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Posts Tagged ‘precision medicine’

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.

http://www.ncbi.nlm.nih.gov/gene/17869

 

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

http://med.stanford.edu/news/all-news/2016/03/protein-increases-signals-that-protect-cancer-cells.html

Depiction of the Myc protein

http://med.stanford.edu/news/all-news/2016/03/protein-increases-signals-that-protect-cancer-cells/_jcr_content/main/image.img.full.high.jpg

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]

http://www.researchcancerimmunotherapy.com/images/pathways/pd-l1-hero.jpg

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]

PD-L1

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

B7.1

Receptor expressed on activated T cells and dendritic cells3

PD-1

Receptor expressed primarily on activated T cells3

CONVERSELY, PD-L2 BINDS PRIMARILY TO PD-13

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

PD-L2

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

PD-1

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

 

PD-L1 MAY INHIBIT CYTOTOXIC T-CELL ACTIVITY IN THE TUMOR MICROENVIRONMENT

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 MAY INHIBIT CANCER IMMUNITY CYCLE PROPAGATION IN THE LYMPH NODES

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

http://www.researchcancerimmunotherapy.com/pathways/pd-l1-immune-evasion

 

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

 

STEPS 1-3: INITIATING AND PROPAGATING ANTICANCER IMMUNITY1

  • 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

STEPS 4-5: ACCESSING THE TUMOR1

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

STEPS 6-7: CANCER-CELL RECOGNITION AND INITIATION OF CYTOTOXICITY1

  • 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

http://www.researchcancerimmunotherapy.com/pathways/pd-l1

 

REFERENCES

  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.

 

An external file that holds a picture, illustration, etc. Object name is nihms384451f1.jpg

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386352/bin/nihms384451f1.jpg

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

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” (http://j.mp/ihclens_webinar), 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).

Fig1

http://www.captodayonline.com/wordpress/wp-content/uploads/2016/02/Fig1.jpg

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).

Fig2

http://www.captodayonline.com/wordpress/wp-content/uploads/2016/02/Fig2.gif

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)

http://www.captodayonline.com/wordpress/wp-content/uploads/2016/02/Fig3.gif

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).

Fig4

http://www.captodayonline.com/wordpress/wp-content/uploads/2016/02/Fig4.gif

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.

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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).

Fig6

“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  http://www.jci.org/articles/view/80011

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-...

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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 ClinicalTrials.gov 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

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

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

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

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

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

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: http://www.cancernetwork.com/oncology-journal/prospects-targeting-pd-1-and-pd-l1-various-tumor-types#sthash.an8uOYLi.dpuf

 

Immune Checkpoint Blockade in Cancer Therapy

Michael A. PostowMargaret K. Callahan and Jedd D. Wolchok
http://jco.ascopubs.org/content/early/2015/01/20/JCO.2014.59.4358.full
 http://dx.doi.org:/10.1200/JCO.2014.59.4358

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.

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http://ascopubs.org/doi/figure/10.1200/JCO.2014.59.4358

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http://jco.ascopubs.org/content/early/2015/01/20/JCO.2014.59.4358/F1.medium.gif

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.

http://news.mit.edu/sites/mit.edu.newsoffice/files/styles/news_article_image_top_slideshow/public/images/2016/MIT-Cancer-Migration-1_0.jpg?itok=aEOCRQpn

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  http://dx.doi.org:/10.1158/2159-8290.CD-15-1183

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
http://www.dddmag.com/news/2016/02/researchers-find-link-between-death-tumor-support-cells-and-cancer-metastasis#.VuatbTol_kI.linkedin

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)

http://www.dddmag.com/sites/dddmag.com/files/20160219-metastasized-cells%20%281%29.jpg

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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AACR and Philly New Media Present a Town Hall Discussion on Precision Medicine

Reporter: Stephen J. Williams, PhD

Cancer Precision Medicine: Big Ideas in Research, Treatment, and Prevention

A Town Hall Forum will discuss the latest findings with regard to precision medicine, its impact currently in cancer treatment, and future directions, discussed by some of the preeminent cancer researchers and oncologists in the country. This unprecedented event is being hosted by the American Association for Cancer Research (AACR) and Philadelphia Media Network – publisher of The Philadelphia Inquirer, Daily News, and Philly.com.

