Posts Tagged ‘oncogene’

Myc and Cancer Resistance

Curator: Larry H. Bernstein, MD, FCAP


Myc (c-Myc) is a regulator gene that codes for atranscription factor. The protein encoded by this gene is a multifunctional, nuclear phosphoprotein that plays a role in cell cycle progression, apoptosis and cellular transformation.[1]

Myc gene was first discovered in Burkitt lymphoma patients. In Burkitt lymphoma, cancer cells showchromosomal translocations, in which Chromosome 8 is frequently involved. Cloning the break-point of the fusion chromosomes revealed a gene that was similar to myelocytomatosis viral oncogene (v-Myc). Thus, the newfound cellular gene was named c-Myc.


Protein increases signals that protect cancer cells

Researchers have identified a link between the expression of a cancer-related gene and cell-surface molecules that protect tumors from the immune system

Depiction of the Myc protein

The Myc protein, depicted here, is mutated in more than half of all human cancers.   Petarg/Shutterstock


A cancer-associated protein called Myc directly controls the expression of two molecules known to protect tumor cells from the host’s immune system, according to a study by researchers at the Stanford University School of Medicine.

The finding is the first to link two critical steps in the development of a successful tumor: uncontrolled cell growth — when mutated or misregulated, Myc causes an increase in the levels of proteins that promote cell division — and an ability to outwit the immune molecules meant to stop it.

The study was published online March 10 inScience. Dean Felsher, MD, PhD, a professor of oncology and of pathology, is the senior author. The lead author is postdoctoral scholar Stephanie Casey, PhD. The work was conducted in collaboration with researchers at the University of Wurzburg.

“Our findings describe an intimate, causal connection between how oncogenes like Myc cause cancer and how those cancer cells manage to evade the immune system,” Felsher said.

‘Don’t eat me’ and ‘don’t find me’

One of the molecules is the CD47 protein, which researchers in the Stanford laboratory of Irving Weissman, MD, have discovered serves as a “don’t eat me” signal to ward off cancer-gobbling immune cells called macrophages. Weissman is the Virginia and D.K. Ludwig Professor for Clinical Investigation in Cancer Research and the director of Stanford’s Institute for Stem Cell Biology and Regenerative Medicine.

Nearly all human cancers express high levels of CD47 on their surfaces, and an antibody targeting the CD47 protein is currently in phase-1 clinical trials for a variety of human cancers.

The other molecule is a “don’t find me” protein called PD-L1, known to suppress the immune system during cancer and autoimmune diseases but also in normal pregnancy. It’s often overexpressed on human tumor cells. An antibody that binds to PD-L1 has been approved by the U.S. Food and Drug Administration to treat bladder and non-small-cell lung cancer, but it has been shown to be effective in the treatment of many cancers.

Dean Felsher

Programmed death-ligand 1 (PD-L1): an inhibitory immune pathway exploited by cancer

Image of PD-L1 binding to B7.1 and PD-1, deactivating T cell]

In cancer, Myc a usual suspect

Researchers in Felsher’s laboratory have been studying the Myc protein for more than a decade. It is encoded by a type of gene known as an oncogene. Oncogenes normally perform vital cellular functions, but when mutated or expressed incorrectly they become powerful cancer promoters. The Myc oncogene is mutated or misregulated in over half of all human cancers.

In particular, Felsher’s lab studies a phenomenon known as oncogene addiction, in which tumor cells are completely dependent on the expression of the oncogene. Blocking the expression of the Myc gene in these cases causes the complete regression of tumors in animals.

In 2010, Felsher and his colleagues showed that this regression could only occur in animals with an intact immune system, but it wasn’t clear why.

“Since then, I’ve had it in the back of my mind that there must be a relationship between Myc and the immune system,” said Felsher.

Turning off Myc expression

Casey and Felsher decided to see if there was a link between Myc expression and the levels of CD47 and PD-L1 proteins on the surface of cancer cells. To do so, they investigated what would happen if they actively turned off Myc expression in tumor cells from mice or humans. They found that a reduction in Myc caused a similar reduction in the levels of CD47 and PD-L1 proteins on the surface of mouse and human acute lymphoblastic leukemia cells, mouse and human liver cancer cells, human skin cancer cells, and human non-small-cell lung cancer cells. In contrast, levels of other immune regulatory molecules found on the surface of the cells were unaffected.

I’ve had it in the back of my mind that there must be a relationship between Myc and the immune system.

In publicly available gene expression data on tumor samples from hundreds of patients, they found that the levels of Myc expression correlated strongly with expression levels of CD47 and PD-L1 genes in liver, kidney and colorectal tumors.

The researchers then looked directly at the regulatory regions in the CD47 and PD-L1 genes. They found high levels of the Myc protein bound directly to the promoter regions of both CD47 and PD-L1 in mouse leukemia cells, as well as in a human bone cancer cell line. They were also able to verify that this binding increased the expression of the CD47 gene in a human blood cell line.

Possible treatment synergy

Finally, Casey and Felsher engineered mouse leukemia cells to constantly express CD47 or PD-L1 genes regardless of Myc expression status. These cells were better able than control cells to evade the detection of immune cells like macrophages and T cells, and, unlike in previous experiments from Felsher’s laboratory, tumors arising from these cells did not regress when Myc expression was deactivated.

“What we’re learning is that if CD47 and PD-L1 are present on the surfaces of cancer cells, even if you shut down a cancer gene, the animal doesn’t mount an adequate immune response, and the tumors don’t regress,” said Felsher.

The work suggests that a combination of therapies targeting the expression of both Myc and CD47 or PD-L1 could possibly have a synergistic effect by slowing or stopping tumor growth, and also waving a red flag at the immune system, Felsher said.

“There is a growing sense of tremendous excitement in the field of cancer immunotherapy,” said Felsher. “In many cases, it’s working. But it’s not been clear why some cancers are more sensitive than others. Our work highlights a direct link between oncogene expression and immune regulation that could be exploited to help patients.”

The research is an example of Stanford Medicine’s focus on precision health, the goal of which is to anticipate and prevent disease in the healthy and precisely diagnose and treat disease in the ill.

Other Stanford co-authors of the paper are oncology instructor Yulin Li, MD, PhD; postdoctoral scholars Ling Tong, PhD, Arvin Gouw, PhD, and Virginie Baylot, PhD; former research assistant Kelly Fitzgerald; and undergraduate student Rachel Do.

The research was supported by the National Institutes of Health (grants RO1CA089305, CA170378, CA184384, CA105102, P50 CA114747, U56CA112973, U01CA188383, 1F32CA177139 and 5T32AI07290).


The PD-L1 pathway downregulates cytotoxic T-cell activity to maintain immune homeostasis

Under normal conditions, the inhibitory ligands PD-L1 and PD-L2 play an important role in maintaining immune homeostasis.1 PD-L1 and PD-L2 bind to specific receptors on T cells. When bound to their receptors, cytotoxic T-cell activity is downregulated, thereby protecting normal cells from collateral damage.1,2

Image showing PD-L1 binding to B7.1 and PD-1 to deactivate T cells during immune response]


Broadly expressed in multiple tissue types, including hematopoietic, endothelial, and epithelial cells1,4


Receptor expressed on activated T cells and dendritic cells3


Receptor expressed primarily on activated T cells3


Image showing PD-L1 binding to B7.1 and PD-1 to deactivate T cells during immune response]


Restricted expression on immune cells and in some organs, such as the lung and colon1,4,5


Receptor expressed primarily on activated T cells3


Many tumors can exploit the PD-L1 pathway to inhibit the antitumor response

In cancer, the PD-L1/B7.1 and PD-L1/PD-1 pathways can protect tumors from cytotoxic T cells, ultimately inhibiting the antitumor immune response in 2 ways.1-3

  • Deactivating cytotoxic T cells in the tumor microenvironment
  • Preventing priming and activation of new T cells in the lymph nodes and subsequent recruitment to the tumor



Upregulation of PD-L1 can inhibit the last stages of the cancer immunity cycle by deactivating cytotoxic T cells in the tumor microenvironment.1

Activated T cells in the tumor microenvironment release interferon gamma.2

As a result, tumor cells and tumor-infiltrating immune cells overexpress PD-L1.2

PD-L1 binds to T-cell receptors B7.1 and PD-1, deactivating cytotoxic T cells. Once deactivated, T cells remain inhibited in the tumor microenvironment.1,2


PD-L1 overexpression can also inhibit propagation of the cancer immunity cycle by preventing the priming and activation of T cells in the lymph nodes.1-3

PD-L1 expression is upregulated on dendritic cells within the tumor microenvironment.2,3

PD-L1–expressing dendritic cells travel from the tumor site to the lymph node.4

PD-L1 binds to B7.1 and PD-1 receptors on cytotoxic T cells, leading to their deactivation.3


The cancer immunity cycle characterizes the complex interactions between the immune system and cancer

The cancer immunity cycle describes a process of how one’s own immune system can protect the body against cancer. When performing optimally, the cycle is self-sustaining. With subsequent revolutions of the cycle, the breadth and depth of the immune response can be increased.1



  • Oncogenesis leads to the expression of neoantigens that can be captured by dendritic cells
  • Dendritic cells can present antigens to T cells, priming and activating cytotoxic T cells to attack the cancer cells


  • Activated T cells travel to the tumor and infiltrate the tumor microenvironment


  • Activated T cells can recognize and kill target cancer cells
  • Dying cancer cells release additional cancer antigens, propagating the cancer immunity cycle




Image of immunity cycle; explore Genentech cancer immunotherapy research on the cancer immunity cycle



  1. Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity. 2013;39:1-10. PMID: 23890059
  2. Chen DS, Irving BA, Hodi FS. Molecular pathways: next-generation immunotherapy—inhibiting programmed death-ligand 1 and programmed death-1. Clin Cancer Res. 2012;18:6580-6587. PMID: 23087408
  3. Keir ME, Butte MJ, Freeman GJ, Sharpe AH. PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol. 2008;26:677-704. PMID: 18173375
  4. Motz GT, Coukos G. Deciphering and reversing tumor immune suppression. Immunity. 2013;39:61-73. PMID: 23890064



MYC regulates the antitumor immune response through CD47 and PD-L1

The clinical efficacy of monoclonal antibodies as cancer therapeutics is largely dependent upon their ability to target the tumor and induce a functional antitumor immune response. This two-step process of ADCC utilizes the response of innate immune cells to provide antitumor cytotoxicity triggered by the interaction of the Fc portion of the antibody with the Fc receptor on the immune cell. Immunotherapeutics that target NK cells, γδ T cells, macrophages and dendritic cells can, by augmenting the function of the immune response, enhance the antitumor activity of the antibodies. Advantages of such combination strategies include: the application to multiple existing antibodies (even across multiple diseases), the feasibility (from a regulatory perspective) of combining with previously approved agents and the assurance (to physicians and trial participants) that one of the ingredients – the antitumor antibody – has proven efficacy on its own. Here we discuss current strategies, including biologic rationale and clinical results, which enhance ADCC in the following ways: strategies that increase total target–monoclonal antibody–effector binding, strategies that trigger effector cell ‘activating’ signals and strategies that block effector cell ‘inhibitory’ signals.

Keywords: γδ T cells, ADCC, cancer, cytokines, IMiD, immunocytokines, immunomodulators, interleukins, monoclonal antibodies, NK cells, passive immunotherapy

Monoclonal antibodies (mAbs) can target tumor antigens on the surface of cancer cells and have a favorable toxicity profile in comparison with cytotoxic chemotherapy. Expression of tumor antigens is dynamic and inducible through agents such as Toll-like receptor (TLR) agonists, immunomodulatory drugs (IMiDs) and hypomethylating agents [1]. Following binding of the mAb to the tumor antigen, the Fc portion of the mAb interacts with the Fc receptor (FcR) on the surface of effector cells (i.e., NK cells, γδ T cells and macrophages), leading to antitumor cytotoxicity and/or phagocytosis of the tumor cell. FcR interactions can be stimulatory or inhibitory to the killer cell, depending on which FcR is triggered and on which cell. Stimulatory effects are mediated through FcγRI on macrophages, dendritic cells (DCs) and neutrophils, and FcγRIIIa on NK cells, DCs and macrophages. In murine models, the cytotoxicity resulting from FcR activation on a NK cell, γδ T cell and macrophage is responsible for antitumor activity [2]. The role of DCs should be noted: although not considered to be primary ADCC effector cells, they can respond to mAb-bound tumor cells via their own FcR-mediated activation and probably play a significant role in activating effector cells. Preclinical models have shown that, although not the effector cell, DCs are critical to the efficacy of mAb-mediated tumor elimination [3]. Equally, mAb-activated ADCC effector cells can induce DC activation [4] and the importance of this crosstalk is an increasing focus of study [5].

The antitumor effects of mAbs are caused by multiple mechanisms of action, including cell signaling agonism/antagonism, complement activation and ligand sequestration, although ADCC probably plays a predominant role in the efficacy of some mAbs. In a clinical series, a correlation between the affinity of the receptor FcγRIIIa (determined by inherited FcR polymorphisms) and the clinical response to mAb therapy, supporting the significance of the innate immune response [610]. Several strategies could potentially improve the innate response following FcR activation by a mAb (Figure 1):

Quantitatively increasing the density of the bound target, mAb or the effector cells;

Stimulation of the effector cell by targeting the NK cell, γδ T cell and/or macrophage with small molecules, cytokines or agonistic antibodies;

Blocking an inhibitory interaction between the NK cell or macrophage and the tumor cell.


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Enhancing ADCC

FcR: Fc receptor; HDACi: Histone deacetylase inhibitor; IMiD: Immunomodulator; KIR: Killer immunoglobulin-like receptor;

The ability of the combination approaches to enhance ADCC is largely determined by the capacity of the mAb to induce ADCC. Since the approval of the first mAb for the treatment of non-Hodgkin’s lymphoma, rituximab (RTX), in 1997, several mAbs have become standard of care for the treatment of both solid tumors and hematologic malignancies, including trastuzumab (TRAST), alemtuzumab, cetuximab, panitumumab and ofatumumab [11]. As noted above, clinical series among lymphoma patients treated with an anti-CD20 mAb (RTX) [6,7], HER2-expressing breast cancer receiving anti-HER2 mAb therapy (TRAST) [8] or colorectal cancer patients treated with an anti-EGFR mAb (cetuximab) [9,10] observed a correlation between clinical benefit and FcγRIIIa genotype, with patients who have higher-affinity polymorphisms demonstrating superior clinical outcomes. By contrast, the anti-EGFR mAb panitumumab does not induce ADCC, owing to a different Fc isotype that does not bind to the FcγRIIIa. Therefore, when considering enhancement of ADCC, such approaches are limited to combinations with mAbs that activate the FcR. Nonetheless, an advantage of this dual therapy strategy is that mAbs yet to be discovered against currently unknown tumor antigens may be combined with the therapeutics discussed herein.

Increasing target–mAb–effector binding

As the central element in the target–mAb–effector cell unit, the mAb seems to be a probable candidate for improvements, either in its antigen-binding or its Fc-binding domains. This approach has been heavily pursued with some degree of success [1215]. Antibody engineering to improve interaction between the target or FcR requires that each new antibody be individually developed and tested as a new entity.

Increasing the antigen target

Tumor cells with a lower density of antigen targets are less responsive to mAbs than higher antigen-expressing diseases [16]. Therefore, it seems logical to try to increase the expression of the target on tumor cells. Antigen expression can be upregulated by cytokines [17], ionizing radiation [18], natural metabolites [19] and hypomethylating agents such as decitabine [20]. In addition, the family of TLR9 agonists known as CpG oligodeoxynucleotides (CpG ODN) can induce CD20 expression on malignant B cells [2123]. Taken together with data showing the activating effect of CpG ODN on effector cells (discussed below), it seems reasonable that the combination of CpG ODN with mAb might have synergistic efficacy. Clinical series, however, have tested CpG ODN administered intravenously or subcutaneously and have observed little efficacy in Phase I and II studies [2426] in low-grade lymphoma. One possible limitation of these studies has been their application to diseases (primarily follicular and mantle cell lymphoma) known to already have high expression of the relevant antigen (CD20). It is plausible that increasing antigen expression on low antigen-expressing diseases such as chronic lymphocytic leukemia could have a greater increase in relative efficacy. To this end, monotherapy studies have recently been undertaken [27,301] and should lead to combination trials.


