Posts Tagged ‘Herceptin’

Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

Larry H Bernstein, MD, FCAP, Author and Curator

This article has two parts:

  • Part 1: Novel Genomics Industry Trends in Diagnostics and Biomarkers vs Present Market Transient Conditions


  • Part 2: Historical Scientific Leaders Memoirs


Part 1: Novel Genomics Industry Trends in Diagnostics and Biomarkers vs Present Market Transient Conditions


Based on “Forging a path from companion diagnostics to holistic decision support”, L.E.K.

Executive Insights, 2013;14(12). http://www.LEK.com

Companion diagnostics and their companion therapies is defined here as a method enabling

  • LIKELY responders to therapies that are specific for patients with ma specific molecular profile.

The result of this statement is that the diagnostics permitted to specific patient types gives access to

  • novel therapies that may otherwise not be approve or reimbursed in other, perhaps “similar” patients
  • who lack a matching identification of the key identifier(s) needed to permit that therapy,
  • thus, entailing a poor expected response.

The concept is new because:

(1) The diagnoses may be closely related by classical criteria, but at the same time they are
not alike with respect to efficacy of treatment with a standard therapy.
(2) The companion diagnostics is restricted to dealing with a targeted drug-specific question
without regard to other clinical issues.
(3) The efficacy issue it clarifies is reliant on a deep molecular/metabolic insight that is not available, except through
emergent genomic/proteomic analysis that has become available and which has rapidly declining cost to obtain.

The limitation example given is HER2 testing for use of Herceptin in therapy for non-candidates (HER2 negative patients).
The problem is that the current format is a “one test/one drug” match, but decision support  may require a combination of

  • validated biomakers obtained on a small biopsy sample (technically manageable) with confusing results.

While HER2 negative patients are more likely to be pre-menopausal with a more aggressive tumor than postmenopausal,

  • the HER2 negative designation does not preclude treatment with Herceptin.

So the Herceptin would be given in combination, but with what other drug in a non-candidate?

The point that L.E.K. makes is that providing highly validated biomarkers linked to approved therapies, it is necessary to pursue more holistic decision support tests that interrogate multiple biomarkers (panels of companion diagnostic markers) and discovery of signatures for treatments that are also used with a broad range of information, such as,

  • traditional tests,
  • imaging,
  • clinical trials,
  • outcomes data,
  • EMR data,
  • reimbursement and coverage data.

A comprehensive solution of this nature appears to be a distance from realization.  However, is this the direction that will lead to tomorrows treatment decision support approaches?

 Surveying the Decision Support Testing Landscape

As a starting point, L.E.K. characterized the landscape of available tests in the U.S. that inform treatment decisions compiled from ~50 leading diagnostics companies operating in the U.S. between 2004-2011. L.E.K. identified more than 200 decision support tests that were classified by test purpose, and more specifically,  whether tests inform treatment decisions for a single drug/class (e.g., companion diagnostics) vs. more holistic treatment decisions across multiple drugs/classes (i.e., multiagent response tests).

 Treatment Decision Support Tests

Companion Diagnostics
Single drug/class
Predict response/safety or guide dosing of a single drug or class

HercepTest   Dako
Determines HER2 protein overexpression for Herceptin treatment selection

Multiple drugs/classes

Vysis ALK Break
Apart FISH
Abbott Labs Predicts the NSCLC patient response to Xalkori

Other Decision Support
Provide prognostic and predictive information on the benefit of treatment

Oncotype Dx    Genomic Health, Inc.
Predicts both recurrence of breast cancer and potential patient benefit to chemotherapy regimens

PML-RARα     Clarient, Inc.
Predicts response to all-trans retinoic acid (ATRA) and other chemotherapy agents

TRUGENE    Siemens
Measures resistence to multiple  HIV-1 anti-retroviral agents

Multi-agent Response

Inform targeted therapy class selection by interrogating a panel of biomarkers
Target Now  Caris Life Sciences
Examines tumor’s molecular profile to tailor treatment options

ResponseDX: Lung    Response Genetics, Inc.
Examines multiple biomarkers to guide therapeutic treatment decisions for NSCLC patients

Source: L.E.K. Analysis

Includes IVD and LDT tests from

  1. top-15 IVD test suppliers,
  2. top-four large reference labs,
  3. top-five AP labs, and
  4. top-20 specialty reference labs.

