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Gene Editing with CRISPR gets Crisper

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, BioIT: BioInformatics, Biological Networks, Gene Regulation and Evolution, BioTechnology - Venture Creation, Cancer and Current Therapeutics, Cancer Informatics, Cancer Screening, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Clinical & Translational, Clinical Diagnostics, Clinical Genomics, CRISPR/Cas9 & Gene Editing, Diagnostics and Lab Tests, Disease Biology, Small Molecules in Development of Therapeutic Drugs, DNA repair, FDA Regulatory Affairs, Gene Regulation, Genetics & Pharmaceutical, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, Innovations, Loss of function gene, Mutant Gene Expression, Nanotechnology for Drug Delivery, Next Generation Sequencing (NGS), Oligonucleotide Therapeutics & Delivery, tagged , , , , , , , , , , , on May 3, 2016| Leave a Comment »

Gene Editing with CRISPR gets Crisper, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Gene Editing with CRISPR gets Crisper

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

 

 

CRISPR Moves from Butchery to Surgery   

More Genomes Are Going Under the CRISPR Knife, So Surgical Standards Are Rising

http://www.genengnews.com/gen-articles/crispr-moves-from-butchery-to-surgery/5759/

  • http://www.genengnews.com/Media/images/Article/thumb_GE_Dharmacon_cas9tracrrna1332182551.jpg
    The Dharmacon subsidary of GE Healthcare provides the Edit-R Lentiviral Gene Engineering platform. It is based on the natural S. pyrogenes system, but unlike that system, which uses a single guide RNA (sgRNA), the platform uses two component RNAs, a gene-specific CRISPR RNA (crRNA) and a universal trans-activating crRNA (tracrRNA). Once hybridized to the universal tracrRNA (blue), the crRNA (green) directs the Cas9 nuclease to a specific genomic region to induce a double- strand break.

    Scientists recently convened at the CRISPR Precision Gene Editing Congress, held in Boston, to discuss the new technology. As with any new technique, scientists have discovered that CRISPR comes with its own set of challenges, and the Congress focused its discussion around improving specificity, efficiency, and delivery.

    In the naturally occurring system, CRISPR-Cas9 works like a self-vaccination in the bacterial immune system by targeting and cleaving viral DNA sequences stored from previous encounters with invading phages. The endogenous system uses two RNA elements, CRISPR RNA (crRNA) and trans-activating RNA (tracrRNA), which come together and guide the Cas9 nuclease to the target DNA.

    Early publications that demonstrated CRISPR gene editing in mammalian cells combined the crRNA and tracrRNA sequences to form one long transcript called asingle-guide RNA (sgRNA). However, an alternative approach is being explored by scientists at the Dharmacon subsidiary of GE Healthcare. These scientists have a system that mimics the endogenous system through a synthetic two-component approach thatpreserves individual crRNA and tracrRNA. The tracrRNA is universal to any gene target or species; the crRNA contains the information needed to target the gene of interest.

    Predesigned Guide RNAs

    In contrast to sgRNAs, which are generated through either in vitro transcription of a DNA template or a plasmid-based expression system, synthetic crRNA and tracrRNA eliminate the need for additional cloning and purification steps. The efficacy of guide RNA (gRNA), whether delivered as a sgRNA or individual crRNA and tracrRNA, depends not only on DNA binding, but also on the generation of an indel that will deliver the coup de grâce to gene function.

    “Almost all of the gRNAs were able to create a break in genomic DNA,” said Louise Baskin, senior product manager at Dharmacon. “But there was a very wide range in efficiency and in creating functional protein knock-outs.”

    To remove the guesswork from gRNA design, Dharmacon developed an algorithm to predict gene knockout efficiency using wet-lab data. They also incorporated specificity as a component of their algorithm, using a much more comprehensive alignment tool to predict potential off-target effects caused by mismatches and bulges often missed by other alignment tools. Customers can enter their target gene to access predesigned gRNAs as either two-component RNAs or lentiviral sgRNA vectors for multiple applications.

    “We put time and effort into our algorithm to ensure that our guide RNAs are not only functional but also highly specific,” asserts Baskin. “As a result, customers don’t have to do any design work.”

    Donor DNA Formats

    http://www.genengnews.com/Media/images/Article/thumb_MilliporeSigma_CRISPR3120824917.jpg
    MilliporeSigma’s CRISPR Epigenetic Activator is based on fusion of a nuclease-deficient Cas9 (dCas9) to the catalytic histone acetyltransferase (HAT) core domain of the human E1A-associated protein p300. This technology allows researchers to target specific DNA regions or gene sequences. Researchers can localize epigenetic changes to their target of interest and see the effects of those changes in gene expression.

    Knockout experiments are a powerful tool for analyzing gene function. However, for researchers who want to introduce DNA into the genome, guide design, donor DNA selection, and Cas9 activity are paramount to successful DNA integration.MilliporeSigma offers two formats for donor DNA: double-stranded DNA (dsDNA) plasmids and single-stranded DNA (ssDNA) oligonucleotides. The most appropriate format depends on cell type and length of the donor DNA. “There are some cell types that have immune responses to dsDNA,” said Gregory Davis, Ph.D., R&D manager, MilliporeSigma.

  • The ssDNA format can save researchers time and money, but it has a limited carrying capacity of approximately 120 base pairs.In addition to selecting an appropriate donor DNA format, controlling where, how, and when the Cas9 enzyme cuts can affect gene-editing efficiency. Scientists are playing tug-of-war, trying to pull cells toward the preferred homology-directed repair (HDR) and away from the less favored nonhomologous end joining (NHEJ) repair mechanism.One method to achieve this modifies the Cas9 enzyme to generate a nickase that cuts only one DNA strand instead of creating a double-strand break. Accordingly, MilliporeSigma has created a Cas9 paired-nickase system that promotes HDR, while also limiting off-target effects and increasing the number of sequences available for site-dependent gene modifications, such as disease-associated single nucleotide polymorphisms (SNPs).“The best thing you can do is to cut as close to the SNP as possible,” advised Dr. Davis. “As you move the double-stranded break away from the site of mutation you get an exponential drop in the frequency of recombination.”

 

  • Ribonucleo-protein Complexes

    Another strategy to improve gene-editing efficiency, developed by Thermo Fisher, involves combining purified Cas9 protein with gRNA to generate a stable ribonucleoprotein (RNP) complex. In contrast to plasmid- or mRNA-based formats, which require transcription and/or translation, the Cas9 RNP complex cuts DNA immediately after entering the cell. Rapid clearance of the complex from the cell helps to minimize off-target effects, and, unlike a viral vector, the transient complex does not introduce foreign DNA sequences into the genome.

    To deliver their Cas9 RNP complex to cells, Thermo Fisher has developed a lipofectamine transfection reagent called CRISPRMAX. “We went back to the drawing board with our delivery, screened a bunch of components, and got a brand-new, fully  optimized lipid nanoparticle formulation,” explained Jon Chesnut, Ph.D., the company’s senior director of synthetic biology R&D. “The formulation is specifically designed for delivering the RNP to cells more efficiently.”

    Besides the reagent and the formulation, Thermo Fisher has also developed a range of gene-editing tools. For example, it has introduced the Neon® transfection system for delivering DNA, RNA, or protein into cells via electroporation. Dr. Chesnut emphasized the company’s focus on simplifying complex workflows by optimizing protocols and pairing everything with the appropriate up- and downstream reagents.

From Mammalian Cells to Microbes

One of the first sources of CRISPR technology was the Feng Zhang laboratory at the Broad Institute, which counted among its first licensees a company called GenScript. This company offers a gene-editing service called GenCRISPR™ to establish mammalian cell lines with CRISPR-derived gene knockouts.

“There are a lot of challenges with mammalian cells, and each cell line has its own set of issues,” said Laura Geuss, a marketing specialist at GenScript. “We try to offer a variety of packages that can help customers who have difficult-to-work-with cells.” These packages include both viral-based and transient transfection techniques.

However, the most distinctive service offered by GenScript is its microbial genome-editing service for bacteria (Escherichia coli) and yeast (Saccharomyces cerevisiae). The company’s strategy for gene editing in bacteria can enable seamless knockins, knockouts, or gene replacements by combining CRISPR with lambda red recombineering. Traditionally one of the most effective methods for gene editing in microbes, recombineering allows editing without restriction enzymes through in vivo homologous recombination mediated by a phage-based recombination system such as lambda red.

On its own, lambda red technology cannot target multiple genes, but when paired with CRISPR, it allows the editing of multiple genes with greater efficiency than is possible with CRISPR alone, as the lambda red proteins help repair double-strand breaks in E. coli. The ability to knockout different gene combinations makes Genscript’s microbial editing service particularly well suited for the optimization of metabolic pathways.

Pooled and Arrayed Library Strategies

Scientists are using CRISPR technology for applications such as metabolic engineering and drug development. Yet another application area benefitting from CRISPR technology is cancer research. Here, the use of pooled CRISPR libraries is becoming commonplace. Pooled CRISPR libraries can help detect mutations that affect drug resistance, and they can aid in patient stratification and clinical trial design.

Pooled screening uses proliferation or viability as a phenotype to assess how genetic alterations, resulting from the application of a pooled CRISPR library, affect cell growth and death in the presence of a therapeutic compound. The enrichment or depletion of different gRNA populations is quantified using deep sequencing to identify the genomic edits that result in changes to cell viability.

MilliporeSigma provides pooled CRISPR libraries ranging from the whole human genome to smaller custom pools for these gene-function experiments. For pharmaceutical and biotech companies, Horizon Discovery offers a pooled screening service, ResponderSCREEN, which provides a whole-genome pooled screen to identify genes that confer sensitivity or resistance to a compound. This service is comprehensive, taking clients from experimental design all the way through to suggestions for follow-up studies.

Horizon Discovery maintains a Research Biotech business unit that is focused on target discovery and enabling translational medicine in oncology. “Our internal backbone gives us the ability to provide expert advice demonstrated by results,” said Jon Moore, Ph.D., the company’s CSO.

In contrast to a pooled screen, where thousands of gRNA are combined in one tube, an arrayed screen applies one gRNA per well, removing the need for deep sequencing and broadening the options for different endpoint assays. To establish and distribute a whole-genome arrayed lentiviral CRISPR library, MilliporeSigma partnered with the Wellcome Trust Sanger Institute. “This is the first and only arrayed CRISPR library in the world,” declared Shawn Shafer, Ph.D., functional genomics market segment manager, MilliporeSigma. “We were really proud to partner with Sanger on this.”

Pooled and arrayed screens are powerful tools for studying gene function. The appropriate platform for an experiment, however, will be determined by the desired endpoint assay.

Detection and Quantification of Edits

 

http://www.genengnews.com/Media/images/Article/BioRad_QX200_System4276117210.jpg

The QX200 Droplet Digital PCR System from Bio-Rad Laboratories can provide researchers with an absolute measure of target DNA molecules for EvaGreen or probe-based digital PCR applications. The system, which can provide rapid, low-cost, ultra-sensitive quantification of both NHEJ- and HDR-editing events, consists of two instruments, the QX200 Droplet Generator and the QX200 Droplet Reader, and their associated consumables.

Finally, one last challenge for CRISPR lies in the detection and quantification of changes made to the genome post-editing. Conventional methods for detecting these alterations include gel methods and next-generation sequencing. While gel methods lack sensitivity and scalability, next-generation sequencing is costly and requires intensive bioinformatics.

To address this gap, Bio-Rad Laboratories developed a set of assay strategies to enable sensitive and precise edit detection with its Droplet Digital PCR (ddPCR) technology. The platform is designed to enable absolute quantification of nucleic acids with high sensitivity, high precision, and short turnaround time through massive droplet partitioning of samples.

Using a validated assay, a typical ddPCR experiment takes about five to six hours to complete. The ddPCR platform enables detection of rare mutations, and publications have reported detection of precise edits at a frequency of <0.05%, and of NHEJ-derived indels at a frequency as low as 0.1%. In addition to quantifying precise edits, indels, and computationally predicted off-target mutations, ddPCR can also be used to characterize the consequences of edits at the RNA level.

According to a recently published Science paper, the laboratory of Charles A. Gersbach, Ph.D., at Duke University used ddPCR in a study of muscle function in a mouse model of Duchenne muscular dystrophy. Specifically, ddPCR was used to assess the efficiency of CRISPR-Cas9 in removing the mutated exon 23 from the dystrophin gene. (Exon 23 deletion by CRISPR-Cas9 resulted in expression of the modified dystrophin gene and significant enhancement of muscle force.)

Quantitative ddPCR showed that exon 23 was deleted in ~2% of all alleles from the whole-muscle lysate. Further ddPCR studies found that 59% of mRNA transcripts reflected the deletion.

“There’s an overarching idea that the genome-editing field is moving extremely quickly, and for good reason,” asserted Jennifer Berman, Ph.D., staff scientist, Bio-Rad Laboratories. “There’s a lot of exciting work to be done, but detection and quantification of edits can be a bottleneck for researchers.”

The gene-editing field is moving quickly, and new innovations are finding their way into the laboratory as researchers lay the foundation for precise, well-controlled gene editing with CRISPR.

 

Are Current Cancer Drug Discovery Methods Flawed?

GEN May 3, 2016   http://www.genengnews.com/gen-news-highlights/are-current-cancer-drug-discovery-methods-flawed/81252682/

 

Researchers utilized a systems biology approach to develop new methods to assess drug sensitivity in cells. [The Institute for Systems Biology]

Understanding how cells respond and proliferate in the presence of anticancer compounds has been the foundation of drug discovery ideology for decades. Now, a new study from scientists at Vanderbilt University casts significant suspicion on the primary method used to test compounds for anticancer activity in cells—instilling doubt on methods employed by the entire scientific enterprise and pharmaceutical industry to discover new cancer drugs.

“More than 90% of candidate cancer drugs fail in late-stage clinical trials, costing hundreds of millions of dollars,” explained co-senior author Vito Quaranta, M.D., director of the Quantitative Systems Biology Center at Vanderbilt. “The flawed in vitro drug discovery metric may not be the only responsible factor, but it may be worth pursuing an estimate of its impact.”

The Vanderbilt investigators have developed what they believe to be a new metric for evaluating a compound’s effect on cell proliferation—called the DIP (drug-induced proliferation) rate—that overcomes the flawed bias in the traditional method.

The findings from this study were published recently in Nature Methods in an article entitled “An Unbiased Metric of Antiproliferative Drug Effect In Vitro.”

For more than three decades, researchers have evaluated the ability of a compound to kill cells by adding the compound in vitro and counting how many cells are alive after 72 hours. Yet, proliferation assays that measure cell number at a single time point don’t take into account the bias introduced by exponential cell proliferation, even in the presence of the drug.

“Cells are not uniform, they all proliferate exponentially, but at different rates,” Dr. Quaranta noted. “At 72 hours, some cells will have doubled three times and others will not have doubled at all.”

Dr. Quaranta added that drugs don’t all behave the same way on every cell line—for example, a drug might have an immediate effect on one cell line and a delayed effect on another.

The research team decided to take a systems biology approach, a mixture of experimentation and mathematical modeling, to demonstrate the time-dependent bias in static proliferation assays and to develop the time-independent DIP rate metric.

“Systems biology is what really makes the difference here,” Dr. Quaranta remarked. “It’s about understanding cells—and life—as dynamic systems.”This new study is of particular importance in light of recent international efforts to generate data sets that include the responses of thousands of cell lines to hundreds of compounds. Using the

  • Cancer Cell Line Encyclopedia (CCLE) and
  • Genomics of Drug Sensitivity in Cancer (GDSC) databases

will allow drug discovery scientists to include drug response data along with genomic and proteomic data that detail each cell line’s molecular makeup.

“The idea is to look for statistical correlations—these particular cell lines with this particular makeup are sensitive to these types of compounds—to use these large databases as discovery tools for new therapeutic targets in cancer,” Dr. Quaranta stated. “If the metric by which you’ve evaluated the drug sensitivity of the cells is wrong, your statistical correlations are basically no good.”

The Vanderbilt team evaluated the responses from four different melanoma cell lines to the drug vemurafenib, currently used to treat melanoma, with the standard metric—used for the CCLE and GDSC databases—and with the DIP rate. In one cell line, they found a glaring disagreement between the two metrics.

“The static metric says that the cell line is very sensitive to vemurafenib. However, our analysis shows this is not the case,” said co-lead study author Leonard Harris, Ph.D., a systems biology postdoctoral fellow at Vanderbilt. “A brief period of drug sensitivity, quickly followed by rebound, fools the static metric, but not the DIP rate.”

Dr. Quaranta added that the findings “suggest we should expect melanoma tumors treated with this drug to come back, and that’s what has happened, puzzling investigators. DIP rate analyses may help solve this conundrum, leading to better treatment strategies.”

The researchers noted that using the DIP rate is possible because of advances in automation, robotics, microscopy, and image processing. Moreover, the DIP rate metric offers another advantage—it can reveal which drugs are truly cytotoxic (cell killing), rather than merely cytostatic (cell growth inhibiting). Although cytostatic drugs may initially have promising therapeutic effects, they may leave tumor cells alive that then have the potential to cause the cancer to recur.

The Vanderbilt team is currently in the process of identifying commercial entities that can further refine the software and make it widely available to the research community to inform drug discovery.

 

An unbiased metric of antiproliferative drug effect in vitro

Leonard A Harris, Peter L Frick, Shawn P Garbett, Keisha N Hardeman, B Bishal Paudel, Carlos F Lopez, Vito Quaranta & Darren R Tyson
Nature Methods 2 May (2016)                 doi:10.1038/nmeth.3852

In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current metrics of antiproliferative small molecule effect suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.

  1. Zuber, J. et al. Nat. Biotechnol. 29, 79–83 (2011).
  2. Berns, K. et al. Nature 428, 431–437 (2004).
  3. Bonnans, C., Chou, J. & Werb, Z. Nat. Rev. Mol. Cell Biol. 15, 786–801 (2014).
  4. Garnett, M.J. et al. Nature 483, 570–575 (2012)

 

Mapping Traits to Genes with CRISPR

Researchers develop a technique to direct chromosome recombination with CRISPR/Cas9, allowing high-resolution genetic mapping of phenotypic traits in yeast.

By Catherine Offord | May 5, 2016

http://www.the-scientist.com/?articles.view/articleNo/46029/title/Mapping-Traits-to-Genes-with-CRISPR

 

http://www.the-scientist.com/images/News/May2016/sciencefigure.jpg

Researchers used CRISPR/Cas9 to make a targeted double-strand break (DSB) in one arm of a yeast chromosome labeled with a green fluorescent protein (GFP) gene. A within-cell mechanism called homologous repair (HR) mends the broken arm using its homolog, resulting in a recombined region from the site of the break to the chromosome tip. When this cell divides by mitosis, each daughter cell will contain a homozygous section in an outcome known as “loss of heterozygosity” (LOH). One of the daughter cells is detectable because, due to complete loss of the GFP gene, it will no longer be fluorescent.REPRINTED WITH PERMISSION FROM M.J. SADHU ET AL., SCIENCE

When mapping phenotypic traits to specific loci, scientists typically rely on the natural recombination of chromosomes during meiotic cell division in order to infer the positions of responsible genes. But recombination events vary with species and chromosome region, giving researchers little control over which areas of the genome are shuffled. Now, a team at the University of California, Los Angeles (UCLA), has found a way around these problems by using CRISPR/Cas9 to direct targeted recombination events during mitotic cell division in yeast. The team described its technique today (May 5) in Science.

“Current methods rely on events that happen naturally during meiosis,” explained study coauthor Leonid Kruglyak of UCLA. “Whatever rate those events occur at, you’re kind of stuck with. Our idea was that using CRISPR, we can generate those events at will, exactly where we want them, in large numbers, and in a way that’s easy for us to pull out the cells in which they happened.”

Generally, researchers use coinheritance of a trait of interest with specific genetic markers—whose positions are known—to figure out what part of the genome is responsible for a given phenotype. But the procedure often requires impractically large numbers of progeny or generations to observe the few cases in which coinheritance happens to be disrupted informatively. What’s more, the resolution of mapping is limited by the length of the smallest sequence shuffled by recombination—and that sequence could include several genes or gene variants.

“Once you get down to that minimal region, you’re done,” said Kruglyak. “You need to switch to other methods to test every gene and every variant in that region, and that can be anywhere from challenging to impossible.”

But programmable, DNA-cutting champion CRISPR/Cas9 offered an alternative. During mitotic—rather than meiotic—cell division, rare, double-strand breaks in one arm of a chromosome preparing to split are sometimes repaired by a mechanism called homologous recombination. This mechanism uses the other chromosome in the homologous pair to replace the sequence from the break down to the end of the broken arm. Normally, such mitotic recombination happens so rarely as to be impractical for mapping purposes. With CRISPR/Cas9, however, the researchers found that they could direct double-strand breaks to any locus along a chromosome of interest (provided it was heterozygous—to ensure that only one of the chromosomes would be cut), thus controlling the sites of recombination.

Combining this technique with a signal of recombination success, such as a green fluorescent protein (GFP) gene at the tip of one chromosome in the pair, allowed the researchers to pick out cells in which recombination had occurred: if the technique failed, both daughter cells produced by mitotic division would be heterozygous, with one copy of the signal gene each. But if it succeeded, one cell would end up with two copies, and the other cell with none—an outcome called loss of heterozygosity.

“If we get loss of heterozygosity . . . half the cells derived after that loss of heterozygosity event won’t have GFP anymore,” study coauthor Meru Sadhu of UCLA explained. “We search for these cells that don’t have GFP out of the general population of cells.” If these non-fluorescent cells with loss of heterozygosity have the same phenotype as the parent for a trait of interest, then CRISPR/Cas9-targeted recombination missed the responsible gene. If the phenotype is affected, however, then the trait must be linked to a locus in the recombined, now-homozygous region, somewhere between the cut site and the GFP gene.

