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Posts Tagged ‘gene expression profiles (GEPs)’


Loss of Normal Growth Regulation

Curator: Larry H Bernstein, MD, FCAP

 

 

Reposted from Dr. Melvin Crasso

Cells from most major human solid and hematologic malignancies exhibit abnormal cellular localization of a variety of oncogenic proteins, tumor suppressor proteins, and cell cycle regulators (Cronshaw et al. 2004, Falini et al 2006). For example, certain p53 mutations lead to localization in the cytoplasm rather than in the nucleus. This results in the loss of normal growth regulation, despite intact tumor suppressor function. In other tumors, wild-type p53 is sequestered in the cytoplasm or rapidly degraded, again leading to loss of its suppressor function. Restoration of appropriate nuclear localization of functional p53 protein can normalize some properties of neoplastic cells (Cai et al. 2008; Hoshino et al. 2008; Lain et al. 1999a; Lain et al. 1999b; Smart et al. 1999), can restore sensitivity of cancer cells to DNA damaging agents (Cai et al. 2008), and can lead to regression of established tumors (Sharpless & DePinho 2007, Xue et al. 2007). Similar data have been obtained for other tumor suppressor proteins such as forkhead (Turner and Sullivan 2008) and c-Abl (Vignari and Wang 2001). In addition, abnormal localization of several tumor suppressor and growth regulatory proteins may be involved in the pathogenesis of autoimmune diseases (Davis 2007, Nakahara 2009). CRMl inhibition may provide particularly interesting utility in familial cancer syndromes (e.g. , Li-Fraumeni Syndrome due to loss of one p53 allele,

BRCA1 or 2 cancer syndromes), where specific tumor suppressor proteins (TSP) are deleted or dysfunctional and where increasing TSP levels by systemic (or local) administration of CRMl inhibitors could help restore normal tumor suppressor function. Specific proteins and R As are carried into and out of the nucleus by specialized transport molecules, which are classified as importins if they transport molecules into the nucleus, and exportins if they transport molecules out of the nucleus (Terry et al. 2007;

Sorokin et al. 2007). Proteins that are transported into or out of the nucleus contain nuclear import/localization (NLS) or export (NES) sequences that allow them to interact with the relevant transporters. Chromosomal Region Maintenance 1 (Crml or CRM1), which is also called exportin-1 or Xpol, is a major exportin.

Overexpression of Crml has been reported in several tumors, including human ovarian cancer (Noske et al. 2008), cervical cancer (van der Watt et al. 2009), pancreatic cancer (Huang et al. 2009), hepatocellular carcinoma (Pascale et al. 2005) and osteosarcoma (Yao et al. 2009) and is independently correlated with poor clinical outcomes in these tumor types.

Inhibition of Crml blocks the exodus of tumor suppressor proteins and/or growth regulators such as p53, c-Abl, p21, p27, pRB, BRCA1, IkB, ICp27, E2F4, KLF5, YAP1, ZAP, KLF5, HDAC4, HDAC5 or forkhead proteins (e.g., FOX03a) from the nucleus that are associated with gene expression, cell proliferation, angiogenesis and epigenetics. Crml inhibitors have been shown to induce apoptosis in cancer cells even in the presence of activating oncogenic or growth stimulating signals, while sparing normal (untransformed) cells. Most studies of Crml inhibition have utilized the natural product Crml inhibitor Leptomycin B (LMB). LMB itself is highly toxic to neoplastic cells, but poorly tolerated with marked gastrointestinal toxicity in animals (Roberts et al. 1986) and humans (Newlands et al. 1996). Derivatization of LMB to improve drug-like properties leads to compounds that retain antitumor activity and are better tolerated in animal tumor models (Yang et al. 2007, Yang et al. 2008, Mutka et al. 2009). Therefore, nuclear export inhibitors could have beneficial effects in neoplastic and other proliferative disorders.