For descriptive purposes only, may not map to exact regulatory labeling

Most tests are companion diagnostics and other decision support tests that provide guidance on

  • single drug/class therapy decisions.

However, holistic decision support tests (e.g., multi-agent response) are growing the fastest at 56% CAGR.
The emergence of multi-agent response tests suggests diagnostics companies are already seeing the need to aggregate individual tests (e.g., companion diagnostics) into panels of appropriate markers addressing a given clinical decision need. L.E.K. believes this trend is likely to continue as

  • increasing numbers of  biomarkers become validated for diseases and multiplexing tools
  • enabling the aggregation of multiple biomarker interrogations into a single test

to become deployed in the clinic.

Personalized Medicine Partnerships

L.E.K. also completed an assessment of publicly available personalized medicine partnership activity from 2009-2011 for ~150 leading organizations operating in the U.S. to look at broader decision support trends and emergence of more holistic solutions beyond diagnostic tests.

Survey of partnerships deals was conducted for

  • top-10 academic medical centers research institutions,
  • top-25 biopharma,
  • top-four healthcare IT companies,
  • top-three healthcare imaging companies,
  • top-20 IVD manufacturers,
  • top-20 laboratories,
  • top-10 payers/PBMs,
  • top-15 personalized healthcare companies,
  • top-10 regulatory/guideline entities, and
  • top-20 tools vendors for the period of 01/01/2009 – 12/31/2011.
    Source: Company websites, GenomeWeb, L.E.K. analysis

Across the sample we identified 189 publicly announced partnerships of which ~65% focused on more traditional areas (biomarker discovery, companion diagnostics and targeted therapies). However, a significant portion (~30%) included elements geared towards creating more holistic decision support models.

Partnerships categorized as holistic decision support by L.E.K. were focused on

  • mining large patient datasets (e.g., from payers or providers),
  • molecular profiling (e.g., deploying next-generation sequencing),
  • creating information technology (IT) infrastructure needed to enable holistic decision support models and
  • integrating various datasets to create richer decision support solutions.

Interestingly, holistic decision support partnerships often included stakeholders outside of biopharma and diagnostics such as

  • research tools,
  • payers/PBMs,
  • healthcare IT companies as well as
  • emerging personalized healthcare (PHC) companies (e.g., Knome, Foundation Medicine and 23andMe).

This finding suggests that these new stakeholders will be increasingly important in influencing care decisions going forward.

Holistic Treatment Decision Support

Holistic Decision   Support Focus

Technology Provider Partners
Stakeholder Deploying the Solution

Holistic Decision
Support Activities
Molecular Profiling

Life Technologies


Sequencing of triple-negative breast  cancer patients to identify potential treatment strategies

Foundation Medicine


Deployment of cancer genomics analysis platform to support Novartis clinical research efforts
Predictive genomics

Clarient, Inc.
(GE Healthcare)


Biomarker profiling of patients within Acorn’s network of providers to support clinical research efforts


Beth Israel Deaconess
Medical Center

Whole genome analysis and to guide patient management
Outcomes Data Mining



Evaluate comparative effectiveness of selected marketed therapies



Leverage information linking drug response and CYP2C9/CYP2C19 variation



Leverage patient genotype, phenotype and outcome for treatment decisions and target therapeutics
Healthcare IT Infrastructure



Deploy IBM’s Watson-based solution to evidence-based healthcare decision-making support


Moffitt Cancer Center

Deploy Oracle’s informatics platform to store and manage patient medical information
Data Integration

Siemens Diagnostics

Susquehanna Health

Integration of imaging and laboratory diagnostics



Integration of advanced tissue diagnostics, digital pathology, annotated biorepository and EMR
to create solutions
next-generation treatment decision support solutions


GE Healthcare

Integration of genomics with imaging data in CVD


L.E.K. believes the likely debate won’t center on which models and companies will prevail. It appears that the industry is now moving along the continuum to a truly holistic capability.
The mainstay of personalized medicine today will become integrated and enhanced by other data.