By systematically making cuts using CRISPR/Cas9 along chromosomes in a hybrid, diploid strain ofSaccharomyces cerevisiae yeast, picking out non-fluorescent cells, and then observing the phenotype, the UCLA team demonstrated that it could rapidly identify the phenotypic contribution of specific gene variants. “We can simply walk along the chromosome and at every [variant] position we can ask, does it matter for the trait we’re studying?” explained Kruglyak.

For example, the team showed that manganese sensitivity—a well-defined phenotypic trait in lab yeast—could be pinpointed using this method to a single nucleotide polymorphism (SNP) in a gene encoding the Pmr1 protein (a manganese transporter).

Jason Moffat, a molecular geneticist at the University of Toronto who was not involved in the work, toldThe Scientist that researchers had “dreamed about” exploiting these sorts of mechanisms for mapping purposes, but without CRISPR, such techniques were previously out of reach. Until now, “it hasn’t been so easy to actually make double-stranded breaks on one copy of a pair of chromosomes, and then follow loss of heterozygosity in mitosis,” he said, adding that he hopes to see the approach translated into human cell lines.

Applying the technique beyond yeast will be important, agreed cell and developmental biologist Ethan Bier of the University of California, San Diego, because chromosomal repair varies among organisms. “In yeast, they absolutely demonstrate the power of [this method],” he said. “We’ll just have to see how the technology develops in other systems that are going to be far less suited to the technology than yeast. . . . I would like to see it implemented in another system to show that they can get the same oomph out of it in, say, mammalian somatic cells.”

Kruglyak told The Scientist that work in higher organisms, though planned, is still in early stages; currently, his team is working to apply the technique to map loci responsible for trait differences between—rather than within—yeast species.

“We have a much poorer understanding of the differences across species,” Sadhu explained. “Except for a few specific examples, we’re pretty much in the dark there.”

M.J. Sadhu, “CRISPR-directed mitotic recombination enables genetic mapping without crosses,” Science, doi:10.1126/science.aaf5124, 2016.

 

CRISPR-directed mitotic recombination enables genetic mapping without crosses

Meru J Sadhu, Joshua S Bloom, Laura Day, Leonid Kruglyak
http://biorxiv.org/content/biorxiv/early/2016/02/19/040428.full.pdf     doi: http://dx.doi.org/10.1101/040428
Linkage and association studies have mapped thousands of genomic regions that contribute to phenotypic variation, but narrowing these regions to the underlying causal genes and variants has proven much more challenging. Resolution of genetic mapping is limited by the recombination rate. We developed a method that uses CRISPR to build mapping panels with targeted recombination events. We tested the method by generating a panel with recombination events spaced along a yeast chromosome arm, mapping trait variation, and then targeting a high density of recombination events to the region of interest. Using this approach, we fine-mapped manganese sensitivity to a single polymorphism in the transporter Pmr1. Targeting recombination events to regions of interest allows us to rapidly and systematically identify causal variants underlying trait differences.
The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Meru Sadhu • 2 months ago

Thank you, David, for the kind words and comments. We agree that the most immediate applications of the CRISPR-based recombination mapping will be in unicellular organisms and cell culture. We also think the method holds a lot of promise for research in multicellular organisms, although we did not mean to imply that it “will be an efficient mapping method for all multicellular organisms”. Every organism will have its own set of constraints as well as experimental tools that will be relevant when adapting a new technique. To best help experts working on these organisms, here are our thoughts on your questions.

You asked about mutagenesis during recombination. We Sanger sequenced 72 of our LOH lines at the recombination site and did not observe any mutations, as described in the supplementary materials. We expect the absence of mutagenesis is because we targeted heterozygous sites where the untargeted allele did not have a usable PAM site; thus, following LOH, the targeted site is no longer present and cutting stops. In your experiments you targeted sites that were homozygous; thus, following recombination, the CRISPR target site persisted, and continued cutting ultimately led to repair by NHEJ and mutagenesis.

As to the more general question of the optimal mapping strategies in different organisms, they will depend on the ease of generating and screening for editing events, the cost and logistics of maintaining and typing many lines, and generation time, among other factors. It sounds like in Drosophila today, your related approach of generating markers with CRISPR, and then enriching for natural recombination events that separate them, is preferable. In yeast, we’ve found the opposite to be the case. As you note, even in Drosophila, our approach may be preferable for regions with low or highly non-uniform recombination rates.

Finally, mapping in sterile interspecies hybrids should be straightforward for unicellular hybrids (of which there are many examples) and for cells cultured from hybrid animals or plants. For studies in hybrid multicellular organisms, we agree that driving mitotic recombination in the early embryo may be the most promising approach. Chimeric individuals with mitotic clones will be sufficient for many traits. Depending on the system, it may in fact be possible to generate diploid individuals with uniform LOH genotype, but this is certainly beyond the scope of our paper. The calculation of the number of lines assumes that the mapping is done in a single step; as you note in your earlier comment, mapping sequentially can reduce this number dramatically.

David Stern 3 months ago

This is a lovely method and should find wide applicability in many settings, especially for microorganisms and cell lines. However, it is not clear that this approach will be, as implied by the discussion, an efficient mapping method for all multicellular organisms. I have performed similar experiments in Drosophila, focused on meiotic recombination, on a much smaller scale, and found that CRISPR-Cas9 can indeed generate targeted recombination at gRNA target sites. In every case I tested, I found that the recombination event was associated with a deletion at the gRNA site, which is probably unimportant for most mapping efforts, but may be a concern in some specific cases, for example for clinical applications. It would be interesting to know how often mutations occurred at the targeted gRNA site in this study.

The wider issue, however, is whether CRISPR-mediated recombination will be more efficient than other methods of mapping. After careful consideration of all the costs and the time involved in each of the steps for Drosophila, we have decided that targeted meiotic recombination using flanking visible markers will be, in most cases, considerably more efficient than CRISPR-mediated recombination. This is mainly due to the large expense of injecting embryos and the extensive effort and time required to screen injected animals for appropriate events. It is both cheaper and faster to generate markers (with CRISPR) and then perform a large meiotic recombination mapping experiment than it would be to generate the lines required for CRISPR-mediated recombination mapping. It is possible to dramatically reduce costs by, for example, mapping sequentially at finer resolution. But this approach would require much more time than marker-assisted mapping. If someone develops a rapid and cheap method of reliably introducing DNA into Drosophila embryos, then this calculus might change.

However, it is possible to imagine situations where CRISPR-mediated mapping would be preferable, even for Drosophila. For example, some genomic regions display extremely low or highly non-uniform recombination rates. It is possible that CRISPR-mediated mapping could provide a reasonable approach to fine mapping genes in these regions.

The authors also propose the exciting possibility that CRISPR-mediated loss of heterozygosity could be used to map traits in sterile species hybrids. It is not entirely obvious to me how this experiment would proceed and I hope the authors can illuminate me. If we imagine driving a recombination event in the early embryo (with maternal Cas9 from one parent and gRNA from a second parent), then at best we would end up with chimeric individuals carrying mitotic clones. I don’t think one could generate diploid animals where all cells carried the same loss of heterozygosity event. Even if we could, this experiment would require construction of a substantial number of stable transgenic lines expressing gRNAs. Mapping an ~20Mbp chromosome arm to ~10kb would require on the order of two-thousand transgenic lines. Not an undertaking to be taken lightly. It is already possible to perform similar tests (hemizygosity tests) using D. melanogaster deficiency lines in crosses with D. simulans, so perhaps CRISPR-mediated LOH could complement these deficiency screens for fine mapping efforts. But, at the moment, it is not clear to me how to do the experiment.

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Somatic Mutation Theory – Why it’s Wrong for Most Cancers

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Cancer Screening, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Disease Biology, Epigenetics and Environmental Factors, Gene Regulation, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genome Biology, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Methylation, Mutagenesis, tagged on May 3, 2016| Leave a Comment »

Somatic Mutation Theory – Why it’s Wrong for Most Cancers, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Somatic Mutation Theory – Why it’s Wrong for Most Cancers

Reporter: Aviva Lev-Ari, PhD, RN

 

 

Somatic Mutation Theory – Why it’s Wrong for Most Cancers.

Cell Physiol Biochem 2016;38:1663-1680. http://www.karger.com/Article/FullText/443106

Björn L.D.M. Brücher and Ijaz S. Jamall

Brucher [a to d] and Jamall [a,b,e]

a Theodor-Billroth-Academy®, Munich, Germany – Sacramento, California, USA;

b INCORE, International Consortium of Research Excellence of the Theodor-Billroth-Academy®, Munich, Germany – Sacramento, California, USA;

c Bon Secours Cancer Institute, Richmond, VA, USA;

d Dep. of Surgery, Carl- Thiem-Klinikum, Cottbus, Germany;

e Risk-Based Decisions Inc., Sacramento, CA, USA

 

Key Words

Carcinogenesis • Somatic mutation theory • Microenvironment • Cell communication • Signaling • Inflammation • Chronic inflammation • Fibrosis • Cell transition • Precancerous niche

Abstract

Hysteron proteron reverses both temporal and logical order and this syllogism occurs in carcinogenesis and the somatic mutation theory (SMT): the first (somatic mutation) occurs only after the second (onset of cancer) and, therefore, observed somatic mutations in most cancers appear well after the early cues of carcinogenesis are in place. It is no accident that mutations are increasingly being questioned as the causal event in the origin of the vast majority of cancers as clinical data show little support for this theory when compared against the metrics of patient outcomes. Ever since the discovery of the double helical structure of DNA, virtually all chronic diseases came to be viewed as causally linked to one degree or another to mutations, even though we now know that genes are not simply blueprints, but rather an assemblage of alphabets that can, under non-genetic influences, be used to assemble a business letter or a work of Shakespearean literature. A minority of all cancers is indeed caused by mutations but the SMT has been applied to all cancers, and even to chemical carcinogenesis, in the absence of hard evidence of causality. Herein, we review the 100 year story of SMT and aspects that show why genes are not just blueprints, how radiation and mutation are associated in a more nuanced view, the proposed risk of cancer and bad luck, and the in vitro and in vivo evidence for a new cancer paradigm. This paradigm is scientifically applicable for the majority of non-heritable cancers and consists of a six-step sequence for the origin of cancer. This new cancer paradigm proclaims that somatic mutations are epiphenomena or later events occurring after carcinogenesis is already underway. This serves not just as a plausible alternative to SMT and explains the origin of the majority of cancers, but also provides opportunities for early interventions and prevention of the onset of cancer as a disease.

  

Conclusions

The incorrect interpretation of data can sometimes appear to be the more parsimonious explanation especially when it has acquired the mantle of a paradigm, as in the case of the SMT. Summa Cancerologica is not hypothetical or ontological. Its syllogism of carcinogenesis needs the consideration of all reasonable perspectives such as whether somatic mutations are later events or epiphenomena occurring at the end of the sequence of events in carcinogenesis. This mutatio praemissarum leads to a reflection of reasoned judgments of correct findings in cancer (mutations within tumors) together with clinical observations (relevance of such mutations to cancer therapy). An overemphasis of the SMT as the sole reason of the origin of carcinogenesis elevated it to the status of a dogma which downplays significant findings of mutations and genetics in different fields of nature, biology and science. However, there is hope that hereditary cancers can be treated in the near future as new technologies make it possible to manipulate proteins packaging DNA to turn on specific gene promoters and enhancers [164]. If this were applicable to the mass of non-hereditary cancers this approach would still be only symptomatic as the genesis of non-hereditary cancers is not caused by somatic mutations though somatic mutations occur within tumors. Focusing on the tumor cell without its origin including the microenvironment won’t be enough [165]. The reasoning on the origin of carcinogenesis, including different step-wise sequences, may help unmask mechanisms of the transition of a normal into a cancer cell (cancer genesis) as well as its different primary pathogenic stimulus, which can serve to prevent or retard cancers instead of concentrating on symptomatic strategies or for a cure for all cancers. It is scientifically valid based on in vitro and in vivo genetic findings that carcinogenesis consists of a six-step multi sequence process [17, 18]. This serves not just as a plausible alternative to the SMT to explain the origin of the majority of cancers, but could also suggest early interventions and thereby prevent the onset of cancer as a disease.

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Clinical Biomarkers Overview

Posted in Cell Biology, Chemical Genetics, Clinical Genomics, Disease Biology, Epigenetics and Environmental Factors, Gene Regulation, Genetics & Pharmaceutical, tagged , , , , , on May 2, 2016| Leave a Comment »

Clinical Biomarkers Overview

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Paving the Road for Clinical Biomarkers

Where Trackless Terrain Once Challenged Biomarker Development, Clearer Paths Are Emerging

http://www.genengnews.com/gen-articles/paving-the-road-for-clinical-biomarkers/5757/

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Fusion detection can be carried out with traditional opposing primer-based library preparation methods, which require target- and fusion-specific primers that define the region to be sequenced. With these methods, primers are needed that flank the target region and the fusion partner, so only known fusions can be detected. An alternative method, ArcherDX’ Anchored Multiplex PCR (AMP), can be used to detect the target of interest, plus any known and unknown fusion partners. This is because AMP uses target-specific unidirectional primers, along with reverse primers, that hybridize to the sequencing adapter that is ligated to each fragment prior to amplification.

 

  • In time, the narrow, tortuous paths followed by pioneers become wider and straighter, whether the pioneers are looking to settle new land or bring new biomarkers to the clinic.

In the case of biomarkers, we’re still at the stage where pioneers need to consult guides and outfitters or, in modern parlance, consultants and technology providers. These hardy souls tend to congregate at events like the Biomarker Conference, which was held recently in San Diego.

At this event, biomarker experts discussed ways to avoid unfortunate detours on the trail from discovery and development to clinical application and regulatory approval. Of particular interest were topics such as the identification of accurate biomarkers, the explication of disease mechanisms, the stratification of patient groups, and the development of standard protocols and assay platforms. In each of these areas, presenters reported progress.

Another crucial subject is the integration of techniques such as next-generation sequencing (NGS). This particular technique has been instrumental in advancing clinical cancer genomics and continues to be the most feasible way of simultaneously interrogating multiple genes for driver mutations.

Enriching nucleic acid libraries for target genes of interest prior to NGS greatly enhances the sensitivity of
detecting mutations, as the enriched regions are sequenced multiple times. This is particularly useful when analyzing clinical samples, which generate low amounts of poor-quality nucleic acids.

However, NGS has been limited in its ability to identify gene fusions and translocations, which underlie oncogenesis in a variety of cancers. “These challenges are largely related to the enrichment chemistry used to produce sequencing libraries,” commented Joshua Stahl, chief scientific officer and general manager, ArcherDX.

Most target-enrichment strategies require prior knowledge of both ends of the target region to be sequenced. Therefore, only gene fusions with known partners can be amplified for downstream NGS assays.

Archer’s Anchored Multiplex PCR (AMP™) technology overcomes this limitation, as it can enrich for novel fusions, while only requiring knowledge of one end of the fusion pair. At the heart of the AMP chemistry are unique Molecular Barcode (MBC) adapters, ligated to the 5′ ends of DNA fragments prior to amplification. The MBCs contain universal primer binding sites for PCR and a molecular barcode for identifying unique molecules. When combined with 3′ gene-specific primers, MBCs enable amplification of target regions with unknown 5′ ends.

“AMP is ideal for identifying gene fusions and other driver mutations from FFPE samples,” asserted Mr. Stahl. “Its robust utility was demonstrated for detection of gene fusions, point mutations, insertions, deletions, and copy number changes from low amounts of clinical formalin-fixed, paraffin-embedded (FFPE) RNA and DNA samples.

“Tagging each molecule of input nucleic acid with a unique molecular barcode allows for de-duplication, error correction, and quantitative analysis, resulting in high sequencing consensus. With its low error rate and low limits of detection, AMP is revolutionizing the field of cancer genomics.”

In a proof-of-concept study, a single-tube 23-plex panel was designed to amplify the kinase domains of ALK, RET, ROS1, and MUSK genes by AMP. This enrichment strategy enabled identification of gene fusions with multiple partners and alternative splicing events in lung cancer, thyroid cancer, and glioblastoma specimens by NGS.

Ignyta, a precision medicine company, adopted Archer’s AMP technology in Trailblaze Pharos™, a multiplex assay employed in their STARTRK-2 trial for identifying actionable NTRK, ROS1, and ALK gene rearrangements in solid tumors that can be treated with the novel kinase inhibitor, entrectinib. “Gene fusions are incredibly important in personalized medicine right now,” stated Mr. Stahl. “Archer’s FusionPlex assays are quickly becoming the new gold standard.”

Reading Cancer Signatures

This image, from the Massachusetts General Hospital Cancer Center, shows multicolor fluorescence in situ hybridization (FISH) analysis of cells from a patient with esophagogastric cancer. Remarkably, the FISH analysis revealed that co-amplification of the MET gene (red signal) and the EGFR gene (green signal) existed simultaneously in the same tumor cells. A chromosome 7 control probe is shown in blue.
“Each year 23,000 kidneys are transplanted, and over 175,000 kidney transplants are functional today,” noted Daniel R. Salomon, M.D., medical program director, Scripps Center for Organ Transplantation, Scripps Research Institute. “However, in just 5 years, 3 out of every 10 patients will be back on dialysis, and in 15 years, at least 75% of all patients will lose their kidney grafts.“Tumor biomarkers are critical for predicting and following patient responses to today’s cancer therapies,” said Darrell Borger, Ph.D., co-director of the Translational Research Laboratory and director of the Biomarker Laboratory, Massachussetts General Hospital (MGH) Cancer Center, Harvard Medical School. “If we understand what drives the malignancy in any given patient, we are able to match existing therapies to the patient’s genotype.”

Over the last decade, the Biomarker/Translational Research Laboratory has focused on developing clinical genotyping and fluorescent in situ hybridization (FISH) assays for rapid personalized genomic testing.

“Initially, we analyzed the most prevalent hotspot mutations, about 160 in 25 cancer genes,” continued Dr. Borger. “However, this approach revealed mutations in only half of our patients. With the advent of NGS, we are able to sequence 190 exons in 39 cancer genes and obtain significantly richer genetic fingerprints, finding genetic aberrations in 92% of our cancer patients.”

Using multiplexed approaches, Dr. Borger’s team within the larger Center for Integrated Diagnostics (CID) program at MGH has established high-throughput genotyping service as an important component of routine care. While only a few susceptible molecular alterations may currently have a corresponding drug, the NGS-driven analysis may supply new information for inclusion of patients into ongoing clinical trials, or bank the result for future research and development.

“A significant impediment to discovery of clinically relevant genomic signatures is our current inability to interconnect the data,” explained Dr. Borger. “On the local level, we are striving to compile the data from clinical observations, including responses to therapy and genotyping. Globally, it is imperative that comprehensive public databases become available to the research community.”

Tumor profiling at MGH have already yielded significant discoveries. Dr. Borger’s lab, in collaboration with oncologists at the MGH Cancer Center, found significant correlations between mutations in the genes encoding the metabolic enzymes isocitrate dehydrogenase (IDH1 and IDH2) and certain types of cancers, such as cholangiocarcinoma and acute myelogenous leukemia (AML).

Historically, cancer signatures largely focus on signaling proteins. Discovery of a correlative metabolic enzyme offered a promise of diagnostics based on metabolic byproducts that may be easily identified in blood. Indeed, the metabolite 2-hydroxyglutarate accumulates to high levels in the tissues of patients carrying IDH1 and IDH2 mutations. They have reported that circulating 2-hydroxyglutarate as measured in the blood correlates with tumor burden, and could serve as an important surrogate marker of treatment response.

Tuning Immunosuppression, Preventing Chronic Rejection

“We believe that this is caused by chronic immune-mediated rejection. Failure of effective immunosuppression reduces functional life of these patients and adds in $9–13 billion in yearly healthcare costs.” Dr. Salomon emphasized that ineffective use of immunosuppressive drugs is partially due to the lack of an objective biomarker which could provide decision support for just-in-time adjustment in therapeutic regimens.

“Our research aims to provide that objective measure to clinicians,” explained Dr. Salomon.

To date, kidney transplant biopsies remain the gold standard, even though they are not suitable for continuous monitoring and have both costs and risks. Dr. Salomon’s team developed a minimally invasive diagnostic approach based on unbiased whole-genome expression profiling of blood samples. Using Affymetrix Human Genome U133 Plus 2.0 Gene Chips, the team analyzed 275 bloodsamples of kidney transplant patients with biopsy-proved acute rejection, acute dysfunction without rejection and transplant excellent phenotype.

The data was passed through several machine-learning algorithms to identify a group of about 250 classifiers that predict subacute or acute rejection with 80% accuracy. This signature is locked while the team continues to expand the core dataset aiming to reach a thousand samples by the end of this year.

“As opposed to classical approaches to biomarker discoveries limited to just a few classifiers, our methodology provides for the first use of unbiased whole-genome profiling in the identification of multiple molecular predictors,” declared Dr. Salomon. “We can use this molecular diagnostic strategy to reveal a subacute rejection prior to significant tissue injury leading to transplant dysfunction. Continuous monitoring would inform physicians on the balance between over-suppression and effective/optimal therapy.”

Dr. Salomon is a chief scientific advisor for Transplant Genomics (TGI), a start-up company created to translate the blood-based molecular diagnostics into clinical tests. In late 2016, TGI will begin providing its TruGraf blood tests for kidney transplant recipients for use by four to six U.S. transplant centers through an early-access program (EAP).

Additional tests designed to be used serially to diagnose and treat subclinical episodes of rejection including biopsy gene profiling are in the final stages of development. Validation and will be made available through the EAP in the upcoming months.

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BioAgilytix’ MultiMuscle Analysis is a process that can split sample analysis into multiple parallel tracks to minimize antibody cross-reactivity and allow for use of the best-fit platform or kit for each biomarker analysis. The process may require only one tube of sample with only one F/T cycle.

