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In Focus: Identity of Cancer Stem Cells

Posted in Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Genome Biology, Stem Cells for Regenerative Medicine, tagged ALDH, anti-cancer therapy, biomarkers, breast cancer, Cancer - General, cancer stem cells, CD34, CD38, CD44, CD96, CML, CSC, CSCs, EMT, Epithelial-mesenchymal transition, genetic heterogeneity, Glioma, Hematopoietic stem cells, hierarchy, HSCs, IL-6, Imatinib, Interleukin 6, LSCs, polarized epithelial cells, self-renewal, stem cells, tumors, tyrosin kinase inhibitor on March 22, 2013| 9 Comments »

Author and Curator: Ritu Saxena, PhD

Word Cloud By Danielle Smolyar

What are cancer stem cells?

Cancer is a debilitating disease estimated to be responsible for about 7.6 million deaths in 2008 (Jemal A, et al, CA Cancer J Clin, Mar-Apr 2011;61(2):69-90). Thus, extensive research is underway to deal with the various types of cancer. The concept of cancer stem cells (CSC) has surfaced in in the past decade after identification and characterization of CSC-enriched populations in several different types of cancer (Lapidot T, et al, Nature, 17 Feb 1994;367(6464):645-8; Reya T, et al, Nature, 1 Nov 2001;414(6859):105-11; Trumpp A and Wiestler OD, et al, Nat Clin Pract Oncol, Jun 2008;5(6):337-47). Although there has been lot of debate on the cell of origin of CSC, according to the classical concept CSC are defined by their functional properties.

Functional properties of CSC

CSCs are at the top of tumor hierarchy. Regenerative tissues follow a hierarchical organization with adult stem cells at the top maintaining tissues and normal adult cells during homeostasis and regeneration during cell loss from injury. Similarly, several tumors follow the hierarchy with CSC at the top. Hierarchical organization has been reported in several cancer types including but not limited to breast cancer, brain cancer, colon cancer, leukemia and pancreatic cancer (Lapidot T, et al, Nature, 17 Feb 1994;367(6464):645-8; Al-Hajj M, et al, PNAS USA, 1 Apr 200;100(7):3983-8; Singh SK, et al, Nature, 18 Nov 2004;432(7015):396-401; Dalerba P, et al, PNAS USA, 12 Jun 2007;104(24):10158-63; Hermann PC, et al, Cell Stem Cell, 13 Sep 2007;1(3):313-23).
CSCs possess unlimited self-renewal capacity similar to that of physiological stem cells and unlike other differentiated cell types within the tumor. Cancer stem cells can also generate non-CSC progeny that is comprised of differentiated cells and forms tumor bulk.
Some CSs exhibit quiescent or dormant stage. Although not observed in all CSC types, some CSCs have been found to shuttle between quiescent, slow-cycling, and active states. The CSCs in their dormant and slow-cycling stage are less likely to be affected by conventional anti-tumor therapies which generally target rapidly dividing cells. Dormant stage is exhibited even in adult stem cells and the dormant normal stem cells can regain cell division potential during tissue injury (Wilson A, et al, Cell, 12 Dec 2008;135(6):1118-29). Thus, it has been speculated that dormant CSC might be a reason for tumor relapse even after pathologic complete response is observed post therapy.
Some CSCs are resistant to conventional anti-cancer therapies. This leads to accumulation of CSC that might result in relapse after anti-cancer therapy. For instance, Li et al (2008) reported that CSC accumulated in the breast of women with locally advanced tumors after cytotoxic chemotherapy had eliminated the bulk of the tumor cells (Li X,et al, J Natl Cancer Inst, 7 May 2008;100(9):672-9). A similar observation was made by Oravecz-Wilson et al (2009) stating that despite remarkable responses to the tyrosine kinase inhibitor imatinib, CML patients show imatinib refractoriness because leukemia stem cells in CML are resistant tyrosine kinase (Oravecz-Wilson KI, et al, Cancer Cell, 4 Aug 2009;16(2):137-48).
The CSC niche. CSC functional traits might be sustained by this microenvironment, termed “niche”. The niche is the environment in which stem cells reside and is responsible for the maintenance of unique stem cell properties such as self-renewal and an undifferentiated state. The heterogeneous populations which constitute a niche include both stem cells and surrounding differentiated cells. The necessary intrinsic pathways that are utilized by this cancer stem cell population to maintain both self-renewal and the ability to differentiate are believed to be a result of the environment where cancer stem cells reside. (Cabarcas SM, et al, Int J Cancer, 15 Nov 2011;129(10):2315-27). For instance, properties of CSC in glioma in a mouse xenograft model were maintained by vascular endothelial cells (Calabrese C, et al, Cancer Cell, Jan 2007;11(1):69-82). Several molecules including interleukin 6 have been observed to play a role in tumor proliferation and hence, participate in maintaining tumorigenic and self-renewal potential of CSC. Moreover, the CSC niche might not only regulate CSCs traits but might also directly provide CSC features to non-CSC population.