The companies that succeed will be able to capture vast amounts of information

  • and synthesize it for personalized care.

Holistic models will be powered by increasingly larger datasets and sophisticated decision-making algorithms.
This will require the participation of an increasingly broad range of participants to provide the

  • science, technologies, infrastructure and tools necessary for deployment.

There are a number of questions posed by this study, but only some are of interest to this discussion:

Group A.    Pharmaceuticals and Devices

  •  How will holistic decision support impact the landscape ?
    (e.g., treatment /testing algorithms, decision making, clinical trials)

Group B.     Diagnostics and   Decision Support

  •   What components will be required to build out holistic solutions?

– Testing technologies

– Information (e.g., associations, outcomes, trial databases, records)

– IT infrastructure for data integration and management, simulation and reporting

  •  How can various components be brought together to build seamless holistic  decision support solutions?

Group C.      Providers and Payers

  •  In which areas should models be deployed over time?
  • Where are clinical and economic arguments  most compelling?

Part 2: Historical Scientific Leaders Memoirs – Realtime Clinical Expert Support

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 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.

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 the workflow and the decision-making process with a reduction of medical error.

Continuing work is in progress in extending the capabilities with model datasets, and sufficient data because

  • 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 elevated cardiac biomarker (troponin), 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.

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 into 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 many characteristic features of classification. In the diagnosis of hematological disorders

  • proliferation of marrow precursors, the
  • domination of a cell line, and features of
  • suppression of hematopoiesis

provide a two dimensional model.  Other dimensions are created by considering

  • the maturity of the circulating cells.

The application of rules-based, automated problem solving should provide a valid approach to

  • the classification and interpretation of the data used to determine a knowledge-based clinical opinion.

The exponential growth of knowledge since the mapping of the human genome enabled by parallel advances in applied mathematics that have not been a part of traditional clinical problem solving.

As the complexity of statistical models has increased

  • the dependencies have become less clear to the individual.

Contemporary statistical modeling has a primary goal of finding an underlying structure in studied data sets.
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”

could improve clinical decision-making 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 by the clinician.

  • temperature
  • heart rate
  • respiratory rate and
  • WBC count

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
  • procalcitonin
  • mean arterial pressure

Immature granulocyte (IG) measurement has been proposed as a

  • 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, provides
  • a promising approach to enhance workflow and decision making.

Such a system utilizes the conjoined syndromic features of

  • disparate data elements with an anticipated reduction of medical error.

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.

The problem stated by LL  WEED in “Idols of the Mind” (Dec 13, 2006): “ a root cause of a major defect in the health care system is that, while we falsely admire and extol the intellectual powers of highly educated physicians, we do not search for the external aids their minds require”.  HIT use has been

  • focused on information retrieval, leaving
  • the unaided mind burdened with information processing.

We deal with problems in the interpretation of data presented to the physician, and how through better

  • design of the software that presents this data the situation could be improved.

The computer architecture that the physician uses to view the results is more often than not presented

  • as the designer would prefer, and not as the end-user would like.

In order to optimize the interface for physician, the system would have a “front-to-back” design, with
the call up for any patient ideally consisting of a dashboard design that presents the crucial information

  • that the physician would likely act on in an easily accessible manner.

The key point is that each item used has to be closely related to a corresponding criterion needed for a decision.

Feature Extraction.

This further breakdown in the modern era is determined by genetically characteristic gene sequences
that are transcribed into what we measure.  Eugene Rypka contributed greatly to clarifying the extraction
of features in a series of articles, which

  • set the groundwork for the methods used today in clinical microbiology.

The method he describes is termed S-clustering, and

  • will have a significant bearing on how we can view laboratory data.

He describes S-clustering as extracting features from endogenous data that

  • amplify or maximize structural information to create distinctive classes.

The method classifies by taking the number of features

  • with sufficient variety to map into a theoretic standard.

The mapping is done by

  • a truth table, and each variable is scaled to assign values for each: message choice.

The number of messages and the number of choices forms an N-by N table.  He points out that the message

  • choice in an antibody titer would be converted from 0 + ++ +++ to 0 1 2 3.

Even though there may be a large number of measured values, the variety is reduced

  • by this compression, even though there is risk of loss of information.

Yet the real issue is how a combination of variables falls into a table with meaningful information. We are concerned with accurate assignment into uniquely variable groups by information in test relationships. One determines the effectiveness of each variable by

  • its contribution to information gain in the system.

The reference or null set is the class having no information.  Uncertainty in assigning to a classification is

  • only relieved by providing sufficient information.

The possibility for realizing a good model for approximating the effects of factors supported by data used

  • for inference owes much to the discovery of Kullback-Liebler distance or “information”, and Akaike
  • found a simple relationship between K-L information and Fisher’s maximized log-likelihood function.

In the last 60 years the application of entropy comparable to

  • the entropy of physics, information, noise, and signal processing,
  • has been fully developed by Shannon, Kullback, and others, and has been integrated with modern statistics,
  • as a result of the seminal work of Akaike, Leo Goodman, Magidson and Vermunt, and work by Coifman.

Gil David et al. introduced an AUTOMATED processing of the data available to the ordering physician and

  • can anticipate an enormous impact in diagnosis and treatment of perhaps half of the top 20 most common
  • causes of hospital admission that carry a high cost and morbidity.

For example: anemias (iron deficiency, vitamin B12 and folate deficiency, and hemolytic anemia or myelodysplastic syndrome); pneumonia; systemic inflammatory response syndrome (SIRS) with or without bacteremia; multiple organ failure and hemodynamic shock; electrolyte/acid base balance disorders; acute and chronic liver disease; acute and chronic renal disease; diabetes mellitus; protein-energy malnutrition; acute respiratory distress of the newborn; acute coronary syndrome; congestive heart failure; disordered bone mineral metabolism; hemostatic disorders; leukemia and lymphoma; malabsorption syndromes; and cancer(s)[breast, prostate, colorectal, pancreas, stomach, liver, esophagus, thyroid, and parathyroid].

Rudolph RA, Bernstein LH, Babb J: Information-Induction for the diagnosis of myocardial infarction. Clin Chem 1988;34:2031-2038.

Bernstein LH (Chairman). Prealbumin in Nutritional Care Consensus Group.

Measurement of visceral protein status in assessing protein and energy malnutrition: standard of care. Nutrition 1995; 11:169-171.

Bernstein LH, Qamar A, McPherson C, Zarich S, Rudolph R. Diagnosis of myocardial infarction: integration of serum markers and clinical descriptors using information theory. Yale J Biol Med 1999; 72: 5-13.

Kaplan L.A.; Chapman J.F.; Bock J.L.; Santa Maria E.; Clejan S.; Huddleston D.J.; Reed R.G.; Bernstein L.H.; Gillen-Goldstein J. Prediction of Respiratory Distress Syndrome using the Abbott FLM-II amniotic fluid assay. The National Academy of Clinical Biochemistry (NACB) Fetal Lung Maturity Assessment Project.  Clin Chim Acta 2002; 326(8): 61-68.

Bernstein LH, Qamar A, McPherson C, Zarich S. Evaluating a new graphical ordinal logit method (GOLDminer) in the diagnosis of myocardial infarction utilizing clinical features and laboratory data. Yale J Biol Med 1999; 72:259-268.

Bernstein L, Bradley K, Zarich SA. GOLDmineR: Improving models for classifying patients with chest pain. Yale J Biol Med 2002; 75, pp. 183-198.

Ronald Raphael Coifman and Mladen Victor Wickerhauser. Adapted Waveform Analysis as a Tool for Modeling, Feature Extraction, and Denoising. Optical Engineering, 33(7):2170–2174, July 1994.