Focusing on Large Molecules 

BioAgilytix, a specialized bioanalytical laboratory, is a global leader in large molecule bioanalysis. The company’s business encompasses pharmacokinetic/pharmacodynamic (PK/PD) studies of large biomolecules, in addition to immunogenicity, biomarkers, and cell-based assays. In less than 10 years,BioAgilytix has grown from a start-up to an international powerhouse with over 100 employees—more than half possessing advanced scientific degrees—because of its team’s expertise in the complexities of large molecule drug development.

“In contrast to small molecule analysis, which has become more of a commodity due to its semiautomated and process-oriented nature, large molecule analysis is inherently challenging,” said Afshin Safavi, Ph.D., founder and chief science officer of BioAgilytix. “In large molecule bioanalysis, we rely heavily on analytical reagents, such as antibodies and recombinant proteins, which are known to show considerable variability from lot to lot.

BioAgilytix’ MultiMuscle Analysis is a process that can split sample analysis into multiple parallel tracks to minimize antibody cross-reactivity and allow for use of the best-fit platform or kit for each biomarker analysis. The process may require only one tube of sample with only one F/T cycle.

“Therefore, designing an effective analytical process for large biomolecules requires scientific personnel with years of experience. It also requires careful management of critical reagents, and a deep understanding of the capabilities and limitations of the platforms selected for use.”

Dr. Safavi explains that the biomarker field has been trending away from a gunshot approach traditionally favored by large pharma to more focused analyses of a few key biomarkers.

“Unlike several years ago, most biotech and pharma companies now perform careful due diligence and literature research before approaching us, to narrow down their investigation to just a handful of biomarkers,” he explained. Limited samples may drive the desire to multiplex as many biomarkers as possible, but a multiplex approach may often result in low quality data due to reagent cross-reactivity.

A recent process innovation developed by BioAgilytix, called MultiMuscle Analysis™, uses a customized parallel process to drastically reduce analytical process time and increase data quality. MultiMuscle Analysis splits the sample analysis into multiple parallel tracks, each performed on specialized equipment by scientists experienced in that particular platform.

“Say, for example, a customer requests measurements of 10 biomarkers,” ventured Dr. Safavi. “If we know some of the antibodies may cross-react, then we may, for example, end up with one heptaplex and three as uniplexes, all done in parallel.”

Using this approach, BioAgilytix is able to perform large biomarker analyses on a very large number of samples in near real-time. “We now receive samples from over 20 countries,” Dr. Safavi stated. “We have used the MultiMuscle approach successfully over and over.”

Feature ArticlesMore » May 1, 2016 (Vol. 36, No. 9)

Paving the Road for Clinical Biomarkers

Where Trackless Terrain Once Challenged Biomarker Development, Clearer Paths Are Emerging

Kate Marusina, Ph.D.

Focusing on Large Molecules

BioAgilytix’ MultiMuscle Analysis is a process that can split sample analysis into multiple parallel tracks to minimize antibody cross-reactivity and allow for use of the best-fit platform or kit for each biomarker analysis. The process may require only one tube of sample with only one F/T cycle.

BioAgilytix, a specialized bioanalytical laboratory, is a global leader in large molecule bioanalysis. The company’s business encompasses pharmacokinetic/pharmacodynamic (PK/PD) studies of large biomolecules, in addition to immunogenicity, biomarkers, and cell-based assays. In less than 10 years, BioAgilytix has grown from a start-up to an international powerhouse with over 100 employees—more than half possessing advanced scientific degrees—because of its team’s expertise in the complexities of large molecule drug development.

“In contrast to small molecule analysis, which has become more of a commodity due to its semiautomated and process-oriented nature, large molecule analysis is inherently challenging,” said Afshin Safavi, Ph.D., founder and chief science officer of BioAgilytix. “In large molecule bioanalysis, we rely heavily on analytical reagents, such as antibodies and recombinant proteins, which are known to show considerable variability from lot to lot.

“Therefore, designing an effective analytical process for large biomolecules requires scientific personnel with years of experience. It also requires careful management of critical reagents, and a deep understanding of the capabilities and limitations of the platforms selected for use.”

Dr. Safavi explains that the biomarker field has been trending away from a gunshot approach traditionally favored by large pharma to more focused analyses of a few key biomarkers.

“Unlike several years ago, most biotech and pharma companies now perform careful due diligence and literature research before approaching us, to narrow down their investigation to just a handful of biomarkers,” he explained. Limited samples may drive the desire to multiplex as many biomarkers as possible, but a multiplex approach may often result in low quality data due to reagent cross-reactivity.

A recent process innovation developed by BioAgilytix, called MultiMuscle Analysis™, uses a customized parallel process to drastically reduce analytical process time and increase data quality. MultiMuscle Analysis splits the sample analysis into multiple parallel tracks, each performed on specialized equipment by scientists experienced in that particular platform.

“Say, for example, a customer requests measurements of 10 biomarkers,” ventured Dr. Safavi. “If we know some of the antibodies may cross-react, then we may, for example, end up with one heptaplex and three as uniplexes, all done in parallel.”

Using this approach, BioAgilytix is able to perform large biomarker analyses on a very large number of samples in near real-time. “We now receive samples from over 20 countries,” Dr. Safavi stated. “We have used the MultiMuscle approach successfully over and over.”

Predicting Clotting or Hemorrhaging

Venous thromboembolism (VTE) is a disease that includes both deep vein thrombosis (DVT) and pulmonary embolism (PE). It is a common, lethal disorder, symptoms of which are often overlooked. VTE is the third most common cardiovascular illness after acute coronary syndrome and stroke.

Venous thrombi, composed predominately of red blood cells bound together by fibrin, form in sites of vessel damage and areas of stagnant blood flow. Once VTE is diagnosed, anticoagulation therapy is indicated.

A novel anticoagulant that reversibly and directly inhibits factor Xa, a key factor in the coagulation system, has been developed by Daiichi Sankyo. “Once on the path of development of an anticoagulant, we recognized the lack of a rapid and sensitive coagulation test that would not be affected by blood traces of anticoagulant therapies,” said Michele Mercuri, M.D., Ph.D., the company’s senior vice president. “An improved diagnostic test would speed up recognition and treatment of thrombosis, and would aid in development of reversing agents that reduce the effect of anticoagulant therapies when needed.”

When Daiichi Sankyo entered in collaboration with Perosphere to develop a novel broad-spectrum reversing agent, the company also supported development of a point-of-care coagulometer (still under development), a hand-held device designed for broad-spectrum monitoring of the activity of anticoagulants and their corresponding reversing agents, across drug classes. A single test requires only 10 µL of fresh or citrated whole blood from a venous draw or finger stick. It optically measures clotting starting with Factor XII activation to fibrin assembly.

Dr. Mercuri explains that none of the existing tests are able to predict whether a patient is at risk for either clotting or hemorrhaging. “Together with Prof. Zahi Fayad’s Team from the Icahn School of Medicine at Mt Sinai, we used magnetic resonance imaging with the gadolinium-based contrast reagent to detect the venous thrombi and follow their dissolution with edoxaban treatment,” reported Dr. Mercuri.

This study, the edoxaban Thrombus Reduction Imaging Study (eTRIS), was focused on developing and validating a magnetic resonance venography (MRV) image acquisition and analysis protocol for the quantification of thrombus volume in deep vein thrombosis. The multicenter study demonstrated excellent reproducibility of analysis of quantifying thrombus volume.

 

Sequence and Epigenetic Factors Determine Overall DNA Structure

Researchers at Ulsan National Institute of Science and Technology (UNIST) in South Korea found that DNA molecules directly interact with one another in ways that are dependent on the sequence of the DNA and epigenetic factors.

The researchers found evidence for sequence-dependent attractive interactions between double-stranded DNA molecules that neither involve intermolecular strand exchange nor are mediated by DNA-binding proteins.

“DNA molecules tend to repel each other in water, but in the presence of special types of cations, they can attract each other just like nuclei pulling each other by sharing electrons in between,” explained lead study author Hajin Kim, Ph.D., assistant professor of biophysics at UNIST. “Our study suggests that the attractive force strongly depends on the nucleic acid sequence and also the epigenetic modifications.”

The investigators used atomic-level simulations to measure forces between double-stranded DNA helices, proposing that the distribution of methyl groups on DNA were the key to regulating this sequence-dependent attraction.

The findings from this study were published recently in Nature Communications through an article entitled “Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation.”

The researchers surmised that direct DNA-DNA interactions could play a central role in how chromosomes are organized and packaged, determining the ultimate fate of many cell types.

Dr. Kim concluded by stating that “in our lab, we try to unravel the mysteries within human cells based on the principles of physics and the mechanisms of biology—seeking for ways to prevent chronic illnesses and diseases associated with aging.”

Searches Related to Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation

 

Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation

Jejoong Yoo, Hajin Kim, Aleksei Aksimentiev  & Taekjip Ha

Nature Communications 22 Mar 2016; 7(11045)    http://dx.doi.org:/10.1038/ncomms11045

Although proteins mediate highly ordered DNA organization in vivo, theoretical studies suggest that homologous DNA duplexes can preferentially associate with one another even in the absence of proteins. Here we combine molecular dynamics simulations with single-molecule fluorescence resonance energy transfer experiments to examine the interactions between duplex DNA in the presence of spermine, a biological polycation. We find that AT-rich DNA duplexes associate more strongly than GC-rich duplexes, regardless of the sequence homology. Methyl groups of thymine acts as a steric block, relocating spermine from major grooves to interhelical regions, thereby increasing DNA–DNA attraction. Indeed, methylation of cytosines makes attraction between GC-rich DNA as strong as that between AT-rich DNA. Recent genome-wide chromosome organization studies showed that remote contact frequencies are higher for AT-rich and methylated DNA, suggesting that direct DNA–DNA interactions that we report here may play a role in the chromosome organization and gene regulation.

Formation of a DNA double helix occurs through Watson–Crick pairing mediated by the complementary hydrogen bond patterns of the two DNA strands and base stacking. Interactions between double-stranded (ds)DNA molecules in typical experimental conditions containing mono- and divalent cations are repulsive1, but can turn attractive in the presence of high-valence cations2. Theoretical studies have identified the ion–ion correlation effect as a possible microscopic mechanism of the DNA condensation phenomena3, 4, 5. Theoretical investigations have also suggested that sequence-specific attractive forces might exist between two homologous fragments of dsDNA6, and this ‘homology recognition’ hypothesis was supported by in vitro atomic force microscopy7 and in vivo point mutation assays8. However, the systems used in these measurements were too complex to rule out other possible causes such as Watson–Crick strand exchange between partially melted DNA or protein-mediated association of DNA.

Here we present direct evidence for sequence-dependent attractive interactions between dsDNA molecules that neither involve intermolecular strand exchange nor are mediated by proteins. Further, we find that the sequence-dependent attraction is controlled not by homology—contradictory to the ‘homology recognition’ hypothesis6—but by a methylation pattern. Unlike the previous in vitro study that used monovalent (Na+) or divalent (Mg2+) cations7, we presumed that for the sequence-dependent attractive interactions to operate polyamines would have to be present. Polyamine is a biological polycation present at a millimolar concentration in most eukaryotic cells and essential for cell growth and proliferation9, 10. Polyamines are also known to condense DNA in a concentration-dependent manner2, 11. In this study, we use spermine4+(Sm4+) that contains four positively charged amine groups per molecule.

 

Methylation determines the strength of DNA–DNA attraction

Analysis of the MD simulations revealed the molecular mechanism of the polyamine-mediated sequence-dependent attraction (Fig. 2). In the case of the AT-rich fragments, the bulky methyl group of thymine base blocks Sm4+ binding to the N7 nitrogen atom of adenine, which is the cation-binding hotspot21, 22. As a result, Sm4+ is not found in the major grooves of the AT-rich duplexes and resides mostly near the DNA backbone (Fig. 2a,d). Such relocated Sm4+ molecules bridge the two DNA duplexes better, accounting for the stronger attraction16, 23, 24, 25. In contrast, significant amount of Sm4+ is adsorbed to the major groove of the GC-rich helices that lacks cation-blocking methyl group (Fig. 2b,e).

Figure 2: Molecular mechanism of polyamine-mediated DNA sequence recognition.

(a–c) Representative configurations of Sm4+ molecules at the DNA–DNA distance of 28 Å for the (AT)10–(AT)10 (a), (GC)10–(GC)10 (b) and (GmC)10–(GmC)10 (c) DNA pairs. The backbone and bases of DNA are shown as ribbon and molecular bond, respectively; Sm4+ molecules are shown as molecular bonds. Spheres indicate the location of the N7 atoms and the methyl groups. (d–f) The average distributions of cations for the three sequence pairs featured in a–c. Top: density of Sm4+ nitrogen atoms (d=28 Å) averaged over the corresponding MD trajectory and the z axis. White circles (20 Å in diameter) indicate the location of the DNA helices. Bottom: the average density of Sm4+ nitrogen (blue), DNA phosphate (black) and sodium (red) atoms projected onto the DNA–DNA distance axis (x axis). The plot was obtained by averaging the corresponding heat map data over y=[−10, 10] Å. See Supplementary Figs 4 and 5 for the cation distributions at d=30, 32, 34 and 36 Å.

Genome-wide investigations of chromosome conformations using the Hi–C technique revealed that AT-rich loci form tight clusters in human nucleus27, 28. Gene or chromosome inactivation is often accompanied by increased methylation of DNA29 and compaction of facultative heterochromatin regions30. The consistency between those phenomena and our findings suggest the possibility that the polyamine-mediated sequence-dependent DNA–DNA interaction might play a role in chromosome folding and epigenetic regulation of gene expression.

 

Phenotypic and Biomarker-based Drug Discovery

Organizers: Michael Foley (Tri-Institutional Therapeutics Discovery Institute), Ralph Garippa (Memorial Sloan-Kettering Cancer Center), David Mark (F. Hoffmann-La Roche), Lorenz Mayr (Astra Zeneca), John Moffat (Genentech), Marco Prunotto (F. Hoffmann-La Roche), and Sonya Dougal (The New York Academy of Sciences)Presented by the Biochemical Pharmacology Discussion Group

Reported by Robert Frawley | Posted January 12, 2016
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Overview

There are two major methods for designing pharmaceutical drugs. In traditional drug discovery (TDD), or empiric design, researchers target a particular domain or protein after working to understand its mechanisms and molecular biology. In phenotypic drug discovery (PDD), many different compounds are tested on a system until one results in an observable phenotype of success, and the compounds’ mechanisms of action are not considered. The Phenotypic and Biomarker-based Drug Discovery symposium, presented by the Academy’s Biochemical Pharmacology Discussion Group on October 27, 2015, featured current work in PDD and highlighted the need to bridge commercial and academic research to improve phenotypic drug design.

Phenotypic drug discovery—screening of thousands of substances for functional cellular outputs such as gene expression, growth arrest, and cancer cell death—has led to the development of more commercial drugs than TDD, the more common method of discovery. Indeed, as Jonathan A. Lee of Eli Lilly noted, spending on TDD is out of sync with the rate of new drugs reaching approval; the number of new drugs per billion dollars spent dropped sharply in the last few decades. He argued that the need for functionally validated drugs could be met through a renewed focus on PDD.

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Bruce A. Posner started the morning session with a discussion of a phenotypic screen conducted at the University of Texas Southwestern Medical Center which identified two chemical scaffolds that are effective in killing non-small cell lung cancer (NSCLC) cells but are harmless to the non-cancer cells tested. In further studies, the group showed that an optimized analog of one scaffold arrested tumor growth in a mouse xenograft model of NSCLC. Both chemical scaffolds appear to work through a novel mechanism targeting stearoyl-CoA desaturase (SCD), which is known to be important in unsaturated fatty acid synthesis. These compounds were found to be specific, effective, and potent in NSCLC cell lines that express elevated levels of Cyp4F11 and/or related Cyp family members. The group also showed that these scaffolds function as prodrugs that are activated only in cancer cells expressing these Cyp isoforms and that the Cyps produce metabolites of the prodrug that bring about cancer-specific cell toxicity. The group is working to improve these scaffolds and to develop a putative biomarker based on Cyp expression.

The Broad Institute’s LINCS (Library of Network-based Cellular Signatures) database is designed to keep track of small-molecule therapeutics, collecting data on cellular responses to “perturbagens” (drugs, factors, and others stimuli). Data are generated using the L1000 assay, which assesses the expression of 1000 genes known to explain 80% of genetic variation in assayed cell lines. Aravind Subramanian explained that the technique can identify the majority of drug effects for a fraction of the cost of RNA sequencing. Although it examines only a subset of molecules and relies on measuring genetic responses, the technique can help predict the likelihood that new compounds will elicit desired effects.

Martin Main of AstraZeneca described phenotypic drug discovery at AstraZeneca. The company’s model for discovery is to check phenotypic markers at every step, as drugs are moved from cell lines to patients. Main’s team identified a molecule that enhances the regenerative function of cardiac myocytes after infarction. Using cells from several donors, the team validated a promising compound that increases proliferation of cardiac myocytes and drives epicardium-derived progenitor cells to assume a myocyte lineage. In another discovery, the team used islet β-cell regeneration as the phenotype, discovering a compound the researchers believe will reach clinical trials for type 2 diabetes.

Andras J. Bauer of Boehringer Ingelheim discussed a method to increase predictive strength in compound selection before phenotypic screening. By cataloging the structures of known target–reference compound binding pairs, the team can compare those structures to untested compounds, and then assess only the most promising compounds. The THICK (Target Hypothesis Information from Curated Knowledge bases) database gives interaction-probability scores to untested compounds on the basis of structure. Bauer also described a method to verify target–compound interaction without labeling the molecules, in which phenotypic results were verified with mass spectrometry.

In the afternoon session, Myles Fennell of Memorial Sloan-Kettering Cancer Center described his work testing small interfering RNA (siRNA) libraries to find siRNAs that alter macropinocytosis (MP), cell-surface ruffling that is seen in prostate cancer cells. The surface phenotype allows TMR-dextran uptake, which the researchers measured in the screen. MP is driven by RAS (a commonly affected gene family in cancers) and the pathways are already popular drug targets. The researchers tested two libraries of siRNAs, which block translation of specific proteins, using TMR as a marker to report MP severity, as well as sensitive single-cell assays to determine siRNA efficacy. The team identified promising target sequences and used a data-analysis pipeline called KNIME to define several hits, which the researchers are pursuing in therapeutic development.

http://www.nyas.org/image.axd?id=0b4496f6-28fb-435c-bd11-06b4d31fc0ad&t=635863102714400000

TMR-dextran is able to work into cells undergoing macropinocytosis and thus these cells can be separated by phenotype as seen in the controls above. (Image courtesy of Myles Fennell)

Giulio Superti-Furga of the Austrian Academy of Sciences is a proponent of understanding the mechanisms of action (MOA) of candidate drugs. He began by explaining that the genome is an incomplete indicator of disease; epigenetics, altered protein function, metabolism, and other factors are also important. He then introduced pharmacoscopy and the “thermal shiftome” as methods to phenotypically screen compounds. Pharmacoscopy uses high-power automated microscopy to describe how compounds affect cell populations by using specific stains for different cell types; a computer then counts the cells expressing each stain, yielding results similar to those obtained via fluorescence-activated cell sorting but generated through an automated process. The thermal shiftome catalogs changes in thermal stability after protein binding in known reactions and is used to characterize the stability of new reactions. Superti-Furga offered a perspective that tempered the enthusiasm for pure PDD and advocated a mechanistic approach to drug discovery.

Michael R. Jackson, at one of the largest academic screening facilities, the Sanford Burnham Prebys Medical Discovery Institute, led a reexamination of drug screens performed by pharmaceutical companies. His team conducted millions of assays and accumulated a large data library with few new hits. However, the researchers were able to closely characterize the chemistry of one hit, an undisclosed interaction, and Jackson’s group is proceeding to develop a drug to modulate nuclear receptor signaling. The researchers also have a procedure that can screen for the differentiation of human induced pluripotent stem cells (iPSCs) into neurons for potential neuro-regenerative therapies. They developed high-throughput morphology, endpoint-measurement, and proliferation assays that generate tightly clustered, repeatable data. The team has produced consistent results screening 10 immune modulators and various cytokines to assess the reactivity and stability of the cells, providing reliable compound characterization. This success in human cells shows that a disease-relevant patient-derived screening platform to characterize differentiation and immune response is possible with robust assays.

In the next set of talks, Friedrich Metzger and Susanne Swalley described the parallel work of Hoffmann-La Roche and Novartis, respectively, toward treating spinal muscular atrophy (SMA). A devastating disease that leads to loss of motor function and affects motor nerve cells in the spinal cord, SMA presents a unique drug development opportunity. The condition is caused by the loss of function of a single gene product called survival of motor neuron (SMN1). Humans encode an unstable gene product, called SMN2, which is nearly homologous to SMN1.

Metzger explained that the inactive SMN2 variant is largely the same as active SMN1 but, missing exon 7, cannot compensate in its absence. The group from Hoffmann-La Roche aimed to stabilize SMN2 by promoting the inclusion of exon 7. The researchers conducted a phenotypic screen seeking a compound that could change the splicing in patient fibroblasts in vitro and produce a stable, functional SMN2 protein including exon 7. In studies with an SMN2Δ7 mouse model (lacking exon 7), mice drugged with the compound experienced full phenotypic rescue. The compound has been shown to induce alternative splicing of SMN2 to include exon 7 in healthy human volunteers; it was well tolerated and is moving to human patient trials.

Swalley discussed the target identification and MOA of the Novartis compound. After a screening process similar to Roche’s, Novartis moved its compound into animal models while also beginning parallel experimentation to find out why it worked. The group found that U1-snRNP, a spliceosome component required for the splicing process, is bound at two essential nucleotides by the compound. In the SMN2Δ7 mice, the compound improved survival and rescued full SMN2 protein expression. The Novartis compound stabilizes the appropriate spliceosome components to produce SMN2 with exon 7 intact. This novel mechanism demonstrates that a sequence-selective small molecule therapy can alter splicing activity to treat SMA. Together these talks demonstrated the power of PDD and the importance of validating drug mechanisms.