Given the following speakers, this event will have a large focus on use of cancer immunotherapy as well as new targets in the precision medicine arena.

Register today: Philly.com/CancerEvent – Use the promo code “AACR” for discounted $45 tickets.

When: Thursday, January 21, 2016 • Program: 2 pm • Networking reception: 5:30 pm.

Where:  The College of Physicians of Philadelphia • 19 South 22nd Street, Philadelphia, Pa.

The event will be held in Philadelphia at the College of Physicians of Philadelphia, home of the famous Mutter Museum.

Please follow the meeting coverage on @pharma_BI and using the following @ handle and # hastags of Twitter:

@AACR

@pharma_BI

@PhillyInquirer

#cbi16

#precisionmedicine

#endcancer

 

From Penn Medicine News Blog: Archives (please click on link below)

Penn’s Center for Personalized Diagnostics (CPD), which recently named Kojo S.J. Elenitoba-Johnson, MD, as its founding director, is diving deeper into cancer patients’ tumors with next generation DNA sequencing.

The genetic tests help refine diagnoses with greater precision than standard imaging tests and blood work by spotting known mutations that can inform the treatment plan. Since it launched in February 2013, the CPD has performed more than 4,000 advanced diagnostics, representing a wide range of cancers.  It’s also producing actionable findings: Of those tests, 75 percent found disease-associated mutations, revealing possible new treatment pathways.

This new CPD video helps breakdown how the process works, but a patient story can really help connect the dots. We’ve written about several people who benefited from the CPD, including one acute myeloid leukemia patient with an FLT3 mutation that made her a candidate for a targeted therapy, and another whose cholangiocarcinoma was successfully treated with a BRAF-targeted therapy after the mutation—typically associated with melanoma—was spotted.

ACC’s role as a national leader in personalized cancer care was also reinforced with the opening of the Center for Rare Cancers and Personalized Therapy in 2015.

Directed by Marcia Brose, MD, PhD, this virtual center enrolls patients into clinical trials based on genetic markers rather than tumor origin.  Patients with the same mutation, BRAF for instance, but different cancers, are part of the same clinical study investigating a targeted therapy.  A story, set to air on TV news affiliates across the country in the upcoming weeks, will feature a patient with a rare salivary tumor who ran out of treatment options, until a HRAS mutation identified through the CPD put him back on track, after switching to the drug tipifarnib. The patient responded well, and a recent scan revealed that his disease has stabilized.

“Philadelphia is a hotbed for healthcare innovation and groundbreaking scientific research—which becomes even more apparent as the ACC continues to move the needle in the precision medicine world,”Abramson Cancer Center (ACC) director Chi Van Dang, MD, PhD, said.  “Quickly evolving diagnostics and genetic tests, cancer vaccines, and powerful personalized therapies that use the body’s own immune system to fight off cancer: These are just a few of the medical advances being utilized today that are giving patients the greatest chance.”

For Media Inquiries see the following AACR contact information:

Julia Gunther
Assistant Director, Media and Public Relations
215-446-6896
Cell: 267-250-5441
Fax: 215-861-5937
julia.gunther@aacr.org
Gunther promotes the AACR’s meetings, journals, and initiatives to the media and the public.

Lauren Walens
Senior Manager, Media and Public Relations
215-446-7163
Fax: 267-765-1050
lauren.walens@aacr.org
Walens promotes the AACR’s meetings, journals, and initiatives to the media and the public. She also manages the AACR’s blog, Cancer Research Catalyst.

Lauren Riley
Senior Coordinator, Media and Public Relations
215-446-7155
Fax: 215-446-7291
lauren.riley@aacr.org
Riley is responsible for media relations promotion and support, conference newsroom logistics, writing and proofreading, website and news release copy, as well as office support for the Communications and Public Relations Department staff.

 

 

 

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Multiple factors related to initial trial design may predict low patient accrual for cancer clinical trials

Reporter: Stephen J. Williams, Ph.D.

