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Archive for the ‘Cancer Informatics’ Category

Identifying Cancers and Resistance, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Identifying Cancers and Resistance

Larry H/ Bernstein, MD, FCAP, Curator

LPBI

 

New Way of Detecting Cancer

http://www.technologynetworks.com/Diagnostics/news.aspx?ID=185201

A new RNA test of blood platelets can be used to detect, classify and pinpoint the location of cancer by analysing a sample equivalent to one drop of blood.

Using this new method for blood-based RNA tests of blood platelets, researchers have been able to identify cancer with 96 per cent accuracy and classifying the type of cancer at an accuracy of 71 per cent.

“Being able to detect cancer at an early stage is vital. We have studied how a whole new blood-based method of biopsy can be used to detect cancer, which in the future renders an invasive cell tissue sample unnecessary in diagnosing lung cancer, for instance. In the study, nearly all forms of cancer were identified, which proves that blood-based biopsies have an immense potential to improve early detection of cancer,” according to Jonas Nilsson, cancer researcher at Umeå University and co-author of the article.

In the study, researchers from Umeå University, in collaborations with researchers from the Netherlands and the US, have investigated how a new method of blood-based RNA tests of the part of the blood called platelets could be used in detecting and classifying cancer.

The results show that blood platelets could constitute a complete and easily accessible blood-based source for sampling and hence be used in diagnosing cancer as well as in the choice of treatment method.

Blood samples from 283 individuals were studied of which 228 people had some form of cancer and 55 showed no evidence of cancer. By comparing the blood samples RNA profiles, researchers could identify the presence of cancer with an accuracy of 96 per cent among patients. Among the 39 patients in the study in which an early detection of cancer had been made, 100 per cent of the cases could be identified and classified.

In follow-up tests using the same method, researchers could identify the origin of tumours with a so far unsurpassed accuracy of 71 per cent in patients with diagnosed cancer in the lung, breast, pancreas, brain, liver, colon and rectum. The samples could also be sorted in subdivisions depending on molecular differences in the cancer form, which can be of great use in the choice of treatment method.

 

Blood Test Picks Out Prostate Cancer Drug Resistance

http://www.technologynetworks.com/Diagnostics/news.aspx?ID=185032

Scientists have developed a blood test that can identify key mutations driving resistance to a widely used prostate cancer drug, and identify in advance patients who will not respond to treatment.

The new research paves the way for information from a blood test to inform prostate cancer treatment in future, with only those patients whose cancers are free of resistance mutations taking the drug, abiraterone.

The study is also a proof of principle that tests for cancer DNA in the bloodstream can be used to detect drug resistance mutations – allowing patients who will not benefit from one drug to be given an alternative treatment instead.

Researchers at The Institute of Cancer Research, London, the Royal Marsden NHS Foundation Trust, and the University of Trento, Italy, analysed 274 blood samples from 97 patients using state-of-the-art DNA sequencing techniques.

They found that mutations in a gene called the androgen receptor (AR) predicted resistance to the prostate cancer drug abiraterone, and that patients with these mutations had poorer survival.

Abiraterone, which was discovered at the ICR, is now standard treatment for men with advanced prostate cancer – but while it is highly effective in many patients, 30-60% do not respond.

So researchers have been searching for a marker that will help predict in advance which men will benefit from the drug, and who should be given a different treatment.

Researchers discovered that men who harbour either a specific mutation or an increase in the number of copies of the AR gene, were 7.8 times less likely to have a reduction of more than 90% in their PSA levels, a widely used test to monitor the response of prostate cancer.

The study also found that in about 15% of men given abiraterone who did not have either mutation before starting treatment, this was acquired as the drug stopped working and appeared in the bloodstream several months before patients developed any symptoms.

Blood tests are particularly valuable in cancer patients because biopsies are often difficult to perform and can carry risks. Even when biopsies are possible, they only give a snapshot of cancer genetics in a small specific area, whereas blood tests can give information that is more representative of multiple different tumours around the body.

Dr Gerhardt Attard, Clinician Scientist at The Institute of Cancer Research, London, and Consultant Medical Oncologist at The Royal Marsden NHS Foundation Trust, said: “The discovery of abiraterone was an important step forward for patients with advanced prostate cancer, but we know it doesn’t work for all men, and we’ve been searching for a marker that will tell us in advance which men will benefit. We’re delighted to have developed a test that appears to predict very accurately whether a patient will respond to abiraterone, and that it can be performed on blood samples – removing the need to take a biopsy.

“We are now planning a clinical trial involving up to 600 men in which we aim to prospectively show that men who are positive with our test have significantly greater benefit from chemotherapy in preference to abiraterone or similar drugs. Critically, we believe that this sort of technology would be relatively straightforward to implement in NHS hospitals, making it accessible to a large number of patients. Additionally, looking at tumour DNA in the blood of patients could potentially give us an overall picture of why the cancer is progressing all over the body, unlike a biopsy that only tells us about the area sampled.”

Professor Paul Workman, Chief Executive of The Institute of Cancer Research, London, said: “Abiraterone has extended the lives of many thousands of men in the UK, with fewer side-effects than chemotherapy, and we are really proud that it was discovered here at the ICR. But we don’t stop at taking a new treatment like abiraterone to patients – it’s also essential to continue to conduct research on these new drugs, to make sure they are being used as effectively as possible.

“This new study finds that by analysing tumour DNA present in the bloodstream, we should be able to personalise treatment with abiraterone, so that only those who will benefit from the drug will receive it. It is the latest step forward in the new era we are in of precision cancer medicine, where rather than using a treatment in the hope that a patient will benefit, we can look at tumour DNA in advance and be much more confident that a particular treatment will work.”

Associate Professor Francesca Demichelis, leader of the Computational Oncology Group at the University of Trento, Italy, said:

“Being able to quantify tumor DNA from a blood sample and to characterize structural changes of the DNA that are predictive of treatment response is powerful. This is especially true as a blood-based test gives us a global picture of the patient’s tumor burden rather than a focal snapshot as with a standard biopsy.

“In principle the same computational approach we adopted in this study is applicable across tumor types. This study represents a step towards sensitive and specific detection of resistance to targeted therapies.”

Dr Emma Smith, Cancer Research UK’s senior science information officer, said:

“Abiraterone, a drug which Cancer Research UK helped to develop, gives men with advanced prostate cancer a much-needed treatment option and more time with their loved ones. But it doesn’t work for everyone.  If these important early results bear up in larger clinical trials it could lead to a test which would indicate which patients might benefit more from trying other therapies instead.”

Dr Iain Frame, Director of Research at Prostate Cancer UK said: “We know that a one-size-fits-all approach to treating prostate cancer doesn’t work, and research like this is crucially important in helping us understand which treatments will – and won’t – work best for each individual. When the clock is ticking for a man with advanced prostate cancer, finding out early that his treatment needs changing can not only save precious time, but can also help avoid unpleasant side effects from a treatment that longer works for him.

“Research like this wouldn’t be possible without the thousands of people who take part in Movember every year – and so with another campaign upon us, we can all do something to help the fight against prostate cancer.”

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Biomarkers for metastatic Renal Cell Carcinoma

Larry H. Berntein, MD, FCAP, Curator

LPBI

 

Biomarkers May Help Predict Treatment Response in mRCC

Roxanne Nelson, BSN, RN

Medscape Medical News    http://www.medscape.com/viewarticle/854483

 

 

http://img.medscape.com/news/2015/wc_150224_renal_cell_carcinoma_800x600.jpg

see also – VEGF Inhibitors Disappoint in Adjuvant Renal Cell Carcinoma

 

Patients with metastatic renal cell carcinoma (mRCC) have variable clinical outcomes to treatment, and identifying the underlying molecular markers may help predict response to therapy. Two studies presented here at the 14th International Kidney Cancer Symposium (IKCS) report preliminary data that may eventually allow for more individualized therapy.

“We need to keep looking for biomarkers in renal cancer, and we are making progress, but we still have a long way to go,” commented Jaime Cajigas, MD, director of the oncology section, Sociedad Colombiana de Urología, Bogotá, Colombia.

Dr Cajigas, who was approached by Medscape Medical Newsfor an independent comment, noted that the “biomarkers are there, and we need to find them in order to give the best therapy.”

Poor Outcome With PD-L1 Expression

In the first study, David Gill, MD, from the Huntsman Cancer Institute, University of Utah, Salt Lake City, and colleagues found that PD-L1 expression correlated with poor outcomes in patients with metastatic clear cell renal cell carcinoma (mccRCC) who were treated with high-dose interleukin-2 (HD IL-2).

Renal cell carcinoma has been recognized as an immunoresponsive tumor, which has led researchers to look at immunomodulatory strategies to stimulate antitumor activity. For treatment with HD IL-2, the response rate is about 16% to 20%, with about 10% of patients achieving a complete response, explained Dr Gill, who presented the findings of their study.

“Notably, many of the complete responses are long-term responses,” he said.

However, he noted that treatment is limited by an acute toxicity profile, although relatively few long-term toxicities occur between treatments and after completion of therapy.

“But because of the toxicity profile, the NCCN [National Comprehensive Cancer Network] recommends this treatment for a select group of patients who have an excellent performance status and good organ function,” Dr Gill said. “Biomarkers predicting response to therapy are needed to better select patients most likely to benefit from high-dose IL-2.”

Clear cell carcinoma expresses PD-L1 to a grater degree than other renal cell variants, and it has been associated with decreased immune response, he pointed out.

The cohort included 58 mccRCC patients who were receiving HD IL-2 and who were available for immunohistochemistry analysis. The patients’ median age was 55 years; the majority were men (78%). Of the 58 patients, 8 (13%) were classified as being of favorable risk, in accordance with the International mRCC Database Consortium (IMDC) risk categories; 42 (72%) were classified as being of intermediate risk; and 8 (14%) were classified as being of poor risk.

PD-L1 tumor positivity was defined as 1% tumor cell membrane staining. Nine patients (16%) had 1% PD-Ll expression; 6 (67%) were IMDC intermediate risk, and 3 (33%) were poor risk.

The findings showed that an increase in PD-LI was associated with decreased overall survival (OS) and decreased progression-free survival (PFS); both were statistically significant. OS was decreased to about 13.7 months from 37.3 months when PD-LI was expressed (P = .0069). Results for PFS were similar: 3.1 months vs 7.3 months (P = .088).

Six patients (12%) had a complete response to treatment, compared with no patients with PD-L1 expression; four patients (8%) had a partial response vs one (11%) among the patients with PD-L1 expression.

The overall objective response rate was 20% for those without PD-L1 expression, compared with 11% for those with it.

 

“We conclude that for patients treated with high-dose IL-2, PD-LI expression correlated with worse outcomes,” said Dr Gill. “This leads us to suggest the immunostimulation achieved with IL-2 may be inferior to the immune dampening from PD-L1 expression.”

Predicting Response to VEGF TKIs

In the second study, the researchers looked at molecular predictors of response and survival outcomes in patients treated with vascular endothelial growth factor tyrosine kinase inhibitors (VEGF TKIs).

VEGF TKIs are the standard frontline treatment option for patients with mRCC, explained lead author Neeraj Agarwal, MD, associate professor of oncology at the the University of Utah School of Medicine, Salt Lake City.

“However, patients have very variable clinical outcomes, and identifying clinical markers predictive of response to therapy has the potential to allow for better selection of patients,” said Dr Agarwal.

To assess potential biomarkers that might be indicative of therapeutic response and survival patterns, Dr Agarwal and colleagues assessed genomic DNA that was extracted from macrodissected formaldehyde fixed-paraffin embedded (FFPE) tumor tissue sections of 86 mccRCC patients.

A total of 65 patients had responded to therapy. Of those, the responses of 16 were long term (defined as PFS > 18 months), and of 21, short term (PFS < 6 months).

For the genomic analysis, the patients were divided into three groups, explained Dr Agarwal. These consisted of patients with long-term response vs short-term response; those with a clinical benefit vs those without a benefit; and patients with an objective response vs those who did not have one.

Gain/loss was evaluated by array-CGH and differential copy number alterations (CNAs) associated with survival outcomes. Best objective responses (complete response, partial response, stable disease, progressive disease) were identified using Fisher’s exact test. Nucleotide variants were detected by massively parallel sequencing using a custom hybrid capture panel of 76 frequently mutated genes and 16 prognostic single-nucleotide polymorphisms in RCC.

They found that the gain of 10p15.2-p15.1 was significantly enriched (P < .05) in 20 patients with short-term response to VEGF TKIs, as compared with patients with long-term response. It correlated with poorer outcome (P = .04) in the remaining patients.

In all 16 patients with long-term response, 11 CNAs were significantly enriched, and only one, 5q14.3 gain, was associated with favorable outcome (P = .01).

When looking at objective responses, nine CNAs were significantly different among patients who had a complete/partial response or who had stable disease vs progressive disease, and eight CNAs differed between complete/partial response vs stable disease/progressive disease.

Sequence information was available for 18 patients with short-term response and for 10 with long-term response. Mutation frequencies of VHL, PBRM1, BAP1, and SET02 were as expected, said Dr Agarwal.

“Several copy numbers in metastatic RCC were predictive of response and outcomes to treatment with VEGF TKIs, and we are hoping to further validate in a larger cohort,” said Dr Agarwal.

14th International Kidney Cancer Symposium (IKCS). Presented November 7, 2015.

 

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Clinical Laboratory Challenges

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

CLINICAL LABORATORY NEWS   

The Lab and CJD: Safe Handling of Infectious Prion Proteins

Body fluids from individuals with possible Creutzfeldt-Jakob disease (CJD) present distinctive safety challenges for clinical laboratories. Sporadic, iatrogenic, and familial CJD (known collectively as classic CJD), along with variant CJD, kuru, Gerstmann-Sträussler-Scheinker, and fatal familial insomnia, are prion diseases, also known as transmissible spongiform encephalopathies. Prion diseases affect the central nervous system, and from the onset of symptoms follow a typically rapid progressive neurological decline. While prion diseases are rare, it is not uncommon for the most prevalent form—sporadic CJD—to be included in the differential diagnosis of individuals presenting with rapid cognitive decline. Thus, laboratories may deal with a significant number of possible CJD cases, and should have protocols in place to process specimens, even if a confirmatory diagnosis of CJD is made in only a fraction of these cases.

The Lab’s Role in Diagnosis

Laboratory protocols for handling specimens from individuals with possible, probable, and definitive cases of CJD are important to ensure timely and appropriate patient management. When the differential includes CJD, an attempt should be made to rule-in or out other causes of rapid neurological decline. Laboratories should be prepared to process blood and cerebrospinal fluid (CSF) specimens in such cases for routine analyses.

Definitive diagnosis requires identification of prion aggregates in brain tissue, which can be achieved by immunohistochemistry, a Western blot for proteinase K-resistant prions, and/or by the presence of prion fibrils. Thus, confirmatory diagnosis is typically achieved at autopsy. A probable diagnosis of CJD is supported by elevated concentration of 14-3-3 protein in CSF (a non-specific marker of neurodegeneration), EEG, and MRI findings. Thus, the laboratory may be required to process and send CSF samples to a prion surveillance center for 14-3-3 testing, as well as blood samples for sequencing of the PRNP gene (in inherited cases).

Processing Biofluids

Laboratories should follow standard protective measures when working with biofluids potentially containing abnormally folded prions, such as donning standard personal protective equipment (PPE); avoiding or minimizing the use of sharps; using single-use disposable items; and processing specimens to minimize formation of aerosols and droplets. An additional safety consideration is the use of single-use disposal PPE; otherwise, re-usable items must be either cleaned using prion-specific decontamination methods, or destroyed.

Blood. In experimental models, infectivity has been detected in the blood; however, there have been no cases of secondary transmission of classical CJD via blood product transfusions in humans. As such, blood has been classified, on epidemiological evidence by the World Health Organization (WHO), as containing “no detectible infectivity,” which means it can be processed by routine methods. Similarly, except for CSF, all other body fluids contain no infectivity and can be processed following standard procedures.

In contrast to classic CJD, there have been four cases of suspected secondary transmission of variant CJD via transfused blood products in the United Kingdom. Variant CJD, the prion disease associated with mad cow disease, is unique in its distribution of prion aggregates outside of the central nervous system, including the lymph nodes, spleen, and tonsils. For regions where variant CJD is a concern, laboratories should consult their regulatory agencies for further guidance.

CSF. Relative to highly infectious tissues of the brain, spinal cord, and eye, infectivity has been identified less often in CSF and is considered to have “low infectivity,” along with kidney, liver, and lung tissue. Since CSF can contain infectious material, WHO has recommended that analyses not be performed on automated equipment due to challenges associated with decontamination. Laboratories should perform a risk assessment of their CSF processes, and, if deemed necessary, consider using manual methods as an alternative to automated systems.

Decontamination

The infectious agent in prion disease is unlike any other infectious pathogen encountered in the laboratory; it is formed of misfolded and aggregated prion proteins. This aggregated proteinacious material forms the infectious unit, which is incredibly resilient to degradation. Moreover, in vitro studies have demonstrated that disrupting large aggregates into smaller aggregates increases cytotoxicity. Thus, if the aim is to abolish infectivity, all aggregates must be destroyed. Disinfectant procedures used for viral, bacterial, and fungal pathogens such as alcohol, boiling, formalin, dry heat (<300°C), autoclaving at 121°C for 15 minutes, and ionizing, ultraviolet, or microwave radiation, are either ineffective or variably effective against aggregated prions.

