Posts Tagged ‘diagnostics’

Clinical Biomarkers Overview

Larry H. Bernstein, MD, FCAP, Curator



Paving the Road for Clinical Biomarkers

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

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.

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)

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


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.

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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Digital PCR

Larry H. Bernstein, MD, FCAP, Curator



GEN Roundup: Digital PCR Advances Partition by Partition  

By Partitioning Samples Digital PCR Is Lowering Detection Limits and Enabling New Applications

GEN  Mar 1, 2016 (Vol. 36, No. 5)


  • Digital PCR (dPCR) has generated intense interest because it is showing potential as a clinical diagnostics tool. It has already proven to be a useful technique for any application where extreme sensitivity or precise quantification is essential, such as identifying mutations or copy number variations in tumor cells, or examining gene expression at the single-cell level.

    GEN interviewed several dPCR experts to find out specifically why the technique is increasing in popularity. GEN also asked the experts to envision dPCR’s future capabilities.

  • GEN: What makes dPCR technology such a superior tool for discovery and diagnostic applications?

    Dr. Shelton The high levels of sensitivity, precision, and reproducibility in DNA and quantification are the major strengths of dPCR. The technology is robust where differences in primer efficiency or the presence of sample-specific PCR inhibitors are trivial to the final quantification through an end-point amplification reaction.

    This provides value to discovery as a trusted tool for validating potential biomarkers and hypotheses generated by broad profiling techniques such as microarrays or next-generation sequencing (NGS). In diagnostics applications, the reproducibility and rapid results of dPCR are critical for labs around the world to quickly compare and share data, especially for ultra-low detection of DNA where variability is high.

    Dr. Garner Digital PCR provides a precise direct counting approach for single molecule detection, thereby providing a straightforward process for the absolute quantification of nucleic acids in samples. One of the biggest advantages of using a system such as ours is its ability to do real-time reads on digital samples. When samples go through PCR, their results are recorded after each cycle.

    These results build a curve, and customers can analyze the data if something went wrong. If it isn’t a clean read—from either a contamination issue, primer-dimer issue, or off-target issue—the curve isn’t the classic PCR curve.

    Dr. Menezes Digital PCR allows absolute quantification of target concentration in samples without the need for standard curves. Obtaining consistent, precise, and absolute quantification with regular qPCR is dependent on standard curve generation and amplification efficiency calculations, which can introduce errors.

    Ms. Hibbs At MilliporeSigma Cell Design Studio, the implementation of dPCR has improved and accelerated the custom cell engineering workflow. After the application of zinc finger nuclease or CRISPR/Cas to create precise genetic modifications in mammalian cell lines, dPCR is used to characterize the expected frequency of homologous recombination and develop a screening strategy based on this expected frequency.

    In some cell lines, homologous recombination occurs at a low frequency. In such cases, dPCR is used to screen cell pools and subsequently identify rare clones having the desired mutation. Digital PCR is also used to accurately and expeditiously measure target gene copy number. It is used this way, for example, in polyploid cell lines.

    Dr. Price The ability to partition genomic samples to a level that enables robust detection of single target molecules is what sets dPCR apart as an innovative tool. Each partition (droplet in the case of the RainDrop System) operates as an individual PCR reaction, allowing for sensitive, reproducible, and precise quantification of nucleic acid molecules without the need for reference standards or endogenous controls.

    Partitioning also provides greater tolerance to PCR inhibitors compared to quantitative PCR (qPCR). In doing so, dPCR can remedy many shortcomings of qPCR by transforming the analog, exponential nature of PCR into a digital signal.

    Mr. Wakida Digital PCR is an ideal technology for detecting rare targets at concentrations of 0.1% or lower. By partitioning samples prior to PCR, exceptionally rare targets can be isolated into individual partitions and amplified.

    Digital PCR produces absolute quantitative results, so in some respects, it is easier than qPCR because it doesn’t require a standard curve, with the added advantages of being highly tolerant of inhibitors and being able to detect more minute fold changes. Absolute quantification is useful for generating reference standards, detecting viral load, and preparing NGS libraries.

  • GEN: In what field do you think dPCR will have the greatest impact in the future?

    Dr. Shelton dPCR will have a great impact on precision medicine, especially in liquid biopsy analysis. Cell-free DNA from bodily fluids such as urine or blood plasma can be analyzed quickly and cost-effectively using dPCR. For example, a rapid dPCR test can be performed to determine mutations present in a patient’s tumor and help drive treatment decisions.

    Iterative monitoring of disease states can also be achieved due to the relatively low cost of dPCR, providing faster response times when medications are failing. Gene editing will also be greatly impacted by dPCR. Digital PCR enables refinement and optimization of gene-editing tools and conditions. Digital PCR also serves as quality control of therapeutically modified cells and viral transfer vectors used in gene-therapy efforts.

    Dr. Garner The BioMark™ HD system combines dPCR with simultaneous real-time data for counting and validation. This capability is important for applications such as rare mutation detection, GMO quantitation, and aneuploidy detection—where false positives are intolerable and precision is paramount.

    Any field that requires precision and the ability to detect false positives is a likely target for Fluidigm’s dPCR. Suitable applications include detecting and quantifying cancer-causing genes in patients’ cells, viral RNA that infects bacteria, or fetal DNA in an expectant mother’s plasma.

    Dr. Menezes This technology is particularly useful for samples with low frequency sequences as, for example, those containing rare alleles, low levels of pathogen, or low levels of target gene expression. Teasing out fine differences in copy number variants is another area where this technology delivers more precise data.

    Ms. Hibbs Digital PCR overcomes limitations associated with low-abundance template material and quantification of rare mutations in a high background of wild-type DNA sequence. For this reason, dPCR is poised to have significant impacts in diverse clinical applications such as detection and quantification of rare mutations in liquid biopsies, detection of viral pathogens, and detection of copy number variation and mosaicism.

    Dr. Price Due to its high sensitivity, precision, and absolute quantification, the RainDrop dPCR has the potential to extend the range of nucleic acid analysis beyond the reach of other methods in a number of applications that could lend themselves to diagnostic, prognostic, and predictive applications. The precision of dPCR can be extremely useful in applications that require finer measures of fold change and rare variant detection.

    Digital PCR is suitable for addressing varied research and clinical challenges. These include the early detection of cancer, pathogen/viral detection and quantitation, copy number variation, rare mutation detection, fetal genetic screening, and predicting transplant rejection. Additional applications include gene expression analysis, microRNA analysis, and NGS library quantification.

    Mr. Wakida Digital PCR will have an impact on applications for detecting rare targets by enabling investigators to complement and extend their capabilities beyond traditionally employed methods. One such application is using dPCR to monitor rare targets in peripheral blood, as in liquid biopsies.

    The monitoring of peripheral blood by means of dPCR has been described in several peer-reviewed articles. In one such article, investigators considered the clinical value of Thermo’s QuantStudio™ 3D Digital PCR system for the detection of circulating DNA in metastatic colorectal cancer (Dig Liver Dis. 2015 Oct; 47(10): 884–90).

  • GEN: Is there a new technology on the horizon that will increase the speed and/or efficiency of dPCR?

    Dr. Shelton High-throughput sample analysis can be an issue with some dPCR systems. However, Bio-Rad’s Automated Droplet Generator allows labs to process 96 samples simultaneously, a capability that eliminates user-to-user variability and minimizes hands-on time.

    We also want users to get the most information from one sample. Therefore, we are focused on expanding the multiplexing capabilities of our system. In development at Bio-Rad are new technologies that increase the multiplexing capabilities without loss of specificity or accuracy in the downstream workflow.

    Dr. Garner Much of the industry direction seems to be in offering ever-higher resolution, or the ability to run more samples at the same resolution. Thus far, however, customers haven’t found commercial uses for these tools. Also, with increasing resolution and the search for even rarer mutations, the challenge of detecting false positives becomes an even bigger issue.

    Dr. Menezes Use of ZEN™ Double-Quenched Probes by IDT in digital PCR provides increased sensitivity and a lower limit of detection. Due to the second quencher, ZEN probes provide even lower background than traditional single-quenched probes. And this lower background enables increased sensitivity when analyzing samples with low copy number targets, where every droplet matters.

    Ms. Hibbs Quantification relies upon counting the number of positive partitions at the end point of the reaction. Accordingly, precision and resolution can be increased by increasing the number of partitions. We are now capable of analyzing on the order of millions of partitions per run, further extending the lower limit of detection. Additionally, the workflow is amenable to the integration of automation in order to increase throughput and standardize reaction set up.

    Dr. Price Although dPCR is still an emerging technology, there is tremendous interest in its potential clinical diagnostics applications. Enabling adoption of dPCR in the clinical lab requires addressing current gaps in workflow, cost, throughput, and turnaround time.

    Digital PCR technology has the potential for being improved significantly in two dimensions. First, one can address the problem of serially detecting positive versus negative partitions by leveraging lower-cost imaging detection technologies. Alternatively, one may capitalize on the small partition volumes to dramatically reduce the time to perform PCR. Ideally, the future will bring both capabilities to bear.

    Mr. Wakida Compared to qPCR, dPCR currently requires more hands-on time to set up experiments. We are investigating methods to address this.


PCR Shows Off Its Clinical Chops   

Thanks to Advances in Genomics, PCR Is Becoming More Common in Clinical Applications

  • Last May, Roche Molecular Systems announced that its cobas Liat Strep A assay received a CLIA waiver. This clinic-ready assay can detect Streptococcus pyogenes (group A ß-hemolytic streptococcus) DNA in throat swabs by targeting a segment of the S. pyogenes genome.

    Since its invention by Kary B. Mullis in 1985, the polymerase chain reaction (PCR) has become well established, even routine, in research laboratories. And now PCR is becoming more common in clinical applications, thanks to advances in genomics and the evolution of more sensitive quantitative PCR methodologies.

    Examples of clinical applications of PCR include point-of-care (POC) molecular tests for bacterial and viral detection, as well as mutation detection in liquid or tumor biopsies for patient stratification and treatment monitoring.
    Industry leaders recently participated in a CHI conference that was held in San Francisco. This conference—PCR for Molecular Medicine—encompassed research and clinical perspectives and emphasized advanced techniques and tools for effective disease diagnosis.
    To kick off the event, speakers shared their views on POC molecular tests. These tests, the speakers insisted, can provide significant value to healthcare only if they support timely decision making.
    Clinic-ready PCR platforms need to combine speed, ease of use, and accuracy. One such platform, the cobas Liat (“laboratory in a tube”), is manufactured by Roche Molecular Systems. The system employs nucleic acid purification and state-of-art PCR-based assay chemistry to enable POC sites to rapidly provide lab-quality results.
    The cobas Liat Strep A Assay detects Streptococcus pyogenes (group A β-hemolytic streptococcus) DNA by targeting a segment of the S. pyogenes genome. The operator transfers an aliquot of a throat swab sample in Amies medium into a cobas Liat Strep A Assay tube, scans the relevant tube and sample identification barcodes, and then inserts the tube into the analyzer for automated processing and result interpretation. No other operator intervention or interpretation is required. Results are ready in approximately 15 minutes.

    According to Shuqi Chen, Ph.D., vp of Point-of-Care R&D at Roche Molecular Systems, clinical studies of the cobas Liat Strep A Assay demonstrated 97.7% sensitivity when the test was used at CLIA-waived, intended-use sites, such as physicians’ offices. In comparison, rapid antigen tests and diagnostic culture have sensitivities of 70% and 81%, respectively (according to a 2009 study Tanz et al. in Pediatrics).

    The cobas Liat assay preserved the same ease-of-use and rapid turnaround as the rapid antigen tests. It addition, it provided significantly faster turnaround than the lab-based culture test, which can take 24–48 hours.

    A CLIA waiver was announced for the cobas Liat Strep A assay in May 2015. CLIA wavers have been submitted for cobas Liat flu assays, and Roche intends to extend the assay menu.

    POC tests are also moving into field applications. Coyote Bioscience has developed a novel method for one-step gene testing without nucleic acid extraction that can be as fast as 10 minutes from blood sample to result. Their portable devices for molecular diagnostics can be used as genetic biosensors to bring complex clinical testing directly to the patient.

    “Instead of sequential steps, reactions happen in parallel, significantly reducing analysis time. Buffer, enzyme, and temperature profiles are optimized to maximize sensitivity,” explained Sabrina Li, CEO, Coyote Bioscience. “Both RNA and DNA can be analyzed simultaneously from a drop of blood in the same reaction.”