The final talk of the day was given by Hoffmann-La Roche’s Jitao David Zhang, who suggested that pathway reporter genes, which are only modulated when a specific signaling pathway is activated or inhibited, can be used as phenotypic readouts. It is known that gene expression data can predict cell phenotype. Using transcriptomics as a surrogate for downstream phenotypes, for example by using expression data from a gene subset to predict outcomes, would save time and effort. In an iPSC cardiomyocyte model of diabetic stress, machine learning (guided by pathway information) characterizes the response of the iPSCs to a library of compounds, highlighting compounds and pathways worthy of further investigation. This new platform for molecular phenotyping using pathway reporter genes, sequencing, and early analysis speeds compound characterization.

Use the tabs above to find multimedia from this event.

Presentations available from:
Andras J. Bauer, PhD, PharmD (Boehringer Ingelheim)
Myles Fennell, PhD (Memorial Sloan-Kettering Cancer Center)
Jonathan A. Lee, PhD (Eli Lilly)
Martin Main, PhD (AstraZeneca)
Yao Shen, PhD (Columbia University)
Susanne Swalley, PhD (Novartis Institutes for BioMedical Research)
Jitao David Zhang, PhD (F. Hoffmann-La Roche)

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What about PDL-1 in oncotherapy diagnostics for NSCLC?

Posted in Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cancer and Current Therapeutics, Cell Biology, Signaling & Cell Circuits, Clinical & Translational, Clinical Diagnostics, Conference Coverage with Social Media, Diagnostics and Lab Tests, Disease Biology, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Gene Regulation, Genetics & Pharmaceutical, Human aging, Human Immune System in Health and in Disease, Immunodiagnostics, Lung and pulmonology, tagged , , , , , , on May 2, 2016| Leave a Comment »

What about PDL-1 in oncotherapy diagnostics for NSCLC?

Larry H. Bernstein, MD, FCAP, Curator

LPBI

UPDATED 5/15/2019

Questions on PD-L1 Diagnostics for Immunotherapy in NSCLC
Alexander M. Castellino, PhD
http://www.medscape.com/viewarticle/862275

Two immunotherapies that target the cell programmed death (PD) pathway are now available, and both nivolumab (Opdivo, Bristol-Myers Squibb Company) and pembrolizumab (Keytruda, Merck Sharp & Dohme Corp) are approved for treating advanced, refractory, non–small cell lung cancer (NSCLC). Across several studies in patients with NSCLC, response to these agents has been correlated with PD-L1 staining, which determines PD-L1 levels in the tumor tissue. How do the available assays for PD-L1 compare?

The linear correlation between three commercially available assays is good across a range of cutoff points, concluded a presentation at the 2016 American Association for Clinical Research Annual Meeting.

Cutoffs are defined as the percentage of cells expressing PD-L1 when analyzed histochemically. “The dataset builds confidence that the assays may be used according to the cutoff clinically validated for the drug in question,” Marianne J. Radcliffe, MD, diagnostic associate director at AstraZeneca, toldMedscape Medical News.

“The correlation is good between the assays across the range examined,” she added.

However, a recently published study showed a high rate of discordance between another set of PD-L1 assays that were tested.
Dr Marianne Radcliffe

“Different diagnostic tests yield different results, depending on the cutoff for each assay. We need to harmonize the assays so clinicians are talking about the same thing,” Brendon Stiles, MD, associate professor of cardiothoracic surgery at Weill Cornell Medicine and New York-Presbyterian Hospital, New York City, told Medscape Medical News.

For Dr Stiles, these studies raise the issue that it is difficult to compare results of diagnostic testing across the different drugs and even with the same drug that are derived from different assays. “More importantly, it raises confusion in clinical practice when a patient’s sample stains positive for PD-L1 with one assay and negative with another,” he said.

“The commercial strategy for developing companion diagnostics for each drug is not in the best interests of the patients. It generates confusion among both clinicians and patients,” Dr Stiles commented. “We need to know if these assays can be used interchangeably,” he said.

As new agents come into the clinic, Dr Stiles believes there should be a universal yes-or-no answer, so that clinicians can use the assay to help decide on the use of immunotherapy.

Three Assays Tested

The study presented by Dr Radcliffe and colleagues investigated three commercially available assays, Ventana SP263, Dako 22C3, and Dako 28-8, with regard to how they compare at different cutoffs. Different studies use different cutoffs to express positivity.

Ventana SP263 was developed as a companion diagnostic for durvalumab (under development by AstraZeneca) using a rabbit monoclonal antibody. Positivity is defined as ≥25% staining of tumor cells.

Dako 22C3 was developed, and is approved, as a companion diagnostic for pembrolizumab. It uses a mouse monoclonal antibody. Positivity is defined as ≥1% and ≥50% staining of tumor cells.

Dako 28-8 was developed as a companion diagnostic for nivolumab and uses a rabbit monoclonal antibody (different from the one used in the Ventana SP263). In clinical practice, this assay is used as a complementary diagnostic for nivolumab, but the drug is approved for use regardless of PD-L1 expression. Positivity is defined as ≥1%, ≥5%, or ≥10% staining of tumor cells.

Ventana SP142 was not included in the study because it is not commercially available, Dr Ratcliffe indicated.
The three assays were used on consecutive sections of 500 archival NSCLC tumor samples obtained from commercial vendors. A single pathologist trained by the manufacturer read all samples in batches on an assay-by-assay basis. Samples were assessed per package inserts provided by Ventana and Dako in a Clinical Laboratory Improvement Amendments program-certified laboratory.

Dr Ratcliffe indicated that between reads of samples from the same patient, there was a washout period for the pathologist to remove bias.

The NSCLC samples included patients with stage I (38%), II (39%), III (20%), and IV (<1%) disease. Histologies included nonsquamous (54%) and squamous (43%) cancers.

All three PD-L1 assays showed similar patterns of staining in the range of 0% to 100%, Dr Ratcliffe indicated.

 

The correlation between any two of the assays was determined from tumor cell membrane staining. The correlation was linear with Spearman correlation of 0.911 for Ventana SP263 vs Dako 22C3; 0.935 for Ventana SP263 vs Dako 28-8; and 0.954 for Dako 28-8 vs Dako 22C3.

“With an overall predictive value of >90%, the assays have closely aligned dynamic ranges, but more work is needed,” Dr Ratcliffe said. “In general, scoring of immunohistochemical assays can be more variable between 1% and 10%, and we plan to look at this in more detail,” she said. These samples need to be reviewed by an independent pathologist, she added.

Dr Radcliffe said that currently, “Direct clinical efficacy data supporting a specific diagnostic test should still be considered as the highest standard of proof for diagnostic clinical utility.”

Why Correlations Are Needed

Pembrolizumab is approved for use only in patients with PD-L1-positive, previously treated NSCLC. A similar patient profile is being considered for nivolumab, for which testing for PD-L1 expression is not required.

For new PD-immunotherapy agents in clinical development, it is not clear whether PD-L1 testing will be mandated.

However, in clinical practice, it is clear that some patients respond to therapy, even if they are PD-L1 negative, as defined from the study. “Is it a failure of the assay, tumor heterogeneity, or is there another time point when PD-L1 expression is turned on?” Dr Stiles asked.

Dr Stiles also pointed out that a recent publication from Yale researchers showed a high a rate of discordance. In this study, PD-L1 expression was determined using two rabbit monoclonal antibodies. Both of these were different from the ones used in the Ventana SP263 and Dako 28-8 assays.

In this study, whole-tissue sections from 49 NSCLC samples were used, and a corresponding tissue microarray was also used with the same 49 samples. Researchers showed that in 49 NSCLC tissue samples, there was intra-assay variability, with results showing fair to poor concordance with the two antibodies. “Assessment of 588 serial section fields of view from whole tissue showed discordant expression at a frequency of 25%.

“Objective determination of PD-L1 protein levels in NSCLC reveals heterogeneity within tumors and prominent interassay variability or discordance. This could be due to different antibody affinities, limited specificity, or distinct target epitopes. Efforts to determine the clinical value of these observations are under way,” the study authors conclude.

The Blueprint Proposal

Coincidentally, a blueprint proposal was announced here at the AACR meeting at a workshop entitled FDA-AACR-ASCO Complexities in Personalized Medicine: Harmonizing Companion Diagnostics across a Class of Targeted Therapies.

The blueprint proposal was developed by four pharmaceutical giants (Bristol-Myers Squibb Company, Merck & Co, Inc, AstraZeneca PLC, and Genentech, Inc) and two diagnostic companies (Agilent Technologies, Inc/Dako Corp and Roche/Ventana Medical Systems, Inc).

In this proposal, the development of an evidence base for PD-1/PD-L1 companion diagnostic characterization for NSCLC would be built into studies conducted in the preapproval stage. Once the tests are approved, the information will lay the foundation for postapproval studies to inform stakeholders (eg, patients, physicians, pathologists) on how the test results can best be used to make treatment decisions.

The blueprint proposal is available online.

Dr Ratcliffe is an employee and shareholder of AstraZeneca. Dr Stiles has disclosed no relevant financial relationships.

 American Association for Cancer Research (AACR) 2016 Annual Meeting: Abstract LB-094, presented April 18, 2016.
Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non–Small-Cell Lung Cancer
Joseph McLaughlin, 1,2; Gang Han, 3; Kurt A. Schalper, 2; ….,  Roy Herbst, 1; Patricia LoRusso, 1; David L. Rimm, 2

JAMA Oncol. 2016;2(1):46-54.       http://dx.doi.org:/10.1001/jamaoncol.2015.3638.

Importance  Early-phase trials with monoclonal antibodies targeting PD-1 (programmed cell death protein 1) and PD-L1 (programmed cell death 1 ligand 1) have demonstrated durable clinical responses in patients with non–small-cell lung cancer (NSCLC). However, current assays for the prognostic and/or predictive role of tumor PD-L1 expression are not standardized with respect to either quantity or distribution of expression.

Objective  To demonstrate PD-L1 protein distribution in NSCLC tumors using both conventional immunohistochemistry (IHC) and quantitative immunofluorescence (QIF) and compare results obtained using 2 different PD-L1 antibodies.

Design, Setting, and Participants  PD-L1 was measured using E1L3N and SP142, 2 rabbit monoclonal antibodies, in 49 NSCLC whole-tissue sections and a corresponding tissue microarray with the same 49 cases. Non–small-cell lung cancer biopsy specimens from 2011 to 2012 were collected retrospectively from the Yale Thoracic Oncology Program Tissue Bank. Human melanoma Mel 624 cells stably transfected with PD-L1 as well as Mel 624 parental cells, and human term placenta whole tissue sections were used as controls and for antibody validation. PD-L1 protein expression in tumor and stroma was assessed using chromogenic IHC and the AQUA (Automated Quantitative Analysis) method of QIF. Tumor-infiltrating lymphocytes (TILs) were scored in hematoxylin-eosin slides using current consensus guidelines. The association between PD-L1 protein expression, TILs, and clinicopathological features were determined.

Main Outcomes and Measures  PD-L1 expression discordance or heterogeneity using the diaminobenzidine chromogen and QIF was the main outcome measure selected prior to performing the study.

Results  Using chromogenic IHC, both antibodies showed fair to poor concordance. The PD-L1 antibodies showed poor concordance (Cohen κ range, 0.124-0.340) using conventional chromogenic IHC and showed intra-assay heterogeneity (E1L3N coefficient of variation [CV], 6.75%-75.24%; SP142 CV, 12.17%-109.61%) and significant interassay discordance using QIF (26.6%). Quantitative immunofluorescence showed that PD-L1 expression using both PD-L1 antibodies was heterogeneous. Using QIF, the scores obtained with E1L3N and SP142 for each tumor were significantly different according to nonparametric paired test (P < .001). Assessment of 588 serial section fields of view from whole tissue showed discordant expression at a frequency of 25%. Expression of PD-L1 was correlated with high TILs using both E1L3N (P = .007) and SP142 (P = .02).

Conclusions and Relevance  Objective determination of PD-L1 protein levels in NSCLC reveals heterogeneity within tumors and prominent interassay variability or discordance. This could be due to different antibody affinities, limited specificity, or distinct target epitopes. Efforts to determine the clinical value of these observations are under way.

 

Invited Commentary
PD-L1 Expression as a Predictive Biomarker: Is Absence of Proof the Same as Proof of Absence?
JAMA Oncol. 2016;2(1):54-55.  http://dx.doi.org:/10.1001/jamaoncol.2015.3782.
Viewpoint
PD-L1 Testing in Cancer: Challenges in Companion Diagnostic Development
JAMA Oncol. 2016;2(1):15-16.   http://dx.doi.org:/10.1001/jamaoncol.2015.4685.

A Blueprint Proposal for Companion Diagnostic Comparability

A highlight of the FDA-AACR-ASCO Complexities in Personalized Medicine: Harmonizing Companion Diagnostics across a Class of Targeted Therapies workshop was the unveiling of a blueprint proposal developed by four pharmaceutical companies (Bristol-Myers Squibb Company, Merck & Co. Inc., AstraZeneca PLC, and Genentech, Inc.) and two diagnostic companies (Agilent Technologies, Inc./Dako Corp and Roche/Ventana Medical Systems, Inc.). The proposed study would help build an evidence base for PD-1/PD-L1 companion diagnostic characterization for non-small cell lung cancer in the pre-approval stage, such that once the tests are approved, the information generated can lay the groundwork for post-approval studies that will help inform patients, physicians, pathologists, and others on how best to use the test results to determine treatment decisions.

Read the Blueprint Proposal.

During the workshop the blueprint development team solicited input and feedback from interested parties. Read the feedback on the blueprint from

The blueprint team has considered the feedback received. Read the team’s combined response to both groups.Back to the Complexities in Personalized Medicine Workshop page.

 
A Blueprint Proposal for Companion Diagnostic Comparability
http://www.aacr.org/AdvocacyPolicy/GovernmentAffairs/Documents/FDA-AACR-ASCO-Complexities-in-Personalized-Medicine-Blueprint-Proposal.pdf
Introduction We are in an era of rapid incorporation of basic scientific discoveries into the drug development pipeline. Currently, numerous sponsors are developing therapeutic products that may use similar or identical biomarkers for therapeutic selection, measured or detected by an in vitro companion diagnostic device. The current practice is to independently develop a companion diagnostic for each therapeutic. Thus, the matrix of therapeutics and companion diagnostics, if each therapeutic were approved in conjunction with a companion diagnostic, may present a complex challenge for testing and decision making in the clinic, potentially putting patients at risk if inappropriate diagnostic tests were used to make treatment decisions. To address this challenge, there is a desire to understand assay comparability and/or standardize analytical and clinical performance characteristics supporting claims that are shared across companion diagnostic devices. Pathologists and oncologists also need clarity on how to interpret test results to inform downstream treatment options for their patients.
Clearly using each of the companion diagnostics to select one of the several available targeted therapies in the same class is not practical and may be impossible. Likewise, having a single test or assay as a sole companion test for all of the multiple therapeutic options within a class is also impractical since the individual therapies have differing modes of action, intended use populations, specificities, safety and efficacy outcomes. Thus, a single assay or test may not adequately capture the appropriate patient population that may benefit (or not) from each individual therapeutic option within a class of therapies. Furthermore, aligning multiple sponsors’ study designs and timelines in order that they all adopt a single companion test may inadvertently slow down development of critical therapeutic products and delay patient access to these life-saving products.
Any solution to this challenge will be multifaceted and will, by necessity, involve multiple stakeholders. Thus, the US Food and Drug Administration (FDA), the American Association for Cancer Research (AACR) and American Society of Clinical Oncology (ASCO) convened a workshop titled “Complexities in Personalized Medicine: Harmonizing Companion Diagnostics Across a Class of Targeted Therapies” to draw out and assess possible solutions. Recognizing that the complex scientific, regulatory and market forces at play here require a collaborative effort, an industry workgroup volunteered to develop a blueprint proposal of potential solutions using nonsmall cell lung cancer (NSCLC) as the use case indication.
Goal and Scope of Blueprint The imminent arrival to the market of multiple PD1 / PD-L1 compounds and the possibility of one or more associated companion diagnostics is unprecedented in the field of oncology. Some may assume that since these products target the same biological pathway, they are interchangeable; however, each PD1/PD-L1 compound is unique with respect to its clinical pharmacology and each compound is being developed in the context of a unique biological scientific hypothesis and registration strategy. Similarly, each companion diagnostic has been optimized within the individual therapeutic development programs to meet specific development goals, e.g., 1) validation for patient selection, 2) subgroup analysis as a prognostic variable, or 3) enrichment.
Further, each companion diagnostic test is optimized for its specific therapy and with its own unique performance characteristics and scoring/interpretation guidelines.
The blueprint development group recognizes that to assume that any one of the available tests could be used for guiding the treatment decision with any one or all of the drugs available in this class presents a potential risk to patients that must be addressed.
The goal of this proposal is to agree and deliver, via cross industry collaboration, a package of information /data upon which analytic comparison of the various diagnostic assays may be conducted, potentially paving the way for post-market standardization and/or practice guideline development as appropriate.
A comparative study of PD-L1 diagnostic assays and the classification of patients as PD-L1 positive and PD-L1 negative
Presentation Time: Monday, Apr 18, 2016, 8:00 AM -12:00 PM
Location: Section 10
Poster Board Number: 18
Author Block: Marianne J. Ratcliffe1, Alan Sharpe2, Anita Midha1, Craig Barker2, Paul Scorer2, Jill Walker2. 1AstraZeneca, Alderley Park, United Kingdom; 2AstraZeneca, Cambridge, United Kingdom
Abstract Body: Background: PD-1/PD-L1 directed antibodies are emerging as effective therapeutics in multiple oncology settings. Keynote 001 and Checkmate 057 have shown more frequent response to PD-1 targeted therapies in NSCLC patients with high tumour PD-L1 expression than patients with low or no PD-L1 expression. Multiple diagnostic PD-L1 tests are available using different antibody clones, different staining protocols and diverse scoring algorithms. It is vital to compare these assays to allow appropriate interpretation of clinical outcomes. Such understanding will promote harmonization of PD-L1 testing in clinical practice.
Methods: Approximately 500 tumour biopsy samples from NSCLC patients, including squamous and non-squamous histologies, will be assessed using three leading PD-L1 diagnostics assays. PD-L1 assessment by the Ventana SP263 assay that is currently being used in Durvalumab clinical trials (positivity cut off: ≥25% tumour cells with membrane staining) will be compared with the Dako 28-8 assay (used in the Nivolumab Checkmate 057 trial at the 1%, 5% and 10% tumour membrane positivity cut offs), and the Dako 22C3 assay (used in the Pembrolizumab Keynote 001 trial) at the 1% and 50% cut offs).
Results: Preliminary data from 81 non-squamous patients indicated good concordance between the Ventana SP263 and Dako 28-8 assays. Optimal overall percent agreement (OPA) was observed between Dako 28-8 at the 10% cut off and the Ventana SP263 assay (OPA; 96%, Positive percent agreement (PPA); 91%, Negative percent agreement (NPA); 98%), where the Ventana SP263 assay was set as the reference. Data on the full cohort will be presented for all three assays, and a lower 95% confidence interval calculated using the Clopper-Pearson method.
Conclusions: This study indicates that the patient population defined by Ventana SP263 at the 25% cut off is similar to that identified by the Dako-28-8 assay at the 10% tumour membrane cut off. This, together with data on the 22C3 assay, will enable cross comparison of studies using different PD-L1 tests, and widen options for harmonization of PD-L1 diagnostic testing.

http://www.abstractsonline.com/Plan/ViewAbstract.aspx

Table 1
Reference: Ventana SP-263 (≥25% tumour membrane staining)
Dako 28-8 assay cut off PPA
(%)		 NPA
(%)		 OPA
(%)
>1% 58 100 81
>5% 72 100 90
>10% 91 98 96

UPDATED 5/19/2019

Incidence of Adverse Events for PD-1/PD-L1 Inhibitors Underscores Toxicity Risk

https://www.cancernetwork.com/immuno-oncology/incidence-adverse-events-pd-1pd-l1-inhibitors-underscores-toxicity-risk

May 7, 2019

Approximately two-thirds of cancer patients who received a programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1) inhibitor in clinical trials experienced treatment-related adverse events, according to a systematic review and meta-analysis recently published in JAMA Oncology. The study findings may facilitate discussions with cancer patients who are considering PD-1 or PD-L1 therapy.

“The vast majority of patients with advanced cancer want to be on the [PD-1 or PD-L1] therapy,” Eric H. Bernicker, MD, a thoracic medical oncologist with Houston Methodist Cancer Center, told Cancer Network. Not involved in the current study, Bernicker explained that patients perceive these therapies to have “very different” side effects and risks from chemotherapy.

While they do, Bernicker explained, it’s important to underscore, which this study does, that these are not “completely innocuous” therapies. The study findings allow physicians to give numbers to patients and families when counseling them about the risks involved, he said.

The systematic review and meta-analysis is based on data from 125 clinical trials and 20,128 participants. Clinical trials were identified by systematically searching for published clinical trials that evaluated single-agent PD-1 and PD-L1 inhibitors and reported treatment-related adverse events in PubMed, Web of Science, Embase, and Scopus. The majority of trials evaluated nivolumab (n = 46) or pembrolizumab (n = 49), and the most common cancer types were lung cancer (n = 26), genitourinary cancer (n = 22), melanoma (n = 16), and gastrointestinal cancer (n = 14).

In all, 66.0% of clinical trial participants reported at least 1 adverse event of any grade, and 14.0% reported at least 1 grade 3 or higher adverse event. The most frequently reported adverse events of any grade were fatigue (18.26%), pruritus (10.61%), and diarrhea (9.47%). As for grade 3 or higher events, the most commonly reported were fatigue (0.89%), anemia (0.78%), and aspartate aminotransferase (AST) increase (0.75%).

