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Posts Tagged ‘cancer signatures’


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 phenomena345. 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 proliferation910. Polyamines are also known to condense DNA in a concentration-dependent manner211. 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 hotspot2122. 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 attraction16232425. 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.

(ac) 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. (df) The average distributions of cations for the three sequence pairs featured in ac. 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 nucleus2728. 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

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.

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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