    The first-generation Mini-8 system was used for Ebola detection in Africa where close to 600 samples were tested with 98.8% sensitivity. Recently in China, the Mini-8 system was applied in hospitals and small community clinics for hepatitis B and C and Bunia virus detection. The second-generation InstantGene system is currently being tested internally with clinical samples.

  • Digital PCR

    Conventional real-time PCR technology, while suited to the analysis of high-quality clinical samples, may effectively conceal amplification efficiency changes when sample quality is inconsistent. A more effective alternative, Bio-Rad suggests, is its droplet-digital PCR (ddPCR) technology, which can provide absolute quantification of target DNA or RNA, a critical advantage when samples are limited, degraded, or contain PCR inhibitors. The company says that of the half-dozen clinical trials that are using digital PCR, half rely on the Bio-Rad QX200 ddPCR system.

    Personalized cancer care requires ultra-sensitive detection and monitoring of actionable mutations from patient samples. The high sensitivity and precision of droplet-digital PCR (ddPCR) from Bio-Rad Laboratories offers critical advantages when clinical samples are limited, degraded, or contain PCR inhibitors.

    Typically, formalin-fixed and paraffin-embedded (FFPE) tissue samples are processed. FFPE samples work well for immunohistochemistry and protein analysis; however, the formalin fixation can damage nucleic acids and inhibit the PCR reaction. Samples may yield 100 ng of purified nucleic acid, but the actual amplifiable material is less than 1%, or 1 ng, in most cases.

    “Current qPCR technology depends on real-time fluorescence accumulation as the PCR is occurring, which can be an effective means of detecting and quantifying DNA targets in nondegraded samples,” commented Dawne Shelton, Ph.D., staff scientist, Digital Biology Center, Applications Development Group, Bio-Rad Laboratories. “Amplification efficiency is critical; if that amplification efficiency changes because of sample quality it is hidden in the qPCR methodology.”

    “In ddPCR, that is a big red flag,” Dr. Shelton continued. “It changes the format of how the data look immediately so you know the amount of inhibition and which samples are too inhibited to use.”

    Tissue types vary and contain different degrees of fat or other content that can also act as PCR inhibitors. In blood monitoring, the small circulating fragments of DNA are extremely degraded; in addition, food, supplements, or other compounds ingested by the patient may have an inhibitory effect.

    Clinical labs test for these variabilities and clean the blood, but remnant PCR inhibitors can remain. In ddPCR, a single template is partitioned into a droplet. If the droplet contains a good template, it produces a signal; otherwise, it does not—a simple yes or no answer.

    “Even if there is no PCR inhibition, most clinical samples yield very small amounts of nucleic acid,” Dr. Shelton added. “To make a secure decision using qPCR is difficult because you are in a gray zone at the very end of its linear range. ddPCR operates best with small sample amounts and provides good statistics for confidence in your results.”

    Currently, at least a half dozen clinical trials worldwide are using digital PCR, half of them are using the Bio-Rad QX200 Droplet Digital PCR system. Examples of studies include examining BCR-ABL monitoring in patients with chronic myelogenous leukemia (CML); identifying activating mutations in epidermal growth factor receptor (EGFR) for first-line therapy of new drugs in patients with lung cancer; and the monitoring of resistance mutations such as EFGR T790M in patients with non-small cell lung cancer (NSCLC).

    Clovis Oncology used a technology called BEAMing (Beads, Emulsions, Amplification, and Magnetics), a type of digital PCR for blood-based molecular testing, to perform EGFR testing on almost 250 patients in clinical trials. In BEAMing, individual EGFR gene copies from plasma are separated into individual water droplets in a water-in-oil emulsion. The gene copies are then amplified by PCR on magnetic beads.

    The beads are counted by flow cytometry using fluorescently labeled probes to distinguish mutant beads from wild-type. Because each bead can be traced to an individual EGFR molecule in the patient’s plasma, the method is highly quantitative.

    “BEAMing is particularly well-suited for the detection of known mutations in circulating tumor DNA. In this circumstance, the mutation of interest often occurs at low levels, perhaps only 1–2 copies per milliliter or even less, and in a high background of wild-type DNA that comes from normal tissue. BEAMing can detect one mutant molecule in a background of 5,000 wild-type molecules in clinical samples,” stated Andrew Allen, MRCP, Ph.D., chief medical officer, Clovis Oncology.

    In the studies, the EGFR-resistance mutation T790M could be identified in plasma 81% of the time that it was seen in the matched patient tumor biopsy. Additionally, about 10% of patients in the study had a T790M mutation in plasma that was not identified in tissue, presumably because of tumor heterogeneity. Another 5–10% of the patients did not provide an EGFR result, usually because the tissue biopsy had no tumor cells.

    In aggregate, these results suggest that plasma EGFR testing can be a valuable complement to tumor testing in the clinical management of NSCLC patients, and can provide an alternative when a biopsy is not available. Tumor biopsies may provide only limited tissue, if in fact any tissue is available, for molecular analysis. Also, mutations may be missed due to tumor heterogeneity. These mutations may be captured by sampling the blood, which acts as a reservoir for mutations from all parts of a patient’s tumor burden.

    In the last few years, a panoply of clinically actionable driver mutations have been identified for NSCLC, including mutations in EGFR, BRAF, and HER2, as well as ALK, ROS, and RET rearrangements. These driver mutations will migrate NSCLC molecular diagnostic testing in the next few years toward panel testing of relevant cancer genes using various digital technologies, including next-generation sequencing.


PCR Has a History of Amplifying Its Game

A GEN 35th Anniversary Retrospective

PCR Has a History of Amplifying Its Game

PCR is a fast and inexpensive technique used to amplify segments of DNA that continues to adapt and evolve for the demanding needs of molecular biology researchers. This diagram shows the basic principles of PCR amplification. [NHGRI]

  • The influence that the polymerase chain reaction (PCR) has had on modern molecular biology is nothing short of remarkable. This technique, which is akin to molecular photocopying, has been the centerpiece of everything from the OJ Simpson Trial to the completion of the Human Genome Project. Clinical laboratories use this DNA amplification method for infectious disease testing and tissue typing in organ transplantation. Most recently, with the explosion of the molecular diagnostics field and meteoric rise in the use of next-generation sequencing platforms, PCR has enhanced its standing as an essential pillar of genomic science.

    Let’s open the door to the past and take a look back around 35 years ago when GEN started reporting on the relatively new disciplines of genetic engineering and molecular biology. At that time, GEN was among the first to hear the buzz surrounding a new method to synthesize and amplify DNA in the laboratory. In reviewing the fascinating history of PCR, we will see how the molecular diagnostics field took shape and where it could be headed in the future.

  • Some Like It Hot

    The biological sciences rarely advance within a vacuum—rather they rely on previous discoveries to promote directly or indirectly our understanding. The contributions made by scientists in the field of molecular biology that contributed to the functional pieces of PCR were numerous and spread out over more than two decades.

    It began with H. Gobind Khorana’s advances in understanding the genetic code, leading to the use of synthetic DNA oligonucleotides, continued through Kjell Kleepe’s 1971 vision of a two-primer system for replicating DNA segments, to Fredrick Sanger’s method of DNA sequencing—a process that would win him the Nobel prize in 1980—which utilized DNA oligo primers, nucleotide precursors, and a DNA synthesis enzyme.

    All of the previous discoveries were essential to PCR’s birth, yet it would be an egregious mistake to begin a retrospective on PCR and not discuss the enzyme upon which the entire reaction hinges upon—DNA polymerase. In 1956, Nobel laureate Arthur Kornberg and his colleagues discovered DNA polymerase (Pol I), in Escherichia coli. Moreover, the researchers described the fundamental process by which the polymerase enzyme copies the base sequence of a DNA template strand. However, it would take biologists another 20 years to discover a version of DNA polymerase that was stable enough for use for any meaningful laboratory purposes.

    That discovery came in 1976 when a team of researchers from the University of Cincinnati described the activity of a DNA polymerase (Taq) they isolated from the extreme thermophile bacteria, Thermus aquaticus, which lives in hot springs and hydrothermal vents. The fact that this enzyme could withstand typical protein-denaturing temperatures and function optimally around 75–80°C fortuitously set the stage for the development of PCR.

    By 1983, all of the ingredients to bake the molecular cake were sitting in the biological cupboard waiting to be assembled in the proper order. At that time, Nobel laureate Kary Mullis was working as a scientist for the Cetus Corporation trying to perfect oligonucleotide synthesis. Mullis stumbled upon the idea of amplifying segments of DNA using multiple rounds of replication and the two primer system—essentially modifying and expanding upon Sanger’s sequencing reaction. Mullis discovered that the temperatures for each step (melting, annealing, and extension) in the reaction would need to be painstakingly controlled by hand. In addition, he realized that since the reactions were using a non-thermostable DNA polymerase, fresh enzyme would need to be “spiked in” after each successive cycle.

    Mullis’ hard work and persistence paid off as the reaction was successful at amplifying a particular segment of DNA that was flanked by two opposing nucleotide primer molecules. Two years later, the Cetus team presented their work at the annual meeting of the American Society for Human Genetics, and the first mention of the method was published in Science that same year; however, that article did not go into detail about the specifics of the newly developed PCR method—a paper that would be rejected by roughly 15 journals and would not be published until 1987.

    Although scientists were a bit slow on the uptake for the new method, the researchers at Cetus were developing ways to improve upon the original assay. In 1986, the scientists substituted the original heat-liable DNA polymerase for the temperature-resistant Taq polymerase, removing the need to spike in enzyme and dramatically reducing errors while increasing sensitivity. A year later, PerkinElmer launched their creation of a thermal cycler, allowing scientists to regulate the heating and cooling parts of the PCR reaction with greater efficiency.

    Extremely soon after the introduction of Taq and the launch of the thermal cycler, the PCR reaction exploded exponentially among research laboratories and not only vaulted molecular biology to the pinnacle of researcher interests, it also launched a molecular diagnostics revolution that continues today and shows no signs of slowing down.

  • Molecular Workhorse

    In the years since PCR first burst onto the scene, there have been a number of significant advancements to the technique that have widely improved the overall method. For example, in 1991, a new DNA polymerase from the hyperthermophilic bacteria Pyrococcus furiosus, or Pfu, was introduced as a high-fidelity alternative enzyme to Taq. Unlike Taq polymerase, Pfu has built in 3′ to 5′ exonuclease proofreading activity, which allows the enzyme to correct nucleotide incorporation errors on the fly—dramatically increasing base specificity, albeit at a reduced rate of amplification versusTaq.

    In 1995, two advancements were introduced to PCR users. The first, called antibody “hot-start” PCR, utilized an immunoglobulin molecule that is directed to the DNA polymerase and inhibits its activity until the first 95°C melt stage, denaturing the antibody and allowing the polymerase to become active. Although this process was effective in increasing the specificity of the PCR reaction, many researchers found that the technique was time consuming and often caused cross-contamination of samples.

    The second innovation introduced that year began another revolution for molecular biology and the PCR method. Real-time PCR, or quantitative PCR (qPCR), allowed researchers to quantitatively create DNA templates for PCR amplification from RNA transcripts through the use of the reverse-transcriptase enzyme and specifically incorporated fluorescent reporter dyes. The technique is still widely used by researchers to monitor gene expression extremely accurately. Over the past 20 years, many companies have spent many R&D dollars to create more accurate, higher throughput, and simple qPCR machines to meet researcher demands.

    With the advent of next-generation sequencing techniques—and the rise of techniques that started commanding the attention of more and more researchers—PCR machines and methods needed to evolve and modernize to keep pace. PCR remained the lynchpin in almost all the next-generation sequencing reactions that came along, but the traditional technique wasn’t nearly as precise as required.

    Digital PCR (dPCR) was introduced as a refinement of the conventional method, with the first real commercial system emerging around 2006. dPCR can be used to quantify directly and clonally amplify DNA or RNA.

    The apparatus carries out a single reaction within a sample. The sample, however, is separated into a large number of partitions. Moreover, the reaction is performed in each partition individually—allowing a more reliable measurement of nucleic acid content. Researchers often use this method for studying gene-sequence variations, such as copy number variants (CNV), point mutations, rare-sequence detection, and microRNA analysis, as well as for routine amplification of next-generation sequencing samples.

  • Future of PCR: Better, Faster, Stronger!

    It is almost impossible to envision a future laboratory setting that wouldn’t utilize PCR in some fashion, especially due to the heavy reliance of next-generation sequencing techniques for accurate PCR samples and at the very least using the method as a simple amplification tool for creating DNA fragments of interest.