The only means to ensure no risk of residual infectious prions is to use disposable materials. This is not always practical, as, for instance, a biosafety cabinet cannot be discarded if there is a CSF spill in the hood. Fortunately, there are several protocols considered sufficient for decontamination. For surfaces and heat-sensitive instruments, such as a biosafety cabinet, WHO recommends flooding the surface with 2N NaOH or undiluted NaClO, letting stand for 1 hour, mopping up, and rinsing with water. If the surface cannot tolerate NaOH or NaClO, thorough cleaning will remove most infectivity by dilution. Laboratories may derive some additional benefit by using one of the partially effective methods discussed previously. Non-disposable heat-resistant items preferably should be immersed in 1N NaOH, heated in a gravity displacement autoclave at 121°C for 30 min, cleaned and rinsed in water, then sterilized by routine methods. WHO has outlined several alternate decontamination methods. Using disposable cover sheets is one simple solution to avoid contaminating work surfaces and associated lengthy decontamination procedures.

With standard PPE—augmented by a few additional safety measures and prion-specific decontamination procedures—laboratories can safely manage biofluid testing in cases of prion disease.

 

The Microscopic World Inside Us  

Emerging Research Points to Microbiome’s Role in Health and Disease

Thousands of species of microbes—bacteria, viruses, fungi, and protozoa—inhabit every internal and external surface of the human body. Collectively, these microbes, known as the microbiome, outnumber the body’s human cells by about 10 to 1 and include more than 1,000 species of microorganisms and several million genes residing in the skin, respiratory system, urogenital, and gastrointestinal tracts. The microbiome’s complicated relationship with its human host is increasingly considered so crucial to health that researchers sometimes call it “the forgotten organ.”

Disturbances to the microbiome can arise from nutritional deficiencies, antibiotic use, and antiseptic modern life. Imbalances in the microbiome’s diverse microbial communities, which interact constantly with cells in the human body, may contribute to chronic health conditions, including diabetes, asthma and allergies, obesity and the metabolic syndrome, digestive disorders including irritable bowel syndrome (IBS), and autoimmune disorders like multiple sclerosis and rheumatoid arthritis, research shows.

While study of the microbiome is a growing research enterprise that has attracted enthusiastic media attention and venture capital, its findings are largely preliminary. But some laboratorians are already developing a greater appreciation for the microbiome’s contributions to human biochemistry and are considering a future in which they expect to measure changes in the microbiome to monitor disease and inform clinical practice.

Pivot Toward the Microbiome

Following the National Institutes of Health (NIH) Human Genome Project, many scientists noted the considerable genetic signal from microbes in the body and the existence of technology to analyze these microorganisms. That realization led NIH to establish the Human Microbiome Project in 2007, said Lita Proctor, PhD, its program director. In the project’s first phase, researchers studied healthy adults to produce a reference set of microbiomes and a resource of metagenomic sequences of bacteria in the airways, skin, oral cavities, and the gastrointestinal and vaginal tracts, plus a catalog of microbial genome sequences of reference strains. Researchers also evaluated specific diseases associated with disturbances in the microbiome, including gastrointestinal diseases such as Crohn’s disease, ulcerative colitis, IBS, and obesity, as well as urogenital conditions, those that involve the reproductive system, and skin diseases like eczema, psoriasis, and acne.

Phase 1 studies determined the composition of many parts of the microbiome, but did not define how that composition affects health or specific disease. The project’s second phase aims to “answer the question of what microbes actually do,” explained Proctor. Researchers are now examining properties of the microbiome including gene expression, protein, and human and microbial metabolite profiles in studies of pregnant women at risk for preterm birth, the gut hormones of patients at risk for IBS, and nasal microbiomes of patients at risk for type 2 diabetes.

Promising Lines of Research

Cystic fibrosis and microbiology investigator Michael Surette, PhD, sees promising microbiome research not just in terms of evidence of its effects on specific diseases, but also in what drives changes in the microbiome. Surette is Canada research chair in interdisciplinary microbiome research in the Farncombe Family Digestive Health Research Institute at McMaster University
in Hamilton, Ontario.

One type of study on factors driving microbiome change examines how alterations in composition and imbalances in individual patients relate to improving or worsening disease. “IBS, cystic fibrosis, and chronic obstructive pulmonary disease all have periods of instability or exacerbation,” he noted. Surette hopes that one day, tests will provide clinicians the ability to monitor changes in microbial composition over time and even predict when a patient’s condition is about to deteriorate. Monitoring perturbations to the gut microbiome might also help minimize collateral damage to the microbiome during aggressive antibiotic therapy for hospitalized patients, he added.

Monitoring changes to the microbiome also might be helpful for “culture negative” patients, who now may receive multiple, unsuccessful courses of different antibiotics that drive antibiotic resistance. Frustration with standard clinical biology diagnosis of lung infections in cystic fibrosis patients first sparked Surette’s investigations into the microbiome. He hopes that future tests involving the microbiome might also help asthma patients with neutrophilia, community-acquired pneumonia patients who harbor complex microbial lung communities lacking obvious pathogens, and hospitalized patients with pneumonia or sepsis. He envisions microbiome testing that would look for short-term changes indicating whether or not a drug is effective.

Companion Diagnostics

Daniel Peterson, MD, PhD, an assistant professor of pathology at Johns Hopkins University School of Medicine in Baltimore, believes the future of clinical testing involving the microbiome lies in companion diagnostics for novel treatments, and points to companies that are already developing and marketing tests that will require such assays.

Examples of microbiome-focused enterprises abound, including Genetic Analysis, based in Oslo, Norway, with its high-throughput test that uses 54 probes targeted to specific bacteria to measure intestinal gut flora imbalances in inflammatory bowel disease and irritable bowel syndrome patients. Paris, France-based Enterome is developing both novel drugs and companion diagnostics for microbiome-related diseases such as IBS and some metabolic diseases. Second Genome, based in South San Francisco, has developed an experimental drug, SGM-1019, that the company says blocks damaging activity of the microbiome in the intestine. Cambridge, Massachusetts-based Seres Therapeutics has received Food and Drug Administration orphan drug designation for SER-109, an oral therapeutic intended to correct microbial imbalances to prevent recurrent Clostridium difficile infection in adults.

One promising clinical use of the microbiome is fecal transplantation, which both prospective and retrospective studies have shown to be effective in patients with C. difficile infections who do not respond to front-line therapies, said James Versalovic, MD, PhD, director of Texas Children’s Hospital Microbiome Center and professor of pathology at Baylor College of Medicine in Houston. “Fecal transplants and other microbiome replacement strategies can radically change the composition of the microbiome in hours to days,” he explained.

But NIH’s Proctor discourages too much enthusiasm about fecal transplant. “Natural products like stool can have [side] effects,” she pointed out. “The [microbiome research] field needs to mature and we need to verify outcomes before anything becomes routine.”

Hurdles for Lab Testing

While he is hopeful that labs someday will use the microbiome to produce clinically useful information, Surette pointed to several problems that must be solved beforehand. First, molecular methods commonly used right now should be more quantitative and accurate. Additionally, research on the microbiome encompasses a wide variety of protocols, some of which are better at extracting particular types of bacteria and therefore can give biased views of communities living in the body. Also, tests may need to distinguish between dead and live microbes. Another hurdle is that labs using varied bioinfomatic methods may produce different results from the same sample, a problem that Surette sees as ripe for a solution from clinical laboratorians, who have expertise in standardizing robust protocols and in automating tests.

One way laboratorians can prepare for future, routine microbiome testing is to expand their notion of clinical chemistry to include both microbial and human biochemistry. “The line between microbiome science and clinical science is blurring,” said Versalovic. “When developing future assays to detect biochemical changes in disease states, we must consider the contributions of microbial metabolites and proteins and how to tailor tests to detect them.” In the future, clinical labs may test for uniquely microbial metabolites in various disease states, he predicted.

 

Automated Review of Mass Spectrometry Results  

Can We Achieve Autoverification?

Author: Katherine Alexander and Andrea R. Terrell, PhD  // Date: NOV.1.2015  // Source:Clinical Laboratory News

https://www.aacc.org/publications/cln/articles/2015/november/automated-review-of-mass-spectrometry-results-can-we-achieve-autoverification

 

Paralleling the upswing in prescription drug misuse, clinical laboratories are receiving more requests for mass spectrometry (MS) testing as physicians rely on its specificity to monitor patient compliance with prescription regimens. However, as volume has increased, reimbursement has declined, forcing toxicology laboratories both to increase capacity and lower their operational costs—without sacrificing quality or turnaround time. Now, new solutions are available enabling laboratories to bring automation to MS testing and helping them with the growing demand for toxicology and other testing.

What is the typical MS workflow?

A typical workflow includes a long list of manual steps. By the time a sample is loaded onto the mass spectrometer, it has been collected, logged into the lab information management system (LIMS), and prepared for analysis using a variety of wet chemistry techniques.

Most commercial clinical laboratories receive enough samples for MS analysis to batch analyze those samples. A batch consists of a calibrator(s), quality control (QC) samples, and patient/donor samples. Historically, the method would be selected (i.e. “analysis of opiates”), sample identification information would be entered manually into the MS software, and the instrument would begin analyzing each sample. Upon successful completion of the batch, the MS operator would view all of the analytical data, ensure the QC results were acceptable, and review each patient/donor specimen, looking at characteristics such as peak shape, ion ratios, retention time, and calculated concentration.

The operator would then post acceptable results into the LIMS manually or through an interface, and unacceptable results would be rescheduled or dealt with according to lab-specific protocols. In our laboratory we perform a final certification step for quality assurance by reviewing all information about the batch again, prior to releasing results for final reporting through the LIMS.

What problems are associated with this workflow?

The workflow described above results in too many highly trained chemists performing manual data entry and reviewing perfectly acceptable analytical results. Lab managers would prefer that MS operators and certifying scientists focus on troubleshooting problem samples rather than reviewing mounds of good data. Not only is the current process inefficient, it is mundane work prone to user errors. This risks fatigue, disengagement, and complacency by our highly skilled scientists.

Importantly, manual processes also take time. In most clinical lab environments, turnaround time is critical for patient care and industry competitiveness. Lab directors and managers are looking for solutions to automate mundane, error-prone tasks to save time and costs, reduce staff burnout, and maintain high levels of quality.

How can software automate data transfer from MS systems to LIMS?

Automation is not a new concept in the clinical lab. Labs have automated processes in shipping and receiving, sample preparation, liquid handling, and data delivery to the end user. As more labs implement MS, companies have begun to develop special software to automate data analysis and review workflows.

In July 2011, AIT Labs incorporated ASCENT into our workflow, eliminating the initial manual peak review step. ASCENT is an algorithm-based peak picking and data review system designed specifically for chromatographic data. The software employs robust statistical and modeling approaches to the raw instrument data to present the true signal, which often can be obscured by noise or matrix components.

The system also uses an exponentially modified Gaussian (EMG) equation to apply a best-fit model to integrated peaks through what is often a noisy signal. In our experience, applying the EMG results in cleaner data from what might appear to be poor chromatography ultimately allows us to reduce the number of samples we might otherwise rerun.

How do you validate the quality of results?

We’ve developed a robust validation protocol to ensure that results are, at minimum, equivalent to results from our manual review. We begin by building the assay in ASCENT, entering assay-specific information from our internal standard operating procedure (SOP). Once the assay is configured, validation proceeds with parallel batch processing to compare results between software-reviewed data and staff-reviewed data. For new implementations we run eight to nine batches of 30–40 samples each; when we are modifying or upgrading an existing implementation we run a smaller number of batches. The parallel batches should contain multiple positive and negative results for all analytes in the method, preferably spanning the analytical measurement range of the assay.

The next step is to compare the results and calculate the percent difference between the data review methods. We require that two-thirds of the automated results fall within 20% of the manually reviewed result. In addition to validating patient sample correlation, we also test numerous quality assurance rules that should initiate a flag for further review.

What are the biggest challenges during implementation and continual improvement initiatives?

On the technological side, our largest hurdle was loading the sequence files into ASCENT. We had created an in-house mechanism for our chemists to upload the 96-well plate map for their batch into the MS software. We had some difficulty transferring this information to ASCENT, but once we resolved this issue, the technical workflow proceeded fairly smoothly.

The greater challenge was changing our employees’ mindset from one of fear that automation would displace them, to a realization that learning this new technology would actually make them more valuable. Automating a non-mechanical process can be a difficult concept for hands-on scientists, so managers must be patient and help their employees understand that this kind of technology leverages the best attributes of software and people to create a powerful partnership.

We recommend that labs considering automated data analysis engage staff in the validation and implementation to spread the workload and the knowledge. As is true with most technology, it is best not to rely on just one or two super users. We also found it critical to add supervisor level controls on data file manipulation, such as removing a sample that wasn’t run from the sequence table. This can prevent inadvertent deletion of a file, requiring reinjection of the entire batch!

 

Understanding Fibroblast Growth Factor 23

Author: Damien Gruson, PhD  // Date: OCT.1.2015  // Source: Clinical Laboratory News

https://www.aacc.org/publications/cln/articles/2015/october/understanding-fibroblast-growth-factor-23

What is the relationship of FGF-23 to heart failure?

A Heart failure (HF) is an increasingly common syndrome associated with high morbidity, elevated hospital readmission rates, and high mortality. Improving diagnosis, prognosis, and treatment of HF requires a better understanding of its different sub-phenotypes. As researchers gained a comprehensive understanding of neurohormonal activation—one of the hallmarks of HF—they discovered several biomarkers, including natriuretic peptides, which now are playing an important role in sub-phenotyping HF and in driving more personalized management of this chronic condition.

Like the natriuretic peptides, fibroblast growth factor 23 (FGF-23) could become important in risk-stratifying and managing HF patients. Produced by osteocytes, FGF-23 is a key regulator of phosphorus homeostasis. It binds to renal and parathyroid FGF-Klotho receptor heterodimers, resulting in phosphate excretion, decreased 1-α-hydroxylation of 25-hydroxyvitamin D, and decreased parathyroid hormone (PTH) secretion. The relationship to PTH is important because impaired homeostasis of cations and decreased glomerular filtration rate might contribute to the rise of FGF-23. The amino-terminal portion of FGF-23 (amino acids 1-24) serves as a signal peptide allowing secretion into the blood, and the carboxyl-terminal portion (aa 180-251) participates in its biological action.

How might FGF-23 improve HF risk assessment?

Studies have shown that FGF-23 is related to the risk of cardiovascular diseases and mortality. It was first demonstrated that FGF-23 levels were independently associated with left ventricular mass index and hypertrophy as well as mortality in patients with chronic kidney disease (CKD). FGF-23 also has been associated with left ventricular dysfunction and atrial fibrillation in coronary artery disease subjects, even in the absence of impaired renal function.

FGF-23 and FGF receptors are both expressed in the myocardium. It is possible that FGF-23 has direct effects on the heart and participates in the physiopathology of cardiovascular diseases and HF. Experiments have shown that for in vitro cultured rat cardiomyocytes, FGF-23 stimulates pathological hypertrophy by activating the calcineurin-NFAT pathway—and in wild-type mice—the intra-myocardial or intravenous injection of FGF-23 resulted in left ventricular hypertrophy. As such, FGF-23 appears to be a potential stimulus of myocardial hypertrophy, and increased levels may contribute to the worsening of heart failure and long-term cardiovascular death.

Researchers have documented that HF patients have elevated FGF-23 circulating levels. They have also found a significant correlation between plasma levels of FGF-23 and B-type natriuretic peptide, a biomarker related to ventricular stretch and cardiac hypertrophy, in patients with left ventricular hypertrophy. As such, measuring FGF-23 levels might be a useful tool to predict long-term adverse cardiovascular events in HF patients.

Interestingly, researchers have documented a significant relationship between FGF-23 and PTH in both CKD and HF patients. As PTH stimulates FGF-23 expression, it could be that in HF patients, increased PTH levels increase the bone expression of FGF-23, which enhances its effects on the heart.

 

The Past, Present, and Future of Western Blotting in the Clinical Laboratory

Author: Curtis Balmer, PhD  // Date: OCT.1.2015  // Source: Clinical Laboratory News

https://www.aacc.org/publications/cln/articles/2015/october/the-past-present-and-future-of-western-blotting-in-the-clinical-laboratory

Much of the discussion about Western blotting centers around its performance as a biological research tool. This isn’t surprising. Since its introduction in the late 1970s, the Western blot has been adopted by biology labs of virtually every stripe, and become one of the most widely used techniques in the research armamentarium. However, Western blotting has also been employed in clinical laboratories to aid in the diagnosis of various diseases and disorders—an equally important and valuable application. Yet there has been relatively little discussion of its use in this context, or of how advances in Western blotting might affect its future clinical use.

Highlighting the clinical value of Western blotting, Stanley Naides, MD, medical director of Immunology at Quest Diagnostics observed that, “Western blotting has been a very powerful tool in the laboratory and for clinical diagnosis. It’s one of many various methods that the laboratorian brings to aid the clinician in the diagnosis of disease, and the selection and monitoring of therapy.” Indeed, Western blotting has been used at one time or the other to aid in the diagnosis of infectious diseases including hepatitis C (HCV), HIV, Lyme disease, and syphilis, as well as autoimmune disorders such as paraneoplastic disease and myositis conditions.

However, Naides was quick to point out that the choice of assays to use clinically is based on their demonstrated sensitivity and performance, and that the search for something better is never-ending. “We’re constantly looking for methods that improve detection of our target [protein],” Naides said. “There have been a number of instances where we’ve moved away from Western blotting because another method proves to be more sensitive.” But this search can also lead back to Western blotting. “We’ve gone away from other methods because there’s been a Western blot that’s been developed that’s more sensitive and specific. There’s that constant movement between methods as new tests are developed.”