Effector cells: γδ T cells

The role of NK cells and macrophages in mediating ADCC has been well established; however, only recently have γδ T cells been found to play a role as ADCC effectors. Typically, this population is considered as a minor subset (<5% of circulating T cells), although they may infiltrate tumors of epithelial origin preferentially and constitute a large portion of the tumor-infiltrating lymphocytes in cancers such as breast carcinoma. The combination of HLA-unrestricted cytotoxicity against multiple tumor cell lines of various histologies, secretion of cytolytic granules and proinflammatory cytokines such as TNF-α, IL-17 and IFN-γ make γδ T cells potentially potent antitumor effectors [32,33].


TLR agonists    

In addition to its aforementioned induction of CD20, CpG ODN also indirectly augments innate immune function. TLRs are specialized to recognize pathogen-associated molecular patterns; they stimulate plasmacytoid DCs and B cells [53], and one of many plasmacytoid DC responses to stimulation by CpG ODNs is activation of local NK cells, thus improving spontaneous cytotoxicity and ADCC [54]. CpG ODN effects on NK cells appeared to be indirect and IFN-γ production by T cells (possibly in response to plasmacytoid DC activation) has been hypothesized as the intermediary of NK cell activation.


Immunomodulatory drugs

IMiDs have shown clinical activity in multiple hematologic malignancies despite their primary mechanism of action being unclear. Among their biologic effects (particularly lenalidomide) there are demonstrable and pleiotropic effects on immune cells and signaling molecules. These include enhancement of in vitro NK cell- and monocyte-mediated ADCC on RTX-coated [68] as well as TRAST- and cetuximab-coated tumor cells [69]. In vivo studies in a human lymphoma severe combined immune deficiency mouse model demonstrated significant increases in NK cell recruitment to tumors mediated via microenvironment cytokine changes and augmented RTX-associated ADCC [70]. Studies suggest that IMiD activation of NK cells occurs indirectly; partly via IL-2 induction by T cells [71]. Clinically, a recent study noted significant increases in peripheral blood NK cells, NK cell cytotoxicity and serum IL-2, IL-15 and GM-CSF [72], the potential ADCC-promoting effects of which are discussed below.



PD-1 is a negative regulatory member of the CD28 superfamily expressed on the surface of activated T cells, B cells, NK cells and macrophages, similar to but more broadly regulatory than CTLA-4. Its two known ligands, PD-L1 and PD-L2, are both expressed on a variety of tumor cell lines. The PD-1–PD-L1 axis modulates the NK cell versus multiple myeloma effect, as seen by its blockade enhancing NK cell function against autologous primary myeloma cells, seemingly through effects on NK cell trafficking, immune complex formation with myeloma cells and cytotoxicity specifically toward PD-L1(+) tumor cells [179]. Two anti-PD-1 mAbs (BMS-936558 and CT-011) are currently in clinical trials, the latter in a combination study with RTX for patients with low-grade follicular lymphoma [314].

ConclusionThe recent approval of an anti-CTLA4 mAb has demonstrated that modulating the immune response can improve patient survival [180,181]. As the immune response is a major determinant of mAb efficacy, the opportunity now exists to combine mAb therapy with IMiDs to enhance their antitumor efficacy. Remarkable advances in the basic science of cellular immunology have increased our understanding of the effector mechanisms of mAb antitumor efficacy. Whereas the earliest iterations of such combinations, for example IL-2 and GM-CSF, may have augmented both effector and suppressive cells, newer approaches such as IL-15 and TLR agonists may more efficiently activate effector cells while minimizing the influence of suppressive cells. Despite these encouraging rationale and preliminary data, clinical evidence is still required to demonstrate whether combination therapies will increase the antitumor effects of mAb.

Still, this approach is unique in combining a tumor-targeting therapy, the mAb, with an immune-enhancing therapy. If successful, these therapies may be combined with multiple mAbs in routine practice, as well as novel mAbs yet to be developed. Various approaches including augmenting antigen expression, stimulating the innate response and blocking inhibitory signals are being explored to determine the optimal synergy with mAb therapies. Therapies targeting NK cells, γδ T cells, macrophages and DCs may ultimately be used in combination to further augment ADCC. Encouraging preclinical studies have led to a number of promising therapeutics, and the results of proof-of-concept clinical trials are eagerly awaited.

PD-L1, other targeted therapies await more standardized IHC

February 2016—Immunohistochemistry is heading down a path toward more standardization, and that’s essential as it plays an increasing role in rapidly expanding immunotherapy, says David L. Rimm, MD, PhD, professor of pathology and of medicine (oncology) and director of translational pathology at Yale University School of Medicine. As a co-presenter of a webinar produced by CAP TODAY in collaboration with Horizon Diagnostics, titled “Immunohistochemistry Through the Lens of Companion Diagnostics” (, he analyzes the core challenges of IHC’s adaptation to the needs of precision medicine: binary versus continuous IHC, measuring as opposed to counting or viewing by the pathologist, automation, and assay performance versus protein measurement.

“Immunohistochemistry is 99 percent binary already,” Dr. Rimm points out. “There are only a few assays in our labs—ER, PR, HER2, Ki-67, and maybe a few more—where we really are looking at a continuous curve or a level of expression.”

Two criteria in the 2010 ASCO/CAP guidelines on ER and PR testing in breast cancer patients are key, he says: 1) the percentage of cells staining and 2) any immunoreactivity. “The first is hard to estimate, but the guidelines recommend the use of greater than or equal to one percent of cells that are immunoreactive. That means they could have a tiny bit of signal or they could have a huge amount of signal and they would be considered immunoreactive, which thereby makes this a binary test.”

Having the test be binary can be a problem for companion diagnostic purposes because any immunoreactivity is dependent on the laboratory threshold and counterstain. For example, if two of the same spots, serial sections on a tissue microarray, were shown side by side, one with and one without the hematoxylin counterstain, “you might see the counterstain make this positive test into a negative by eye, which is a potential problem with IHC when you have a binary stain.” (Fig. 1).


Dr. Rimm describes a small study done with three different CLIA-certified labs, each using a different FDA-approved antibody and measuring about 500 breast cancer cases on a tissue microarray. The study showed there can be fairly significant discordance between labs—between 18 and 30 percent discordance—in terms of the cases that were positive. “In fact, if we look at outcome, 18 percent of the cases were called positive in Lab Two but were negative in Lab Three. Lab Three showed outcomes similar to the double positives whereas Lab Two had false-negatives.” This is an important problem that occurs when we try to binarize our immunohistochemistry, he says.

Counting is more variable in a real-world setting due to the variability of the threshold for considering a case positive. “You can easily calculate that if your threshold was five percent, then you’d have 70 percent positive cells. And you would easily call this positive. But if you added more hematoxylin because that’s how your pathologist liked it, then perhaps you’d only have 30 percent positive. So this is the risk of using thresholds.” (Fig. 2).


Although this is done in all of immunohistochemistry today, Dr. Rimm thinks it is an important consideration as IHC transitions to more standardized form. “An H score—intensity times area, which has been attempted many times, can’t be done by human beings. Pathologists try but have failed.”

“We can’t do those intensities by eye. We have to measure them with a machine. But we get a very different piece of information content when we measure intensity, as opposed to measuring the percentage of cells above a threshold. In sum, more information is present in a measurement than in counting.”

Pathologists read slides for a living, so it’s uncomfortable to think about giving that up in order to use a machine to measure the slides. “But I think if we want to serve our clients and our patients, we really owe them the accuracy of the 21st century as opposed to the methods of the 20th century.” (Fig. 3).

A shows comparison of a quantitative fluorescence score on the x axis versus an H-score on the y axis. Note the noncontinuous nature of human estimation of intensity times area (H-score). B) The survival curve in a population of lung cancer cases using the H-score. C) The survival curve in the same population using the quantitative score. (Source: David Rimm, MD, PhD)

A shows comparison of a quantitative fluorescence score on the x axis versus an H-score on the y axis. Note the noncontinuous nature of human estimation of intensity times area (H-score). B) The survival curve in a population of lung cancer cases using the H-score. C) The survival curve in the same population using the quantitative score. (Source: David Rimm, MD, PhD)

Among the currently available quantitative measuring devices are the Visiopharm, VIAS (Ventana), Aperio (Leica), InForm (Perkin-Elmer), and Definiens platforms. “We use the platform invented in my lab, called Aqua [Automated Quantitative Analysis], but this is now owned by Genoptix/Novartis. Genoptix intends to provide commercial tests using Aqua internally,” Dr. Rimm says, “as well as enable platform and commercial testing through partnership with additional reference lab providers.

“There are many quantification platforms,” he adds, “and I believe that any of them, used properly, can be effective in measurement.”

(Of the 265 participants in the CAP PM2 Survey, 2015 B mailing, who reported using an imaging system for quantification, 4.6 percent use VIAS, 4.1 percent use ACIS, 0.8 use Applied Imaging, and 10 percent use “other” imaging systems. Of the 1,359 Survey participants who responded to the question about use of an imaging system to analyze hormone receptor slides, 1,094, or 80.5 percent, reported not using any imaging system for quantification.)

Says Dr. Rimm: “The first platform we used to try to quantitate some DAB stain slides was actually the Aperio Nuclear Image Analysis algorithm. But the problem with DAB is that you can’t see through it. And so inherently it’s physically flawed as a method for accurate measurement.” He compares DAB to looking at stacks of pennies from above, where their height and quantity can’t be surmised, as opposed to from the side, where their numbers can be accurately estimated. “This is why I don’t use, in general, DAB-type technologies or any chromogen.”

Fluorescence doesn’t have this problem, and that is the reason Dr. Rimm began using fluorescence as a quantitative method. “We try to be entirely quantitative without any feature extraction. So we define epithelial tumors using a mask of cytokeratin. We define a mask by bleeding and dilating, filling some holes, and then ultimately measure the intensity of each cell, or of each target we’re looking for. In this case, in a molecularly defined compartment.”

Compartments can be defined by any type of molecular interactions. “We defined DAPI-positive pixels as nuclei, and we measure the intensity of the estrogen receptor within the compartment. And that gives us an intensity over an area or the equivalent of a concentration.” Many other fluorescent tools can be used in this same manner, but he cautions against use of fluorescent tools that group and count. “That’s a second approach that can be used, but the result gives you a count instead of a measurement.”

When comparing a pathologist’s reading versus a quantitative immunofluorescence score, he notes, pathologists actually don’t generate a continuous score. Instead, pathologists tend to use groups. “We tend to use a 100 or a 200 or an even number. We never say, ‘Well, it’s 37 percent positive.’ We say, ‘It’s 40 percent positive,’ because we know we can’t reproducibly tell 37 from 38 from 40 percent positive.”

The result of that is a noncontinuous scoring result, which doesn’t give the information content of quantitative measurement. A comparison between the two methods shows that at times, where quantitative measurement shows a significant difference in outcome, nonquantitative measure or an H-score difference may not show a difference in outcome. (Fig. 3 illustrates this concept.)

“Pathologists tend to group things, and we also tend to overestimate. It’s not that pathologists are bad readers. It’s just the tendency of the human eye because of our ability to distinguish different intensities and the subtle difference between intensities. But even if you compare two quantitative methods, you can see that the method where light absorbance occurs—that is the percent positive nuclei by Aperio, which is a chromogen-based method—tends to saturate. This is, in fact, amplified dramatically when you look at something with a wide dynamic range like HER2.” (Fig. 4).


In one study, researchers found less than one percent discordance—essentially no discordance—between two antibodies (Dekker TJ, et al. Breast Cancer Res. 2012;14[3]:R93). But looking at these results graphed quantitatively, you would see a very different result, Dr. Rimm says. “You can see a whole group of cases down below where there’s very low extracellular domain and very high cytoplasmic domain. In fact, some of these cases have essentially no extracellular domain, but high levels of cytoplasmic domain, and other cases have roughly equal levels of each” (Carvajal-Hausdorf DE, et al. J Natl Cancer Inst.2015;107[8]:pii:djv136).

Recent studies by Dr. Rimm’s group have shown this to have clinical implications. He looked at patients treated with trastuzumab in the absence of chemotherapy, in an unusual study called the HeCOG (Hellenic Cooperative Oncology Group) trial.

“We found that patients who had high levels of both extracellular and intracellular domain have much more benefit than patients who are missing the extracellular domain and thereby missing the trastuzumab binding site.” Follow-up studies are being done to validate this finding in larger cohorts.

Preanalytical variables, Dr. Rimm emphasizes, can have significant effects on IHC results, and more than 175 of them have been identified. “These are basically all the things we can’t control, which is the ultimate argument for standardization.”

In a surprising study by Flory Nkoy, et al., he says, it was shown that breast cancer specimens were more likely to be ER negative if the patient’s surgery was on a Friday because there was a higher ER-negative rate on Friday than on Monday. “So how could that be? Well, it was clearly the fact that the tissue was sitting over the weekend. And when it sat over the weekend, the ER positivity rate was going down” (Arch Pathol Lab Med. 2010;134:606–612).

Another study showed that after one hour, four hours, and eight hours of storage at room temperature, you lose significant amounts of staining, Dr. Rimm says. “And perhaps the best nonquantitative study or H-score-based study of this phenomenon was done by Isil Yildiz-Aktas, et al., where a significant decrease in the estrogen receptor score was found after only three hours in delay to fixation” (Mod Pathol. 2012;25:1098–1105).

How long the slide is left to sit after it is cut is another preanalytical variable to be concerned with. “In the clinical lab, that’s not often a problem since we cut them, then stain them right away. But in a research setting, a fresh-cut slide can look very different from a slide that’s two days old, six days old, or 30 days old, where a 2+ spot on a breast cancer patient becomes negative after 30 days sitting on a lab bench. So those are both key variables to be mindful of.”

One solution for those preanalytic variables is trying to prevent delayed time to fixation. “And probably time to fixation is one of the main preanalytic variables, although it’s only one of the many hundreds of variables. The method we use to try to get around this problem is to use core biopsies or allow rapid and complete fixation, and then other things can be done.”

Finally, he warns, don’t cut your tissue until right before you stain it. “If you’re asked to send a tissue out to a collaborator or someone who is going to use it for research purposes later, we recommend coring and re-embedding the core, or sending the whole block. Unstained sections, when not properly stored in a vacuum, will ultimately be damaged by hydration or oxidation, both of which lead to loss of antigenicity.”

The crux of the matter is assay performance versus protein measurement, Dr. Rimm says. “In the last six to nine months, we really are faced with this problem in spades, as PD-L1 has become a very important companion diagnostic.”

There are now four PD-L1 drugs with complementary or companion diagnostic tests (Fig. 5). One of the FDA-approved drugs, nivolumab (Opdivo, Bristol-Myers Squibb), for example, uses a clone called 28-8, which is provided by Dako in an assay, a complementary diagnostic assay, and with the following suggested scoring system: one percent, five percent, or 10 percent. In contrast, pembrolizumab (Keytruda, Merck) is also now FDA-approved but requires a companion diagnostic test that uses a different antibody, although the same Dako Link 48 platform. This diagnostic has a different scoring system of less than one percent, one to 49 percent, and 50 percent and over.

Two other companies, Roche/Genentech and AstraZeneca, also have drugs in trials that may or may not have companion diagnostic testing, though both have already identified a partner and a unique antibody (neither of those listed above) and companion diagnostic testing scores used in their clinical trials.

“So what’s a pathologist to do?” Dr. Rimm says. “Well, there are a few problems with this. First of all, what we really should be doing is measuring PD-L1. That’s the target and that’s what should ultimately predict response. But instead what we’re stuck with, through the intricacies of the way our field has grown and our legacy, is closed-system assays. While these probably do measure PD-L1, we do not know how these compare to each other.” Two parallel large multi-institutional studies are addressing this issue now, he says.

There are solutions for managing these closed-system assays to be sure the assay is working in your lab and that you can get the right answer, Dr. Rimm says. His laboratory uses a closed-system assay for PD-L1, relying not on the defined system but rather on a test system it has developed in doing a study with different investigators.