In addition to tumor suppressor proteins, Crml also exports several key proteins that are involved in many inflammatory processes. These include IkB, NF-kB, Cox-2, RXRa, Commdl, HIFl, HMGBl, FOXO, FOXP and others. The nuclear factor kappa B (NF-kB/rel) family of transcriptional activators, named for the discovery that it drives immunoglobulin kappa gene expression, regulate the mRNA expression of variety of genes involved in inflammation, proliferation, immunity and cell survival. Under basal conditions, a protein inhibitor of NF-kB, called IkB, binds to NF-kB in the nucleus and the complex IkB-NF-kB renders the NF-kB transcriptional function inactive. In response to inflammatory stimuli, IkB dissociates from the IkB-NF-kB complex, which releases NF-kB and unmasks its potent transcriptional activity. Many signals that activate NF-kB do so by targeting IkB for proteolysis (phosphorylation of IkB renders it “marked” for ubiquitination and then proteolysis). The nuclear IkBa-NF-kB complex can be exported to the cytoplasm by Crml where it dissociates and NF-kB can be reactivated. Ubiquitinated IkB may also dissociate from the NF-kB complex, restoring NF-kB transcriptional activity. Inhibition of Crml induced export in human neutrophils and macrophage like cells (U937) by LMB not only results in accumulation of transcriptionally inactive, nuclear IkBa-NF-kB complex but also prevents the initial activation of NF-kB even upon cell stimulation (Ghosh 2008, Huang 2000). In a different study, treatment with LMB inhibited IL-Ιβ induced NF-kB DNA binding (the first step in NF-kB transcriptional activation), IL-8 expression and intercellular adhesion molecule expression in pulmonary microvascular endothelial cells (Walsh 2008). COMMDl is another nuclear inhibitor of both NF-kB and hypoxia-inducible factor 1 (HIFl) transcriptional activity. Blocking the nuclear export of COMMDl by inhibiting Crml results in increased inhibition of NF-kB and HIFl transcriptional activity (Muller 2009).

Crml also mediates retinoid X receptor a (RXRa) transport. RXRa is highly expressed in the liver and plays a central role in regulating bile acid, cholesterol, fatty acid, steroid and xenobiotic metabolism and homeostasis. During liver inflammation, nuclear RXRa levels are significantly reduced, mainly due to inflammation-mediated nuclear export of RXRa by Crml . LMB is able to prevent IL-Ιβ induced cytoplasmic increase in RXRa levels in human liver derived cells (Zimmerman 2006).

The role of Crml -mediated nuclear export in NF-kB, HIF-1 and RXRa signalling suggests that blocking nuclear export can be potentially beneficial in many inflammatory processes across multiple tissues and organs including the vasculature (vasculitis, arteritis, polymyalgia rheumatic, atherosclerosis), dermatologic (see below), rheumatologic

(rheumatoid and related arthritis, psoriatic arthritis, spondyloarthropathies, crystal arthropathies, systemic lupus erythematosus, mixed connective tissue disease, myositis syndromes, dermatomyositis, inclusion body myositis, undifferentiated connective tissue disease, Sjogren’s syndrome, scleroderma and overlap syndromes, etc.).

CRM1 inhibition affects gene expression by inhibiting/activating a series of transcription factors like ICp27, E2F4, KLF5, YAP1, and ZAP.

Crml inhibition has potential therapeutic effects across many dermatologic syndromes including inflammatory dermatoses (atopy, allergic dermatitis, chemical dermatitis, psoriasis), sun-damage (ultraviolet (UV) damage), and infections. CRMl inhibition, best studied with LMB, showed minimal effects on normal keratinocytes, and exerted anti-inflammatory activity on keratinocytes subjected to UV, TNFa, or other inflammatory stimuli (Kobayashi & Shinkai 2005, Kannan & Jaiswal 2006). Crml inhibition also upregulates NRF2 (nuclear factor erythroid-related factor 2) activity, which protects keratinocytes (Schafer et al. 2010, Kannan & Jaiswal 2006) and other cell types (Wang et al. 2009) from oxidative damage. LMB induces apoptosis in keratinocytes infected with oncogenic human papillomavirus (HPV) strains such as HPV 16, but not in uninfected keratinocytes (Jolly et al. 2009).