    Yet there is at least one new next-generation sequencing technique that can identify native DNA sequences without an amplification step—nanopore sequencing. Although this technique has performed well in many preliminary trials, it is in its relative infancy. It will probably undergo additional development lasting several years before it approaches large-scale adoption by researchers. Even then, PCR has become so engrained into daily laboratory life that to try to phase out the technique would be like asking molecular biologists to give up their pipettes or restriction enzymes.

    Most PCR equipment manufacturers continue to seek ways to improve the speed and sensitivity of their thermal cyclers, while biologists continue to look toward ways to genetically engineer better DNA polymerase molecules with even greater fidelity than their naturally occurring cousins. Whatever the new advancements are, and wherever they lead the life sciences field, you can count on us at GEN to continue to provide our readers with detailed information for another 35 years … at least!


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Next Generation Sequencing in Clinical Laboratory

Curator: Larry H. Bernstein, MD, FCAP

INSIGHTS on Next-Generation Sequencing

Next-generation (NGS) sequencing brings scalability and sensitivity to diagnostics in ways that traditional DNA analysis could not

Enabling Technology for Diagnosis, Prognosis, and Personalized Medicine

Significantly higher speed, lower cost, smaller sample size, and higher accuracy compared with conventional Sanger sequencing make next-generation sequencing (NGS) an attractive platform for medical diagnostics. By practically eliminating cost and time barriers, NGS allows testing of virtually any gene or genetic mutation associated with diseases.

Scalability and Sensitivity

NGS brings scalability and sensitivity to diagnostics in ways that traditional DNA analysis could not. “NGS analyzes hundreds of gene variants or biomarkers simultaneously. Traditional sequencing is better suited for analysis of single genes or fewer than 100 variants,” notes Joseph Bernardo, president of next-generation sequencing and oncology at Thermo Fisher Scientific (Waltham, MA).

Related Article: Computational Changes in Next-Generation Sequencing

Thermo Fisher’s Oncomine Focus Assay for NGS, for example, analyzes close to 1,000 biomarkers associated with the 52-gene panel. These biomarkers constitute about 1,000 different locations on the 52 genes that correlate with the efficacy of certain drugs. The assay allows single-workflow concurrent analysis of DNA and RNA, enabling sequencing of 35 hot-spot genes, 19 genes associated with copy number gain, and 23 fusion genes.

NGS is also better suited to detect lower levels of variants present in heterogeneous material, such as tumor samples. And while both NGS and Sanger sequencing are versatile, NGS can analyze both DNA and RNA, including RNA fusions, at a much more cost-efficient price point.

“When interrogating a limited number of analytes, Sanger sequencing is the standard for many laboratory- developed tests, offering fast turnaround times and lower cost than NGS,” Bernardo says. “We view the two methods as complementary.”

Diagnostic NGS is moving inexorably toward targeted sequencing, particularly for tumor analysis. The targets are specific regions within a tumor’s DNA or individual genes, or specific locations on single genes.

“Targeted sequencing lends itself to diagnostic testing, particularly in oncology, as the goal is to analyze multiple genes associated with cancer using a platform that offers high sensitivity, reliability, and rapid turnaround time,” Bernardo tells Lab Manager. “It is simply more cost-effective.”

That is why the National Cancer Institute (NCI) chose Thermo Fisher’s Ion Torrent sequencing system and the Oncomine reagents for NCI-MATCH, the most ambitious trial to date of NGS oncology diagnostics.

NCI-MATCH will use a 143-gene panel to test submitted tumor samples at four centers (NCI, MD Anderson Cancer Center, Massachusetts General Hospital, and Yale University). The centers then provide sequencing data that helps direct appropriate treatments.

The NCI test protocol ensures consistency across multiple instruments and sites.

Personalized Treatments

Another great opportunity for NGS-based diagnostics is in personalized or precision medicine for both new and existing drugs. Companion diagnostics—co-approved with the relevant drug—drive this entire business. “The only way personalized medicine can succeed commercially is if pharmaceutical companies incorporate a universal assay philosophy in their development programs instead of developing a unique assay for each new drug,” Bernardo explains. For example, in late 2015, Thermo Fisher partnered with Pfizer and Novartis to develop a universal companion diagnostic with the goal of identifying personalized therapy selection from a menu of drugs targeting non-small-cell lung cancer, which annually causes more deaths than breast, colon, and prostate cancer combined.

While advances in sequencing have been remarkable in recent years, the eventual success of NGS-based diagnostics will not depend on instrumentation alone. “What [ensures] ease of use and commonality of results is the cohesiveness of the entire workflow, from sample prep to rapid sequencing systems and bioinformatics,” Bernardo says. “Those components working together will drive NGS into a realizable solution for the clinical market.”

In addition to confirming a disease condition (diagnosis), NGS also provides valuable information on disease susceptibility, prognosis, and the potential effect of drugs on individual patients. The latter idea, known as precision medicine or personalized medicine, uses an individual’s molecular profile to guide treatment. The idea is to differentiate diseases into subtypes based on molecular (usually genetic) characteristics and tailor therapies accordingly.

Precision medicine is still in its infancy, but dozens of pharmaceutical, diagnostics, and genetics firms have bought into the idea.

“We are just at the beginning of connecting genomic and genetic information to target specific therapies for patients,” says T.J. Johnson, president and CEO of HTG Molecular Diagnostics (Tuscon, AZ). “Precision medicine will have a bright future as we gain better understanding of the root causes of disease.”

In 2013, HTG commercialized its HTG Edge instrument platform and a portfolio of RNA assays, which fully automate the company’s proprietary nuclease protection chemistry. This chemistry measures mRNA and miRNA gene expression levels from very small quantities of difficult-to-handle samples.

HTG entered the NGS market in 2014 with the launch of the first HTG EdgeSeq product, an assay that targets and digitally measures the expression of more than 2,000 microRNAs. The assay utilizes the HTG Edge for sample and library preparation, and it uses a suitable NGS instrument (from either Illumina or Thermo Fisher) for quantitation. The data is imported back into the HTG EdgeSeq instrument for analytics and reporting.

In 2015, the company launched four additional HTG EdgeSeq panels: immuno- oncology and pan-oncology biomarker panels, a lymphoma profiling panel, and a classifier for subtyping diffuse large B-cell lymphomas (DLBCL).

Eliminating Biopsies?

Traditional biopsies for tumor DNA analysis are invasive, risky, and often impossible to obtain, and they may not uncover the heterogeneity often present in tumors. It was recently discovered that dying tumor cells release small pieces of DNA into the bloodstream. This cell-free circulating tumor DNA (ctDNA) is detectable in samples through NGS.

In September 2015, Memorial Sloan Kettering Cancer Center (MSK) and NGS leader Illumina (San Diego, CA) entered a collaboration to study ctDNA for cancer diagnosis and monitoring. The aim is to establish ctDNA as a relevant cancer biomarker.

Heterogeneity as it pertains to cancer traditionally refers to multiple tissues located within a tumor, as determined histologically. A number of recent studies suggest that tumor heterogeneity occurs at the genetic level as well. “In particular, there appears to be a tremendous variety of sequence variants within the same tumor, even resulting in situations where one tumor can have multiple mutated genes that have been demonstrated to drive cancer,” says John Leite, PhD, vice president, oncology—market development and product marketing at Illumina.

Heterogeneity challenges the search for treatments that target a specific gene product or pathway. Once the patient is treated, biopsies tell very little about how that patient is responding. “Our hope is that ctDNA provides clinicians with a real-time measure of the abundance of those mutated genes and that a decrease in the relative abundance is synonymous with a lower tumor burden,” Leite adds.

Clinical trials are needed to demonstrate that patients whose therapy was selected using ctDNA versus traditional tissue biopsy testing had a significantly improved outcome or that the analysis might be informative for prognosis.

What about cancer cells that do not release DNA? “Studies show that tumors from different organs or tissues release more or less ctDNA into the peripheral blood,” Leite explains, “but in general the possibility that some cells might not release ctDNA is an open area of research.”

For the MSK-Illumina collaboration, the cancer center will collect samples, and Illumina will apply its sequencing technology to detect ctDNA in those samples. The big draw here is the potential to reduce the number of invasive, expensive diagnostic and monitoring procedures with a simple blood test. This would not be possible without deep next-generation sequencing—the genomics vernacular for sequencing at great depths of coverage.

“Whereas sequencing to identify germline variants can be performed at a nominal depth of coverage—for example, reading a DNA strand 30 times—sequencing rare variants such as in ctDNA requires a much higher level of sensitivity, which is driven by increasing depth of coverage [as much] as 25,000 times,” Leite tells Lab Manager.

In addition to the Illumina MSK collaboration and the work of Thermo Fisher Scientific described above, many more studies involving research consortia and pharmaceutical companies are under way.

“This is a really exciting time for oncology,” Leite says.

Reducing Sample Size

Similarly, in November 2015, Circulogene Theranostics (Birmingham, AL) launched its cfDNA (cell-free DNA) liquid biopsy products for testing ten tumor types, including breast, lung, and colon cancers. The company claims the ability to enrich circulating cfDNA from a single drop of blood.

“While all liquid biopsy companies are focusing on the downstream novel technologies to selectively enrich or amplify tumor-specific cfDNA from a dominantly normal population, the upstream 40 to 90 percent material loss during cfDNA extraction leads to potential false negative results of cancer mutation detection,” explains Chen Yeh, Circulogene’s chief scientific officer. “This is why 10 to 20 mL of blood [are] generally required for conventional cfDNA liquid biopsies.”

Related Article: Researcher Using Next-Generation Sequencing, Other New Methods to Rapidly Identify Pathogens

Released cfDNA fragments often complex with proteins and lipids, which shift their densities to values much lower than those of pure DNA or protein while protecting the corresponding cfDNA from attack by circulating nucleases. Circulogene’s cfDNA breakthrough concentrates and enriches these genetic fragments through density fractionation followed by enzyme-based DNA modification and manipulation, eliminating extraction-associated loss. The technology ensures near-full recovery of both small-molecular-weight (apoptotic cell death) and high-molecular-weight (necrotic cell death) cell-free DNA species from droplet volumes of plasma, serum, or other body fluids.

“The 50-gene panel is our first offering,” says Yeh. “We will continue to develop and cover more comprehensive, informative, and actionable genes and tests.”

The current bottleneck in personalized and precision medicine is the severe shortage of anticancer drugs. Yeh provides perspective, saying, “We have about 60 FDA-approved drugs for cancer-targeted therapies on market, while there are approximately 150 cancer driver genes to aim for. If counting all mutations within these 150 genes, the numbers will be overwhelming.”

Circulogene’s cell-free DNA enrichment technology may be followed up with NGS, conventional Sanger sequencing, or any DNA assay based on PCR or mass spectrometry. However, the sensitivity of Sanger sequencing is insufficient for detecting variants with volumes below 15 percent. Moreover, the company’s multiplex NGS liquid biopsy test captures and monitors real-time, longitudinal tumor heterogeneity or tumor clonal dynamic evolution, which goes well beyond testing of a single mutation on a single sample in traditional sequencing.


Gene Editing Casts a Wide Net 

With CRISPR, Gene Editing Can Trawl the Murk, Catching Elusive Phenotypes amidst the Epigenetic Ebb and Flow

  • Genome editing, a much-desired means of accomplishing gene knockout, gene activation, and other tasks, once seemed just beyond the reach of most research scientists and drug developers. But that was before the advent of CRISPR technology, an easy, versatile, and dependable means of implementing genetic modifications. It is in the process of democratizing genome editing.

    CRISPR stands for “clustered, regularly interspaced, short palindromic repeats,” segments of DNA that occur naturally in many types of bacteria. These segments function as part of an ancient immune system. Each segment precedes “spacer DNA,” a short base sequence that is derived from a fragment of foreign DNA. Spacers serve as reminders of past encounters with phages or plasmids.

    The CRISPR-based immune system encompasses several mechanisms, including one in which CRISPR loci are transcribed into small RNAs that may complex with a nuclease called CRISPR-associated protein (Cas). Then the RNA guides Cas, which cleaves invading DNA on the basis of sequence complementarity.

    In the laboratory, CRISPR sequences are combined with a short RNA complementary to a target gene site. The result is a complex in which the RNA guides Cas to a preselected target.