Frequently reported immune-related adverse events of any grade included diarrhea (9.47%), AST increase (3.39%), vitiligo (3.26%), alanine aminotransferase (ALT) increase (3.14%), pneumonitis (2.79%), and colitis (1.24%). Grade 3 or higher immune-related adverse events included AST increase (0.75%), ALT increase (0.70%), pneumonitis (0.67%), diarrhea (0.59%), and colitis (0.47%).

If present, certain adverse events had increased likelihood of being grade 3 or higher, including hepatitis (risk ratio [RR], 50.59%), pneumonitis (RR, 24.01%), type 1 diabetes (RR, 41.86%), and colitis (RR, 37.90%).

“In terms of the rough percentage of side effects and the breadth of the side effects, this is pretty much what most of us see in the clinic,” Bernicker said, noting that none of the findings were particularly surprising.

Although no differences in adverse event incidence were found across different cancer types, differences were found between PD-1 and PD-L1 inhibitors in a subgroup analysis. Overall, compared with PD-L1 inhibitors, PD-1 inhibitors had a higher mean incidence of grade 3 or higher events (odds ratio [OR], 1.58; 95% CI, 1.00–2.54). Specifically, nivolumab had a higher mean incidence of grade 3 or higher events (OR, 1.81; 95% CI, 1.04–3.01) compared with PD-L1 inhibitors.

Bernicker commented that these incidence differences on the basis of drug type were “intriguing” but not clinically useful, given that PD-1 and PD-L1 inhibitors are not interchangeable. He said the finding “needs to be further looked at.”

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Immune System Stimulants: Articles of Note @pharmaceuticalintelligence.com

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Gene Regulation, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genome Biology, Immuno-Oncology & Genomics, Immunodiagnostics, Immunology, Innovation in Immunology Diagnostics, Viral diseases, tagged , , , , , , on May 1, 2016| Leave a Comment »

Immune System Stimulants: Articles of Note @pharmaceuticalintelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

 

  • New Approaches to Immunotherapy

 

New Class of Immune System Stimulants: Cyclic Di-Nucleotides (CDN): Shrink Tumors and bolster Vaccines, re-arm the Immune System’s Natural Killer Cells, which attack Cancer Cells and Virus-infected Cells

Three Methods for Design of a Novel Immune Therapy for Cancer: Conceptual Foundation for Development of a Novel Mechanism of Action for a Combination Therapy of Biologics — Password protected

Basic Research in Immune Oncology and Molecular Genomics: Methods to Stimulate Immunity by Alteration of Tumor Antigens – Reporting on R&D @MGH

New insights in cancer, cancer immunogenesis and circulating cancer cells

Perspectives on Anti-metastatic Effects in Cancer Research 2015

 

 

Issues Need to be Resolved With Immuno-Modulatory Therapies: NK cells, mAbs, and adoptive T cells

 

  •  Current Methods of Immuno-Therapy

 

 

Checkpoint inhibitors for gastrointestinal cancers

Immunomodulatory Therapeutic Antibodies for Cancer, August 13-15, 2013 – Boston, MA – Final Agenda

Tang Prize for 2014: Immunity and Cancer

LIVE 10:25 am – 12:00 pm 4/26/2016 Fireside Chat: Robert Bradway, CEO, Amgen & Immunotherapy I: Checkpoint Activation and Cancer Vaccines @2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Westin Hotel, Boston

Natural Killer Cell Response: Treatment of Cancer

CANCER IMMUNOTHERAPY

Cancer Immunotherapy Conference & Biomarkers for Cancer Immunotherapy Symposium, March 6-11, 2016 | Moscone North Convention Center | San Francisco, CA

Viruses, Vaccines and Immunotherapy

Advances in Cancer Immunotherapy

Perspectives on Anti-metastatic Effects in Cancer Research 2015

 

  • Evolving Approaches including Combination Oncotherapy

 

LIVE – 8:00 am – 12:00 pm 4/25/2016 – First Look: The Next Wave of Cancer Breakthroughs @2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Westin Hotel, Boston 2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Partners HealthCare, Boston, at the Westin Hotel, Boston

Brain Cancer Vaccine in Development and other considerations

Rapid regression of HER2 breast cancer

Breakthrough work in cancer

Novel biomarkers for targeting cancer immunotherapy

Humanized Mice May Revolutionize Cancer Drug Discovery

Immunomodulatory Therapeutic Antibodies for Cancer, August 13-15, 2013 – Boston, MA – Final Agenda

Melanoma: Molecule in Immune System Could Help Treat Dangerous Skin Cancer

NIH Study Demonstrates that a New Cancer Immunotherapy Method could be Effective against a wide range of Cancers

Aptamers and Scaffolds

 

  • Microbiological Factors in Cancer Growth

 

Microbe meets cancer

Gut microbiome and anti-tumor response

Malaria Protein Anti-cancer Activity

Retroviruses and Immunity

Oncolytic Viruses in Cancer Therapy @ CHI’s PreClinical Congress, June 14, 2016 Westin Boston Waterfront, Boston

Oncolytic Virus Immuno-Therapy: New Approach for a New Class of Immunotherapy Drugs

 

  • Signaling Pathways in Oncotherapy

 

Protein heals wounds, boosts immunity and protects from cancer – Lactoferrin

Programmed Cell Death and Cancer Therapy

BET Proteins Connect Diabetes and Cancer

Signaling of Immune Response in Colon Cancer

Myc and Cancer Resistance

Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle and Histone Modulation

Pancreatic Cancer and Crossing Roads of Metabolism

Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches

A Curated Census of Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy

Biology, Physiology and Pathophysiology of Heat Shock Proteins

Heat Shock Proteins (HSP) and Molecular Chaperones

The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

What is the key method to harness Inflammation to close the doors for many complex diseases?

IDO for Commitment of a Life Time: The Origins and Mechanisms of IDO, indolamine 2, 3-dioxygenase

Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Hemeostasis of Immune Responses for Good and Bad

Insight on Cell Senescence

Neutrophil Serine Proteases in Disease and Therapeutic Considerations

T cell-mediated immune responses & signaling pathways activated by TLRs

 

  • Immunogenetics in Oncotherapy

 

CRISPR/Cas9: Contributions on Endoribonuclease Structure and Function, Role in Immunity and Applications in Genome Engineering

CRISPR-Cas9 and Regenerative Medicine

CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development

GEN Tech Focus: Rethinking Gene Expression Analysis

Gene Expression and Adaptive Immune Resistance Mechanisms in Lymphoma

Serpins: A Review in Human Genomics

Upcoming Meetings on Cancer Immunogenetics

ipilimumab, a Drug that blocks CTLA-4 Freeing T cells to Attack Tumors @DM Anderson Cancer Center

NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

Cancer Labs at School of Medicine @ Technion: Janet and David Polak Cancer and Vascular Biology Research Center

Host – Tumor Interactions during Cancer Therapy – Dr. Yuval Shaked’s Lab @Technion

Demythologizing sharks, cancer, and shark fins

Naked Mole Rats Cancer-Free

From the Walter and Eliza Hall Institute of Medical Research: Genes Needed for Local Tissue Immune Response

 

  • Immunotherapy Market

 

Next-generation Universal Cell Immunotherapy startup Adicet Bio, Menlo Park, CA is launched with $51M Funding by OrbiMed

Juno Acquires AbVitro for $125M: high-throughput and single-cell sequencing capabilities for Immune-Oncology Drug Discovery

Monoclonal Antibody Therapy and Market

Monoclonal Antibody Therapy: What is in the name or clear description?

Tumor Associated Macrophages: The Double-Edged Sword Resolved?

Targeting Glucose Deprived Network Along with Targeted Cancer Therapy Can be a Possible Method of Treatment

Immunoreactivity of Nanoparticles 

Tofacitinib, an Oral Janus Kinase Inhibitor, in Active Ulcerative Colitis

Acute Lung Injury

Peroxisome proliferator-activated receptor (PPAR-gamma) Receptors Activation: PPARγ transrepression for Angiogenesis in Cardiovascular Disease and PPARγ transactivation for Treatment of Diabetes

Inflammatory Disorders: Articles published @ pharmaceuticalintelligence.com

Cytokines in IBD

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Chemotherapy Benefit in Early Breast Cancer Patients

Posted in Anticancer Resistance, Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, BioTechnology - Venture Creation, Cancer and Current Therapeutics, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Clinical & Translational, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Curation, Disease Biology, Drug Delivery Platform Technology, Experimental validation, Gene Regulation, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genomic Expression, Innovations, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, Medical and Population Genetics, Metabolism, Metabolomics, Molecular Genetics & Pharmaceutical, Pharmaceutical Analytics, Pharmaceutical Drug Discovery, Pharmacologic toxicities, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Signaling, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, Technology Advance Assessment of, tagged , , , , , , , , , , on April 20, 2016| Leave a Comment »

Chemotherapy Benefit in Early Breast Cancer Patients

Larry H Bernstein, MD, FCAP, Curator

LPBI

 

Agendia’s MammaPrint® First and Only Genomic Assay to Receive Level 1A Clinical Utility Evidence for Chemotherapy Benefit in Early Breast Cancer Patients

http://www.b3cnewswire.com/201604191373/agendias-mammaprintr-first-and-only-genomic-assay-to-receive-level-1a-clinical-utility-evidence-for-chemotherapy-benefit-in-early-breast-cancer-patients.

  • Clinical high-risk patients with a low-risk MammaPrint® result, including 48 percent node-positive, had five-year distant metastasis-free survival rate in excess of 94 percent, whether randomized to receive adjuvant chemotherapy or not
  • MammaPrint could change clinical practice by substantially de-escalating the use of adjuvant chemotherapy and sparing many patients an aggressive treatment they will not benefit from
  • Forty-six percent overall reduction in chemotherapy prescription among clinically high-risk patients

April 19, 2016 / B3C newswire / —Agendia, Inc., together with the European Organisation for Research and Treatment of Cancer (EORTC) and Breast International Group (BIG), announced results from the initial analysis of the primary objective of the Microarray In Node-negative (and 1 to 3 positive lymph node) Disease may Avoid ChemoTherapy (MINDACT) study at the American Association for Cancer Research Annual Meeting 2016 in New Orleans, LA.

Using the company’s MammaPrint® assay, patients with early-stage breast cancer who were considered at high risk for disease recurrence based on clinical and biological criteria had a distant metastasis-free survival at five years in excess of 94 percent.The MammaPrint test—the first and only genomic assay with FDA 510(k) clearance for use in risk assessment for women of all ages with early stage breast cancer—identified a large group of patients for whom five-year distant metastasis–free survival was equally good whether or not they received adjuvant chemotherapy (chemotherapy given post-surgery).

“The MINDACT trial design is the optimal way to prove clinical utility of a genomic assay,” said Prof. Laura van ’t Veer, CRO at Agendia, Leader, Breast Oncology Program, and Director, Applied Genomics at UCSF Helen Diller Family Comprehensive Cancer Center. “It gives the level 1A clinical evidence (prospective, randomized and controlled) that empowers physicians to clearly and confidently know when chemotherapy is part of optimal early-stage breast cancer therapy.  In this trial, MammaPrint (70-gene assay) was compared to the standard of care physicians use today, to decide what is the best treatment option for an early-stage breast cancer patient.”

The MINDACT trial is the first prospective randomized controlled clinical trial of a breast cancer recurrence genomic assay with level 1A clinical evidence and the first prospective translational research study of this magnitude in breast cancer to report the results of its primary objective.

Among the 3,356 patients enrolled in the MINDACT trial, who were categorized as having a high risk of breast cancer recurrence based on common clinical and pathological criteria (C-high), the MammaPrint assay reduced the chemotherapy treatment prescription by 46 percent.Using the 70-gene assay, MammaPrint, 48 percent of lymph-node positive breast cancer patients considered clinically high-risk (Clinical-high) and genomic low-risk (MammaPrint-low) had an excellent distant metastasis-free survival at five years in excess of 94 percent.

“Traditionally, physicians have relied on clinical-pathological factors such as age, tumor size, tumor grade, lymph node involvement, and hormone receptor status to make breast cancer treatment decisions,” said Massimo Cristofanilli, MD, Associate Director of Translational Research and Precision Medicine at the Robert H. Lurie Comprehensive Cancer Center, Northwestern University in Chicago. “These findings provide level 1A clinical utility evidence by demonstrating that the detection of low-risk of distant recurrence reported by the MammaPrint test can be safely used in the management of thousands of women by identifying those who can be spared from a toxic and unnecessary treatment.”

MINDACT is a randomized phase III trial that investigates the clinical utility of MammaPrint, when compared (or – “used in conjunction with”) to the standard clinical pathological criteria, for the selection of patients unlikely to benefit from adjuvant chemotherapy. From 2007 to 2011, 6,693 women who had undergone surgery for early-stage breast cancer enrolled in the trial (111 centers in nine countries). Participants were categorized as low or high risk for tumor recurrence in two ways: first, through analysis of tumor tissue using MammaPrint at a central location in Amsterdam; and second, using Adjuvant! Online, a tool that calculates risk of breast cancer recurrence based on common clinical and biological criteria.

Patients characterized in both clinical and genomic assessments as “low- risk” are spared chemotherapy, while patients characterized as “high- risk” are advised chemotherapy. Those with conflicting results are randomized to use either clinical or genomic risk (MammaPrint) evaluation to decide on chemotherapy treatment.

The MINDACT trial is managed and sponsored by the EORTC as part of an extensive and complex partnership in collaboration with Agendia and BIG, and many other academic and commercial partners, as well as patient advocates.

“These MINDACT trial results are a testament that the science of the MammaPrint test is the most robust in the genomic breast recurrence assay market.  Agendia will continue to collaborate with pharmaceutical companies, leading cancer centers and academic groups on additional clinical research and in the pursuit of bringing more effective, individualized treatments within reach of cancer patients,” said Mark Straley, Chief Executive Officer at Agendia. “We value the partnership with the EORTC and BIG and it’s a great honor to share this critical milestone.”

Breast cancer is the most frequently diagnosed cancer in women worldwide(1). In 2012, there were nearly 1.7 million new breast cancer cases among women worldwide, accounting for 25 percent of all new cancer cases in women(2).

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Imaging of Cancer Cells

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, Biological Networks, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, BioPrinting in Regenerative Medicine, BioTechnology - Venture Creation, Cancer and Current Therapeutics, Cancer Screening, Cell Biology, Cell Level, Clinical Diagnostics, Curation, Developmental biology, Diagnostics and Lab Tests, Disease Biology, Drug Toxicity, Experimental validation, Gene Regulation, Genetics & Pharmaceutical, Genome Biology, Genomic Expression, Intelligent Information Systems, Lasers and photonics, Metabolomics, Pharmaceutical Analytics, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmacologic toxicities, Proteins, Proteomics, Technology Advance Assessment of, tagged , , , , , , on April 20, 2016| Leave a Comment »

Imaging of Cancer Cells, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microscope uses nanosecond-speed laser and deep learning to detect cancer cells more efficiently

April 13, 2016

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Time-stretch quantitative phase imaging (TS-QPI) and analytics system

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

Abstract of Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

references:

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi & Bahram Jalali. Deep Learning in Label-free Cell Classification. Scientific Reports 6, Article number: 21471 (2016); doi:10.1038/srep21471 (open access)

Supplementary Information

 

Deep Learning in Label-free Cell Classification

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang,Kayvan Reza Niazi & Bahram Jalali

Scientific Reports 6, Article number: 21471 (2016)    http://dx.doi.org:/10.1038/srep21471

Deep learning extracts patterns and knowledge from rich multidimenstional datasets. While it is extensively used for image recognition and speech processing, its application to label-free classification of cells has not been exploited. Flow cytometry is a powerful tool for large-scale cell analysis due to its ability to measure anisotropic elastic light scattering of millions of individual cells as well as emission of fluorescent labels conjugated to cells1,2. However, each cell is represented with single values per detection channels (forward scatter, side scatter, and emission bands) and often requires labeling with specific biomarkers for acceptable classification accuracy1,3. Imaging flow cytometry4,5 on the other hand captures images of cells, revealing significantly more information about the cells. For example, it can distinguish clusters and debris that would otherwise result in false positive identification in a conventional flow cytometer based on light scattering6.

In addition to classification accuracy, the throughput is another critical specification of a flow cytometer. Indeed high throughput, typically 100,000 cells per second, is needed to screen a large enough cell population to find rare abnormal cells that are indicative of early stage diseases. However there is a fundamental trade-off between throughput and accuracy in any measurement system7,8. For example, imaging flow cytometers face a throughput limit imposed by the speed of the CCD or the CMOS cameras, a number that is approximately 2000 cells/s for present systems9. Higher flow rates lead to blurred cell images due to the finite camera shutter speed. Many applications of flow analyzers such as cancer diagnostics, drug discovery, biofuel development, and emulsion characterization require classification of large sample sizes with a high-degree of statistical accuracy10. This has fueled research into alternative optical diagnostic techniques for characterization of cells and particles in flow.

Recently, our group has developed a label-free imaging flow-cytometry technique based on coherent optical implementation of the photonic time stretch concept11. This instrument overcomes the trade-off between sensitivity and speed by using Amplified Time-stretch Dispersive Fourier Transform12,13,14,15. In time stretched imaging16, the object’s spatial information is encoded in the spectrum of laser pulses within a pulse duration of sub-nanoseconds (Fig. 1). Each pulse representing one frame of the camera is then stretched in time so that it can be digitized in real-time by an electronic analog-to-digital converter (ADC). The ultra-fast pulse illumination freezes the motion of high-speed cells or particles in flow to achieve blur-free imaging. Detection sensitivity is challenged by the low number of photons collected during the ultra-short shutter time (optical pulse width) and the drop in the peak optical power resulting from the time stretch. These issues are solved in time stretch imaging by implementing a low noise-figure Raman amplifier within the dispersive device that performs time stretching8,11,16. Moreover, warped stretch transform17,18can be used in time stretch imaging to achieve optical image compression and nonuniform spatial resolution over the field-of-view19. In the coherent version of the instrument, the time stretch imaging is combined with spectral interferometry to measure quantitative phase and intensity images in real-time and at high throughput20. Integrated with a microfluidic channel, coherent time stretch imaging system in this work measures both quantitative optical phase shift and loss of individual cells as a high-speed imaging flow cytometer, capturing 36 million images per second in flow rates as high as 10 meters per second, reaching up to 100,000 cells per second throughput.

Figure 1: Time stretch quantitative phase imaging (TS-QPI) and analytics system; A mode-locked laser followed by a nonlinear fiber, an erbium doped fiber amplifier (EDFA), and a wavelength-division multiplexing (WDM) filter generate and shape a train of broadband optical pulses. http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

 

Box 1: The pulse train is spatially dispersed into a train of rainbow flashes illuminating the target as line scans. The spatial features of the target are encoded into the spectrum of the broadband optical pulses, each representing a one-dimensional frame. The ultra-short optical pulse illumination freezes the motion of cells during high speed flow to achieve blur-free imaging with a throughput of 100,000 cells/s. The phase shift and intensity loss at each location within the field of view are embedded into the spectral interference patterns using a Michelson interferometer. Box 2: The interferogram pulses were then stretched in time so that spatial information could be mapped into time through time-stretch dispersive Fourier transform (TS-DFT), and then captured by a single pixel photodetector and an analog-to-digital converter (ADC). The loss of sensitivity at high shutter speed is compensated by stimulated Raman amplification during time stretch. Box 3: (a) Pulse synchronization; the time-domain signal carrying serially captured rainbow pulses is transformed into a series of one-dimensional spatial maps, which are used for forming line images. (b) The biomass density of a cell leads to a spatially varying optical phase shift. When a rainbow flash passes through the cells, the changes in refractive index at different locations will cause phase walk-off at interrogation wavelengths. Hilbert transformation and phase unwrapping are used to extract the spatial phase shift. (c) Decoding the phase shift in each pulse at each wavelength and remapping it into a pixel reveals the protein concentration distribution within cells. The optical loss induced by the cells, embedded in the pulse intensity variations, is obtained from the amplitude of the slowly varying envelope of the spectral interferograms. Thus, quantitative optical phase shift and intensity loss images are captured simultaneously. Both images are calibrated based on the regions where the cells are absent. Cell features describing morphology, granularity, biomass, etc are extracted from the images. (d) These biophysical features are used in a machine learning algorithm for high-accuracy label-free classification of the cells.

On another note, surface markers used to label cells, such as EpCAM21, are unavailable in some applications; for example, melanoma or pancreatic circulating tumor cells (CTCs) as well as some cancer stem cells are EpCAM-negative and will escape EpCAM-based detection platforms22. Furthermore, large-population cell sorting opens the doors to downstream operations, where the negative impacts of labels on cellular behavior and viability are often unacceptable23. Cell labels may cause activating/inhibitory signal transduction, altering the behavior of the desired cellular subtypes, potentially leading to errors in downstream analysis, such as DNA sequencing and subpopulation regrowth. In this way, quantitative phase imaging (QPI) methods24,25,26,27 that categorize unlabeled living cells with high accuracy are needed. Coherent time stretch imaging is a method that enables quantitative phase imaging at ultrahigh throughput for non-invasive label-free screening of large number of cells.

In this work, the information of quantitative optical loss and phase images are fused into expert designed features, leading to a record label-free classification accuracy when combined with deep learning. Image mining techniques are applied, for the first time, to time stretch quantitative phase imaging to measure biophysical attributes including protein concentration, optical loss, and morphological features of single cells at an ultrahigh flow rate and in a label-free fashion. These attributes differ widely28,29,30,31 among cells and their variations reflect important information of genotypes and physiological stimuli32. The multiplexed biophysical features thus lead to information-rich hyper-dimensional representation of the cells for label-free classification with high statistical precision.

We further improved the accuracy, repeatability, and the balance between sensitivity and specificity of our label-free cell classification by a novel machine learning pipeline, which harnesses the advantages of multivariate supervised learning, as well as unique training by evolutionary global optimization of receiver operating characteristics (ROC). To demonstrate sensitivity, specificity, and accuracy of multi-feature label-free flow cytometry using our technique, we classified (1) OT-IIhybridoma T-lymphocytes and SW-480 colon cancer epithelial cells, and (2) Chlamydomonas reinhardtii algal cells (herein referred to as Chlamydomonas) based on their lipid content, which is related to the yield in biofuel production. Our preliminary results show that compared to classification by individual biophysical parameters, our label-free hyperdimensional technique improves the detection accuracy from 77.8% to 95.5%, or in other words, reduces the classification inaccuracy by about five times.     ……..

