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Posts Tagged ‘Trastuzumab’


Circulating Protein Breast Carcinoma Biomarkers

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Circulating Protein Biomarkers a Boon to Breast Cancer Management

Immunoassays Are the Gold Standard for Measuring Soluble Breast-Cancer-Related Proteins

Mary F. Lopez, Ph.D

http://www.genengnews.com/insight-and-intelligence/circulating-protein-biomarkers-a-boon-to-breast-cancer-management/77900557/

 

Circulating Protein Biomarkers a Boon to Breast Cancer Management

http://www.genengnews.com/media/images/AnalysisAndInsight/Nov4_2015_MaryLopez_HER2Oncoprotein2272426032.jpg

HER-2 is an oncogene and a member of the human epidermal growth factor receptor (HER/EGFR/ERBB) family. Amplification or overexpression of HER-2 plays an important role in the development and progression of certain aggressive types of breast cancer.

 

Breast cancer patients often display elevated levels of proteins in plasma or serum, and many of these proteins correlate to active or recurrent disease, metastasis, prognosis, and therapeutic response. Several of these markers have demonstrated clinical utility, particularly in the area of monitoring disease recurrence after or during therapeutic treatment in advanced disease.

The application of immunoassays to monitor levels of breast-cancer-related proteins in blood or serum is complementary to other diagnostics such as imaging and can be a valuable part of the routine management of disease. The availability of a group of easily applied blood tests is a convenient and powerful addition to the arsenal of technologies available to physicians for patient management. Immunoassays are available for serum-borne breast-cancer-related markers such as cancer antigen 15-3 (CA 15-3), carcinoembryonic antigen (CEA), tissue inhibitor of metalloproteinases-1 (TIMP-1), and human epidermal growth factor receptor 2 (HER2)—all of which are discussed in this article.

Clinical Utility Considered by Multiple Studies 

While essential for diagnosis and typing, tissue testing during long-term breast cancer management is impractical, costly, and painful for patients. A growing number of studies support serum-based immunoassay testing to monitor drug response, disease progression, and potential for metastases.

In 2012, Tsai et al.1 studied serum levels of HER2 and TIMP-1 in 185 breast cancer patients in Taiwan. They concluded that TIMP-1 was significantly associated with serum HER2-positive status in circulation, as well as poorer disease-free survival—suggesting that monitoring both of these biomarkers may be beneficial.

In the same year, Kontani et al.2 reported that serum HER2 is a useful biomarker not only for detecting breast cancer recurrence but also for predicting tumor responses to trastuzumab. In 2014, Shao et al.3 found that elevated serum HER2 levels were significantly associated with short-term response to trastuzumab treatment. The median progression-free survival was significantly longer in patients with low levels of serum HER2. Furthermore, they found that over time patients with remaining low serum HER2 levels or those who achieved low serum HER2 levels after treatment had significantly longer progression-free survival than those whose levels remained high or converted from low to high.

In 2015, Di Gioia et al.4 reported that they had investigated the combination of CEA, CA 15-3, and serum HER2 in the pretherapeutic serum of 241 patients as biomarkers for prognosis in early breast cancer. Their retrospective analysis confirmed that serum HER-2 and CA15-3 (but not CEA) were independent and better prognostic tools than HER-2 in tissue. However, they concluded that prospective validation is necessary to confirm usefulness in routine clinical practice.

In a review published in 2015, Ravelli et al.5 discussed the advantages, drawbacks, and new insights with respect to measuring circulating breast-cancer-related biomarkers with immunoassays as compared to standard tissue-based biopsies. They suggested that a reliable panel of circulating cancer biomarkers would be helpful for the following tasks:

  1. Screening and diagnostic procedures
  2. Predicting prognosis
  3. Selecting therapeutic options, including experimental ones
  4. Detecting a lack of efficacy of an ongoing therapy and predicting side-effects
  5. Identifying recurrence

In this review, the authors emphasized “classic” markers such as CA 15-3, CEA, and serum HER2. With respect to CA 15-3 and CEA, the authors noted that they might be most useful in combination. “[More] reliable prediction power have been obtained with the combination of the two biomarkers, whereas when measured singularly, both sensitivity and specificity values were drastically decreased,” the authors wrote. “As a result, the association of the two markers may be a useful independent tool in the follow-up of [breast cancer] patients.”

In their discussion of HER2, the researchers pointed out that increased serum HER2 levels have been clearly associated with the tissue HER2 status, the presence and number of metastases, and the levels of CA 15-3 and CEA.