In recent years, this quest has been leading clinical laboratories away from Western blotting toward more sensitive and specific diagnostic assays, at least for some diseases. Using confirmatory diagnosis of HCV infection as an example, Sai Patibandla, PhD, director of the immunoassay group at Siemens Healthcare Diagnostics, explained that movement away from Western blotting for confirmatory diagnosis of HCV infection began with a technical modification called Recombinant Immunoblotting Assay (RIBA). RIBA streamlines the conventional Western blot protocol by spotting recombinant antigen onto strips which are used to screen patient samples for antibodies against HCV. This approach eliminates the need to separate proteins and transfer them onto a membrane.

The RIBA HCV assay was initially manufactured by Chiron Corporation (acquired by Novartics Vaccines and Diagnostics in 2006). It received Food and Drug Administration (FDA) approval in 1999, and was marketed as Chiron RIBA HCV 3.0 Strip Immunoblot Assay. Patibandla explained that, at the time, the Chiron assay “…was the only FDA-approved confirmatory testing for HCV.” In 2013 the assay was discontinued and withdrawn from the market due to reports that it was producing false-positive results.

Since then, clinical laboratories have continued to move away from Western blot-based assays for confirmation of HCV in favor of the more sensitive technique of nucleic acid testing (NAT). “The migration is toward NAT for confirmation of HCV [diagnosis]. We don’t use immunoblots anymore. We don’t even have a blot now to confirm HCV,” Patibandla said.

Confirming HIV infection has followed a similar path. Indeed, in 2014 the Centers for Disease Control and Prevention issued updated recommendations for HIV testing that, in part, replaced Western blotting with NAT. This change was in response to the recognition that the HIV-1 Western blot assay was producing false-negative or indeterminable results early in the course of HIV infection.

At this juncture it is difficult to predict if this trend away from Western blotting in clinical laboratories will continue. One thing that is certain, however, is that clinicians and laboratorians are infinitely pragmatic, and will eagerly replace current techniques with ones shown to be more sensitive, specific, and effective. This raises the question of whether any of the many efforts currently underway to improve Western blotting will produce an assay that exceeds the sensitivity of currently employed techniques such as NAT.

Some of the most exciting and groundbreaking work in this area is being done by Amy Herr, PhD, a professor of bioengineering at University of California, Berkeley. Herr’s group has taken on some of the most challenging limitations of Western blotting, and is developing techniques that could revolutionize the assay. For example, the Western blot is semi-quantitative at best. This weakness dramatically limits the types of answers it can provide about changes in protein concentrations under various conditions.

To make Western blotting more quantitative, Herr’s group is, among other things, identifying losses of protein sample mass during the assay protocol. About this, Herr explains that the conventional Western blot is an “open system” that involves lots of handling of assay materials, buffers, and reagents that makes it difficult to account for protein losses. Or, as Kevin Lowitz, a senior product manager at Thermo Fisher Scientific, described it, “Western blot is a [simple] technique, but a really laborious one, and there are just so many steps and so many opportunities to mess it up.”

Herr’s approach is to reduce the open aspects of Western blot. “We’ve been developing these more closed systems that allow us at each stage of the assay to account for [protein mass] losses. We can’t do this exactly for every target of interest, but it gives us a really good handle [on protein mass losses],” she said. One of the major mechanisms Herr’s lab is using to accomplish this is to secure proteins to the blot matrix with covalent bonding rather than with the much weaker hydrophobic interactions that typically keep the proteins in place on the membrane.

Herr’s group also has been developing microfluidic platforms that allow Western blotting to be done on single cells, “In our system we’re doing thousands of independent Westerns on single cells in four hours. And, hopefully, we’ll cut that down to one hour over the next couple years.”

Other exciting modifications that stand to dramatically increase the sensitivity, quantitation, and through-put of Western blotting also are being developed and explored. For example, the use of capillary electrophoresis—in which proteins are conveyed through a small electrolyte-filled tube and separated according to size and charge before being dropped onto a blotting membrane—dramatically reduces the amount of protein required for Western blot analysis, and thereby allows Westerns to be run on proteins from rare cells or for which quantities of sample are extremely limited.

Jillian Silva, PhD, an associate specialist at the University of California, San Francisco Helen Diller Family Comprehensive Cancer Center, explained that advances in detection are also extending the capabilities of Western blotting. “With the advent of fluorescence detection we have a way to quantitate Westerns, and it is now more quantitative than it’s ever been,” said Silva.

Whether or not these advances produce an assay that is adopted by clinical laboratories remains to be seen. The emphasis on Western blotting as a research rather than a clinical tool may bias advances in favor of the needs and priorities of researchers rather than clinicians, and as Patibandla pointed out, “In the research world Western blotting has a certain purpose. [Researchers] are always coming up with new things, and are trying to nail down new proteins, so you cannot take Western blotting away.” In contrast, she suggested that for now, clinical uses of Western blotting remain “limited.”

 

Adapting Next Generation Technologies to Clinical Molecular Oncology Service

Author: Ronald Carter, PhD, DVM  // Date: OCT.1.2015  // Source: Clinical Laboratory News

https://www.aacc.org/publications/cln/articles/2015/october/adapting-next-generation-technologies-to-clinical-molecular-oncology-service

Next generation technologies (NGT) deliver huge improvements in cost efficiency, accuracy, robustness, and in the amount of information they provide. Microarrays, high-throughput sequencing platforms, digital droplet PCR, and other technologies all offer unique combinations of desirable performance.

As stronger evidence of genetic testing’s clinical utility influences patterns of patient care, demand for NGT testing is increasing. This presents several challenges to clinical laboratories, including increased urgency, clinical importance, and breadth of application in molecular oncology, as well as more integration of genetic tests into synoptic reporting. Laboratories need to add NGT-based protocols while still providing old tests, and the pace of change is increasing.What follows is one viewpoint on the major challenges in adopting NGTs into diagnostic molecular oncology service.

Choosing a Platform

Instrument selection is a critical decision that has to align with intended test applications, sequencing chemistries, and analytical software. Although multiple platforms are available, a mainstream standard has not emerged. Depending on their goals, laboratories might set up NGTs for improved accuracy of mutation detection, massively higher sequencing capacity per test, massively more targets combined in one test (multiplexing), greater range in sequencing read length, much lower cost per base pair assessed, and economy of specimen volume.

When high-throughput instruments first made their appearance, laboratories paid more attention to the accuracy of base-reading: Less accurate sequencing meant more data cleaning and resequencing (1). Now, new instrument designs have narrowed the differences, and test chemistry can have a comparatively large impact on analytical accuracy (Figure 1). The robustness of technical performance can also vary significantly depending upon specimen type. For example, LifeTechnologies’ sequencing platforms appear to be comparatively more tolerant of low DNA quality and concentration, which is an important consideration for fixed and processed tissues.

https://www.aacc.org/~/media/images/cln/articles/2015/october/carter_fig1_cln_oct15_ed.jpg

Figure 1 Comparison of Sequencing Chemistries

Sequence pile-ups of the same target sequence (2 large genes), all performed on the same analytical instrument. Results from 4 different chemistries, as designed and supplied by reagent manufacturers prior to optimization in the laboratory. Red lines represent limits of exons. Height of blue columns proportional to depth of coverage. In this case, the intent of the test design was to provide high depth of coverage so that reflex Sanger sequencing would not be necessary. Courtesy B. Sadikovic, U. of Western Ontario.

 

In addition, batching, robotics, workload volume patterns, maintenance contracts, software licenses, and platform lifetime affect the cost per analyte and per specimen considerably. Royalties and reagent contracts also factor into the cost of operating NGT: In some applications, fees for intellectual property can represent more than 50% of the bench cost of performing a given test, and increase substantially without warning.

Laboratories must also deal with the problem of obsolescence. Investing in a new platform brings the angst of knowing that better machines and chemistries are just around the corner. Laboratories are buying bigger pieces of equipment with shorter service lives. Before NGTs, major instruments could confidently be expected to remain current for at least 6 to 8 years. Now, a major instrument is obsolete much sooner, often within 2 to 3 years. This means that keeping it in service might cost more than investing in a new platform. Lease-purchase arrangements help mitigate year-to-year fluctuations in capital equipment costs, and maximize the value of old equipment at resale.

One Size Still Does Not Fit All

Laboratories face numerous technical considerations to optimize sequencing protocols, but the test has to be matched to the performance criteria needed for the clinical indication (2). For example, measuring response to treatment depends first upon the diagnostic recognition of mutation(s) in the tumor clone; the marker(s) then have to be quantifiable and indicative of tumor volume throughout the course of disease (Table 1).

As a result, diagnostic tests need to cover many different potential mutations, yet accurately identify any clinically relevant mutations actually present. On the other hand, tests for residual disease need to provide standardized, sensitive, and accurate quantification of a selected marker mutation against the normal background. A diagnostic panel might need 1% to 3% sensitivity across many different mutations. But quantifying early response to induction—and later assessment of minimal residual disease—needs a test that is reliably accurate to the 10-4 or 10-5 range for a specific analyte.

Covering all types of mutations in one diagnostic test is not yet possible. For example, subtyping of acute myeloid leukemia is both old school (karyotype, fluorescent in situ hybridization, and/or PCR-based or array-based testing for fusion rearrangements, deletions, and segmental gains) and new school (NGT-based panel testing for molecular mutations).

Chemistries that cover both structural variants and copy number variants are not yet in general use, but the advantages of NGTs compared to traditional methods are becoming clearer, such as in colorectal cancer (3). Researchers are also using cell-free DNA (cfDNA) to quantify residual disease and detect resistance mutations (4). Once a clinically significant clone is identified, enrichment techniques help enable extremely sensitive quantification of residual disease (5).

Validation and Quality Assurance

Beyond choosing a platform, two distinct challenges arise in bringing NGTs into the lab. The first is assembling the resources for validation and quality assurance. The second is keeping tests up-to-date as new analytes are needed. Even if a given test chemistry has the flexibility to add analytes without revalidating the entire panel, keeping up with clinical advances is a constant priority.

Due to their throughput and multiplexing capacities, NGT platforms typically require considerable upfront investment to adopt, and training staff to perform testing takes even more time. Proper validation is harder to document: Assembling positive controls, documenting test performance criteria, developing quality assurance protocols, and conducting proficiency testing are all demanding. Labs meet these challenges in different ways. Laboratory-developed tests (LDTs) allow self-determined choice in design, innovation, and control of the test protocol, but can be very expensive to set up.

Food and Drug Administration (FDA)-approved methods are attractive but not always an option. More FDA-approved methods will be marketed, but FDA approval itself brings other trade-offs. There is a cost premium compared to LDTs, and the test methodologies are locked down and not modifiable. This is particularly frustrating for NGTs, which have the specific attraction of extensive multiplexing capacity and accommodating new analytes.

IT and the Evolution of Molecular Oncology Reporting Standards

The options for information technology (IT) pipelines for NGTs are improving rapidly. At the same time, recent studies still show significant inconsistencies and lack of reproducibility when it comes to interpreting variants in array comparative genomic hybridization, panel testing, tumor expression profiling, and tumor genome sequencing. It can be difficult to duplicate published performances in clinical studies because of a lack of sufficient information about the protocol (chemistry) and software. Building bioinformatics capacity is a key requirement, yet skilled people are in short supply and the qualifications needed to work as a bioinformatician in a clinical service are not yet clearly defined.

Tumor biology brings another level of complexity. Bioinformatic analysis must distinguish tumor-specific­ variants from genomic variants. Sequencing of paired normal tissue is often performed as a control, but virtual normal controls may have intriguing advantages (6). One of the biggest challenges is to reproducibly interpret the clinical significance of interactions between different mutations, even with commonly known, well-defined mutations (7). For multiple analyte panels, such as predictive testing for breast cancer, only the performance of the whole panel in a population of patients can be compared; individual patients may be scored into different risk categories by different tests, all for the same test indication.

In large scale sequencing of tumor genomes, which types of mutations are most informative in detecting, quantifying, and predicting the behavior of the tumor over time? The amount and complexity of mutation varies considerably across different tumor types, and while some mutations are more common, stable, and clinically informative than others, the utility of a given tumor marker varies in different clinical situations. And, for a given tumor, treatment effect and metastasis leads to retesting for changes in drug sensitivities.

These complexities mean that IT must be designed into the process from the beginning. Like robotics, IT represents a major ancillary decision. One approach many labs choose is licensed technologies with shared databases that are updated in real time. These are attractive, despite their cost and licensing fees. New tests that incorporate proprietary IT with NGT platforms link the genetic signatures of tumors to clinically significant considerations like tumor classification, recommended methodologies for monitoring response, predicted drug sensitivities, eligible clinical trials, and prognostic classifications. In-house development of such solutions will be difficult, so licensing platforms from commercial partners is more likely to be the norm.

The Commercial Value of Health Records and Test Data

The future of cancer management likely rests on large-scale databases that link hereditary and somatic tumor testing with clinical outcomes. Multiple centers have such large studies underway, and data extraction and analysis is providing increasingly refined interpretations of clinical significance.

Extracting health outcomes to correlate with molecular test results is commercially valuable, as the pharmaceutical, insurance, and healthcare sectors focus on companion diagnostics, precision medicine, and evidence-based health technology assessment. Laboratories that can develop tests based on large-scale integration of test results to clinical utility will have an advantage.

NGTs do offer opportunities for net reductions in the cost of healthcare. But the lag between availability of a test and peer-evaluated demon­stration of clinical utility can be considerable. Technical developments arise faster than evidence of clinical utility. For example, immuno­histochemistry, estrogen receptor/progesterone receptor status, HER2/neu, and histology are still the major pathological criteria for prognostic evaluation of breast cancer at diagnosis, even though multiple analyte tumor profiling has been described for more than 15 years. Healthcare systems need a more concerted assessment of clinical utility if they are to take advantage of the promises of NGTs in cancer care.

Disruptive Advances

Without a doubt, “disruptive” is an appropriate buzzword in molecular oncology, and new technical advances are about to change how, where, and for whom testing is performed.

• Predictive Testing

Besides cost per analyte, one of the drivers for taking up new technologies is that they enable multiplexing many more analytes with less biopsy material. Single-analyte sequential testing for epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase, and other targets on small biopsies is not sustainable when many more analytes are needed, and even now, a significant proportion of test requests cannot be completed due to lack of suitable biopsy material. Large panels incorporating all the mutations needed to cover multiple tumor types are replacing individual tests in companion diagnostics.

• Cell-Free Tumor DNA

Challenges of cfDNA include standardizing the collection and processing methodologies, timing sampling to minimize the effect of therapeutic toxicity on analytical accuracy, and identifying the most informative sample (DNA, RNA, or protein). But for more and more tumor types, it will be possible to differentiate benign versus malignant lesions, perform molecular subtyping, predict response, monitor treatment, or screen for early detection—all without a surgical biopsy.

cfDNA technologies can also be integrated into core laboratory instrumentation. For example, blood-based EGFR analysis for lung cancer is being developed on the Roche cobas 4800 platform, which will be a significant change from the current standard of testing based upon single tests of DNA extracted from formalin-fixed, paraffin-embedded sections selected by a pathologist (8).

• Whole Genome and Whole Exome Sequencing

Whole genome and whole exome tumor sequencing approaches provide a wealth of biologically important information, and will replace individual or multiple gene test panels as the technical cost of sequencing declines and interpretive accuracy improves (9). Laboratories can apply informatics selectively or broadly to extract much more information at relatively little increase in cost, and the interpretation of individual analytes will be improved by the context of the whole sequence.

• Minimal Residual Disease Testing

Massive resequencing and enrichment techniques can be used to detect minimal residual disease, and will provide an alternative to flow cytometry as costs decline. The challenge is to develop robust analytical platforms that can reliably produce results in a high proportion of patients with a given tumor type, despite using post-treatment specimens with therapy-induced degradation, and a very low proportion of target (tumor) sequence to benign background sequence.

The tumor markers should remain informative for the burden of disease despite clonal evolution over the course of multiple samples taken during progression of the clinical course and treatment. Quantification needs to be accurate and sensitive down to the 10-5 range, and cost competitive with flow cytometry.

• Point-of-Care Test Methodologies

Small, rapid, cheap, and single use point-of-care (POC) sequencing devices are coming. Some can multiplex with analytical times as short as 20 minutes. Accurate and timely testing will be possible in places like pharmacies, oncology clinics, patient service centers, and outreach programs. Whether physicians will trust and act on POC results alone, or will require confirmation by traditional laboratory-based testing, remains to be seen. However, in the simplest type of application, such as a patient known to have a particular mutation, the advantages of POC-based testing to quantify residual tumor burden are clear.

Conclusion

Molecular oncology is moving rapidly from an esoteric niche of diagnostics to a mainstream, required component of integrated clinical laboratory services. While NGTs are markedly reducing the cost per analyte and per specimen, and will certainly broaden the scope and volume of testing performed, the resources required to choose, install, and validate these new technologies are daunting for smaller labs. More rapid obsolescence and increased regulatory scrutiny for LDTs also present significant challenges. Aligning test capacity with approved clinical indications will require careful and constant attention to ensure competitiveness.

References

1. Liu L, Li Y, Li S, et al. Comparison of next-generation sequencing systems. J Biomed Biotechnol 2012; doi:10.1155/2012/251364.

2. Brownstein CA, Beggs AH, Homer N, et al. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biol 2014;15:R53.

3. Haley L, Tseng LH, Zheng G, et al. Performance characteristics of next-generation sequencing in clinical mutation detection of colorectal ­cancers. [Epub ahead of print] Modern Pathol July 31, 2015 as doi:10.1038/modpathol.2015.86.