 

Feature Extraction

The decomposed components of sequential line scans form pairs of spatial maps, namely, optical phase and loss images as shown in Fig. 2 (see Section Methods: Image Reconstruction). These images are used to obtain biophysical fingerprints of the cells8,36. With domain expertise, raw images are fused and transformed into a suitable set of biophysical features, listed in Table 1, which the deep learning model further converts into learned features for improved classification.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f2.jpg

The optical loss images of the cells are affected by the attenuation of multiplexed wavelength components passing through the cells. The attenuation itself is governed by the absorption of the light in cells as well as the scattering from the surface of the cells and from the internal cell organelles. The optical loss image is derived from the low frequency component of the pulse interferograms. The optical phase image is extracted from the analytic form of the high frequency component of the pulse interferograms using Hilbert Transformation, followed by a phase unwrapping algorithm. Details of these derivations can be found in Section Methods. Also, supplementary Videos 1 and 2 show measurements of cell-induced optical path length difference by TS-QPI at four different points along the rainbow for OT-II and SW-480, respectively.

Table 1: List of extracted features.

Feature Name    Description         Category

 

Figure 3: Biophysical features formed by image fusion.

(a) Pairwise correlation matrix visualized as a heat map. The map depicts the correlation between all major 16 features extracted from the quantitative images. Diagonal elements of the matrix represent correlation of each parameter with itself, i.e. the autocorrelation. The subsets in box 1, box 2, and box 3 show high correlation because they are mainly related to morphological, optical phase, and optical loss feature categories, respectively. (b) Ranking of biophysical features based on their AUCs in single-feature classification. Blue bars show performance of the morphological parameters, which includes diameter along the interrogation rainbow, diameter along the flow direction, tight cell area, loose cell area, perimeter, circularity, major axis length, orientation, and median radius. As expected, morphology contains most information, but other biophysical features can contribute to improved performance of label-free cell classification. Orange bars show optical phase shift features i.e. optical path length differences and refractive index difference. Green bars show optical loss features representing scattering and absorption by the cell. The best performed feature in these three categories are marked in red.

Figure 4: Machine learning pipeline. Information of quantitative optical phase and loss images are fused to extract multivariate biophysical features of each cell, which are fed into a fully-connected neural network.

The neural network maps input features by a chain of weighted sum and nonlinear activation functions into learned feature space, convenient for classification. This deep neural network is globally trained via area under the curve (AUC) of the receiver operating characteristics (ROC). Each ROC curve corresponds to a set of weights for connections to an output node, generated by scanning the weight of the bias node. The training process maximizes AUC, pushing the ROC curve toward the upper left corner, which means improved sensitivity and specificity in classification.

….   How to cite this article: Chen, C. L. et al. Deep Learning in Label-free Cell Classification.

Sci. Rep. 6, 21471; http://dx.doi.org:/10.1038/srep21471

 

Computer Algorithm Helps Characterize Cancerous Genomic Variations

http://www.genengnews.com/gen-news-highlights/computer-algorithm-helps-characterize-cancerous-genomic-variations/81252626/

To better characterize the functional context of genomic variations in cancer, researchers developed a new computer algorithm called REVEALER. [UC San Diego Health]

Scientists at the University of California San Diego School of Medicine and the Broad Institute say they have developed a new computer algorithm—REVEALER—to better characterize the functional context of genomic variations in cancer. The tool, described in a paper (“Characterizing Genomic Alterations in Cancer by Complementary Functional Associations”) published in Nature Biotechnology, is designed to help researchers identify groups of genetic variations that together associate with a particular way cancer cells get activated, or how they respond to certain treatments.

REVEALER is available for free to the global scientific community via the bioinformatics software portal GenePattern.org.

“This computational analysis method effectively uncovers the functional context of genomic alterations, such as gene mutations, amplifications, or deletions, that drive tumor formation,” said senior author Pablo Tamayo, Ph.D., professor and co-director of the UC San Diego Moores Cancer Center Genomics and Computational Biology Shared Resource.

Dr. Tamayo and team tested REVEALER using The Cancer Genome Atlas (TCGA), the NIH’s database of genomic information from more than 500 human tumors representing many cancer types. REVEALER revealed gene alterations associated with the activation of several cellular processes known to play a role in tumor development and response to certain drugs. Some of these gene mutations were already known, but others were new.

For example, the researchers discovered new activating genomic abnormalities for beta-catenin, a cancer-promoting protein, and for the oxidative stress response that some cancers hijack to increase their viability.

REVEALER requires as input high-quality genomic data and a significant number of cancer samples, which can be a challenge, according to Dr. Tamayo. But REVEALER is more sensitive at detecting similarities between different types of genomic features and less dependent on simplifying statistical assumptions, compared to other methods, he adds.

“This study demonstrates the potential of combining functional profiling of cells with the characterizations of cancer genomes via next-generation sequencing,” said co-senior author Jill P. Mesirov, Ph.D., professor and associate vice chancellor for computational health sciences at UC San Diego School of Medicine.

 

Characterizing genomic alterations in cancer by complementary functional associations

Jong Wook Kim, Olga B Botvinnik, Omar Abudayyeh, Chet Birger, et al.

Nature Biotechnology (2016)              http://dx.doi.org:/10.1038/nbt.3527

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

 

Figure 2: REVEALER results for transcriptional activation of β-catenin in cancer.close

(a) This heatmap illustrates the use of the REVEALER approach to find complementary genomic alterations that match the transcriptional activation of β-catenin in cancer. The target profile is a TCF4 reporter that provides an estimate of…

 

An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models

Jonathan P. Celli, Imran Rizvi, Adam R. Blanden, Iqbal Massodi, Michael D. Glidden, Brian W. Pogue & Tayyaba Hasan

Scientific Reports 4; 3751  (2014)    http://dx.doi.org:/10.1038/srep03751

While it is increasingly recognized that three-dimensional (3D) cell culture models recapitulate drug responses of human cancers with more fidelity than monolayer cultures, a lack of quantitative analysis methods limit their implementation for reliable and routine assessment of emerging therapies. Here, we introduce an approach based on computational analysis of fluorescence image data to provide high-content readouts of dose-dependent cytotoxicity, growth inhibition, treatment-induced architectural changes and size-dependent response in 3D tumour models. We demonstrate this approach in adherent 3D ovarian and pancreatic multiwell extracellular matrix tumour overlays subjected to a panel of clinically relevant cytotoxic modalities and appropriately designed controls for reliable quantification of fluorescence signal. This streamlined methodology reads out the high density of information embedded in 3D culture systems, while maintaining a level of speed and efficiency traditionally achieved with global colorimetric reporters in order to facilitate broader implementation of 3D tumour models in therapeutic screening.

The attrition rates for preclinical development of oncology therapeutics are particularly dismal due to a complex set of factors which includes 1) the failure of pre-clinical models to recapitulate determinants of in vivo treatment response, and 2) the limited ability of available assays to extract treatment-specific data integral to the complexities of therapeutic responses1,2,3. Three-dimensional (3D) tumour models have been shown to restore crucial stromal interactions which are missing in the more commonly used 2D cell culture and that influence tumour organization and architecture4,5,6,7,8, as well as therapeutic response9,10, multicellular resistance (MCR)11,12, drug penetration13,14, hypoxia15,16, and anti-apoptotic signaling17. However, such sophisticated models can only have an impact on therapeutic guidance if they are accompanied by robust quantitative assays, not only for cell viability but also for providing mechanistic insights related to the outcomes. While numerous assays for drug discovery exist18, they are generally not developed for use in 3D systems and are often inherently unsuitable. For example, colorimetric conversion products have been noted to bind to extracellular matrix (ECM)19 and traditional colorimetric cytotoxicity assays reduce treatment response to a single number reflecting a biochemical event that has been equated to cell viability (e.g. tetrazolium salt conversion20). Such approaches fail to provide insight into the spatial patterns of response within colonies, morphological or structural effects of drug response, or how overall culture viability may be obscuring the status of sub-populations that are resistant or partially responsive. Hence, the full benefit of implementing 3D tumour models in therapeutic development has yet to be realized for lack of analytical methods that describe the very aspects of treatment outcome that these systems restore.

Motivated by these factors, we introduce a new platform for quantitative in situ treatment assessment (qVISTA) in 3D tumour models based on computational analysis of information-dense biological image datasets (bioimage-informatics)21,22. This methodology provides software end-users with multiple levels of complexity in output content, from rapidly-interpreted dose response relationships to higher content quantitative insights into treatment-dependent architectural changes, spatial patterns of cytotoxicity within fields of multicellular structures, and statistical analysis of nodule-by-nodule size-dependent viability. The approach introduced here is cognizant of tradeoffs between optical resolution, data sampling (statistics), depth of field, and widespread usability (instrumentation requirement). Specifically, it is optimized for interpretation of fluorescent signals for disease-specific 3D tumour micronodules that are sufficiently small that thousands can be imaged simultaneously with little or no optical bias from widefield integration of signal along the optical axis of each object. At the core of our methodology is the premise that the copious numerical readouts gleaned from segmentation and interpretation of fluorescence signals in these image datasets can be converted into usable information to classify treatment effects comprehensively, without sacrificing the throughput of traditional screening approaches. It is hoped that this comprehensive treatment-assessment methodology will have significant impact in facilitating more sophisticated implementation of 3D cell culture models in preclinical screening by providing a level of content and biological relevance impossible with existing assays in monolayer cell culture in order to focus therapeutic targets and strategies before costly and tedious testing in animal models.

Using two different cell lines and as depicted in Figure 1, we adopt an ECM overlay method pioneered originally for 3D breast cancer models23, and developed in previous studies by us to model micrometastatic ovarian cancer19,24. This system leads to the formation of adherent multicellular 3D acini in approximately the same focal plane atop a laminin-rich ECM bed, implemented here in glass-bottom multiwell imaging plates for automated microscopy. The 3D nodules resultant from restoration of ECM signaling5,8, are heterogeneous in size24, in contrast to other 3D spheroid methods, such as rotary or hanging drop cultures10, in which cells are driven to aggregate into uniformly sized spheroids due to lack of an appropriate substrate to adhere to. Although the latter processes are also biologically relevant, it is the adherent tumour populations characteristic of advanced metastatic disease that are more likely to be managed with medical oncology, which are the focus of therapeutic evaluation herein. The heterogeneity in 3D structures formed via ECM overlay is validated here by endoscopic imaging ofin vivo tumours in orthotopic xenografts derived from the same cells (OVCAR-5).

 

Figure 1: A simplified schematic flow chart of imaging-based quantitative in situ treatment assessment (qVISTA) in 3D cell culture.

(This figure was prepared in Adobe Illustrator® software by MD Glidden, JP Celli and I Rizvi). A detailed breakdown of the image processing (Step 4) is provided in Supplemental Figure 1.

A critical component of the imaging-based strategy introduced here is the rational tradeoff of image-acquisition parameters for field of view, depth of field and optical resolution, and the development of image processing routines for appropriate removal of background, scaling of fluorescence signals from more than one channel and reliable segmentation of nodules. In order to obtain depth-resolved 3D structures for each nodule at sub-micron lateral resolution using a laser-scanning confocal system, it would require ~ 40 hours (at approximately 100 fields for each well with a 20× objective, times 1 minute/field for a coarse z-stack, times 24 wells) to image a single plate with the same coverage achieved in this study. Even if the resources were available to devote to such time-intensive image acquisition, not to mention the processing, the optical properties of the fluorophores would change during the required time frame for image acquisition, even with environmental controls to maintain culture viability during such extended imaging. The approach developed here, with a mind toward adaptation into high throughput screening, provides a rational balance of speed, requiring less than 30 minutes/plate, and statistical rigour, providing images of thousands of nodules in this time, as required for the high-content analysis developed in this study. These parameters can be further optimized for specific scenarios. For example, we obtain the same number of images in a 96 well plate as for a 24 well plate by acquiring only a single field from each well, rather than 4 stitched fields. This quadruples the number conditions assayed in a single run, at the expense of the number of nodules per condition, and therefore the ability to obtain statistical data sets for size-dependent response, Dfrac and other segmentation-dependent numerical readouts.

 

We envision that the system for high-content interrogation of therapeutic response in 3D cell culture could have widespread impact in multiple arenas from basic research to large scale drug development campaigns. As such, the treatment assessment methodology presented here does not require extraordinary optical instrumentation or computational resources, making it widely accessible to any research laboratory with an inverted fluorescence microscope and modestly equipped personal computer. And although we have focused here on cancer models, the methodology is broadly applicable to quantitative evaluation of other tissue models in regenerative medicine and tissue engineering. While this analysis toolbox could have impact in facilitating the implementation of in vitro 3D models in preclinical treatment evaluation in smaller academic laboratories, it could also be adopted as part of the screening pipeline in large pharma settings. With the implementation of appropriate temperature controls to handle basement membranes in current robotic liquid handling systems, our analyses could be used in ultra high-throughput screening. In addition to removing non-efficacious potential candidate drugs earlier in the pipeline, this approach could also yield the additional economic advantage of minimizing the use of costly time-intensive animal models through better estimates of dose range, sequence and schedule for combination regimens.

 

Microscope Uses AI to Find Cancer Cells More Efficiently

Thu, 04/14/2016 – by Shaun Mason

http://www.mdtmag.com/news/2016/04/microscope-uses-ai-find-cancer-cells-more-efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.

There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The study, which was published in the journal Nature Scientific Reports, was led by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering; Claire Lifan Chen, a UCLA doctoral student; and Ata Mahjoubfar, a UCLA postdoctoral fellow.

Photonic time stretch was invented by Jalali, and he holds a patent for the technology. The new microscope is just one of many possible applications; it works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly — in nanoseconds, or billionths of a second — that the images would be too weak to be detected and too fast to be digitized by normal instrumentation.

The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.

“Each frame is slowed down in time and optically amplified so it can be digitized,” Mahjoubfar said. “This lets us perform fast cell imaging that the artificial intelligence component can distinguish.”

Normally, taking pictures in such minuscule periods of time would require intense illumination, which could destroy live cells. The UCLA approach also eliminates that problem.

“The photonic time stretch technique allows us to identify rogue cells in a short time with low-level illumination,” Chen said.

The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.   …..  see also http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

Chen, C. L. et al. Deep Learning in Label-free Cell Classification.    Sci. Rep. 6, 21471;   http://dx.doi.org:/10.1038/srep21471

 

 

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Insight into Dark Matter DNA

Posted in Gene Regulation, Genome Biology, Innovations, tagged on April 19, 2016| Leave a Comment »

Insight into Dark Matter DNA

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Interpreting “Dark Matter” DNA

http://www.technologynetworks.com/Informatics/news.aspx?ID=190224

Scientists at the Gladstone Institutes have invented a new way to read and interpret the human genome.

The computational method, called TargetFinder, can predict where non-coding DNA—the DNA that does not code for proteins—interacts with genes. This technology helps researchers connect mutations in the so-called genomic “dark matter” with the genes they affect, potentially revealing new therapeutic targets for genetic disorders.

In the study the researchers looked at fragments of non-coding DNA called enhancers. Enhancers act like an instruction manual for a gene, dictating when and where a gene is turned on. Genes can be separated from their enhancers by long stretches of DNA that contain many other genes.

“Most genetic mutations that are associated with disease occur in enhancers, making them an incredibly important area of study,” said senior author Katherine Pollard, PhD, a senior investigator at the Gladstone Institutes. “Before now, we struggled to understand how enhancers find the distant genes they act upon.”

Scientists originally believed that enhancers mostly affect the gene nearest to them. However, the new study revealed that, on a strand of DNA, enhancers can be millions of letters away from the gene they influence, skipping over the genes in between. When an enhancer is far away from the gene it affects, the two connect by forming a three-dimensional loop, like a bow on the genome.

Using machine learning technology, the researchers analyzed hundreds of existing datasets from six different cell types to look for patterns in the genome that identify where a gene and enhancer interact. They discovered several patterns that exist on the loops that connect enhancers to genes. This pattern accurately predicted whether a gene-enhancer interaction occurred 85 percent of the time.

“It’s remarkable that we can predict complex three-dimensional interactions from relatively simple data,” said first author Sean Whalen, PhD, a biostatistician at Gladstone. “No one had looked at the information stored on loops before, and we were surprised to discover how important that information is.”

Performing experiments in the lab to identify all of these gene-enhancer interactions can take millions of dollars and years of research. The new computational approach is a much cheaper and less time-consuming way to identify gene-enhancer connections in the genome. The technology also provides insight into how DNA loops form and how they might break in disease. The scientists have offered all of the code and data from TargetFinder online for free.

“Our ability to predict the gene targets of enhancers so accurately enables us to link mutations in enhancers to the genes they target,” said Pollard. “Having that link is the first step towards using these connections to treat diseases.

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Colon cancer and organoids

Posted in 3D Printing for Medical Application, Anticancer Resistance, BioBanking, Biological Networks, Biological Networks, Gene Regulation and Evolution, BioPrinting in Regenerative Medicine, Ca2+ triggered activation, Calcium Signaling, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Competition in regenerative stem cell science, Curation, Developmental biology, Disease Biology, Drug Toxicity, Gastroenterology, Gene Regulation, Genome Biology, MicroEngineering Cell-Tissue & Systems, tagged , , , , , , , , on April 15, 2016| Leave a Comment »

Colon cancer and organoids

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

 

 

Guts and Glory

An open mind and collaborative spirit have taken Hans Clevers on a journey from medicine to developmental biology, gastroenterology, cancer, and stem cells.

By Anna Azvolinsky    http://www.the-scientist.com/?articles.view/articleNo/45580/title/Guts-and-Glory

Ihave had to talk a lot about my science recently and it’s made me think about how science works,” says Hans Clevers. “Scientists are trained to think science is driven by hypotheses, but for [my lab], hypothesis-driven research has never worked. Instead, it has been about trying to be as open-minded as possible—which is not natural for our brains,” adds the Utrecht University molecular genetics professor. “The human mind is such that it tries to prove it’s right, so pursuing a hypothesis can result in disaster. My advice to my own team and others is to not preformulate an answer to a scientific question, but just observe and never be afraid of the unknown. What has worked well for us is to keep an open mind and do the experiments. And find a collaborator if it is outside our niche.”

“One thing I have learned is that hypothesis-driven research tends not to be productive when you are in an unknown territory.”

Clevers entered medical school at Utrecht University in The Netherlands in 1978 while simultaneously pursuing a master’s degree in biology. Drawn to working with people in the clinic, Clevers had a training position in pediatrics lined up after medical school, but then mentors persuaded him to spend an additional year converting the master’s degree to a PhD in immunology. “At the end of that year, looking back, I got more satisfaction from the research than from seeing patients.” Clevers also had an aptitude for benchwork, publishing four papers from his PhD year. “They were all projects I had made up myself. The department didn’t do the kind of research I was doing,” he says. “Now that I look back, it’s surprising that an inexperienced PhD student could come up with a project and publish independently.”

Clevers studied T- and B-cell signaling; he set up assays to visualize calcium ion flux and demonstrated that the ions act as messengers to activate human B cells, signaling through antibodies on the cell surface. “As soon as the experiment worked, I got T cells from the lab next door and did the same experiment. That was my strategy: as soon as something worked, I would apply it elsewhere and didn’t stop just because I was a B-cell biologist and not a T-cell biologist. What I learned then, that I have continued to benefit from, is that a lot of scientists tend to adhere to a niche. They cling to these niches and are not that flexible. You think scientists are, but really most are not.”

Here, Clevers talks about promoting a collaborative spirit in research, the art of doing a pilot experiment, and growing miniature organs in a dish.

Clevers Creates

Re-search? Clevers was born in Eindhoven, in the south of The Netherlands. The town was headquarters to Philips Electronics, where his father worked as a businessman, and his mother took care of Clevers and his three brothers. Clevers did well in school but his passion was sports, especially tennis and field hockey, “a big thing in Holland.” Then in 1975, at age 18, he moved to Utrecht University, where he entered an intensive, biology-focused program. “I knew I wanted to be a biology researcher since I was young. In Dutch, the word for research is ‘onderzoek’ and I knew the English word ‘research’ and had wondered why there was the ‘re’ in the word, because I wanted to search but I didn’t want to do re-search—to find what someone else had already found.”

Opportunity to travel. “I was very disappointed in my biology studies, which were old-fashioned and descriptive,” says Clevers. He thought medicine might be more interesting and enrolled in medical school while still pursuing a master’s degree in biology at Utrecht. For the master’s, Clevers had to do three rotations. He spent a year at the International Laboratory for Research on Animal Diseases (ILRAD) in Nairobi, Kenya, and six months in Bethesda, Maryland, at the National Institutes of Health. “Holland is really small, so everyone travels.” Clevers saw those two rotations more as travel explorations. In Nairobi, he went on safaris and explored the country in Land Rovers borrowed from the institute. While in Maryland in 1980, Clevers—with the consent of his advisor, who thought it was a good idea for him to get a feel for the U.S.—flew to Portland, Oregon, and drove back to Boston with a musician friend along the Canadian border. He met the fiancé of political activist and academic Angela Davis in New York City and even stayed in their empty apartment there.

Life and lab lessons. Back in Holland, Clevers joined Rudolf Eugène Ballieux’s lab at Utrecht University to pursue his PhD, for which he studied immune cell signaling. “I didn’t learn much science from him, but I learned that you always have to create trust and to trust people around you. This became a major theme in my own lab. We don’t distrust journals or reviewers or collaborators. We trust everyone and we share. There will be people who take advantage, but there have only been a few of those. So I learned from Ballieux to give everyone maximum trust and then change this strategy only if they fail that trust. We collaborate easily because we give out everything and we also easily get reagents and tools that we may need. It’s been valuable to me in my career. And it is fun!”