    Cas produces precise site-specific DNA breaks, which, with imperfect repair, cause gene mutagenesis. In more recent applications, Cas can serve as an anchor for other proteins, such as transcriptional factors and epigenetic enzymes. This system, it seems, has almost limitless versatility.

  • Edited Stem Cells

    The Sanger Institute Mouse Genetic Program, along with other academic institutions around the world, provides access to thousands of genetically modified mouse strains. “Genetic engineering of mouse embryonic stem (ES) cells by homologous recombination is a powerful technique that has been around since the 1980s,” says William Skarnes, Ph.D., senior group leader at the Wellcome Trust Sanger Institute.

    “A significant drawback of the ES technology is the time required to achieve a germline transmission of the modified genetic locus,” he continues. “While we have an exhaustive collection of modified ES cells, only about 5,000 knockout mice, or a quarter of mouse genome, were derived on the basis of this methodology.”

    The dominant position of the mouse ES cell engineering is now effectively challenged by the CRISPR technology. Compared with very low rates of homologous recombination in fertilized eggs, CRISPR generates high levels of mutations, and off-target effects may be so few as to be undetectable.

    “We used the whole-genome sequencing to thoroughly assess off-target mutations in the offspring of CRISPR-engineered founder animals,” informs Dr. Skarnes. “A mutated Cas9 nuclease was deployed to increase specificity, resulting in nearly perfect targeting.”

    Dr. Skarnes explains that the major mouse genome centers are now switching to CRISPR to complete the creation of the world-wide repository of mouse knockouts. His own research is now focused on genetically engineered induced pluripotent stem cells (iPSCs). These cells are adult cells that have been reprogrammed to an embryonic stem cell–like state, and are thus devoid of ethical issues associated with research on human embryonic stem cells. The ultimate goal is to establish a world-wide panel of reference iPSCs created by high-throughput genetic editing of every single human gene.

    “We are poised to begin a large-scale phenotypic analysis of human genes,” declares Dr. Skarnes. His lab is releasing the first set of functional data on 100 DNA repair genes. “By knocking out individual proteins involved in DNA repair and sequencing the genomes of mutant cells,” declares Dr. Skarnes, “we hope to better understand the mutational signatures that occur in cancer.”

  • Pooled CRISPR Libraries

    Researchers hope to gain a better understanding of the mutational signatures found in cancers by using CRISPR techniques to knock out individual proteins involved in DNA repair and then sequencing the genomes of mutant cells. [iStock/zmeel]

    Connecting a phenotype to the underlying genomics requires an unbiased screening of multiple genes at once. “Pooled CRISPR libraries provide an opportunity to cast a wide net at a reasonably low cost,” says Donato Tedesco, Ph.D., lead research scientist at Cellecta. “Screening one gene at a time on genome scale is a significant investment of time and money that not everyone can afford, especially when looking for common genetic drivers across many cell models.”

    Building on years of experience with shRNA libraries, Cellecta is uniquely positioned to prepare pooled CRISPR libraries for genome-wide or targeted screens of gene families. While shRNA interferes with gene translation, CRISPR disrupts a gene and the genomic level due to imperfections in the DNA repair mechanism.

    To determine if these different mechanisms for inactivating genes affect the results of genetic screens, the team conducted a side-by-side comparison of Cellecta’s Human Genome-Wide Module 1 shRNA Library, which expresses 50,000 shRNA targeting 6,300 human genes, with the library of 50,000 gRNA targeting the same gene set. The concordance between approaches was very high, suggesting that these technologies may be complementary and used for cross-confirmation of results.

    Also, a recently completed Phase I NIH SBIR Grant was used to create and test guiding strand RNA (sgRNA) structures to drastically improve efficiency of gene targeting. For this work, Cellecta used a pool library strategy to simultaneously test multiple sgRNA structures for their efficiency and specificity. An early customized Cellecta pooled gRNA library was successfully utilized for screening for epigenetic genes. This particular screen is highly dependent on a complete loss of function, and could not have been accomplished by shRNA inhibition.

    Scientists from Epizyme interrogated 600 genes in a panel of 100 cell lines and, in addition to finding many epigenetic genes required for proliferation in nearly all cell lines, were able to identify validate several essential epigenetic genes required only in subsets of cells with specific genetic lesions. In other words, pooled cell line screening was able to distinguish targets that are likely to produce toxic side effects in certain types of cancer cells from gene targets that are essential in most cells.

    “A more complicated application of CRISPR technology is to use it for gene activation,” adds Dr. Tedesco. “Cellecta plans to optimize this application to bring forth highly efficient, inexpensive, high-throughput genetic screens based on their pooled libraries.

  • Chemically Modified sgRNA

    Scientists based at Agilent Research Laboratories and Stanford University worked together to demonstrate that chemically modified single guide RNA can be used to enhance the genome editing of primary hepatopoietic stem cells and T cells. This image, which is from the Stanford laboratory of Matthew Porteus, M.D., Ph.D., shows CD34+ human hematopoietic stem cells that were edited to turn green. Editing involved inserting a construct for green fluorescent protein. About 1,000 cells are pictured here.

    Researchers at Agilent Technologies applied their considerable experience in DNA and RNA synthesis to develop a novel chemical synthesis method that can generate long RNAs of 100 nucleotides or more, such as single guide RNAs (sgRNAs) for CRISPR genome editing. “We have used this capability to design and test numerous chemical modifications at different positions of the RNA molecule,” said Laurakay Bruhn, Ph.D., section manager, biological chemistry, Agilent.

    Agilent Research Laboratories worked closely with the laboratory of Matthew Porteus, M.D., Ph.D., an associate professor of pediatrics and stem cell transplantation at Stanford University. The Agilent and Stanford researchers collaborated to further explore the benefits of chemically modified sgRNAs in genome editing of primary hematopoetic stem cells and T cells.

    Dr. Porteus’ lab chose three key target genes implicated in the development of severe combined immunodeficiency (SCID), sickle cell anemia, and HIV transmission. Editing these genes in the patient-derived cells offers an opportunity for novel precision therapies, as the edited cells can renew, expand, and colonize the donor’s bone marrow.

    Dr. Bruhn emphasized the importance of editing specificity, so that no other cellular function is affected by the change. The collaborators focused on three chemical modifications strategically placed at each end of sgRNAs that Agilent had previously tested to show they maintained sgRNA function. A number of other optimization strategies in cell culturing and transfection were explored to ensure high editing yields.

    “Primary cells are difficult to manipulate and edit in comparison with cell lines already adapted to cell culture,” maintains Dr. Bruhn. Widely varied cellular properties of primary cells may require experimental adaptation of editing techniques for each primary cell type.

    The resulting data showed that chemical modifications can greatly enhance efficiency of gene editing. The next step would translate these findings into animal models. Another advantage of chemical synthesis of RNA is that it can potentially be used to make large enough quantities for therapeutics.

    “We are working with Agilent’s Nucleic Acid Solution Division—a business focused on GMP manufacturing of oligonucleotides for therapeutics—to engage with customers interested in this capability and better understand how we might be able to help them accomplish their goals,” says Dr. Bruhn.

  • Customized Animal Models

    “Given their gene-knockout capabilities, zinc-finger-based technologies and CRISPR-based technologies opened the doors for creation of animal models that more closely resemble human disease than mouse models,” says Myung Shin, Ph.D., senior principal scientist, Merck & Co. Dr. Shin’s team supports Merck’s drug discovery and development program by creating animal models mimicking human genetics.

    For example, Dr. Shin’s team has worked with the Dahl salt-sensitive strain of rats, a widely studied model of hypertension. “We used zinc-finger nucleases to generate a homozygous knockout of a renal outer medullary potassium channel (ROMK) gene,” elaborates Dr. Shin. “The resulting model represents a major advance in elucidating the role of ROMK gene.”

    According to Dr. Shin, the model may also provide a bridge between genetics and physiology, particularly in studies of renal regulation and blood pressure. In one study, the model generated animal data that suggest ROMK plays a key role in kidney development and sodium absorption. Work along these lines may lead to a pharmacological strategy to manage hypertension.

    In another study, the team applied zinc-finger nuclease strategy to knockout the coagulation Factor XII, and thoroughly characterize them in thrombosis and hemostasis studies. Results confirmed and extended previous literature findings suggesting Factor XII as a potential target for antithrombotic therapies that carry minimal bleeding risk. The model can be further utilized to study safety profiles and off-target effects of such novel Factor XII inhibitors.

    “We use one-cell embryos to conduct genome editing with zinc-fingers and CRISPR,” continues Dr. Shin. “The ease of this genetic manipulation speeds up generation of animal models for testing of various hypotheses.”

    A zinc finger–generated knockout of the multidrug resistance protein MDR 1a P-glycoprotein became an invaluable tool for evaluating drug candidates for targets located in the central nervous system. For example, it demonstrated utility in pharmacological analyses.

    Dr. Shin’s future research is directed toward preclinical animal models that would contain specific nucleotide changes corresponding to those of humans. “CRISPR technology,” insists Dr. Shin, “brings an unprecedented power to manipulate genome at the level of a single nucleotide, to create gain- or loss-of-function genetic alterations, and to deeply understand the biology of a disease.”

  • Transcriptionally Active dCas9

    “Epigenome editing is important for several reasons,” says Charles Gersbach, Ph.D., an associate professor of biomedical engineering at Duke University. “It is a tool that helps us answer fundamental questions about biology. It advances disease modeling and drug screening. And it may, in the future, serve as mode of genetic therapy.”

    “One part of our research focuses on studying the function of epigenetic marks,” Dr. Gersback continues. “While many of these marks are catalogued, and some have been associated with the certain gene-expression states, the exact causal link between these marks and their effect on gene expression is not known. CRISPR technology can potentially allow for targeted direct manipulation of each epigenetic mark, one at a time.”

    Dr. Gersback’s team mutated the Cas9 nuclease to create deactivated Cas9 (dCas9), which is devoid of endonuclease activity. Although the dCas9 protein lacks catalytic activity, it may still serve as an anchor for a plethora of other important proteins, such as transcription factors and methyltransferases.

    In an elegant study, Dr. Gersbach and colleagues demonstrated that recruitment of a histone acetyltransferase by dCas9 to a genomic site activates nearby gene expression. Moreover, the activation occurred even when the acetyltransferase domain was targeted to a distal enhancer. Similarly, recruitment of KRAB repressor to a distant site silenced the target gene in a very specific manner. These findings support the important role of three-dimensional chromatin structures in gene activation.

    “Genome regulation by epigenetic markers is not static,” maintains Dr. Gersbach. “It responds to changes in the environment and other stimuli. It also changes during cell differentiation. We designed an inducible system providing us with an ability to execute dynamic control over the target genes.”

    In a light-activated CRISPR-Cas9 effector (LACE) system, blue light may be used to control the recruitment of the transcriptional factor VP64 to target DNA sequences. The system has been used to provide robust activation of four target genes with only minimal background activity. Selective illumination of culture plates created a pattern of gene expression in a population of cells, which could be used to mimic what is observed in natural tissues.

    Together with collaborators at Duke University, Dr. Gersbach intends to carry out the high-throughput analysis of all currently identified regulatory elements in the genome. “Our ultimate goal,” he declares, “is to assign function to all of these elements.”

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World’s First 3D-printed ‘Sneezeometer’ Will Help Asthma Patients

Reported by : Irina Robu, PhD

Researchers at University of Surrrey has developed the world’s first sneezeometer using an airflow sensor that is sensitive enough to measure the speed of sneeze to help diagnose diverse respiratory conditions twice as fast. The current devices are expensive and  lack sensitivy in difficult diagnostic situations.

Surrey’s sneezometer is ultra-sensitive and measures the flow rate of air through a patient’s lungs. The sneezometer is fast and sensitive enough to pick up tiny fluctuations int he breath’s flow rate when the patient breathes through the instrument. After the development of the Surrey’s sneezometer, researchers are currently exploring its diagnostic capabilities.

According to Dr. Birch from University of Surrey’s Aerodynamics and Environmental Flow research Group explained, “Breathing disorders are highly prevalent in both the developed and developing world”. The diagnosis and monitoring of respiratory diseases is crucial to proper treatment. This sneezometer that has been developed is a simple, low-cost and non-intrusive diagnostic solution that will make doctors’ lives easier.

The device is currently used in clinical trials at King’s College Hospital in London to help develop a wide range conditions from neonatal settings through to animal diseases. The ability to monitor the sensitivity of airflow detection makes this very useful for both research and clinical perspective.