Sample runs by these different investigators show the potentially high variability, he says. “In a scan of results, no one would deny which spots are the positive spots and which are the negative.” But the difference in staining prevents accurate measurement of these things and shows the variability inherent even in a closed-box system.

A comparison of two closed-box systems, the SP1 run on the Discovery Ultra on Ventana, and the SP1, same antibody, run on the Dako closed-box system, also shows that, in fact, there’s not 100 percent agreement using same-day, same-FDA-cleared antibody staining and different autostainers. So automation may not solve the problem, Dr. Rimm notes (Fig. 6).


“When running these in a quantitative fashion and measuring them quantitatively, there are actually differences in the way these closed-box systems run. And so you, as the pathologist, have to be the one who makes sure your assays are correct, your thresholds are correct, and your measurements are accurate.”

The way to do that, he believes, is to use standardization or index arrays. An index array of HER2 that his laboratory developed has 3+ amplified, 2+ amplified, not amplified, and so on from 80 cases in the lab’s archive, shown stained with immunofluorescence and quantitative and DAB stain. “It was only with this standardization array, run every time we ran our stainer, that we were able to draw the conclusions in the previous study about extracellular versus cytoplasmic domain.”

Companies have realized the importance of this, and specifically companies like NantOmics (formerly OncoPlexDx) have realized they can exactly quantitate the amount of tissue on a slide using a specialized mass spectrometry method, he says. “They can actually give you amol/µg of total protein.”

He and colleagues are working with NantOmics now to try to convert from amols to protein to average quantitative fluorescent scores to help build these standards and make standard arrays more accurate. “This is still a work in progress, but I believe this is ultimately the kind of accuracy that can standardize all of our labs. We have shown that the quantitative fluorescence system is truly linear and quantitative for EGFR measurements when using mass spectrometry as a gold standard.” They are preparing to submit a manuscript with this data.

In the interim, Dr. Rimm’s laboratory has begun working also with Horizon Diagnostics, employing Horizon’s experimental 15-spot positive-control array. “When you use this array and quantitate it with quantitative fluorescence, you get a very interesting profile. If a cut point is set at one point, you would see three clearly positive cells or spots and 12 clearly negative spots with two different antibodies. But is that the threshold?”

“In fact, using a little higher score and a very quantitative test, you might find that the threshold may, in fact, be a little bit lower than that.” It turns out that only three of these 12 spots are true negatives. The others at least have some level of RNA, and some have a lot. “So how do we handle these? And are these behaving the same way with multiple antibodies?” Parallel results, finding nearly the same threshold case, have been found using SP142 from Ventana, E1L3N from Cell Signaling, and SP263 from Ventana.

Studies to address those issues are still in the early stage, he says. He cautions that there is variance in these assays, and more work is being done to reproduce the data. “But I think the important point is that, using these kinds of arrays, you can definitively determine whether your lab has the same cut point as every other lab. And were we to quantitate this with mass spectrometry, we would know exactly the break point for use in the future.”

Dr. Rimm’s laboratory has also built its own PD-L1 index tissue microarray with a number of its own tumor slides ranging from very low to very high expressors, a series of cell lines, and including some placenta-positive controls on normal tumor. He has found that generating an index array has advantages, and he encourages other laboratories to prepare their own index arrays to increase the accuracy and reproducibility of their laboratory-developed tests. “You can produce these in your own lab so that you can be sure you can standardize your tests run in your clinical lab from day to day and week to week as part of an LDT.”

“If we think about it, there really are no clinical antibodies today that are truly quantitative,” Dr. Rimm says. “And when there are, new protocols will be required, but I believe those protocols are now in existence. We just await the clinical trials that require truly quantitative protein measurement or in situ proteomics.”

In that process of moving toward in situ proteomics, suggests web-inar co-presenter Clive Taylor, MD, DPhil, professor of pathology in the Keck School of Medicine at the University of Southern California, FDA approval, per se, will not solve any of the problems discussed in the webinar. (See the January 2016 issue for the full report of Dr. Taylor’s presentation.) “I think what the FDA approval will do is demand that we find solutions to these problems ourselves. The FDA’s attitude is, to a large degree, dependent on the claim. So if we just use immunohistochemistry as a simple stain, then the FDA classes that as sort of class I, level 1. And we can do that [IHC stain] without having to get preapproval by the FDA.

“On the other hand, if we take something like the well-established HercepTest, where based on the result of that test alone, it’s decided whether or not the patient gets treatment, treatment that’s very expensive and treatment that has benefits and…side effects. That claim is, in fact, a very high-level claim. And for that, the FDA is demanding high-level data, which I think is entirely appropriate,” Dr. Taylor says.

Most of these upcoming companion diagnostics, if not all, he says, will be regarded by the FDA as class III, high level or high complexity. They will require a premarket approval study in conjunction with a clinical trial. And the FDA will demand high standards of control and performance, eventually. “There are not many labs that can produce those high standards as in-house or lab-developed tests today. And even the companies currently in trials are not producing the improved performance level for these tests that we are talking about today, as being required for high-quality quantitative and reproducible companion diagnostics. Eventually, I am convinced we will have to do that. It’s just that it will take time to get there.”

The FDA can only approve what is brought to it, Dr. Rimm points out. And so a true, fully quantitative IHC-based assay has presumably never been submitted, or at least never been approved by the FDA. “What we’re seeing instead are the assays that the FDA has approved, which are well defined and rigorously submitted. However, the result is a closed system that we use, which may or may not accurately measure PD-L1 on the slide, depending upon preanalytic variables and individual laboratories’ methods.”

“So questions keep popping up. And I can only say that we, as pathologists, have the final responsibility to our patients. And while it may not be recommended and it may change in the future, right now lab-derived tests or LDTs may be more accurate than FDA-approved platforms.”

“If you think about it, in molecular diagnostics where I’m familiar with EFGR and BRAF and KRAS tests, in that testing setting, less than 25 percent of the labs that do that test actually use the FDA-approved test,” Dr. Rimm says. “The remainder of the labs do their own LDTs, including our labs here at Yale.”

It wouldn’t surprise him if the same thing happens for PD-L1. “I’m aware of at least two labs—and we probably will be the third—that devise our own LDT for PD-L1 testing using the standards I’ve discussed, using array-type controls to be sure that our levels are correct, and then using a scoring system that we derived.”

“We aren’t really in a position to know at the time that we receive a piece of lung cancer tissue whether the oncologist is going to use pembrolizumab, which requires a companion diagnostic, or nivolumab, or the other drugs, which may or may not require a companion diagnostic. So in that sense, we’re almost bound to use an LDT,” Dr. Rimm says, since his lab can’t actually run four different potentially incongruent, though FDA-approved, tests for PD-L1.

Until a truly quantitative approach is developed and submitted to the FDA and approved, Dr. Taylor believes we won’t see things changing. “The algorithms that currently are approved have been approved on the basis that they can produce a similar result to a consensus group of pathologists. So they’re only as good as the pathologists.”

“As Dr. Rimm has discussed, I actually believe we can get a much better result than the pathologists can get with their naked eye. We have to get away from comparing it to what we currently can do and start to try to construct a proper test, just like we did in the clinical lab 30 years ago when we automated the clinical lab,” Dr. Taylor says. “We need to automate anatomic pathology, including the sample preparation, the assay process, and the reading, all three together in a closed system. And we’re nibbling away at the edges of it. We’ll get there, but it’ll take some time.”

Dr. Rimm is skeptical that the diagnostics field has learned any lessons from HercepTest and the companion diagnostics world of almost 20 years ago. “The submissions to the FDA for PD-L1 look very similar to what was submitted in 1998 for the HercepTest, the companion diagnostic test for trastuzumab [Herceptin]. And that’s disappointing. I think that is 20-year-old technology and we can do better. But even if we want to use the 20- or 40-year-old DAB-based technology, we should still be standardizing it and having a mechanism for standardization and having defined thresholds.”

As future FDA submissions come in, Dr. Rimm hopes that “even if they’re not quantitated, they can be standardized as to where the thresholds occur, so that we can be sure we deliver the best possible care to patients. And in the interim, I think we, as pathologists, will have to do that standardization with an LDT to be sure we’re giving our best results.”

Dr. Taylor warns that there is only a limited number of labs in the country and in the world that will be able to produce these LDTs, because of the complexity. “The FDA has already said in a position paper that it believes it may have to regulate LDTs to some extent. And what that will mean is that in the validation process, your own LDT will start to approach what is required for an FDA-approved test. And most labs are in no position to be able to do that.”

“So I think we’re going to come to a blending here, all forced by companion diagnostics. This is in situ proteomics,” Dr. Taylor says. “It’s a new test, essentially. It’s not straightforward immunohistochemistry, but a new test. And I think the fluorescence approach that Dr. Rimm has used has a lot of advantages in relating signal to target in terms of figure out what the best test is and stop comparing it to the pathologists. We should compare it to the best assay we can produce.”

With respect to the PD-L1 problem, Dr. Rimm notes, “I would point out that there is a so-called ‘Blueprint’ for comparison of the different antibodies and the different FDA assays, or potentially FDA-submitted tests anyway, to see how equivalent they are.” Similarly, he adds, the National Comprehensive Cancer Network recently issued a press release describing a multi-institutional study to assess the FDA-approved assay but also including an LDT (the Cell Signaling antibody E1L3N using the Leica Bond staining platform).

He points to a newly published study by his group (McLaughlin J, et al. JAMA Oncol. 2016;2[1]:46–54), finding that objective determination of PD-L1 protein levels in non-small cell lung cancer reveals heterogeneity within tumors and prominent interassay variability or discordance. The authors concluded that future studies measuring PD-L1 quantitatively in patients treated with anti-PD-1 and anti PD-L1 therapies may better address the prognostic or predictive value of these biomarkers. With future rigorous studies, including tissues with known responses to anti-PD-1 and anti-PD-L1 therapies, researchers could determine the optimal assay, PD-L1 antibody, and the best cut point for PD-L1 positivity.

Other work that will probably come out in mid-2016 from Dr. Rimm’s group has shown that expression of PD-L1 is largely bimodal, he says. “That is, there’s a group of patients that express a lot, and then there’s another group of patients that expresses a little or none.”

So time will tell how PD-L1 will be scored. “But if you look at the data from the Merck study and their cut point of greater than 50 percent, or even the cut point from the AstraZeneca studies of greater than 25 percent, you’re really dichotomizing the population into patients who are truly PD-LI positive from patients who are negative or almost negative.”

“Of course, we don’t want to miss patients in that negative to almost-negative group who will respond,” Dr. Rimm says. “On the other hand, we probably will have fairly good specificity and sensitivity with the assay defined by Merck and Dako with 22C3 as was recently published” (Robert C, et al. N Engl J Med. 2015;372[26]:2521–2532).

Many difficulties lie ahead, as researchers try to weigh the merits of different drugs with different approved tests on different platforms, involving different antibodies, Dr. Taylor says. “Does the lab try to set up four different PD-L1s, and if we only have one platform and not another, what do we do about that?” He thinks the tests may often be sent out to larger reference labs or academic centers as a result.

Dr. Rimm confirms that his own lab’s LDT—although literally thousands of PD-L1 tests have been conducted using it—is not yet up and running in the Yale CLIA laboratory, and in the meantime the IHC slides are being sent out to a commercial vendor.

Eventually, Dr. Taylor believes, the pressure of these dilemmas will lead the diagnostics field to develop an immunoassay on tissue sections. “We’ve never been forced to do that before, but once we are, that will produce a huge change in diagnostic capability and research capability.”

Anti–PD-1/PD-L1 therapy of human cancer: past, present, and future

Lieping Chen and

The cDNA of programmed cell death 1 (PD-1) was isolated in 1992 from a murine T cell hybridoma and a hematopoietic progenitor cell line undergoing apoptosis (1). Genetic ablation studies showed that deficiencies in PD-1 resulted in different autoimmune phenotypes in various mouse strains (2, 3). PD-1–deficient allogeneic T cells with transgenic T cell receptors exhibited augmented responses to alloantigens, indicating that the PD-1 on T cells plays a negative regulatory role in response to antigen (2).

Several studies contributed to the discovery of the molecules that interact with PD-1. In 1999, the B7 homolog 1 (B7-H1, also called programmed death ligand-1 [PD-L1]) was identified independently from PD-1 using molecular cloning and human expressed-sequence tag database searches based on its homology with B7 family molecules, and it was shown that PD-L1 acts as an inhibitor of human T cell responses in vitro (4). These two independent lines of study merged one year later when Freeman, Wood, and Honjo’s laboratories showed that PD-L1 is a binding and functional partner of PD-1 (5). Next, it was determined that PD-L1–deficient mice (Pdl1 KO mice) were prone to autoimmune diseases, although this strain of mice did not spontaneously develop such diseases (6). It became clear later that the PD-L1/PD-1 interaction plays a dominant role in the suppression of T cell responses in vivo, especially in the tumor microenvironment (7, 8).

In addition to PD-L1, another PD-1 ligand called B7-DC (also known as PD-L2) was also identified by the laboratories of Pardoll (9) and Freeman (10). This PD-1 ligand was found to be selectively expressed on DCs and delivered its suppressive signal by binding PD-1. Mutagenesis studies of PD-L1 and PD-L2 molecules guided by molecular modeling revealed that both PD-L1 and PD-L2 could interact with other molecules in addition to PD-1 and suggested that these interactions had distinct functions (11). The functional predictions from these mutagenesis studies were later confirmed when PD-L1 was found to interact with CD80 on activated T cells to mediate an inhibitory signal (12, 13). This finding came as a surprise because CD80 had been previously identified as a functional ligand for CD28 and cytotoxic T lymphocyte antigen-4 (CTLA-4) (14, 15). PD-L2 was also found to interact with repulsive guidance molecule family member b (RGMb), a molecule that is highly enriched in lung macrophages and may be required for induction of respiratory tolerance (16). With at least five interacting molecules in the PD-1/PD-L1 pathway (referred to as the PD pathway) (Figure 1), further studies will be required to understand the relative contributions of these molecules during activation or suppression of T cells.

The PD pathway. The PD pathway has at least 5 interacting molecules. PD-...

The PD pathway.

The PD pathway has at least 5 interacting molecules. PD-L1 and PD-L2, with different expression patterns, were identified as ligands of PD-1, and the interaction of PD-L1 or PD-L2 with PD-1 may induce T cell suppression. PD-L1 was found to interact with B7-1 (CD80) on activated T cells and inhibit T cell activity. PD-L2 has a second receptor, RGMb; initially, this interaction activates T cells, but it subsequently induces respiratory tolerance. PD-L1 on tumor cells can also act as a receptor, and the signal delivered from PD-1 on T cells can protect tumor cells from cytotoxic lysis.

The discovery of the PD pathway did not automatically justify its application to cancer therapy, especially after the initial PD-1–deficient mouse studies, which suggested that PD-1 deficiency increases the incidence of autoimmune diseases (2, 3). In our initial work to characterize PD-L1 and its function, PDL1 mRNA was found to be broadly expressed in various tissues (17). However, normal human tissues seldom express PD-L1 protein on their cell surface, with the exception of tonsil (17), placenta (18), and a small fraction of macrophage-like cells in lung and liver (17), suggesting that, under normal physiological conditions, PDL1 mRNA is under tight posttranscriptional regulation. In sharp contrast, PD-L1 protein is abundantly expressed on the cell surface in various human cancers, as indicated by immunohistochemistry in frozen human tumor sections. Additionally, the pattern of PD-L1 expression was found to be focal rather than diffuse in most human cancers (17). In fact, the majority of in vitro–cultured tumor lines of both human and mouse origin are PD-L1–negative on the cell surface, despite overwhelming PD-L1 signal in specimens that are freshly isolated from patients with cancer (17, 19). This discrepancy was explained by the finding that IFN-γ upregulates PD-L1 on the cell surface of normal tissues and in various tumor lines (7, 17, 19). It was widely thought that IFN-γ typically promotes, rather than suppresses, T cell responses by stimulating antigen processing and presentation machinery (20, 21); therefore, the role of IFN-γ in downregulating immune responses in the tumor microenvironment via induction of PD-L1 was not well accepted until more recently. This finding is vital to our current understanding of the unique immunology that takes place in the tumor microenvironment and provided an important clue that led to the “adaptive resistance” hypothesis (see below) that explains this pathway’s mechanism of action to evade tumor immunity.

