualized clusters, whereas there was more classification overlap between C1 and C5. In other words:

  • Tumors classified as C1, C5, and C6 were more frequently CIN+, CIMP−TP53 mutant, and distal (p<0.001), without any other molecular or clinicopathological features able to discriminate these three clusters clearly.
  • Tumors classified as C2, C4, and C3 were more frequently CIMP+ (59%, 34%, and 18%, respectively, versus <5% in other clusters) and proximal.
  • C2 was enriched for dMMR (68%) and BRAF- mutant tumors (40%).
  • C3 was enriched for KRAS- mutant tumors (87%).

Note: No association between clusters and TNM stage (histopathology) was found, except enrichment for metastatic (31%) tumors in C4.

Figure: These signaling pathways associated with the molecular subtype (by cluster)

Figure 2 Signaling pathways associated with each molecular subtype.

Marisa L et al. Signaling pathways associated with each molecular subtype

These clusters fall into several signaling pathways:

  • up-regulated immune system and cell growth pathways were found in C2, the subtype enriched for dMMR tumors
  • C4 and C6 both showed down-regulation of cell growth and death pathways and up-regulation of the epithelial–mesenchymal transition/motility pathways. displaying “stem cell phenotype–like” GEPs (91%)
  • Most signaling pathways were down-regulated in C1 and C3.
  • In C1, cell communication and immune pathways were down-regulated.
  • In C5, cell communication, Wnt, and metabolism pathways were up-regulated.

These results are further summarized in table 2:

Figure 3 Summary of the main characteristics of the six subtypes.

Marisa L et al. Gene Expression Classification of Colon Cancer into Molecular Subtypes

The authors have identified six robust molecular subtypes of CC individualized by distinct clinicobiological characteristics (as summarized in table 2).

This classification successfully identified the dMMR tumor subtype, and also individualized five other distinct subtypes among pMMR tumors, including three CIN+ CIMP− subtypes representing slightly more than half of the tumors. As expected, mutation of BRAF was associated with the dMMR subtype, but was also frequent in the C4 CIMP+ poor prognosis subtype. TP53– andKRAS-mutant tumors were found in all the subtypes; nevertheless, the C3 subtype, highly enriched in KRAS-mutant CC, was individualized and validated, suggesting a specific role of this mutation in this particular subgroup of CC.

Current Treatments for colon cancer- Table 3 (11) .

Constant S et al. Colon Cancer: Current Treatments and Preclinical Models for the Discovery and Development of New Therapies

Exploratory analysis of each subtype GEP with previously published supervised signatures and relevant deregulated signaling pathways improved the biological relevance of the classification.

The biological relevance of our subtypes was highlighted by significant differences in prognosis. In our unsupervised hierarchical clustering, patients whose tumors were classified as C4 or C6 had poorer RFS than the other patients.

Prognostic analyses based solely on common DNA alterations can distinguish between risk groups, but are still inadequate, as most CCs are pMMR CIMP− BRAFwt.

The markers BRAF-mutant, CIMP+, and dMMR may be useful for classifying a small proportion of cases, but are uninformative for a large number of patients.

Unfortunately, 5 of the 9 anti-CRC drugs approved by the FDA today are basic cytotoxic chemotherapeutics that attack cancer cells at a very fundamental level (i.e. the cell division machinery) without specific targets, resulting in poor effectiveness and strong side-effects (Table 3) (11).

An example for side effects induction mechanisms have also been reported in CRC for the BRAF(V600E) inhibitor Vemurafenib that triggers paradoxical EGFR activation (12).


The authors of this study “report a new classification of CC into six robust molecular subtypes that arise through distinct biological pathways and represent novel prognostic subgroups. Our study clearly demonstrates that these gene signatures reflect the molecular heterogeneity of CC. This classification therefore provides a basis for the rational design of robust prognostic signatures for stage II–III CC and for identifying specific, potentially targetable markers for the different subtypes”.

These results further underline the urgent need to expand the standard therapy options by turning to more focused therapeutic strategies: a targeted therapy-for specific subtype profile.. Accordingly, the expansion and the development of new path of therapy, like drugs specifically targeting the self-renewal of intestinal cancer stem cells – a tumor cell population from which CRC is supposed to relapse, remains relevant.