Clevers Concentrates

On a mission. “Once I decided to become a scientist, I knew I needed to train seriously. Up to that point, I was totally self-trained.” From an extensive reading of the immunology literature, Clevers became interested in how T cells recognize antigens, and headed off to spend a postdoc studying the problem in Cox Terhorst’s lab at Dana-Farber Cancer Institute in Boston. “Immunology was young, but it was very exciting and there was a lot to discover. I became a professional scientist there and experienced how tough science is.” In 1988, Clevers cloned and characterized the gene for a component of the T-cell receptor (TCR) called CD3-epsilon, which binds antigen and activates intracellular signaling pathways.

On the fast track in Holland. Clevers returned to Utrecht University in 1989 as a professor of immunology. Within one month of setting up his lab, he had two graduate students and a technician, and the lab had cloned the first T cell–specific transcription factor, which they called TCF-1, in human T cells. When his former thesis advisor retired, Clevers was asked, at age 33, to become head of the immunology department. While the appointment was high-risk for him and for the department, Clevers says, he was chosen because he was good at multitasking and because he got along well with everyone.

Problem-solving strategy. “My strategy in research has always been opportunistic. One thing I have learned is that hypothesis-driven research tends not to be productive when you are in an unknown territory. I think there is an art to doing pilot experiments. So we have always just set up systems in which something happens and then you try and try things until a pattern appears and maybe you formulate a small hypothesis. But as soon as it turns out not to be exactly right, you abandon it. It’s a very open-minded type of research where you question whether what you are seeing is a real phenomenon without spending a year on doing all of the proper controls.”

Trial and error. Clevers’s lab found that while TCF-1 bound to DNA, it did not alter gene expression, despite the researchers’ tinkering with promoter and enhancer assays. “For about five years this was a problem. My first PhD students were leaving and they thought the whole TCF project was a failure,” says Clevers. His lab meanwhile cloned TCF homologs from several model organisms and made many reagents including antibodies against these homologs. To try to figure out the function of TCF-1, the lab performed a two-hybrid screen and identified components of the Wnt signaling pathway as binding partners of TCF-1. “We started to read about Wnt and realized that you study Wnt not in T cells but in frogs and flies, so we rapidly transformed into a developmental biology lab. We showed that we held the key for a major issue in developmental biology, the final protein in the Wnt cascade: TCF-1 binds b-catenin when b-catenin becomes available and activates transcription.” In 1996, Clevers published the mechanism of how the TCF-1 homolog in Xenopus embryos, called XTcf-3, is integrated into the Wnt signaling pathway.

Clevers Catapults

COURTESY OF HANS CLEVERS AND JEROEN HUIJBEN, NYMUS

3DCrypt building and colon cancer.

Clevers next collaborated with Bert Vogelstein’s lab at Johns Hopkins, linking TCF to Wnt signaling in colon cancer. In colon cancer cell lines with mutated forms of the tumor suppressor gene APC, the APC protein can’t rein in b-catenin, which accumulates in the cytoplasm, forms a complex with TCF-4 (later renamed TCF7L2) in the nucleus, and caninitiate colon cancer by changing gene expression. Then, the lab showed that Wnt signaling is necessary for self-renewal of adult stem cells, as mice missing TCF-4 do not have intestinal crypts, the site in the gut where stem cells reside. “This was the first time Wnt was shown to play a role in adults, not just during development, and to be crucial for adult stem cell maintenance,” says Clevers. “Then, when I started thinking about studying the gut, I realized it was by far the best way to study stem cells. And I also realized that almost no one in the world was studying the healthy gut. Almost everyone who researched the gut was studying a disease.” The main advantages of the murine model are rapid cell turnover and the presence of millions of stereotypic crypts throughout the entire intestine.

Against the grain. In 2007, Nick Barker, a senior scientist in the Clevers lab, identified the Wnt target gene Lgr5 as a unique marker of adult stem cells in several epithelial organs, including the intestine, hair follicle, and stomach. In the intestine, the gene codes for a plasma membrane protein on crypt stem cells that enable the intestinal epithelium to self-renew, but can also give rise to adenomas of the gut. Upon making mice with adult stem cell populations tagged with a fluorescent Lgr5-binding marker, the lab helped to overturn assumptions that “stem cells are rare, impossible to find, quiescent, and divide asymmetrically.”

On to organoids. Once the lab could identify adult stem cells within the crypts of the gut, postdoc Toshiro Sato discovered that a single stem cell, in the presence of Matrigel and just three growth factors, could generate a miniature crypt structure—what is now called an organoid. “Toshi is very Japanese and doesn’t always talk much,” says Clevers. “One day I had asked him, while he was at the microscope, if the gut stem cells were growing, and he said, ‘Yes.’ Then I looked under the microscope and saw the beautiful structures and said, ‘Why didn’t you tell me?’ and he said, ‘You didn’t ask.’ For three months he had been growing them!” The lab has since also grown mini-pancreases, -livers, -stomachs, and many other mini-organs.

Tumor Organoids. Clevers showed that organoids can be grown from diseased patients’ samples, a technique that could be used in the future to screen drugs. The lab is also building biobanks of organoidsderived from tumor samples and adjacent normal tissue, which could be especially useful for monitoring responses to chemotherapies. “It’s a similar approach to getting a bacterium cultured to identify which antibiotic to take. The most basic goal is not to give a toxic chemotherapy to a patient who will not respond anyway,” says Clevers. “Tumor organoids grow slower than healthy organoids, which seems counterintuitive, but with cancer cells, often they try to divide and often things go wrong because they don’t have normal numbers of chromosomes and [have] lots of mutations. So, I am not yet convinced that this approach will work for every patient. Sometimes, the tumor organoids may just grow too slowly.”

Selective memory. “When I received the Breakthrough Prize in 2013, I invited everyone who has ever worked with me to Amsterdam, about 100 people, and the lab organized a symposium where many of the researchers gave an account of what they had done in the lab,” says Clevers. “In my experience, my lab has been a straight line from cloning TCF-1 to where we are now. But when you hear them talk it was ‘Hans told me to try this and stop this’ and ‘Half of our knockout mice were never published,’ and I realized that the lab is an endless list of failures,” Clevers recalls. “The one thing we did well is that we would start something and, as soon as it didn’t look very good, we would stop it and try something else. And the few times when we seemed to hit gold, I would regroup my entire lab. We just tried a lot of things, and the 10 percent of what worked, those are the things I remember.”

Greatest Hits

  • Cloned the first T cell–specific transcription factor, TCF-1, and identified homologous genes in model organisms including the fruit fly, frog, and worm
  • Found that transcriptional activation by the abundant β-catenin/TCF-4 [TCF7L2] complex drives cancer initiation in colon cells missing the tumor suppressor protein APC
  • First to extend the role of Wnt signaling from developmental biology to adult stem cells by showing that the two Wnt pathway transcription factors, TCF-1 and TCF-4, are necessary for maintaining the stem cell compartments in the thymus and in the crypt structures of the small intestine, respectively
  • Identified Lgr5 as an adult stem cell marker of many epithelial stem cells including those of the colon, small intestine, hair follicle, and stomach, and found that Lgr5-expressing crypt cells in the small intestine divide constantly and symmetrically, disproving the common belief that stem cell division is asymmetrical and uncommon
  • Established a three-dimensional, stable model, the “organoid,” grown from adult stem cells, to study diseased patients’ tissues from the gut, stomach, liver, and prostate
 Regenerative Medicine Comes of Age   
“Anti-Aging Medicine” Sounds Vaguely Disreputable, So Serious Scientists Prefer to Speak of “Regenerative Medicine”

  • Induced pluripotent stem cells (iPSCs) and genome-editing techniques have facilitated manipulation of living organisms in innumerable ways at the cellular and genetic levels, respectively, and will underpin many aspects of regenerative medicine as it continues to evolve.

    An attitudinal change is also occurring. Experts in regenerative medicine have increasingly begun to embrace the view that comprehensively repairing the damage of aging is a practical and feasible goal.

    A notable proponent of this view is Aubrey de Grey, Ph.D., a biomedical gerontologist who has pioneered an regenerative medicine approach called Strategies for Engineered Negligible Senescence (SENS). He works to “develop, promote, and ensure widespread access to regenerative medicine solutions to the disabilities and diseases of aging” as CSO and co-founder of the SENS Research Foundation. He is also the editor-in-chief of Rejuvenation Research, published by Mary Ann Liebert.

    Dr. de Grey points out that stem cell treatments for age-related conditions such as Parkinson’s are already in clinical trials, and immune therapies to remove molecular waste products in the extracellular space, such as amyloid in Alzheimer’s, have succeeded in such trials. Recently, there has been progress in animal models in removing toxic cells that the body is failing to kill. The most encouraging work is in cancer immunotherapy, which is rapidly advancing after decades in the doldrums.

    Many damage-repair strategies are at an  early stage of research. Although these strategies look promising, they are handicapped by a lack of funding. If that does not change soon, the scientific community is at risk of failing to capitalize on the relevant technological advances.

    Regenerative medicine has moved beyond boutique applications. In degenerative disease, cells lose their function or suffer elimination because they harbor genetic defects. iPSC therapies have the potential to be curative, replacing the defective cells and eliminating symptoms in their entirety. One of the biggest hurdles to commercialization of iPSC therapies is manufacturing.

  • Building Stem Cell Factories

    Cellular Dynamics International (CDI) has been developing clinically compatible induced pluripotent stem cells (iPSCs) and iPSC-derived human retinal pigment epithelial (RPE) cells. CDI’s MyCell Retinal Pigment Epithelial Cells are part of a possible therapy for macular degeneration. They can be grown on bioengineered, nanofibrous scaffolds, and then the RPE cell–enriched scaffolds can be transplanted into patients’ eyes. In this pseudo-colored image, RPE cells are shown growing over the nanofibers. Each cell has thousands of “tongue” and “rod” protrusions that could naturally support rod and cone cells in the eye.

    “Now that an infrastructure is being developed to make unlimited cells for the tools business, new opportunities are being created. These cells can be employed in a therapeutic context, and they can be used to understand the efficacy and safety of drugs,” asserts Chris Parker, executive vice president and CBO, Cellular Dynamics International (CDI). “CDI has the capability to make a lot of cells from a single iPSC line that represents one person (a capability termed scale-up) as well as the capability to do it in parallel for multiple individuals (a capability termed scale-out).”

    Minimally manipulated adult stem cells have progressed relatively quickly to the clinic. In this scenario, cells are taken out of the body, expanded unchanged, then reintroduced. More preclinical rigor applies to potential iPSC therapy. In this case, hematopoietic blood cells are used to make stem cells, which are manufactured into the cell type of interest before reintroduction. Preclinical tests must demonstrate that iPSC-derived cells perform as intended, are safe, and possess little or no off-target activity.

    For example, CDI developed a Parkinsonian model in which iPSC-derived dopaminergic neurons were introduced to primates. The model showed engraftment and enervation, and it appeared to be free of proliferative stem cells.

    • “You will see iPSCs first used in clinical trials as a surrogate to understand efficacy and safety,” notes Mr. Parker. “In an ongoing drug-repurposing trial with GlaxoSmithKline and Harvard University, iPSC-derived motor neurons will be produced from patients with amyotrophic lateral sclerosis and tested in parallel with the drug.” CDI has three cell-therapy programs in their commercialization pipeline focusing on macular degeneration, Parkinson’s disease, and postmyocardial infarction.

    • Keeping an Eye on Aging Eyes
      The California Project to Cure Blindness is evaluating a stem cell–based treatment strategy for age-related macular degeneration. The strategy involves growing retinal pigment epithelium (RPE) cells on a biostable, synthetic scaffold, then implanting the RPE cell–enriched scaffold to replace RPE cells that are dying or dysfunctional. One of the project’s directors, Dennis Clegg, Ph.D., a researcher at the University of California, Santa Barbara, provided this image, which shows stem cell–derived RPE cells. Cell borders are green, and nuclei are red.

      The eye has multiple advantages over other organ systems for regenerative medicine. Advanced surgical methods can access the back of the eye, noninvasive imaging methods can follow the transplanted cells, good outcome parameters exist, and relatively few cells are needed.

      These advantages have attracted many groups to tackle ocular disease, in particular age-related macular degeneration, the leading cause of blindness in the elderly in the United States. Most cases of age-related macular degeneration are thought to be due to the death or dysfunction of cells in the retinal pigment epithelium (RPE). RPE cells are crucial support cells for the rods, cones, and photoreceptors. When RPE cells stop working or die, the photoreceptors die and a vision deficit results.

      A regenerated and restored RPE might prevent the irreversible loss of photoreceptors, possibly via the the transplantation of functionally polarized RPE monolayers derived from human embryonic stem cells. This approach is being explored by the California Project to Cure Blindness, a collaborative effort involving the University of Southern California (USC), the University of California, Santa Barbara (UCSB), the California Institute of Technology, City of Hope, and Regenerative Patch Technologies.

      The project, which is funded by the California Institute of Regenerative Medicine (CIRM), started in 2010, and an IND was filed early 2015. Clinical trial recruitment has begun.

      One of the project’s leaders is Dennis Clegg, Ph.D., Wilcox Family Chair in BioMedicine, UCSB. His laboratory developed the protocol to turn undifferentiated H9 embryonic stem cells into a homogenous population of RPE cells.

      “These are not easy experiments,” remarks Dr. Clegg. “Figuring out the biology and how to make the cell of interest is a challenge that everyone in regenerative medicine faces. About 100,000 RPE cells will be grown as a sheet on a 3 × 5 mm biostable, synthetic scaffold, and then implanted in the patients to replace the cells that are dying or dysfunctional. The idea is to preserve the photoreceptors and to halt disease progression.”

      Moving therapies such as this RPE treatment from concept to clinic is a huge team effort and requires various kinds of expertise. Besides benefitting from Dr. Clegg’s contribution, the RPE project incorporates the work of Mark Humayun, M.D., Ph.D., co-director of the USC Eye Institute and director of the USC Institute for Biomedical Therapeutics and recipient of the National Medal of Technology and Innovation, and David Hinton, Ph.D., a researcher at USC who has studied how actvated RPE cells can alter the local retinal microenvironment.

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Fat Cells Reprogrammed to Make Insulin

Posted in Biochemical pathways, Biological Networks, Gene Regulation and Evolution, Cell Biology, Signaling & Cell Circuits, Clinical & Translational, Clinical Genomics, Curation, Developmental biology, Diabetes Mellitus, Disease Biology, Endocrine Diseases, Epigenetics and Environmental Factors, Gene Regulation, Genome Biology, Genomic Endocrinology, Innovations, Metabolism, Metabolomics, tagged , , on April 14, 2016| Leave a Comment »

Fat Cells Reprogrammed to Make Insulin

Curator: Larry H. Bernstein, MD, FCAP

 

A New Use for Love Handles, Insulin-Producing Beta Cells

http://www.genengnews.com/gen-news-highlights/a-new-use-for-love-handles-insulin-producing-beta-cells/81252612/

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Scientists at the Swiss Federal Institute of Technology (ETH) in Zurich have found an exciting new use for the cells that reside in the undesirable flabby tissue—creating pancreatic beta cells. The ETH researchers extracted stem cells from a 50-year-old test subject’s fatty tissue and reprogrammed them into mature, insulin-producing beta cells.

The findings from this study were published recently in Nature Communications in an article entitled “A Programmable Synthetic Lineage-Control Network That Differentiates Human IPSCs into Glucose-Sensitive Insulin-Secreting Beta-Like Cells.”

The investigators added a highly complex synthetic network of genes to the stem cells to recreate precisely the key growth factors involved in this maturation process. Central to the process were the growth factors Ngn3, Pdx1, and MafA; the researchers found that concentrations of these factors change during the differentiation process.

For instance, MafA is not present at the start of maturation. Only on day 4, in the final maturation step, does it appear, its concentration rising steeply and then remaining at a high level. The changes in the concentrations of Ngn3 and Pdx1, however, are very complex: while the concentration of Ngn3 rises and then falls again, the level of Pdx1 rises at the beginning and toward the end of maturation.

Senior study author Martin Fussenegger, Ph.D., professor of biotechnology and bioengineering at ETH Zurich’s department of biosystems science and engineering stressed that it was essential to reproduce these natural processes as closely as possible to produce functioning beta cells, stating that “the timing and the quantities of these growth factors are extremely important.”

The ETH researchers believe that their work is a real breakthrough, in that a synthetic gene network has been used successfully to achieve genetic reprogramming that delivers beta cells. Until now, scientists have controlled such stem cell differentiation processes by adding various chemicals and proteins exogenously.

“It’s not only really hard to add just the right quantities of these components at just the right time, but it’s also inefficient and impossible to scale up,” Dr. Fussenegger noted.

While the beta cells not only looked very similar to their natural counterparts—containing dark spots known as granules that store insulin—the artificial beta cells also functioned in a very similar manner. However, the researchers admit that more work needs to be done to increase the insulin output.

“At the present time, the quantities of insulin they secrete are not as great as with natural beta cells,” Dr. Fussenegger stated. Yet, the key point is that the researchers have for the first time succeeded in reproducing the entire natural process chain, from stem cell to differentiated beta cell.

In future, the ETH scientists’ novel technique might make it possible to implant new functional beta cells in diabetes sufferers that are made from their adipose tissue. While beta cells have been transplanted in the past, this has always required subsequent suppression of the recipient’s immune system—as with any transplant of donor organs or tissue.

“With our beta cells, there would likely be no need for this action since we can make them using endogenous cell material taken from the patient’s own body,” Dr. Fussenegger said. “This is why our work is of such interest in the treatment of diabetes.”

A programmable synthetic lineage-control network that differentiates human IPSCs into glucose-sensitive insulin-secreting beta-like cells

Pratik Saxena, Boon Chin Heng, Peng Bai, Marc Folcher, Henryk Zulewski & Martin Fussenegger
Nature Communications7,Article number:11247         doi:10.1038/ncomms11247

Synthetic biology has advanced the design of standardized transcription control devices that programme cellular behaviour. By coupling synthetic signalling cascade- and transcription factor-based gene switches with reverse and differential sensitivity to the licensed food additive vanillic acid, we designed a synthetic lineage-control network combining vanillic acid-triggered mutually exclusive expression switches for the transcription factors Ngn3 (neurogenin 3; OFF-ON-OFF) and Pdx1 (pancreatic and duodenal homeobox 1; ON-OFF-ON) with the concomitant induction of MafA (V-maf musculoaponeurotic fibrosarcoma oncogene homologue A; OFF-ON). This designer network consisting of different network topologies orchestrating the timely control of transgenic and genomic Ngn3, Pdx1 and MafA variants is able to programme human induced pluripotent stem cells (hIPSCs)-derived pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells, whose glucose-stimulated insulin-release dynamics are comparable to human pancreatic islets. Synthetic lineage-control networks may provide the missing link to genetically programme somatic cells into autologous cell phenotypes for regenerative medicine.

Cell-fate decisions during development are regulated by various mechanisms, including morphogen gradients, regulated activation and silencing of key transcription factors, microRNAs, epigenetic modification and lateral inhibition. The latter implies that the decision of one cell to adopt a specific phenotype is associated with the inhibition of neighbouring cells to enter the same developmental path. In mammals, insights into the role of key transcription factors that control development of highly specialized organs like the pancreas were derived from experiments in mice, especially various genetically modified animals1, 2, 3, 4. Normal development of the pancreas requires the activation of pancreatic duodenal homeobox protein (Pdx1) in pre-patterned cells of the endoderm. Inactivating mutations of Pdx1 are associated with pancreas agenesis in mouse and humans5, 6. A similar cell fate decision occurs later with the activation of Ngn3 that is required for the development of all endocrine cells in the pancreas7. Absence of Ngn3 is associated with the loss of pancreatic endocrine cells, whereas the activation of Ngn3 not only allows the differentiation of endocrine cells but also induces lateral inhibition of neighbouring cells—via Delta-Notch pathway—to enter the same pancreatic endocrine cell fate8. This Ngn3-mediated cell-switch occurs at a specific time point and for a short period of time in mice9. Thereafter, it is silenced and becomes almost undetectable in postnatal pancreatic islets. Conversely, Pdx1-positive Ngn3-positive cells reduce Pdx1 expression, as Ngn3-positive cells are Pdx1 negative10. They re-express Pdx1, however, as they go on their path towards glucose-sensitive insulin-secreting cells with parallel induction of MafA that is required for proper differentiation and maturation of pancreatic beta cells11. Data supporting these expression dynamics are derived from mice experiments1, 11, 12. A synthetic gene-switch governing cell fate decision in human induced pluripotent stem cells (hIPSCs) could facilitate the differentiation of glucose-sensitive insulin-secreting cells.