Due to the lack of cell surface expression of PD-L1 on most cultured tumor lines, it is necessary to reexpress PD-L1 on the surface using transfection to recapitulate the effects of cell surface PD-L1 in human cancers and to create models to study how tumor-associated PD-L1 interacts with immune cells. We now know that cancer cells and other cells in the tumor microenvironment can upregulate the expression of PD-L1 after encountering T cells, mostly via IFN-γ, which may make the transfection-mediated expression of PD-L1 unnecessary in some tumor models. Nevertheless, our results demonstrated that PD-L1+ human tumor cells could eliminate activated effector T cells (Teffs) via apoptosis in coculture systems, and this effect could be blocked by inclusion of an anti-human PD-L1 mAb (clone 2H1). Next, we generated a hamster mAb (clone 10B5) against mouse PD-L1 to block its interaction with T cells and test its role in tumor immunity in vitro and in vivo. We demonstrated that progressive growth of PD-L1+ murine P815 tumors in syngeneic mice could be suppressed using anti–PD-L1 mAb (17). Altogether, these studies represented the initial attempt at using mAb to block the PD pathway as an approach for cancer therapy. These proof-of-concept studies (17) were confirmed by several subsequent studies. A study from Nagahiro Minato’s laboratory showed that the J558L mouse myeloma line constitutively expressed high levels of cell surface PD-L1 and the growth of these cells in syngeneic BALB/c mice could be partially suppressed by administering anti–PD-L1 mAb (22). Our laboratory showed that regression of progressively growing squamous cell carcinomas in syngeneic mice could also be suppressed using a combination of adoptively transferred tumor-draining lymphocytes and anti–PD-L1 mAb (23). Furthermore, the Zou laboratory demonstrated that ovarian cancer–infiltrating human T cells could be activated in vitro using DCs, which showed enhanced activity in the presence of anti–PD-L1 mAb; upon transfer, these cells could eliminate established human ovarian cancers in immune-deficient mice (24). These early studies established the concept that the PD pathway could be used by tumors to escape immune attack in the tumor microenvironment. More importantly, these studies built a solid foundation for the development of anti-PD therapy for the treatment of human cancers.  …..

Anti-PD therapy has taken center stage in immunotherapies for human cancer, especially for solid tumors. This therapy is distinct from the prior immune therapeutic agents, which primarily boost systemic immune responses or generate de novo immunity against cancer; instead, anti-PD therapy modulates immune responses at the tumor site, targets tumor-induced immune defects, and repairs ongoing immune responses. While the clinical success of anti-PD therapy for the treatment of a variety of human cancers has validated this approach, we are still learning from this pathway and the associated immune responses, which will aid in the discovery and design of new clinically applicable approaches in cancer immunotherapy.


PD-1 Pathway Inhibitors: Changing the Landscape of Cancer Immunotherapy

Dawn E. Dolan, PharmD, and Shilpa Gupta, MD

Background: Immunotherapeutic approaches to treating cancer have been evaluated during the last few decades with limited success. An understanding of the checkpoint signaling pathway involving the programmed death 1 (PD-1) receptor and its ligands (PD-L1/2) has clarified the role of these approaches in tumor-induced immune suppression and has been a critical advancement in immunotherapeutic drug development. Methods: A comprehensive literature review was performed to identify the available data on checkpoint inhibitors, with a focus on anti–PD-1 and anti–PD-L1 agents being tested in oncology. The search included Medline, PubMed, the registry, and abstracts from the American Society of Clinical Oncology meetings through April 2014. The effectiveness and safety of the available anti–PD-1 and anti–PD-L1 drugs are reviewed. Results: Tumors that express PD-L1 can often be aggressive and carry a poor prognosis. The anti–PD-1 and anti–PD-L1 agents have a good safety profile and have resulted in durable responses in a variety of cancers, including melanoma, kidney cancer, and lung cancer, even after stopping treatment. The scope of these agents is being evaluated in various other solid tumors and hematological malignancies, alone or in combination with other therapies, including other checkpoint inhibitors and targeted therapies, as well as cytotoxic chemotherapy. Conclusions: The PD-1/PD-L1 pathway in cancer is implicated in tumors escaping immune destruction and is a promising therapeutic target. The development of anti–PD-1 and anti–PD-L1 agents marks a new era in the treatment of cancer with immunotherapies. Early clinical experience has shown encouraging activity of these agents in a variety of tumors, and further results are eagerly awaited from completed and ongoing studies.


Role of PD-1/PD-L1 Pathway PD-1 is an immunoinhibitory receptor that belongs to the CD28 family and is expressed on T cells, B cells, monocytes, natural killer cells, and many tumor-infiltrating lymphocytes (TILs)10; it has 2 ligands that have been described (PD-L1 [B7H1] and PD-L2 [B7-DC]).11 Although PD-L1 is expressed on resting T cells, B cells, dendritic cells, macrophages, vascular endothelial cells, and pancreatic islet cells, PD-L2 expression is seen on macrophages and dendritic cells alone.10 Certain tumors have a higher expression of PD-L1.12 PD-L1 and L2 inhibit T-cell proliferation, cytokine production, and cell adhesion.13 PD-L2 controls immune T-cell activation in lymphoid organs, whereas PD-L1 appears to dampen T-cell function in peripheral tissues.14 PD-1 induction on activated T cells occurs in response to PD-L1 or L2 engagement and limits effector T-cell activity in peripheral organs and tissues during inflammation, thus preventing autoimmunity. This is a crucial step to protect against tissue damage when the immune system is activated in response to infection.15-17 Blocking this pathway in cancer can augment the antitumor immune response.18 Like the CTLA-4, the PD-1 pathway down-modulates Tcell responses by regulating overlapping signaling proteins that are part of the immune checkpoint pathway; however, they function slightly differently.14,16 Although the CTLA-4 focuses on regulating the activation of T cells, PD-1 regulates effector T-cell activity in peripheral tissues in response to infection or tumor progression.16 High levels of CTLA-4 and PD-1 are expressed on regulatory T cells and these regulatory T cells and have been shown to have immune inhibitory activity; thus, they are important for maintaining self-tolerance.16 The role of the PD-1 pathway in the interaction of tumor cells with the host immune response and the PD-L1 tumor cell expression may provide the basis for enhancing immune response through a blockade of this pathway.16 Drugs targeting the PD-1 pathway may provide antitumor immunity, especially in PD-L1 positive tumors. Various cancers, such as melanoma, hepatocellular carcinoma, glioblastoma, lung, kidney, breast, ovarian, pancreatic, and esophageal cancers, as well as hematological malignancies, have positive PD-L1 expression, and this expression has been correlated with poor prognosis.8,19 Melanoma and kidney cancer are prototypes of immunogenic tumors that have historically been known to respond to immunotherapeutic approaches with interferon alfa and interleukin 2. The CTLA-4 antibody ipilimumab is approved by the US Food and Drug Administration for use in melanoma. Clinical activity of drugs blocking the PD-1/PD-L1 pathway has been demonstrated in melanoma and kidney cancer.20-24 In patients with kidney cancer, tumor, TIL-associated PD-L1 expression, or both were associated with a 4.5-fold increased risk of mortality and lower cancer-specific survival rate, even after adjusting for stage, grade, and performance status.18,19,25,26 A correlation between PD-L1 expression and tumor growth has been described in patients with melanoma, providing the rationale for using drugs that block the PD-1/PD-L1 pathway.19,27 Historically, immunotherapy has been ineffective in cases of non–small-cell lung cancer (NSCLC), which has been thought to be a type of nonimmunogenic cancer; nevertheless, lung cancer can evade the immune system through various complex mechanisms.28 In patients with advanced lung cancer, the peripheral and tumor lymphocyte counts are decreased, while levels of regulatory T cells (CD4+), which help suppress tumor immune surveillance, have been found at higher levels.29-32 Immune checkpoint pathways involving the CTLA-4 or the PD-1/PD-L1 are involved in regulating T-cell responses, providing the rationale for blocking this pathway in NSCLC with antibodies against CTLA-4 and the PD-1/PD-L1 pathway.32 Triple negative breast cancer (TNBC) is an aggressive subset of breast cancer with limited treatment options. PD-L1 expression has been reported in patients with TNBC. When PD-L1 expression was evaluated in TILs, it correlated with higher grade and larger-sized tumors.33 Tumor PD-L1 expression also correlates with the infiltration of T-regulatory cells in TNBC, findings that suggest the role of PD-L1–expressing tumors and the PD-1/PD-L1–expressing TILs in regulating immune response in TNBC.34


Preclinical evidence exists for the complementary roles of CTLA-4 and PD-1 in regulating adaptive immunity, and this provides rationale for combining drugs targeting these pathways.44-46 Paradoxically and originally believed to be immunosuppressive, new data allow us to recognize that cytotoxic agents can antagonize immunosuppression in the tumor microenvironment, thus promoting immunity based on the concept that tumor cells die in multiple ways and that some forms of apoptosis may lead to an enhanced immune response.8,15 For example, nivolumab was combined with ipilimumab in a phase 1 trial of patients with advanced melanoma.46 The combination had a manageable safety profile and produced clinical activity in the majority of patients, with rapid and deep tumor regression seen in a large proportion of patients. Based on the results of this study, a phase 3 study is being undertaken to evaluate whether this combination is better than nivolumab alone in melanoma (NCT01844505). Several other early-phase studies are underway to explore combinations of various anti–PD-1/PD-L1 drugs with other therapies across a variety of tumor types (see Tables 1 and 2), possibly paving the way for future combination studies.


Development of PD-1/PD-L1 Pathway in Tumor Immune Microenvironment and Treatment for Non-Small Cell Lung Cancer

Jiabei He, Ying Hu, Mingming Hu & Baolan Li

Lung cancer is currently the leading cause of cancer-related death in worldwide, non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancers. Surgery, platinum-based chemotherapy, molecular targeted agents and radiotherapy are the main treatment of NSCLC. With the strategies of treatment constantly improving, the prognosis of NSCLC patients is not as good as before, new sort of treatments are needed to be exploited. Programmed death 1 (PD-1) and its ligand PD-L1 play a key role in tumor immune escape and the formation of tumor microenvironment, closely related with tumor generation and development. Blockading the PD-1/PD-L1 pathway could reverse the tumor microenvironment and enhance the endogenous antitumor immune responses. Utilizing the PD-1 and/or PD-L1 inhibitors has shown benefits in clinical trials of NSCLC. In this review, we discuss the basic principle of PD-1/PD-L1 pathway and its role in the tumorigenesis and development of NSCLC. The clinical development of PD-1/PD-L1 pathway inhibitors and the main problems in the present studies and the research direction in the future will also be discussed.

Lung cancer is currently the leading cause of cancer-related death in the worldwide. In China, the incidence and mortality of lung cancer is 5.357/10000, 4.557/10000 respectively, with nearly 600,000 new cases every year1. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancers, the early symptoms of patients with NSCLC are not very obvious, especially the peripheral lung cancer. Though the development of clinic diagnostic techniques, the majority of patients with NSCLC have been at advanced stage already as they are diagnosed. Surgery is the standard treatment in the early stages of NSCLC, for the advanced NSCLC, the first-line therapy is platinum-based chemotherapy. In recent years, patients with specific mutations may effectively be treated with molecular targeted agents initially. The prognosis of NSCLC patients is still not optimistic even though the projects of chemotherapy as well as radiotherapy are continuously ameliorating and the launch of new molecular targeted agents is never suspended, the five-year survival rate of NSCLC patients is barely more than 15%2, the new treatment is needed to be opened up.

During the last few decades, significant efforts of the interaction between immune system and immunotherapy to NSCLC have been acquired. Recent data have indicated that the lack of immunologic control is recognized as a hallmark of cancer currently. Programmed death-1 (PD-1) and its ligand PD-L1 play a key role in tumor immune escape and the formation of tumor microenvironment, closely related with tumor generation and development. Blockading the PD-1/PD-L1 pathway could reverse the tumor microenvironment and enhance the endogenous antitumor immune responses.

In this review, we will discuss the PD-1/PD-L1 pathway from the following aspects: the basic principle of PD-1/PD-L1 pathway and its role in the tumorigenesis and development of NSCLC, the clinical development of several anti-PD-1 and anti-PD-L1 drugs, including efficacy, toxicity, and application as single agent, or in combination with other therapies, the main problems in the present studies and the research direction in the future.


Cancer as a chronic, polygene and often inflammation-provoking disease, the mechanism of its emergence and progression is very complicated. There are many factors which impacted the development of the disease, such as: environmental factors, living habits, genetic mutations, dysfunction of the immune system and so on. At present, increasing evidence has revealed that the development and progression of tumor are accompanied by the formation of special tumor immune microenvironment. Tumor cells can escape the immune surveillance and disrupt immune checkpoint of host in several methods, therefore, to avoid the elimination from the host immune system. Human cancers contain a number of genetic and epigenetic changes, which can produce neoantigens that are potentially recognizable by the immune system3, thus trigger the body’s T cells immune response. The T cells of immune system recognize cancer cells as abnormal primarily, generate a population of cytotoxic T lymphocytes (CTLs) that can traffic to and infiltrate cancers wherever they reside, and specifically bind to and then kill cancer cells. Effective protective immunity against cancer depends on the coordination of CTLs4. Under normal physiological conditions, there is a balance status in the immune checkpoint molecule which makes the immune response of T cells keep a proper intensity and scope in order to minimize the damage to the surrounding normal tissue and avoid autoimmune reaction. However, numerous pathways are utilized by cancers to up-regulate the negative signals through cell surface molecules, thus inhibit T-cell activation or induce apoptosis and promote the progression and metastasis of cancers5. Increasing experiments and clinical trails show that immunotherapeutic approaches utilizing antagonistic antibodies to block checkpoint pathways, can release cancer inhibition and facilitate antitumor activity, so as to achieve the purpose of treating cancer.

The present research of immune checkpoint molecules are mainly focus on cytotoxic T lymphocyte-associated antigen 4 (CLTA-4), Programmed death-1 (PD-1) and its ligands PD-L1 (B7H1) and PD-L2 (B7-DC). CTLA-4 regulates T cell activity in the early stage predominantly, and PD-1 mainly limits the activity of T-cell in the tumor microenvironment at later stage of tumor growth6. Utilizing the immune checkpoint blockers to block the interactions between PD-1 and its ligands has shown benefits in clinical trials, including the NSCLC patients. PD-1 and its ligands have been rapidly established as the currently most important breakthrough targets in the development of effective immunotherapy.

PD-1/PD-L1 pathway and its expression, regulation

PD-1 is a type 1 trans-membrane protein that encoded by the PDCD1 gene7. It is a member of the extended CD28/CTLA-4 immunoglobulin family and one of the most important inhibitory co-receptors expressed by T cells. The structure of the PD-1 includes an extracellular IgV domain, a hydrophobic trans-membrane region and an intracellular domain. The intracellular tail includes separate potential phosphorylation sites that are located in the immune receptor tyrosine-based inhibitory motif (ITIM) and in the immunoreceptor tyrosine-based switch motif (ITSM). Mutagenetic researches indicated that the activated ITSM is essential for the PD-1 inhibitory effect on T cells8. PD-1 is expressed on T cells, B cells, monocytes, natural killer cells, dendritic cells and many tumor-infiltrating lymphocytes (TILs)9. In addition, the research of Francisoet et al. showed that PD-1 was also expressed on regulatory T cells (Treg) and able to facilitate the proliferation of Treg and restrain immune response10.

PD-1 has two ligands: PD-L1 (also named B7-H1; CD274) and PD-L2 (B7-DC; CD273), that are both coinhibitory. PD-L1 is expressed on resting T cells, B cells, dendritic cells, macrophage, vascular endothelial cells and pancreatic islet cells. PD-L2 expression is seen on macrophages and dendritic cells alone and is far less prevalent than PD-L1 across tumor types. It shows much more restricted expression because of its more restricted tissue distribution. Differences in expression patterns suggest distinct functions in immune regulation across distinct cell types. The restricted expression of PD-L2, largely to antigen-presenting cells, is consistent with a role in regulating T-cell priming or polarization, whereas broad distribution of PD-L1 suggests a more general role in protecting peripheral tissues from excessive inflammation.

