Posts Tagged ‘Ras’

Reporter and Curator: Dr. Sudipta Saha, Ph.D.


A mutated gene called RAS gives rise to a signalling protein Ral which is involved in tumour growth in the bladder. Many researchers tried and failed to target and stop this wayward gene. Signalling proteins such as Ral usually shift between active and inactive states.


So, researchers next tried to stop Ral to get into active state. In inacvtive state Ral exposes a pocket which gets closed when active. After five years, the researchers found a small molecule dubbed BQU57 that can wedge itself into the pocket to prevent Ral from closing and becoming active. Now, BQU57 has been licensed for further development.


Researchers have a growing genetic data on bladder cancer, some of which threaten to overturn the supposed causes of bladder cancer. Genetics has also allowed bladder cancer to be reclassified from two categories into five distinct subtypes, each with different characteristics and weak spots. All these advances bode well for drug development and for improved diagnosis and prognosis.


Among the groups studying the genetics of bladder cancer are two large international teams: Uromol (named for urology and molecular biology), which is based at Aarhus University Hospital in Denmark, and The Cancer Genome Atlas (TCGA), based at institutions in Texas and Boston. Each team tackled a different type of cancer, based on the traditional classification of whether or not a tumour has grown into the muscle wall of the bladder. Uromol worked on the more common, earlier form, non-muscle-invasive bladder cancer, whereas TCGA is looking at muscle-invasive bladder cancer, which has a lower survival rate.


The Uromol team sought to identify people whose non-invasive tumours might return after treatment, becoming invasive or even metastatic. Bladder cancer has a high risk of recurrence, so people whose non-invasive cancer has been treated need to be monitored for many years, undergoing cystoscopy every few months. They looked for predictive genetic footprints in the transcriptome of the cancer, which contains all of a cell’s RNA and can tell researchers which genes are turned on or off.


They found three subgroups with distinct basal and luminal features, as proposed by other groups, each with different clinical outcomes in early-stage bladder cancer. These features sort bladder cancer into genetic categories that can help predict whether the cancer will return. The researchers also identified mutations that are linked to tumour progression. Mutations in the so-called APOBEC genes, which code for enzymes that modify RNA or DNA molecules. This effect could lead to cancer and cause it to be aggressive.


The second major research group, TCGA, led by the National Cancer Institute and the National Human Genome Research Institute, that involves thousands of researchers across USA. The project has already mapped genomic changes in 33 cancer types, including breast, skin and lung cancers. The TCGA researchers, who study muscle-invasive bladder cancer, have looked at tumours that were already identified as fast-growing and invasive.


The work by Uromol, TCGA and other labs has provided a clearer view of the genetic landscape of early- and late-stage bladder cancer. There are five subtypes for the muscle-invasive form: luminal, luminal–papillary, luminal–infiltrated, basal–squamous, and neuronal, each of which is genetically distinct and might require different therapeutic approaches.


Bladder cancer has the third-highest mutation rate of any cancer, behind only lung cancer and melanoma. The TCGA team has confirmed Uromol research showing that most bladder-cancer mutations occur in the APOBEC genes. It is not yet clear why APOBEC mutations are so common in bladder cancer, but studies of the mutations have yielded one startling implication. The APOBEC enzyme causes mutations early during the development of bladder cancer, and independent of cigarette smoke or other known exposures.


The TCGA researchers found a subset of bladder-cancer patients, those with the greatest number of APOBEC mutations, had an extremely high five-year survival rate of about 75%. Other patients with fewer APOBEC mutations fared less well which is pretty surprising.


This detailed knowledge of bladder-cancer genetics may help to pinpoint the specific vulnerabilities of cancer cells in different people. Over the past decade, Broad Institute researchers have identified more than 760 genes that cancer needs to grow and survive. Their genetic map might take another ten years to finish, but it will list every genetic vulnerability that can be exploited. The goal of cancer precision medicine is to take the patient’s tumour and decode the genetics, so the clinician can make a decision based on that information.





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Targeting KRAS Mutations In Pancreatic Tumours

Curator: David Orchard-Webb, PhD


In pancreatic cancer KRAS is mutated between 70-90%. Next generation sequencing suggests 90%, however it must be understood that the allelic frequency of KRAS mutation will not be 100% – not all the cells within the tumour will contain a KRAS mutation due to tumour heterogeneity [1]. Regardless of the exact figure KRAS mutation is more frequent in pancreatic ductal adenocarcinoma (PDAC) than in any other cancer and plays a role in cancer initiation and progression. The four most common KRAS mutations in PDAC are G12D > G12V > G12C > G13D [2]. The use of mutant RAS peptides as a cancer vaccine immunotherapy is an established concept and the nordic company Targovax has a RAS vaccine called TG01 in phase I/II clinical trials for pancreatic cancer.


Inhibiting the signalling activity of KRAS in pancreatic cancers is also attractive however failures most notably of the farnesyl transferase inhibitors in clinical trials has led to unwarranted pessimism. Two farnesyl transferase inhibitors have been tested and failed in clinical trials of pancreatic cancer, Tipifarnib (Johnson & Johnson), and L-778.123 (Merck & Co.) [3]. However farnesyltransferase inhibitors may still have a role in the treatment of a rare subset of pancreatic cancer patients. Importantly rare cases of complete response to farnesyltransferase inhibitors are known in pancreatic cancer patients [4]. Recently new drug targets that shut-down mutated RAS signalling have emerged.


Small molecule competitive binders of the prenyl-binding protein PDEδ have been developed by researchers at the Max Planck Institute of Molecular Physiology, Dortmund, Germany. RAS localisation to the membrane is essential for its signalling in cancer. RAS proteins are farnesylated and this modulates their localisation to the plasma membrane. The farnesyl tail of RAS is bound by PDEδ which facilitates its transport to the plasma membrane. A series of compounds with ever greater affinity for the PDEδ pocket that binds RAS have been synthesised. Deltazinone was their first generation compound and Deltarasin their second generation compound [5, 6]. The Dortmund group have shown in pancreatic cancer cell lines with mutated KRAS that these compounds reduce cell proliferation.


A new small molecule called Rigosertib has been developed by the laboratory of E. Premkumar Reddy (New York) and collaborators which binds to the RAS-binding domain (RBD) present in proteins that interact with RAS and thus block this interaction, nullifying activated RAS [7]. Rigosertib dramatically reduced the growth of human HCT116 colon cancer cell line implanted as a mouse xenograft. It also reduced the number of Pancreatic Intraepithelial Neoplasia (PanIN) lesions, precursors of PDAC, present in KRAS mutant mice.


It will be important to further refine these compounds into leads for preclinical development through to investigational new drug (IND) submission as they are very promising developments for RAS active tumours such as PDAC.




  1. Lennerz, Jochen K., and Albrecht Stenzinger. ‘Allelic Ratio of KRAS Mutations in Pancreatic Cancer’. The Oncologist 20, no. 4 (2015): e8–e9.
  2. Stephen, Andrew G., Dominic Esposito, Rachel K. Bagni, and Frank McCormick. ‘Dragging Ras Back in the Ring’. Cancer Cell 25, no. 3 (March 2014): 272–81. doi:10.1016/j.ccr.2014.02.017.
  3. Orchard­-Webb D. 2015. Future Directions in Pancreatic Cancer Therapy. JOP. Journal of the Pancreas 16:249­-255.
  4. Ledford, Heidi. ‘Cancer Researchers Revisit “Failed” Clinical Trials’. Nature, 18 April 2013. doi:10.1038/nature.2013.12835.
  5. Zimmermann, Gunther, Björn Papke, Shehab Ismail, Nachiket Vartak, Anchal Chandra, Maike Hoffmann, Stephan A. Hahn, et al. ‘Small Molecule Inhibition of the KRAS-PDEδ Interaction Impairs Oncogenic KRAS Signalling’. Nature 497, no. 7451 (30 May 2013): 638–42. doi:10.1038/nature12205.
  6. Papke, Björn, Sandip Murarka, Holger A Vogel, Pablo Martín-Gago, Marija Kovacevic, Dina C Truxius, Eyad K Fansa, et al. ‘Identification of Pyrazolopyridazinones as PDEδ Inhibitors’. Nature Communications 7 (20 April 2016): 11360. doi:10.1038/ncomms11360.
  7. Athuluri-Divakar, Sai Krishna, Rodrigo Vasquez-Del Carpio, Kaushik Dutta, Stacey J. Baker, Stephen C. Cosenza, Indranil Basu, Yogesh K. Gupta, et al. ‘A Small Molecule RAS-Mimetic Disrupts RAS Association with Effector Proteins to Block Signaling’. Cell 165, no. 3 (21 April 2016): 643–55. doi:10.1016/j.cell.2016.03.045.


Other Related Articles Published In This Open Access Online Journal Include The Following:



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Mutations in RAS genes

Larry H Bernstein, MD, FCAP, Curator



Novel Mechanism Targets “Undruggable” RAS Oncogenes

Dr. Reddy discussing findings from new research on developing drugs toward RAS.

Mechanism of new research targeting previously undruggable RAS gene. [Cell, Volume 165, 21 April 2016]


For greater than 30% of human cancers, RAS genes are mutated and have been implicated as key tumor drivers—making them some of the most sought after cancer drug targets. However, the absence of any drug-binding pockets in the mutant RAS proteins has made drug development extremely difficult. Yet now, a new study from researchers at the Icahn School of Medicine at Mount Sinai has identified a new mechanism for targeting this important cancer gene.

Mutations in RAS genes—such as HRAS, KRAS, and NRAS—are frequently observed in many of the most common and lethal tumors, including cancers of the pancreas, lung, and colon. While scientists have made significant headway in understanding these mutations and their impact on cellular signaling, little headway has been made toward developing drugs that systematically target the RAS oncogenes. This lack of progress has led many in the field to label RAS an “undruggable” cancer gene.

The Mt. Sinai team identified what they believe to be the first small molecule able to simultaneously inhibit the different signaling pathways activated by RAS oncogenes. This compound called rigosertib or ON01910.Na, acts as a protein-protein interaction inhibitor that prevents binding between RAS and signaling proteins (including RAF, PI3K, and others) that drive a cell into a cancer cell.

“Here, we present evidence that rigosertib, a styryl-benzyl sulfone, acts as a RAS-mimetic and interacts with the RBDs of RAF kinases, resulting in their inability to bind to RAS, disruption of RAF activation, and inhibition of the RAS-RAF-MEK pathway,” the authors wrote. “We also find that rigosertib binds to the RBDs of Ral-GDS and PI3Ks. These results suggest that targeting of RBDs across multiple signaling pathways by rigosertib may represent an effective strategy for inactivation of RAS signaling.”

The findings from this study were published recently in Cell through an article entitled “A Small Molecule RAS-Mimetic Disrupts RAS Association with Effector Proteins to Block Signaling.”

Additionally, the investigators performed structural experiments to confirm the mode of action for rigosertib and also demonstrated the potential for this targeted mechanism in the treatment of several RAS-driven cancers.

“This discovery is a significant breakthrough for the cancer field,” explained senior study author E. Premkumar Reddy, Ph.D., professor of oncological sciences at the Icahn School of Medicine at Mount Sinai. “Rigosertib’s mechanism of action represents a new paradigm for attacking the intractable RAS oncogenes. Our current focus is to use the information from our studies with rigosertib to design the next generation of small molecule RAS-targeting therapies, and we are excited to have recently identified several compounds which we think improve on the qualities of rigosertib.”