Surrey’s researchers envisions that the new device could be in clinical service as soon as 2018 and will have a true impact on the lives of patience living with chronic illnesses. The device will make the diagnosis more accurate, faster, and cheaper.


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Signaling Stiffness Changes

Larry H. Bernstein, MD, FCAP, Curator



Sound Waves Levitate Cells to Detect Disease-Signaling Stiffness Changes

Wed, 11/04/2015  Acoustical Society of America (ASA)


Utah Valley University physicists are literally applying rocket science to the field of medical diagnostics. With a few key changes, the researchers used a noninvasive ultrasonic technique originally developed to detect microscopic flaws in solid fuel rockets, such as space shuttle boosters, to successfully detect cell stiffness changes associated with certain cancers and other diseases.

Brian Patchett, a research assistant and instructor within the Department of Physics at Utah Valley University, will describe the group’s method, which uses sound waves to manipulate and probe cells, during the Acoustical Society of America’s Fall 2015 Meeting, held Nov. 2-6, in Jacksonville, Fla.

The method combines a low-frequency ultrasonic wave to levitate the cells and confine them to a single layer within a fluid and a high-frequency ultrasonic wave to measure the cell’s stiffness.

University Basic setup of the group’s acoustic levitation device during normal use with an ultrahigh-frequency pulser taking readings of the monolayer. (Credit: Brian Patchett/Utah Valley)


“An acoustic wave is a pressure wave so it travels as a wave of high and low pressure. By trapping a sound wave between a transducer — such as a speaker — and a reflective surface, we can create a ‘standing wave’ in the space between,” explained Patchett. “This standing wave has stationary layers of high and low pressure, a.k.a. ‘anti-nodes,’ and areas, ‘the nodes’ where the pressure remains the same.”

This standing wave allowed the group to acoustically levitate the cells and isolate them in manner similar to their natural state — as they would be within human tissue or the bloodstream. Previous work in this realm relied on “growing the cell cultures in a Petri dish, which tends to deform the structure, as well as create all sorts of interference,” Patchett said.

The significance of the group’s work is that it focuses on an unexplored method of measuring the properties of cells and how they change during the process of cancer and disease development. “The stiffness of the cell is the primary change detected with our high-frequency ultrasound; it reveals detailed information about the internal structure of the cell and how it changes in certain diseases,” Patchett said.

The group’s method can also help distinguish between different types of cancer — such as aggressive breast cancer vs. less aggressive forms. “By isolating the cells in a monolayer of fluid via acoustic levitation, we’re providing a better method for the detection of cell stiffness,” Patchett said. “This method can be used to explore the aspect of cells that changes during Alzheimer’s disease, the metastasis of cancer, or during the onset of autoimmune responses to better understand these conditions and provide insight into possible treatment methods.”

UHFSine photo of the layers created by Sine waves. (Credit: Brian Patchett/Utah Valley University)


One of the group’s key findings is that “by manipulating the shape of the wave that we use for levitation in specific ways, we’re able to create more precise, sharply defined layers,” Patchett said. “And, borrowing from previous cell culture work done by our group, our high-frequency ultrasound method detects changes within the stiffness of cells with high accuracy. By isolating the cells in a levitated monolayer, we hope to see these changes more clearly so that we can gain a better understanding of what’s happening within the cell and why.”

What kinds of applications might this method find? “It’s a really fantastic research method for exploring autoimmune disorders,” pointed out Timothy Doyle, lead scientist on the project and an assistant professor of physics at Utah Valley University.

As far as other applications, the group’s method may find use in clinics, hospitals, and surgical centers as a way to immediately detect and characterize cancer or other diseases.

“Our method identifies aggressive types of breast cancer, for example, while in the operating room,” Patchett noted. “Faster than current pathology methods, it will enable doctors to ensure speedier assessments and more effective treatment plans for patients — personalized to their specific needs, which, in turn, will end up being more cost effective in the long term.”

In the near future, the group plans to apply their method to a wide range of biological materials, including white blood cells undergoing activation, which is part of the immune response to an illness.

“We’re collaborating with the Huntsman Cancer Institute — part of the University of Utah healthcare system — to explore various types of breast tissues under levitation to refine our pathology detection methods,” Patchett said. “Our goal is to provide potentially life-saving, personalized medical treatments based on our ability to quickly and effectively detect cancers and diseases in patients.”




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

Curator: Demet Sag, PhD, CRA, GCP

Through Histone Modulation

Renal cell carcinoma accounts for only 3% of total human malignancies but it is still the most common type of urological cancer with a high prevalence in elderly men (>60 years of age).

ICD10 C64
ICD9-CM 189.0
ICD-O M8312/3
OMIM 144700 605074
DiseasesDB 11245
MedlinePlus 000516
eMedicine med/2002

Most kidney cancers are renal cell carcinomas (RCC). RCC lacks early warning signs and 70 % of patients with RCC develop metastases. Among them, 50 % of patients having skeletal metastases developed a dismal survival of less than 10 % at 5 years.

There are three main histopathological entities:

  1. Clear cell RCC (ccRCC), dominant in histology (65%)
  2. Papillary (15-20%) and
  3. Chromophobe RCC (5%).

There are very rare forms of RCC shown in collecting duct, mucinous tubular, spindle cell, renal medullary, and MiTF-TFE translocation carcinomas.

Subtypes of clear cell and papillary RCC, and a new subtype, clear cell papillary

Different subtypes of clear cell RCC can be defined by HIF patterns as well as by transcriptomic expression as defined by ccA and ccB subtypes. Papillary RCC also demonstrates distinct histological subtypes. A recently described variant denoted as clear cell papillary RCC is VHL wildtype (VHL WT), while other clear cell tumors are characterized by VHL mutation, loss, or inactivation (VHL MT).


  • Renal cell cancer is a disease in which malignant (cancer) cells form in tubules of the kidney.
  • Smoking and misuse of certain pain medicines can affect the risk of renal cell cancer.
  • Signs of renal cell cancer include
  • Blood in your urine, which may appear pink, red or cola colored
  • A lump in the abdomen.
  • Back pain just below the ribs that doesn’t go away
  • Weight loss
  • Fatigue
  • Intermittent fever


Factors that can increase the risk of kidney cancer include:

  • Older age.
  • High blood pressure (hypertension).
  • Treatment for kidney failure.(long-term dialysis to treat chronic kidney failure)
  • Certain inherited syndromes.
  • von Hippel-Lindau disease

Tests that examine the abdomen and kidneys are used to detect (find) and diagnose renal cell cancer.

The following tests and procedures may be used:

There are 3 treatment approaches for Renal Cancer:

Stages of Renal Cancer:

Stage I Tumour of a diameter of 7 cm (approx. 23⁄4 inches) or smaller, and limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage II Tumour larger than 7.0 cm but still limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage III
any of the following
Tumor of any size with involvement of a nearby lymph node but no metastases to distant organs. Tumour of this stage may be with or without spread to fatty tissue around the kidney, with or without spread into the large veins leading from the kidney to the heart.
Tumour with spread to fatty tissue around the kidney and/or spread into the large veins leading from the kidney to the heart, but without spread to any lymph nodes or other organs.
Stage IV
any of the following
Tumour that has spread directly through the fatty tissue and the fascia ligament-like tissue that surrounds the kidney.
Involvement of more than one lymph node near the kidney
Involvement of any lymph node not near the kidney
Distant metastases, such as in the lungs, bone, or brain.
Grade Level Nuclear Characteristics
Grade I Nuclei appear round and uniform, 10 μm; nucleoli are inconspicuous or absent.
Grade II Nuclei have an irregular appearance with signs of lobe formation, 15 μm; nucleoli are evident.
Grade III Nuclei appear very irregular, 20 μm; nucleoli are large and prominent.
Grade IV Nuclei appear bizarre and multilobated, 20 μm or more; nucleoli are prominent



90% or more of kidney cancers are believed to be of epithelial cell origin, and are referred to as renal cell carcinoma (RCC), which are further subdivided based on histology into clear-cell RCC (75%), papillary RCC (15%),

chromophobe tumor (5%), and oncocytoma (5%).

Nephrectomy continues to be the cornerstone of treatment for localized renal cell carcinoma (RCC). Research is still underway to developed targeted agents against the vascular endothelial growth factor (VEGF) molecule and related pathways as well as inhibitors of the mammalian target of rapamycin (mTOR),

clear cell RCC (ccRCC) doesn’t respond well to radiation chemotherapy due to high radiation resistancy.  The hallmark genetic features of solid tumors such as KRAS or TP53 mutations are also absent. However, there is a well-designed association presented between ccRCC and mutations in the VHL gene

Hereditary RCC, accounts for around 4% of cases, has been a relatively dominant area of RCC genetics.

Causative genes have been identified in several familial cancer syndromes that predispose to RCC including

  • VHLmutations in von Hippel-Lindau disease that predispose to ccRCC and VHL is somatically mutated in up to 80% of ccRCC
  • METmutations in familial papillary renal cancer,
  • dominantly activating kinase domainMET mutation reported in 4–10% of sporadic papillary RCC[2].
  • FH (fumarate hydratase) mutations in hereditary leiomyomatosis and renal cell cancer that predispose to papillary RCC
  • FLCN(folliculin) mutations in Birt-Hogg-Dubé syndrome that predispose to primarily chromophobe RCC.

In addition, there are germline mutations:

  • in theTSC1/2 genes predispose to tuberous sclerosis complex where approximately 3% of cases develop ccRCC,
  • in the SDHB(succinate dehydrogenase type B) in patients with paraganglioma syndrome shows elevated risk to develop multiple types of RCC.

GWAS in almost 6000 RCC cases demonstrated that loci on 2p21 and 11q13.3 play a role in RCC. Although EPAS1 gene encoding a transcription factor operative in hypoxia-regulated responses in  2p21 , 11q13.3 has no known coding genes.

There has been, however, comparatively less progress in the elaboration of the somatic genetics of sporadic RCC.

Absent mutations in sporadic RCC:

  • somaticFH mutations
  • somatic mutations ofTSC12 and SDHB

Present mutations in sporadic ccRCC (chromophobe RCC) are

  • TSC1mutations occur in 5% of ccRCCs and
  • somatic mutations inFLCN  rare
  • may predict for extraordinary sensitivity to mTORC1 inhibitors clinically.

The COSMIC database reports somatic point mutations in TP53 in 10% of cases, KRAS/HRAS/NRAS combined ≤1%, CDKN2A 10%, PTEN 3%, RB1 3%, STK11/LKB1 ≤1%, PIK3Ca ≤1%, EGFR1% and BRAF ≤1% in all histological samples. Further information can be found at ( genetics/CGP/cosmic/) for the  RCC somatic genetics.

HIF- and hypoxia-mediated epigenetic regulation work together due to histone modification because HIF activate several chromatin demethylases, including JMJD1A (KDM3A), JMJD2B (KDM4B), JMJD2C (KDM4C) and JARID1B (KDM5B), all of which are directly targeted by HIF.

Overview of Histone 3 modifications implicated in RCC genetics

A number of histone modifying genes are mutated in renal cell carcinoma. These include the H3K36 trimethylase SETD2, the H3K27 demethylase UTX/KDM6A, the H3K4 demethylase JARID1C/KDM5C and the SWI/SNF complex compenent PBRM1, shown in this cartoon to represent their relative activities on Histone H3.

Hyper-methylation is observed on RASSF1 highly (50% f RCC) yet less on VHL and CDKN2A, yet there is a methylation and silencing observed on TIMP3 and secreted frizzled-related protein 2.

RCC is ONE OF THE “CILIOPATHIES” among Polycystic Kidney Disease (PKD), Tuberous Sclerosis Complex (TSC) and VHL Syndrome. The main display of cysts is dysfunctional primary cilia.

Mol Cancer Res. Author manuscript; available in PMC 2013 Jan 1.

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117

pVHL mutants are categorized as Class A, B and C depending on the affected step in pVHL protein quality control

VHL proteostasis involves the chaperone mediated translocation of nascent VHL peptide from the ribosome to the TRiC/CCT chaperonin, where folding occurs in an ATP dependent process. The VBC complex is formed while VHL is bound to TRiC, and the mature complex is then released. Three different classes of mutation exist: Class A mutations prevent binding of VHL to TRiC, and abrogate folding into a mature complex. Class B mutations prevent association of Elongins C and B to VHL. Class C mutations inhibit interaction between VHL and HIF1 a.