PD-L1 is expressed in various types of cancers, especially in NSCLC11,12, melanoma, renal cell carcinoma, gastric cancer, hepatocellular as well as cutaneous and various leukemias, multiple myeloma and so on13,14,15. It is present in the cytoplasm and plasma membrane of cancer cells, but not all cancers or all cells within a cancer express PD-L116,17. The expression of PD-L1 is induced by multiple proinflammatory molecules, including types I and II IFN-γ, TNF-α, LPS, GM-CSF and VEGF, as well as the cytokines IL-10 and IL-4, with IFN-γ being the most potent inducer18,19. IFN-γ and TNF-α are produced by activated type 1 T cells, and GM-CSF and VEGF are produced by a variety of cancer stromal cells, the tumor microenvironment upregulates PD-L1 expression, thereby, promotes immune suppression. This latter effect is called “adaptive immune resistance”, because the tumor protects itself by inducing PD-L1 in response to IFN-γ produced by activated T cells17. PD-L1 is regulated by oncogenes, also known as the inherent immune resistance. PD-L1 expression is suppressed by the tumor suppressor gene: PTEN (phosphatase and tension homolog deleted on chromosome ten) gene. Cancer cells frequently contain mutated PTEN, which can activate the S6K1 gene, thus results in PD-L1 mRNA to polysomes increase greatly20, hence increases the translation of PD-L1 mRNA and plasma membrane expression of PD-L1. Parsa et al.’s research also demonstrated that neuroglioma with PTEN gene deletion regulate PD-L1 expression at the translational level by activating the PI3K/AKT downstream mTOR-S6K1signal pathway and, hence increase the PD-L1 expression21. Micro-RNAs also translationally regulate PD-L1 expression. MiRNA-513 is complementary to the 3′ untranslated region of PD-L1 and prevents PD-L1 mRNA translation22. In addition, a later literature reported that in the model of melanoma, the up-regulation of PD-L1 is closely related to the CD8 T cell, independent of regulation by oncogenes13. Noteworthily, the PD-L1 can bind to T cell expressed CD80, and at this point CD80 is a receptor instead of ligand to transmit negative regulated signals23.


PD-1/PD-L1 mediate immune suppression by multiple mechanisms

Like the CTLA-4, the PD-1/PD-L1 pathway down-modulates T-cell response by regulating overlapping signal proteins in the immune checkpoint pathway. However, their functions are slightly different24. The CTLA-4 focuses on regulating the activation of T cells, while PD-1 regulates effector T-cell activity in peripheral tissues in response to infection or tumor progression25. Tregs that high-level expression of PD-1 have been shown to have immune inhibitory activity, thus, they are important for maintaining self-tolerance. In normal human bodies, this is a crucial step to protect against tissue damage when the immune system is activated in response to infection26. However, in response to immune attack, cancer cells overexpress PD-L1 and PD-L2. They bind to PD-1 receptor on T cells, inhibiting the activation of T-cells, thus suppressing T-cell attack and inducing tumor immune escape. Thus tumor cells effectively form a suitable tumor microenvironment and continue to proliferate27. PD-1/PD-L1 pathway regulates immune suppression by multiple mechanisms, specific performance of the following: Induce apoptosis of activated T cells: PD-1 reduces T cell survival by impacting apoptotic genes. During T cell activation, CD28 ligation sustains T cell survival by driving expression of the antiapoptotic gene Bcl-xL. PD-1 prevents Bcl-xL expression by inhibiting PI3K activation, which is essential for upregulation of Bcl-xL. Early studies demonstrated that PD-L1+ murine and human tumor cells induce apoptosis of activated T cells and that antibody blocking of PD-L1 can decrease the apoptosis of T cells and facilitate antitumor immunity28,16. Facilitate T cell anergy and exhaustion: A research shown that the occurrence of tumor is associated with chronic infection29. According to the study of chronic infection, PD-1 overexpressed on the function exhausted T cells, blocking the PD-1/PD-L1 pathway can restore the proliferation, secretion and cytotoxicity30. In addition, later research demonstrated that the exhaustion of TILs in the tumor microenvironment is closely related to the PD-L1 expression of tumor cells, myeloid cells derived from tumor31. Enhance the function of regulatory T cells: PD-L1 can promote the generation of induced Tregs by down-regulating the mTOR, AKT, S6 and the phosphorylation of ERK2 and increasing PTEN, thus restrain the activity of effector T-cell32. Blocking the PD-1/PD-L1 pathway can increase the function of effector CD8 T-cell and inhibt the function of Tregs, bone marrow derived inhibition cells, thus enhance the anti-tumor response. Inhibit the proliferation of T cells: PD-1 ligation also prevents phosphorylation of PKC-theta, which is essential for IL-2 production33, and arrests T cells in the G1 phase, blocking proliferation. PD-1 mediates this effect by activating Smad3, a factor that arrests cycling34. Restrain impaired T cell activation and IL-2 production: PD-1/PD-L1 blocks the downstream signaling events triggered by Ag/MHC engagement of the TCR and co-stimulation through CD28, resulting in impaired T cell activation and IL-2 production. Signaling through the TCR requires phosphorylation of the tyrosine kinase ZAP70. PD-1 engagement reduces the phosphorylation of ZAP70 and, hence, inhibits downstream signaling events. In addition, signaling through PD-1 also prevents the conversion of functional CD8+ T effector memory cells into CD8+ central memory cells35 and, thus, reduces long-term immune memory that might protect against future metastatic disease. PD-L1 also promotes tumor progression by reversing signaling through CD80 into T cells. CD80-PD-L1 interactions restrain self-reactive T cells in an autoimmune setting36, therefore, their inhibition may facilitate antitumor immunity.

Researches on the mechanism of PD-1/PD-L1 pathway mediating immune escape are still ongoing, especially the mechanism of PD-L2 is still unclear. These researches provide the theoretical basis and research direction for the further immunotherapy targets research.


Anti-PD-1 antibodies


Nivolumab (BMS-936558, Brand name: Opdivo) is a human monoclonal IgG4 antibody that essentially lacks detectable antibody-dependent cellular cytotoxicity (ADCC). Inhibition by monoclonal antibody of PD-1 on CD8+ TILs within lung cancers can restore cytokine secretion and T-cell proliferation48. Results of a larger phase I study in 296 patients (236 patients evaluated) reported that the objective response (complete or partial responses) of patients with NSCLC was 18%. A total of 65% of responders had durable responses lasting for more than 1 year. Stable disease lasting 24 weeks was seen in patients with NSCLC. PD-L1expression was tested in 42 patients: 9 of 25(36%) patients whose PD-L1 expression positive were objectively response to PD-1 blockade treatment, while the remaining 17 nonresponsive patients were negative45.

In another early phase I trial of nivolumab49, an objective response was observed in 22 patients (17%; 95% CI, 11%–25%) in a dose-expansion cohort of 129 previously treated patients with advanced NSCLC. Six additional patients who had an unconventional immune-related response were not included. Moreover, the median duration of response was exceptional for 17 months. Although the median PFS in the cohort was 2.3 months and the median overall survival was 9.9 months, it seemed clear that those who responded had sustained benefit. Specifically, the 2-year overall survival rate was 24%, and many remained in remission after completing 96 weeks of continuous therapy.

Single-agent trials of nivolumab are planning or ongoing on NSCLC (NCT01721759, NCT02066636). In addition, there are clinical randomized trials which focus on the comparison of nivolumab and plain-based combination chemotherapy (NCT02041533, NCT01673867). In March 4, 2015, nivolumab was approved by the US Food and Drug Administration for treatment of patients with metastatic NSCLC (squamous cell carcinoma), when progression of their diseases during or after chemotherapy with platinum-based drugs.


Pembrolizumab (MK-3475) is a highly selective, humanized monoclonal antibody with activity against PD-1 that contains a mutation at C228P designed to prevent Fc-mediated ADCC. It is now in the clinical research phases for patients with advanced solid tumors. Its safety and efficacy were evaluated in a phase I clinical trial of KEYNOTE-001. The best response according of 38 cases of patients which initially accepted pembrolizumab 10 mg/kg q3wwas 21% (based on RECIST1.1 evaluation) and the median PFS of responder still has not reached until 62 weeks. The researchers also found that the antitumor activity of pembrolizumab was associated with the PD-L1expression44,50. The critical values of the expression of PD-L1 will be validated in 300 cases of patients which will soon been rolled into the study.

Clinical trial of pembrolizumab monotherapy is ongoing for patients with NSCLC (NCT01840579). Randomized trials comparing pembrolizumab to combination chemotherapy (NCT02142738) or single-agent docetaxel (NCT01905657) are ongoing in PD-L1 positive patients with NSCLC.

Pidilizumab (CT-011)

Pidilizumab is a humanized IgG-1K recombinant anti-PD-1 monoclonal antibody that has demonstrated antitumor activity in mouse cancer models. In a first-in-human phase I dose-escalation study in patients with only advanced hematologic cancers, there is no clinical trials of NSCLC presently51.


Anti-PD-L1 antibodies

Another therapeutic method based on the PD-1/PD-L1 pathway is by specific binding between antibody and PD-L1, thus preventing its activity. It was speculated that utilizing PD-L1 as therapeutic target maybe accompanied by less toxicity in part by modulating the immune response selectively in the tumor microenvironment. However, since PD-L2 expressed by tumor cells or some other tumor-associated molecules may play a role in mediating PD-1-expressing lymphocytes, it is conceivable that the magnitude of the anti-tumor immune response could also be blunted.


BMS-936559/MDX1105 is a fully humanized, high affinity, IgG4 monoclonal antibody that react specifically with PD-L1, thus inhibiting the binding of PD-L1 and PD-1, CD80 (which binds not only PD-L1 but also CTLA-4 and CD28). Initial results from a multicenter and dose-escalation phase I trial of 207 patients(including 75 cases of patients with NSCLC) showed durable tumor regression (objective response rate of 6%–17%) and prolonged stabilization of disease (12%–41% at 24weeks) in patients with advanced cancers, including NSCLC, melanoma and kidney cancer. In patients with NSCLC, there were five objective responses (in 4 patients with the nonsquamous subtype and 1 with the squamous subtype) at doses of 3 mg/kg and 10 mg/kg, with response rates of 8% and 16%, respectively. Six additional patients with NSCLC had stable disease lasting at least 24 weeks52.


MPDL3280A is a human IgG1 antibody that targets PD-L1. Its Fc component has been engineered to not activate antibody-dependent cell cytotoxicity. In a recently reported phase I study, a 21% response rate was noted in patients with metastatic melanoma, RCC or NSCLC53, including several patients who demonstrated shrinkage of tumor within a few days of initiating treatment.

Fifty-two patients were enrolled in an expansion cohort of the phase I trial of MPDL3280A, 62% of them were heavily pretreated NSCLC (≥3 lines of systemic therapy) and the ORR was 22%54. Analysis of biomarker data from archival tumor samples demonstrated a correlation between PD-L1 status and response and lack of progressive disease55.


MEDI4736 is a human IgG1 antibody that binds specifically to PD-L1, thus preventing PD-L1 binding to PD-1 and CD80. Interim results from a phase I trial reported no colitis or pneumonitis of any grade, with several durable remissions, including NSCLC patients56. An ongoing phase I dose-escalation study (NCT01693562) of MEDI-4736 in 26 patients, 4 partial responses (3 in patients with NSCLC and 1 with melanoma) were observed and 5 additional patients exhibited lesser degrees of tumor shrinkage. The disease control rate at 12 weeks was 46%57. Expansion cohorts was opened in Sep 2013, 10 mg/kg q2w dose. 151 patients was enrolled so far in the expansion cohorts, tumor shrinkage was reported as early as the first assessment at 6 weeks and among the 13 patients with NSCLC, responses were sustained at 10 or more to 14.9 or more months58. In the NSCLC expansion cohort, the response rate was 16% in 58 evaluable patients and the disease control rate at 12 weeks was 35% with responses seen in all histologic subtypes as well as in a smaller proportion of PD-L1- tumors.

On the basis of the favorable toxicity profile and promising activity in a heavily pretreated NSCLC population, a global Phase III placebo controlled trial using the 10 mg/kg biweekly dose has been launched in Stage III patients who have not progressed following chemo-radiation (NCT02125461). The primary outcome measures are overall survival and progression-free survival.


AMP-224 was a B7-DC-Fc fusion protein which can block the PD-1 receptor competitively59. Some NSCLC patients were included in a first-in-man phase I trial of this fusion protein drug. A dose-dependent reduction in PD-1-high TILs was observed at 4 hours and 2 weeks after drug administration60.


A variety of approaches for combining PD-1/PD-L1 pathway inhibitors with other therapeutic methods have been explored over the past few years in an effort to offer more feasible therapeutic options for clinic to improve treatment outcomes. Approaches have included combinations with other immune checkpoint inhibitors, immunostimulatory cytokines (e.g. IFN-y) cytotoxic chemotherapy, platinum-based chemotherapy, radiotherapy, anti-angiogenic inhibitors, tumor vaccine and small-molecule molecularly targeted therapies many with promising results61,62. Studies indicated that PD-1/PD-L1 pathway inhibitors were most effective when combined with treatments that activating the immune system63.

Preclinical evidence exists for the complementary roles of CTLA-4 and PD-1 in regulating adaptive immunity, and this provides rationale for combining drugs targeting these pathways. In a Phase I study in 46 chemotherapy-naive patients with NSCLC, four cohorts of patients received ipilimumab (3 mg/kg) plus nivolumab for four cycles followed by nivolumab 3 mg/kg intravenously every 2 weeks. The ORR was 22% and did not correlate with PD-L1 status64.

In another Phase I study, 56 patients with advanced NSCLC were assigned based on histology to four cohorts to receive nivolumab (5–10 mg/kg) intravenously every 3 weeks plus one of four concurrent standard “platinum doublet” chemotherapy regimens. No dose de-escalation was required for dose-limiting toxicity. The ORR was 33–50% across arms and the 1-year OS rates were promising at 59–87%65.


The research of cancer immunotherapy provides a new wide space for cancer treatment (including NSCLC), and compared with other therapeutic method, immunotherapy has its unique advantages, such as: relative safety, effectivity, less and low grade side effect and so on. Especially with the discovery and continued in-depth study of PD-1/PD-L1 pathway in the immune regulation mechanism, many significative conclusions were reported. Data from many clinical trails suggest that some patients with NSCLC have been benefited from the drugs of anti-PD-1 and anti-PD-L1 already. However, summarized what have been discussed above, only a small fraction of patients benefit from PD-1 or PD-L1 inhibitors treatment. But with the continuous studies on biomarker and combined treatment in PD-1/PD-L1 pathway, new research progress will be acquired as well. We will make significant progress on treatment and in control of NSCLC.


Prospects for Targeting PD-1 and PD-L1 in Various Tumor Types     

Table 1: Selected Anti–PD-1 and Anti–PD-L1 Antibodies
Table 2: Selected Adverse Events
Table 3: Selected Clinical Trials for Metastatic Melanoma
Table: 4 Selected Trials for Metastatic Renal Cell Carcinoma
Table 5: Selected Trials for Non–Small-Cell Lung Cancer (NSCLC )
Table 6: Selected Trials for Other Tumor Types

Immune checkpoints, such as programmed death ligand 1 (PD-L1) or its receptor, programmed death 1 (PD-1), appear to be Achilles’ heels for multiple tumor types. PD-L1 not only provides immune escape for tumor cells but also turns on the apoptosis switch on activated T cells. Therapies that block this interaction have demonstrated promising clinical activity in several tumor types. In this review, we will discuss the current status of several anti–PD-1 and anti–PD-L1 antibodies in clinical development and their direction for the future.