What is the origin of CSC?

According to current thinking, CSC result from epithelial-mesenchymal transition (EMT) when cells switch from a polarized epithelial to a non-polarized mesenchymal cell type with stem cell properties, including migratory behavior, self-renewal and generation of differentiated progeny, and reduced responsiveness to conventional cancer therapies (Scheel C and Weinberg RA, Semin Cancer Biol, Oct 2012;22(5-6):396-403; Crews LA and Jamieson CH, Cancer Lett, 17 Aug 2012). Evidence is accumulating that cancers of distinct subtypes within an organ may derive from different ‘cells of origin’. The tumor cell of origin is the cell type from which the disease is derived after it undergoes oncogenic mutation. It might take a series of mutations to achieve the CSC phenotype (Visvader JE, Nature, 20 Jan 2011;469(7330):314-22). Also, CSCs have been reported to originate from stem cells in some cases.

Biomarkers for CSC

CSC targeting therapy could either eliminate CSCs by either killing them after differentiating them from other tumor population, and/or by disrupting their niche. Efficient eradication of CSCs may require the combined ablation of CSCs themselves and their niches. Identifying appropriate biomarkers of CSC is a very important aim for CSCs to be useful as targets of anti-cancer therapies in order to possibly prevent relapse. Using cell surface markers, CSCs have been isolated and purified from cancers of breast, brain, thyroid, cervix, lung, blood (leukemia), skin (melanoma), organs of the gastrointestinal and reproductive tracts, and the retina. The challenge, however, is that CSCs share similar markers with normal cells which makes CSCs targeting difficult as it would harm normal cells in the process. More recently, advanced techniques such as signal sequence trap (SST) PCR screening methods have been developed to identify a leukemia-specific stem cell marker (CD96). After a small subset of human AML cells displayed tumorigenic properties, Leukemia Stem Cells (LSCs) were identified as leukemia cells with CD23+/CD38+ markers. These cells closely resemble hematopeotic stem cells (HSCs) (Bonnet D and Dick JR, Nat Med, Jul 1997;3(7):730-7). In solid tumors, a significant discovery was made when CSCs in breast cancer were identified within the ESA+/CD44+/CD24low-neg population of mammary pleural effusion and tumor samples (Al-Hajj M, et al, PNAS USA, 1 Apr 200;100(7):3983-8).

After these two landmark publications, CSCs were identified in many more solid and hematopoietic human tumors as well. In addition, within a tumor type, CSC-enriched populations display heterogeneity in markers. For example, only 1% of breast cancer cells simultaneously express both reported CSC phenotypes ESA+/CD44+/

CD24low-neg and ALDH-1+ (Ginestier C, et al, Cell Stem Cell, 1 Nov 2007;1(5):555-67). The discrepancy might be due to different techniques used to identify the markers and also a reflection of the molecular heterogeneity within the tumors. Recent advances in genome wide expression profiling studies have led to the identification of different subtypes in a particular type of cancer. Breast cancer was recently classified into different subtypes and this genetic heterogeneity is likely paralleled by a heterogeneous CSC complexity.

Conclusion

A lot of research is currently underway on various aspects of CSCs including biomarker identification, cell of origin, and clinical trials targeting CSC population in cancer. The concept of CSCs has evolved quite a bit since their discovery. Recently, identification of high genetic heterogeneity within a tumor has been in focus and subsequently it has been observed that several CSC clones can coexist and compete with each other within a tumor. Adding complexity to their identity is the fact that CSCs may have unstable phenotypes and genotypes. Taken together, the dynamics associated with CSCs makes it difficult to identify reliable and robust biomarkers and develop efficient targeted therapies. Thus, a major thrust of research should be to focus on the unfolding of the dynamic identity of CSCs in tumor types and at different that might lead to the identification and targeting of highly specific CSCs biomarkers.