In recent years, synthetic biology has significantly advanced the rational design of synthetic gene networks that can interface with host metabolism, correct physiological disturbances13 and provide treatment strategies for a variety of metabolic disorders, including gouty arthritis14, obesity15 and type-2 diabetes16. Currently, synthetic biology principles may provide the componentry and gene network topologies for the assembly of synthetic lineage-control networks that can programme cell-fate decisions and provide targeted differentiation of stem cells into terminally differentiated somatic cells. Synthetic lineage-control networks may therefore provide the missing link between human pluripotent stem cells17 and their true impact on regenerative medicine18, 19, 20. The use of autologous stem cells in regenerative medicine holds great promise for curing many diseases, including type-1 diabetes mellitus (T1DM), which is characterized by the autoimmune destruction of insulin-producing pancreatic beta cells, thus making patients dependent on exogenous insulin to control their blood glucose21, 22. Although insulin therapy has changed the prospects and survival of T1DM patients, these patients still suffer from diabetic complications arising from the lack of physiological insulin secretion and excessive glucose levels23. The replacement of the pancreatic beta cells either by pancreas transplantation or by transplantation of pancreatic islets has been shown to normalize blood glucose and even improve existing complications of diabetes24. However, insulin independence 5 years after islet transplantation can only be achieved in up to 55% of the patients even when using the latest generation of immune suppression strategies25, 26. Transplantation of human islets or the entire pancreas has allowed T1DM patients to become somewhat insulin independent, which provides a proof-of-concept for beta-cell replacement therapies27, 28. However, because of the shortage of donor pancreases and islets, as well as the significant risk associated with transplantation and life-long immunosuppression, the rational differentiation of stem cells into functional beta-cells remains an attractive alternative29, 30. Nevertheless, a definitive cure for T1DM should address both the beta-cell deficit and the autoimmune response to cells that express insulin. Any beta-cell mimetic should be able to store large amounts of insulin and secrete it on demand, as in response to glucose stimulation29, 31. The most effective protocols for the in vitro generation of bonafide insulin-secreting beta-like cells that are suitable for transplantation have been the result of sophisticated trial-and-error studies elaborating timely addition of complex growth factor and small-molecule compound cocktails to human pancreatic progenitor cells32, 33, 34. The differentiation of pancreatic progenitor cells to beta-like cells is the most challenging part as current protocols provide inconsistent results and limited success in programming pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells35, 36, 37. One of the reasons for these observations could be the heterogeneity in endocrine differentiation and maturation towards a beta cell phenotype. Here we show that a synthetic lineage-control network programming the dynamic expression of the transcription factors Ngn3, Pdx1 and MafA enables the differentiation of hIPSC-derived pancreatic progenitor cells to glucose-sensitive insulin-secreting beta-like cells (Supplementary Fig. 1).

 

Vanillic acid-programmable positive band-pass filter

The differentiation pathway from pancreatic progenitor cells to glucose-sensitive insulin-secreting pancreatic beta-cells combines the transient mutually exclusive expression switches of Ngn3 (OFF-ON-OFF) and Pdx1 (ON-OFF-ON) with the concomitant induction of MafA (OFF-ON) expression10,11. Since independent control of the pancreatic transcription factors Ngn3, Pdx1 and MafA by different antibiotic transgene control systems responsive to tetracycline, erythromycin and pristinamycin did not result in the desired differential control dynamics (Supplementary Fig. 2), we have designed a vanillic acid-programmable synthetic lineage-control network that programmes hIPSC-derived pancreatic progenitor cells to specifically differentiate into glucose-sensitive insulin-secreting beta-like cells in a seamless and self-sufficient manner. The timely coordination of mutually exclusive Ngn3 and Pdx1 expression with MafA induction requires the trigger-controlled execution of a complex genetic programme that orchestrates two overlapping antagonistic band-pass filter expression profiles (OFF-ON-OFF and ON-OFF-ON), a positive band-pass filter for Ngn3 (OFF-ON-OFF) and a negative band-pass filter, also known as band-stop filter, for Pdx1 (ON-OFF-ON), the ramp-up expression phase of which is linked to a graded induction of MafA (OFF-ON).

The core of the synthetic lineage-control network consists of two transgene control devices that are sensitive to the food component and licensed food additive vanillic acid. These devices are a synthetic vanillic acid-inducible (ON-type) signalling cascade that is gradually induced by increasing the vanillic acid concentration and a vanillic acid-repressible (OFF-type) gene switch that is repressed in a vanillic acid dose-dependent manner (Fig. 1a,b). The designer cascade consists of the vanillic acid-sensitive mammalian olfactory receptor MOR9-1, which sequentially activates the G protein Sα (GSα) and adenylyl cyclase to produce a cyclic AMP (cAMP) second messenger surge38 that is rewired via the cAMP-responsive protein kinase A-mediated phospho-activation of the cAMP-response element-binding protein 1 (CREB1) to the induction of synthetic promoters (PCRE) containing CREB1-specific cAMP response elements (CRE; Fig. 1a). The co-transfection of pCI-MOR9-1 (PhCMV-MOR9-1-pASV40) and pCK53 (PCRE-SEAP-pASV40) into human mesenchymal stem cells (hMSC-TERT) confirmed the vanillic acid-adjustable secreted alkaline phosphatase (SEAP) induction of the designer cascade (>10 nM vanillic acid; Fig. 1a). The vanillic acid-repressible gene switch consists of the vanillic acid-dependent transactivator (VanA1), which binds and activates vanillic acid-responsive promoters (for example, P1VanO2) at low and medium vanillic acid levels (<2 μM). At high vanillic acid concentrations (>2 μM), VanA1 dissociates from P1VanO2, which results in the dose-dependent repression of transgene expression39 (Fig. 1b). The co-transfection of pMG250 (PSV40-VanA1-pASV40) and pMG252 (P1VanO2-SEAP-pASV40) into hMSC-TERT corroborated the fine-tuning of the vanillic acid-repressible SEAP expression (Fig. 1b).

Figure 1: Design of a vanillic acid-responsive positive band-pass filter providing an OFF-ON-OFF expression profile.

Design of a vanillic acid-responsive positive band-pass filter providing an OFF-ON-OFF expression profile.

http://www.nature.com/ncomms/2016/160411/ncomms11247/images_article/ncomms11247-f1.jpg

a) Vanillic acid-inducible transgene expression. The constitutively expressed vanillic acid-sensitive olfactory G protein-coupled receptor MOR9-1 (pCI-MOR9-1; PhCMV-MOR9-1-pA) senses extracellular vanillic acid levels and triggers G protein (Gs)-mediated activation of the membrane-bound adenylyl cyclase (AC) that converts ATP into cyclic AMP (cAMP). The resulting intracellular cAMP surge activates PKA (protein kinase A), whose catalytic subunits translocate into the nucleus to phosphorylate cAMP response element-binding protein 1 (CREB1). Activated CREB1 binds to synthetic promoters (PCRE) containing cAMP-response elements (CRE) and induces PCRE-driven expression of human placental secreted alkaline phosphatase (SEAP; pCK53, PCRE-SEAP-pA). Co-transfection of pCI-MOR9-1 and pCK53 into human mesenchymal stem cells (hMSC-TERT) grown for 48 h in the presence of increasing vanillic acid concentrations results in a dose-inducible SEAP expression profile. (b) Vanillic acid-repressible transgene expression. The constitutively expressed, vanillic acid-dependent transactivator VanA1(pMG250, PSV40-VanA1-pA, VanA1, VanR-VP16) binds and activates the chimeric promoter P1VanO2 (pMG252, P1VanO2-SEAP-pA) in the absence of vanillic acid. In the presence of increasing vanillic acid concentrations, VanA1 is released from P1VanO2, and transgene expression is shut down. Co-transfection of pMG250 and pMG252 into hMSC-TERT grown for 48 h in the presence of increasing vanillic acid concentrations results in a dose-repressible SEAP expression profile. (c) Positive band-pass expression filter. Serial interconnection of the synthetic vanillic acid-inducible signalling cascade (a) with the vanillic acid-repressible transcription factor-based gene switch (b) by PCRE-mediated expression of VanA1 (pSP1, PCRE-VanA1-pA) results in a two-level feed-forward cascade. Owing to the opposing responsiveness and differential sensitivity to vanillic acid, this synthetic gene network programmes SEAP expression with a positive band-pass filter profile (OFF-ON-OFF) as vanillic acid levels are increased. Medium vanillic acid levels activate MOR9-1, which induces PCRE-driven VanA1 expression. VanA1remains active and triggers P1VanO2-mediated SEAP expression in feed-forward manner, which increases to maximum levels. At high vanillic acid concentrations, MOR9-1 maintains PCRE-driven VanA1 expression, but the transactivator dissociates from P1VanO2, which shuts SEAP expression down. Co-transfection of pCI-MOR9-1, pSP1 and pMG252 into hMSC-TERT grown for 48 h in the presence of increasing vanillic acid concentrations programmes SEAP expression with a positive band-pass profile (OFF-ON-OFF). Data are the means±s.d. of triplicate experiments (n=9).

The opposing responsiveness and differential sensitivity of the control devices to vanillic acid are essential to programme band-pass filter expression profiles. Upon daisy-chaining the designer cascade (pCI-MOR9-1; PhCMV-MOR9-1-pASV40; pSP1, PCRE-VanA1-pASV40) and the gene switch (pSP1, PCRE-VanA1-pASV40; pMG252, P1VanO2-SEAP-pASV40) in the same cell, the network executes a band-pass filter SEAP expression profile when exposed to increasing concentrations of vanillic acid (Fig. 1c). Medium vanillic acid levels (10 nM to 2 μM) activate MOR9-1, which induces PCRE-driven VanA1 expression. VanA1 remains active within this concentration range and, in a feed-forward amplifier manner, triggers P1VanO2-mediated SEAP expression, which gradually increases to maximum levels (Fig. 1c). At high vanillic acid concentrations (2 μM to 400 μM), MOR9-1 maintains PCRE-driven VanA1 expression, but the transactivator is inactivated and dissociates from P1VanO2, which results in the gradual shutdown of SEAP expression (Fig. 1c).

Vanillic acid-programmable lineage-control network

For the design of the vanillic acid-programmable synthetic lineage-control network, constitutive MOR9-1 expression and PCRE-driven VanA1 expression were combined with pSP12 (pASV40-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pASV40) for endocrine specification and pSP17(PCREm-Pdx1cm-2A-MafAcm-pASV40) for maturation of developing beta-cells (Fig. 2a,b). ThepSP12-encoded expression unit enables the VanA1-controlled induction of the optimized bidirectional vanillic acid-responsive promoter (P3VanO2) that drives expression of a codon-modified Ngn3cm, the nucleic acid sequence of which is distinct from its genomic counterpart (Ngn3g) to allow for quantitative reverse transcription–PCR (qRT–PCR)-based discrimination. In the opposite direction, P3VanO2 transcribes miR30Pdx1g-shRNA, which exclusively targets genomicPdx1 (Pdx1g) transcripts for RNA interference-based destruction and is linked to the production of a blue-to-red medium fluorescent timer40 (mFT) for precise visualization of the unit’s expression dynamics in situ. pSP17 contains a dicistronic expression unit in which the modified high-tightness and lower-sensitivity PCREm promoter (see below) drives co-cistronic expression of Pdx1cm andMafAcm, which are codon-modified versions producing native transcription factors that specifically differ from their genomic counterparts (Pdx1g, MafAg) in their nucleic acid sequence. After individual validation of the vanillic acid-controlled expression and functionality of all network components (Supplementary Figs 2–9), the lineage-control network was ready to be transfected into hIPSC-derived pancreatic progenitor cells. These cells are characterized by high expression of Pdx1g and Nkx6.1 levels and the absence of Ngn3g and MafAg production32, 33, 34 (day 0:Supplementary Figs 10–16).

 

Figure 2: Synthetic lineage-control network programming differential expression dynamics of pancreatic transcription factors.

Synthetic lineage-control network programming differential expression dynamics of pancreatic transcription factors.

 

http://www.nature.com/ncomms/2016/160411/ncomms11247/images/ncomms11247-f2.jpg

(a) Schematic of the synthetic lineage-control network. The constitutively expressed, vanillic acid-sensitive olfactory G protein-coupled receptor MOR9-1 (pCI-MOR9-1; PhCMV-MOR9-1-pA) senses extracellular vanillic acid levels and triggers a synthetic signalling cascade, inducing PCRE-driven expression of the transcription factor VanA1 (pSP1, PCRE-VanA1-pA). At medium vanillic acid concentrations (purple arrows), VanA1 binds and activates the bidirectional vanillic acid-responsive promoter P3VanO2 (pSP12, pA-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pA), which drives the induction of codon-modified Neurogenin 3 (Ngn3cm) as well as the coexpression of both the blue-to-red medium fluorescent timer (mFT) for precise visualization of the unit’s expression dynamics and miR30pdx1g-shRNA (a small hairpin RNA programming the exclusive destruction of genomic pancreatic and duodenal homeobox 1 (Pdx1g) transcripts). Consequently, Ngn3cm levels switch from low to high (OFF-to-ON), and Pdx1g levels toggle from high to low (ON-to-OFF). In addition, Ngn3cm triggers the transcription of Ngn3g from its genomic promoter, which initiates a positive-feedback loop. At high vanillic acid levels (orange arrows), VanA1 is inactivated, and both Ngn3cm and miR30pdx1g-shRNA are shut down. At the same time, the MOR9-1-driven signalling cascade induces the modified high-tightness and lower-sensitivity PCREm promoter that drives the co-cistronic expression of the codon-modified variants of Pdx1 (Pdx1cm) and V-maf musculoaponeurotic fibrosarcoma oncogene homologue A (MafAcm; pSP17, PCREm-Pdx1cm-2A-MafAcm-pA). Consequently, Pdx1cm and MafAcm become fully induced. As Pdx1cm expression ramps up, it initiates a positive-feedback loop by inducing the genomic counterparts Pdx1g and MafAg. Importantly, Pdx1cm levels are not affected by miR30Pdx1g-shRNA because the latter is specific for genomic Pdx1g transcripts and because the positive feedback loop-mediated amplification of Pdx1gexpression becomes active only after the shutdown of miR30Pdx1g-shRNA. Overall, the synthetic lineage-control network provides vanillic acid-programmable, transient, mutually exclusive expression switches for Ngn3 (OFF-ON-OFF) and Pdx1 (ON-OFF-ON) as well as the concomitant induction of MafA (OFF-ON) expression, which can be followed in real time (Supplementary Movies 1 and 2). (b) Schematic illustrating the individual differentiation steps from human IPSCs towards beta-like cells. The colours match the cell phenotypes reached during the individual differentiation stages programmed by the lineage-control network shown in a.

Following the co-transfection of pCI-MOR9-1 (PhCMV-MOR9-1-pASV40), pSP1 (PCRE-VanA1-pASV40), pSP12 (pASV40-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pASV40) and pSP17(PCREm-Pdx1cm-2A-MafAcm-pASV40) into hIPSC-derived pancreatic progenitor cells, the synthetic lineage-control network should override random endogenous differentiation activities and execute the pancreatic beta-cell-specific differentiation programme in a vanillic acid remote-controlled manner. To confirm that the lineage-control network operates as programmed, we cultivated network-containing and pEGFP-N1-transfected (negative-control) cells for 4 days at medium (2 μM) and then 7 days at high (400 μM) vanillic acid concentrations and profiled the differential expression dynamics of all of the network components and their genomic counterparts as well as the interrelated transcription factors and hormones in both whole populations and individual cells at days 0, 4, 11 and 14 (Figs 2 and 3 and Supplementary Figs 11–17).

 

Figure 3: Dynamics of the lineage-control network.

Dynamics of the lineage-control network.

http://www.nature.com/ncomms/2016/160411/ncomms11247/images/ncomms11247-f3.jpg

(a,b) Quantitative RT–PCR-based expression profiling of the pancreatic transcription factors Ngn3cm/g, Pdx1cm/g and MafAcm/g in hIPSC-derived pancreatic progenitor cells containing the synthetic lineage-control network at days 4 and 11. Data are the means±s.d. of triplicate experiments (n=9). (c–g) Immunocytochemistry of pancreatic transcription factors Ngn3cm/g, Pdx1cm/g and MafAcm/g in hIPSC-derived pancreatic progenitor cells containing the synthetic lineage-control network at days 4 and 11. hIPSC-derived pancreatic progenitor cells were co-transfected with the lineage-control vectors pCI-MOR9-1 (PhCMV-MOR9-1-pA), pSP1 (PCRE-VanA1-pA), pSP12 (pA-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pA) and pSP17 (PCREm-Pdx1cm-2A-MafAcm) and immunocytochemically stained for (c) VanA1 and Pdx1 (day 4), (d) VanA1 and Ngn3 (day 4), (e) VanA1 and Pdx1 (day 11), (f) MafA and Pdx1 (day 11) as well as (g) VanA1 and insulin (C-peptide) (day 11). The cells staining positive for VanA1 are containing the lineage-control network. DAPI, 4′,6-diamidino-2-phenylindole. Scale bar, 100 μm.

…….

Multicellular organisms, including humans, consist of a highly structured assembly of a multitude of specialized cell phenotypes that originate from the same zygote and have traversed a preprogrammed multifactorial developmental plan that orchestrates sequential differentiation steps with high precision in space and time19, 51. Because of the complexity of terminally differentiated cells, the function of damaged tissues can for most medical indications only be restored via the transplantation of donor material, which is in chronically short supply52.

Despite significant progress in regenerative medicine and the availability of stem cells, the design of protocols that replicate natural differentiation programmes and provide fully functional cell mimetics remains challenging29, 53. For example, efforts to generate beta-cells from human embryonic stem cells (hESCs) have led to reliable protocols involving the sequential administration of growth factors (activin A, bone morphogenetic protein 4 (BMP-4), basic fibroblast growth factor (bFGF), FGF-10, Noggin, vascular endothelial growth factor (VEGF) and Wnt3A) and small-molecule compounds (cyclopamine, forskolin, indolactam V, IDE1, IDE2, nicotinamide, retinoic acid, SB−431542 and γ-secretase inhibitor) that modulate differentiation-specific signalling pathways31, 54, 55. In vitro differentiation of hESC-derived pancreatic progenitor cells into beta-like cells is more challenging and has been achieved recently by a complex media formulation with chemicals and growth factors32, 33, 34.

hIPSCs have become a promising alternative to hESCs; however, their use remains restricted in many countries56. Most hIPSCs used for directed differentiation studies were derived from a juvenescent cell source that is expected to show a higher degree of differentiation potential compared with older donors that typically have a higher need for medical interventions37, 57, 58. We previously succeeded in producing mRNA-reprogrammed hIPSCs from adipose tissue-derived mesenchymal stem cells of a 50-year-old donor, demonstrating that the reprogramming of cells from a donor of advanced age is possible in principle59.

Recent studies applying similar hESC-based differentiation protocols to hIPSCs have produced cells that release insulin in response to high glucose32, 33, 34. This observation suggests that functional beta-like cells can eventually be derived from hIPSCs32, 33. In our hands, the growth-factor/chemical-based technique for differentiating human IPSCs resulted in beta-like cells with poor glucose responsiveness. Recent studies have revealed significant variability in the lineage specification propensity of different hIPSC lines35, 60 and substantial differences in the expression profiles of key transcription factors in hIPSC-derived beta-like cells33. Therefore, the growth-factor/chemical-based protocols may require further optimization and need to be customized for specific hIPSC lines35. Synthetic lineage-control networks providing precise dynamic control of transcription factor expression may overcome the challenges associated with the programming of beta-like cells from different hIPSC lines.

Rather than exposing hIPSCs to a refined compound cocktail that triggers the desired differentiation in a fraction of the stem cell population, we chose to design a synthetic lineage-control network to enable single input-programmable differentiation of hIPSC-derived pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells. In contrast with the use of growth-factor/chemical-based cocktails, synthetic lineage-control networks are expected to (i) be more economical because of in situ production of the required transcription factors, (ii) enable simultaneous control of ectopic and chromosomally encoded transcription factor variants, (iii) tap into endogenous pathways and not be limited to cell-surface input, (iv) display improved reversibility that is not dependent on the removal of exogenous growth factors via culture media replacement, (v) provide lateral inhibition, thereby reducing the random differentiation of neighbouring cells and (vi) enable trigger-programmable and (vii) precise differential transcription factor expression switches.

The synthetic lineage-control network that precisely replicates the endogenous relative expression dynamics of the transcription factors Pdx-1, Ngn3 and MafA required the design of a new network topology that interconnects a synthetic signalling cascade and a gene switch with differential and opposing sensitivity to the food additive vanillic acid. This differentiation device provides different band-pass filter, time-delay and feed-forward amplifier topologies that interface with endogenous positive-feedback loops to orchestrate the timely expression and repression of heterologous and chromosomally encoded Ngn3, Pdx1 and MafA variants. The temporary nature of the engineering intervention, which consists of transient transfection of the genetic lineage-control components in the absence of any selection, is expected to avoid stable modification of host chromosomes and alleviate potential safety concerns. In addition, the resulting beta-cell mass could be encapsulated inside vascularized microcontainers28, a proven containment strategy in prototypic cell-based therapies currently being tested in animal models of prominent human diseases14, 15, 16, 61, 62 as well as in human clinical trials28.

The hIPSC-derived beta-like cells resulting from this trigger-induced synthetic lineage-control network exhibited glucose-stimulated insulin-release dynamics and capacity matching the human physiological range and transcriptional profiling, flow cytometric analysis and electron microscopy corroborated the lineage-controlled stem cells reached a mature beta-cell phenotype. In principle, the combination of hIPSCs derived from the adipose tissue of a 50-year-old donor59 with a synthetic lineage-control network programming glucose-sensitive insulin-secreting beta-like cells closes the design cycle of regenerative medicine63. However, hIPSCs that are derived from T1DM patients, differentiated into beta-like cells and transplanted back into the donor would still be targeted by the immune system, as demonstrated in the transplantation of segmental pancreatic grafts from identical twins64. Therefore, any beta-cell-replacement therapy will require complementary modulation of the immune system either via drugs30, 65, engineering or cell-based approaches66, 67 or packaging inside vascularizing, semi-permeable immunoprotective microcontainers28.

Capitalizing on the design principles of synthetic biology, we have successfully constructed and validated a synthetic lineage-control network that replicates the differential expression dynamics of critical transcription factors and mimicks the native differentiation pathway to programme hIPSC-derived pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells that compare with human pancreatic islets at a high level. The design of input-triggered synthetic lineage-control networks that execute a preprogrammed sequential differentiation agenda coordinating the timely induction and repression of multiple genes could provide a new impetus for the advancement of developmental biology and regenerative medicine.

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

Adipocyte Derived Stroma Cells: Their Usage in Regenerative Medicine and Reprogramming into Pancreatic Beta-Like Cells

Curator: Evelina Cohn, Ph.D.

https://pharmaceuticalintelligence.com/2016/03/03/adipocyte-derived-stroma-cells-their-usage-in-regenerative-medicine-and-reprogramming-into-pancreatic-beta-like-cells/

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