Several PD-1 and PD-L1 antibodies are in clinical development (Table 1). Overall, they are very well tolerated; most did not reach dose-limiting toxicity in their phase I studies. As listed in Table 2, no clinically significant difference in adverse event profiles has been seen between anti–PD-1 and anti–PD-L1 antibodies. Slightly higher rates of infusion reactions (11%) were observed with BMS-936559 (anti–PD-L1) than with BMS-96558 (nivolumab). In an early stage of a nivolumab phase I study, there was concern about fatal pneumonitis.[7] It has been hypothesized that PD-1 interaction with PD-L2 expressed on the normal parenchymal cells of lung and kidney provides unique negative signaling that prevents autoimmunity.[8] Thus, anti–PD-1 antibody blockage of such an interaction may remove this inhibition, allowing autoimmune pneumonitis or nephritis. Anti–PD-L1 antibody, however, would theoretically leave PD-1–PD-L2 interaction intact, preventing the autoimmunity caused by PD-L2 blockade. With implementation of an algorithm to detect early signs of pneumonitis and other immune-related adverse events, many of these side effects have become manageable. However, it does require discerning clinical attention to detect potentially fatal side effects. In terms of antitumor activity, both anti–PD-1 and anti–PD-L1 antibodies have shown responses in overlapping multiple tumor types. Although limited to a fraction of patients, most responses, when observed, were rapid and durable.

– See more at:


Immune Checkpoint Blockade in Cancer Therapy

Michael A. PostowMargaret K. Callahan and Jedd D. Wolchok

Immunologic checkpoint blockade with antibodies that target cytotoxic T lymphocyte–associated antigen 4 (CTLA-4) and the programmed cell death protein 1 pathway (PD-1/PD-L1) have demonstrated promise in a variety of malignancies. Ipilimumab (CTLA-4) and pembrolizumab (PD-1) are approved by the US Food and Drug Administration for the treatment of advanced melanoma, and additional regulatory approvals are expected across the oncologic spectrum for a variety of other agents that target these pathways. Treatment with both CTLA-4 and PD-1/PD-L1 blockade is associated with a unique pattern of adverse events called immune-related adverse events, and occasionally, unusual kinetics of tumor response are seen. Combination approaches involving CTLA-4 and PD-1/PD-L1 blockade are being investigated to determine whether they enhance the efficacy of either approach alone. Principles learned during the development of CTLA-4 and PD-1/PD-L1 approaches will likely be used as new immunologic checkpoint blocking antibodies begin clinical investigation.

CTLA-4 was the first immune checkpoint receptor to be clinically targeted (Fig 1) Normally, after T-cell activation, CTLA-4 is upregulated on the plasma membrane where it functions to downregulate T-cell function through a variety of mechanisms, including preventing costimulation by outcompeting CD28 for its ligand, B7, and also by inducing T-cell cycle arrest.15 Through these mechanisms and others, CTLA-4 has an essential role in maintaining normal immunologic homeostasis, as evidenced by the fact that mice deficient in CTLA-4 die from fatal lymphoproliferation.6,7 Recognizing the role of CTLA-4 as a negative regulator of immunity, investigators led studies demonstrating that antibody blockade of CTLA-4 could result in antitumor immunity in preclinical models.8,9

Fig 1.


Article SOURCE:

Fig 1.

The cytotoxic T lymphocyte–associated antigen 4 (CTLA-4) immunologic checkpoint. T-cell activation requires antigen presentation in the context of a major histocompatibility complex (MHC) molecule in addition to the costimulatory signal achieved when B7 on an antigen-presenting cell (dendritic cell shown) interacts with CD28 on a T cell. Early after activation, to maintain immunologic homeostasis, CTLA-4 is translocated to the plasma membrane where it downregulates the function of T cells.

On the basis of this preclinical rationale, two antibodies targeting CTLA-4, ipilimumab (Bristol-Myers Squibb, Princeton, NJ) and tremelimumab (formerly Pfizer, currently MedImmune/AstraZeneca, Wilmington, DE), entered clinical development. Early reports of both agents showed durable clinical responses in some patients.1012Unfortunately, despite a proportion of patients experiencing a durable response, tremelimumab did not statistically significantly improve overall survival, which led to a negative phase III study comparing tremelimumab to dacarbazine/temozolomide in patients with advanced melanoma.13 It is possible that the lack of an overall survival benefit was a result of the crossover of patients treated with chemotherapy to an expanded access ipilimumab program or a result of the dosing or scheduling considerations of tremelimumab.

Ipilimumab, however, was successful in improving overall survival in two phase III studies involving patients with advanced melanoma.14,15 Although the median overall survival was only improved by several months in each of these studies, landmark survival after treatment initiation favored ipilimumab; in the first phase III study, 18% of patients were alive after 2 years compared with 5% of patients who received the control treatment of gp100 vaccination.14 More recently reported pooled data from clinical trials of ipilimumab confirm that approximately 20% of patients will have long-term survival of at least 3 years after ipilimumab therapy, with the longest reported survival reaching 10 years.1618

For patients with other malignancies, CTLA-4 antibody therapy has also shown some benefits. Ipilimumab, in combination with carboplatin and paclitaxel in a phased treatment schedule, showed improved progression-free survival compared with carboplatin and paclitaxel alone for patients with non–small-cell lung cancer.19Several patients with pancreatic cancer had declines in CA 19-9 when ipilimumab was given with GVAX (Aduro, Berkeley, CA),20and ipilimumab has also resulted in responses in patients with prostate cancer.21 Unfortunately, a phase III study in patients with castrate-resistant prostate cancer who experienced progression on docetaxel chemotherapy demonstrated that after radiotherapy, ipilimumab did not improve overall survival compared with placebo.22 Although this study is felt to have been a negative study, ipilimumab may have conferred a benefit to patients with favorable prognostic features, such as the absence of visceral metastases, but this requires further study. Another CTLA-4–blocking antibody, tremelimumab, has shown responses in patients with mesothelioma, and ongoing trials are under way.23

CTLA-4 blockade has also been administered together with other immunologic agents, such as the indoleamine 2,3-dioxygenase inhibitor INCB024360,106 the oncolytic virus talimogene laherparepvec,107 and granulocyte-macrophage colony-stimulating factor,108 with encouraging early results. We expect subsequent studies involving engineered T-cell–based therapies and checkpoint blockade.

Other promising data involve CTLA-4 combinations with PD-1 blockade. A phase I study of ipilimumab and nivolumab in patients with melanoma resulted in a high durable response rate and impressive overall survival compared with historical data.109,110Although the most recently reported grade 3 or 4 toxicity rate in patients with melanoma was 64%, which is higher than either ipilimumab or nivolumab individually,111 the vast majority of these irAEs were asymptomatic laboratory abnormalities of unclear clinical consequence. For example, elevations in amylase or lipase were reported in 21% of patients, none of whom met clinical criteria for a diagnosis of pancreatitis. The rate of grade 3 or 4 diarrhea was 7%, which is approximately similar to the rate of grade 3 or 4 diarrhea with ipilimumab monotherapy at 3 mg/kg. Whether ipilimumab and nivolumab improve overall survival compared with either nivolumab or ipilimumab alone remains the subject of an ongoing phase III randomized trial, and investigations of the combination of ipilimumab and nivolumab (and tremelimumab and MEDI4736) are ongoing in many other cancers.

Immunotherapy with checkpoint-blocking antibodies targeting CTLA-4 and PD-1/PD-L1 has improved the outlook for patients with a variety of malignancies. Despite the promise of this approach, many questions remain, such as the optimal management of irAEs and how best to evaluate combination approaches to determine whether they will increase the efficacy of CTLA-4 or PD-1/PD-L1 blockade alone. Themes from the experience with CTLA-4 and PD-1/PD-L1 will likely be relevant for investigations of novel immunologic checkpoints in the future.

This is a very important article, Dr. Larry.

It fits so beautiful with our work on Molecules in Development Table.

Thank you


This image depicts the process of metastasis in a mouse tumor, where tumor cells (green) have helped to reorganize the collagen into aligned fibers (blue) that provide the structural support for motility. This helps the tumor cells to enter blood vessels (red), ultimately leading to the formation of metastases in other organs.

This image depicts the process of metastasis in a mouse tumor, where tumor cells (green) have helped to reorganize the collagen into aligned fibers (blue) that provide the structural support for motility. This helps the tumor cells to enter blood vessels (red), ultimately leading to the formation of metastases in other organs.  Image: Madeleine Oudin and Jeff Wyckoff

Paving the way for metastasis

Cancer cells remodel their environment to make it easier to reach nearby blood vessels.

Anne Trafton | MIT News Office     March 15, 2016


A new study from MIT reveals how cancer cells take some of their first steps away from their original tumor sites. This spread, known as metastasis, is responsible for 90 percent of cancer deaths.

Studying mice, the researchers found that cancer cells with a particular version of the Mena protein, called MenaINV (invasive), are able to remodel their environment to make it easier for them to migrate into blood vessels and spread through the body. They also showed that high levels of this protein are correlated with metastasis and earlier deaths among breast cancer patients.

Finding a way to block this protein could help to prevent metastasis, says Frank Gertler, an MIT professor of biology and a member of the Koch Institute for Integrative Cancer Research.

“That’s something that I think would be very promising, because we know that when we genetically remove MenaINV, the tumors become nonmetastatic,” says Gertler, who is the senior author of a paper describing the findings in the journal Cancer Discovery.

Madeleine Oudin, a postdoc at the Koch Institute, is the paper’s lead author.

On the move

For cancer cells to metastasize, they must first become mobile and then crawl through the surrounding tissue to reach a blood vessel. In the new study, the MIT team found that cancer cells follow the trail of fibronectin, a protein that is part of the “extracellular matrix” that provides support for surrounding cells. Fibronectin is found in particularly high concentrations around the edges of tumors and near blood vessels.

“Cancer cells within a tumor environment are constantly faced with differences in fibronectin concentrations, and they need to be able to move from low to high concentrations to reach the blood vessels,” Oudin says.

MenaINV, an alternative form of the normal Mena protein, is key to this process. MenaINV includes a segment not found in the normal version, and this makes it bind more strongly to a receptor known as alpha-5 integrin, which is found on the surfaces of tumor cells and nearby supporting cells, and recognizes fibronectin.

When MenaINV attaches to this receptor, it promotes the binding of fibronectin to the same receptors. Fibronectin is normally a tangled protein, but when it binds to cell surfaces, it gets stretched out into long bundles. This stimulates the organization of collagen, another extracellular matrix protein, into stiff fibrils that radiate from the edges of the tumor.

This pattern, which is typically seen in tumors that are more aggressive, essentially paves the way for tumor cells to move toward blood vessels.

“If you have curly, coiled collagen, that’s associated with a good outcome, but if it gets realigned into these really straight long fibers, that provides highways for these cells to migrate on,” Oudin says.

In studies of mice, cells with the invasive form of Mena were better able to recognize and crawl toward higher concentrations of fibronectin, moving along the collagen pathways, while cells without MenaINV did not move toward the higher concentrations.

Predicting metastasis

The researchers also looked at data from breast cancer patients and found that high levels of MenaINV and fibronectin are associated with metastasis and earlier death. However, there was no link between the normal version of Mena and earlier death.

Gertler’s lab had previously developed antibodies that can detect the normal and invasive forms of Mena, which are now being developed for testing patient biopsy samples. Such tests could help doctors to determine whether a patient’s tumor is likely to spread or not, and possibly to guide the patient’s treatment. In addition, scientists may be able to develop drugs that inhibit MenaINV, which could be useful for treating cancer or preventing it from metastasizing.

The researchers now hope to study how MenaINV may contribute to other types of cancers. Preliminary studies suggest that it plays a similar role in lung and colon cancers as that seen in breast cancer. They are also investigating how the choice between the two forms of the Mena protein is regulated, and how other proteins found in the extracellular matrix might contribute to cancer cell migration.

Facilitating Tumor Cell Migration

Researchers identify a modified form of a migration-regulating protein in cancer cells that remodels the tumor microenvironment to promote metastasis.
By Catherine Offord | March 16, 2016

Emerging evidence suggests that metastasis—the spread of cancer from one organ or tissue to another—is aided by a significant remodeling of the cancer cells’ surroundings. Now, researchers at MIT have made progress toward understanding the mechanisms involved in this process by highlighting the role of a protein that reorganizes the tumor’s extracellular matrix to facilitate cellular migration into blood vessels. The findings were published yesterday (March 15) in Cancer Discovery.

Using a mouse model, the team showed that a cancer-cell-expressed protein called MenaINV—a mutated, “invasive” form of the cell-migration-modulator Mena—binds more strongly than its normal equivalent to a receptor on tumor and nearby support cells. The binding rearranges fibronectin in the tumor microenvironment, which in turn triggers the reorganization of collagen in the extracellular matrix into linear fibers radiating from the tumor.

This collagen restructuring is key in facilitating the migration of tumor cells to the blood vessels, from where they can disseminate throughout the body.

Tumor cell-driven extracellular matrix remodeling enables haptotaxis during metastatic progression

Madeleine J. Oudin1Oliver Jonas1Tatsiana Kosciuk1Liliane C. Broye1Bruna C. Guido1Jeff Wyckoff1, …., James E. Bear2 and Frank B. Gertler1,*
Cancer Discov CD-15-1183  Jan 25, 2016

Fibronectin (FN) is a major component of the tumor microenvironment, but its role in promoting metastasis is incompletely understood. Here we show that FN gradients elicit directional movement of breast cancer cells, in vitro and in vivo. Haptotaxis on FN gradients requires direct interaction between α5β1 integrin and Mena, an actin regulator, and involves increases in focal complex signaling and tumor-cell-mediated extracellular matrix (ECM) remodeling. Compared to Mena, higher levels of the pro-metastatic MenaINV isoform associate with α5, which enables 3D haptotaxis of tumor cells towards the high FN concentrations typically present in perivascular space and in the periphery of breast tumor tissue. MenaINV and FN levels were correlated in two breast cancer cohorts, and high levels of MenaINV were significantly associated with increased tumor recurrence as well as decreased patient survival. Our results identify a novel tumor-cell-intrinsic mechanism that promotes metastasis through ECM remodeling and ECM guided directional migration.


Researchers Find Link Between Death of Tumor-support Cells and Cancer Metastasis       Fri, 02/19/2016

The images show tumors that have metastasized to the lungs (image b) and bones (image d) in mice that had CAFs eliminated after 10 days. (Credit: Biju Parekkadan, Massachusetts General Hospital)

The images show tumors that have metastasized to the lungs (image b) and bones (image d) in mice that had CAFs eliminated after 10 days. (Credit: Biju Parekkadan, Massachusetts General Hospital)

Researchers have discovered that eliminating cells thought to aid tumor growth did not slow or halt the growth of cancer tumors. In fact, when the cancer-associated fibroblasts (CAFs), were eliminated after 10 days, the risk of metastasis of the primary tumor to the lungs and bones of mice increased dramatically. Scientists used bioengineered CAFs equipped with genes that caused those cells to self-destruct at defined moments in tumor progression. The study, published in Scientific Reports on Feb. 19, was conducted by researchers funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at Massachusetts General Hospital (MGH). NIBIB is part of the National Institutes of Health.

What causes cancer to grow and metastasize is not well understood by scientists. CAFs are thought to be fibroblast cells native to the body that cancer cells hijacks and use to sustain their growth. However, because fibroblasts are found throughout the human body, it can be difficult to follow and study cancer effects on these cells.

“This work underscores two important things in solving the puzzle that is cancer,” said Rosemarie Hunziker, Ph.D., program director for Tissue Engineering at NIBIB. “First, we are dealing with a complex disease with so many dimensions that we are really only just beginning to describe it.  Second, this approach shows the power of cell engineering — manipulating a key cell in the cancer environment has led to a significant new understanding of how cancer grows and how it might be controlled in the future.”

Biju Parekkadan, Ph.D., assistant professor of surgery and bioengineering at MGH, and his team designed an experiment with the goal of better understanding the cellular environment in which tumors exist (called tumor microenvironment or TME), and the role of CAFs in tumor growth. In an effort to understand whether targeting CAFs could limit the growth of breast cancer tumors implanted in mice, they bioengineered CAFs with a genetic “kill switch.” The cells were designed to die when exposed to a compound that was not toxic to the surrounding cells.

Parekkadan and his team chose two different stages of tumor growth in which the CAFs were killed off after the tumor was implanted. When the CAFs were eliminated on the third or fourth day, they found no major difference in tumor growth or risk of metastasis compared with the tumors where the CAFs remained. However, there was an increase in tumor-associated macrophages — cells that have been associated with metastasis — in this early stage.