Utility Strongly Suggested by a Large Study  

The review cited a 2014 study that reported how 2,862 primary breast cancer patients had been monitored to demonstrate the correlation between serum HER2 and tissue HER2 overexpression.6This study showed that 15% of tissue-HER2-positive patients also had increased serum HER2 levels and that there was a linear correlation with the increased aggressiveness of tumors. Multivariate analysis confirmed that increased serum HER2 is an independent prognostic factor that can be clinically useful, particularly in patients with tissue-HER2-positive tumors.

The authors of the study concluded that measuring serum HER2 could help oncologists monitor patient response to trastuzumab in the absence of tissue HER2. They added that further clinical validation would be worthwhile.

Summary

Basic immunoassay technology has been in place since the 1950s and has become the gold standard for clinical protein measurement due to high sensitivity and selectivity.7 Growing evidence suggests that these reliable, robust tests can provide valuable insights for physicians managing breast cancer treatment.

 

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Combining Nanotube Technology and Genetically Engineered Antibodies to Detect Prostate Cancer Biomarkers[1]

Writer, Curator: Stephen J. Williams, Ph.D.

acs nanoFigure of  Carbon Nanotube Transistor design with functionalized antibodies for biomarker detection.  From paper of A.T. Johnson; used with permission from A.T. Johnson)

In a literature review of the current status of the breast cancer biomarker field[2], author Dr. Michael Duffy, from University College Dublin, pondered the clinical utility of breast cancer serum markers and suggested that due to lack of sensitivity and specificity none of available markers is of value for detection of early breast cancer however these biomarkers have been shown useful in monitoring patients with advanced disease. For instance high preoperative CA15-3 is indicative of adverse patient outcome.  According to American Society of Clinical Oncology Expert Panel, however CA 15-3 may lack the sensitivity and disease specificity for breast cancer as a prognostic marker.  For panel suggestions please click on the link below:

http://www.asco.org/sites/www.asco.org/files/breast_tm_2007_changes-final.pdf

The same panel also concurred on the lack of prognostic value of other markers (for example CEA for colon cancer) but did agree that 66-73% of patients with advanced disease, who responded to therapy, showed reduction in these serum markers.  Indeed, CA125, long associated as a biomarker for ovarian cancer, does not have the sensitivity and especially the disease specificity to be a stand-alone prognostic marker[3].  Therefore, although “omics” strategies have suggested multiple possible biomarkers  for various cancers, a major issue in translating a putative biomarker to either:

1)      a clinically validated (panel) of disease-relevant biomarkers or

2)      biomarkers useful for therapeutic monitoring

is obtaining the specificity and sensitivity for detection in bio-specimens.   As discussed below, this is being achieved with the merger of nanotechnology-based sensors and bioengineering of biomolecule.

For ASCO panel suggestions of biomarkers useful in Prostate cancer please see the link below:

http://jco.ascopubs.org/site/misc/specialarticles.xhtml#GENITOURINARY_CANCER

As a side note, since 2010, ASCO has focused on reviewing and producing new guidelines for cancer biomarkers including genome sequencing:

http://www.medscape.com/viewarticle/723349

Osteopontin (OPN) and prostate cancer

Osteopontin is a phosphorylated glycoprotein secreted by activated macrophages, leukocytes, activated T lymphocytes and is present at sites of inflammation (for a review of OPN see [4]).  Osteopontin interacts with several integrins and CD44 (a putative cancer stem cell marker).  Binding of OPN to cell integrins mediates cell-matrix and cell-cell communication, stimulating adhesion, migration (through interaction with urokinase plasminogen activator {uPA}) and cell signaling pathways such as the HGF-Met pathway.  Overexpression is found on a variety of cancers including breast, lung, colorectal, ovarian and melanoma[5].  And although OPN is detected in normal tissue, it is known that OPN over-expression can alter the malignant potential of tumor cells.

Roles of osteopontin in cancer include:

  • Binding to CD44
  • Increase in growth factor signaling (HGF/Met pathway)
  • Increase uPA activity- increase invasiveness
  • Angiogenesis thru binding with αvβ3 integrin and increased VEGF expression
  • Protection against apoptosis: OPN activates nuclear factor Κβ

Some researchers have suggested it could be a prognostic marker for breast and lung cancer while there have been conflicting reports as to whether OPN expression is correlated to malignant potential in prostate cancer[6].  Osteopontin is found on tumor infiltrating macrophages, which may contribute to OPN as a prognostic marker. Breast cancer patients (disseminated carcinomas) have 4-10 times higher serum levels of OPN than found in healthy patients, although there is no difference in pre- or post-menopausal women[7].