A Small Molecule RAS-Mimetic Disrupts RAS Association with Effector Proteins to Block Signaling

Sai Krishna Athuluri-Divakar, Rodrigo Vasquez-Del Carpio,….., Aneel K. Aggarwal, E. Premkumar Reddycorrespondence

  • Rigosertib binds to the RAS-binding domains (RBDs) of multiple RAS effectors
  • Binding of rigosertib to RAF-RBD inhibits RAS-RAF interaction and impairs the kinase
  • Rigosertib inhibits MEK-ERK pathway activated by growth factors and oncogenic RAS

Oncogenic activation of RAS genes via point mutations occurs in 20%–30% of human cancers. The development of effective RAS inhibitors has been challenging, necessitating new approaches to inhibit this oncogenic protein. Functional studies have shown that the switch region of RAS interacts with a large number of effector proteins containing a common RAS-binding domain (RBD). Because RBD-mediated interactions are essential for RAS signaling, blocking RBD association with small molecules constitutes an attractive therapeutic approach. Here, we present evidence that rigosertib, a styryl-benzyl sulfone, acts as a RAS-mimetic and interacts with the RBDs of RAF kinases, resulting in their inability to bind to RAS, disruption of RAF activation, and inhibition of the RAS-RAF-MEK pathway. We also find that ribosertib binds to the RBDs of Ral-GDS and PI3Ks. These results suggest that targeting of RBDs across multiple signaling pathways by rigosertib may represent an effective strategy for inactivation of RAS signaling.

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Upregulate Tumor Suppressor Pathways

Writer and Curator: Larry H Bernstein, MD, FCAP


7.5  Upregulate Tumor Suppressor Pathways

7.5.1 NR4A nuclear receptors are orphans but not lonesome

7.5.2 The interplay of NR4A receptors and the oncogene–tumor suppressor networks in cancer

7.5.3 NLRX1 acts as tumor suppressor by regulating TNF-α induced apoptosis

7.5.4 The Mre11 Complex Suppresses Oncogene-Driven Breast Tumorigenesis and Metastasis

7.5.5 Expression of Stromal Cell-derived Factor 1 and CXCR4 Ligand Receptor System in Pancreatic Cancer

7.5.6 DLC1- a significant GAP in the cancer genome

7.5.7 DLC1 is a chromosome 8p tumor suppressor whose loss promotes hepatocellular carcinoma.

7.5.8 Smad7 regulates compensatory hepatocyte proliferation in damaged mouse liver and positively relates to better clinical outcome in human hepatocellular carcinoma



7.5.1 NR4A nuclear receptors are orphans but not lonesome

Kurakula K, Koenis DS, van Tiel CM, de Vries CJ.
Biochim Biophys Acta. 2014 Nov; 1843(11):2543-2555


  • Nuclear receptors Nur77, Nurr1 and NOR-1 are ‘orphan’ receptors of the NR4A subfamily.
  • The NR4A receptors have no ligands.
  • The known protein–protein interactions of all three NR4A receptors are summarized.
  • Interacting proteins are transcription factors, coregulators or protein kinases.
  • Protein–protein interactions modulate NR4A receptor activity and function.


The NR4A subfamily of nuclear receptors consists of three mammalian members: Nur77, Nurr1, and NOR-1. The NR4A receptors are involved in essential physiological processes such as adaptive and innate immune cell differentiation, metabolism and brain function. They act as transcription factors that directly modulate gene expression, but can also form trans-repressive complexes with other transcription factors. In contrast to steroid hormone nuclear receptors such as the estrogen receptor or the glucocorticoid receptor, no ligands have been described for the NR4A receptors. This lack of known ligands might be explained by the structure of the ligand-binding domain of NR4A receptors, which shows an active conformation and a ligand-binding pocket that is filled with bulky amino acid side-chains. Other mechanisms, such as transcriptional control, post-translational modifications and protein–protein interactions therefore seem to be more important in regulating the activity of the NR4A receptors. For Nur77, over 80 interacting proteins (the interactome) have been identified so far, and roughly half of these interactions has been studied in more detail. Although the NR4As show some overlap in interacting proteins, less information is available on the interactome of Nurr1 and NOR-1. Therefore, the present review will describe the current knowledge on the interactomes of all three NR4A nuclear receptors with emphasis on Nur77.
Nur77 in the regulation of endocrine signals and steroid hormone synthesis

Nur77 is expressed in endocrine tissues and in organs that are crucial for steroid hormone synthesis such as the adrenal glands, the pituitary gland and the testes. The first functional NurRE was identified in the promoter of the pro-opiomelanocortin (POMC) gene of pituitary derived AtT-20 cells [2]. Nur77 can bind this NurRE either as a homodimer or as a heterodimer with either one of the other two NR4A receptors Nurr1 and NOR-1. Interestingly, it was shown that these heterodimers enhance POMC gene transcription more potently than homodimers of Nur77 do, suggesting that there is interdependency between the NR4A receptors in activating their target genes [3]. The NurRE sequence in the POMC promoter also partially overlaps with a STAT1-3 response element. Philips et al. showed that Nur77 and STAT1-3 bind simultaneously to this so called NurRE-STAT composite site and synergistically enhance transcription of the POMC gene. However, Nur77 and STAT1-3 do not interact directly, which suggests that oneor more facilitatingfactors are involved in NurRE-STAT driven transcription. Mynard et al. showed that this third factor is cAMP response element binding protein (CREB), which binds both STAT1-3 and Nur77 and indirectly enhances transcription of the POMC gene by facilitating the synergistic activation of the NurRE-STAT composite site by STAT1-3 and Nur77 [4]. Nur77also plays animportant role in the steroidogenic acute regulatory protein (StAR)-mediated testosterone production by Leydig cells. StAR is required for the transport of cholesterol through the mitochondrial membrane to initiate steroid hormone synthesis. Nur77 binds to an NBRE in the StAR promoter, which is in close proximity to an AP-1 response element. In response to cAMP stimulation c-Jun and Nur77 synergistically increase StAR gene expression [5], presumably through a direct interaction between c-Jun and the LBD of Nur77 [6]. On the other hand, c-Jun has also been shown to suppress expression of the hydroxylase P450 c17 gene by blocking the DNA-binding activity o fNur77 in response to stimulation of Leydig cells with reactive oxygen species [7].The effect of c-Jun on the transcriptional activity of Nur77 therefore seems to depend on other factors as well. One of these factors could be the atypical nuclear receptor DAX1 (NR0B1), which lacks a DBD and associates with multiple coregulatory proteins. DAX1 binds Nur77 directly and represses its ability to enhance transcription of the previously mentioned P450 c17 gene.

Fig.1.Schematic representation of the domain structure of nuclear receptors. Nuclear receptors are composed of an N-terminal domain (N-term), a central DNA-binding domain (DBD) and a ligand-binding domain (LBD). The amino acid similarity between the individual domains of Nur77 with Nurr1 and NOR-1 is given in percentages below the domains.

The interactome of NOR-1

NOR-1 is less well studied than Nur77 and Nurr1 and most of the data on interacting proteins of NOR-1 are presented in studies that are mainly focused on its homologues. As a consequence, NOR-1 protein– protein interactions are described with limited detail, for example the HATp300/CBPacetylatesNOR-1similarlyasNur77,however,theeffect on NOR-1 activity has not been described [79]. Likewise, NOR-1 interacts with the co-regulator TIF1β resulting in enhanced NOR-1 activity, but the domain involved in the interaction is unknown [48]. Similar to Nur77, PKC and RSK1/2 were shown to induce NOR-1 mitochondrial translocation [73,79] and DNA-PK binds the DBD of NOR-1. Even though Nurr1 and Nur77 are both essential for optimal DSB repair the function of NOR-1 in this process remains to be studied [68]. Both FHL2 and the peptidyl-prolyl isomerase Pin1 bind the N-terminal domain and DBD of NOR-1, resulting in reduced or enhanced transcriptional activity of NOR-1, respectively [59,64]. Muscat and co-workers performed detailed studies to identify coregulatorsofNOR-1andwerethefirsttorevealtheabsenceofaconventional ligand-binding pocket in the LBD of NOR-1, through molecular modeling and hydrophobicity analysis of the LBD [104]. Based on these analyses, the relative importance of the N-terminal domain of NOR-1 in regulation of the transcriptional activity of NOR-1 became apparent and direct interaction of a number of crucial co-regulators to this domain was shown;SRC-2 (GRIP-1), SRC-1, SRC-3, p300, DRIP250/ TRAP220 and PCAF [104]. The interaction between the N-terminal domain of NOR-1 and TRAP220 is independent of PKA- and PKC phosphorylation sites in TRAP220. Most interestingly, the purine derivative 6-mercaptopurine, which enhances the activity of NR4As without directly binding these nuclear receptors promotes the interaction between NOR-1 and TRAP220 [105]. Both Nur77 and NOR-1 are involved in T-cell receptor mediated apoptosis of developing T cells [106]. During activation of T cells the expressionofNOR-1isinducedandproteinkinaseC(PKC)becomesactive.NOR-1is aPKCsubstratethat isphosphorylatedand subsequently translocatesfromthenucleustothemitochondriawhereitbindsBcl-2. Most interestingly, as already indicated above the interaction between NOR-1/Nur77 and Bcl-2 causes a conformational change in Bcl-2 allowing its BH3 domain to be exposed, resulting in the conversion of Bcl-2 from an anti-apoptotic into a pro-apoptotic protein. For Nur77 it is exactly known which amino acids are involved to provoke the functional switchin Bcl-2, whichis not thecasefor NOR-1 [57,79]. Initially, the homeobox domain containing protein Six3 was identified in a yeast-two-hybrid study as a protein that interacts uniquely withtheDBDandLBDofNOR-1withoutbindingorinhibitingtheactivity of Nur77 or Nurr1. Of interest, NOR-1 and Six3 show overlap in expression in the rat fetal forebrain on embryonic day 18 [107]. In a later study this specificity of Six3 forNOR-1 was not found, rather interaction with all three NR4As was observed [108]. NOR-1 is part of the EWS/NOR-1 fusion protein that is expressed in human extraskeletal myxoid chondrosarcoma tumors. Six3 enhances the activity of NOR-1 (and Nur77 and Nurr1), whereas the activity of EWS/NOR-1 is inhibited and the interaction only requires the DBD of NOR-1. The opposing data in these two studies may be explained by the use of different cell types for the activity assays, as well as the use of Gal4-fusion proteins in the latter study. PARP-1 specifically and effectively interacts with theDBD of NOR-1 independent of the enzymatic activity of PARP-1 [69]. Nurr1 interacts with lower affinity, whereas EWS/NOR-1 and Nur77 do not bind PARP-1, unless the N-terminal domain of Nur77 is deleted. The latter experiment nicely illustrates that the N-terminal domains of Nur77 and EWS/NOR-1 disturb PARP-1 interaction with the DBD. This may be the underlying mechanism of differential function of NOR-1 and the EWS/NOR-1 fusion protein. In line with the binding characteristics, PARP-1 only inhibits the activity of NOR-1 effectively, again independently of the ribose polymerase activity of PARP-1.

Table 5 NOR-1 interacting proteins.

Fig.2. Nur77 and its interacting proteins. Schematic overview of the protein–protein interactions with Nur77 for which the domains of interaction have been elucidated. Details are described in the text and in Tables 1–3, which also contain the full names of the indicated proteins. N-term, N-terminal domain; DBD, DNA-binding domain; LBD, ligand-binding domain.

Fig.3. Nur77 and kinases modulating its activity and localization. A, Overview of the amino-acid sequence of Nur77 with known phosphorylation sites and associated kinases indicated (T= threonine,S= serine). B,Schematic illustration of effects of different kinases on Nur77 transcriptional activity and subcellular localization. See Table3 for definitions of the abbreviations of the kinases shown.