4. Butler TM, Johnson-Camacho K, Peto M, et al. Exome sequencing of cell-free DNA from metastatic cancer patients identifies clinically actionable mutations distinct from primary ­disease. PLoS One 2015;10:e0136407.

5. Castellanos-Rizaldos E, Milbury CA, Guha M, et al. COLD-PCR enriches low-level variant DNA sequences and increases the sensitivity of genetic testing. Methods Mol Biol 2014;1102:623–39.

6. Hiltemann S, Jenster G, Trapman J, et al. Discriminating somatic and germline mutations in tumor DNA samples without matching normals. Genome Res 2015;25:1382–90.

7. Lammers PE, Lovly CM, Horn L. A patient with metastatic lung adenocarcinoma harboring concurrent EGFR L858R, EGFR germline T790M, and PIK3CA mutations: The challenge of interpreting results of comprehensive mutational testing in lung cancer. J Natl Compr Canc Netw 2015;12:6–11.

8. Weber B, Meldgaard P, Hager H, et al. Detection of EGFR mutations in plasma and biopsies from non-small cell lung cancer patients by allele-specific PCR assays. BMC Cancer 2014;14:294.

9. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science 2013;339:1546–58.

10. Heitzer E, Auer M, Gasch C, et al. Complex tumor genomes inferred from single circulating tumor cells by array-CGH and next-generation sequencing. Cancer Res 2013;73:2965–75.

11. Healy B. BRCA genes — Bookmaking, fortunetelling, and medical care. N Engl J Med 1997;336:1448–9.

 

 

 

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Biomarker Development

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

Biomarker Development

Curator: Larry H. Bernstein, MD, FCAP

 

 

NBDA’s Biomarker R&D Modules

http://nbdabiomarkers.org/

“collaboratively creating the NBDA Standards* required for end-to-end, evidence – based biomarker development to advance precision (personalized) medicine”

http://nbdabiomarkers.org/sites/all/themes/nbda/images/nbda_logo.jpg

http://nbdabiomarkers.org/about/what-we-do/pipeline-overview/assay-development

 

Successful biomarkers should move systematically and seamlessly through specific R&D “modules” – from early discovery to clinical validation. NBDA’s end-to-end systems approach is based on working with experts from all affected multi-sector stakeholder communities to build an in-depth understanding of the existing barriers in each of these “modules” to support decision making at each juncture.  Following extensive “due diligence” the NBDA works with all stakeholders to assemble and/or create the enabling standards (guidelines, best practices, SOPs) needed to support clinically relevant and robust biomarker development.

Mission: Collaboratively creating the NBDA Standards* required for end-to-end, evidence – based biomarker development to advance precision (personalized) medicine.
NBDA Standards include but are not limited to: “official existing standards”, guidelines, principles, standard operating procedures (SOP), and best practices.

https://vimeo.com/83266065

 

“The NBDA’s vision is not to just relegate the current biomarker development processes to history, but also to serve as a working example of what convergence of purpose, scientific knowledge and collaboration can accomplish.”

NBDA Workshop VII – “COLLABORATIVELY BUILDING A FOUNDATION FOR FDA BIOMARKER QUALIFICATION”
NBDA Workshop VII   December 14-15, 2015   Washington Court Hotel, Washington, DC

The upcoming meeting was preceded by an NBDA workshop held on December 1-2, 2014, “The Promising but Elusive Surrogate Endpoint:  What Will It Take?” where we explored in-depth with FDA leadership and experts in the field the current status and future vison for achieving success in surrogate endpoint development.  Through panels and workgroups, the attendees extended their efforts to pursue the FDA’s biomarker qualification pathway through the creation of sequential contexts of use models to support qualification of drug development tools – and ultimately surrogate endpoints.

Although the biomarker (drug development tools) qualification pathway (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentTools…) represents an opportunity to increase the value of predictive biomarkers, animal models, and clinical outcomes across the drug (and biologics) development continuum, there are myriad challenges.  In that regard, the lack of evidentiary standards to support contexts of use-specific biomarkers emerged from the prior NBDA workshop as the major barrier to achieving the promise of biomarker qualification.  It also became clear that overall, the communities do not understand the biomarker qualification process; nor do they fully appreciate that it is up to the stakeholders in the field (academia, non-profit foundations, pharmaceutical and biotechnology companies, and patient advocate organizations) to develop these evidentiary standards.

This NBDA workshop will feature a unique approach to address these problems.  Over the past two years, the NBDA has worked with experts in selected disease areas to develop specific case studies that feature a systematic approach to identifying the evidentiary standards needed for sequential contexts of use for specific biomarkers to drive biomarker qualification.   These constructs, and accompanying whitepapers are now the focus of collaborative discussions with FDA experts.

The upcoming meeting will feature in-depth panel discussions of 3-4 of these cases, including the case leader, additional technical contributors, and a number of FDA experts.  Each of the panels will analyze their respective case for strengths and weaknesses – including suggestions for making the biomarker qualification path for the specific biomarker more transparent and efficient. In addition, the discussions will highlight the problem of poor reproducibility of biomarker discovery results, and its impact on the qualification process.

 

Health Care in the Digital Age

Mobile, big data, the Internet of Things and social media are leading a revolution that is transforming opportunities in health care and research. Extraordinary advancements in mobile technology and connectivity have provided the foundation needed to dramatically change the way health care is practiced today and research is done tomorrow. While we are still in the early innings of using mobile technology in the delivery of health care, evidence supporting its potential to impact the delivery of better health care, lower costs and improve patient outcomes is apparent. Mobile technology for health care, or mHealth, can empower doctors to more effectively engage their patients and provide secure information on demand, anytime and anywhere. Patients demand safety, speed and security from their providers. What are the technologies that are allowing this transformation to take place?

 

https://youtu.be/WeXEa2cL3oA    Monday, April 27, 2015  Milken Institute

Moderator


Michael Milken, Chairman, Milken Institute

 

Speakers


Anna Barker, Fellow, FasterCures, a Center of the Milken Institute; Professor and Director, Transformative Healthcare Networks, and Co-Director, Complex Adaptive Systems Network, Arizona State University
Atul Butte, Director, Institute of Computational Health Sciences, University of California, San Francisco
John Chen, Executive Chairman and CEO, BlackBerry
Victor Dzau, President, Institute of Medicine, National Academy of Sciences; Chancellor Emeritus, Duke University
Patrick Soon-Shiong, Chairman and CEO, NantWorks, LLC

 

Mobile, big data, the Internet of Things and social media are leading a revolution that is transforming opportunities in health care and research. Extraordinary advancements in mobile technology and connectivity have provided the foundation needed to dramatically change the way health care is practiced today and research is done tomorrow. While we are still in the early innings of using mobile technology in the delivery of health care, evidence supporting its potential to impact the delivery of better health care, lower costs and improve patient outcomes is apparent. Mobile technology for health care, or mHealth, can empower doctors to more effectively engage their patients and provide secure information on demand, anytime and anywhere. Patients demand safety, speed and security from their providers. What are the technologies that are allowing this transformation to take place?

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Cancer Companion Diagnostics

Curator: Larry H. Bernstein, MD, FCAP

 

Companion Diagnostics for Cancer: Will NGS Play a Role?

Patricia Fitzpatrick Dimond, Ph.D.

http://www.genengnews.com/insight-and-intelligence/companion-diagnostics-for-cancer/77900554/

Companion diagnostics (CDx), in vitro diagnostic devices or imaging tools that provide information essential to the safe and effective use of a corresponding therapeutic product, have become indispensable tools for oncologists.  As a result, analysts expect the global CDx market to reach $8.73 billion by 2019, up from from $3.14 billion in 2014.

Use of CDx during a clinical trial to guide therapy can improve treatment responses and patient outcomes by identifying and predicting patient subpopulations most likely to respond to a given treatment.

These tests not only indicate the presence of a molecular target, but can also reveal the off-target effects of a therapeutic, predicting toxicities and adverse effects associated with a drug.

For pharma manufacturers, using CDx during drug development improves the success rate of drugs being tested in clinical trials. In a study estimating the risk of clinical trial failure during non-small cell lung cancer drug development in the period between 1998 and 2012 investigators analyzed trial data from 676 clinical trials with 199 unique drug compounds.

The data showed that Phase III trial failure proved the biggest obstacle to drug approval, with an overall success rate of only 28%. But in biomarker-guided trials, the success rate reached 62%. The investigators concluded from their data analysis that the use of a CDx assay during Phase III drug development substantially improves a drug’s chances of clinical success.

The Regulatory Perspective

According to Patricia Keegen, M.D., supervisory medical officer in the FDA’s Division of Oncology Products II, the agency requires a companion diagnostic test if a new drug works on a specific genetic or biological target that is present in some, but not all, patients with a certain cancer or disease. The test identifies individuals who would benefit from the treatment, and may identify patients who would not benefit but could also be harmed by use of a certain drug for treatment of their disease. The agency classifies companion diagnosis as Class III devices, a class of devices requiring the most stringent approval for medical devices by the FDA, a Premarket Approval Application (PMA).

On August 6, 2014, the FDA finalized its long-awaited “Guidance for Industry and FDA Staff: In Vitro Companion Diagnostic Devices,” originally issued in July 2011. The final guidance stipulates that FDA generally will not approve any therapeutic product that requires an IVD companion diagnostic device for its safe and effective use before the IVD companion diagnostic device is approved or cleared for that indication.

Close collaboration between drug developers and diagnostics companies has been a key driver in recent simultaneous pharmaceutical-CDx FDA approvals, and partnerships between in vitro diagnostics (IVD) companies have proliferated as a result.  Major test developers include Roche Diagnostics, Abbott Laboratories, Agilent Technologies, QIAGEN), Thermo Fisher Scientific, and Myriad Genetics.

But an NGS-based test has yet to make it to market as a CDx for cancer.  All approved tests include PCR–based tests, immunohistochemistry, and in situ hybridization technology.  And despite the very recent decision by the FDA to grant marketing authorization for Illumina’s MiSeqDx instrument platform for screening and diagnosis of cystic fibrosis, “There still seems to be a number of challenges that must be overcome before we see NGS for targeted cancer drugs,” commented Jan Trøst Jørgensen, a consultant to DAKO, commenting on presentations at the European Symposium of Biopathology in June 2013.

Illumina received premarket clearance from the FDA for its MiSeqDx system, two cystic fibrosis assays, and a library prep kit that enables laboratories to develop their own diagnostic test. The designation marked the first time a next-generation sequencing system received FDA premarket clearance. The FDA reviewed the Illumina MiSeqDx instrument platform through its de novo classification process, a regulatory pathway for some novel low-to-moderate risk medical devices that are not substantially equivalent to an already legally marketed device.

Dr. Jørgensen further noted that “We are slowly moving away from the ‘one biomarker: one drug’ scenario, which has characterized the first decades of targeted cancer drug development, toward a more integrated approach with multiple biomarkers and drugs. This ‘new paradigm’ will likely pave the way for the introduction of multiplexing strategies in the clinic using gene expression arrays and next-generation sequencing.”

The future of CDxs therefore may be heading in the same direction as cancer therapy, aimed at staying ahead of the tumor drug resistance curve, and acknowledging the reality of the shifting genomic landscape of individual tumors. In some cases, NGS will be applied to diseases for which a non-sequencing CDx has already been approved.

Illumina believes that NGS presents an ideal solution to transforming the tumor profiling paradigm from a series of single gene tests to a multi-analyte approach to delivering precision oncology. Mya Thomae, Illumina’s vice president, regulatory affairs, said in a statement that Illumina has formed partnerships with several drug companies to develop a universal next-generation sequencing-based oncology test system. The collaborations with AstraZeneca, Janssen, Sanofi, and Merck-Serono, announced in 2014 and 2015 respectively, seek to  “redefine companion diagnostics for oncology  focused on developing a system for use in targeted therapy clinical trials with a goal of developing and commercializing a multigene panel for therapeutic selection.”

On January 16, 2014 Illumina and Amgen announced that they would collaborate on the development of a next-generation sequencing-based companion diagnostic for colorectal cancer antibody Vectibix (panitumumab). Illumina will develop the companion test on its MiSeqDx instrument.

In 2012, the agency approved Qiagen’s Therascreen KRAS RGQ PCR Kit to identify best responders to Erbitux (cetuximab), another antibody drug in the same class as Vectibix. The label for Vectibix, an EGFR-inhibiting monoclonal antibody, restricts the use of the drug for those metastatic colorectal cancer patients who harbor KRAS mutations or whose KRAS status is unknown.

The U.S. FDA, Illumina said, hasn’t yet approved a companion diagnostic that gauges KRAS mutation status specifically in those considering treatment with Vectibix.  Illumina plans to gain regulatory approval in the U.S. and in Europe for an NGS-based companion test that can identify patients’ RAS mutation status. Illumina and Amgen will validate the test platform and Illumina will commercialize the test.

Treatment Options

Foundation Medicine says its approach to cancer genomic characterization will help physicians reveal the alterations driving the growth of a patient’s cancer and identify targeted treatment options that may not have been otherwise considered.

FoundationOne, the first clinical product from Foundation Medicine, interrogates the entire coding sequence of 315 cancer-related genes plus select introns from 28 genes often rearranged or altered in solid tumor cancers.  Based on current scientific and clinical literature, these genes are known to be somatically altered in solid cancers.

These genes, the company says, are sequenced at great depth to identify the relevant, actionable somatic alterations, including single base pair change, insertions, deletions, copy number alterations, and selected fusions. The resultant fully informative genomic profile complements traditional cancer treatment decision tools and often expands treatment options by matching each patient with targeted therapies and clinical trials relevant to the molecular changes in their tumors.

As Foundation Medicine’ s NGS analyses are increasingly applied, recent clinical reports describe instances in which comprehensive genomic profiling with the FoundationOne NGS-based assay result in diagnostic reclassification that can lead to targeted drug therapy with a resulting dramatic clinical response. In several reported instances, NGS found, among the spectrum of aberrations that occur in tumors, changes unlikely to have been discovered by other means, and clearly outside the range of a conventional CDx that matches one drug to a specific genetic change.

TRK Fusion Cancer

In July 2015, the University of Colorado Cancer Center and Loxo Oncology published a research brief in the online edition of Cancer Discovery describing the first patient with a tropomyosin receptor kinase (TRK) fusion cancer enrolled in a LOXO-101 Phase I trial. LOXO-101 is an orally administered inhibitor of the TRK kinase and is highly selective only for the TRK family of receptors.

While the authors say TRK fusions occur rarely, they occur in a diverse spectrum of tumor histologies. The research brief described a patient with advanced soft tissue sarcoma widely metastatic to the lungs. The patient’s physician submitted a tumor specimen to Foundation Medicine for comprehensive genomic profiling with FoundationOne Heme, where her cancer was demonstrated to harbor a TRK gene fusion.

Following multiple unsuccessful courses of treatment, the patient was enrolled in the Phase I trial of LOXO-101 in March 2015. After four months of treatment, CT scans demonstrated almost complete tumor disappearance of the largest tumors.

The FDA’s Elizabeth Mansfield, Ph.D., director, personalized medicine staff, Office of In Vitro Diagnostics and Radiological Health, said in a recent article,  “FDA Perspective on Companion Diagnostics: An Evolving Paradigm” that “even as it seems that many questions about co-development have been resolved, the rapid accumulation of new knowledge about tumor biology and the rapid evolution of diagnostic technology are challenging FDA to continually redefine its thinking on companion diagnostics.” It seems almost inevitable that a consolidation of diagnostic testing should take place, to enable a single test or a few tests to garner all the necessary information for therapeutic decision making.”

Whether this means CDx testing will begin to incorporate NGS sequencing remains to be seen.

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Immune T-cell Regulation

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Team Taps Immune T-Cell Regulators to Find Early Markers of Disease in Blood

An international team led by investigators in Sweden team used a series of array-based expression and methylomic profiling experiments to put together a so-called gene regulatory network for T cells differentiating into four T helper cell groups.

NEW YORK (GenomeWeb) – The regulatory network controlling differentiation of the immune system’s T cells may contain markers of disease that differ in individuals’ blood samples even before symptoms appear, according to a new study in Science Translational Medicine.

A validated gene regulatory network and GWAS identifies early regulators of T cell–associated diseases

Diseases may be easier to treat if caught early. However, means of identifying early disease—especially before symptoms appear—are in short supply. Now, Gustafsson et al.identify early regulators of T cell–mediated disease by finding transcription factors involved in T cell differentiation that are enriched in disease-associated polymorphisms. Three such experimentally validated transcription factors—GATA3, MAF, and MYB—and their targets were found to be differentially expressed in asymptomatic stages of two different T cell–mediated diseases—multiple sclerosis and seasonal allergic rhinitis. These data not only provide potential markers of disease development but also shed light on the mechanistic underpinning of T cell–mediated disease.

By cross-referencing transcription factors from this analysis with information from past genome-wide association studies on common human diseases, the group narrowed in on three transcription factors showing disease-related differences in their expression, SNP profiles, and splice variant patterns.

To test their hypothesis that the transcription factors might offer a window on symptom development, the researchers then tracked their expression in the blood of individuals with two relapsing diseases: multiple sclerosis and seasonal allergic rhinitis. Indeed, their results pointed to distinct expression profiles in those with or without symptoms, suggesting similar T-cell regulators could help in finding, treating, or perhaps preventing other common diseases down the road.

“[We think] that different functional variants of these three transcription factors or their expression levels … can be used to predict many T cell-associated diseases with high accuracy,” senior author Mikael Benson, a physician researcher with Linköping University’s Centre for Individualized Medicine in Sweden, told GenomeWeb.

“But,” he cautioned, “to really make it clinically relevant, I think you have to perform more studies of at-risk populations for certain diseases and study many different types of molecules, like methylation and proteins, and pick the most discriminating molecules for discriminating purposes.”