R. Coifman and N. Saito. Constructions of local orthonormal bases for classification and regression. C. R. Acad. Sci. Paris, 319 Série I:191-196, 1994.

Realtime Clinical Expert Support and validation System

We have developed 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 primary purpose is to

  1. gather medical information,
  2. generate metrics,
  3. analyze them in realtime and
  4. provide a differential diagnosis,
  5. meeting the highest standard of accuracy.

The system builds its unique characterization and provides a list of other patients that share this unique profile, therefore utilizing the vast aggregated knowledge (diagnosis, analysis, treatment, etc.) of the medical community. The

  • main mathematical breakthroughs are provided by accurate patient profiling and inference methodologies
  • in which anomalous subprofiles are extracted and compared to potentially relevant cases.

As the model grows and its knowledge database is extended, the diagnostic and the prognostic become more accurate and precise. We anticipate that the effect of implementing this diagnostic amplifier would result in

  • higher physician productivity at a time of great human resource limitations,
  • safer prescribing practices,
  • rapid identification of unusual patients,
  • better assignment of patients to observation, inpatient beds,
    intensive care, or referral to clinic,
  • shortened length of patients ICU and bed days.

The main benefit is a real time assessment as well as diagnostic options based on

  • comparable cases,
  • flags for risk and potential problems

as illustrated in the following case acquired on 04/21/10. The patient was diagnosed by our system with severe SIRS at a grade of 0.61 .

Graphical presentation of patient status

The patient was treated for SIRS and the blood tests were repeated during the following week. The full combined record of our system’s assessment of the patient, as derived from the further hematology tests, is illustrated below. The yellow line shows the diagnosis that corresponds to the first blood test (as also shown in the image above). The red line shows the next diagnosis that was performed a week later.

Progression changes in patient ICU stay with SIRS

Chemistry of Herceptin [Trastuzumab] is explained with images in




The Cost Burden of Disease: U.S. and Michigan CHRT Brief. January 2010.

The National Hospital Bill: The Most Expensive Conditions by Payer, 2006. HCUP Brief #59.

Rudolph RA, Bernstein LH, Babb J: Information-Induction for the diagnosis of myocardial infarction. Clin Chem 1988;34:2031-2038.

Bernstein LH, Qamar A, McPherson C, Zarich S, Rudolph R. Diagnosis of myocardial infarction: integration of serum markers and clinical descriptors using information theory. Yale J Biol Med 1999; 72: 5-13.

Kaplan L.A.; Chapman J.F.; Bock J.L.; Santa Maria E.; Clejan S.; Huddleston D.J.; Reed R.G.; Bernstein L.H.; Gillen-Goldstein J. Prediction of Respiratory Distress Syndrome using the Abbott FLM-II amniotic fluid assay. The National Academy of Clinical Biochemistry (NACB) Fetal Lung Maturity Assessment Project.  Clin Chim Acta 2002; 326(8): 61-68.

Bernstein LH, Qamar A, McPherson C, Zarich S. Evaluating a new graphical ordinal logit method (GOLDminer) in the diagnosis of myocardial infarction utilizing clinical features and laboratory data. Yale J Biol Med 1999; 72:259-268.

Bernstein L, Bradley K, Zarich SA. GOLDmineR: Improving models for classifying patients with chest pain. Yale J Biol Med 2002; 75, pp. 183-198.

Ronald Raphael Coifman and Mladen Victor Wickerhauser. Adapted Waveform Analysis as a Tool for Modeling, Feature Extraction, and Denoising. Optical Engineering 1994; 33(7):2170–2174.

  1. Coifman and N. Saito. Constructions of local orthonormal bases for classification and regression. C. R. Acad. Sci. Paris, 319 Série I:191-196, 1994.

W Ruts, S De Deyne, E Ameel, W Vanpaemel,T Verbeemen, And G Storms. Dutch norm data for 13 semantic categories and 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.

Retinal prosthetic strategy with the capacity to restore normal vision, Sheila Nirenberg and Chethan Pandarinath



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