UPDATED 5/15/2019

A recently published paper in JCNI highlights results determining factors which may affect cancer trial patient accrual and the development of a predictive model of accrual issues based on those factors.

To hear a JCNI podcast on the paper click here

but below is a good posting from scienmag.com which describes their findings:

Factors predicting low patient accrual in cancer clinical trials

source: http://scienmag.com/factors-predicting-low-patient-accrual-in-cancer-clinical-trials/

Nearly one in four publicly sponsored cancer clinical trials fail to enroll enough participants to draw valid conclusions about treatments or techniques. Such trials represent a waste of scarce human and economic resources and contribute little to medical knowledge. Although many studies have investigated the perceived barriers to accrual from the patient or provider perspective, very few have taken a trial-level view and asked why certain trials are able to accrue patients faster than expected while others fail to attract even a fraction of the intended number of participants. According to a study published December 29 in the JNCI: Journal of the National Cancer Institute, a number of measurable trial characteristics are predictive of low patient accrual.

Caroline S. Bennette, M.P.H., Ph.D., of the Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, and colleagues from the University of Washington and the Fred Hutchinson Cancer Research Center analyzed information on 787 phase II/III clinical trials sponsored by the National Clinical Trials Network (NCTN; formerly the Cooperative Group Program) launched between 2000 and 2011. After excluding trials that closed because of toxicity or interim results, Bennette et al. found that 145 (18%) of NCTN trials closed with low accrual or were accruing at less than 50% of target accrual 3 years or more after opening.

The authors identified potential risk factors from the literature and interviews with clinical trial experts and found multiple trial-level factors that were associated with poor accrual to NCTN trials, such as increased competition for patients from currently ongoing trials, planning to enroll a higher proportion of the available patient population, and not evaluating a new investigational agent or targeted therapy. Bennette et al. then developed a multivariable prediction model of low accrual using 12 trial-level risk factors, which they reported had good agreement between predicted and observed risks of low accrual in a preliminary validation using 46 trials opened between 2012 and 2013.

The researchers conclude that “Systematically considering the overall influence of these factors could aid in the design and prioritization of future clinical trials…” and that this research provides a response to the recent directive from the Institute of Medicine to “improve selection, support, and completion of publicly funded cancer clinical trials.”

In an accompanying editorial, Derek Raghavan, M.D., Levine Cancer Institute, writes that the focus needs to be on getting more patients involved in trials, saying, “we should strive to improve trial enrollment, giving the associated potential for improved results. Whether the basis is incidental, because of case selection bias, or reflects the support available to trial patients has not been determined, but the fact remains that outcomes are better.”

###

Contact info:

Article: Caroline S. Bennette, M.P.H., Ph.D., cb11@u.washington.edu

Editorial: Derek Raghavan, M.D., derek.raghavan@carolinashealthcare.org

Other investigators also feel that initial trial design is of UTMOST importance for other reasons, especially in the era of “precision” or “personalized” medicine and why the “basket trial” or one size fits all trial strategy is not always feasible.

In Why the Cancer Research Paradigm Must Transition to “N-of-1” Approach

Dr. Maurie Markman, MD gives insight into why the inital setup of a trial and the multi-center basket type of  accrual can be a problematic factor in obtaining meaningful cohorts of patients with the correct mutational spectrum.

The anticancer clinical research paradigm has rapidly evolved so that subject selection is increasingly based on the presence or absence of a particular molecular biomarker in the individual patient’s malignancy. Even where eligibility does not mandate the presence of specific biological features, tumor samples are commonly collected and an attempt is subsequently made to relate a particular outcome (eg, complete or partial objective response rate; progression-free or overall survival) to the individual cancer’s molecular characteristics.