Piezoelectric sensors have been used by the same group at Fox Chase Cancer Center to detect serum levels of the HER2 protein in breast cancer patients, for the purpose of therapeutic monitoring after anti-HER2 antibody trastuzumab (Herceptin™) therapy.  Lina Loo, in the laboratory of Dr. Gregory Adams showed the utility of using (scFv) to trastuzumab (anti-HER2) with pizo-electric nanotubes to accurately and reproducibly determine levels of serum HER2[8].  This method improved the sensitivity of serum HER2 detection over other methods such as:

  • ELISA {enzyme-linked immunoassay}
  • Luminex platforms

Please watch the following video interview concerning genetically engineered scFV antibody fragments and their use in cancer detection and treatment (with Dr. Matt Robinson and Dr. Greg Adams, from Fox Chase Cancer Center)

PLEASE WATCH VIDEO

However the advent of nanotechnology-based detection system combined with engineered affinity-based biomolecules has increased both the sensitivity and specificity of biomarker detection from complex fluids such as plasma and urine.  The advent of multiple types of biosensors, including

has given the ability to measure, with enhanced sensitivity and specificity,  putative biomarkers of disease in minute volumes of precious bio-samples.

The basic design of a biosensor is made of three components:

  1. A recognition element (I.e. antibodies, nucleic acids, enzymes)
  2. A signal transducer (electrochemical, optical, piezoelectric)
  3. Signal processor (relays and displays)

In the journal ACS Nano Mitchel Lerner from Dr. Charlie Johnson’s laboratory at University of Pennsylvania in collaboration with Fox Chase Cancer researchers in the laboratory of Dr. Matthew Robinson, describe a piezoelectric detection system for quantifying levels of osteopontin (OPN), a putative biomarker for prostate cancer[1].  In this paper Dr. Robinson’s group at Fox Chase, genetically engineered a single chain variable fragment (scFv) protein {the binding portion of the antibody} which had high affinity for OPN.  This scFv was attached to a carbon nanotube field-effect transistor (NT-FET), designed by Dr. Johnson’s group, using a chemical process called chemical functionalization {a process using diazonium salts to covalently attach scFV to NT-FET.

functionalization

Figure. Functionalization scheme for OPN attachment to carbon nanotubes. As figure 1 legend in paper states: “First, sp8 hybridized-sites are created o the nanotube sidewall by incubation in a diazonium salt solution.  The carboxylic acid group is then activated by EDC and stabilized with NHS.ScFv antibody displaces the NHS and forms an amide bond.  OPN epitope is shown in yellow and the C and N-terminuses are in orange and green respectively.” (used by permision for A.T. Charlie Johnson)

This system was then used to determine the selectivity and sensitivity of OPN from complex solutions.

Methods: 

Nanotube (NT) design

  • Grown by catalytic vapor deposition
  • Electrical contacts patterned using photo-lithography
  • Atomic Force microscopy was used to verify structure of nanotube

Chemical Linking of scFv to nanotube

  • Diazonium treatment resulted in activation and subsequent stabilization of amino (NHS) side chain
  • Amine group on lysine of scFV displaced NHS group => covalent attachment of scFV to NT
  • Atomic Force Spectroscopy used to verify linkage of scFv to nanotube

Results showed there was

  • minimal non-specific binding of OPN to the scFv
  • system allowed for detection limit of 1 pg/ml OPN (pictogram/milliliter) or 30 fM (fentomolar) in a phosphate buffered saline solution.
  •  Only a minute volume (10 µl) of sample is needed
  • Sensor able to measure million-fold  range of OPN concentrations ( from 10-3 to 103 ng/mL OPN)

Two experiments were conducted to determine the specificity of OPN to the antibody-detection system.

1st experiment

–          scFv functionalized  sensor was incubated in a solution of high concentration of BSA (450 mg/ml) to approximate nonspecific proteins in patient samples

–           minimal signal was detected

        2nd experiment

–          Functionalized NT-FET devices with a scFv based on the HER2 therapeutic antibody trastuzumab

–          There was no binding of OPN to anti-HER2 devices

–          Therefore anti OPN (23C3) scFv-functionalized carbon nanotube sensors exhibit high levels of specificity to OPN

The authors conclude “the functionalization procedure described here is expected to be generalizable to any antibody containing an accessible amine group, and to result in biosensors appropriate for detection of corresponding complementary proteins at fM concentrations”.