Discussion and concluding remarks
This review summarizes the currently available knowledge on the protein–protein interactions of the NR4A nuclear receptor family and their downstream effects. When looking at the information gathered in this review three main observations can be made. First, there are a large number of protein–protein interactions that regulate the activity of Nur77 and there is a large variation in the effects of these interactions on the ‘target’ protein, be it Nur77 or the interacting protein itself. These effects include modulation of transcriptional activity, protein stability, post-translational modification and cellular localization: all processes that are tightly regulated by ligand binding in other nuclear receptors. In light of the many interactions it undergoes with other proteins, Nur77 could also be considered to be a molecular ‘chameleon’: a protein that selectively adopts the responsiveness of other proteins by directly interacting with them. Secondly, the protein–protein interactions with Nur77 described in this review have been studied in a wide range of cell types, such as immune cells (T-cells, thymocytes, monocytes and macrophages); somatic cells(neurons,smooth muscle cells,endothelial
cells and hepatocytes) and cancer cells from diverse origins.We reason that a stimulus- and cell type-specific expression pattern of interacting proteins may be decisive in determining both the interactions of NR4 As with other proteins and their activity in general.The well-studied interaction between Nur77 and RXRα, which has unique outcomes depending on both the cell type studied and the stimulus used, is one such interaction that is modulated by stimulus- or cell type- specific auxiliary proteins. Lastly, there is a large amount of overlap in interacting proteins between the three NR4A nuclear receptors. All three domains of the NR4As are involved in interactions with other proteins (Tables 1–5, Fig. 2), and we think that the unstructured N-terminal domains are of special interest as they have the lowest overall amino acid similarity (Fig. 1). Based on this dissimilarity, it could be hypothesized that the N-terminal domain of each NR4A receptor interacts with a unique set of proteins that specifically regulates each of their activities, if it were not for the fact that this review has shown that the interacting partners of the NR4As strongly overlap. However, a closer look at the N-terminal domains of Nur77, Nurr1 and NOR-1 reveals small stretches of relatively high similarity within the amino acid sequences (Fig. 4). The possible importance of these small stretches of high similarity is most readily apparent when looking at phosphorylation sites of the NR4As.

Fig. 4. Amino-acid sequence similarity between the N-terminal domains of the NR4A receptors. The amino-acid sequence of the N-terminal domains of Nur77, Nurr1 and NOR-1 was aligned and the extent of sequence similarity is indicated with colors; e.g. blue indicates the regions where the sequence of the three NR4As is identical. In the Nur77 sequence, the CHEK2 target Thr88, the JNK1 target Ser95, the ERK2 target Thr143, the CK2 target Ser152, and the DNA-PK target Ser164 are indicated with arrows. In the Nurr1 sequence, the ERK2 targets Ser126 and Thr132, and the ERK5 targets Thr168 and Ser177 are indicated with arrows.



7.5.2 The interplay of NR4A receptors and the oncogene–tumor suppressor networks in cancer

Beard JA, Tenga A, Chen T
Cell Signal. 2015 Feb; 27(2):257-66


  • The expression and function of NR4As are dysregulated in multiple cancer types.
  • NR4As are positively regulated by oncogenic signaling pathways.
  • NR4As are capable of inhibiting tumor suppressor signaling.
  • The connectedness of NR4As with these pathways mediate their functions in cancer.
  • NR4A agonists and antagonists offer therapeutic strategies for cancer treatment.


Nuclear receptor (NR) subfamily 4 group A (NR4A) is a family of three highly homologous orphan nuclear receptors that have multiple physiological and pathological roles, including some in cancer. These NRs are reportedly dysregulated in multiple cancer types, with many studies demonstrating pro-oncogenic roles for NR4A1 (Nur77) and NR4A2 (Nurr1). Additionally, NR4A1 and NR4A3 (Nor-1) are described as tumor suppressors in leukemia. The dysregulation and functions of the NR4A members are due to many factors, including transcriptional regulation, protein-protein interactions, and post-translational modifications. These various levels of intracellular regulation result from the signaling cross-talk of the NR4A members with various signaling pathways, many of which are relevant to cancer and likely explain the family members’ functions in oncogenesis and tumor suppression. In this review, we discuss the multiple functions of the NR4A receptors in cancer and summarize a growing body of scientific literature that describes the interconnectedness of the NR4A receptors with various oncogene and tumor suppressor pathways.

NR4As are positively regulated by oncogenic signaling pathways

NR4A subfamily of nuclear receptors

NR4A subfamily of nuclear receptors

intracellular regulation result from the signaling cross-talk of the NR4A members


7.5.3 NLRX1 acts as tumor suppressor by regulating TNF-α induced apoptosis

Singh K, Poteryakhina A, Zheltukhin A, …Chumakov PM, Singh R.
Biochim Biophys Acta. 2015 May; 1853(5):1073-86


  • NLRX1 sensitizes cancer cells to TNF induced cell death by regulating Caspase-8.
  • NLRX1 localizes to mitochondria (mt) and regulates TNF induced mt-ROS generation.
  • Mitochondrial association of Caspase-8 with NLRX1 may regulate mt-ETC function.
  • NLRX1 expression in cancer cells suppresses tumorigenicity in nude mice.

Chronic inflammation in tumor microenvironment plays an important role at different stages of tumor development. The specific mechanisms of the association and its role in providing a survival advantage to the tumor cells are not well understood. Mitochondria are emerging as a central platform for the assembly of signaling complexes regulating inflammatory pathways, including the activation of type-I IFN and NF-κB. These complexes in turn may affect metabolic functions of mitochondria and promote tumorigenesis. NLRX1, a mitochondrial NOD-like receptor protein, regulate inflammatory pathways, however its role in regulation of cross talk of cell death and metabolism and its implication in tumorigenesis is not well understood. Here we demonstrate that NLRX1 sensitizes cells to TNF-α induced cell death by activating Caspase-8. In the presence of TNF-α, NLRX1 and active subunits of Caspase-8 are preferentially localized to mitochondria and regulate the mitochondrial ROS generation. NLRX1 regulates mitochondrial Complex I and Complex III activities to maintain ATP levels in the presence of TNF-α. The expression of NLRX1 compromises clonogenicity, anchorage-independent growth, migration of cancer cells in vitro and suppresses tumorigenicity in vivo in nude mice. We conclude that NLRX1 acts as a potential tumor suppressor by regulating the TNF-α induced cell death and metabolism.


7.5.4 The Mre11 Complex Suppresses Oncogene-Driven Breast Tumorigenesis and Metastasis

Gupta GPVanness KBarlas AManova-Todorova KOWen YHPetrini JH
Mol Cell. 2013 Nov 7;52(3):353-65

The DNA damage response (DDR) is activated by oncogenic stress, but the mechanisms by which this occurs, and the particular DDR functions that constitute barriers to tumorigenesis, remain unclear. We established a mouse model of sporadic onco-gene-driven breast tumorigenesis in a series of mutant mouse strains with specific DDR deficiencies to reveal a role for the Mre11 complex in the response to oncogene activation. We demonstrate that an Mre11-mediated DDR restrains mammary hyperplasia by effecting an oncogene-induced G2 arrest. Impairment of Mre11 complex functions promotes the progression of mammary hyperplasias into invasive and metastatic breast cancers, which are often associated with secondary inactivation of the Ink4a-Arf (CDKN2a) locus. These findings provide insight into the mechanism of DDR engagement by activated oncogenes and highlight genetic interactions between the DDR and Ink4a-Arf pathways in suppression of oncogene-driven tumorigenesis and metastasis.

The DNA damage response (DDR) network comprises DNA repair, DNA damage signaling, apoptosis, and cell-cycle checkpoint functions (Ciccia and Elledge, 2010). Two lines of evidence support the view that the DDR is a barrier to tumorigenesis. Mutations affecting components of the DDR are frequently associated with predisposition to cancer (Ciccia and Elledge, 2010). Also, indices of DDR activation are evident in preneoplastic lesions or in cultured cells harboring activated oncogenes (Bart-kova et al., 2005Gorgoulis et al., 2005). Despite supportive genetic data from in vitro and tumor inoculation studies (Bartkova et al., 2006;Di Micco et al., 2006), causal demonstration that the oncogene-induced DDR suppresses tumorigenesis within a tissue context remains limited (Gorrini et al., 2007Squatrito et al., 2010Takacova et al., 2012). In certain contexts, the role for ataxia telangiectasia mutated (ATM) in suppressing onco-gene-driven tumorigenesis was relatively minor, although these mouse models were limited by the fact that ATM−/− mice are prone to early spontaneous lymphomagenesis (Efeyan et al., 2009).

The mechanism for DDR activation in response to oncogene expression remains incompletely understood, but the prevailing view posits that oncogene activation leads to replication stress in the form of stalled, and subsequently collapsed, DNA replication forks (Halazonetis et al., 2008). Analysis of the ATRSeckel mouse has indicated that ATR may be required for cell viability upon oncogene activation, suggesting that DNA replication stress may indeed underlie these effects of oncogene activation (López-Contreras et al., 2012;Murga et al., 2011Schoppy et al., 2012). However, since ATR promotes viability, rather than elimination of the oncogene-expressing cells, this outcome is not consistent with a barrier function for that component of the DDR. The purpose of this study was to delineate the particular aspects of the DDR network that constitute barriers to oncogenesis using a mouse model of sporadic, oncogene-driven breast cancer.

The Mre11 complex is a sensor of DNA double-strand breaks (Stracker and Petrini, 2011). Hypomorphic mutations in this complex, modeled in the mouse after alleles inherited in ataxiatelangiectasia-like disorder (A-TLD) and Nijmegen breakage syndrome (NBS), have facilitated the elucidation of the Mre11 complex’s role in the ATM-dependent DDR. Here, we utilize these and other mutant mouse strains, individually and in combination, to define the tumor-suppressive functions of the DDR in mammary epithelium.

A Mouse Model of Sporadic, Oncogene-Induced Mammary Neoplasia

Expression of activated NeuT (Bargmann and Weinberg, 1988), the rodent ortholog of the ERBB2/HER2oncogene, in the mammary epithelium of adult mice via the RCAS/MMTVTVA system (Du et al., 2006) results in early DDR activation, and oligoclonal tumors with an average latency of 5 months (Reddy et al., 2010). To delineate the aspects of the DDR primarily relevant for tumor suppression in the face of oncogene activation, we interbred MMTV-TVA mice with a variety of mutant mouse strains with established DDR deficiencies. Age-matched cohorts of female animals (12–18 weeks old) were injected with either RCAS-HA-NeuT or control virus via mammary intraductal injection. The genotypes analyzed wereMre11ATLD1/ATLD1Nbs1ΔBBChk2−/−Nbs1ΔCChk2−/−p53515C/515Cp53−/−, and 53BP1−/−, each of which exhibits defects in DNA-damage-induced cell-cycle checkpoint activation, apoptosis, and/or DNA repair (Figures S1A and S1B available online; Liu et al., 2004Shibata et al., 2010Stracker et al., 20072008Stracker and Petrini, 2011Theunissen et al., 2003Williams et al., 2002). These mouse strains did not exhibit any histopathological deficits in mammary gland development (data not shown), circumventing the potential problem of differences in mammary tissue among the various genetic backgrounds confounding the analyses.

We performed digital quantification of glandular structures relative to total cellular content in the oncogene-expressing mammary glands and normalized this value to the glandular content observed in the matched control mammary glands (Figure 1C). These variations in mammary ductal enlargement, luminal filling, cellular turnover, and glandular density across the different genotypes are summarized in Figure 1D.

NeuT expression in Chk2−/− and Nbs1ΔCChk2−/− mammary epithelium produced hyperplasias that were only modestly dissimilar from WT (Figures 1B–1D; data not shown), suggesting that apoptosis and the intra-S phase checkpoint—diminished in both mutants (Stracker et al., 2008)—do not mediate the early response to oncogene activation. Consistent with that interpretation, p53515C/515C mutants, in which p53-dependent apoptosis is lost (Liu et al., 2004), also exhibited relatively modest hyper-plasia, although some morphological changes were noted (Figures 1B–1D). In contrast, p53−/− mammary glands resembled p53515C/515C morphologically, but exhibited more extensive NeuT-induced hyperplasia (Figures 1B–1D), consistent with additional deficiencies of the null mutant—including, but not limited to, induction of the G1/S checkpoint and senescence pathways.