For the most part, diseases aren’t successfully diagnosed until symptoms are obvious, creating problems for those tasked with trying to treat conditions that may already have caused irreparable damage, Benson noted. Along with suffering for patients, this treatment failure leads to health care costs associated with both ineffective drugs and new drug development.

“Ideally, you should actually start treatment for preventing disease before symptoms occur, early in the disease process,” he explained.

In their search for early blood markers of disease, Benson and his colleagues drew from their ongoing work on T cells — a group of white blood cells tasked with patrolling the body to find and stave off forms of disease ranging from metabolic conditions and heart disease to inflammatory conditions and cancer.

“If you can tap into that information — what’s happening in those T cells — ideally you could diagnose disease processes early before symptoms occur,” Benson said.

With that in mind, the researchers did array-based gene expression and methylation profiling on T cells over time as they differentiated into four subsets of T helper cells, hoping to find regulators relevant to early-stage disease.

After assessing such in vitro T-cell differentiation events at six time points in four replicate experiments, the researchers plugged their data into a mathematical model designed by first author Mika Gustafsson, also at Linköping University, to tease apart factors involved in T-cell differentiation regulation.

And from the set of transcription factors pinned to T cell differentiation, the team narrowed in further by folding in information on the genes and variants implicated in common human diseases through past GWAS.

The search led to three transcription factors — GATA3, MYB, and MAF — that were confirmed as T-cell regulators through the researchers’ subsequent chromatin immunoprecipitation sequencing, gene expression profiling, and gene knockdown experiments in differentiated T helper 1 and T helper 3 cells.

From publicly available gene expression data, the researchers found that these three transcription factors and/or their target genes were differentially expressed in T cell-associated diseases such as rheumatoid arthritis, acute myeloid leukemia, or systemic lupus erythematosus.

Meanwhile, their own experiments in T cell samples from individuals with seasonal allergic rhinitis, individuals with MS, and unaffected control individuals suggested splicing patterns and gene expression of the three transcription factor markers shifted as symptoms appear in each of the relapsing diseases.

While there may not be a clear clinical need to test for allergic seasonal rhinitis, Benson explained, he and his colleagues believe a similar approach could be used for finding informative markers in individuals at risk of certain diseases such as hereditary breast cancer, for example, to diagnose, treat, or attempt to prevent disease.

“A likely continuation would be to study various forms of common cancer in at-risk populations to show clinical feasibility,” he said. “We believe that it’s probably easier to find drugs that can stop earlier disease processes than established ones.”

More generally, Benson said, it would be ideal to start testing markers identified through these sorts of studies over decades in prospective studies of thousands or hundreds of thousands of individuals who start out healthy, using longitudinal blood samples such as those planned for President Obama’s Precision Medicine Initiative.

sjwilliamspa

Seems odd to me that they had done microarray as T cell differentiate. I would have felt this has been done with other methodology such as differential display. The finding of GATA3 does not seem unique though as this factor is commonly differenctially expressed as cells differentiate and is involved in the differentiation of many cell types. It only seems to me coincidental that a cross search in expression databases revealed GATA involved in disease states, which it is however does not seem a fair comparison. They would need to constitutively express GATA3 in an autologous transfer to see if mice develop symtomology of these diseases, which does not seem totally plausible.

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

Sequence the Human Genome

Curator: Larry H Bernstein, MD, FCAP

 

 

Geneticist Craig Venter helped sequence the human genome. Now he wants yours.

By CARL ZIMMER   NOVEMBER 5, 2015   http://www.statnews.com/2015/11/05/geneticist-craig-venter-helped-sequence-the-human-genome-now-he-wants-yours/

If you enter Health Nucleus, a new facility in San Diego cofounded by J. Craig Venter, one of the world’s best-known living scientists, you will get a telling glimpse into the state of medical science in 2015.

Your entire genome will be sequenced with extraordinary resolution and accuracy. Your body will be scanned in fine, three-dimensional detail. Thousands of compounds in your blood will be measured. Even the microbes that live inside you will be surveyed. You will get a custom-made iPad app to navigate data about yourself. Also, your wallet will be at least $25,000 lighter.

Venter, who came to the world’s attention in the 1990s when he led a campaign to produce the first draft of a human genome, launched Health Nucleus last month as part of his new company, Human Longevity. He has made clear that his aim is just as lofty as it was when he and his team sequenced the human genome or built a flu vaccine from a genetic sequence delivered to them over the Internet.

“We’re trying to show the value of actual scientific data that can change people’s lives,” Venter told STAT in some of his most extensive remarks yet about the project. “Our goal is to interpret everything in the genome that we can.”

Still, the initiative is drawing deep suspicion among some doctors who question whether Venter’s existing tests can tell patients anything meaningful at all. In interviews, they said they see Health Nucleus as the latest venture that could lead consumers to believe that more testing means improved health. That notion, they say, could drive customers to get procedures they don’t need, which might even be harmful.

“I think there is absolutely no evidence that any of those tests have any benefit for healthy people,” Dr. Rita Redberg, a cardiologist at the University of California at San Diego and the editor-in-chief of JAMA Internal Medicine, said when asked about Venter’s new project.

Venter has a black belt in media savvy — he can make the details of molecular biology alluring for viewers of 60 Minutes and TED talks alike — but off screen he has earned a reputation even from his critics for serious scientific achievements. His non-profit J. Craig Venter Institute, which he founded in 1992, now has a staff of 300. Scientists at the institute have explored everything from the ocean’s biodiversity to the Ebola virus.

Last year, at age 67, Venter cofounded Human Longevity, a company based in San Diego with branches in Mountain View, Calif., and Singapore that is building the largest human genome-sequencing operation on Earth, equipped with massive computing resources to analyze the data being generated. The firm’s database now contains highly accurate genome sequences from 20,000 people; another 3,000 genomes are being added each month.

Franz Och, the former head of Google Translate and an expert on machine learning, is leading a team that’s teaching computers to recognize patterns in the company’s databases that scientists themselves may not be able to see. To demonstrate the power of this approach, Human Longevity researchers are using machine learning to discover how genetic variations shape the human face.

“We can determine a good resemblance of your photograph straight from your genetic code,” said Venter.

Venter and his colleagues will be publishing the results of that study soon — most likely generating another round of headlines. But headlines don’t pay the bills, and at a company that’s got $70 million in funding from private investors, bills matter. The company is now exploring a number of avenues for generating income from its database. It has partnered with Discovery, an insurance company in England and South Africa, to read the DNA of their clients. For $250 apiece, it will sequence the protein-coding regions of the genome, known as exomes, and offer an interpretation of the data.

Health Nucleus could become yet another source of income for Human Longevity. The San Diego facility can handle eight to 12 people a day. There are plans to open more sites both in the United States and abroad. “You can do the math,” Venter said.

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Huge Data Network Bites into Cancer Genomics

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Closer to a Cure for Gastrointestinal Cancer

Suzanne Tracy, Editor-in-Chief, Scientific Computing and HPC Source
http://www.scientificcomputing.com/news/2015/11/closer-cure-gastrointestinal-cancer

In order to streamline workflows and keep pace with data-intensive discovery demands, CCS integrated its HPC environment with data capture and analytics capabilities, allowing data to move transparently between research steps, and driving discoveries such as a link between certain viruses and gastrointestinal cancers.

 

SANTA CLARA, CA — At the University of Miami’s Center for Computational Science (CCS), more than 2,000 internal researchers and a dozen expert collaborators across academic and industry sectors worldwide are working together in workflow management, data management, data mining, decision support, visualization and cloud computing. CCS maintains one of the largest centralized academic cyberinfrastructures in the country, which fuels vital and critical discoveries in Alzheimer’s, Parkinson’s, gastrointestinal cancer, paralysis and climate modeling, as well as marine and atmospheric science research.

In order to streamline workflows and keep pace with data-intensive discovery demands, CCS integrated its high performance computing (HPC) environment with data capture and analytics capabilities, allowing data to move transparently between research steps. To speed scientific discoveries and boost collaboration with researchers around the world, the center deployed high-performance DataDirect Networks (DDN) GS12K scale-out file storage. CCS now relies on GS12K storage to handle bandwidth-driven workloads while serving very high IOPS demand resulting from intense user interaction, which simplifies data capture and analysis. As a result, the center is able to capture, store and distribute massive amounts of data generated from multiple scientific models running different simulations on 15 Illumina HiSeq sequencers simultaneously on DDN storage. Moreover, number-crunching time for genome mapping and SNP calling has been reduced from 72 to 17 hours.

“DDN enabled us to analyze thousands of samples for the Cancer Genome Atlas, which amounts to nearly a petabyte of data,” explained Dr. Nicholas Tsinoremas, director of the Center for Computational Sciences at the University of Miami. “Having a robust storage platform like DDN is essential to driving discoveries, such as our recent study that revealed a link between certain viruses and gastrointestinal cancers. Previously, we couldn’t have done that level of computation.”

In addition to providing significant storage processing power to meet both high I/O and interactive processing requirements, CCS needed a flexible file system that could support large parallel and short serial jobs. The center also needed to address “data in flight” challenges that result from major data surges during analysis, and which often cause a 10x spike in storage. The system’s performance for genomics assembly, alignment and mapping is enabling CCS to support all its application needs, including the use of BWA and Bowtie for initial mapping, as well as SamTools and GATK for variant analysis and SNP calling.

“Our arrangement is to share data or make it available to anyone asking, anywhere in the world,” added Tsinoremas. “Now, we have the storage versatility to attract researchers from both within and outside the HPC community … we’re well-positioned to generate, analyze and integrate all types of research data to drive major scientific discoveries and breakthroughs.”

About DDN

DataDirect Networks is a big data storage supplier to data-intensive, global organizations. For more than 15 years, the company has designed, developed, deployed and optimized systems, software and solutions that enable enterprises, service providers, universities and government agencies to generate more value and to accelerate time to insight from their data and information, on premise and in the cloud. Organizations leverage DDN technology and the technical expertise of its team to capture, store, process, analyze, collaborate and distribute data, information and content at largest scale in the most efficient, reliable and cost effective manner. DDN customers include financial services firms and banks, healthcare and life science organizations, manufacturing and energy companies, government and research facilities, and web and cloud service providers.

 

“Where DDN really stood out is in the ability to adapt to whatever we would need. We have both IOPS-centric storage and the deep, slower I/O pool at full bandwidth. No one else could do that.”

Joel P. Zysman

Director of High Performance Computing

Center for Computational Science at the University of Miami

The University of Miami maintains one of the largest centralized, academic, cyber infrastructures in the US, which is integral to addressing and solving major scientific challenges. At its Center for Computational Science (CCS), more than 2,000 researchers, faculty, staff and students across multiple disciplines collaborate on diverse and interdisciplinary projects requiring HPC resources.

With 50% of the center’s users come from University of Miami’s Miller School of Medicine with ongoing projects at the Hussman Institute for Human Genomics, the explosion of next-generation sequencing has had a major impact on compute and storage demands. At CCS, the heavy I/O required to create four billion reads from one genome in a couple of days only intensifies when the data from the reads needs to be managed and analyzed

Aside from providing sufficient storage power to meet both high I/O and interactive processing demands, CCS needed a powerful file system that was flexible enough to handle very large parallel jobs as well as smaller, shorter serial jobs. CCS also needed to address as much as 10X spikes in storage, so it was critical to scale and support petabytes of machine-generated data without adding a layer of complexity or creating inefficiencies.

Read their success story to learn how high-performance DDN® Storage I/O has helped the University of Miami:

  • Establish links between certain viruses and gastrointestinal cancers discovered with computation that were not possible before
  • Reduce genomics compute and analysis time from 72 to 17 hours
CHALLENGES

  • Diverse, interdisciplinary research projects required massive compute and storage power as well as integrated data lifecycle movement and management
  • Highly demanding I/O and heavy interactivity requirements from next-gen sequencing intensified data generation, analysis and management
  • Handle large parallel jobs and smaller, shorter serial jobs
  • Data surges during analysis created “data-in-flight” challenges

SOLUTION

An end-to-end, high performance DDN GRIDScaler® solution featuring a GS12K™ scale-out appliance with an embedded IBM® GPFS™ parallel file system

TECHNICAL BENEFITS

  • Centralized storage with an embedded file system makes it easy to add storage where needed—in the high-performance, high-transaction or slower storage pools—and then manage it all through a single pane of glass
  • DDN’s transparent data movement enables using one platform for data capture, download, analysis and retention
  • The ability to maintain an active archive of storage lets the center accommodate different types of analytics with varied I/O needs

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Renal Cell Carcinoma Classified

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

TCGA Analysis Points to Kidney Cancer Subtypes With Prognosis, Treatment Clues

NEW YORK (GenomeWeb) – Members of the Cancer Genome Atlas Research Network have described two molecularly distinct types within the kidney cancer papillary renal cell carcinoma, including one group containing three prognostically informative subtypes.

As they reported in the New England Journal of Medicine last night, the researchers used whole-exome sequencing, transcriptome sequencing, microRNA sequencing, proteomic analyses, and array-based methylation and copy number profiling to characterize 161 primary papillary renal cell carcinomas.

Bringing these data together, the researchers defined two main papillary renal cell carcinoma groups: type 1 tumors, which were frequently marked by glitches in the MET gene pathway, and type 2 tumors, which fell into three further subtypes with variable molecular features and patient outcomes.

The findings “really help us understand the phenotypes of sporadic papillary kidney cancer,” corresponding author Marston Linehan, a urologic oncology researcher with the National Cancer Institute, told GenomeWeb. “It also confirms that type 1 and type 2 [papillary renal cell carcinoma] really are two very separate diseases.”

It’s expected that molecular patterns within these groups could help predict disease aggressiveness in papillary renal cell carcinoma patients and, in some cases, highlight possible treatment targets.

 

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma

The Cancer Genome Atlas Research Network Group

NEJM   Nov 4, 2015     DOI: http://dx.doi.org:/10.1056/NEJMoa1505917

Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.

Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with METalterations, whereas type 2 tumors were characterized by CDKN2Asilencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2–antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH).

Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.)

Figure 1. Somatic Alterations in Papillary Renal-Cell Carcinoma and Molecular Differences between Type 1 and Type 2 Cancers.

http://www.nejm.org/na101/home/literatum/publisher/mms/journals/content/nejm/0/nejm.ahead-of-print/nejmoa1505917/20151104-02/images/small/nejmoa1505917_f1.gif

Unsupervised clustering of DNA copy profiles of 161 papillary renal-cell carcinomas (PRCCs) (Panel A) revealed three molecular subtypes, one of which was highly enriched for type 1 tumors and the other two for type 2 tumors. SCNA denotes somatic copy-number alterations. Significantly mutated genes (SMGs) in PRCC (Panel B) were determined by considering all genes (q<0.1 [range, 0.0 to 1.0]) or focusing on the set of 260 genes previously implicated in cancer by large-scale, pan-cancer exome analyses15 (q<0.1). P values were calculated with the MutSigCV algorithm, version 2.0. A pathway-centric view of gene mutations in PRCC (Panel C) shows key pathways and genes implicated in cancer, either in the current study or elsewhere.15 The tumors were classified according to histologic type (from left to right) and according to gene or pathway altered (from top to bottom). Pathways and genes represented include MET, the Hippo pathway (NF2, SAV1, and WWC1), the NRF2 pathway (NFE2L2, KEAP1, CUL3, SIRT1, and FH), chromatin modification (CREBBP, DOTL1, EHMT1/2, EP300, EZH1/2, KAT2A/B, KDM1A/B, KDM4A/B, KDM5A/B/C, KDM6A/B, MLL1/2/3/4/5, NSD1, SETD2, SMYD4, and SRCAP), the SWI/SNF complex (ACTB, ACTL6A/B, ARID1A/B, ARID2, BCL6A/B/C, BCL11A/B, BRD7/9, DPF1/2/3, PHF10, PBRM1, SMARCA2/4

Figure 2. Alterations in Papillary Renal-Cell Carcinoma Involving the MET Oncogene.

http://www.nejm.org/na101/home/literatum/publisher/mms/journals/content/nejm/0/nejm.ahead-of-print/nejmoa1505917/20151104-02/images/small/nejmoa1505917_f2.gif

Panel A is a schematic representation of somatic mutations in MET, along with germline variant H1112R, which was previously implicated in hereditary papillary renal-cell carcinoma,17 and the novel RNA transcript variant of MET lacking the canonical exons 1 and 2 but containing a novel exon 1 that splices to the canonical exon 3. IPT denotes immunoglobulin-like, plexins, and transcription factors, and PSI plexins, semaphorins, and integrins. Panel B shows the crystal structure for the MET tyrosine kinase catalytic domain (RCSB-PDB 3I5 N18), on which are mapped the residues that are altered in papillary renal-cell carcinoma. All numbering of amino acids is based on the MET protein sequences.

We used a comprehensive genomics approach to characterize the biologic foundation of papillary renal-cell carcinoma and found that type 1 and type 2 papillary renal-cell carcinoma are distinctly different diseases and that type 2 papillary renal-cell carcinoma is a heterogeneous disease with multiple distinct subgroups. Common driver mutations among the different subtypes were relatively rare, as had been observed in two recent studies.7,30 Molecular and phenotypic differences between type 1 and type 2 papillary renal-cell carcinoma were reflected in individual and combined analyses of various data platforms. The usefulness of CDKN2A alterations as an independent prognostic marker associated with type 2 tumors requires validation. This study suggests that gene fusions involving TFE3 or TFEB are underappreciated in type 2 tumors in adults and should be considered in any patient with type 2 disease. Although papillary renal-cell carcinomas with fusions involving TFE3 or TFEB are generally considered to be diseases of children and young adults,16the mean age in our study was 52 years, and we found tumors with TFEB fusions in patients 64 and 71 years of age.