One important result of this effort has been the recognition that there are an increasing number of patient subsets within what was previously—and incorrectly—considered a much larger homogenous patient population; for example, non–small cell lung cancer (NSCLC) versus EGFR-mutation–positive NSCLC. And, while it may still be possible to conduct phase III randomized trials involving a relatively limited percentage of patients within a large malignant entity, extensive and quite expensive effort may be required to complete this task. For example, the industry-sponsored phase III trial comparing first-line crizotinib with chemotherapy (pemetrexed plus either carboplatin or cisplatin) in ALK-rearrangement–positive NSCLC, which constitutes 3% to 5% of NSCLCs, required an international multicenter effort lasting 2.5 years to accrue the required number of research subjects.1

But what if an investigator, research team, or biotech company desired to examine the clinical utility of an antineoplastic in a patient population representing an even smaller proportion of patients with NSCLC such as in the 1% of the patient population with ROS1 abnormalities,2 or in a larger percentage of patients representing 4%-6% of patients with a less common tumor type such as ovarian cancer? How realistic is it that such a randomized trial could ever be conducted?

Further, considering the resources required to initiate and successfully conduct a multicenter international phase III registration study, it is more than likely that in the near future only the largest pharmaceutical companies will be in a position to definitively test the clinical utility of an antineoplastic in a given clinical situation.

One proposal to begin to explore the benefits of targeted antineoplastics in the setting of specific molecular abnormalities has been to develop a socalled “basket trial” where patients with different types of cancers with varying treatment histories may be permitted entry, assuming a well-defined molecular target is present within their cancer. Of interest, several pharmaceutical companies have initiated such clinical research efforts.

Yet although basket trials represent an important research advance, they may not provide the answer to the molecular complexities of cancer that many investigators believe they will. The research establishment will have to take another step toward innovation to “N-of-1” designs that truly explore the unique nature of each individual’s cancer.

Trial Illustrates Weaknesses

A recent report of the results of one multicenter basket trial focused on thoracic cancers demonstrates both the strengths but also a major fundamental weakness of the basket trial approach.3

However, the investigators were forced to conclude that despite accrual of more than 600 patients onto a study conducted at two centers over a period of approximately 2 years, “this basket trial design was not feasible for many of the arms with rare mutations.”3

They concluded that they needed a larger number of participating institutions and the ability to adapt the design for different drugs and mutations. So the question to be asked is as follows: Is the basket-type approach the only alternative to evaluate the clinical relevance of a targeted antineoplastic in the presence of a specific molecular abnormality?

Of course, the correct answer to this question is surely: No!

– See more at: http://www.onclive.com/publications/Oncology-live/2015/July-2015/Why-the-Cancer-Research-Paradigm-Must-Transition-to-N-of-1-Approach#sthash.kLGwNzi3.dpuf

The following is a video on the website ClinicalTrials.gov which is a one-stop service called EveryClinicalTrial to easily register new clinical trials and streamline the process:

 

UPDATED 5/15/2019

Another possible roadblock to patient accrual has always been the fragmentation of information concerning the availability of clinical trails and coordinating access among the various trial centers, as well as performing analytics on trial data to direct new therapeutic directions.  The NIH has attempted to circumvent this problem with the cancer trials webpage trials.gov however going through the vast number of trials, patient accrual requirements, and finding contact information is a daunting task.  However certain clinical trial marketplaces are now being developed which may ease access problems to clinical trials as well as data analytic issues, as highlighted by the Scientist.com article below:

Scientist.com Launches Trial Insights, A Transformative Clinical Trials Data Analytics Solution

The world’s largest online marketplace rolls out first original service, empowering researchers with on demand insights into clinical trials to help drive therapeutic decisions

SAN DIEGO–(BUSINESS WIRE)–Scientist.com, the online marketplace for outsourced research, announced today the launch of Trial Insights, a digital reporting solution that simplifies data produced through clinical trial, biomarker and medical diagnostic studies into an intuitive and user-friendly dashboard. The first of its kind, Trial Insights curates publicly available data nightly from information hubs such as clinicaltrials.gov and customizes it to fit a researcher or research organization’s specific project needs.

Trial Insights, new clinical trial reporting solution, allows researchers to keep track of the evolving landscape of drugs, diseases, sponsors, investigators and medical devices important to their work.

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“Trial Insights offers researchers an easy way to navigate the complexity of clinical trials information,” said Ron Ranauro, Founder of Incite Advisors. “Since Trial Insights’ content is digitally curated, researchers can continuously keep track of the evolving landscape of drugs, diseases, sponsors, investigators and medical devices important to their work.”