In contrast to the aforementioned genotypes, oncogene-induced hyperplasia was markedly distinct in Mre11ATLD1/ATLD1 and Nbs1ΔBB mammary glands relative to WT mammary glands (Figures 1B–1D). The Mre11 complex mutant genotypes exhibited florid hyperplasia in response to oncogene expression that frequently filled the lumen of the enlarged mammary ducts. Quantification of hyperplasia across the entire mammary gland revealed that Mre11ATLD1/ATLD1 was associated with the most significant degree of oncogene-induced proliferative change (Figure 1C).

We examined oncogene-dependent activation of the DDR in WT and Mre11ATLD1/ATLD1 mammary hyperplasias. Consistent with prior reports (Reddy et al., 2010), we observed the formation of γH2AX foci and accumulation of 53BP1 nuclear staining in WT hyperplasias after the introduction of NeuT (Figures 2A and 2B). We observed a highly significant, >2-fold reduction in both NeuT-induced γH2AX foci formation and 53BP1 accumulation within Mre11ATLD1/ATLD1 lesions relative to WT (p < 0.0001; Figures 2A and 2B). In contrast to the effects of Mre11 complex hypomorphism, oncogene-dependent DDR activation was unperturbed in p53−/− mammary glands (Figure 2A; data not shown). These data demonstrate that the Mre11 complex is required for DDR activation upon NeuT expression.

The oncogene-driven, Mre11 complex-dependent DDR exhibited dissimilarities from that induced by ionizing radiation (IR). First, oncogene expression in the WT mammary gland resulted in finely punctate 53BP1 staining and did not induce the large foci that develop after irradiation of the mammary gland (Figure S4). In addition, phosphorylation of the ATM target KAP1 at Ser824 was not observed in the oncogene-expressing mammary gland, but was readily detected in IR-treated mammary tissue (Figure 2C). Similarly, we observed significantly less p53 stabilization in mammary epithelial cells after oncogene expression in comparison to irradiated tissue (Figure S4). Hence, the Mre11 complex-mediated response to oncogene activation appears to be qualitatively distinct from the response to clastogen-induced DNA damage.

We examined apoptosis and growth arrest—functional outcomes of DDR activation—in hyperplastic lesions. While NeuT expression was associated with increased proliferation and apoptosis rates relative to control mammary glands, we did not observe a statistically significant difference in TUNEL or Ki67 positivity between WT and Mre11ATLD1/ATLD1 oncogene-induced hyperplasias (Figures 3A and 3B). We observed a 4-fold increase in pHH3-S10 staining in WT versus Mre11ATLD1/ATLD1 hyperplasias (p < 0.001; Figure 3C), which was unexpected given the significantly increased cellularity of Mre11ATLD1/ATLD1 hyperplasias. The pHH3-S10 staining pattern that we observed was punctate, and pHH3-S10-positive nuclei did not exhibit morphological features of mitosis (Figure 3C, inset), suggesting that the pHH3-S10 signal represented pericentromeric staining characteristic of late G2 cells rather than mitotic cells.

Centriole duplication was evident in 84% of pHH3-S10-positive cells, compared to only 16% of pHH3-S10-negative cells (p < 0.0001; Figure 4B), indicating a cell-cycle state that is beyond the G1/S transition. These observations collectively suggest that NeuT expression in mammary epithelium activates a Mre11 complex-dependent G2 arrest or accumulation. Notably, this G2 arrest is distinct from the canonical IR-induced G2/M checkpoint, which is also Mre11 dependent (Theunissen et al., 2003). In that context, pHH3-S10 is not induced, suggesting that the heterochromatin-associated accumulation of this marker is oncogene specific.

The variable and prolonged latency of tumor onset in Mre11ATLD1/ATLD1 animals suggests that additional genetic alterations may be required for NeuT-mediated transformation of mammary epithelial cells. We examined p19Arf expression—a well-established oncogene-induced tumor-suppressive pathway (Sherr, 2001)—in the 3-week-old NeuT-expressing mammary hyperplasias from WT and Mre11ATLD1/ATLD1animals. We observed >10-fold induction of p19Arf after oncogene expression in Mre11ATLD1/ATLD1relative to control-injected mammary glands (Figure 6A). The extent of p19Arf induction in NeuT-expressingWT mammary glands was <50% of that observed in Mre11ATLD1/ATLD1 (p < 0.007, Figure 6A). Notably, there was no difference in HA-NeuT expression levels between the WT and Mre11ATLD1/ATLD1 mice that could account for the elevated levels of p19Arf (Figure S6A). As expected, p53 levels were modestly elevated in Mre11ATLD1/ATLD1 hyperplasias relative to WT (Figure S6B).

Collectively, the findings presented here indicate that the Mre11 complex constitutes an inducible barrier to oncogene-driven neoplasia. In response to oncogene activation, the Mre11 complex mediates a G2 arrest that appears to be qualitatively distinct from that revealed in previous analyses of Mre11 complex-dependent DDR functions (Figure 7EStracker et al., 2004). The arrest is associated with heterochromatin changes, including the appearance of macroH2A and histone H3 (Ser10) phosphorylation. Histone H3 phosphorylation at pericentric heterochromatin begins early in G2 phase and expands as cells enter mitosis (Crosio et al., 2002). That fact, along with the finding that H3 phosphorylation arises in cells that have undergone centriole duplication, indicates that cells in oncogene-expressing hyperplasias accumulate in G2. We cannot exclude the possibility that other NeuT-expressing cells also arrest in G1 without the observed heterochromatic changes. In Mre11ATLD1/ATLD1 mammary epithelium, the NeuT-induced arrest is lost, and macroH2A and histone H3 phosphorylation are not detected in hyperplastic tissue, demonstrating that the G2 accumulation depends on the Mre11 complex.

The Mre11 complex-dependent G2 arrest does not appear permanent, as WT cells are capable at low frequency of progressing to tumors. When the arrest is attenuated, as in Mre11ATLD1/ATLD1, we observe more extensive oncogene-induced mammary hyperplasia, and a significantly greater likelihood of progression to invasive breast cancer. Although previous studies show that the Mre11 complex suppresses genome instability, and thus the risk of spontaneous DNA-damage-associated tumorigenesis (Stracker et al., 2008Theunissen et al., 2003), this study demonstrates that the Mre11 complex also suppresses oncogene-driven neoplasia and tumorigenesis.

An important question concerns the underlying basis of the response to oncogene activation. Given the importance of the Mre11 complex in sensing DNA double-strand breaks and initiating an ATM-dependent DDR, a parsimonious interpretation is that oncogene activation results in DNA damage. Indeed, there are compelling genetic data supporting the induction of DNA replication stress upon oncogene activation (Bartkova et al., 2006Campaner and Amati, 2012Di Micco et al., 2006Dominguez-Sola et al., 2007;López-Contreras and Fernandez-Capetillo, 2010). DNA replication stress is a common precursor of frank DNA damage when forks collapse (Allen et al., 2011), which would readily account for the induction of DNA damage upon oncogene induction.

Potential crosstalk between the oncogene-induced DDR and the Arf tumor suppressor pathways has recently been described (Evangelou et al., 2013Monasor et al., 2013Velimezi et al., 2013). Our data provide direct evidence for a genetic interaction between these pathways during oncogene-driven tumorigenesis. We demonstrate that when Mre11 complex function is impaired, oncogene expression induces Arf expression, and Ink4a-Arf inactivation is commonly observed in the mammary tumors that ensue. The mechanism for how Mre11 hypomorphism promotes oncogene-induced Arf expression remains unclear.  We observe that 40% of the NeuT-induced mammary tumors that developed in Mre11ATLD1/ATLD1 mice had genetic inactivation of the Ink4a-Arf locus, and the remaining tumors exhibited reduced p19Arf expression, suggesting alternative modes of pathway suppression. These findings provide compelling genetic evidence for the cooperative roles of the Mre11 complex and Ink4a-Arf pathways in the suppression of oncogene-driven tumorigenesis and metastasis.

The behavior of the emergent tumors in Mre11ATLD1/ATLD1mice suggests a link between increased chromosomal instability and an elevated rate of metastatic dissemination from the primary tumor. The observation that all of the Ink4a-Arf mutated mammary tumors were lung metastatic also raises the possibility that Arf loss promotes metastatic progression in the context of Mre11 complex impairment.

Our genetic data suggest that functional hypomorphism of this pathway may be a driver of breast tumorigenesis, genomic instability, and metastasis. Given the profound DDR defects associated with Mre11 complex hypomorphism (Stracker and Petrini, 2011), this subset of human breast cancer may exhibit exquisite DNA damage sensitivities that could be therapeutically exploited to improve clinical outcomes.



7.5.5 Expression of Stromal Cell-derived Factor 1 and CXCR4 Ligand Receptor System in Pancreatic Cancer

Koshiba T, Hosotani R, Miyamoto Y, Ida J, …, Fujii N, Imamura M
Clin Cancer Res Sep; 6(9):3530-5
NR4A subfamily of nuclear receptors

To examine the expression of the stromal cell-derived factor 1 (SDF-1)/CXCR4 receptor ligand system in pancreatic cancer cells and endothelial cells, we performed immunohistochemical analysis for 52 pancreatic cancer tissue samples with anti-CXCR4 antibody and reverse transcription-PCR analysis for CXCR4 and SDF-1 in five pancreatic cancer cell lines (AsPC-1, BxPC-3, CFPAC-1, HPAC, and PANC-1), an endothelial cell line (HUVEC), and eight pancreatic cancer tissues. We then performed cell migration assay on AsPC-1 cells, HUVECs, and CFPAC-1 cells in the presence of SDF-1 or MRC-9 fibroblast cells. Immunoreactive CXCR4 was found mainly in pancreatic cancer cells and endothelial cells of relatively large vessels around a tumorous lesion. The immunopositive ratio in the pancreatic cancer was 71.2%. There was no statistically significant correlation with clinicopathological features. SDF-1 mRNA expressions were detected in all pancreatic cancer tissues but not in pancreatic cancer cell lines and HUVECs; meanwhile, CXCR4 mRNA was detected in all pancreatic cancer tissues, cancer cell lines, and HUVECs. The results indicate that the paracrine mechanism is involved in the SDF-1/CXCR4 receptor ligand system in pancreatic cancer. In vitro studies demonstrated that SDF-1 significantly increased the migration ability of AsPC-1 and HUVECs, and these effects were inhibited by CXCR4 antagonist T22, and that the coculture system with MRC-9 also increased the migration ability of CFPAC-1 cells, and this effect was significantly inhibited by T22. Our results suggested that the SDF-1/CXCR4 receptor ligand system may have a possible role in the pancreatic cancer progression through tumor cell migration and angiogenesis.

Chemokines belong to the small molecule chemoattractive cytokine family and are grouped into CXC chemokines and CC chemokines, on the basis of the characteristic presence of four conserved cysteine residues (123) . Chemokines mediate the chemical effect on target cells through G-protein-coupled receptors, which are characterized structurally by seven transmembrane spanning domains and are involved in the attraction and activation of mononuclear and polymorphonuclear leukocytes. The effects of CXC chemokines on cancer cells have been investigated in the case of IL3 -8. Several studies have demonstrated the presence of IL-8 and its receptor in tumor tissues, which were involved in vascular endothelial cell proliferation and tumor neovascularization ,(4567) . It was also reported that IL-8 inhibited non-small cell lung cancer proliferation via the autocrine and paracrine pathway (8) . IL-8 produced by malignant melanoma was found to induce cell proliferation via the autocrine pathway in vitro (9) . These studies indicate that IL-8 is involved in the regulation of tumor progression through tumor angiogenesis and/or direct cancer cell growth.