Cytogenetic locations: 3p25.3 , 11q13.3
Matching terms: lindau, disease, von, hippellindau, hippel
  • Birt-Hogg-Dube syndrome,
Cytogenetic location: 17p11.2 
Matching terms: birthoggdube, syndrome, birt, hogg, dube
  • tuberous sclerosis
# 191100. TUBEROUS SCLEROSIS 1; TSC1 ICD+, Links
Cytogenetic location: 9q34.13 
Matching terms: tuber, sclerosi, tuberous
  • familial papillary renal cell carcinoma.
Cytogenetic locations: 3p25.3 3p25.3 3q21.1 8q24.13 12q24.31 17p11.2 17q12 
Matching terms: renal, familial, papillary, carcinoma, cell

Model for the control of the fate of nephron progenitor cells. Eya1 lies genetically upstream of Six2. Six2 labels the nephron progenitor cells, which can either maintain a progenitor state and self-renew or differentiate via the Wnt4-mediated MET. Wnt4 expression is under the direct control of Wt1. β-Catenin is involved in both progenitor cell fates through activation of different transcriptional programs. Active nuclear phosphorylated Yap/Taz shifts the progenitor balance toward the self-renewal fate. Eya1 and Six2 interact directly with Mycn, leading to dephosphorylation of Mycn pT58, stabilization of the protein, increased proliferation, and potentially a shift of the nephron progenitor toward self-renewal. Genes activated in Wilms’ tumors are depicted in green, and inactivated genes are in blue. Deregulation of Yap/Taz in Wilms’ tumors results in phosphorylated Yap not being retained in the cytoplasm as it should, but it translocates to the nucleus and thus shifts the progenitor cell balance toward self-renewal. This model is likely a simplification, as it presumes that all Wilms’ tumors, regardless of causative mutation, are caused by the same mechanism.

Epigenetic aberrations associated with Wilms’ tumor

Chinese Case Study: PMCID: PMC4471788

They u8ndertook this study based on association of low circulating adiponectin concentrations with a higher risk of several cancers, including renal cell carcinoma. Thus they demonstrated that by case–control study that ADIPOQ rs182052 is significantly associated with ccRCC risk.

They investigated the frequency of three single nucleotide polymorphisms (SNPs), rs182052G>A, rs266729C>G, rs3774262G>A, in the adiponectin gene (ADIPOQ).  1004 registered patients with clear cell renal cell carcinoma (ccRCC) compared with 1108 healthy subjects (= 1108).

The first table presents the characteristics of 1004 patients with clear cell renal cell carcinoma and 1108 cancer-free controls from a Chinese Han population. The Second and third table shows the SNP results.

Table 1: The characteristics of the examined population.

Variable Cases, n (%) Controls, n (%) P-value
1004 (100) 1108 (100)
Age, years
 ≤44 195 (19.4) 230 (20.8) 0.559
 45–64 580 (57.8) 644 (58.1)
 ≥65 229 (22.8) 234 (21.1)
 Male 711 (70.8) 815 (73.6) 0.160
 Female 293 (29.2) 293 (26.4)
BMI, kg/m2
 <25 480 (47.8) 589 (53.2) 0.014
 ≥25 524 (52.2) 519 (46.8)
Smoking status
 Never 455 (45.3) 529 (47.7) 0.265
 Ever/current 549 (54.7) 579 (52.3)
 No 639 (63.6) 780 (70.4) 0.001
 Yes 365 (36.4) 328 (29.6)
Fuhrman grade
 I 40 (4.0)
 II 380 (37.8)
 III 347 (34.6)
 IV 175 (17.4)
 Missing 62 (6.2)
Stage at diagnosis
 I 738 (73.5)
 II 71 (7.1)
 III 19 (1.9)
 IV 176 (17.5)

Pearson’s χ2-test.

Table 2:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Table 3:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Molecular Genetics Level for Physiology (Function):

a The protein–protein interaction for the identified 8 proteins in STRING (10 necessary proteins/genes were added into the network so as to find the potential strong connection among them. The red dotted lines circled three main pathways. b The ingenuity pathway analysis (IPA) for all these 18 genes showing that oxidative phosphorylation, mitochondria dysfunction and granzyme A are the significantly activated pathways (fold change over 1.5, P < 0.05). c The possible mechanism related mitochondria functions: unspecific condition like inflammation, carcinogens, radiation (ionizing or ultraviolet), intermittent hypoxia, viral infections which is carcinogenesis in our study that damages a cell’s oxidative phosphorylation. Any of these conditions can damage the structure and function of mitochondria thus activating a respiratory chain changes (Complex I, II, III, IV) and also cytochrome c release. When the mitochondrial dysfunction persists, it produces genome instability (mtDNA mutation), and further lead to malignant transformation (metastasis) via increased ROS and apoptotic resistance. (Color figure online)

RENAL CELL CARCINOMA AND METABOLISM goes hand to hand in genes encoding enzymes of the Krebs cycle suppress tumor formation in kidney cells. This includes Succinate dehydrogenase (SDH), Fumarate hydratase (FH).  As a result of accumulation of succinate or fumarate causes the inhibition of a family of 2-oxoglutarate-dependent dioxygeneases.

The FH and SDH genes function as two-hit tumor suppressor genes.

SDH has a complex of 4 different polypeptides (SDHA-D) function in electron transfer, catalyzes the conversion of succinate to fumarate. Furthermore, heterozygous germline mutations in SDHsubunits predispose to pheochromocytoma/paraganglioma. FH function to convert fumarate to malate.  When its mutations presented as heterozygous germline, it predisposes hereditary leiomyomatosis and renal cell cancer (HLRCC). Among them about 20–50% of HLRCC families are typically papillary-type 2 (pRCC-2) and overwhelmingly aggressive.RCC is increasingly being recognized as a metabolic disease, and key lesions in nutrient sensing and processing have been detected.

Regulation of Prolyl Hydroxylases and Keap1 by Krebs cycle

Regulation of Prolyl Hydroxylases by Tricarboxylic Acid (TCA) Cycle Intermediates. Prolyl hydroxylases use TCA cycle intermediates to help catalyze the oxygen, iron and ascorbate dependent- addition of a hydroxyl side chain to a Pro402 and Pro564 of HIF alpha subunits, leading to VHL binding and degradation. Defects in either fumarate hydratase or succinate dehydrogenase will drive up levels of fumarate and succinate, which competitively bind prolyl hydroxylases, and prevent HIF prolyl hydroxylation. This results in higher intracellular HIF levels.

Regulation of mTORC1

HIF regulation and mTOR pathway connections. Hypoxia blocks HIF expression in a TSC1/2 and REDD dependent pathway [155]. HIF1α appears to be both TORC1 and TORC2 dependent, whereas HIF2α is only TORC2 dependent [275]. Signaling via TORC2 appears to upregulate HIF2α in an AKT dependent manner [69].


Based on the types of renal cancers the treatment method may vary but the general scheme is:


Drugs Approved for Kidney (Renal Cell) Cancer

Food and Drug Administration (FDA) approved drugs for kidney (renal cell) cancer. Some of the drug names link to NCI’s Cancer Drug Information summaries.

T cell regulation in RCC

Immune regulation of renal tumor cells. A: When an antigen presenting cell (APC) engages a T-cell via a cognate T-cell receptor (TCR) and CD28, T-cell cell activation occurs. B: Early and late T-cell inhibitory signals are mediated via CTLA-4 and PD-1 receptors, and this occurs via engagement of the APC via B7 and PD-L1, respectively. C: Inhibitory antibodies against CTLA-4 and PD-1 can overcome T-cell downregulation and once again allow cytokine production.

Phase III Trials of Targeted Therapy in Metastatic Renal Cell Carcinoma

Trial Number
Clinical setting RR (%) PFS (months) OS (months)
VEGF-Targeted Therapy

Bevacizumab +

649 First-line 31 vs. 12 10.2 vs. 5.5
23.3 vs. 21.3
*CALBG 90206

Bevacizumab +

732 First-line 25.5 vs. 13 8.4 vs. 4.9
18.3 vs. 17.4
Sunitinib vs.
750 First-line 47 vs. 12 11 vs. 5
26.4 vs. 21.8

Sorafenib vs.

903 Second-line


10 vs. 2 5.5 vs. 2.8
Pazopanib vs.
435 First line/second line


30 vs. 3 9.2 vs. 4.2
22.9 vs. 20.5

Axitinib vs.
sorafenib [269]

723 Second line

(post-sunitinib, cytokine,
bevacizumab or

19 vs. 9
6.7 vs. 4.7
Not reported
mTOR-Targeted Therapy
vs. Tem + IFNa
vs. IFNa[249]
624 First line, ≥ 3 poor risk
9 vs. 5 3.8 vs. 1.9 for
10.9 vs. 7.3 for
Everolimus vs.
placebo [274]
410 Second line
(post sunitinib and/or
2 vs. 0 4.9 vs. 1.9


14.8 vs. 14.5

RCC renal cell carcinoma, RR response rate, OS overall survival, PFS progression free survival, VEGFvascular endothelial growth factor, IFNa interferon alphamTOR mammalian target of rapamycin. AVORENAVastin fOr RENal cell cancer, CALBG Cancer and Leukemia Group B. TARGET Treatment Approaches in Renal Cancer Global Evaluation Trial. AXIS Axitinib in Second Line. ARCC Advanced Renal-Cell Carcinoma. RECORD-1 REnal Cell cancer treatment withOral RAD001 given Daily.

aIncluding serum lactate dehydrogenase level of more than 1.5 times the upper limit of the normal range, a hemoglobin level below the lower limit of the normal range; a corrected serum calcium level of more than 10 mg per deciliter (2.5 mmol per liter), a time from initial diagnosis of renal-cell carcinoma to randomization of less than 1 year, a Karnofsky performance score of 60 or 70, or metastases in multiple organs.

PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242

Table: RCC-Associated Antigens (RCCAA) Recognized by T Cells.

Antigen Antigen
Frequency of
Among RCC
Tumors (%)
CD8+ T cell
Patients with
HLA Class I
CD4+ T cell
Patients with
HLA Class II
References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Survivina ML 100 Multiple Multiple 114
OFA-iLR OF 100 A2 NR 115116
IGFBP3ab ML 97 NR Multiple 117118
EphA2a ML > 90 A2 DR4 1744119
RU2AS Antisense
> 90 B7 NR 120
(CA-IX) ab
RCC 90 A2, A24 Multiple 4751
EGFRab ML 85 A2 NR 121122
HIFPH3a ML 85 A24 NR 123
c-Meta ML > 80 A2 NR 124
WT-1a ML 80 A2, A24 NR 125128
MUC1ab ML 76 A2 DR3 46129130
5T4 ML 75 A2, Cw7 DR4 54131133
iCE aORF 75 B7 NR 134
MMP7a ML 75 A3 Multiple 117135136
Cyclin D1a ML 75 A2 Multiple 117137138
HAGE b CT 75 A2 DR4 139
hTERT ab ML > 70 Mutliple Multiple 140142
FGF-5 Protein splice variant > 60 A3 NR 143
mutVHLab ML > 60 NR NR 144
MAGE-A3 b CT 60 Multiple Multiple 145
SART-3 ML 57 Mulitple NR 146149
SART-2 ML 56 A24 NR 150
PRAME b CT 40 Multiple NR 151154
p53ab Mutant/WT
32 Mutliple Multiple 155156
MAGE-A9b CT >30 A2 NR 157
MAGE-A6b CT 30 Mutliple DR4 18158
MAGE-D4b CT 30 A25 NR 159
Her2/neua ML 1030 Multiple Multiple 45160164
SART-1a ML 25 Multiple NR 165167
RAGE-1 CT (ORF2/5) 21 Mutliple Multiple 151157168169
TRP-1/ gp75 ML 11 A31 DR4 151170172

A summary is provided for RCCAA that have been defined at the molecular level. RCCAA are characterized with regard to their antigen category, their prevalence of (over)expression among total RCC specimens evaluated, whether RCCAA expression is modulated by hypoxia or tumor DNA methylation status, and which HLA class I and class II alleles have been reported to serve as presenting molecules for T cell recognition of peptides derived from a given RCCAA.