As the velocity, variety and veracity of data available on sites like clinicaltrials.gov continues to increase, the ability to curate it becomes more valuable to different audiences. With the advancement of personalized medicine, it is important to make the data accessible to the health care and patient communities. Information found on the Trial Insights platform can help guide decision making across the pharmaceutical, biotechnology and contract research organization industries as clinical trial data is a primary information source for competitive intelligence, research planning and clinical study planning.

“We are extremely excited to launch the first Scientist.com exclusive, original service offering to our clients in the life sciences,” said Mark Herbert, Scientist.com Chief Business Officer. “Our goal at Scientist.com is to help cure all diseases by 2050, and we believe solutions like Trial Insights, which greatly simplifies access to and reporting of clinical trial data, will get us one step closer to reaching that goal.”

source: https://www.businesswire.com/news/home/20190416005362/en/Scientist.com-Launches-Trial-Insights-Transformative-Clinical-Trials?utm_source=TrialIO+List

 

Other article on this Open Access Journal on Cancer Clinical Trial Design include:

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Precision Medicine for Future of Genomics Medicine is The New Era

Demet Sag, PhD, CRA, GCP

 

Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

I remember when I was screening the X-chromosome by using deletion/duplication mapping and using P elements and bar balancers as a tool to keep the genome stable to identify transregulating elements of ovo gene, female germline specific Drosophila melanogaster germline sex determination gene. At the time for my dissertation, I screened X-chromosome using 45 deficiency strains, I found that these trans-regulating regions were grouped into 12 loci based on overlapping cytology. Five regions were trans-regulating activators, and seven were trans-regulating repressors; extrapolating to the entire genome, this result predicted nearly 85 loci. This one gene may expressed three proteins at different time of development and activate/downregulate various regions to accommadate proper system development in addition to auto-regulate and gene dose responses. Drosophila has only four chromosomes but the cellular interactions and signaling mechanisms are still complicated yet as not complicated as human. I do appreciate the new applications and upcoming changes.

Now, the technology is much better and precision is the key to establish to use in clinics.  However, we have new issues to overcome like computing such a big data, align properly, analyze effectively, compare and contrast the outcomes to identify the variations that may function in on  population, or two etc. At the end of the day collaboration, standardization, and data sharing are few of the key factors.

It is necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data so  Gene Ontology Consortium is created. Three independent ontologies can be reached at  (http://www.geneontology.org) developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.

Precision-Medicine-Timeline2

We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more. some examples include:

  • the budding yeast, Saccharomyces cerevisiae,
  • the nematode worm Caenorhabditis elegans
  • the fruitfly Drosophila melanogaster,
  • the flowering plant Arabidopsis thaliana
  • fission yeast Schizosaccharomyces pombe
  • the  mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of common diseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with unrelevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies), an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component in complex diseases and
  • to fully understand the genetic pathways contributing to complex disease

Examples of Gene Ontology

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.

 

With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.

 

This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.

 

There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics

 

Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.

 

As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a main factor:

 

  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6, which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.Published online 2009 May 27. doi:  10.1073/pnas.0903103106

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Allele-Frequency Data for Nine Reproducible Associations

frequency
gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.

 

PMC full text:

Am J Hum Genet. 2006 Jan; 78(1): 130–136.Published online 2005 Nov 16. doi:  10.1086/499287Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

frequency
gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

 

They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs given  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1.

Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”

 

In 2002 the International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed.

In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population.

In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability. These are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center located at (http://www.hapmap.org) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP).

In the Phase II HapMap we identified 32,996 recombination hotspots (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence.

In addition to many previously identified regions in HapMap Phase I including LARGESYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.

HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily

  • to recognize and
  • to annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.

 

PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is ” a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database)”.

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured profile methods (Gribskov et al. 1987;Henikoff and Henikoff 1991Attwood et al. 1994):

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases:

A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.

 

Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985Rollins et al. 1991).

 

The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating position-specific evolutionary conservation” (PSEC) scores.

 

The process for building PANTHER families include:

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).

 

Conclusion:

Precision medicine effort is the beginning of a new journey to provide better health solutions.

 

Further Reading and References:

Human Phenome Project:

Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.

Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogasterScience.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

Chervitz SA, et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 1999;27:74–78.

The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res. 1999;27:85–88.

Blake JA, et al. The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res. 2000;28:108–111.

Ball CA, et al. Integrating functional genomic information into the Saccharomyces Genome Database. Nucleic Acids Res. 2000;28:77–80.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001. The sequence of the human genome. Science 291: 1304–1351.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860–921.

Mi, H., Vandergriff, J., Campbell, M., Narechania, A., Lewis, S., Thomas, P.D., and Ashburner, M. 2003. Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res.

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25: 25–29.

Computational Biology

Attwood TK, Beck ME, Bleasby AJ, Parry-Smith DJ. PRINTS–a database of protein motif fingerprints. Nucleic Acids Res. 1994 Sep;22(17):3590-6.

Obenauer JC, Yaffe MB. Computational prediction of protein-protein interactions.

Methods Mol Biol. 2004;261:445-68. Review.

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Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

Spencer CC, et al. The influence of recombination on human genetic diversity.PLoS Genet. 2006;2:e148.

Petes TD. Meiotic recombination hot spots and cold spots. Nature Rev. Genet.2001;2:360–369.

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Gauderman WJ. Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med. 2002;21(1):35–50. doi: 10.1002/sim.973.

Attwood, T.K., Beck, M.E., Bleasby, A.J., and Parry-Smith, D.J. 1994. PRINTS—A database of protein motif fingerprints. Nucleic Acids Res. 22: 3590–3596.

Bairoch, A. 1991. PROSITE: A dictionary of sites and patterns in proteins. Nucleic Acids Res. 19 Suppl: 2241–2245.

Bairoch, A. and Apweiler, R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28: 45–48.

Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. 2002. The Pfam protein families database. Nucleic Acids Res. 30: 276–280.

Sonnhammer, E.L., Eddy, S.R., and Durbin, R. 1997. Pfam: A comprehensive database of protein domain families based on seed alignments. Proteins 28:405–420.

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HUGO Gene Nomenclature Committee (2011). HGNC Database.http://www.genenames.org/.

Population Genomics, GWAS, Inheritance, Heritability, Migration, Selection  an Evolution:

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Joseph Lachance, Sarah A. Tishkoff  Population Genomics of Human Adaptation

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TOTAL Views of Presentation Content per Presentation: 10th Annual Personalized Medicine Conference at the Harvard Medical School, November 12-13, 2014 2012pharmaceutical
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11:30AM 11/13/2014 – Role of Genetics and Genomics in Pharmaceutical Development @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
10:15AM 11/13/2014 – Panel Discussion — IT/Big Data @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:30AM 11/13/2014 – Harvard Business School Case Study: 23andMe @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:00AM 11/13/2014 – Welcome from Gary Gottlieb, M.D., Partners HealthCare @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
4:00PM 11/12/2014 – Panel Discussion Novel Approaches to Personalized Medicine @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
3:15PM 11/12/2014 – Discussion Complex Disorders @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
1:45PM 11/12/2014 – Panel Discussion – Oncology @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
1:15PM 11/12/2014 – Keynote Speaker – International Genetics Health and Disease @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
11:30AM 11/12/2014 – Personalized Medicine Coalition Award & Award Recipient Speech @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
11:00AM 11/12/2014 – Keynote Speaker – Past, Present and Future of Personalized Medicine @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
9:20AM 11/12/2014 – Panel Discussion – Genomic Technologies @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:50AM 11/12/2014 – Keynote Speaker – CEO, American Medical Association @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:20AM 11/12/2014 – Special Guest Keynote Speaker – The Future of Personalized Medicine @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:00AM 11/12/2014 – Welcome & Opening Remarks @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
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The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference,11/28-29, 2012, Harvard Medical School, Boston, MA 2012pharmaceutical
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Highlights from 8th Annual Personalized Medicine Conference, November 28-29, 2012, Harvard Medical School, Boston, MA 2012pharmaceutical
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Geneticist George Church: A Future Without Limits

Reporter: Aviva Lev-Ari, PhD, RN

Article ID #155: Geneticist George Church: A Future Without Limits. Published on 10/24/2014