Reference

Jemal A, et al, CA Cancer J Clin, Mar-Apr 2011;61(2):69-90

Reya T, et al, Nature, 1 Nov 2001;414(6859):105-11

Trumpp A and Wiestler OD, et al, Nat Clin Pract Oncol, Jun 2008;5(6):337-47

Lapidot T, et al, Nature, 17 Feb 1994;367(6464):645-8

Singh SK, et al, Nature, 18 Nov 2004;432(7015):396-401

Dalerba P, et al, PNAS USA, 12 Jun 2007;104(24):10158-63

Hermann PC, et al, Cell Stem Cell, 13 Sep 2007;1(3):313-23

Wilson A, et al, Cell, 12 Dec 2008;135(6):1118-29

Li X,et al, J Natl Cancer Inst, 7 May 2008;100(9):672-9

Oravecz-Wilson KI, et al, Cancer Cell, 4 Aug 2009;16(2):137-48

Cabarcas SM, et al, Int J Cancer, 15 Nov 2011;129(10):2315-27

Calabrese C, et al, Cancer Cell, Jan 2007;11(1):69-82

Scheel C and Weinberg RA, Semin Cancer Biol, Oct 2012;22(5-6):396-403

Crews LA and Jamieson CH, Cancer Lett, 17 Aug 2012

Visvader JE, Nature, 20 Jan 2011;469(7330):314-22

Bonnet D and Dick JR, Nat Med, Jul 1997;3(7):730-7

Al-Hajj M, et al, PNAS USA, 1 Apr 200;100(7):3983-8

Ginestier C, et al, Cell Stem Cell, 1 Nov 2007;1(5):555-67

Baccelli I and Trumpp AJ, Cell Biol, 6 Aug 2012;198(3):281-93

Zhao L, et al, Eur Surg Res, 2012;49(1):8-15

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http://pharmaceuticalintelligence.com/2012/08/15/to-die-or-not-to-die-time-and-order-of-combination-drugs-for-triple-negative-breast-cancer-cells-a-systems-level-analysis/

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http://pharmaceuticalintelligence.com/2013/03/03/treatment-for-metastatic-her2-breast-cancer/ Reporter: Larry H Bernstein, MD

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http://pharmaceuticalintelligence.com/2012/11/30/histone-deacetylase-inhibitors-induce-epithelial-to-mesenchymal-transition-in-prostate-cancer-cells/ Reporter and Curator: Stephen J. Williams, PhD

http://pharmaceuticalintelligence.com/2012/10/22/blood-vessel-generating-stem-cells-discovered/ Reporter: Ritu Saxena, PhD

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In focus: Melanoma Genetics

Posted in Biological Networks, Gene Regulation and Evolution, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, tagged Akt, Apoptosis, ARF, BRAF, CDK4, CDK6, CDKN2A, cell cycle, CGH array, cutaneous melanoma, genetic heterogeneity, genome, genomics, INK4A, locus, malignant, Melanoma, naevi, nodularity, NRAS, p53, PI3K, pigmentation, Raf, Ras, RB, skin cancer, sunburn, ubiquitination, UV-A, UV-B on February 18, 2013| 4 Comments »

Curator: Ritu Saxena, Ph.D.

Melanoma

Melanoma represents approximately 4% of human skin cancers, yet accounts for approximately 80% of deaths from cutaneous neoplasms. It remains one of the most common types of cancer among young adults. Melanoma is recognized as the most common fatal skin cancer with its incidence rising to 15 fold in the past 40 years in the United States. Melanoma develops from the malignant transformation of melanocytes, the pigment-producing cells that reside in the basal epidermal layer in human skin. (Greenlee RT, et al, Cancer J Clin. Jan-Feb 2001;51(1):15-36 ; Weinstock MA, et al, Med Health R I. Jul 2001;84(7):234-6). Classic clinical signs of melanoma include change in color, recent enlargement, nodularity, irregular borders, and bleeding. Cardinal signs of melanoma are sometimes referred to by the mnemonic ABCDEs (asymmetry, border irregularity, color, diameter, elevation) (Chudnovsky Y, et al. J Clin Invest, 1 April 2005; 115(4): 813–824).