Abbreviations: CT = Cancer-Testis Antigens; ML = Multi-lineage Antigens; NR = Not Reported; OF = Oncofetal Antigen; aORF = altered open reading frame; ORF = open reading frame; RCC = Renal cell carcinoma; WT = Wild-Type;



PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242

Expected Impact on Teff versus Suppressor Cells
Co-Therapeutic Agent Teff
References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
IL-2 +/− ↑ (Treg) 173175
IL-7 ↑ (Treg) 176178
IL-12 – (Treg), ↓ (MDSC) 179181
IL-15 ↑ (Treg)* 182183
IL-18 ↓ (Treg) 184186
IL-21 ? +/− (Treg) 187190
IFN-α +/− (Treg) 175191194
IFN-γ -? ? ↑ ↑ (Treg); ↑ ?(MDSC) 195197
GM-CSF ? ↑ (Treg); ↑(MDSC) 198202
Coinhibitory Antagonist
CTLA-4 ? ↓ (Treg) 203204
PD1/PD1L ↓ (Treg) 205207
Costimulatory Agonist
CD40/CD40L ↑ (Treg); ↑(MDSC) 208211
GITR/GITRL ↓ (Treg); ↓ (MDSC) 212213
OX40/OX86 ↑↓ (Treg); ↓ (MDSC) 214219
4-1BB/4-1BBL ↑ (Treg) 220224
TLR Agonists
Imiquimod (TLR7) ? 225227
Resiquimod (TLR8) ? ? 228229
CpG (TLR9) ↓ (Treg) 230232
VEGF-Trap ? ? 233
Sunitinib ? ↓ (Treg/MDSC) 98100234
Sorafenib ? ↓ (MDSC) 235
Bevacizumab ? ? ↓ (MDSC) 236237
Gefitinib (IRESSA) ? ? ? ? ? 238239
Cetuximab ? ? ? ? 240
mTOR Inhibitors
Temsirolimus/Everolimus ? ↓ (Treg) 241
Treg/MDSC Inhibitors
Iplimumab (CTLA-4) ? ↓ (Treg) 242243
ONTAK (CD25) +/− +/− ? ? ↓ (Treg) 244
Anti-TGFβ/TGFβR ↓ (Treg) 245247
Anti-IL10/IL10R +/− ↓ (Treg) 248249
Anti-IL35/IL35R ↑? ↑? ↑? ↑? ↓ (Treg) 250
1-methyl trytophan ? ? ↓ (MDSC) 251
ATRA ? ? ↑ (Treg), ↓ (MDSC) 9093

Agents that are currently or soon-to-be in clinical trials are summarized with regard to their anticipated impact(s) on Type-1 anti-tumor T cell (Te) activation, function, survival and recruitment into the TME. Additional anticipated effects of drugs on suppressor cells (Treg and MDSC) are also summarized. Key: ↑, agent is expected to increase parameter; ↓, agent is expected to inhibit parameter; +/−, minimal increase or decrease is expected in parameter as a consequence of treatment with agent; ?, unknown effect of agent on parameter.

Abbreviations: ATRA, all-trans retinoic acid; CTLA-4, cytotoxic T Lymphocyte antigen 4; GITR(L), glucocorticoid-induced TNF receptor (ligand); GM-CSF, granulocyte-macrophage colony stimulating factor; IFN, interferon; IL, interleukin; MDSC, myeloid-derived suppressor cell; PD1/PD1L, programmed cell death 1 (ligand); TGF-β(R), tumor necrosis factor-β(receptor); TLR, Toll-like receptor; TME, tumor microenvironment; Treg, regulatory T cell; VEGF, vascular endothelial growth factor.

Alternative and Complementary Therapies for Cancer:

  • Art therapy
  • Dance or movement therapy
  • Exercise
  • Meditation
  • Music therapy
  • Relaxation exercises

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117 PMCID: PMC3399969 NIHMSID: NIHMS380694

State-of-the-science: An update on renal cell carcinoma

Eric Jonasch,1 Andrew Futreal,1 Ian Davis,2 Sean Bailey,2 William Y. Kim,2 James Brugarolas,3 Amato Giaccia,4 Ghada Kurban,5 Armin Pause,6 Judith Frydman,4 Amado Zurita,1 Brian I. Rini,7 Pam Sharma,8Michael Atkins,9 Cheryl Walker,8,* and W. Kimryn Rathmell2,*

Go to:


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[Discovery Medicine; ISSN: 1539-6509; Discov Med 18(101):341-350, December 2014.Copyright © Discovery Medicine. All rights reserved.]


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Hematological Malignancy Diagnostics

Author and Curator: Larry H. Bernstein, MD, FCAP


2.4.3 Diagnostics Computer-aided diagnostics

Back-to-Front Design

Robert Didner
Bell Laboratories

Decision-making in the clinical setting
Didner, R  Mar 1999  Amer Clin Lab

Mr. Didner is an Independent Consultant in Systems Analysis, Information Architecture (Informatics) Operations Research, and Human Factors Engineering (Cognitive Psychology),  Decision Information Designs, 29 Skyline Dr., Morristown, NJ07960, U.S.A.; tel.: 973-455-0489; fax/e-mail:

A common problem in the medical profession is the level of effort dedicated to administration and paperwork necessitated by various agencies, which contributes to the high cost of medical care. Costs would be reduced and accuracy improved if the clinical data could be captured directly at the point they are generated in a form suitable for transmission to insurers or machine transformable into other formats. Such a capability could also be used to improve the form and the structure of information presented to physicians and support a more comprehensive database linking clinical protocols to outcomes, with the prospect of improving clinical outcomes. Although the problem centers on the physician’s process of determining the diagnosis and treatment of patients and the timely and accurate recording of that process in the medical system, it substantially involves the pathologist and laboratorian, who interact significantly throughout the in-formation-gathering process. Each of the currently predominant ways of collecting information from diagnostic protocols has drawbacks. Using blank paper to collect free-form notes from the physician is not amenable to computerization; such free-form data are also poorly formulated, formatted, and organized for the clinical decision-making they support. The alternative of preprinted forms listing the possible tests, results, and other in-formation gathered during the diagnostic process facilitates the desired computerization, but the fixed sequence of tests and questions they present impede the physician from using an optimal decision-making sequence. This follows because:

  • People tend to make decisions and consider information in a step-by-step manner in which intermediate decisions are intermixed with data acquisition steps.
  • The sequence in which components of decisions are made may alter the decision outcome.
  • People tend to consider information in the sequence it is requested or displayed.
  • Since there is a separate optimum sequence of tests and questions for each cluster of history and presenting symptoms, there is no one sequence of tests and questions that can be optimal for all presenting clusters.
  • As additional data and test results are acquired, the optimal sequence of further testing and data acquisition changes, depending on the already acquired information.

Therefore, promoting an arbitrary sequence of information requests with preprinted forms may detract from outcomes by contributing to a non-optimal decision-making sequence. Unlike the decisions resulting from theoretical or normative processes, decisions made by humans are path dependent; that is, the out-come of a decision process may be different if the same components are considered in a different sequence.

Proposed solution

This paper proposes a general approach to gathering data at their source in computer-based form so as to improve the expected outcomes. Such a means must be interactive and dynamic, so that at any point in the clinical process the patient’s presenting symptoms, history, and the data already collected are used to determine the next data or tests requested. That de-termination must derive from a decision-making strategy designed to produce outcomes with the greatest value and supported by appropriate data collection and display techniques. The strategy must be based on the knowledge of the possible outcomes at any given stage of testing and information gathering, coupled with a metric, or hierarchy of values for assessing the relative desirability of the possible outcomes.

A value hierarchy

  • The numbered list below illustrates a value hierarchy. In any particular instance, the higher-numbered values should only be considered once the lower- numbered values have been satisfied. Thus, a diagnostic sequence that is very time or cost efficient should only be considered if it does not increase the likelihood (relative to some other diagnostic sequence) that a life-threatening disorder may be missed, or that one of the diagnostic procedures may cause discomfort.
  • Minimize the likelihood that a treatable, life-threatening disorder is not treated.
  • Minimize the likelihood that a treatable, discomfort-causing disorder is not treated.
  • Minimize the likelihood that a risky procedure(treatment or diagnostic procedure) is inappropriately administered.
  • Minimize the likelihood that a discomfort-causing procedure is inappropriately administered.
  • Minimize the likelihood that a costly procedure is inappropriately administered.
  • Minimize the time of diagnosing and treating thepatient.8.Minimize the cost of diagnosing and treating the patient.

The above hierarchy is relative, not absolute; for many patients, a little bit of testing discomfort may be worth a lot of time. There are also some factors and graduations intentionally left out for expository simplicity (e.g., acute versus chronic disorders).This value hierarchy is based on a hypothetical patient. Clearly, the hierarchy of a health insurance carrier might be different, as might that of another patient (e.g., a geriatric patient). If the approach outlined herein were to be followed, a value hierarchy agreed to by a majority of stakeholders should be adopted.


Once the higher values are satisfied, the time and cost of diagnosis and treatment should be minimized. One way to do so would be to optimize the sequence in which tests are performed, so as to minimize the number, cost, and time of tests that need to be per-formed to reach a definitive decision regarding treatment. Such an optimum sequence could be constructed using Claude Shannon’s information theory.

According to this theory, the best next question to ask under any given situation (assuming the question has two possible outcomes) is that question that divides the possible outcomes into two equally likely sets. In the real world, all tests or questions are not equally valuable, costly, or time consuming; therefore, value(risk factors), cost, and time should be used as weighting factors to optimize the test sequence, but this is a complicating detail at this point.

A value scale

For dynamic computation of outcome values, the hierarchy could be converted into a weighted value scale so differing outcomes at more than one level of the hierarchy could be readily compared. An example of such a weighted value scale is Quality Adjusted Life Years (QALY).

Although QALY does not incorporate all of the factors in this example, it is a good conceptual starting place.

The display, request, decision-making relationship

For each clinical determination, the pertinent information should be gathered, organized, formatted, and formulated in a way that facilitates the accuracy, reliability, and efficiency with which that determination is made. A physician treating a patient with high cholesterol and blood pressure (BP), for example, may need to know whether or not the patient’s cholesterol and BP respond to weight changes to determine an appropriate treatment (e.g., weight control versus medication). This requires searching records for BP, certain blood chemicals (e.g., HDLs, LDLs, triglycerides, etc.), and weight from several

sources, then attempting to track them against each other over time. Manually reorganizing this clinical information each time it is used is extremely inefficient. More important, the current organization and formatting defies principles of human factors for optimally displaying information to enhance human information-processing characteristics, particularly for decision support.

While a discussion of human factors and cognitive psychology principles is beyond the scope of this paper, following are a few of the system design principles of concern:

  • Minimize the load on short-term memory.
  • Provide information pertinent to a given decision or component of a decision in a compact, contiguous space.
  • Take advantage of basic human perceptual and pat-tern recognition facilities.
  • Design the form of an information display to com-plement the decision-making task it supports.

F i g u re 1 shows fictitious, quasi-random data from a hypothetical patient with moderately elevated cholesterol. This one-page display pulls together all the pertinent data from six years of blood tests and related clinical measurements. At a glance, the physician’s innate pattern recognition, color, and shape perception facilities recognize the patient’s steadily increasing weight, cholesterol, BP, and triglycerides as well as the declining high-density lipoproteins. It would have taken considerably more time and effort to grasp this information from the raw data collection and blood test reports as they are currently presented in independent, tabular time slices.

Design the formulation of an information display to complement the decision-making task.

The physician may wish to know only the relationship between weight and cardiac risk factors rather than whether these measures are increasing or decreasing, or are within acceptable or marginal ranges. If so, Table 1 shows the correlations between weight and the other factors in a much more direct and simple way using the same data as in Figure 1. One can readily see the same conclusions about relations that were drawn from Figure 1.This type of abstract, symbolic display of derived information also makes it easier to spot relationships when the individual variables are bouncing up and down, unlike the more or less steady rise of most values in Figure 1. This increase in precision of relationship information is gained at the expense of other types of information (e.g., trends). To display information in an optimum form then, the system designer must know what the information demands of the task are at the point in the task when the display is to be used.

Present the sequence of information display clusters to complement an optimum decision-making strategy.

Just as a fixed sequence of gathering clinical, diagnostic information may lead to a far from optimum outcome, there exists an optimum sequence of testing, considering information, and gathering data that will lead to an optimum outcome (as defined by the value hierarchy) with a minimum of time and expense. The task of the information system designer, then, is to provide or request the right information, in the best form, at each stage of the procedure. For ex-ample, Figure 1 is suitable for the diagnostic phase since it shows the current state of the risk factors and their trends. Table 1, on the other hand, might be more appropriate in determining treatment, where there may be a choice of first trying a strict dietary treatment, or going straight to a combination of diet plus medication. The fact that Figure 1 and Table 1 have somewhat redundant information is not a problem, since they are intended to optimally provide information for different decision-making tasks. The critical need, at this point, is for a model of how to determine what information should be requested, what tests to order, what information to request and display, and in what form at each step of the decision-making process. Commitment to a collaborative relationship between physicians and laboratorians and other information providers would be an essential requirement for such an undertaking. The ideal diagnostic data-collection instrument is a flexible, computer-based device, such as a notebook computer or Personal Digital Assistant (PDA) sized device.