When the team waited to eliminate the CAFs until the 10th or 11th day, they discovered that in addition to the increase in macrophages, the cancer was more likely to spread to the lungs and bones of the mice. The unexpected results from this experiment could spur more research into the role of CAFs in cancer growth and metastasis.

More research may reveal whether or not there is a scientific basis for targeting CAFs for destruction — and if so, the awareness that timing matters when it comes to the response of the tumor. While neither treatment affected the growth of the initial tumor, it is important to understand that most cancer deaths result from metastases to vital organs rather than from the direct effects of the primary tumor.




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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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War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert

War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert

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

Is one of the world’s most prominent cancer researchers throwing in the towel on the War On Cancer? Not throwing in the towel, just reminding us that cancer is more complex than just a genetic disease, and in the process, giving kudos to those researchers who focus on non-genetic aspects of the disease (see Dr. Larry Bernstein’s article Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?).


National Public Radio (NPR) has been conducting an interview series with MIT cancer biology pioneer, founding member of the Whitehead Institute for Biomedical Research, and National Academy of Science member and National Medal of Science awardee Robert A. Weinberg, Ph.D., who co-discovered one of the first human oncogenes (Ras)[1], isolation of first tumor suppressor (Rb)[2], and first (with Dr. Bill Hahn) proved that cells could become tumorigenic after discrete genetic lesions[3].   In the latest NPR piece, Why The War On Cancer Hasn’t Been Won (seen on NPR’s blog by Richard Harris), Dr. Weinberg discusses a comment in an essay he wrote in the journal Cell[4], basically that, in recent years, cancer research may have focused too much on the genetic basis of cancer at the expense of multifaceted etiology of cancer, including the roles of metabolism, immunity, and physiology. Cancer is the second most cause of medically related deaths in the developed world. However, concerted efforts among most developed nations to eradicate the disease, such as increased government funding for cancer research and a mandated ‘war on cancer’ in the mid 70’s has translated into remarkable improvements in diagnosis, early detection, and cancer survival rates for many individual cancer. For example, survival rate for breast and colon cancer have improved dramatically over the last 40 years. In the UK, overall median survival times have improved from one year in 1972 to 5.8 years for patients diagnosed in 2007. In the US, the overall 5 year survival improved from 50% for all adult cancers and 62% for childhood cancer in 1972 to 68% and childhood cancer rate improved to 82% in 2007. However, for some cancers, including lung, brain, pancreatic and ovarian cancer, there has been little improvement in survival rates since the “war on cancer” has started.

(Other NPR interviews with Dr. Weinberg include How Does Cancer Spread Through The Body?)

As Weinberg said, in the 1950s, medical researchers saw cancer as “an extremely complicated process that needed to be described in hundreds, if not thousands of different ways,”. Then scientists tried to find a unifying principle, first focusing on viruses as the cause of cancer (for example rous sarcoma virus and read Dr. Gallo’s book on his early research on cancer, virology, and HIV in Virus Hunting: AIDS, Cancer & the Human Retrovirus: A Story of Scientific Discovery).

However (as the blog article goes on) “that idea was replaced by the notion that cancer is all about wayward genes.”

“The thought, at least in the early 1980s, was that were a small number of these mutant, cancer-causing oncogenes, and therefore that one could understand a whole disparate group of cancers simply by studying these mutant genes that seemed to be present in many of them,” Weinberg says. “And this gave the notion, the illusion over the ensuing years, that we would be able to understand the laws of cancer formation the way we understand, with some simplicity, the laws of physics, for example.”

According to Weinberg, this gene-directed unifying theory has given way as recent evidences point back once again to a multi-faceted view of cancer etiology.

But this is not a revolutionary or conflicting idea for Dr. Weinberg, being a recipient of the 2007 Otto Warburg Medal and focusing his latest research on complex systems such as angiogenesis, cell migration, and epithelial-stromal interactions.

In fact, it was both Dr. Weinberg and Dr. Bill Hanahan who formulated eight governing principles or Hallmarks of cancer:

  1. Maintaining Proliferative Signals
  2. Avoiding Immune Destruction
  3. Evading Growth Suppressors
  4. Resisting Cell Death
  5. Becoming Immortal
  6. Angiogenesis
  7. Deregulating Cellular Energy
  8. Activating Invasion and Metastasis

Taken together, these hallmarks represent the common features that tumors have, and may involve genetic or non-genetic (epigenetic) lesions … a multi-modal view of cancer that spans over time and across disciplines. As reviewed by both Dr. Larry Bernstein and me in the e-book Volume One: Cancer Biology and Genomics for Disease Diagnosis, each scientific discipline, whether the pharmacologist, toxicologist, virologist, molecular biologist, physiologist, or cell biologist has contributed greatly to our total understanding of this disease, each from their own unique perspective based on their discipline. This leads to a “multi-modal” view on cancer etiology and diagnosis, treatment. Many of the improvements in survival rates are a direct result of the massive increase in the knowledge of tumor biology obtained through ardent basic research. Breakthrough discoveries regarding oncogenes, cancer cell signaling, survival, and regulated death mechanisms, tumor immunology, genetics and molecular biology, biomarker research, and now nanotechnology and imaging, have directly led to the advances we now we in early detection, chemotherapy, personalized medicine, as well as new therapeutic modalities such as cancer vaccines and immunotherapies and combination chemotherapies. Molecular and personalized therapies such as trastuzumab and aromatase inhibitors for breast cancer, imatnib for CML and GIST related tumors, bevacizumab for advanced colorectal cancer have been a direct result of molecular discoveries into the nature of cancer. This then leads to an interesting question (one to be tackled in another post):

Would shifting focus less on cancer genome and back to cancer biology limit the progress we’ve made in personalized medicine?


In a 2012 post Genomics And Targets For The Treatment Of Cancer: Is Our New World Turning Into “Pharmageddon” Or Are We On The Threshold Of Great Discoveries? Dr. Leonard Lichtenfield, MD, Deputy Chief Medical Officer for the ACS, comments on issues regarding the changes which genomics and personalized strategy has on oncology drug development. As he notes, in the past, chemotherapy development was sort of ‘hit or miss’ and the dream and promise of genomics suggested an era of targeted therapy, where drug development was more ‘rational’ and targets were easily identifiable.

To quote his post

That was the dream, and there have been some successes–even apparent cures or long term control–with the used of targeted medicines with biologic drugs such as Gleevec®, Herceptin® and Avastin®. But I think it is fair to say that the progress and the impact hasn’t been quite what we thought it would be. Cancer has proven a wily foe, and every time we get answers to questions what we usually get are more questions that need more answers. The complexity of the cancer cell is enormous, and its adaptability and the genetic heterogeneity of even primary cancers (as recently reported in a research paper in the New England Journal of Medicine) has been surprising, if not (realistically) unexpected.


Indeed the complexity of a given patient’s cancer (especially solid tumors) with regard to its genetic and mutation landscape (heterogeneity) [please see post with interview with Dr. Swanton on tumor heterogeneity] has been at the forefront of many clinicians minds [see comments within the related post as well as notes from recent personalized medicine conferences which were covered live on this site including the PMWC15 and Harvard Personalized Medicine conference this past fall].

In addition, Dr. Lichtenfeld makes some interesting observations including:

  • A “pharmageddon” where drug development risks/costs exceed the reward so drug developers keep their ‘wallets shut’. For example even for targeted therapies it takes $12 billion US to develop a drug versus $2 billion years ago
  • Drugs are still drugs and failure in clinical trials is still a huge risk
  • “Eroom’s Law” (like “Moore’s Law” but opposite effect) – increasing costs with decreasing success
  • Limited market for drugs targeted to a select mutant; what he called “slice and dice”

The pros and cons of focusing solely on targeted therapeutic drug development versus using a systems biology approach was discussed at the 2013 Institute of Medicine’s national Cancer Policy Summit.

  • Andrea Califano, PhD – Precision Medicine predictions based on statistical associations where systems biology predictions based on a physical regulatory model
  • Spyro Mousses, PhD – open biomedical knowledge and private patient data should be combined to form systems oncology clearinghouse to form evolving network, linking drugs, genomic data, and evolving multiscalar models
  • Razelle Kurzrock, MD – What if every patient with metastatic disease is genomically unique? Problem with model of smaller trials (so-called N=1 studies) of genetically similar disease: drugs may not be easily acquired or re-purposed, and greater regulatory burdens

So, discoveries of oncogenes, tumor suppressors, mutant variants, high-end sequencing, and the genomics and bioinformatic era may have led to advent of targeted chemotherapies with genetically well-defined patient populations, a different focus in chemotherapy development

… but as long as we have the conversation open I have no fear of myopia within the field, and multiple viewpoints on origins and therapeutic strategies will continue to develop for years to come.


  1. Parada LF, Tabin CJ, Shih C, Weinberg RA: Human EJ bladder carcinoma oncogene is homologue of Harvey sarcoma virus ras gene. Nature 1982, 297(5866):474-478.
  2. Friend SH, Bernards R, Rogelj S, Weinberg RA, Rapaport JM, Albert DM, Dryja TP: A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature 1986, 323(6089):643-646.
  3. Hahn WC, Counter CM, Lundberg AS, Beijersbergen RL, Brooks MW, Weinberg RA: Creation of human tumour cells with defined genetic elements. Nature 1999, 400(6743):464-468.
  4. Weinberg RA: Coming full circle-from endless complexity to simplicity and back again. Cell 2014, 157(1):267-271.


Other posts on this site on The War on Cancer and Origins of Cancer include:


2013 Perspective on “War on Cancer” on December 23, 1971

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

World facing cancer ‘tidal wave’, warns WHO

2013 American Cancer Research Association Award for Outstanding Achievement in Chemistry in Cancer Research: Professor Alexander Levitzki

Genomics and Metabolomics Advances in Cancer

The Changing Economics of Cancer Medicine: Causes for the Vanishing of Independent Oncology Groups in the US

Cancer Research Pioneer, after 71 years of Immunology Lab Research, Herman Eisen, MD, MIT Professor Emeritus of Biology, dies at 96

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials

Articles on Cancer-Related Topic in Scientific Journal

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

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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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Loss of Gene Islands May Promote a Cancer Cell’s Survival, Proliferation and Evolution: A new Hypothesis (and second paper validating model) on Oncogenesis from the Elledge Laboratory

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

It is well established that a critical event in the transformation of a cell to the malignant state involves the mutation of hosts of oncogenes and tumor suppressor genes, which in turn, confer on a cell the inability to properly control its proliferation.    On a genomic scale, these mutations can result in gene amplifications, loss of heterozygosity (LOH), and epigenetic changes resulting in tumorigenesis.  The “two hit hypothesis”, proposed by Dr. Al Knudson of Fox Chase Cancer Center[1], proposes that two mutations in the same gene are required for tumorigenesis, initially proposed to explain the progression of retinoblastoma in children, indicating a recessive disease.

(Excerpts from a great article explaining the two-hit-hypothesis is given at the end of this post).

And, although many tumor genomes display haploinsufficeint tumor suppressor genes, and fit the two hit model quite nicely, recent data show that most tumors display hemizygous recurrent deletions within their genomes.  Tumors display numerous recurrent hemizygous focal deletions that seem to contain no known tumor suppressor genes. For instance a recent analysis of over three thousand tumors including breast, bladder, pancreatic, ovarian and gastric cancers averaged greater than 10 deletions/tumor and 82 regions of recurrent focal deletions,

It has been proposed these great number of hemizygous deletions may be a result of:

  • a recessive tumor suppressor gene requiring mutation or silencing of second allele
  • the mutation may recur as they are located in fragile sites (unstable genomic regions)
  • single-copy loss may provide selective advantage regardless of the other allele

Note: some definitions of hemizygosity are given below.  In general at any locus, each parental chromosome can have 3 deletion states:

  1. wild type
  2. large deletion
  3. small deletion

Hemizygous deletions only involve one allele, not both alleles which is unlike the classic tumor suppressor like TP53

To see if it is possible that only one mutated allele of a tumor suppressor gene may be a casual event for tumorigenesis, Dr. Nicole Solimini and colleagues, from Dr. Stephen Elledge’s lab at Harvard, proposed a hypothesis they termed the cancer gene island model, after analyzing the regions of these hemizygous deletions for cancer related genes[2].  Dr. Soliin and colleagues analyzed whole-genome sequence data for 526 tumors in the COSMIC database comparing to a list generated from the Cancer Gene Census for homozygous loss-of-function mutations (mutations which result in a termination codon or frame-shift mutation: {this produces a premature stop in the protein or an altered sequence leading to a nonfunctional protein}.

Results of this analysis revealed:

  1. although tumors have a wide range of deletions per tumor (most epithelial high grade like ovarian, bladder, pancreatic, and esophageal adenocarcinomas had 10-14 deletions per tumor
  2. and although tumors exhibited a wide range (2- 16 ) loss of function mutations
  3. ONLY 14 of 82 recurrent deletions contained a known tumor suppressor gene and was a low frequency event
  4. Most recurrent cancer deletions do not contain putative tumor suppressor genes.

Therefore, as the authors suggest, an alternate method to the two-hit hypothesis may account for a selective growth advantage for these types of deletions, defining these low frequency hemizygous mutations in two general classes

  1. STOP genes: suppressors of tumor growth and proliferation
  2. GO genes: growth enhancers and oncogenes

Identifying potential STOP genes

To identify the STOP and GO genes the authors performed a primary screen of an shRNA library in telomerase (hTERT) immortalized human mammary epithelial cells using increased PROLIFERATION as a screening endpoint to determine STOP genes and decreased proliferation and lethality (essential genes) to determine possible GO genes. An initial screen identified 3582 possible STOP genes.  Using further screens and higher stringency criteria which focused on:

  • Only genes which increased proliferation in independent triplicate screens
  • Validated by competition assays
  • Were enriched more than four fold in three independent shRNA screens

the authors were able to focus on and validate 878 genes to determine the molecular pathways involved in proliferation.

These genes were involved in cell cycle regulation, apoptosis, and autophagy (which will be discussed in further posts).

To further validate that these putative STOP genes are relevant in human cancer, the list of validated STOP genes found in the screen was compared to the list of loss-of-function mutations in the 526 tumors in the COSMIC databaseSurprisingly, the validated STOP gene list were significantly enriched for known and possibly NOVEL tumor suppressor genes and especially loss of function and deletion mutations but also clustered in gene deletions in cancer.  This not only validated the authors’ model system and method but suggests that hemizygous deletions in multiple STOP genes may contribute to tumorigenesis

as the function of the majority of STOP genes is to restrain tumorigenesis

A few key conclusions from this study offer strength to an alternative view of oncogenesis NAMELY:

  • Loss of multiple STOP genes per deletion optimize a cancer cell’s proliferative capacity
  • Cancer cells display an insignificant loss of GO genes, minimizing negative impacts on cellular fitness
  • Haploinsufficiency in multiple STOP genes can result in similar alteration of function similar to complete loss of both alleles of
  • Cancer evolution may result from selection of hemizygous loss of high number of STOP and low number of GO genes
  • Leads to a CANCER GENE ISLAND model where there is a clonal evolution of transformed cells due to selective pressures

A link to the supplemental data containing STOP and GO genes found in validation screens and KEGG analysis can be found at the following link:

A link to an interview with the authors, originally posted on Harvard’s site can be found here.

Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome; a new paper from the Elledge group in the journal Cell

A concern of the authors was the extent to which gene silencing could have on their model in tumors.  The validation of the model was performed in cancer cell lines and compared to tumor genome sequence in publicly available databases however a followup paper by the same group shows that haploinsufficiency contributes a greater impact on the cancer genome than these studies have suggested.

In a follow-up paper by the Elledge group in the journal Cell[3], Theresa Davoli and colleagues, after analyzing 8,200 tumor-normal pairs, show there are many more cancer driver genes than once had been predicted.  In addition, the distribution and potency of STOP genes, oncogenes, and essential genes (GO) contribute to the complex picture of aneuploidy seen in many sporadic tumors.  The authors proposed that, together with these and their previous findings, that haploinsufficiency plays a crucial role in shaping the cancer genome.