Clinical characteristics

Melanoma primarily affects fair-haired and fair-skinned individuals, and those who burn easily or have a history of severe sunburn are at higher risk than their darkly pigmented, age-matched controls. The exact mechanism and wavelengths of UV light that are the most critical remain controversial, but both UV-A (wavelength 320–400 nm) and UV-B (290–320 nm) have been implicated (Jhappan C, et al, Oncogene, 19 May 2003;22(20):3099-112). Case-control studies have identified several risk factors in populations susceptible to developing melanoma. MacKie RM et al (1989) stated that the relative risk of cutaneous melanoma is estimated from the four strongest risk factors identified by conditional logistic regression. These factors are

total number of benign pigmented naevi above 2 mm diameter;
freckling tendency;
number of clinically atypical naevi (over 5 mm diameter and having an irregular edge, irregular pigmentation, or inflammation); and
a history of severe sunburn at any time in life.

Use of this risk-factor chart should enable preventive advice for and surveillance of those at greatest risk (MacKie RM, et al, Lancet 26 Aug1989;2(8661):487-90).

Cutaneous melanoma can be subdivided into several subtypes, primarily based on anatomic location and patterns of growth (Table 1).

Table 1: Clinical Classification of Melanoma (Chudnovsky Y, et al, 2005)

The genetics of melanoma

As in many cancers, both genetic predisposition and exposure to environmental agents are risk factors for melanoma development. Many studies conducted over several decades on benign and malignant melanocytic lesions as well as melanoma cell lines have implicated numerous genes in melanoma development and progression.

Table 2: Genes involved in Melanoma (Chudnovsky Y, et al, 2005)

Apart from the risk factors such as skin pigmentation, freckling, and so on, another significant risk factor is ‘strong family history of melanoma’. Older case-control studies of patients with familial atypical mole-melanoma (FAMM) syndrome suggested an elevated risk of ∼434-to 1000-fold over the general population (Greene MH, et al, Ann Intern Med, Apr 1985;102(4):458-65). A more recent meta-analysis of family history found that the presence of at least one first-degree relative with melanoma increases the risk by 2.24-fold (Gandini S, et al, Eur J Cancer, Sep 2005;41(14):2040-59). Genetic studies of melanoma-prone families have given important clues regarding melanoma susceptibility loci.

CDKN2A, the familial melanoma locus

CDKN2A is located at chromosome 9p21 and is composed of 4 exons (E) – 1α, 1β, 2, and 3. LOH or mutations at this locus cosegregated with melanoma susceptibility in familial melanoma kindred and 9p21 mutations have been observed in different cancer cell lines. The locus encodes two tumor suppressors via alternate reading frames, INK4 (p16^INK4a) and ARF (p14^ARF). INK4A and ARF encode alternative first exons, 1α and 1β respectively and different promoters. INK4A is translated from the splice product of E1α, E2, and E3, while ARF is translated from the splice product of E1β, E2, and E3. Second exons of the two proteins are shared and two translated proteins share no amino acid homology.

INK4A is the founding member of the INK4 (Inhibitor of cyclin-dependent kinase 4) family of proteins and inhibits the G1 cyclin-dependent kinases (CDKs) 4/6, which phosphorylate and inactivate the retinoblastoma protein (RB), thereby allowing for S-phase entry. Thus, loss of INK4K function promotes RB inactivation through hyperphosphorylation, resulting in unconstrained cell cycle progression.

ARF (Alternative Reading Frame) protein of the locus inhibits HDM2-mediated ubiquitination and subsequent degradation of p53. Thus, loss of ARF inactivates another tumor suppressor, p53. The loss of p53 impairs mechanisms that normally target genetically damaged cells for cell cycle arrest and/or apoptosis, which leads to proliferation of damaged cells. Loss of CDKN2A therefore contributes to tumorigenesis by disruption of both the pRB and p53 pathways.

Figure 1: Genetic encoding and mechanism of action of INK4A and ARF.