Barriers to interactive, computer-driven data collection at the source

As with any major change, it may be difficult to induce many physicians to change their behavior by interacting directly with a computer instead of with paper and pen. Unlike office workers, who have had to make this transition over the past three decades, most physicians’ livelihoods will not depend on converting to computer interaction. Therefore, the transition must be made attractive and the changes less onerous. Some suggestions follow:

  1. Make the data collection a natural part of the clinical process.
  2. Ensure that the user interface is extremely friendly, easy to learn, and easy to use.
  3. Use a small, portable device.
  4. Use the same device for collection and display of existing information (e.g., test results and his-tory).
  5. Minimize the need for free-form written data entry (use check boxes, forms, etc.).
  6. Allow the entry of notes in pen-based free-form (with the option of automated conversion of numeric data to machine-manipulable form).
  7. Give the physicians a more direct benefit for collecting data, not just a means of helping a clerk at an HMO second-guess the physician’s judgment.
  8. Improve administrative efficiency in the office.
  9. Make the data collection complement the clinical decision-making process.
  10. Improve information displays, leading to better outcomes.
  11. Make better use of the physician’s time and mental effort.


The medical profession is facing a crisis of information. Gathering information is costing a typical practice more and more while fees are being restricted by third parties, and the process of gathering this in-formation may be detrimental to current outcomes. Gathered properly, in machine-manipulable form, these data could be reformatted so as to greatly improve their value immediately in the clinical setting by leading to decisions with better outcomes and, in the long run, by contributing to a clinical data warehouse that could greatly improve medical knowledge. The challenge is to create a mechanism for data collection that facilitates, hastens, and improves the outcomes of clinical activity while minimizing the inconvenience and resistance to change on the part of clinical practitioners. This paper is intended to provide a high-level overview of how this may be accomplished, and start a dialogue along these lines.


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Table 1

Correlation of weight with other cardiac risk factors

Cholesterol 0.759384
HDL 0.53908
LDL 0.177297
BP-syst. 0.424728
BP-dia. 0.516167
Triglycerides 0.637817

Figure 1  Hypothetical patient data.

(not shown)

Realtime Clinical Expert Support

Regression: A richly textured method for comparison and classification of predictor variables

Converting Hematology Based Data into an Inferential Interpretation

Larry H. Bernstein, Gil David, James Rucinski and Ronald R. Coifman
In Hematology – Science and Practice
Lawrie CH, Ch 22. Pp541-552.
InTech Feb 2012, ISBN 978-953-51-0174-1

A model for Thalassemia Screening using Hematology Measurements A model for automated screening of thalassemia in hematology (math study).

Kneifati-Hayek J, Fleischman W, Bernstein LH, Riccioli A, Bellevue R.
Lab Hematol. 2007; 13(4):119-23.

The results of 398 patient screens were collected. Data from the set were divided into training and validation subsets. The Mentzer ratio was determined through a receiver operating characteristic (ROC) curve on the first subset, and screened for thalassemia using the second subset. HgbA2 levels were used to confirm beta-thalassemia.

RESULTS: We determined the correct decision point of the Mentzer index to be a ratio of 20. Physicians can screen patients using this index before further evaluation for beta-thalassemia (P < .05).

CONCLUSION: The proposed method can be implemented by hospitals and laboratories to flag positive matches for further definitive evaluation, and will enable beta-thalassemia screening of a much larger population at little to no additional cost.

Measurement of granulocyte maturation may improve the early diagnosis of the septic state. Bernstein LH, Rucinski J. Clin Chem Lab Med. 2011 Sep 21;49(12):2089-95. The automated malnutrition assessment.

David G, Bernstein LH, Coifman RR. Nutrition. 2013 Jan; 29(1):113-21. Molecular Diagnostics

Genomic Analysis of Hematological Malignancies

Acute lymphoblastic leukemia (ALL) is the most common hematologic malignancy that occurs in children. Although more than 90% of children with ALL now survive to adulthood, those with the rarest and high-risk forms of the disease continue to have poor prognoses. Through the Pediatric Cancer Genome Project (PCGP), investigators in the Hematological Malignancies Program are identifying the genetic aberrations that cause these aggressive forms of leukemias. Here we present two studies on the genetic bases of early T-cell precursor ALL and acute megakaryoblastic leukemia.

  • Early T-Cell Precursor ALL Is Characterized by Activating Mutations
  • The CBFA2T3-GLIS2Fusion Gene Defines an Aggressive Subtype of Acute Megakaryoblastic Leukemia in Children

Early T-cell precursor ALL (ETP-ALL), which comprises 15% of all pediatric T-cell leukemias, is an aggressive disease that is typically resistant to contemporary therapies. Children with ETP-ALL have a high rate of relapse and an extremely poor prognosis (i.e., 5-year survival is approximately 20%). The genetic basis of ETP-ALL has remained elusive. Although ETP-ALL is associated with a high burden of DNA copy number aberrations, none are consistently found or suggest a unifying genetic alteration that drives this disease.

Through the efforts of the PCGP, Jinghui Zhang, PhD (Computational Biology), James R. Downing, MD (Pathology), Charles G. Mullighan, MBBS(Hons), MSc, MD (Pathology), and colleagues analyzed the whole-genome sequences of leukemic cells and matched normal DNA from 12 pediatric patients with ETP-ALL. The identified genetic mutations were confirmed in a validation cohort of 52 ETP-ALL specimens and 42 non-ETP T-lineage ALLs (T-ALL).

In the journal Nature, the investigators reported that each ETP-ALL sample carried an average of 1140 sequence mutations and 12 structural variations. Of the structural variations, 51% were breakpoints in genes with well-established roles in hematopoiesis or leukemogenesis (e.g., MLH2,SUZ12, and RUNX1). Eighty-four percent of the structural variations either caused loss of function of the gene in question or resulted in the formation of a fusion gene such as ETV6-INO80D. The ETV6 gene, which encodes a protein that is essential for hematopoiesis, is frequently mutated in leukemia. Among the DNA samples sequenced in this study, ETV6 was altered in 33% of ETP-ALL but only 10% of T-ALL cases.

Next-generation sequencing in hematologic malignancies: what will be the dividends?

Jason D. MerkerAnton Valouev, and Jason Gotlib
Ther Adv Hematol. 2012 Dec; 3(6): 333–339.

The application of high-throughput, massively parallel sequencing technologies to hematologic malignancies over the past several years has provided novel insights into disease initiation, progression, and response to therapy. Here, we describe how these new DNA sequencing technologies have been applied to hematolymphoid malignancies. With further improvements in the sequencing and analysis methods as well as integration of the resulting data with clinical information, we expect these technologies will facilitate more precise and tailored treatment for patients with hematologic neoplasms.

Leveraging cancer genome information in hematologic malignancies.

Rampal R1Levine RL.
J Clin Oncol. 2013 May 20; 31(15):1885-92.

The use of candidate gene and genome-wide discovery studies in the last several years has led to an expansion of our knowledge of the spectrum of recurrent, somatic disease alleles, which contribute to the pathogenesis of hematologic malignancies. Notably, these studies have also begun to fundamentally change our ability to develop informative prognostic schema that inform outcome and therapeutic response, yielding substantive insights into mechanisms of hematopoietic transformation in different tissue compartments. Although these studies have already had important biologic and translational impact, significant challenges remain in systematically applying these findings to clinical decision making and in implementing new technologies for genetic analysis into clinical practice to inform real-time decision making. Here, we review recent major genetic advances in myeloid and lymphoid malignancies, the impact of these findings on prognostic models, our understanding of disease initiation and evolution, and the implication of genomic discoveries on clinical decision making. Finally, we discuss general concepts in genetic modeling and the current state-of-the-art technology used in genetic investigation.

p53 mutations are associated with resistance to chemotherapy and short survival in hematologic malignancies

E Wattel, C Preudhomme, B Hecquet, M Vanrumbeke, et AL.
Blood, (Nov 1), 1994; 84(9): pp 3148-3157

We analyzed the prognostic value of p53 mutations for response to chemotherapy and survival in acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and chronic lymphocytic leukemia (CLL). Mutations were detected by single-stranded conformation polymorphism (SSCP) analysis of exons 4 to 10 of the P53 gene, and confirmed by direct sequencing. A p53 mutation was found in 16 of 107 (15%) AML, 20 of 182 (11%) MDS, and 9 of 81 (11%) CLL tested. In AML, three of nine (33%) mutated cases and 66 of 81 (81%) nonmutated cases treated with intensive chemotherapy achieved complete remission (CR) (P = .005) and none of five mutated cases and three of six nonmutated cases treated by low-dose Ara C achieved CR or partial remission (PR) (P = .06). Median actuarial survival was 2.5 months in mutated cases, and 15 months in nonmutated cases (P < lo-‘). In the MDS patients who received chemotherapy (intensive chemotherapy or low-dose Ara C), 1 of 13 (8%) mutated cases and 23 of 38 (60%) nonmutated cases achieved CR or PR (P = .004), and median actuarial survival was 2.5 and 13.5 months, respectively (P C lo-’). In all MDS cases (treated and untreated), the survival difference between mutated cases and nonmutated cases was also highly significant. In CLL, 1 of 8 (12.5%) mutated cases treated by chemotherapy (chlorambucil andlor CHOP andlor fludarabine) responded, as compared with 29 of 36 (80%) nonmutated cases (P = .02). In all CLL cases, survival from p53 analysis was significantly shorter in mutated cases (median 7 months) than in nonmutated cases (median not reached) (P < IO-’). In 35 of the 45 mutated cases of AML, MDS, and CLL, cytogenetic analysis or SSCP and sequence findings showed loss of the nonmutated P53 allele. Our findings show that p53 mutations are a strong prognostic indicator of response to chemotherapy and survival in AML, MDS, and CLL. The usual association of p53 mutations to loss of the nonmutated P53 allele, in those disorders, ie, to absence of normal p53 in tumor cells, suggests that p53 mutations could induce drug resistance, at least in part, by interfering with normal apoptotic pathways in tumor cells.

Genomic approaches to hematologic malignancies

Benjamin L. Ebert and Todd R. Golub
Blood. 2004; 104:923-932,%20Blood%202004.pdf

In the past several years, experiments using DNA microarrays have contributed to an increasingly refined molecular taxonomy of hematologic malignancies. In addition to the characterization of molecular profiles for known diagnostic classifications, studies have defined patterns of gene expression corresponding to specific molecular abnormalities, oncologic phenotypes, and clinical outcomes. Furthermore, novel subclasses with distinct molecular profiles and clinical behaviors have been identified. In some cases, specific cellular pathways have been highlighted that can be therapeutically targeted. The findings of microarray studies are beginning to enter clinical practice as novel diagnostic tests, and clinical trials are ongoing in which therapeutic agents are being used to target pathways that were identified by gene expression profiling. While the technology of DNA microarrays is becoming well established, genome-wide surveys of gene expression generate large data sets that can easily lead to spurious conclusions. Many challenges remain in the statistical interpretation of gene expression data and the biologic validation of findings. As data accumulate and analyses become more sophisticated, genomic technologies offer the potential to generate increasingly sophisticated insights into the complex molecular circuitry of hematologic malignancies. This review summarizes the current state of discovery and addresses key areas for future research. Flow cytometry

Introduction to Flow Cytometry: Blood Cell Identification

Dana L. Van Laeys

No other laboratory method provides as rapid and detailed analysis of cellular populations as flow cytometry, making it a valuable tool for diagnosis and management of several hematologic and immunologic diseases. Understanding this relevant methodology is important for any medical laboratory scientist.

Whether you have no previous experience with flow cytometry or just need a refresher, this course will help you to understand the basic principles, with the help of video tutorials and interactive case studies.

Basic principles include:

  1. Immunophenotypic features of various types of hematologic cells
  2. Labeling cellular elements with fluorochromes
  3. Blood cell identification, specifically B and T lymphocyte identification and analysis
  4. Cell sorting to isolate select cell population for further analysis
  5. Analyzing and interpreting result reports and printouts

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