WordCloud Image Produced by Adam Tubman

UPDATED 12/05/2020

 

In the future, George Church believes, almost everything will be better because of genetics. If you have a medical problem, your doctor will be able to customize a treatment based on your specific DNA pattern. When you fill up your car, you won’t be draining the world’s dwindling supply of crude oil, because the fuel will come from microbes that have been genetically altered to produce biofuel. When you visit the zoo, you’ll be able to take your children to the woolly mammoth or passenger pigeon exhibits, because these animals will no longer be extinct. You’ll be able to do these things, that is, if the future turns out the way Church envisions it—and he’s doing everything he can to see that it does.

UPDATED 12/05/2020

George Church backs a startup solution to the massive gene therapy manufacturing bottleneck

Source: https://endpts.com/george-church-backs-a-startup-solution-to-the-massive-gene-therapy-manufacturing-bottleneck/
Jason Mast: Associate Editor
George Church and his graduate students have spent the last decade seeding startups on the razor’s edge between biology and science fiction: gene therapy to prevent aging, CRISPRed pigs that can be used to harvest organs for transplant, and home kits to test your poop for healthy or unhealthy bacteria. (OK, maybe they’re not all on that razor’s edge.)

But now a new spinout from the Department of Genetics’ second floor is tackling a far humbler problem — one that major company after major company has stumbled over as they tried to get cures for rare diseases and other gene therapies into the clinic and past regulators: How the hell do you build these?

CEO Lex Vovner of 64x Bio

“There’s a lot happening for new therapies but not enough attention around this problem,” Lex Rovner, who was a post-doc at Church’s lab from 2015 to 2018, told Endpoints News. “And if we don’t figure out how to fix this, many of these therapies won’t even reach patients.”

This week, with Church and a couple other prominent scientists as co-founders, Rovner launched 64x Bio to tackle one key part of the manufacturing bottleneck. They won’t be looking to retrofit plants or build gene therapy factories, as Big Pharma and big biotech are now spending billions to do. Instead, with $4.5 million in seed cash, they will try to engineer the individual cells that churn out a critical component of the therapies.

George Church
The goal is to build cells that are fine-tuned to do nothing but spit out the viral vectors that researchers and drug developers use to shuttle gene therapies into the body. Different vectors have different demands; 64x Bio will look to make efficient cellular factories for each.

“While a few general ways to increase vector production may exist, each unique vector serotype and payload poses a specific challenge,” Church said in an emailed statement. “Our platform enables us to fine tune custom solutions for these distinct combinations that are particularly hard to overcome.”

Before joining Church’s lab, Rovner did her graduate work at Yale, where she studied how to engineer bacteria to produce new kinds of protein for drugs or other purposes. And after leaving Church’s lab in 2018, she initially set out to build a manufacturing startup with a broad focus.

Yet as she spoke with hundreds of biotech executives on LinkedIn and in coffee shops around Cambridge, the same issue kept popping up: They liked their gene therapy technology in the lab but they didn’t know how to scale it up.

“Everyone kept saying the same thing,” Rovner said. “We basically realized there’s this huge problem.”

The issue would soon make headlines in industry publications: bluebird delaying the launch of Zynteglo, Novartis delaying the launch of Zolgensma in the EU, Axovant delaying the start of their Parkinson’s trial.

Part of the problem, Rovner said, is that gene therapies are delivered on viral vectors. You can build these vectors in mammalian cell lines by feeding them a small circular strand of DNA called a plasmid. The problem is that mammalian cells have, over billions of years, evolved tools and defenses precisely to avoid making viruses. (Lest the mammal they live in die of infection).

There are genetic mutations that can turn off some of the internal defenses and unleash a cell’s ability to produce virus, but they’re rare and hard to find. Other platforms, Rovner said, try to find these mutations by using CRISPR to knock out genes in different cells and then screening each of them individually, a process that can require hundreds of thousands of different 100-well plates, with each well containing a different group of mutant cells.

“It’s just not practical, and so these platforms never find the cells,” Rovner said.