SDF-1 was initially cloned by Tashiro et al. (10) and later identified as a growth factor for B cell progenitors, a chemotactic factor for T cells and monocytes, and in B-cell lymphopoiesis and bone marrow myelopoiesis (111213) . SDF-1 is a member of the CXC subfamily of chemokines, and its chemotactic effect is mediated by the chemokine receptor CXCR4 (12 , 14) . Most of the chemokine receptors interact with pleural ligands, and vice versa, but the SDF-1/CXCR4 receptor ligand system has been shown to involve a one-on-one interaction (15 , 16) . Furthermore, CXCR4 has been shown to function as a coreceptor for T lymphocytotrophic HIV-1 isolates (17) . Recent studies have demonstrated that endothelial cells express CXCR4 and are strongly chemoattracted by SDF-1 (1819,20) . Tachibana et al. (15) reported that in the embryo of CXCR4 or SDF-1 knockout mice larger branches of the superior mesenteric artery were missing and that the resultant abnormal circulatory system led to gastrointestinal hemorrhage and intestinal obstruction. These findings suggest that SDF-1 and CXCR4 are involved in organ vascularization, as well as in the immune and hematopoietic system.

To clarify the role of the SDF-1/CXCR4 receptor ligand system in pancreatic cancer, we have investigated the expression of CXCR4 and SDF-1 with the aid of immunohistochemical analysis and RT-PCR in pancreatic cancer tissue and experimental chemotactic activity of SDF-1 in pancreatic cancer cells and vascular endothelial cells in vitro.

The distribution of CXCR4 protein expression in pancreatic cancer tissue was examined by means of immunohistochemical analysis of pancreatic cancer tissue samples obtained at surgical operation. Fig. 1<$REFLINK> shows representative immunostainings of cancerous and noncancerous regions in pancreatic cancer tissues. Staining of the CXCR4 protein was identified in the cytoplasm and/or cell membrane of cancer cells, but was not detected in the normal acinar cells and ductal cells of noncancerous region in pancreatic cancer tissue. Negative or weak staining for the CXCR4 protein was observed in a majority of the infiltrating inflammatory cells in the specimens. The immunopositive ratio of cancer cells in the pancreatic cancer tissue specimens was 71.2% (37 of 52). Table 1<$REFLINK>summarizes the relationship between CXCR4 expression and clinicopathological features of 52 pancreatic cancers. There was no significant correlation between the expression of CXCR4 protein and the clinicopathological variables examined (i.e., tumor extension, lymph node metastasis, liver metastasis, and Union International Contre Cancer stage). CXCR4 immunoreactivities were observed in endothelial cells of relatively large vessels around the tumorous lesions, but were scarcely found in the endothelial cells of microvessels inside tumorous lesions (Fig. 2, A and B)<$REFLINK> .

We performed RT-PCR using specific primers, as described in“ Materials and Methods,” to confirm CXCR4 and SDF-1 mRNA expression in pancreatic cancer cells, endothelial cells (HUVECs), and pancreatic cancer tissues. CXCR4 mRNA expressions were clearly detected in five pancreatic cancer cell lines, HUVECs, and eight pancreatic cancer tissue samples (Fig. 3a)<$REFLINK> . On the other hand, SDF-1 mRNA expression was not detected in five pancreatic cancer cell lines and HUVECs, but was identified in eight pancreatic cancer tissue samples (Fig. 3b)<$REFLINK> .

Transwell migration assays were performed to examine the effects of SDF-1 on motility of pancreatic cancer cells (AsPC-1) and endothelial cells (HUVEC). At a concentration of 100 ng/ml, SDF-1 induced chemotaxis of AsPC-1 cells, which was approximately double that of the control. One micromolar of T22 (CXCR4 antagonist) and 10 μg/ml of IVR7 (neutralizing CXCR4 antibody) completely blocked the chemotaxis of AsPC-1 induced by 100 ng/ml SDF-1 (Fig. 4a)<$REFLINK> . At a concentration of 100 g/ml SDF-1 induced an approximately quadruple chemotaxis of HUVECs. One micromolar of T22 caused a 33% reduction of the chemotaxis of HUVECs in the presence of containing 100 ng/ml SDF-1 (Fig. 4b)<$REFLINK> .

SDF-1 belongs to the CXC chemokine family and is a ligand for CXCR4. The role of the SDF-1/CXCR4 receptor ligand system has been investigated mainly in the field of immunology, especially in the mechanism of infection of T lymphocytotrophic HIV-1 and for the prevention of HIV-1 infection. Investigators have also paid attention to the role of the SDF-1/CXCR4 receptor ligand system in cancer tissues.

In this study, we first used immunohistochemical methods to examine CXCR4 expression in pancreatic cancer tissues. Immunoreactive CXCR4 was found in the cytoplasm and/or cell membrane of pancreatic cancer cells. Although CXCR4 staining in pancreatic cancer tissue was heterogeneous and showed differences between specimens, it was found mainly in cancer cells: the immunopositive ratio for the pancreatic cancer tissue specimens was 71.2% (37 of 52). There was a tendency for the immunopositive ratio of CXCR4 in tumors with lymph node metastasis or liver metastasis to be higher than in tumors without these features, but no statistically significant correlation with clinicopathological features were found. There is a diversity of views on the role of the SDF-1/CXCR4 receptor ligand system in malignant tissues. In the current study, SDF-1 mRNA expressions were detected in all pancreatic cancer tissues (eight of eight) but were not detected in pancreatic cancer cell lines (zero of five), whereas CXCR4 mRNA was detected in both pancreatic cancer tissues (eight of eight) and cancer cell lines (five of five). The results indicate that the paracrine mechanism may be involved in the SDF-1/CXCR4 receptor ligand system in pancreatic cancer.

Our results suggest that the SDF-1/CXCR4 receptor ligand system may have a possible role in the pancreatic cancer progression through tumor cell migration and angiogenesis. Because T22 suppressed the migration of both pancreatic cancer cells and endothelial cells in vitro, additional in vivo studies are warranted to examine whether T22 suppresses the tumor spread and tumor angiogenesis to clarify the role of the SDF-1/CXCR4 receptor ligand system in pancreatic cancer.


7.5.6 DLC1- a significant GAP in the cancer genome

Aurelia Lahoz and Alan Hall
Genes Dev. 2008 Jul 1; 22(13): 1724–1730

Rho GTPases are believed to make important contributions to the development and progression of human cancer, but direct evidence in the form of somatic mutations analogous to those affecting Ras has been lacking. A recent study in Genes & Development by Xue and colleagues (1439–1444) now provides in vivo evidence that DLC1, a negative regulator of Rho, is a tumor suppressor gene deleted almost as frequently as p53 in common cancers such as breast, colon, and lung.

Cancer is a complex set of diseases arising from combinations of genetic and epigenetic events, including base mutations, chromosomal rearrangements, DNA methylation, and chromatin modification. Genetic changes were first seen cytologically and revealed gross chromosomal abnormalities, such as translocations, deletions, amplifications (of entire chromosomes or parts of chromosomes), and inversions. Subsequently, DNA sequencing of candidate genes and then whole genomes has uncovered large numbers of more subtle genetic alterations. The recent and continuing successes of sequencing and other nonfunctional based genomic approaches have raised new problems in how to determine which changes have significance for tumor development. This is not a trivial problem and will require combinations of cell-based assays, in vivo animal models, and ultimately clinical intervention.

The identification of the Ras oncogene was the first major triumph of the early application of molecular biology to the cancer problem (Malumbres and Barbacid 2003). Although originally identified as a viral oncogene in a rodent sarcoma-inducing retrovirus, it was the seminal work of the Weinberg and Cooper laboratories in 1981 (Krontiris and Cooper 1981Shih et al. 1981), using DNA transfection assays of human tumor DNA into immortalized mouse fibroblasts, that led to the identification of Ras as a true human oncogene. Several groups went on to show that any one of the three Ras genes (HRASKRAS, and NRAS) could be converted into a human oncogene by a single base mutation leading to a single amino acid substitution in the encoded Ras protein. Ras mutations are found in ∼30% of most, though not all, cancer types and it remains the most frequently mutated dominant oncogene so far identified (Bos 1989). We now know much about the consequences of those amino acid substitutions and the cellular and physiological importance of Ras in controlling proliferation and differentiation. Ras is an example of a regulatory GTPase that cycles between active (GTP-bound) and inactive (GDP-bound) conformations to control biochemical pathways and processes. These molecular switches are activated by guanine nucleotide exchange factors (GEFs), which catalyze exchange of GDP for GTP, and are inactivated by GTPase-activating proteins (GAPs), which promote the otherwise slow, intrinsic GTPase activity of the proteins (Fig. 1). The amino acid substitutions identified in Ras in human cancers are found at codons 12, 61, and to a lesser extent 13, and the common consequence of these changes is to prevent GAP-mediated stimulation of GTP hydrolysis leading to permanent activation of the switch (Trahey and McCormick 1987). Inspection of Figure 1 suggests possible alternative ways in which this molecular switch could be inappropriately activated. For example, activating mutations in one of the nine RasGEF genes or inactivation of one of the eight RasGAP genes could lead to hyperactivation of the switch. To date, no such mutations have been reported in GEF genes in human cancers, but one of the GAPs, neurofibromin, is encoded by the NF1 tumor suppressor gene. Patients with neurofibromatosis type I inherit only one functional NF1 gene and are then predisposed to cancer through complete loss of NF1. In addition, mutational activation of components of downstream signaling pathways (Fig. 1) could bypass the need for Ras and this is clearly the case with somatic mutations in BRAF (which encodes a Ras effector), found most frequently in malignant melanomas (>50%), but also in thyroid, colorectal, and ovarian cancer (Davies et al. 2002Wellbrock et al. 2004).

The Ras GTP.GDP cycle

The Ras GTP.GDP cycle

Figure 1. The Ras GTP/GDP cycle. Ras GTPases are molecular switches and the GDP/GTP cycle is controlled by GEFs and GAPs. The output of the switch is through the interaction of Ras.GTP with effector proteins.

Rho GTPases can trigger numerous downstream signaling pathways by interacting with distinct effectors—to date, ∼20 such target proteins have been reported that specifically interact with Rho (Etienne-Manneville and Hall 2002). One of the best-characterized is Rho kinase (ROCK), which regulates myosin II and actin filament contractility, through its ability to phosphorylate and inactivate myosin light chain phosphatase (Fukata et al. 2001). Rho kinase is involved in many aspects of normal cell biology, such as cell cycle, morphogenesis, and migration, and in addition has been shown to participate in the proliferation, invasion, and metastasis of cancer cells (Etienne-Manneville and Hall 2002Sahai and Marshall 2002Narumiya and Yasuda 2006). In the final part of their study, Xue et al. (2008) show that two small molecule Rho kinase inhibitors, Y-27632 and to a lesser extent Fasudil, inhibit in vitro colony formation of p53−/− liver progenitor cells expressing c-Myc and DCL1 shRNA. It should be noted, however, that both Y-27632 and Fasudil inhibit PRK/PKN and citron kinase, two other kinases activated by Rho, so the result is not entirely conclusive (Ishizaki et al. 2000).

Embryonic fibroblasts can be obtained from DLC1−/− mice and these display alterations in the organization of actin filaments and focal adhesion (Durkin et al. 2005). Confusingly, however, these knockout cells have fewer stress fibers and focal adhesions—the opposite of what would have been predicted for the loss of a GAP that regulates Rho. In fact the cytoskeletal and adhesion complex changes seen in DLC−/− fibroblasts appear to be more in keeping with Rac activation. Unfortunately the authors did not examine the levels of either Rho.GTP or Rac.GTP in these cells, which might have provided some insight into this unexpected result. In the absence of tissue-specific mouse knockouts, we must look to work in Drosophila on RhoGAP88C, the fly ortholog of DCL1, to provide some in vivo physiological data. Mutations in RhoGAP88C were first identified as crossveinless-c and result in defects in tissue morphogenesis during development (Denholm et al. 2005). Closer examination suggests that this GAP regulates tubulogenesis and convergent extension, two processes driven by reorganization of the actin cytoskeleton. An additional and provocative observation to emerge from this study is that RhoGAP88C acts through Rho in some tissues, but it acts through Rac and not Rho in others. The in vitro biochemical activity of this GAP has not been determined and so it is possible that it shows a different specificity from its mammalian counterpart. Otherwise, tissue-specific modification of its catalytic activity would need to be invoked, rendering the in vitro assays essentially useless for predicting specificity. Two subsequent studies have concluded that RhoGAP88C is localized basolaterally in epithelial cells and serves to restrict Rho activity to the apical surface and thereby generate morphogenetic tissue remodeling through polarized activation of myosin II (Brodu and Casanova 2006Simoes et al. 2006).