Crml also mediates the transport of key neuroprotectant proteins that may be useful in neurodegenerative diseases including Parkinson’s disease (PD), Alzheimer’s disease, and amyotrophic lateral sclerosis (ALS). For example, by (1) forcing nuclear retention of key neuroprotective regulators such as NRF2 (Wang 2009), FOXA2 (Kittappa et al. 2007), parking in neuronal cells, and/or (2) inhibiting NFKB transcriptional activity by sequestering IKB to the nucleus in glial cells, Crml inhibition could slow or prevent neuronal cell death found in these disorders. There is also evidence linking abnormal glial cell proliferation to abnormalities in CRMl levels or CRMl function (Shen 2008).

Intact nuclear export, primarily mediated through CRMl, is also required for the intact maturation of many viruses. Viruses where nuclear export, and/or CRMl itself, has been implicated in their lifecycle include human immunodeficiency virus (HIV), adenovirus, simian retrovirus type 1, Borna disease virus, influenza (usual strains as well as H1N1 and avian H5N1 strains), hepatitis B (HBV) and C (HCV) viruses, human papillomavirus (HPV), respiratory syncytial virus (RSV), Dungee, Severe Acute Respiratory Syndrome coronavirus, yellow fever virus, West Nile virus, herpes simplex virus (HSV), cytomegalovirus (CMV), and Merkel cell polyomavirus (MCV). (Bhuvanakantham 2010, Cohen 2010, Whittaker 1998). It is anticipated that additional viral infections reliant on intact nuclear export will be uncovered in the future.

The HIV-1 Rev protein, which traffics through nucleolus and shuttles between the nucleus and cytoplasm, facilitates export of unspliced and singly spliced HIV transcripts containing Rev Response Elements (RRE) RNA by the CRMl export pathway. Inhibition of Rev-mediated RNA transport using CRMl inhibitors such as LMBor PKF050-638 can arrest the HIV-1 transcriptional process, inhibit the production of new HIV-1 virions, and thereby reduce HIV-1 levels (Pollard 1998, Daelemans 2002). Dengue virus (DENV) is the causative agent of the common arthropod-borne viral disease, Dengue fever (DF), and its more severe and potentially deadly Dengue hemorrhagic fever (DHF). DHF appears to be the result of an over exuberant inflammatory response to DENV. NS5 is the largest and most conserved protein of DENV. CRMl regulates the transport of NS5 from the nucleus to the cytoplasm, where most of the NS5 functions are mediated. Inhibition of CRMl -mediated export of NS5 results in altered kinetics of virus production and reduces induction of the inflammatory chemokine interleukin-8 (IL-8), presenting a new avenue for the treatment of diseases caused by DENV and other medically important flaviviruses including hepatitis C virus (Rawlinson 2009).

Other virus-encoded RNA-binding proteins that use CRMl to exit the nucleus include the HSV type 1 tegument protein (VP 13/14, or hUL47), human CMV protein pp65, the SARS Coronavirus ORF 3b Protein, and the RSV matrix (M) protein (Williams 2008, Sanchez 2007, Freundt 2009, Ghildyal 2009).

Interestingly, many of these viruses are associated with specific types of human cancer including hepatocellular carcinoma (HCC) due to chronic HBV or HCV infection, cervical cancer due to HPV, and Merkel cell carcinoma associated with MCV. CRMl inhibitors could therefore have beneficial effects on both the viral infectious process as well as on the process of neoplastic transformation due to these viruses.