(Chudnovsky Y, et al, 2005)

RAF and RAS pathways

A genetic hallmark of melanoma is the presence of activating mutations in the oncogenes BRAF and NRAS, which are present in 70% and 15% of melanomas, respectively, and lead to constitutive activation of mitogen-activated protein kinase (MAPK) pathway signaling. However, molecules that inhibit MAPK pathway–associated kinases, like BRAF and MEK, have shown only limited efficacy in the treatment of metastatic melanoma. Thus, a deeper understanding of the cross talk between signaling networks and the complexity of melanoma progression should lead to more effective therapy.

NRAS mutations activate both effector pathways, Raf-MEK-ERK and PI3K-Akt in melanoma. The Raf-MEK-ERK pathway may also be activated via mutations in the BRAF gene. In a subset of melanomas, the ERK kinases have been shown to be constitutively active even in the absence of NRAS or BRAF mutations. The PI3K-Akt pathway may be activated through loss or mutation of the tumor suppressor gene PTEN, occurring in 30–50% of melanomas, or through gene amplification of the AKT3 isoform. Activation of ERK and/or Akt3 promotes the development of melanoma by various mechanisms, including stimulation of cell proliferation and enhanced resistance to apoptosis.

Figure 2: Schematic of the canonical Ras effector pathways Raf-MEK-ERK and PI3K-Akt in melanoma.

Curtin et al (2005) compared genome-wide alterations in the number of copies of DNA and mutational status of BRAF and NRAS in 126 melanomas from four groups in which the degree of exposure to ultraviolet light differs: 30 melanomas from skin with chronic sun-induced damage and 40 melanomas from skin without such damage; 36 melanomas from palms, soles, and subungual (acral) sites; and 20 mucosal melanomas. Significant differences were observed in number of copies of DNA and mutation frequencies in BRAF among the four groups of melanomas. Eighty-one percent of the melanomas on skin without sun-induced damaged had mutations in BRAF or NRAS. Melanomas with wild-type BRAF or NRAS frequently had increases in the number of copies of the genes for cyclin-dependent kinase 4 (CDK4) and cyclin D1 (CCND1), downstream components of the RAS-BRAF pathway. Thus, the genetic alterations identified in melanomas at different sites and with different levels of sun exposure indicate that there are distinct genetic pathways in the development of melanoma and implicate CDK4 and CCND1 as independent oncogenes in melanomas without mutations in BRAF or NRAS. (Curtin JA, et al, N Engl J Med, 17 Nov 2005;353(20):2135-47).

Genetic Heterogeneity of Melanoma

Melanoma exhibits molecular heterogeneity with markedly distinct biological and clinical behaviors. Lentigo maligna melanomas, for example, are indolent tumors that develop over decades on chronically sun-exposed area such as the face. Acral lentigenous melanoma, or the other hand, develops on sun-protected regions, tend to be more aggressive. Also, transcription profiling has provided distinct molecular subclasses of melanoma. It is also speculated that alterations at the DNA and RNA and the non-random nature of chromosomal aberrations may segregate melanoma tumors into subtypes with distinct clinical behaviors.

The melanoma gene atlas

Whole-genome screening technologies such as spectral karyotype analysis and array-CGH have identified many recurrent nonrandom chromosomal structural alterations, particularly in chromosomes 1, 6, 7, 9, 10, and 11 (Curtin JA, et al, N Engl J Med, 17 Nov 2005;353(20):2135-47); however, in most cases, no known or validated targets have been linked to these alterations.

In A systematic high-resolution genomic analysis of melanocytic genomes, array-CGH profiles of 120 melanocytic lesions, including 32 melanoma cell lines, 10 benign melanocytic nevi, and 78 melanomas (primary and metastatic) by Chin et al (2006) revealed a level of genomic complexity not previously appreciated. In total, 435 distinct copy number aberrations (CNAs) were defined among the metastatic lesions, including 163 recurrent, high-amplitude events. These include all previously described large and focal events (e.g., 1q gain, 6p gain/6q loss, 7 gain, 9p loss, and 10 loss). Genomic complexity observed in primary and benign nevi melanoma is significantly less than that observed in metastatic melanoma (Figure 3) (Chin L, et al, Genes Dev. 15 Aug 2006;20 (16):2149-2182).

Figure 3: Genome comparisons of melanocyte lesions (Chin L, et al, 2006)

Thus, genomic profiling of various melanoma progression types could reveal important information regarding genetic events those likely drive as metastasis and possibly, reveal provide cues regarding therapy targeted against melanoma.

Reference: