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Big Data in Genomic Medicine

Posted in Bio Instrumentation in Experimental Life Sciences Research, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Beth Israel Deaconess Medical Center, Big data, Digital pathology, DNA, DNA Sequencing, Harvard Medical School, Jim Golden, Laboratory News on December 17, 2012| 9 Comments »

Reporter: Larry H Bernstein, MD, FCAP

Image Source: Created by Noam Steiner Tomer 7/31/2020

Pathologists May Be Healthcare’s Rock Stars of Big Data in Genomic Medicine’s ’Third Wave’

Category: Digital Pathology, Instruments & Equipment, Laboratory Management and Operations, Laboratory News, Laboratory Operations, Laboratory Pathology, Management & Operations

Published: December 17 2012

Pathologists are positioned to be the primary interpreters of big data as genomic medicine further evolves

Pathologists and clinical laboratory managers may be surprised to learn that at least one data scientist has proclaimed pathologists the real big data rock stars of healthcare. The reason has to do with the shift in focus of genomic medicine from therapeutics and presymptomatic disease assessment to big data analytics.

In a recent posting published at Forbes.com, data scientist Jim Golden heralded the pronouncement of Harvard pathologist Mark S. Boguski, M.D., Ph.D., FACM. He declared that “The time of the $1,000 genome meme is over!”

DNA Sequencing Systems and the $1,000 Genome

Golden has designed, built, and programmed DNA sequencing devices. He apprenticed under the Human Genome program and spent 15 years working towards the $1,000 genome. “I’m a believer,” he blogged. “[That’s] why I was so intrigued [by Boguski’s remarks].

Boguski is Associate Professor of Pathology at the Center for Biomedical Informatics at Harvard Medical School and the Department of Pathology at Beth Israel Deaconess Medical Center. It was in a presentation at a healthcare conference in Boston that Boguski pronounced that it is time for the $1,000 genome to go.

Both Golden and Boguski acknowledged the benefits generated by the race to the $1,000 genome. Competition to be first to achieve this milestone motivated scientists and engineers to swiftly drive down the cost of decoding DNA. The result was a series of advances in instrumentation, chemistry, and biology.

Pathologists and Big Data Analytics

“Our notions about how genome science and technology would improve health and healthcare have changed,” Boguski wrote in an editorial published at Future Medicine. He then noted that the focus has shifted to big data analytics.

In the editorial, Boguski described the phases of development of genomic medicine as “waves.” The first wave occurred during the mid- to late-1990s. It focused on single- nucleotide polymorphisms (SNP) and therapeutics.

Medical Laboratories Have Opportunity to Perform Presymptomatic Testing

The second wave focused on presymptomatic testing for disease risk assessment and Genome Wide Association Studies (GWAS). Researchers expected this data to help manage common diseases.

The first two waves of medical genomics were conducted largely by the pharmaceutical industry, as well as with  primary care and public health communities, according to Boguski. Considerable optimism accompanied each wave of medical genomics.

“Despite the earlier optimism, progress in improving human health has been modest and incremental, rather than paradigm-shifting,” noted Boguski, who wrote that,to date, only a handful of genome-derived drugs have reached the market. He further observed that products such as direct-to-consumer genomic testing have proved more educational and recreational than medical.

“Third Wave” of Genomic Medicine

It was rapid declines in the cost of next-generation DNA sequencing technologies that now has triggered the third wave of genomic medicine. Its focus is postsymptomatic genotyping for individualized and optimized disease management.

“This is where genomics is likely to bring the most direct and sustained impact on healthcare for several reasons,” stated Boguski. “Genomics technologies enable disease diagnosis of sufficient precision to drive both cost-effective [patient] management and better patient outcomes. Thus, they are an essential part of the prescription for disruptive healthcare reform.”

Boguski reiterated the case for the value of laboratory medicine. He stated the following critical—but often overlooked—points, each of which is familiar to pathologists and clinical laboratory managers:

1. Pathologist-directed, licensed clinical laboratory testing has a major effect on clinical decision-making.

2. Medical laboratory testing services account for only about 2% of healthcare expenditures in the United States.

3. Medical laboratory services strongly influence the remaining 98% of costs through the information they provide on the prevention, diagnosis, treatment, and management of disease.

Molecular Diagnostics Reaching Maturity for Clinical Laboratory Testing

“Genome analytics are just another technology in the evolution of molecular diagnostics,” Boguski declared in his editorial.

Read more: Pathologists May Be Healthcare’s Rock Stars of Big Data in Genomic Medicine’s ’Third Wave’ | Dark Daily http://www.darkdaily.com/pathologists-may-be-healthcare%e2%80%99s-rock-stars-of-big-data-in-genomic-medicines-third-wave-1217#ixzz2FL24IRAA

English: Created by Abizar Lakdawalla. (Photo credit: Wikipedia)

English: Workflow for DNA nanoball sequencing (Photo credit: Wikipedia)

DNA sequence (Photo credit: Wikipedia)

Big Data: water wordscape (Photo credit: Marius B)

Related articles

• Digital Pathology Market Expected to Reach $336.6 Million at a CAGR of 12% from 2012 to 2017 by MarketsandMarkets (prweb.com)
• Response to UK’s Plan to sequence genomes of 100,000 Cancer Patients (grosenfeld.wordpress.com)
• The Fast Changing Landscape of Sequencing Technologies and Their Impact on Microbial Genome Assemblies and Annotation (plosone.org)
• Stanford to rival Broad Institute with Big Data-focused genomics center (fiercebiotechit.com)
• New Method To Directly Sequence Small Genomes Without Library Preparation (medicalnewstoday.com)
• Three Big Themes from the Personalized Medicine Conference (xconomy.com)
• NIH Grant Moves Pathologists to the Forefront of Genomic Medicine (prweb.com)
• Tool developed to evaluate genome sequencing method (rdmag.com)
• Big Data To Become Powerful Driver Of IT Spending – Gartner (misco.co.uk)
• Video: DDN Powers Genomics at the Virginia Bioinformatics Institute (insidehpc.com)

Comment & Response

Right now the cost of the testing and the turnaround times are not favorable. It is going to take a decade or more for clinical labs to catch up. For some time it will be send out tests to Quest, LabCorp, and State or University lab consortia.

The power of the research technology is pushing this along, but for Personalized Medicine the testing should be coincident with the patient visit, and the best list of probable issues should be accessible on the report screen. The EHR industry is dominated by 2 companies that I see have no interest in meeting the needs of the physicians. The payback has to be on efficient workflow, accurate assessment of the record, and timely information. The focus for 25 years has been on billing structure. But even the revised billing codes (ICD10) can’t be less than 5 years out-of-date because of improvements in the knowledge base and improvements in applied math algorithms.

The medical record still may have information buried min a word heap, and the laboratory work is a go-to-you know where sheet with perhaps 15 variables on a page, with chemistry and hematology, immunology, blood bank, and microbiology on separate pages. The ability of the physician to fully digest the information with “errorless” discrimination is tested, and the stress imposed by the time for each patient compromises performance. There is work going on in moving proteomics along to a high throughput system for improved commercial viability, that was reported by Leigh Anderson a few years ago. The genomics is more difficult, but the genomics is partly moving to rapid micropanel tools.

In summary, there are 3 factors:

1. Automation and interpretation
2. Integration into the EHR in real time and usable by a physician.
3. The sorting out of the highest feature “predictors” and classifying them into clinically meaningful sets and subsets.

When this is done, then the next generation of recoding will be in demand.
The Automated Malnutrition Assessment
Gil David1, Larry Bernstein2, Ronald R. Coifman1

1Department of Mathematics, Program in Applied Mathematics,
Yale University, New Haven, CT 06510, USA,
2Triplex Consulting, Trumbull, CT 06611

Abstract

Introduction: We propose an automated nutritional assessment (ANA) algorithm that provides a method for malnutrition risk prediction with high accuracy and reliability.

Materials and Methods: The database used for this study is a file of 432 patients, where each patient is described by 4 laboratory parameters and 11 clinical parameters. A malnutrition risk assessment of low (1), moderate (2) or high (3) was assigned by a dietitian for each patient. An algorithm for data organization and classification via characteristic metrics is proposed. For each patient, the algorithm characterizes its unique profile and builds a characteristic metric to identify similar patients who are mapped into a classification.

Results: The algorithm assigned a malnutrition risk level for each patient based on different training sizes that were taken out of the data.

Our method resulted in an average error (distance between the automated score and the real score) of 0.386, 0.3507, 0.3454, 0.34 and 0.2907 for 10%, 30%, 50%, 70% and 90% training sizes, respectively.

Our method outperformed the compared method even when our method used a smaller training set then the compared method. In addition, we show that the laboratory parameters themselves are sufficient for the automated risk prediction and organize the patients into clusters that correspond to low, low-moderate, moderate, moderate-high and high risk areas.

Discussion: The problem of rapidly identifying risk and severity of malnutrition is crucial for minimizing medical and surgical complications. These are not easily performed or adequately expedited. We characterize for each patient a unique profile and map similar patients into a classification. We also find that the laboratory parameters themselves are sufficient for the automated risk prediction.

Keywords: Network Algorithm, unsupervised classification, malnutrition screening, protein energy malnutrition (PEM), malnutrition risk, characteristic metric, characteristic profile, data characterization, non-linear differential diagnosis.

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Sunitinib brings Adult Acute Lymphoblastic Leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Bone Disease and Musculoskeletal Disease, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Health Economics and Outcomes Research, Interviews with Scientific Leaders, Medical and Population Genetics, Nephrology, Nephrology & Regenerative medicine, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Population Health Management, Genetics & Pharmaceutical, tagged Acute lymphoblastic leukemia, Cancer - General, Chromosome 13, compassionate use program, DNA, DNA Sequencing, FLT3 gene on CHromosome 13, FLT3 receptor blockage, kidney cancer, New York Times, progression-free survival, RNA sequencing, sequencing, Sunitinib, Washington University in St. Louis on July 9, 2012| 12 Comments »

Sunitinib brings Adult Acute Lymphoblastic Leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

Curator: Aviva Lev-Ari, PhD, RN

Article ID #1: Sunitinib brings Adult Acute Lymphoblastic Leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade. Published on 7/9/2012

WordCloud Image Produced by Adam Tubman

Word Cloud by Daniel Menzin

Updated 11/13/2013

Pazopanib versus Sunitinib in Renal Cancer

N Engl J Med 2013; 369:1968-1970November 14, 2013DOI: 10.1056/NEJMc1311795

Article

To the Editor:

Cancer treatments are expensive. The estimation of the total cost can be challenging because of several factors such as efficacy, toxicity, and the costs and duration of supportive care and end-of-life care. Motzer et al. (Aug. 22 issue)1 report similar efficacy but a favorable safety and quality-of-life profile and less medical resource utilization with pazopanib as compared with sunitinib in first-line therapy for metastatic renal cancer. Since oncology is becoming an increasingly value-based specialty, we wanted to highlight another important aspect of this trial. Pazopanib appears to be favorable not only in terms of safety and quality of life, but also in terms of overall cost. A 30-day supply of pazopanib (at a dose of 800 mg daily) ranges from $3,500 to $8,556, whereas a 30-day supply of sunitinib (at a dose of 50 mg daily) ranges from $4,500 to $13,559.2 The total cost of pazopanib during the median progression-free survival of 8.4 months is $29,400 to $71,870, and the total cost of sunitinib during the median progression-free survival of 9.5 months is $42,750 to $127,454. Less toxicity and less medical resource utilization with pazopanib will most likely further lower the overall costs of treatment with this agent. Comparative-effectiveness trials hold great promise for maximizing patient safety, improving treatment outcomes, and reducing costs.

Ryan Ramaekers, M.D.
Mark Tharnish, Pharm.D.
M. Sitki Copur, M.D.
Saint Francis Cancer Treatment Center, Grand Island, NE
mcopur@sfmc-gi.org

No potential conflict of interest relevant to this letter was reported.

2 References

To the Editor:

Motzer et al. report a combined analysis of two open-label noninferiority trials (927 patients in the original trial and 183 patients in a second trial), each of which compared pazopanib with sunitinib with respect to progression-free survival in renal-cell carcinoma. Quality-of-life outcomes were subjective.

Analysis of noninferiority trials is notoriously difficult.1,2 The authors’ analysis of the trials, which was open-label because of the different administration schedules of the drugs, presents problems in interpreting progression-free survival and quality of life. The studies define disease progression differently. The larger study defined progression-free survival according to independent review. The protocol for the smaller study states that progression-free survival “will be summarized . . . based on the investigator assessment.” Inference from subjective outcomes in unmasked trials (e.g., quality of life in both studies and progression-free survival in the smaller study and therefore in the combined analysis) is subject to well-known bias. Moreover, the article does not state how many of the 379 participants (34%) who discontinued the intervention before death or disease progression (see Fig. S2 in the Supplementary Appendix, available with the full text of the article at NEJM.org) were assessed for progression-free survival. A fair comparison must use rigorous methods to handle missing data.3 Since the article did not deal appropriately with missing data, its conclusions regarding noninferiority are uninterpretable.

Janet Wittes, Ph.D.
Statistics Collaborative, Washington, DC
janet@statcollab.com

Dr. Wittes reports that her company, Statistics Collaborative, has consulting agreements with both GlaxoSmithKline and Pfizer, the manufacturers of the drugs discussed in the article by Motzer et al. In addition, Statistics Collaborative has contracts with several other companies that produce drugs for patients with cancer. No other potential conflict of interest relevant to this letter was reported.

3 References

To the Editor:

Motzer et al. state that “the results of the progression-free survival analysis in the per-protocol population were consistent with the results of the primary analysis.” However, the predefined margin of noninferiority (<1.25) was not met. The upper limit of the confidence interval (1.255) was clearly above the defined threshold.1 In a noninferiority trial, the use of the intention-to-treat population is generally nonconservative,2 the full analysis set and the per-protocol analysis set are considered to have equal importance, and the use of the intention-to-treat population should lead to similar conclusions for a robust interpretation.3 Thus, it is surprising that the authors did not come to or discuss the same conclusions as that of the French National Authority for Health4: “serious doubt exists about the noninferiority result of pazopanib compared to sunitinib” and “the clinical significance of the noninferiority threshold defined in the protocol was an efficacy loss of 2.2 months in the median progression-free survival. This is too large for patients.”

Jochen Casper, M.D.
Silke Schumann-Binarsch, M.D.
Claus-Henning Köhne, M.D.
Klinikum Oldenburg, Oldenburg, Germany
casper.jochen@klinikum-oldenburg.de

Dr. Casper reports receiving consulting fees from Bayer, Novartis, and Pfizer and speaking fees from Novartis and Pfizer. No other potential conflict of interest relevant to this letter was reported.

4 References

The authors reply: In reply to Ramaekers et al.: we agree that decisions regarding the provision of health care include economic evaluations to identify treatments that provide the best clinical benefit at an acceptable cost.

To clarify a point in the letter by Wittes: the primary end point of this phase 3 trial was progression-free survival evaluated by an independent review committee; these data were assessed for all 1110 patients from both trials. This is specified in the protocol. The consistency of the quality-of-life results with the observed differences in the safety profiles for the two drugs speaks to the absence of bias in the quality-of-life outcome. The number of patients in whom follow-up ended before progression was assessed by the independent review committee was balanced between the two groups: 156 patients in the pazopanib group (28%) and 168 patients in the sunitinib group (30%). To Wittes’s final point regarding rigorous methods to handle missing data: the algorithm for assigning disease-progression and censoring dates followed the Guidance for Industry of the Food and Drug Administration1 and is included in the protocol of our article.

In reply to Casper et al.: there is no consensus regarding whether the per-protocol population is more conservative than the intention-to-treat population for the noninferiority analysis.2,3Reviews of noninferiority trials indicate that the per-protocol population is not generally more conservative than the intention-to-treat population, and there are scenarios in which the per-protocol analysis itself could introduce bias.3 A systematic review indicated that more than 70% of published findings from noninferiority trials in oncology show results in only the intention-to-treat population and not in the per-protocol population.4 Our phase 3 trial had a single primary analysis in the intention-to-treat population, with the per-protocol population included as a key sensitivity analysis, as supported by Fleming et al.5 No formal hypothesis testing was planned for the per-protocol population, nor was the trial powered for this. Consistency of the point estimates was desired to show an absence of bias due to the analysis population. This absence of bias was shown by the consistency of the hazard ratios (1.07 in the per-protocol analysis vs. 1.05 in the primary analysis). For an underpowered per-protocol comparison, it is inappropriate for Casper et al. to interpret that the upper bound that barely exceeded 1.25 in our per-protocol analysis is an indication of inconsistency of results across the two populations. The noninferiority margin was selected in consultation with oncology experts, and justification of the margin is in the protocol.

Robert J. Motzer, M.D.
Memorial Sloan-Kettering Cancer Center, New York, NY
motzerr@mskcc.org

Lauren McCann, Ph.D.
Keith Deen, M.S.
GlaxoSmithKline, Collegeville, PA

Since publication of their article, the authors report no further potential conflict of interest.

REFERENCES

Food and Drug Administration. Guidance for industry: clinical trial endpoints for the approval of cancer drugs and biologics. May 2007 (http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm071590.pdf).

Jones B, Jarvis P, Lewis JA, Ebbutt AF. Trials to assess equivalence: the importance of rigorous methods. BMJ 1996;313:36-39[Erratum, BMJ 1996;313:550.]
CrossRef | Web of Science | Medline

Brittain E, Lin D. A comparison of intent-to-treat and per-protocol results in antibiotic non-inferiority trials. Stat Med 2005;24:1-10
CrossRef | Web of Science | Medline

Tanaka S, Kinjo Y, Kataoka Y, Yoshimura K, Termukai S. Statistical issues and recommendations for noninferiority trials in oncology: a systematic review. Clin Cancer Res 2012;18:1837-1847
CrossRef | Web of Science | Medline

Fleming TR, Odem-Davis K, Rothmann MD, Li Shen Y. Some essential considerations in the design and conduct of non-inferiority trials. Clin Trials2011;8:432-439
CrossRef | Web of Science | Medline

SOURCE

http://www.nejm.org/doi/full/10.1056/NEJMc1311795?query=TOC

Original Article Published on 7/9/2012

July 6, 2012 NY Times reports on a new approach based on DNA and RNA sequencing and a cancer drug for kidney cancer to bring REMISSION to Adult acute lymphoblastic leukemia (ALL).

On the lower left corner of this page – Watch the VIDEO

second-chance.html

Dr. Lukas Wartman, is a Cancer Researcher specializing in Leukemia. He suspected he had Leukemia, the very disease he had devoted his medical career to studying.

After years of treatment and two relapses of ALL, he has exhaused all conventional approaches to his disease. At Washington University in St. Louis, his colleagues in the lab, decoded Dr. Wartman’s genetic information by genome sequencing techniques t determine the genetic cause of his ALL. The team found an overactive gne, FLT3 on Chromosome 13. The gene was treated with pfizer’s Suntinib drug for advanced kidney cancer.

Blood samples free of ALL found in days after using the drug. As results were very promising, Pfizer, the drug’s maker who has turned down Dr. Wartman’s request for the drug under their compassionate use program, though he explained that his entire salary was only enough to pay for 7 1/2 months of Sutent (Suntinib). While he does not know why Pfizer gave him the drug finally, he suspects it was the plea of his Nurse Practitioner, Stephanie Bauer, NP.

Identification of the genetic cause for his ALL, thus discovering a breakthough in understanding and treatment for ALL in other patients, involved the following steps:

SAMPLE

two tissue samples taken from Dr. Wartman’s Bone marrow and skin cells

SEQUENCE

Extracts of DNA and RNA from Dr. Wartman’s cells, two types of genetic material tested

COMPARISON

DNA sequesnces showed genetic mutations possibly related to his ALL, none seemed treatable. However, RNA sequencing revealed that a normal Gene, FLT3, on cheomozome 13, was overactive in his leukemia cells

TARGETING

The FLT3 gene helps create new white blod cells in the bone marrow. Dr. Wartman’s marrow bone cells were covered with an extreme number of FLT3 receptors which possibly caused the growth of his leukemia.

TREATMENT – Receptor Blockade 

Drug known to block FLT3 receptor, Sunitinib, used for kedney cancer treatment, was given to Dr. Wartman. Two weeks after Dr, Wartman began taking the drug, tests revealed that his leukenia was in remission.

NEW MARKETS FOR FLT3  GENE BLOCKADE DRUG  – KIDNEY CANCER AND LEUKEMIA

Pfizer has NOW a NEW market for Sunitinib — All CANCER PATIENTS DIAGNOSED WITH Adult acute lymphoblastic leukemia (ALL) where an overactive FLT3 gene on chomosome 13 is found.

NEW TREATMENT OPTIONS FOR Adult acute lymphoblastic leukemia (ALL)

Thus, any (ALL) diagnosed patient needs to be tested for Chromosome 13, ONLY rather then the entire genome sequencing of the Patient. If FLT3 is not found overactive, THEN proceed with entire genome sequencing of the Patient. IF another gene is overactive FIND DRUG FOR RECEPTOR BLOCKADE.

SIZING THE MARKET FOR FLT3 BLOCKADE DRUGS: KIDNEY CANCER vs LEUKEMIA

The Market for Adult ALL is much bigger than the market for kidney cancer. Thus, this discovery regarding the remission of Dr. Wartman’s remission following two relapses is so significant for Pfizer and for any patient with the diagnosis of Adult ALL.

I recommend the reader to click on the links and follow the reactions of the public to this article in The New York Times.

http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-treatment-for-leukemia-glimpses-of-the-future.html?pagewanted=all

Read HUNDREDS of Comments by Cancer Patients and the readers of The New York Times Health Section

http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-treatment-for-leukemia-glimpses-of-the-future.html?pagewanted=all#commentsContainer

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