Hemizygosity and Haploinsufficiency

Below are a few definitions from Wikipedia:

Zygosity is the degree of similarity of the alleles for a trait in an organism.

Most eukaryotes have two matching sets of chromosomes; that is, they are diploid. Diploid organisms have the same loci on each of their two sets of homologous chromosomes, except that the sequences at these loci may differ between the two chromosomes in a matching pair and that a few chromosomes may be mismatched as part of a chromosomal sex-determination system. If both alleles of a diploid organism are the same, the organism is homozygous at that locus. If they are different, the organism is heterozygous at that locus. If one allele is missing, it is hemizygous, and, if both alleles are missing, it is nullizygous.

Haploinsufficiency occurs when a diploid organism has only a single functional copy of a gene (with the other copy inactivated by mutation) and the single functional copy does not produce enough of a gene product (typically a protein) to bring about a wild-type condition, leading to an abnormal or diseased state. It is responsible for some but not all autosomal dominant disorders.

Al Knudsen and The “Two-Hit Hypothesis” of Cancer

Excerpt from a Scientist article by Eugene Russo about Dr. Knudson’s Two hit Hypothesis;

for full article please follow the link–Hypothesis/

The “two-hit” hypothesis was, according to many, among the more significant milestones in that rapid evolution of biomedical science. The theory explains the relationship between the hereditary and nonhereditary, or sporadic, forms of retinoblastoma, a rare cancer affecting one in 20,000 children. Years prior to the age of gene cloning, Knudson’s 1971 paper proposed that individuals will develop cancer of the retina if they either inherit one mutated retinoblastoma (Rb) gene and incur a second mutation (possibly environmentally induced) after conception, or if they incur two mutations or hits after conception.3 If only one Rb gene functions normally, the cancer is suppressed. Knudson dubbed these preventive genes anti-oncogenes; other scientists renamed them tumor suppressors.

When first introduced, the “two-hit” hypothesis garnered more interest from geneticists than from cancer researchers. Cancer researchers thought “even if it’s right, it may not have much significance for the world of cancer,” Knudson recalls. “But I had been taught from the early days that very often we learn fundamental things from unusual cases.” Knudson’s initial motivation for the model: a desire to understand the relationship between nonhereditary forms of cancer and the much rarer hereditary forms. He also hoped to elucidate the mechanism by which common cancers, such as those of the breast, stomach, and colon, become more prevalent with age.

According to the then-accepted somatic mutation theory, the more mutations, the greater the risk of cancer. But this didn’t jibe with Knudson’s own studies on childhood cancers, which suggested that, in the case of cancers such as retinoblastoma, disease onset peaks in early childhood. Knudson set out to determine the smallest number of cancer-inducing events necessary to cause cancer and the role of these events in hereditary vs. nonhereditary cancers. Based on existing data on cancer cases and some mathematical deduction, Knudson came up with the “two-hit” hypothesis.

Not until 1986, when researchers at the Whitehead Institute for Biomedical Research in Cambridge, Mass., cloned the Rb gene, would there be solid evidence to back up Knudson’s pathogenesis paradigm.4 “Even with the cloning of the gene, it wasn’t clear how general it would be,” says Knudson. There are, it turns out, several two-hit lesions, including polyposis, neurofibromitosis, and basal cell carcinoma syndrome. Other cancers show only some correspondence with the two-hit model. In the case of Wilm’s tumor, for example, the model accounts for about 15 percent of the cancer incidence; the remaining cases seem to be more complicated.


His seminal paper on the two-hit hypothesis[1]

A.G. Knudson, “Mutation and cancer: statistical study of retinoblastoma,” Proceedings of the National Academy of Sciences, 68:820-3, 1971.

The two hit hypothesis proposed by A.G. Knudson.  A description with video of Dr. Knudson talk at AACR can be found at the following link (photo creditied to A.G. Knudson and Fox Chase Cancer Center at the following link:


1.            Knudson AG, Jr.: Mutation and cancer: statistical study of retinoblastoma. Proceedings of the National Academy of Sciences of the United States of America 1971, 68(4):820-823.

2.            Solimini NL, Xu Q, Mermel CH, Liang AC, Schlabach MR, Luo J, Burrows AE, Anselmo AN, Bredemeyer AL, Li MZ et al: Recurrent hemizygous deletions in cancers may optimize proliferative potential. Science 2012, 337(6090):104-109.

3.            Davoli T, Xu Andrew W, Mengwasser Kristen E, Sack Laura M, Yoon John C, Park Peter J, Elledge Stephen J: Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome. Cell 2013, 155(4):948-962.

Other papers on this site on CANCER and MUTATION include:

Cancer Mutations Across the Landscape

Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center

Whole exome somatic mutations analysis of malignant melanoma contributes to the development of personalized cancer therapy for this disease

Breast Cancer and Mitochondrial Mutations

Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

Hold on. Mutations in Cancer do good.

Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis.

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Aneuploidy and Carcinogenesis

Curator and Reporter: Larry H. Berntein, MD, FCAP


Curator: Stephen J Williams, PhD


New Theory of Cancer Development

Researchers have been unable to explain why cancer cells contain abnormal numbers of chromosomes for over a century. The phenomenon known as aneuploidy is associated with all types of cancer. Harvard Medical School researchers have hypothesized why cancer cells contain many more chromosome abnormalities than healthy cells. They have devised a way to understand

  • patterns of aneuploidy in tumors and
  • predict which genes in the affected chromosomes are likely to be cancer suppressors or promoters, and
  • they propose that aneuploidy is a driver of cancer, rather than a result of it.

The study, to be published online in Cell, offers a new theory of cancer development and could lead to new treatment targets.  This would be feasible if they could identify key cancers suppressors.

The cancer cell characteristically has many gene deletions and amplifications, chromosome gains and losses. Although it has the appearance of randomness, previous research has shown that there is a pattern to the alterations in chromosomes and chromosome arms, which suggests that we can decipher that pattern and perhaps learn how or if it drives the cancer, according to the senior author, Stephen Elledge, Gregor Mendel professor of Genetics and of Medicine at HMS and professor of medicine at Brigham and Women’s Hospital.  Having proposed the theory about how these cellular genetic changes occur, the team set out to prove it using mathematical analysis.

See “Related Links” for full-size image. (Source: HMS/ University of Cambridge/Joanne Davidson, Mira Grigorova and Paul Edwards)

Mining for answers

Cancer research has focused on mutations for decades since the “oncogene revolution.”  Changes in the DNA code that abnormally activate genes, called oncogenes, either promote cancer or deactivate genes that suppress cancer. The role of aneuploidy— in which entire chromosomes or chromosome arms are added or deleted— has remained largely unstudied.

Elledge and his team, including research fellow and first author Teresa Davoli, suspected that aneuploidy has a significant role to play in cancer because missing or extra chromosomes likely affect genes involved in tumor-related processes such as cell division and DNA repair.

To test their hypothesis, the researchers developed a computer program called TUSON (Tumor Suppressor and Oncogene) Explorer together with Wei Xu and Peter Park at HMS and Brigham and Women’s. The program analyzed genome sequence data from more than 8,200 pairs of cancerous and normal tissue samples in three preexisting databases.

They found many more potential cancer drivers than anticipated

  • after generating a list of suspected oncogenes and tumor suppressor genes based on their mutation patterns.

They ranked the suspects by how powerful an effect their deletion or duplication was likely to have on cancer development.  The team then looked at where the suspects normally appear in chromosomes.

They discovered that

  • the number of tumor suppressor genes or oncogenes in a chromosome
  • correlated with how often the whole chromosome or part of the chromosome was deleted or duplicated in cancers.

Where there were concentrations of tumor suppressor genes alongside

  • fewer oncogenes and fewer genes essential to survival,
  • there was more chromosome deletion.


When the team factored in gene potency, the correlations got even stronger. A cluster of highly potent tumor suppressors was

  • more likely to mean chromosome deletion than a cluster of weak suppressors.

Number matters

Since 1971, the standard tumor suppressor model has held that

  • cancer is caused by a “two-hit” cascade in which first one copy and
  • then the second copy of a gene becomes mutated.

Elledge argues that simply losing or gaining one copy of a gene through aneuploidy can influence tumor growth as well. However, the loss or gain of multiple cancer driver genes that individually have low potency

  • can have big effects by accretion of potency

These novel algorithms that identify tumor suppressors and oncogenes give experimentally verifiable basis for how  aneuploidies evolve in cancer cells, and

  • Indicate that subtle changes in the activity of many different genes at the same time can contribute to tumorigenesis

These findings also may have answered a long-standing question about whether aneuploidy is a cause or effect of cancer, leaving researchers free to pursue the question of how.  “Aneuploidy is driving cancer, not simply a consequence of it,” said Elledge. “Other things also matter, such as gene mutations, rearrangements and changes in expression. We don’t know what the weighting is, but now we should be able to figure it out.”  Elledge and Davoli plan to gather experimental evidence to support their mathematical findings. That will include validating some of the new predicted tumor suppressors and oncogenes as well as “making some deletions and amplifications and seeing if they have the properties we think they do”.

Source: Harvard Medical School

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How Mobile Elements in “Junk” DNA Promote Cancer – Part 1: Transposon-mediated Tumorigenesis

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



Landscape of Somatic Retrotransposition in Human Cancers. Science (2012); Vol. 337:967-971. (1)

Sequencing of the human genome via massive programs such as the Cancer Genome Atlas Program (CGAP) and the Encyclopedia of DNA Elements (ENCODE) consortium in conjunction with considerable bioinformatics efforts led by the National Center for Biotechnology Information (NCBI) have unlocked a myriad of yet unclassified genes (for good review see (2).  The project encompasses 32 institutions worldwide which, so far, have generated 1640 data sets, initially depending on microarray platforms but now moving to the more cost effective new sequencing technology.  Initially the ENCODE project focused on three types of cells: an immature white blood cell line GM12878, leukemic line K562, and an approved human embryonic cell line H1-hESC.  The analysis was rapidly expanded to another 140 cell types.  DNA sequencing had revealed 20,687 known coding regions with hints of 50 more coding regions.  Another 11,224 DNA stretches were classified as pseudogenes.  The ENCODE project reveals that many genes encode for an RNA, not protein product, so called regulatory RNAs.

However some of the most recent and interesting results focus on the noncoding regions of the human genome, previously discarded as uninteresting or “junk” DNA .  Only 2% of the human genome contains coding regions while 98% of this noncoding part of the genome is actually found to be highly active “with about 4 million constantly communicating switches” (3).  Some of these “switches” in the noncoding portion contain small, repetitive elements which are mobile throughout the genome, and can control gene expression and/or predispose to disease such as cancer.  These mobile elements, found in almost all organisms, are classified as transposable elements (TE), inserting themselves into far-reaching regions of the genome.  Retro-transposons are capable of generating new insertions through RNA intermediates.  These transposable elements are normally kept immobile by epigenetic mechanisms(4-6) however some TEs can escape epigenetic repression and insert in areas of the genome, a process described as insertional mutagenesis as the process can lead to gene alterations seen in disease(7).  In addition, this insertional mutagenesis can lead to the transformation of cells and, as described in Post 2, act as a model system to determine drivers of oncogenesis. This insertional mutagenesis is a different mechanism of genetic alteration and rearrangement seen in cancer like recombination and fusion of gene fragments as seen with the Philadelphia chromosome and BCR/ABL fusion protein (8).  The mechanism of transposition and putative effects leading to mutagenesis are described in the following figure:


Figure.  Insertional mutagenesis based on transposon-mediated mechanism.  A) Basic structure of  transposon contains gene/sequence flanked by two inverted repeats (IR) and/or direct repeats (DR).  An enzyme, the transposase (red hexagon) binds and cuts at the IR/DR and transposon is pasted at another site in DNA, containing an insertion site.  B)   Multiple transpositions may results in oncogenic events by inserting in promoters leading to altered expression of genes driving oncogenesis or inserting within coding regions and inactivating tumor suppressors or activating oncogenes.  Deep sequencing of the resultant tumor genomes ( based on nested PCR from IR/DRs) may reveal common insertion sites (CIS) and oncogenic mutations could be identified.

In a bioinformatics study Eunjung Lee et al.(1), in collaboration with the Cancer Genome Atlas Research Network, the authors had analyzed 43 high-coverage whole-genome sequencing datasets from five cancer types to determine transposable element insertion sites.  Using a novel computational method, the authors had identified 194 high-confidence somatic TE insertion sites present in cancers of epithelial origin such as colorectal, prostate and ovarian, but not in brain or blood cancers.  Sixty four of the 194 detected somatic TE insertions were located within 62 annotated genes. Genes with TE insertion in colon cancers have commonly high mutation rates and enriched genes were associated with cell adhesion functions (CDH12, ROBO2,NRXN3, FPR2, COL1A1, NEGR1, NTM and CTNNA2) or tumor suppressor functions (NELL1m ROBO2, DBC1, and PARK2).  None of the somatic events were located within coding regions, with the TE sequences being detected in untranslated regions (UTR) or intronic regions.  Previous studies had shown insertion in these regions (UTR or intronic) can disrupts gene expression (9). Interestingly, most of the genes with insertion sites were down-regulated, suggested by a recent paper showing that local changes in methylation status of transposable elements can drive retro-transposition (10,11).  Indeed, the authors found that somatic insertions are biased toward the hypomethylated regions in cancer cell DNA.  The authors also confirmed that the insertion sites were unique to cancer and were somatic insertions, not germline (germline: arising during embryonic development) in origin by analyzing 44 normal genomes (41 normal blood samples from cancer patients and three healthy individuals).

The authors conclude:

“that some TE insertions provide a selective advantage during tumorigenesis,

rather than being merely passenger events that precede clonal expansion(1).”

The authors also suggest that more bioinformatics studies, which utilize the expansive genomic and epigenetic databases, could determine functional consequences of such transposable elements in cancerThe following Post will describe how use of transposon-mediated insertional mutagenesis is leading to discoveries of the drivers (main genetic events) leading to oncogenesis.

1.            Lee, E., Iskow, R., Yang, L., Gokcumen, O., Haseley, P., Luquette, L. J., 3rd, Lohr, J. G., Harris, C. C., Ding, L., Wilson, R. K., Wheeler, D. A., Gibbs, R. A., Kucherlapati, R., Lee, C., Kharchenko, P. V., and Park, P. J. (2012) Science 337, 967-971

2.            Pennisi, E. (2012) Science 337, 1159, 1161

3.            Park, A. (2012) Don’t Trash These Genes. “Junk DNA may lead to valuable cures. in Time, Time, Inc., New York, N.Y.

4.            Maksakova, I. A., Mager, D. L., and Reiss, D. (2008) Cellular and molecular life sciences : CMLS 65, 3329-3347

5.            Slotkin, R. K., and Martienssen, R. (2007) Nature reviews. Genetics 8, 272-285

6.            Yang, N., and Kazazian, H. H., Jr. (2006) Nature structural & molecular biology 13, 763-771

7.            Hancks, D. C., and Kazazian, H. H., Jr. (2012) Current opinion in genetics & development 22, 191-203

8.            Sattler, M., and Griffin, J. D. (2001) International journal of hematology 73, 278-291

9.            Han, J. S., Szak, S. T., and Boeke, J. D. (2004) Nature 429, 268-274

10.          Reichmann, J., Crichton, J. H., Madej, M. J., Taggart, M., Gautier, P., Garcia-Perez, J. L., Meehan, R. R., and Adams, I. R. (2012) PLoS computational biology 8, e1002486

11.          Byun, H. M., Heo, K., Mitchell, K. J., and Yang, A. S. (2012) Journal of biomedical science 19, 13

Other research paper on ENCODE and Cancer were published on this Scientific Web site as follows:

Expanding the Genetic Alphabet and linking the genome to the metabolome

Junk DNA codes for valuable miRNAs: non-coding DNA controls Diabetes

ENCODE Findings as Consortium

Reveals from ENCODE project will invite high synergistic collaborations to discover specific targets

ENCODE: the key to unlocking the secrets of complex genetic diseases

Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations

Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

Commentary on Dr. Baker’s post “Junk DNA codes for valuable miRNAs: non-coding DNA controls Diabetes”

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

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