64x Bio will try to find them by building a library of millions of mutant mammalian cells and then using a molecular “barcoding” technique to screen those cells in a single pool. The technique, Rovner said, lets them trace how much vector any given cell produces, allowing researchers to quickly identify super-producing cells and their mutations.

The technology was developed partially in-house but draws from IP at Harvard and the Wyss Institute. Harvard’s Pam Silver and Wyss’s Jeffrey Way are co-founders.

The company is now based in SoMa in San Francisco. With the seed cash from Fifty Years, Refactor and First Round Capital, Rovner is recruiting and looking to raise a Series A soon. They’re in talks with pharma and biotech partners, while they try to validate the first preclinical and clinical applications.

Gene therapy is one focus, but Rovner said the platform works for anything that involves viral vector, including vaccines and oncolytic viruses. You just have to find the right mutation.

“It’s the rare cell you’re looking for,” she said.

AUTHOR
Jason Mast
Associate Editor
jason@endpointsnews.com
@JasonMMast
Jason Mas

In 2005 he launched the Personal Genome Project, with the goal of sequencing and sharing the DNA of 100,000 volunteers. With an open-source database of that size, he believes, researchers everywhere will be able to meaningfully pursue the critical task of correlating genetic patterns with physical traits, illnesses, and exposure to environmental factors to find new cures for diseases and to gain basic insights into what makes each of us the way we are. Church, tagged as subject hu43860C, was first in line for testing. Since then, more than 13,000 people in the U.S., Canada, and the U.K. have volunteered to join him, helping to establish what he playfully calls the Facebook of DNA.

Church has made a career of defying the impossible. Propelled by the dizzying speed of technological advancement since then, the Personal Genome Project is just one of Church’s many attempts to overcome obstacles standing between him and the future.

“It’s not for everyone,” he says. “But I see a trend here. Openness has changed since many of us were young. People didn’t use to talk about sexuality or cancer in polite society. This is the Facebook generation.” If individuals were told which diseases or medical conditions they were genetically predisposed to, they could adjust their behavior accordingly, he reasoned. Although universal testing still isn’t practical today, the cost of sequencing an individual genome has dropped dramatically in recent years, from about $7 million in 2007 to as little as $1,000 today.

“It’s all too easy to dismiss the future,” he says. “People confuse what’s impossible today with what’s impossible tomorrow.”, especially through the emerging discipline of “synthetic” biology. The basic idea behind synthetic biology, he explained, was that natural organisms could be reprogrammed to do things they wouldn’t normally do, things that might be useful to people. In pursuit of this, researchers had learned not only how to read the genetic code of organisms but also how to write new code and insert it into organisms. Besides making plastic, microbes altered in this way had produced carpet fibers, treated wastewater, generated electricity, manufactured jet fuel, created hemoglobin, and fabricated new drugs. But this was only the tip of the iceberg, Church wrote. The same technique could also be used on people.

“Every cell in our body, whether it’s a bacterial cell or a human cell, has a genome,” he says. “You can extract that genome—it’s kind of like a linear tape—and you can read it by a variety of methods. Similarly, like a string of letters that you can read, you can also change it. You can write, you can edit it, and then you can put it back in the cell.”

This April, the Broad Institute, where Church holds a faculty appointment, was awarded a patent for a new method of genome editing called CRISPR (clustered regularly interspersed short palindromic repeats), which Church says is one of the most effective tools ever developed for synthetic biology. By studying the way that certain bacteria defend themselves against viruses, researchers figured out how to precisely cut DNA at any location on the genome and insert new material there to alter its function. Last month, researchers at MIT announced they had used CRISPR to cure mice of a rare liver disease that also afflicts humans. At the same time, researchers at Virginia Tech said they were experimenting on plants with CRISPR to control salt tolerance, improve crop yield, and create resistance to pathogens.

The possibilities for CRISPR technology seem almost limitless, Church says. If researchers have stored a genetic sequence in a computer, they can order a robot to produce a piece of DNA from the data. That piece can then be put into a cell to change the genome. Church believes that CRISPR is so promising that last year he co-founded a genome-editing company, Editas, to develop drugs for currently incurable diseases.

Source: news.nationalgeographic.com

See on Scoop.itCardiovascular and vascular imaging

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