Taken together, a picture emerges of spatially localized DLC1 acting to control Rho activity so as to promote changes in the actin cytoskeleton during cell morphogenesis. The disruption of this pathway might be expected to lead to tissue disorganization during differentiation programs, which could promote inappropriate cell proliferation (Fig. 2).

DLC1 is a tumor suppressor.

DLC1 is a tumor suppressor.

Figure 2.  DLC1 is a tumor suppressor. Loss of DLC1 leads to deregulated and/or delocalized activation of Rho. This may disrupt tissue morphogenesis leading to inappropriate proliferation. (PM) Plasma membrane.

Directed therapeutic intervention depends on a deep understanding of the relevant signaling pathways through which DLC1 loss is manifest. It is a sobering thought that the signaling pathways downstream from Ras responsible for human cancer are still debated some 25 years after its discovery as a human oncogene and it would be optimistic to believe that identifying Rho pathways will be any easier. Inhibiting the GTPase itself, whether Ras or Rho, is challenging. One of the most promising potential targets for Ras inactivation has been farnesyltransferase (FT), the enzyme required for carboxy-terminal, post-translational modification by a farnesyl lipid (Wright and Philips 2006). FT inhibitors are currently in clinical trials, though the data reported so far are not encouraging. Inhibiting Rho using a similar strategy seems less attractive, since it uses a geranylgeranyltransferase to add a geranylgeranyl group; a much more widespread modification than farnesyl addition. Two other processing enzymes that act on both Ras and Rho, a carboxyl-protease and an isoprenylcysteine carboxyl methyltransferase, are being considered as Ras targets, but in tissue culture at least these seem not to be essential for Rho function (Michaelson et al. 2005). Another possibility that is distinctive to DLC1 might be to attack the epigenetic mechanisms that appear to be commonly used to silence this gene in human cancers. Inhibitors of DNA methyltransferase and histone deacetylase (HDAC) have already been shown to induce the restoration of DLC1 expression in cancer cells, making Zebularine, a new and highly effective DNA demethylating agent, as well as HDAC inhibitors attractive therapeutic approaches (Guan et al. 2006Neureiter et al. 2007Seng et al. 2007Xu et al. 2007). Finally, if it turns out that Rho kinase mediates the key signaling pathway downstream from DLC1 loss, then there is already a huge effort underway to develop small molecule inhibitors of this protein. Rho kinase has been implicated in various forms of cardiovascular disease—such as pulmonary hypertension, myocardial hypertrophy, and atherosclerosis—and in fact one compound, Fasudil, is already being used clinically in Japan for cerebral ischemia (Rikitake and Liao 2005Tawara and Shimokawa 2007). With over a dozen pharmaceutical companies reportedly working on this problem, and if the work from Xue et al. (2008) implicating Rho kinase downstream from DLC1 turns out to be correct, those companies may end up with a blockbuster!


7.5.7 DLC1 is a chromosome 8p tumor suppressor whose loss promotes hepatocellular carcinoma.

Xue W, Krasnitz A, Lucito R, Sordella R, … , Zender L, Lowe SW.
Genes Dev. 2008 Jun 1;22(11):1439-44

Deletions on chromosome 8p are common in human tumors, suggesting that one or more tumor suppressor genes reside in this region. Deleted in Liver Cancer 1 (DLC1) encodes a Rho-GTPase activating protein and is a candidate 8p tumor suppressor. We show that DLC1 knockdown cooperates with Myc to promote hepatocellular carcinoma in mice, and that reintroduction of wild-type DLC1 into hepatoma cells with low DLC1 levels suppresses tumor growth in situ. Cells with reduced DLC1 protein contain increased GTP-bound RhoA, and enforced expression a constitutively activated RhoA allele mimics DLC1 loss in promoting hepatocellular carcinogenesis. Conversely, down-regulation of RhoA selectively inhibits tumor growth of hepatoma cells with disabled DLC1. Our data validate DLC1 as a potent tumor suppressor gene and suggest that its loss creates a dependence on the RhoA pathway that may be targeted therapeutically.

Tumor suppressor genes act in signaling networks that protect against tumor initiation and progression, and can be inactivated by deletions, point mutations, or promoter hypermethylation. Although tumor suppressors are rarely considered direct drug targets, they can negatively regulate pro-oncogenic signaling proteins that are amenable to small molecule inhibition. For instance, NF1 inhibits the Ras signaling pathway, which is deregulated in many cancers and has been pursued for its therapeutic potential (Downward 2003). Similarly, PTEN inhibits the PI3–kinase pathway, and inhibitors of PI3K pathway components such as PI3K, AKT, and mTORs have entered clinic trials (Luo et al. 2003).

Recurrent chromosomal deletions found in sporadic cancers often contain tumor suppressor genes. For example, PTEN loss on chromosome 10q23 frequently occurs in various cancers and promotes tumorigenesis by deregulating the PI3 kinase pathway (Maser et al. 2007). Similarly, heterozygous deletions on chromosome 8p22 in many hepatocellular carcinomas (HCC) (Jou et al. 2004) and other cancer types, including carcinomas of the breast, prostate, colon, and lung (Matsuyama et al. 2001Durkin et al. 2007). Several genes, including DLC1MTUS1FGL1 and TUSC3, have been identified as candidate tumor suppressors in this region (Yan et al. 2004). Deleted in Liver Cancer 1 (DLC1) is a particularly attractive candidate owing to its genomic deletion, promoter methylation, and underexpressed mRNA in cancer (Yuan et al. 19982003aNg et al. 2000Wong et al. 2003Guan et al. 2006Seng et al. 2007Ying et al. 2007;Zhang et al. 2007Pike et al. 2008; for review, see Durkin et al. 2007).

Despite its potential importance, functional data implicating DLC1 loss in tumorigenesis are lacking. DLC1encodes a RhoGAP protein that catalyzes the conversion of active GTP-bound RhoGTPase (Rho) to the inactive GDP-bound form and thus suppresses Rho activity (Yuan et al. 1998). DLC1 has potent GAP activity for RhoA and limited activity for CDC42 (Wong et al. 2003Healy et al. 2008). When overexpressed, DLC1 inhibits the growth of tumor cells and xenografts (Yuan et al. 2003b2004Zhou et al. 2004Wong et al. 2005Kim et al. 2007), but whether this requires its Rho-GAP activity or other functions remains unresolved (Qian et al. 2007Liao et al. 2007). Most functional studies to date have relied on DLC1 overexpression and, as yet, none have documented that loss of DLC1 promotes transformation in vitro or tumorigenesis in vivo. Indeed, homozygous dlc1 knockout mice die around embryonic day 10.5 (E10.5), and there is no overt phenotype in dlc1 heterozygous mice (Durkin et al. 2005).

Our laboratory recently developed a “mosaic” mouse model whereby liver carcinomas can be rapidly produced with different genetic alterations by manipulation of cultured embryonic liver progenitor cells (hepatoblasts) followed by transplantation into the livers of recipient mice (Zender et al. 20052006). We previously used this model to identify new oncogenes in HCC, which could be characterized in an appropriate biological and genetic context (Zender et al. 2006). Furthermore, using this system, we showed that shRNAs capable of suppressing gene function by RNAi could recapitulate the consequences of tumor suppressor gene loss on liver carcinogenesis (Zender et al. 2005Xue et al. 2007). Here we combine this mosaic model and RNAi to validate DLC1 as a potent tumor suppressor gene and study its action in vivo.

Studies using low-resolution genome scanning methods have identified chromosome 8p deletions as common lesions in liver carcinoma and other tumor types. To confirm and extend these observations, we examined a series of data sets of copy number alterations in HCC obtained using representational oligonucleotide microarray analysis (ROMA), a variation of array-based CGH that enables genome scanning at high resolution (Lucito et al. 2003). In a panel of 86 liver cancers, heterozygous deletions encompassing theDLC1 were observed in 59 tumors (Fig. 1A,B; data not shown). Consistent with previous reports, these deletions were large (>5 Mb), encompassing >20 annotated genes but invariably included the DLC1 locus. Indeed, heterozygous deletions of DLC1 occurred more frequently than those observed for the well-established tumor suppressors such as INK4a/ARFPTEN, and TP53 (Fig. 1C). Furthermore, DLC1deletions were nearly as common as those for TP53 in other major tumor types such as lung, colon, and breast (Fig. 1C). Again, most 8p deletions were large, although in breast cancer DLC1 resided at a local deletion epicenter reminiscent of that surrounding the INK4a/ARF locus on chromosome 9p21 (Fig. 1D,E). Although we did not examine the status of the remaining allele in this tumor cohort, studies suggest that it can be silenced by promoter methylation (Yuan et al. 2003a; for review, see Durkin et al. 2007). Together, these data suggest that DLC1 loss plays an important role in human cancer but, in the absence of functional validation, are not conclusive.

Genetically modified liver progenitors were seeded into the livers of syngeneic recipients to assess their ability to form tumors in situ. In contrast to the modest impact of DLC1 loss in vitro, DLC1 shRNAs significantly accelerated tumor onset in vivo (P value < 0.0001 for shDLC1-1 and P < 0.0005 for shDLC1-2) (Fig. 2D,E). In fact, at 57 d post-transplantation, GFP-positive tumor nodules were observed in the livers of most animals receiving cells harboring DLC1 shRNAs, whereas the control animals showed no macroscopically detectable tumor burden (Fig. 2E). Furthermore, the pathology of tumors derived from DLC1 knockdown resembled aggressive human HCC and displayed a high proliferative index as assessed by Ki67 immunohistochemistry (Fig. 2F). Tumors also expressed the HCC markers α-fetoprotein (AFP) and albumin (Supplemental Fig. S3B). These data demonstrate that loss of DLC1 can efficiently promote the development of HCC.

We also ectopically expressed the murine dlc1 gene in mouse hepatoma cells and tested their ability to form tumors orthotopically. To this end, we cloned a Myc-tagged murine dlc1 cDNA and confirmed its ability to produce a protein of the correct molecular weight (Fig. 3A). A mouse hepatoma cell line harboring a luciferase reporter and expressing oncogenic Ras and undetectable DLC1 (see Fig. 1F, lane 8) was infected with the DLC1-expressing retrovirus or an empty vector. Consistent with the literature (Ng et al. 2000), reintroduction of DLC1 produced a modest effect on proliferation in colony formation assays (Supplemental Fig. S4A,B).

Although RhoA has been identified as a DLC1 effector, overexpression studies suggest that other DLC1 functions can contribute to its anti-proliferative activities (Liao et al. 2007Qian et al. 2007). To determine whether RhoA is required for maintaining tumorigenesis stimulated by DLC1 loss, we tested whether suppression of RhoA in DLC1-suppressed hepatoma lines would impact their expansion as subcutaneous tumors in immunocompromised mice. shRNAs capable of down-regulating RhoA to varying degrees (Fig. 5A) decreased the in vivo growth of two independent murine hepatoma lines with undetectable DLC1 (Fig. 5B, cell lines 1,2; Supplemental Fig. S6A,B). Of note, none of the shRNAs completely suppressed RhoA expression, and their ability to limit tumor expansion was proportional to their knockdown efficiency (Supplemental Fig. S6A). The impact of these shRNAs was less pronounced in hepatoma cell lines with higher DLC1 levels (Fig. 5B, cell lines 3,4; Supplemental Fig. S6C,D). Although complete inhibition of RhoA activity might be generally cytostatic (see Piekny et al. 2005), these data suggest that RhoA is required for maintaining the growth of tumors with attenuated DLC1 activity.

In this study, we combined in vivo RNAi and a mosaic mouse model of HCC to study the impact of DLC1 loss on liver carcinogenesis in mice, which to date has not been possible owing to the embryonic lethality of DLC1 knockout animals. We show that DLC1 loss, when combined with other oncogenic lesions, promotes HCC in vivo and that RhoA activation is both necessary and sufficient for its effects. In our survey of copy number alterations in human tumors, 8p22 deletions encompassing DLC1 occurred in >60% of heptocellular carcinomas as well as a large portion of human lung, breast, and colon carcinomas (see also Durkin et al. 2007). Similarly, RhoA is up-regulated in HCC and many other tumor types (Sahai and Marshall 2002;Fukui et al. 2006). Although other tumor suppressor genes may also reside in the 8p region, our results demonstrate that DLC1 is functionally important and highlight the potential importance of the RhoA signaling network in epithelial cancers.

Molecularly targeted therapies have been devised for inhibiting several oncogenic pathways, including those affected by BCR-ABL, activated Ras and PI3kinase (Downward 2003Luo et al. 2003). Although tumor suppressors are generally not amenable to direct therapeutic targeting, their mutation may confer a cellular dependency on downstream oncogenic proteins that can be inhibited with small molecule drugs. In this regard, the impact of DLC1 loss may parallel that produced by loss of PTEN, which deregulates the PI3K pathway and can sensitize cells to pharmacological inhibitors of downstream effectors such as mTOR (Maser et al. 2007). Our data indicate that RhoA is required for maintaining at least some tumors driven by DLC1 loss, and that cells with disabled DLC1 are particularly sensitive to inhibitors that target at least one RhoA effector. Clearly, more studies will be required to confirm and extend these observations; nevertheless, the high frequency of DLC1 loss in human cancer implies that pharmacologic intervention of the signaling pathways modulated by DLC1 may have broad therapeutic utility.


7.5.8 Smad7 regulates compensatory hepatocyte proliferation in damaged mouse liver and positively relates to better clinical outcome in human hepatocellular carcinoma

Feng T, Dzieran J, Gu X, Marhenke S, Vogel A, …, Dooley S, Meindl-Beinker NM.
Clin Sci (Lond). 2015 Jun 1; 128(11):761-74

Transforming growth factor β (TGF-β) is cytostatic towards damage-induced compensatory hepatocyte proliferation. This function is frequently lost during hepatocarcinogenesis, thereby switching the TGF-β role from tumour suppressor to tumour promoter. In the present study, we investigate Smad7 overexpression as a pathophysiological mechanism for cytostatic TGF-β inhibition in liver damage and hepatocellular carcinoma (HCC). Transgenic hepatocyte-specific Smad7 overexpression in damaged liver of fumarylacetoacetate hydrolase (FAH)-deficient mice increased compensatory proliferation of hepatocytes. Similarly, modulation of Smad7 expression changed the sensitivity of Huh7, FLC-4, HLE and HLF HCC cell lines for cytostatic TGF-β effects. In our cohort of 140 HCC patients, Smad7 transcripts were elevated in 41.4% of HCC samples as compared with adjacent tissue, with significant positive correlation to tumour size, whereas low Smad7 expression levels were significantly associated with worse clinical outcome. Univariate and multivariate analyses indicate Smad7 levels as an independent predictor for overall (P<0.001) and disease-free survival (P=0.0123). Delineating a mechanism for Smad7 transcriptional regulation in HCC, we identified cold-shock Y-box protein-1 (YB-1), a multifunctional transcription factor. YB-1 RNAi reduced TGF-β-induced and endogenous Smad7 expression in Huh7 and FLC-4 cells respectively. YB-1 and Smad7 mRNA expression levels correlated positively (P<0.0001). Furthermore, nuclear co-localization of Smad7 and YB-1 proteins was present in cancer cells of those patients. In summary, the present study provides a YB-1/Smad7-mediated mechanism that interferes with anti-proliferative/tumour-suppressive TGF-β actions in a subgroup of HCC cells that may facilitate aspects of tumour progression.

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Artificial Intelligence Versus the Scientist: Who Will Win?

Will DARPA Replace the Human Scientist: Not So Fast, My Friend!

Writer, Curator: Stephen J. Williams, Ph.D.


Last month’s issue of Science article by Jia You “DARPA Sets Out to Automate Research”[1] gave a glimpse of how science could be conducted in the future: without scientists. The article focused on the U.S. Defense Advanced Research Projects Agency (DARPA) program called ‘Big Mechanism”, a $45 million effort to develop computer algorithms which read scientific journal papers with ultimate goal of extracting enough information to design hypotheses and the next set of experiments,

all without human input.

The head of the project, artificial intelligence expert Paul Cohen, says the overall goal is to help scientists cope with the complexity with massive amounts of information. As Paul Cohen stated for the article:


Just when we need to understand highly connected systems as systems,

our research methods force us to focus on little parts.


The Big Mechanisms project aims to design computer algorithms to critically read journal articles, much as scientists will, to determine what and how the information contributes to the knowledge base.

As a proof of concept DARPA is attempting to model Ras-mutation driven cancers using previously published literature in three main steps:

  1. Natural Language Processing: Machines read literature on cancer pathways and convert information to computational semantics and meaning

One team is focused on extracting details on experimental procedures, using the mining of certain phraseology to determine the paper’s worth (for example using phrases like ‘we suggest’ or ‘suggests a role in’ might be considered weak versus ‘we prove’ or ‘provide evidence’ might be identified by the program as worthwhile articles to curate). Another team led by a computational linguistics expert will design systems to map the meanings of sentences.

  1. Integrate each piece of knowledge into a computational model to represent the Ras pathway on oncogenesis.
  2. Produce hypotheses and propose experiments based on knowledge base which can be experimentally verified in the laboratory.

The Human no Longer Needed?: Not So Fast, my Friend!

The problems the DARPA research teams are encountering namely:

  • Need for data verification
  • Text mining and curation strategies
  • Incomplete knowledge base (past, current and future)
  • Molecular biology not necessarily “requires casual inference” as other fields do


Notice this verification step (step 3) requires physical lab work as does all other ‘omics strategies and other computational biology projects. As with high-throughput microarray screens, a verification is needed usually in the form of conducting qPCR or interesting genes are validated in a phenotypical (expression) system. In addition, there has been an ongoing issue surrounding the validity and reproducibility of some research studies and data.

See Importance of Funding Replication Studies: NIH on Credibility of Basic Biomedical Studies

Therefore as DARPA attempts to recreate the Ras pathway from published literature and suggest new pathways/interactions, it will be necessary to experimentally validate certain points (protein interactions or modification events, signaling events) in order to validate their computer model.

Text-Mining and Curation Strategies

The Big Mechanism Project is starting very small; this reflects some of the challenges in scale of this project. Researchers were only given six paragraph long passages and a rudimentary model of the Ras pathway in cancer and then asked to automate a text mining strategy to extract as much useful information. Unfortunately this strategy could be fraught with issues frequently occurred in the biocuration community namely:

Manual or automated curation of scientific literature?

Biocurators, the scientists who painstakingly sort through the voluminous scientific journal to extract and then organize relevant data into accessible databases, have debated whether manual, automated, or a combination of both curation methods [2] achieves the highest accuracy for extracting the information needed to enter in a database. Abigail Cabunoc, a lead developer for Ontario Institute for Cancer Research’s WormBase (a database of nematode genetics and biology) and Lead Developer at Mozilla Science Lab, noted, on her blog, on the lively debate on biocuration methodology at the Seventh International Biocuration Conference (#ISB2014) that the massive amounts of information will require a Herculaneum effort regardless of the methodology.

Although I will have a future post on the advantages/disadvantages and tools/methodologies of manual vs. automated curation, there is a great article on researchinformation.infoExtracting More Information from Scientific Literature” and also see “The Methodology of Curation for Scientific Research Findings” and “Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison” for manual curation methodologies and A MOD(ern) perspective on literature curation for a nice workflow paper on the International Society for Biocuration site.

The Big Mechanism team decided on a full automated approach to text-mine their limited literature set for relevant information however was able to extract only 40% of relevant information from these six paragraphs to the given model. Although the investigators were happy with this percentage most biocurators, whether using a manual or automated method to extract information, would consider 40% a low success rate. Biocurators, regardless of method, have reported ability to extract 70-90% of relevant information from the whole literature (for example for Comparative Toxicogenomics Database)[3-5].

Incomplete Knowledge Base

In an earlier posting (actually was a press release for our first e-book) I had discussed the problem with the “data deluge” we are experiencing in scientific literature as well as the plethora of ‘omics experimental data which needs to be curated.

Tackling the problem of scientific and medical information overload


Figure. The number of papers listed in PubMed (disregarding reviews) during ten year periods have steadily increased from 1970.

Analyzing and sharing the vast amounts of scientific knowledge has never been so crucial to innovation in the medical field. The publication rate has steadily increased from the 70’s, with a 50% increase in the number of original research articles published from the 1990’s to the previous decade. This massive amount of biomedical and scientific information has presented the unique problem of an information overload, and the critical need for methodology and expertise to organize, curate, and disseminate this diverse information for scientists and clinicians. Dr. Larry Bernstein, President of Triplex Consulting and previously chief of pathology at New York’s Methodist Hospital, concurs that “the academic pressures to publish, and the breakdown of knowledge into “silos”, has contributed to this knowledge explosion and although the literature is now online and edited, much of this information is out of reach to the very brightest clinicians.”

Traditionally, organization of biomedical information has been the realm of the literature review, but most reviews are performed years after discoveries are made and, given the rapid pace of new discoveries, this is appearing to be an outdated model. In addition, most medical searches are dependent on keywords, hence adding more complexity to the investigator in finding the material they require. Third, medical researchers and professionals are recognizing the need to converse with each other, in real-time, on the impact new discoveries may have on their research and clinical practice.

These issues require a people-based strategy, having expertise in a diverse and cross-integrative number of medical topics to provide the in-depth understanding of the current research and challenges in each field as well as providing a more conceptual-based search platform. To address this need, human intermediaries, known as scientific curators, are needed to narrow down the information and provide critical context and analysis of medical and scientific information in an interactive manner powered by web 2.0 with curators referred to as the “researcher 2.0”. This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Yaneer Bar-Yam of the New England Complex Systems Institute was not confident that using details from past knowledge could produce adequate roadmaps for future experimentation and noted for the article, “ “The expectation that the accumulation of details will tell us what we want to know is not well justified.”

In a recent post I had curated findings from four lung cancer omics studies and presented some graphic on bioinformatic analysis of the novel genetic mutations resulting from these studies (see link below)

Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for

Non-Small Cell Lung Cancer

which showed, that while multiple genetic mutations and related pathway ontologies were well documented in the lung cancer literature there existed many significant genetic mutations and pathways identified in the genomic studies but little literature attributed to these lung cancer-relevant mutations.


  This ‘literomics’ analysis reveals a large gap between our knowledge base and the data resulting from large translational ‘omic’ studies.

Different Literature Analyses Approach Yeilding

A ‘literomics’ approach focuses on what we don NOT know about genes, proteins, and their associated pathways while a text-mining machine learning algorithm focuses on building a knowledge base to determine the next line of research or what needs to be measured. Using each approach can give us different perspectives on ‘omics data.

Deriving Casual Inference

Ras is one of the best studied and characterized oncogenes and the mechanisms behind Ras-driven oncogenenis is highly understood.   This, according to computational biologist Larry Hunt of Smart Information Flow Technologies makes Ras a great starting point for the Big Mechanism project. As he states,” Molecular biology is a good place to try (developing a machine learning algorithm) because it’s an area in which common sense plays a minor role”.

Even though some may think the project wouldn’t be able to tackle on other mechanisms which involve epigenetic factors UCLA’s expert in causality Judea Pearl, Ph.D. (head of UCLA Cognitive Systems Lab) feels it is possible for machine learning to bridge this gap. As summarized from his lecture at Microsoft:

“The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analyzing their causal roots.”

According to him first

1) articulate assumptions

2) define research question in counter-inference terms

Then it is possible to design an inference system using calculus that tells the investigator what they need to measure.

To watch a video of Dr. Judea Pearl’s April 2013 lecture at Microsoft Research Machine Learning Summit 2013 (“The Mathematics of Causal Inference: with Reflections on Machine Learning”), click here.

The key for the Big Mechansism Project may me be in correcting for the variables among studies, in essence building a models system which may not rely on fully controlled conditions. Dr. Peter Spirtes from Carnegie Mellon University in Pittsburgh, PA is developing a project called the TETRAD project with two goals: 1) to specify and prove under what conditions it is possible to reliably infer causal relationships from background knowledge and statistical data not obtained under fully controlled conditions 2) develop, analyze, implement, test and apply practical, provably correct computer programs for inferring causal structure under conditions where this is possible.

In summary such projects and algorithms will provide investigators the what, and possibly the how should be measured.

So for now it seems we are still needed.


  1. You J: Artificial intelligence. DARPA sets out to automate research. Science 2015, 347(6221):465.
  2. Biocuration 2014: Battle of the New Curation Methods []
  3. Davis AP, Johnson RJ, Lennon-Hopkins K, Sciaky D, Rosenstein MC, Wiegers TC, Mattingly CJ: Targeted journal curation as a method to improve data currency at the Comparative Toxicogenomics Database. Database : the journal of biological databases and curation 2012, 2012:bas051.
  4. Wu CH, Arighi CN, Cohen KB, Hirschman L, Krallinger M, Lu Z, Mattingly C, Valencia A, Wiegers TC, John Wilbur W: BioCreative-2012 virtual issue. Database : the journal of biological databases and curation 2012, 2012:bas049.
  5. Wiegers TC, Davis AP, Mattingly CJ: Collaborative biocuration–text-mining development task for document prioritization for curation. Database : the journal of biological databases and curation 2012, 2012:bas037.

Other posts on this site on include: Artificial Intelligence, Curation Methodology, Philosophy of Science

Inevitability of Curation: Scientific Publishing moves to embrace Open Data, Libraries and Researchers are trying to keep up

A Brief Curation of Proteomics, Metabolomics, and Metabolism

The Methodology of Curation for Scientific Research Findings

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

The growing importance of content curation

Data Curation is for Big Data what Data Integration is for Small Data

Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation The Art of Scientific & Medical Curation

Exploring the Impact of Content Curation on Business Goals in 2013

Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison

conceived: NEW Definition for Co-Curation in Medical Research

Reconstructed Science Communication for Open Access Online Scientific Curation

Search Results for ‘artificial intelligence’

 The Simple Pictures Artificial Intelligence Still Can’t Recognize

Data Scientist on a Quest to Turn Computers Into Doctors

Vinod Khosla: “20% doctor included”: speculations & musings of a technology optimist or “Technology will replace 80% of what doctors do”

Where has reason gone?

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Curator: Ritu Saxena, Ph.D.


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 (p16INK4a) and ARF (p14ARF). 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

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).

Genetic heterogeneity Melanoma

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.


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  8. Chin L, et al, Genes Dev. 15 Aug 2006;20 (16):2149-2182

Related articles on Melanoma on this Open Access Online Scientific Journal, include the following: 

Thymosin alpha1 and melanoma Author/Editor- Tilda Barliya, Ph.D.

A New Therapy for Melanoma Reporter- Larry H Bernstein, M.D.

Melanoma: Molecule in Immune System Could Help Treat Dangerous Skin Cancer Reporter: Prabodh Kandala, Ph.D.

Why Braf inhibitors fail to treat melanoma. Reporter: Prabodh Kandala, Ph.D.

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Latest research efforts reported in the San Antonio Breast Cancer Symposium, 2012

Curator: Ritu Saxena, Ph.D.

‘Triple negative breast cancer’ or TNBC, as the name suggests, is a classification of breast cancers lacking the expression of estrogen receptor (ER) and progesterone receptor expression as well as amplification of the human epidermal growth factor receptor 2 (HER2).

Unlike other breast cancer types, treating TNBC is a challenge mainly because of the absence of well-defined molecular targets and because of disease heterogeneity. Currently, neoadjuvant chemotherapies are in use to treat TNBC patients. Some, around 30%, patients respond completely to neoadjuvant chemotherapy and have good outcomes after surgery. However, if there is a residual disease after therapy, outcomes are poor.

Therefore, current focus of the field is to first understand the complexity of the disease, both at genomic and molecular level and look for targets. Also, several combination chemotherapies are currently under trial to determine the efficacy, overall response rate, progression-free survival and other relevant factors for patients suffering with different forms of TNBC.

Recently, in the San Antonio Breast Cancer Symposium (SABCS 2012), several abstarcts related to TNBC research, both clinical and pre-clinical. Here is a compilation of some of the abstracts and their relevance in the field of TNBC research:

Triple Negative Breast Cancer: Subtypes, Molecular Targets, and Therapeutic Approaches, Pietenpol JA, Vanderbilt-Ingram Cancer Center; Vanderbilt University School of Medicine (Nashville, TN), Abstract no. ES2-2.

In order to better understand the complexity of TNBC, an integrative and comprehensive genomic and molecular analysis is required. The analysis would give important cues to developing and administering effective therapeutic agents. The group has compiled an extensive number of TNBC gene expression profiles and initiated molecular subtyping of the disease. Differential GE was used to designate 25 TNBC cell line models representative of the following subtypes:

  •  two basel-like TNBC subtypes with cell cycle and DDR gene expression signatures (BL1 and BL2);
  • two mesenchymal subtypes with high expression of genes involved in differentiation and growth factor pathways (M and MSL);
  • an immunomodulatory (IM) type;
  • a luminal subtype driven by androgen signaling (LAR)

The pharmacological drugs were chosen on the basis of the genetic pathways active in the cell lines with the abovementioned TNBC subtypes. It was observed that BL1 and BL2 subtype cell lines respond to cisplatin. Mesenchymal, basal, and luminal subtype lines with aberrations in PI3K signaling and have the greatest sensitivity to PI3K inhibitors.

The LAR subtype cell lines express AR and are uniquely sensitive to bicalutamide (AR antagonist). The experiment was a proof-of-concept that the best therapy could be based on TNBC subtypes.

The group has also developed a web-based subtyping tool referred to as “TNBCtype,” for candidate TNBC tumor samples using our gene expression metadata and classification methods. The approach would enable alignment of TNBC patients to appropriate targeted therapies.

The Clonal and Mutational Composition of Triple Negative Breast Cancers: Aparicio S, University of British Columbia (Vancouver, BC), Canada. Abstract no. ES2-3.

The abstract is on the same lines, TNBC heterogeneity that is. The concept of clonal heterogeneity in cancers, the spatial and temporal variation in clonal composition, is the focal point of the discussion. The group has developed next generation sequencing approaches and applied them to the understanding of mutational and clonal composition of primary TNBC. They have demonstrated that both mutational composition and clonal structure of primary TNBC is in fact a complete spectrum, a notion that is far from the previous one that stated TNBC to be a distinct disease. The authors add “clonal analysis suggests a means by which the genetic complexity might be reduced by following patient evolution over time and space.” The specific implications of the mutational and transcriptome landscapes of TNBC in relation to possible disease biologies were discussed in the symposium.

Profiling of triple-negative breast cancers after neoadjuvant chemotherapy identifies targetable molecular alterations in the treatment-refractory residual disease:

Balko JM, etal, Vanderbilt University (Nashville, TN); Foundation Medicine, (Cambridge, MA); Instituto Nacional de Enfermedades Neoplásicas, Lima, Peru

In the absence of hormone receptors and hence lack of targets, Neoadjuvant chemotherapy (NAC) is increasingly used in patients with TNBC. NAC can induce a pathologic complete response (pCR) in ∼30% of patients which portends a favorable prognosis. In contrast, patients with residual disease (RD) in the breast at surgical resection exhibit worse outcomes. The authors hypothesize that “profiling residual TNBC after NAC would identify molecularly targetable lesions in the chemotherapy resistant component of the tumor and that the persistent tumor cells would mirror micro-metastases which ultimately recur in such patients.” The researchers utilized targeted next generation sequencing (NGS) for 182 oncogenes and tumor suppressors in a CLIA certified lab (Foundation Medicine, Cambridge, MA) and gene expression profiling (NanoString) of the RD after NAC in 102 patients with TNBC. The RD was stained for Ki67, which has been reported to predict outcome after NAC in unselected breast cancers. Out of the 89 evaluable post-NAC tumors, 57 (64%) were basal-like; 19% HER2-enriched; 6% luminal A; 6% luminal B and 5% normal-like. Of 81 tumors evaluated by NGS, 89% demonstrated mutations in TP53, 27% were MCL1-amplified, and 21% were MYC-amplified.

Several pathways were found to be altered:

  • PI3K/mTOR pathway (AKT1-3, PIK3CA, PIK3R1, RAPTOR, PTEN, and TSC1)
  • Cell cycle genes (amplifications of CDK2, CDK4, and CDK6, CCND1-3, and CCNE1); loss of RB
  • DNA repair pathway (BRCA1/2, ATM)
  • Ras/MAPK pathway (KRAS, RAF1, NF1)
  • Sporadic growth factor receptor (amplifications occurred in EGFR, KIT, PDGFRA, PDGFRB, MET, FGFR1, FGFR2, and IGF1R.

NGS identified 7 patients with ERBB2 gene amplification. NGS could assist in the identification of ERBB2-overexpressing tumors misclassified at the time of diagnosis.

Amplifications of MYC were independently associated with poor recurrence-free survival (RFS) and overall survival (OS). In contrast to the earlier notion, high post-NAC Ki67 score did not predict poor RFS or OS in this predominantly TNBC cohort.

The authors concluded that “the diversity of lesions in residual TNBCs after NAC underscores the need for powerful and broad molecular approaches to identify actionable molecular alterations and, in turn, better inform personalized therapy of this aggressive disease.”

Identification of Novel Synthetic-Lethal Targets for MYC-Driven Triple-Negative Breast Cancer: Goga A, etal, UCSF (San Francisco, CA), Abstract No. S3-8

Reiterating the greatest challenge of the TNBC treatment, no targeted agents currently exist against TNBC. The group at UCSF has discovered that TNBC frequently express high levels of the MYC proto-oncogene. The discovery has led them to identify new “synthetic-lethal” strategies to selectively kill TNBC with MYC overexpression. “Synthetic lethality arises when a combination of mutations in two or more genes leads to cell death, whereas a mutation in only one of these genes has little effect. Using this strategy, we can take advantage of the elevated MYC signaling in TNBC to selectively kill them, while sparing normal tissues in which MYC is expressed at much lower levels”

The researchers performed a shRNA synthetic-lethal screen in the human mammary epithelial cells (HMEC), to identify new molecules, such as cell cycle kinases, which when inhibited can preferentially kill TNBC cells. A high-throughput screen of ∼2000 shRNAs, that target the human kinome (∼ 600 kinases) when performed, led to the identification of 13 kinases whose inhibition by >1 shRNAs gave rise to >50% inhibition of cell growth. ARK5 and GSK3A were the two other kinases that were shown to have a synthetic-lethal interaction with MYC. Since these two kinases have been identified in other studies, it gives validity to the ability to the methods of Goga etal in identifying synthetic-lethal targets. The group is currently characterizing and validating the 11 novel targets identified in this screen, using human cancer cell lines as well as mouse cancer models to determine the impact of inhibiting these targets on triple-negative breast cancer development and proliferation.


Dent R, etal.  Triple-negative breast cancer: clinical features and patterns of recurrence (2007) Clin Cancer Res 13, 4429-4434.

Lehmann BD, etal. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies (2011) J Clin Invest. 121: 2750-67.

Chen X, etal. TNBCtype: A Subtyping Tool for Triple- Negative Breast Cancer. (2012) Cancer informatics 11, 147-156.

Abstracts presented in SABCS 2012 can be accessed here.

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