The most distinct of the three type 2 subgroups was the subgroup defined by the CIMP, which was associated with the worst overall survival. CIMP hypermethylation patterns have been observed in a number of other cancer subtypes, including glioblastoma,31 lung adenocarcinoma,32 and gastric adenocarcinoma.33 The CIMP-associated tumors showed low levels of FH mRNA expression, and five had germline or somatic mutation of FH. Germline mutation of FH has been observed in the aggressive type 2 tumor associated with the hereditary leiomyomatosis and renal-cell cancer syndrome.9,34 In this syndrome, the high levels of fumarate accumulating from loss of fumarate hydratase enzyme activity result in impaired function of enzymes such as the TET family of enzymes, which play a role in maintaining appropriate DNA methylation within the genome.35 The subgrouping of type 2 tumors according to molecular features and the presence of specific subsets of type 2 tumors, such as those with TFE3 fusions or CIMP, suggest that substratification of type 2 papillary renal-cell carcinoma according to specific molecular markers may allow more accurate diagnosis that could lead to the development of mechanistic, disease-specific targeted therapies.

This classification of papillary renal-cell carcinoma could potentially have a substantial effect on clinical and therapeutic management and on the design of clinical trials. Alteration of MET or gain of chromosome 7 was observed in a large percentage (81%) of type 1 tumors. Antitumor activity of an agent targeting the MET and VEGFR2 pathways has been shown in a phase 2 trial involving patients with papillary renal-cell carcinoma, with a particularly high response rate among patients who had tumors with MET mutations.36 Mutation of the Hippo pathway tumor suppressor, NF2, was observed in a number of papillary renal-cell carcinomas. This pathway has been targeted in other cancers with agents such as dasatinib, an inhibitor of the YES1 kinase that interacts with the YAP transcription factor that is up-regulated with Hippo pathway dysregulation.37 The CIMP-associated tumors showed a Warburg-like metabolic shift, similar to that observed in fumarate hydratase–deficient tumors in patients with the hereditary leiomyomatosis and renal-cell cancer syndrome.11,25,26 A clinical trial targeting this metabolic shift in papillary renal-cell carcinoma is currently under way (ClinicalTrials.gov number, NCT01130519). Increased expression of the NRF2-ARE pathway has been observed in both hereditary and sporadic type 2 papillary renal-cell carcinomas.12 Immunohistochemical analysis for NQO1 could provide a valuable marker of activation of the NRF2-ARE pathway. Currently, there is intense interest in the NRF2-ARE pathway in cancer,38 and novel strategies have recently been developed to target this pathway.39

The identification of altered genes and pathways provides a comprehensive foundation for an understanding of the molecular basis of papillary renal-cell carcinoma. This refined classification more accurately reflects the genotypic and phenotypic differences among the various types of these tumors and may lead to more appropriate clinical management and development of more effective forms of therapy.

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Inadequacy of EHRs

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

EHRs need better workflows, less ‘chartjunk’

By Marla Durben Hirsch

Electronic health records currently handle data poorly and should be enhanced to better collect, display and use it to support clinical care, according to a new study published in JMIR Medical Informatics.

The authors, from Beth Israel Deaconess Medical Center and elsewhere, state that the next generation of EHRs need to improve workflow, clinical decision-making and clinical notes. They decry some of the problems with existing EHRs, including data that is not displayed well, under-networked, underutilized and wasted. The lack of available data causes errors, creates inefficiencies and increases costs. Data is also “thoughtlessly carried forward or copied and pasted into the current note” creating “chartjunk,” the researchers say.

They suggest ways that future EHRs can be improved, including:

  • Integrating bedside and telemetry monitoring systems with EHRs to provide data analytics that could support real time clinical assessments
  • Formulating notes in real-time using structured data and natural language processing on the free text being entered
  • Formulating treatment plans using information in the EHR plus a review of population data bases to identify similar patients, their treatments and outcomes
  • Creating a more “intelligent” design that capitalizes on the note writing process as well as the contents of the note.

“We have begun to recognize the power of data in other domains and are beginning to apply it to the clinical space, applying digitization as a necessary but insufficient tool for this purpose,” the researchers say. “The vast amount of information and clinical choices demands that we provide better supports for making decisions and effectively documenting them.”

Many have pointed out the flaws in current EHR design that impede the optimum use of data and hinder workflow. Researchers have suggested that EHRs can be part of a learning health system to better capture and use data to improve clinical practice, create new evidence, educate, and support research efforts.

 

Disrupting Electronic Health Records Systems: The Next Generation

1Beth Israel Deaconess Medical Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Boston, MA, US; 2Yale University, Yale-New Haven Hospital, Department of Pulmonary and Critical Care, New Haven, CT, US; 3Center for Urban Science and Progress, New York University, New York, NY, US; 4Center for Wireless Health, Departments of Anesthesiology and Neurological Surgery, University of Virginia, Charlottesville, VA, US

*these authors contributed equally

JMIR  23.10.15 Vol 3, No 4 (2015): Oct-Dec

This paper is in the following e-collection/theme issue:
Electronic Health Records
Clinical Information and Decision Making
Viewpoints on and Experiences with Digital Technologies in Health
Decision Support for Health Professionals

The health care system suffers from both inefficient and ineffective use of data. Data are suboptimally displayed to users, undernetworked, underutilized, and wasted. Errors, inefficiencies, and increased costs occur on the basis of unavailable data in a system that does not coordinate the exchange of information, or adequately support its use. Clinicians’ schedules are stretched to the limit and yet the system in which they work exerts little effort to streamline and support carefully engineered care processes. Information for decision-making is difficult to access in the context of hurried real-time workflows. This paper explores and addresses these issues to formulate an improved design for clinical workflow, information exchange, and decision making based on the use of electronic health records.   JMIR Med Inform 2015;3(4):e34   http://dx.doi.org:/10.2196/medinform.4192

KEYWORDS

Celi LA, Marshall JD, Lai Y, Stone DJ. Disrupting Electronic Health Records Systems: The Next Generation. JMIR Med Inform 2015;3(4):e34  DOI: 10.2196/medinform.4192  PMID: 26500106

Weed introduced the “Subjective, Objective, Assessment, and Plan” (SOAP) note in the late 1960s [1]. This note entails a high-level structure that supports the thought process that goes into decision-making: subjective data followed by ostensibly more reliable objective data employed to formulate an assessment and subsequent plan. The flow of information has not fundamentally changed since that time, but the complexities of the information, possible assessments, and therapeutic options certainly have greatly expanded. Clinicians have not heretofore created anything like an optimal data system for medicine [2,3]. Such a system is essential to streamline workflow and support decision-making rather than adding to the time and frustration of documentation [4].

What this optimal data system offers is not a radical departure from the traditional thought processes that go into the production of a thoughtful and useful note. However, in the current early stage digitized medical system, it is still incumbent on the decision maker/note creator to capture the relevant priors, and to some extent, digitally scramble to collect all the necessary updates. The capture of these priors is a particular challenge in an era where care is more frequently turned over among different caregivers than ever before. Finally, based on a familiarity of the disease pathophysiology, the medical literature and evidence-based medicine (EBM) resources, the user is tasked with creating an optimal plan based on that assessment. In this so-called digital age, the amount of memorization, search, and assembly can be minimized and positively supported by a well-engineered system purposefully designed to assist clinicians in note creation and, in the process, decision-making.

Since 2006, use of electronic health records (EHRs) by US physicians increased by over 160% with 78% of office-based physicians and 59% of hospitals having adopted an EHR by 2013 [5,6]. With implementation of federal incentive programs, a majority of EHRs were required to have some form of built-in clinical decision support tools by the end of 2012 with further requirements mandated as the Affordable Care Act (ACA) rolls out [7]. These requirements recognize the growing importance of standardization and systematization of clinical decision-making in the context of the rapidly changing, growing, and advancing field of medical knowledge. There are already EHRs and other technologies that exist, and some that are being implemented, that integrate clinical decision support into their functionality, but a more intelligent and supportive system can be designed that capitalizes on the note writing process itself. We should strive to optimize the note creation process as well as the contents of the note in order to best facilitate communication and care coordination. The following sections characterize the elements and functions of this decision support system (Figure 1).

http://medinform.jmir.org/article/viewFile/4192/1/68490

Figure 1. Clinician documentation with fully integrated data systems support. Prior notes and data are input for the following note and decisions. Machine analyzes input and displays suggested diagnoses and problem list, and test and treatment recommendations based on various levels of evidence: CPG – clinical practice guidelines, UTD – Up to Date®, DCDM – Dynamic Clinical Data Mining.View this figure

Incorporating Data

Overwhelmingly, the most important characteristic of the electronic note is its potential for the creation and reception of what we term “bidirectional data streams” to inform both decision-making and research. By bidirectional data exchange, we mean that electronic notes have the potential to provide data streams to the entirety of the EHR database and vice versa. The data from the note can be recorded, stored, accessed, retrieved, and mined for a variety of real-time and future uses. This process should be an automatic and intrinsic property of clinical information systems. The incoming data stream is currently produced by the data that is slated for import into the note according to the software requirements of the application and the locally available interfaces [8]. The provision of information from the note to the system has both short- and long-term benefits: in the short term, this information provides essential elements for functions such as benchmarking and quality reporting; and in the long term, the information provides the afferent arm of the learning system that will identify individualized best practices that can be applied to individual patients in future formulations of plans.

Current patient data should include all the electronically interfaced elements that are available and pertinent. In addition to the usual elements that may be imported into notes (eg, laboratory results and current medications), the data should include the immediate prior diagnoses and treatment items, so far as available (especially an issue for the first note in a care sequence such as in the ICU), the active problem list, as well as other updates such as imaging, other kinds of testing, and consultant input. Patient input data should be included after verification (eg, updated reviews of systems, allergies, actual medications being taken, past medical history, family history, substance use, social/travel history, and medical diary that may include data from medical devices). These data priors provide a starting point that is particularly critical for those note writers who are not especially (or at all) familiar with the patient. They represent historical (and yet dynamic) evidence intended to inform decision-making rather than “text” to be thoughtlessly carried forward or copied and pasted into the current note.

Although the amount and types of data collected are extremely important, how it is used and displayed are paramount. Many historical elements of note writing are inexcusably costly in terms of clinician time and effort when viewed at a level throughout the entire health care system. Redundant items such as laboratory results and copy-and-pasted nursing flow sheet data introduce a variety of “chartjunk” that clutters documentation and makes the identification of truly important information more difficult and potentially even introduces errors that are then propagated throughout the chart [9,10]. Electronic systems are poised to automatically capture the salient components of care so far as these values are interfaced into the system and can even generate an active problem list for the providers. With significant amounts of free text and “unstructured data” being entered, EHRs will need to incorporate more sophisticated processes such as natural language processing and machine learning to provide accurate interpretation of text entered by a variety of different users, from different sources, and in different formats, and then translated into structured data that can be analyzed by the system.

Optimally, a fully functional EHR would be able to provide useful predictive data analytics including the identification of patterns that characterize a patient’s normal physiologic state (thereby enabling detection of significant change from that state), as well as mapping of the predicted clinical trajectory, such as prognosis of patients with sepsis under a number of different clinical scenarios, and with the ability to suggest potential interventions to improve morbidity or mortality [11]. Genomic and other “-omic” information will eventually be useful in categorizing certain findings on the basis of individual susceptibilities to various clinical problems such as sepsis, auto-immune disease, and cancer, and in individualizing diagnostic and treatment recommendations. In addition, an embedded data analytic function will be able to recognize a constellation of relatively subtle changes that are difficult or impossible to detect, especially in the presence of chronic co-morbidities (eg, changes consistent with pulmonary embolism, which can be a subtle and difficult diagnosis in the presence of long standing heart and/or lung disease) [12,13].

The data presentation section must be thoughtfully displayed so that the user is not overwhelmed, but is still aware of what elements are available, and directed to those aspects that are most important. The user then has the tools at hand to construct the truly cognitive sections of the note: the assessment and plan. Data should be displayed in a fashion that efficiently and effectively provides a maximally informationally rich and minimally distracting graphic display. The fundamental principle should result in a thoughtfully planned data display created on the ethos of “just enough and no more,” as well as the incorporation of clinical elements such as severity, acuity, stability, and reversibility. In addition to the now classic teachings of Edward Tufte in this regard, a number of new data artists have entered the field [14]. There is room for much innovation and improvement in this area, as medicine transitions from paper to a digital format that provides enormous potential and capability for new types of displays.

Integrating the Monitors

Bedside and telemetry monitoring systems have become an element of the clinical information system but they do not yet interact with the EHR in a bidirectional fashion to provide decision support. In addition to the raw data elements, the monitors can provide data analytics that could support real-time clinical assessment as well as material for predictive purposes apart from the traditional noisy alarms [15,16]. It may be less apparent how the reverse stream (EHR to bedside monitor) would work, but the EHR can set the context for the interpretation of raw physiologic signals based on previously digitally captured vital signs, patient co-morbidities and current medications, as well as the acute clinical context.

In addition, the display could provide an indication of whether technically ”out of normal range” vital signs (or labs in the emergency screen described below) are actually “abnormal” for this particular patient. For example, a particular type of laboratory value for a patient may have been chronically out of normal range and not represent a change requiring acute investigation and/or treatment. This might be accomplished by displaying these types of ”normally abnormal” values in purple or green rather than red font for abnormal, or via some other designating graphic. The purple font (or whatever display mode was utilized) would designate the value as technically abnormal, but perhaps notcontextually abnormal. Such designations are particularly important for caregivers who are not familiar with the patient.

It also might be desirable to use a combination of accumulated historical data from the monitor and the EHR to formulate personalized alarm limits for each patient. Such personalized alarm limits would provide a smarter range of acceptable values for each patient and perhaps also act to reduce the unacceptable number of false positive alarms that currently plague bedside caregivers (and patients) [17]. These alarm limits would be dynamically based on the input data and subject to reformulation as circumstances changed. We realize that any venture into alarm settings becomes a regulatory and potentially medico-legal issue, but these intimidating factors should not be allowed to grind potentially beneficial innovations to a halt. For example, “hard” limits could be built into the alarm machine so that the custom alarm limits could not fall outside certain designated values.

Supporting the Formulation of the Assessment

Building on both prior and new, interfaced and manually entered data as described above, the next framework element would consist of the formulation of the note in real time. This would consist of structured data so far as available and feasible, but is more likely to require real-time natural language processing performed on the free text being entered. Different takes on this kind of templated structure have already been introduced into several electronic systems. These include note templates created for specific purposes such as end-of-life discussions, or documentation of cardiopulmonary arrest. The very nature of these note types provides a robust context for the content. We also recognize that these shorter and more directed types of notes are not likely to require the kind of extensive clinical decision support (CDS) from which an admission or daily progress note may benefit.

Until the developers of EHRs find a way to fit structured data selection seamlessly and transparently into workflow, we will have to do the best we can with the free text that we have available. While this is a bit clunky in terms of data utilization purposes, perhaps it is not totally undesirable, as free text inserts a needed narrative element into the otherwise storyless EHR environment. Medical care can be described as an ongoing story and free text conveys this story in a much more effective and interesting fashion than do selected structured data bits. Furthermore, stories tend to be more distinctive than lists of structured data entries, which sometimes seem to vary remarkably little from patient to patient. But to extract the necessary information, the computer still needs a processed interpretation of that text. More complex systems are being developed and actively researched to act more analogously to our own ”human” form of clinical problem solving [18], but until these systems are integrated into existing EHRs, clinicians may be able to help by being trained to minimize the potential confusion engendered by reducing completely unconstrained free text entries and/or utilizing some degree of standardization within the use of free text terminologies and contextual modifiers.

Employing the prior data (eg, diagnoses X, Y, Z from the previous note) and new data inputs (eg, laboratory results, imaging reports, and consultants’ recommendations) in conjunction with the assessment being entered, the system would have the capability to check for inconsistencies and omissions based on analysis of both prior and new entries. For example, a patient in the ICU has increasing temperature and heart rate, and decreasing oxygen saturation. These continuous variables are referenced against other patient features and risk factors to suggest the possibility that the patient has developed a pulmonary embolism or an infectious ventilator-associated complication. The system then displays these possible diagnoses within the working assessment screen with hyperlinks to the patient’s flow sheets and other data supporting the suggested problems (Figure 2). The formulation of the assessment is clearly not as potentially evidence-based as that of the plan; however, there should still be dynamic, automatic and rapid searches performed for pertinent supporting material in the formulation of the assessment. These would include the medical literature, including textbooks, online databases, and applications such as WebMD. The relevant literature that the system has identified, supporting the associations listed in the assessment and plan, can then be screened by the user for accuracy and pertinence to the specific clinical context. Another potentially useful CDS tool for assessment formulation is a modality we have termed dynamic clinical data mining (DCDM) [19]. DCDM draws upon the power of large sets of population health data to provide differential diagnoses associated with groupings or constellations of symptoms and findings. Similar to the process just described, the clinician would then have the ability to review and incorporate these suggestions or not.

An optional active search function would also be provided throughout the note creation process for additional flexibility—clinicians are already using search engines, but doing so sometimes in the absence of specific clinical search algorithms (eg, a generic search engine such Google). This may produce search results that are not always of the highest possible quality [20,21]. The EHR-embedded search engine would have its algorithm modified to meet the task as Google has done previously for its search engine [22]. The searchable TRIP database provides a search engine for high-quality clinical evidence, as do the search modalities within Up to Date, Dynamed, BMJ Clinical Evidence, and others [23,24].

http://medinform.jmir.org/article/viewFile/4192/1/68491

Figure 2. Mock visualization of symptoms, signs, laboratory results, and other data input and systems suggestion for differential diagnoses.

Supporting the Formulation of the Plan

With the assessment formulated, the system would then formulate a proposed plan using EBM inputs and DCDM refinements for issues lying outside EBM knowledge. Decision support for plan formulation would include items such as randomized control trials (RCTs), observational studies, clinical practice guidelines (CPGs), local guidelines, and other relevant elements (eg, Cochrane reviews). The system would provide these supporting modalities in a hierarchical fashion using evidence of the highest quality first before proceeding down the chain to lower quality evidence. Notably, RCT data are not available for the majority of specific clinical questions, or it is not applicable because the results cannot be generalized to the patient at hand due to the study’s inclusion and exclusion criteria [25]. Sufficiently reliable observational research data also may not be available, although we expect that the holes in the RCT literature will be increasingly filled by observational studies in the near future [16,26]. In the absence of pertinent evidence-based material, the system would include the functionality which we have termed DCDM, and our Stanford colleagues have termed the “green button” [19,27]. This still-theoretical process is described in detail in the references, but in brief, DCDM would utilize a search engine type of approach to examine a population database to identify similar patients on the basis of the information entered in the EHR. The prior treatments and outcomes of these historical patients would then be analyzed to present options for the care of the current patient that were, to a large degree, based on prior data. The efficacy of DCDM would depend on, among other factors, the availability of a sufficiently large population EHR database, or an open repository that would allow for the sharing of patient data between EHRs. This possibility is quickly becoming a reality with the advent of large, deidentified clinical databases such as that being created by the Patient Centered Outcomes Research Institute [26].

The tentative plan could then be modified by the user on the basis of her or his clinical “wetware” analysis. The electronic workflow could be designed in a number of ways that were modifiable per user choice/customization. For example, the user could first create the assessment and plan which would then be subject to comment and modification by the automatic system. This modification might include suggestions such as adding entirely new items, as well as the editing of entered items. In contrast, as described, the system could formulate an original assessment and plan that was subject to final editing by the user. In either case, the user would determine the final output, but the system would record both system and final user outputs for possible reporting purposes (eg, consistency with best practices). Another design approach might be to display the user entry in toto on the left half of a computer screen and a system-formulated assessment (Figure 3) and plan on the right side for comparison. Links would be provided throughout the system formulation so that the user could drill into EHR-provided suggestions for validation and further investigation and learning. In either type of workflow, the system would comparatively evaluate the final entered plan for consistency, completeness, and conformity with current best practices. The system could display the specific items that came under question and why. Users may proceed to adopt or not, with the option to justify their decision. Data reporting analytics could be formulated on the basis of compliance with EBM care. Such analytics should be done and interpreted with the knowledge that EBM itself is a moving target and many clinical situations do not lend themselves to resolution with the current tools supplied by EBM.

Since not all notes call for this kind of extensive decision support, the CDS material could be displayed in a separate columnar window adjacent to the main part of the screen where the note contents were displayed so that workflow is not affected. Another possibility would be an “opt-out” button by which the user would choose not to utilize these system resources. This would be analogous but functionally opposite to the “green button” opt-in option suggested by Longhurst et al, and perhaps be designated the “orange button” to clearly make this distinction [27]. Later, the system would make a determination as to whether this lack of EBM utilization was justified, and provide a reminder if the care was determined to be outside the bounds of current best practices. While the goal is to keep the user on the EBM track as much as feasible, the system has to “realize” that real care will still extend outside those bounds for some time, and that some notes and decisions simply do not require such machine support.

There are clearly still many details to be worked out regarding the creation and use of a fully integrated bidirectional EHR. There currently are smaller systems that use some components of what we propose. For example, a large Boston hospital uses a program called QPID which culls all previously collected patient data and uses a Google-like search to identify specific details of relevant prior medical history which is then displayed in a user-friendly fashion to assist the clinician in making real-time decisions on admission [28]. Another organization, the American Society of Clinical Oncology, has developed a clinical Health IT tool called CancerLinQ which utilizes large clinical databases of cancer patients to trend current practices and compare the specific practices of individual providers with best practice guidelines [29]. Another hospital system is using many of the components discussed in a new, internally developed platform called Fluence that allows aggregation of patient information, and applies already known clinical practice guidelines to patients’ problem lists to assist practitioners in making evidenced-based decisions [30]. All of these efforts reflect inadequacies in current EHRs and are important pieces in the process of selectively and wisely incorporating these technologies into EHRs, but doing so universally will be a much larger endeavor.

http://medinform.jmir.org/article/viewFile/4192/1/68492

Figure 3. Mock screenshot for the “Assessment and Plan” screen with background data analytics. Based on background analytics that are being run by the system at all times, a series of “problems” are identified and suggested by the system, which are then displayed in the EMR in the box on the left. The clinician can then select problems that are suggested, or input new problems that are then displayed in the the box on the right of the EMR screen, and will now be apart of ongoing analytics for future assessment.  View this figure

Conclusions

Medicine has finally entered an era in which clinical digitization implementations and data analytic systems are converging. We have begun to recognize the power of data in other domains and are beginning to apply it to the clinical space, applying digitization as a necessary but insufficient tool for this purpose (personal communication from Peter Szolovits, The Unreasonable Effectiveness of Clinical Data. Challenges in Big Data for Data Mining, Machine Learning and Statistics Conference, March 2014). The vast amount of information and clinical choices demands that we provide better supports for making decisions and effectively documenting them. The Institute of Medicine demands a “learning health care system” where analysis of patient data is a key element in continuously improving clinical outcomes [31]. This is also an age of increasing medical complexity bound up in increasing financial and time constraints. The latter dictate that medical practice should become more standardized and evidence-based in order to optimize outcomes at the lowest cost. Current EHRs, mostly implemented over the past decade, are a first step in the digitization process, but do not support decision-making or streamline the workflow to the extent to which they are capable. In response, we propose a series of information system enhancements that we hope can be seized, improved upon, and incorporated into the next generation of EHRs.

There is already government support for these advances: The Office of the National Coordinator for Health IT recently outlined their 6-year and 10-year plans to improve EHR and health IT interoperability, so that large-scale realizations of this idea can and will exist. Within 10 years, they envision that we “should have an array of interoperable health IT products and services that allow the health care system to continuously learn and advance the goal of improved health care.” In that, they envision an integrated system across EHRs that will improve not just individual health and population health, but also act as a nationwide repository for searchable and researchable outcomes data [32]. The first step to achieving that vision is by successfully implementing the ideas and the system outlined above into a more fully functional EHR that better supports both workflow and clinical decision-making. Further, these suggested changes would also contribute to making the note writing process an educational one, thereby justifying the very significant time and effort expended, and would begin to establish a true learning system of health care based on actual workflow practices. Finally, the goal is to keep clinicians firmly in charge of the decision loop in a “human-centered” system in which technology plays an essential but secondary role. As expressed in a recent article on the issue of automating systems [33]:

In this model (human centered automation)…technology takes over routine functions that a human operator has already mastered, issues alerts when unexpected situations arise, provides fresh information that expands the operator’s perspective and counters the biases that often distort human thinking. The technology becomes the expert’s partner, not the expert’s replacement.

Key Concepts and Terminology

A number of concepts and terms were introduced throughout this paper, and some clarification and elaboration of these follows:

  • Affordable Care Act (ACA): Legislation passed in 2010 that constitutes two separate laws including the Patient Protection and Affordable Care Act and the Health Care and Education Reconciliation Act. These two pieces of legislation act together for the expressed goal of expanding health care coverage to low-income Americans through expansion of Medicaid and other federal assistance programs [34].
  • Clinical Decision Support (CDS) is defined by CMS as “a key functionality of health information technology” that encompasses a variety of tools including computerized alerts and reminders, clinical guidelines, condition-specific order sets, documentations templates, diagnostic support, and other tools that “when used effectively, increases quality of care, enhances health outcomes, helps to avoid errors and adverse events, improves efficiency, reduces costs, and boosts provider and patient satisfaction” [35].
  • Cognitive Computing is defined as “the simulation of human thought processes in a computerize model…involving self learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works” [36]. Defined by IBM as computer systems that “are trained using artificial intelligence and machine learning algorithms to sense, predict, infer and, in some ways, think” [37].
  • Deep learning is a form of machine learning (a more specific subgroup of cognitive computing) that utilizes multiple levels of data to make hierarchical connections and recognize more complex patterns to be able to infer higher level concepts from lower levels of input and previously inferred concepts [38]. Figure 3 demonstrates how this concept relates to patients illustrating the system recognizing patterns of signs and symptoms experienced by a patient, and then inferring a diagnosis (higher level concept) from those lower level inputs. The next level concept would be recognizing response to treatment for proposed diagnosis, and offering either alternative diagnoses, or change in therapy, with the system adapting as the patient’s course progresses.
  • Dynamic clinical data mining (DCDM): First, data mining is defined as the “process of discovering patterns, automatically or semi-automatically, in large quantities of data” [39]. DCDM describes the process of mining and interpreting the data from large patient databases that contain prior and concurrent patient information including diagnoses, treatments, and outcomes so as to make real-time treatment decisions [19].
  • Natural Language Processing (NLP) is a process based on machine learning, or deep learning, that enables computers to analyze and interpret unstructured human language input to recognize and even act upon meaningful patterns [39,40].

References

  1. Weed LL. Medical records, patient care, and medical education. Ir J Med Sci 1964 Jun;462:271-282. [Medline]
  2. Celi L, Csete M, Stone D. Optimal data systems: the future of clinical predictions and decision support. Curr Opin Crit Care 2014 Oct;20(5):573-580. [CrossRef] [Medline]
  3. Cook DA, Sorensen KJ, Hersh W, Berger RA, Wilkinson JM. Features of effective medical knowledge resources to support point of care learning: a focus group study. PLoS One 2013 Nov;8(11):e80318 [FREE Full text] [CrossRef] [Medline]
  4. Cook DA, Sorensen KJ, Wilkinson JM, Berger RA. Barriers and decisions when answering clinical questions at the point of care: a grounded theory study. JAMA Intern Med 2013 Nov 25;173(21):1962-1969. [CrossRef] [Medline]

more ….

The Electronic Health Record: How far we have travelled, and where is journey’s end?

http://pharmaceuticalintelligence.com/2012/09/21/the-electronic-health-record-how-far-we-have-travelled-and-where-is-journeys-end/

A focus of the Accountable Care Act is improved delivery of quality, efficiency and effectiveness to the patients who receive healthcare in US from the providers in a coordinated system.  The largest confounder in all of this is the existence of silos that are not readily crossed, handovers, communication lapses, and a heavy paperwork burden.  We can add to that a large for profit insurance overhead that is disinterested in the patient-physician encounter.  Finally, the knowledge base of medicine has grown sufficiently that physicians are challenged by the amount of data and the presentation in the Medical Record.

I present a review of the problems that have become more urgent to fix in the last decade.  The administration and paperwork necessitated by health insurers, HMOs and other parties today may account for 40% of a physician’s practice, and the formation of large physician practice groups and alliances of the hospital and hospital staffed physicians (as well as hospital system alliances) has increased in response to the need to decrease the cost of non-patient care overhead.   I discuss some of the points made by two innovators from the healthcare and  the communications sectors.

I also call attention to the New York Times front page article calling attention to a sharp rise in inflation-adjusted Medicare payments for emergency-room services since 2006 due to upcoding at the highest level, partly related to the ability to physician ability to overstate the claim for service provided by correctible improvements I discuss below.  (NY Times, 9/22/2012).  The solution still has another built in step that requires quality control of both the input and the output, achievable today.  This also comes at a time that there is a nationwide implementation of ICD-10 to replace ICD-9 coding.

US medical groups' adoption of EHR (2005)

US medical groups’ adoption of EHR (2005) (Photo credit: Wikipedia)

The first finding by Robert S Didner, on “Decision Making in the Clinical Setting”, concludes that the gathering of information has large costs while reimbursements for the activities provided have decreased, detrimental to the outcomes that are measured.  He suggests that this data can be gathered and reformatted to improve its value in the clinical setting by leading to decisions with optimal outcomes.  He outlines how this can be done.

The second is a discussion by Emergency Medicine  physicians, Thomas A Naegele and harry P Wetzler,  who have developed a Foresighted Practice Guideline (FPG) (“The Foresighted Practice Guideline Model: A Win-Win Solution”).   They focus on collecting data from similar patients, their interventions, and treatments to better understand the value of alternative courses of treatment.  Using the FPG model will enable physicians to elevate their practice to a higher level and they will have hard information on what works.  These two views are more than 10 years old, and they are complementary.

Didner points out that there is no one sequence of tests and questions that can be optimal for all presenting clusters.  Even as data and test results are acquired, the optimal sequence of information gathering is changed, depending on the gathered information.  Thus, the dilemma is created of how to collect clinical data.  Currently, the way information is requested and presented does not support the way decisions are made.   Decisions are made in a “path-dependent” way, which is influenced by the sequence in which the components are considered.    Ideally, it would require a separate form for each combination of presenting history and symptoms, prior to ordering tests, which is unmanageable.   The blank paper format is no better, as the data is not collected in the way it would be used, and it constitutes separate clusters (vital signs, lab work{also divided into CBC, chemistry panel, microbiology, immunology, blood bank, special tests}].   Improvements have been made in the graphical presentation of a series of tests. Didner presents another means of gathering data in machine manipulable form that improves the expected outcomes.  The basis for this model is that at any stage of testing and information gathering there is an expected outcome from the process, coupled with a metric, or hierarchy of values to determine the relative desirability of the possible outcomes.

He creates a value hierarchy:

  1. Minimize the likelihood that a treatable, life-threatening disorder is not treated.
  2. Minimize the likelihood that a treatable, permanently-disabling or disfiguring disorder is not treated.
  3. Minimize the likelihood that a treatable, discomfort causing disorder is not treated.
  4. Minimize the likelihood that a risky procedure, (treatment or diagnostic procedure) is inappropriately administered.
  5. Minimize the likelihood that a discomfort causing procedure is inappropriately administered.
  6. Minimize the likelihood that a costly procedure is inappropriately administered.
  7. Minimize the time of diagnosing and treating the patient.
  8. Minimize the cost of diagnosing and treating the patient.

In reference to a way of minimizing the number, time and cost of tests, he determines that the optimum sequence could be found using Claude  Shannon’s Information theory.  As to a hierarchy of outcome values, he refers to the QALY scale as a starting point. At any point where a determination is made there is disparate information that has to be brought together, such as, weight, blood pressure, cholesterol, etc.  He points out, in addition, that the way the clinical information is organized is not opyimal for the way to display information to enhance human cognitive performance in decision support.  Furthermore, he looks at the limit of short term memory as 10 chunks of information at any time, and he compares the positions of chess pieces on the board with performance of a grand master, if the pieces are in an order commensurate with a “line of attack”.  The information has to be ordered in the way it is to be used! By presenting information used for a particular decision component in a compact space the load on short term memory is reduced, and there is less strain in searching for the relevant information.

He creates a Table to illustrate the point.

Correlation of weight with other cardiac risk factors

Chol                       0.759384
HDL                        -0.53908
LDL                         0.177297
bp-syst                 0.424728
bp-dia                   0.516167

Triglyc                   0.637817

The task of the information system designer is to provide or request the right information, in the best form, at each stage of the procedure.

The FPG concept as deployed by Naegele and Wetzler is a model for design of a more effective health record that has already shown substantial proof of concept in the emergency room setting.  In principle, every clinical encounter is viewed as a learning experience that requires the collection of data , learning from similar patients, and comparing the value of alternative courses of treatment.  The framework for standard data collection is the FPG model. The FPG is distinguished from hindsighted guidelines which are utilized by utilization and peer review organizations.  Over time, the data forms patient clusters and enables the physician to function at a higher level.

Hypothesis construction is experiential, and hypothesis generation and testing is required to go from art to science in the complex practice of medicine.  In every encounter there are 3 components: patient, process, and outcome.  The key to the process is to collect data on patients, processes and outcomes in a standard way.  The main problem with a large portion of the chart is that the description is not uniform.  This is not fully resolved with good natural language encryption.  The standard words and phrases that may be used for a particular complaint or condition constitute a guideline.  This type of “guided documentation” is a step in moving toward a guided practice.  It enables physicians to gather data on patients, processes and outcomes of care in routine settings, and they can be reviewed and updated.  This is a higher level of methodology than basing guidelines on “consensus and opinion”.
When Lee Goldman, et al., created the guideline for classifying chest pain in the emergency room, the characteristics of the chest pain was problematic. In dealing with this he determined that if the chest pain was “stabbing”, or if it radiated to the right foot, heart attack is excluded.

The IOM is intensely committed to practice guidelines for care.  The guidelines are the data bases of the science of medical decision making and disposition processing, and are related to process-flow.  However, the hindsighted  or retrospective approach is diagnosis or procedure oriented.  HPGs are the tool used in utilization review.  The FPG model focuses on the physician-patient encounter and is problem oriented.   We can go back further and remember the contribution by Lawrence Weed to the “structured medical record”.
The physicians today use an FPG framework in looking at a problem or pathology (especially in pathology, which extends the classification by used of biomarker staining).  The Standard Patient File Format (SPPF) was developed by Weed and includes: 1. Patient demographics; 2. Front of the chart; 3. Subjective: Objective; Assessment/diagnosis;6. Plan; Back of the chart.  The FPG retains the structure of the SPPF  All of the words and phrases in the FPG are the data base for the problem or condition. The current construct of the chart is uninviting: nurses notes, medications, lab results, radiology, imaging.