Therefore, the complexity of these results supports the arrival of a personalized medicine, where a careful profiling of tumors will be useful to stratify patient population in order to test drugs sensitivity and combination with the ultimate goal to make treatments safer and more effective.


1. Marisa L,  de Reyniès A, Alex Duval A,  Selves J, Pierre Gaub M, Vescovo L, Etienne-Grimaldi MC, Schiappa R, Guenot D, Ayadi M, Kirzin S, Chazal M, Fléjou JF…Boige V. Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value. PLoS Med May 2013 10(5): e1001453. doi:10.1371.

2. Villamil BP, Lopez AR, Prieto SH, Campos GL, Calles A, Lopez- Asenjo JA, Sanz Ortega J, Perez CF, Sastre J, Alfonso R, Caldes T, Sanchez FM and Rubio ED. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior. BMC Cancer 2012, 12:260.

3. Diaz-Rubio E, Tabernero J, Gomez-Espana A, Massuti B, Sastre J, Chaves M, Abad A, Carrato A, Queralt B, Reina JJ, et al.: Phase III study of capecitabine plus oxaliplatin compared with continuous-infusion fluorouracil plus oxaliplatin as first-line therapy in metastatic colorectal cancer: final report of the Spanish Cooperative Group for the Treatment of Digestive Tumors Trial. J Clin Oncol 2007, 25(27):4224-4230.

4. Salazar R, Roepman P, Capella G, Moreno V, Simon I, et al. (2011) Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J Clin Oncol 29: 17–24.

5.  By: Global Genome Knowledge. Colorectal Cancer- Personalized Medicine, Now a Clinical Reality.

6. Popat S, Hubner R and Houlston RS. Systematic review of microsatellite instability and colorectal cancer prognosis. J Clin Oncol. 2005 Jan 20;23(3):609-618.

7. By: Jeffrey Norris. Value of Genomics and Personalized Medicine Is Wrongly Downplayed.

8. By: James C Salwitz. The Future is now: Personalized Medicine.

9. Jeffrey A. Meyerhardt., and Robert J. Mayer. Systemic Therapy for Colorectal Cancer. N Engl J Med 2005;352:476-487.

10. Pritchard CC and Grady WM. Colorectal Cancer Molecular Biology Moves Into Clinical Practice. Gut. Jan 2011 60(1): 116-129.  Gut. 2011 January; 60(1): 116–129

11. Constant S, Huang S, Wiszniewski L andMas C. Colon Cancer: Current Treatments and Preclinical Models for the Discovery and Development of New Therapies.  Pharmacology, Toxicology and Pharmaceutical Science » “Drug Discovery”, book edited by Hany A. El-Shemy, ISBN 978-953-51-0906-8.

12. Prahallad, C. Sun, S. Huang, F. Di Nicolantonio, R. Salazar, D. Zecchin, R. L. Beijersbergen, A. Bardelli, R. Bernards, 2012 Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature Jan 2012 483 (7387): 100-103.

Other related articles on this Open Access Online Scientific Journal include the following:

*. By Tilda Barliya PhD. Colon Cancer.

**. By: Tilda Barliya PhD. CD47: Target Therapy for Cancer.

I. By: Aviva Lev-Ari, PhD, RNCancer Genomic Precision Therapy: Digitized Tumor’s Genome (WGSA) Compared with Genome-native Germ Line: Flash-frozen specimen and Formalin-fixed paraffin-embedded Specimen Needed.

II. By: Aviva Lev-Ari, PhD, RN. Critical Gene in Calcium Reabsorption: Variants in the KCNJ and SLC12A1 genes – Calcium Intake and Cancer Protection.

III.  By: Stephen J. Williams, Ph.DIssues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing.

IV. By: Ritu Saxena, Ph.DIn Focus: Targeting of Cancer Stem Cells.

V.  By: Ziv Raviv PhD. Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv.

VI. By: Ritu Saxena, PhD. In Focus: Identity of Cancer Stem Cells.

VII. By: Dror Nir, PhD. State of the art in oncologic imaging of Colorectal cancers.


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