CRMl controls the nuclear localization and therefore activity of multiple DNA metabolizing enzymes including histone deacetylases (HDAC), histone acetyltransferases (HAT), and histone methyltransferases (HMT). Suppression of cardiomyocyte hypertrophy with irreversible CRMl inhibitors has been demonstrated and is believed to be linked to nuclear retention (and activation) of HDAC 5, an enzyme known to suppress a hypertrophic genetic program (Monovich et al. 2009). Thus, CRMl inhibition may have beneficial effects in hypertrophic syndromes, including certain forms of congestive heart failure and hypertrophic cardiomyopathies.

 

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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
mutation
10% Probable Probable Lynch
Syndrome
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
Syndrome
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
Detection
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
mutations
1–15% Lynch
Syndrome

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)

Results:

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).

Summary:

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.

References:

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. http://www.plosmedicine.org/article/info%3Adoi/10.1371/journal.pmed.1001453

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.  http://www.biomedcentral.com/1471-2407/12/260/

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. http://jco.ascopubs.org/content/25/27/4224.long

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. http://www.ncbi.nlm.nih.gov/pubmed?cmd=Search&doptcmdl=Citation&defaultField=Title%20Word&term=Salazar%5Bauthor%5D%20AND%20Gene%20expression%20signature%20to%20improve%20prognosis%20prediction%20of%20stage%20II%20and%20III%20colorectal%20cancer

5.  By: Global Genome Knowledge. Colorectal Cancer- Personalized Medicine, Now a Clinical Reality.  http://www.srlworld.com/innersense/Voice-135-Colorectal-Cancer-Sept-2012-IS.pdf

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. http://www.ncbi.nlm.nih.gov/pubmed/15659508

7. By: Jeffrey Norris. Value of Genomics and Personalized Medicine Is Wrongly Downplayed.http://www.ucsf.edu/news/2012/04/11864/value-genomics-and-personalized-medicine-wrongly-downplayed

8. By: James C Salwitz. The Future is now: Personalized Medicine. http://www.cancer.org/cancer/news/expertvoices/post/2012/04/18/the-future-is-now-personalized-medicine.aspx

9. Jeffrey A. Meyerhardt., and Robert J. Mayer. Systemic Therapy for Colorectal Cancer. N Engl J Med 2005;352:476-487. http://www.med.upenn.edu/gastro/documents/NEJMchemotherapycolorectalcancer.pdf

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): 116–129http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3006043/

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.  http://www.intechopen.com/books/drug-discovery/colon-cancer-current-treatments-and-preclinical-models-for-the-discovery-and-development-of-new-ther

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. http://www.nature.com/nature/journal/v483/n7387/full/nature10868.html

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

*. By Tilda Barliya PhD. Colon Cancer. https://pharmaceuticalintelligence.com/2013/04/30/colon-cancer/

**. By: Tilda Barliya PhD. CD47: Target Therapy for Cancer. https://pharmaceuticalintelligence.com/2013/05/07/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. https://pharmaceuticalintelligence.com/2013/04/21/cancer-genomic-precision-therapy-digitized-tumors-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. https://pharmaceuticalintelligence.com/2013/04/12/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. https://pharmaceuticalintelligence.com/2013/04/10/issues-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. https://pharmaceuticalintelligence.com/2013/03/27/in-focus-targeting-of-cancer-stem-cells/

V.  By: Ziv Raviv PhD. Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv. https://pharmaceuticalintelligence.com/2013/03/25/tel-aviv-sourasky-medical-center-cancer-prevention-center-excellent-example-for-adopting-prevention-of-cancer-as-a-mean-of-fighting-it/

VI. By: Ritu Saxena, PhD. In Focus: Identity of Cancer Stem Cells. https://pharmaceuticalintelligence.com/2013/03/22/in-focus-identity-of-cancer-stem-cells/

VII. By: Dror Nir, PhD. State of the art in oncologic imaging of Colorectal cancers. https://pharmaceuticalintelligence.com/2013/02/02/state-of-the-art-in-oncologic-imaging-of-colorectal-cancers/

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