Realtime Clinical Expert Support and Validation System
Gil David and Larry Bernstein have developed, in consultation with Prof. Ronald Coifman, in the Yale University Applied Mathematics Program, a software system that is the equivalent of an intelligent Electronic Health Records Dashboard that provides empirical medical reference and suggests quantitative diagnostics options.

The introduction of a DASHBOARD has allowed a presentation of drug reactions, allergies, primary and secondary diagnoses, and critical information about any patient the care giver needing access to the record. The advantage of this innovation is obvious. The startup problem is what information is presented and how it is displayed, which is a source of variability and a key to its success. It is also imperative that the extraction of data from disparate sources will, in the long run, further improve the diagnostic process.  For instance, the finding of both ST depression on EKG coincident with an increase of a cardiac biomarker (troponin). Through the application of geometric clustering analysis the data may interpreted in a more sophisticated fashion in order to create a more reliable and valid knowledge-based opinion.  In the hemogram one can view data reflecting the characteristics of a broad spectrum of medical conditions.  Characteristics expressed as measurements of size, density, and concentration, resulting in more than a dozen composite variables, including the mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular hemoglobin (MCH), total white cell count (WBC), total lymphocyte count, neutrophil count (mature granulocyte count and bands), monocytes, eosinophils, basophils, platelet count, and mean platelet volume (MPV), blasts, reticulocytes and platelet clumps, as well as other features of classification.   This has been described in a previous post.

It is beyond comprehension that a better construct has not be created for common use.

W Ruts, S De Deyne, E Ameel, W Vanpaemel,T Verbeemen, And G Storms. Dutch norm data for 13 semantic categoriesand 338 exemplars. Behavior Research Methods, Instruments, & Computers 2004; 36 (3): 506–515.
De Deyne, S Verheyen, E Ameel, W Vanpaemel, MJ Dry, WVoorspoels, and G Storms. Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts.
Behavior Research Methods 2008; 40 (4): 1030-1048

Landauer, T. K., Ross, B. H., & Didner, R. S. (1979). Processing visually presented single words: A reaction time analysis [Technical memorandum].  Murray Hill, NJ: Bell Laboratories. Lewandowsky , S. (1991).

Weed L. Automation of the problem oriented medical record. NCHSR Research Digest Series DHEW. 1977;(HRA)77-3177.

Naegele TA. Letter to the Editor. Amer J Crit Care 1993;2(5):433.

The potential contribution of informatics to healthcare is more than currently estimated

http://pharmaceuticalintelligence.com/2013/02/18/the-potential-contribution-of-informatics-to-healthcare-is-more-than-currently-estimated/

 The estimate of improved costsavings in healthcare and diagnostic accuracy is extimated to be substantial.   I have written about the unused potential that we have not yet seen.  In short, there is justification in substantial investment in resources to this, as has been proposed as a critical goal.  Does this mean a reduction in staffing?  I wouldn’t look at it that way.  The two huge benefits that would accrue are:

  1. workflow efficiency, reducing stress and facilitating decision-making.
  2. scientifically, primary knowledge-based  decision-support by well developed algotithms that have been at the heart of computational-genomics.
 Can computers save health care? IU research shows lower costs, better outcomes

Cost per unit of outcome was $189, versus $497 for treatment as usual

 Last modified: Monday, February 11, 2013
BLOOMINGTON, Ind. — New research from Indiana University has found that machine learning — the same computer science discipline that helped create voice recognition systems, self-driving cars and credit card fraud detection systems — can drastically improve both the cost and quality of health care in the United States.
 Physicians using an artificial intelligence framework that predicts future outcomes would have better patient outcomes while significantly lowering health care costs.
Using an artificial intelligence framework combining Markov Decision Processes and Dynamic Decision Networks, IU School of Informatics and Computing researchers Casey Bennett and Kris Hauser show how simulation modeling that understands and predicts the outcomes of treatment could
  • reduce health care costs by over 50 percent while also
  • improving patient outcomes by nearly 50 percent.
The work by Hauser, an assistant professor of computer science, and Ph.D. student Bennett improves upon their earlier work that
  • showed how machine learning could determine the best treatment at a single point in time for an individual patient.
By using a new framework that employs sequential decision-making, the previous single-decision research
  • can be expanded into models that simulate numerous alternative treatment paths out into the future;
  • maintain beliefs about patient health status over time even when measurements are unavailable or uncertain; and
  • continually plan/re-plan as new information becomes available.

In other words, it can “think like a doctor.”  (Perhaps better because of the limitation in the amount of information a bright, competent physician can handle without error!)

“The Markov Decision Processes and Dynamic Decision Networks enable the system to deliberate about the future, considering all the different possible sequences of actions and effects in advance, even in cases where we are unsure of the effects,” Bennett said.  Moreover, the approach is non-disease-specific — it could work for any diagnosis or disorder, simply by plugging in the relevant information.  (This actually raises the question of what the information input is, and the cost of inputting.)
The new work addresses three vexing issues related to health care in the U.S.:
  1. rising costs expected to reach 30 percent of the gross domestic product by 2050;
  2. a quality of care where patients receive correct diagnosis and treatment less than half the time on a first visit;
  3. and a lag time of 13 to 17 years between research and practice in clinical care.

 

Framework for Simulating Clinical Decision-Making

“We’re using modern computational approaches to learn from clinical data and develop complex plans through the simulation of numerous, alternative sequential decision paths,” Bennett said. “The framework here easily out-performs the current treatment-as-usual, case-rate/fee-for-service models of health care.”  (see the above)
Bennett is also a data architect and research fellow with Centerstone Research Institute, the research arm of Centerstone, the nation’s largest not-for-profit provider of community-based behavioral health care. The two researchers had access to clinical data, demographics and other information on over 6,700 patients who had major clinical depression diagnoses, of which about 65 to 70 percent had co-occurring chronic physical disorders like diabetes, hypertension and cardiovascular disease.  Using 500 randomly selected patients from that group for simulations, the two
  • compared actual doctor performance and patient outcomes against
  • sequential decision-making models

using real patient data.

They found great disparity in the cost per unit of outcome change when the artificial intelligence model’s
  1. cost of $189 was compared to the treatment-as-usual cost of $497.
  2. the AI approach obtained a 30 to 35 percent increase in patient outcomes
Bennett said that “tweaking certain model parameters could enhance the outcome advantage to about 50 percent more improvement at about half the cost.”
While most medical decisions are based on case-by-case, experience-based approaches, there is a growing body of evidence that complex treatment decisions might be effectively improved by AI modeling.  Hauser said “Modeling lets us see more possibilities out to a further point –  because they just don’t have all of that information available to them.”  (Even then, the other issue is the processing of the information presented.)
Using the growing availability of electronic health records, health information exchanges, large public biomedical databases and machine learning algorithms, the researchers believe the approach could serve as the basis for personalized treatment through integration of diverse, large-scale data passed along to clinicians at the time of decision-making for each patient. Centerstone alone, Bennett noted, has access to health information on over 1 million patients each year. “Even with the development of new AI techniques that can approximate or even surpass human decision-making performance, we believe that the most effective long-term path could be combining artificial intelligence with human clinicians,” Bennett said. “Let humans do what they do well, and let machines do what they do well. In the end, we may maximize the potential of both.”
Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach” was published recently in Artificial Intelligence in Medicine. The research was funded by the Ayers Foundation, the Joe C. Davis Foundation and Indiana University.
For more information or to speak with Hauser or Bennett, please contact Steve Chaplin, IU Communications, at 812-856-1896 or stjchap@iu.edu.
IBM Watson Finally Graduates Medical School
It’s been more than a year since IBM’s Watson computer appeared on Jeopardy and defeated several of the game show’s top champions. Since then the supercomputer has been furiously “studying” the healthcare literature in the hope that it can beat a far more hideous enemy: the 400-plus biomolecular puzzles we collectively refer to as cancer.
Anomaly Based Interpretation of Clinical and Laboratory Syndromic Classes

Larry H Bernstein, MD, Gil David, PhD, Ronald R Coifman, PhD.  Program in Applied Mathematics, Yale University, Triplex Medical Science.

 Statement of Inferential  Second Opinion  
 Realtime Clinical Expert Support and Validation System

Gil David and Larry Bernstein have developed, in consultation with Prof. Ronald Coifman, in the Yale University Applied Mathematics Program, a software system that is the equivalent of an intelligent Electronic Health Records Dashboard that provides
  • empirical medical reference and suggests quantitative diagnostics options.

Background

The current design of the Electronic Medical Record (EMR) is a linear presentation of portions of the record by
  • services, by
  • diagnostic method, and by
  • date, to cite examples.

This allows perusal through a graphical user interface (GUI) that partitions the information or necessary reports in a workstation entered by keying to icons.  This requires that the medical practitioner finds

  • the history,
  • medications,
  • laboratory reports,
  • cardiac imaging and EKGs, and
  • radiology
in different workspaces.  The introduction of a DASHBOARD has allowed a presentation of
  • drug reactions,
  • allergies,
  • primary and secondary diagnoses, and
  • critical information about any patient the care giver needing access to the record.
 The advantage of this innovation is obvious.  The startup problem is what information is presented and how it is displayed, which is a source of variability and a key to its success.

Proposal

We are proposing an innovation that supercedes the main design elements of a DASHBOARD and
  • utilizes the conjoined syndromic features of the disparate data elements.
So the important determinant of the success of this endeavor is that it facilitates both
  1. the workflow and
  2. the decision-making process
  • with a reduction of medical error.
 This has become extremely important and urgent in the 10 years since the publication “To Err is Human”, and the newly published finding that reduction of error is as elusive as reduction in cost.  Whether they are counterproductive when approached in the wrong way may be subject to debate.
We initially confine our approach to laboratory data because it is collected on all patients, ambulatory and acutely ill, because the data is objective and quality controlled, and because
  • laboratory combinatorial patterns emerge with the development and course of disease.  Continuing work is in progress in extending the capabilities with model data-sets, and sufficient data.
It is true that the extraction of data from disparate sources will, in the long run, further improve this process.  For instance, the finding of both ST depression on EKG coincident with an increase of a cardiac biomarker (troponin) above a level determined by a receiver operator curve (ROC) analysis, particularly in the absence of substantially reduced renal function.
The conversion of hematology based data into useful clinical information requires the establishment of problem-solving constructs based on the measured data.  Traditionally this has been accomplished by an intuitive interpretation of the data by the individual clinician.  Through the application of geometric clustering analysis the data may interpreted in a more sophisticated fashion in order to create a more reliable and valid knowledge-based opinion.
The most commonly ordered test used for managing patients worldwide is the hemogram that often incorporates the review of a peripheral smear.  While the hemogram has undergone progressive modification of the measured features over time the subsequent expansion of the panel of tests has provided a window into the cellular changes in the production, release or suppression of the formed elements from the blood-forming organ to the circulation.  In the hemogram one can view data reflecting the characteristics of a broad spectrum of medical conditions.
Progressive modification of the measured features of the hemogram has delineated characteristics expressed as measurements of
  • size,
  • density, and
  • concentration,
resulting in more than a dozen composite variables, including the
  1. mean corpuscular volume (MCV),
  2. mean corpuscular hemoglobin concentration (MCHC),
  3. mean corpuscular hemoglobin (MCH),
  4. total white cell count (WBC),
  5. total lymphocyte count,
  6. neutrophil count (mature granulocyte count and bands),
  7. monocytes,
  8. eosinophils,
  9. basophils,
  10. platelet count, and
  11. mean platelet volume (MPV),
  12. blasts,
  13. reticulocytes and
  14. platelet clumps,
  15. perhaps the percent immature neutrophils (not bands)
  16. as well as other features of classification.
The use of such variables combined with additional clinical information including serum chemistry analysis (such as the Comprehensive Metabolic Profile (CMP)) in conjunction with the clinical history and examination complete the traditional problem-solving construct. The intuitive approach applied by the individual clinician is limited, however,
  1. by experience,
  2. memory and
  3. cognition.
The application of rules-based, automated problem solving may provide a more reliable and valid approach to the classification and interpretation of the data used to determine a knowledge-based clinical opinion.
The classification of the available hematologic data in order to formulate a predictive model may be accomplished through mathematical models that offer a more reliable and valid approach than the intuitive knowledge-based opinion of the individual clinician.  The exponential growth of knowledge since the mapping of the human genome has been enabled by parallel advances in applied mathematics that have not been a part of traditional clinical problem solving.  In a univariate universe the individual has significant control in visualizing data because unlike data may be identified by methods that rely on distributional assumptions.  As the complexity of statistical models has increased, involving the use of several predictors for different clinical classifications, the dependencies have become less clear to the individual.  The powerful statistical tools now available are not dependent on distributional assumptions, and allow classification and prediction in a way that cannot be achieved by the individual clinician intuitively. Contemporary statistical modeling has a primary goal of finding an underlying structure in studied data sets.
In the diagnosis of anemia the variables MCV,MCHC and MCH classify the disease process  into microcytic, normocytic and macrocytic categories.  Further consideration of
proliferation of marrow precursors,
  • the domination of a cell line, and
  • features of suppression of hematopoiesis

provide a two dimensional model.  Several other possible dimensions are created by consideration of

  • the maturity of the circulating cells.
The development of an evidence-based inference engine that can substantially interpret the data at hand and convert it in real time to a “knowledge-based opinion” may improve clinical problem solving by incorporating multiple complex clinical features as well as duration of onset into the model.
An example of a difficult area for clinical problem solving is found in the diagnosis of SIRS and associated sepsis.  SIRS (and associated sepsis) is a costly diagnosis in hospitalized patients.   Failure to diagnose sepsis in a timely manner creates a potential financial and safety hazard.  The early diagnosis of SIRS/sepsis is made by the application of defined criteria (temperature, heart rate, respiratory rate and WBC count) by the clinician.   The application of those clinical criteria, however, defines the condition after it has developed and has not provided a reliable method for the early diagnosis of SIRS.  The early diagnosis of SIRS may possibly be enhanced by the measurement of proteomic biomarkers, including transthyretin, C-reactive protein and procalcitonin.  Immature granulocyte (IG) measurement has been proposed as a more readily available indicator of the presence of
  • granulocyte precursors (left shift).
The use of such markers, obtained by automated systems in conjunction with innovative statistical modeling, may provide a mechanism to enhance workflow and decision making.
An accurate classification based on the multiplicity of available data can be provided by an innovative system that utilizes  the conjoined syndromic features of disparate data elements.  Such a system has the potential to facilitate both the workflow and the decision-making process with an anticipated reduction of medical error.

This study is only an extension of our approach to repairing a longstanding problem in the construction of the many-sided electronic medical record (EMR).  On the one hand, past history combined with the development of Diagnosis Related Groups (DRGs) in the 1980s have driven the technology development in the direction of “billing capture”, which has been a focus of epidemiological studies in health services research using data mining.

In a classic study carried out at Bell Laboratories, Didner found that information technologies reflect the view of the creators, not the users, and Front-to-Back Design (R Didner) is needed.  He expresses the view:

“Pre-printed forms are much more amenable to computer-based storage and processing, and would improve the efficiency with which the insurance carriers process this information.  However, pre-printed forms can have a rather severe downside. By providing pre-printed forms that a physician completes
to record the diagnostic questions asked,
  • as well as tests, and results,
  • the sequence of tests and questions,
might be altered from that which a physician would ordinarily follow.  This sequence change could improve outcomes in rare cases, but it is more likely to worsen outcomes. “
Decision Making in the Clinical Setting.   Robert S. Didner
 A well-documented problem in the medical profession is the level of effort dedicated to administration and paperwork necessitated by health insurers, HMOs and other parties (ref).  This effort is currently estimated at 50% of a typical physician’s practice activity.  Obviously this contributes to the high cost of medical care.  A key element in the cost/effort composition is the retranscription of clinical data after the point at which it is collected.  Costs would be reduced, and accuracy improved, if the clinical data could be captured directly at the point it is generated, in a form suitable for transmission to insurers, or machine transformable into other formats.  Such data capture, could also be used to improve the form and structure of how this information is viewed by physicians, and form a basis of a more comprehensive database linking clinical protocols to outcomes, that could improve the knowledge of this relationship, hence clinical outcomes.
  How we frame our expectations is so important that
  • it determines the data we collect to examine the process.
In the absence of data to support an assumed benefit, there is no proof of validity at whatever cost.   This has meaning for
  • hospital operations, for
  • nonhospital laboratory operations, for
  • companies in the diagnostic business, and
  • for planning of health systems.
In 1983, a vision for creating the EMR was introduced by Lawrence Weed and others.  This is expressed by McGowan and Winstead-Fry.
J J McGowan and P Winstead-Fry. Problem Knowledge Couplers: reengineering evidence-based medicine through interdisciplinary development, decision support, and research.
Bull Med Libr Assoc. 1999 October; 87(4): 462–470.   PMCID: PMC226622    Copyright notice

Example of Markov Decision Process (MDP) trans...

Example of Markov Decision Process (MDP) transition automaton (Photo credit: Wikipedia)

Control loop of a Markov Decision Process

Control loop of a Markov Decision Process (Photo credit: Wikipedia)

English: IBM's Watson computer, Yorktown Heigh...

English: IBM’s Watson computer, Yorktown Heights, NY (Photo credit: Wikipedia)

English: Increasing decision stakes and system...

English: Increasing decision stakes and systems uncertainties entail new problem solving strategies. Image based on a diagram by Funtowicz, S. and Ravetz, J. (1993) “Science for the post-normal age” Futures 25:735–55 (http://dx.doi.org/10.1016/0016-3287(93)90022-L). (Photo credit: Wikipedia)

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