I had the opportunity to speak with co-author Dr. Matthew Robinson, Assistant Professor in the Developmental Therapeutics Program at Fox Chase Cancer Center about the next steps for this work.  Dr. Robinson mentioned that “at this point we have not looked in patient samples yet but our plan is to move in that direction. We need to establish sensitivity/specificity in increasingly complex samples (e.g. spiked normal serum and retrospectively in patient serum with known levels of biomarkers).” 

Cancer patients often present a complex metabolic profile.  The paper notes that OPN has a pI (isoelectric point) of 4.2, which would result in a negative charge at physiologically normal pH of 7.6. I asked Dr. Robinson about if changes in metabolic profile could hinder OPN binding to the NT-FET system would require some preprocessing of blood samples.  Dr. Robinson  agreed “that confounding variables such as additional diseases but even things like diet (i.e. is fasting necessary) need to be addressed before this platform is ready for use in clinical setting.
It is likely that sample prep will be needed to remove albumin, lower salt concentrations, etc. This could end up being problematic for biomarkers that are unstable and would degrade over the time necessary for sample prep. It is also possible that sample prep to remove albumin and other background factors could result in loss of biomarkers. This will need to be determined on a case-by-case basis with validated testing methods.”
One useful advantage of this system is the possibility of measuring multiple biomarkers, clinically important as studies has suggested that

multiple markers result in the higher sensitivity/specificity for many infrequent cancers, such as ovarian. Dr. Robinson agrees “that panels of biomarkers are likely to be better at early detection and diagnosis. In principle the platform that we describe can be set up to allow for detection of  multiple biomarkers at a time. From the biology end of things we have built antibodies against 3 different prostate cancer biomarkers for that purpose.”

Dr. Johnson  commented on the ability of the platform allowed for the simultaneous detection of multiple biomarkers, noting that ”the platform is compatible with the measurement of multiple biomarkers through the use of multiple devices, each functionalized with their own antibody.”

 

ASCO guidelines Expert Panel on Tumor Biomarkers 2007 Update for Breast Cancer:

http://www.asco.org/sites/www.asco.org/files/breast_tm_2007_changes-final.pdf 

ASCO Guidelines for Genitourinary Cancer:

Screening for Prostate Cancer With Prostate-Specific Antigen Testing: American Society of Clinical Oncology Provisional Clinical Opinion

Published in JCO, Vol. 30, Issue 24 (August 20), 2012: 3020-3025

American Society of Clinical Oncology Clinical Practice Guideline on Uses of Serum Tumor Markers in Adult Males With Germ Cell Tumors

Published in JCO, Vol 28, Issue 20 (July 10), 2010: 3388-3404

American Society of Clinical Oncology Endorsement of the Cancer Care Ontario Practice Guideline on Nonhormonal Therapy for Men With Metastatic Hormone-Refractory (castration-resistant) Prostate Cancer

Published in JCO, Vol 25, Issue 33 (November 20), 2007: 5313-5318

Initial Hormonal Management of Androgen-Sensitive Metastatic, Recurrent, or Progressive Prostate Cancer: 2006 Update of an American Society of Clinical Oncology Practice Guideline

Published in JCO, Vol. 25, Issue 12 (April 20), 2007: 1596-1605

References:

1.            Lerner MB, D’Souza J, Pazina T, Dailey J, Goldsmith BR, Robinson MK, Johnson AT: Hybrids of a genetically engineered antibody and a carbon nanotube transistor for detection of prostate cancer biomarkers. ACS nano 2012, 6(6):5143-5149.

2.            Duffy MJ: Serum tumor markers in breast cancer: are they of clinical value? Clinical chemistry 2006, 52(3):345-351.

3.            Meyer T, Rustin GJ: Role of tumour markers in monitoring epithelial ovarian cancer. British journal of cancer 2000, 82(9):1535-1538.

4.            Rodrigues LR, Teixeira JA, Schmitt FL, Paulsson M, Lindmark-Mansson H: The role of osteopontin in tumor progression and metastasis in breast cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2007, 16(6):1087-1097.

5.            Brown LF, Berse B, Van de Water L, Papadopoulos-Sergiou A, Perruzzi CA, Manseau EJ, Dvorak HF, Senger DR: Expression and distribution of osteopontin in human tissues: widespread association with luminal epithelial surfaces. Molecular biology of the cell 1992, 3(10):1169-1180.

6.            Thoms JW, Dal Pra A, Anborgh PH, Christensen E, Fleshner N, Menard C, Chadwick K, Milosevic M, Catton C, Pintilie M et al: Plasma osteopontin as a biomarker of prostate cancer aggression: relationship to risk category and treatment response. British journal of cancer 2012, 107(5):840-846.

7.            Brown LF, Papadopoulos-Sergiou A, Berse B, Manseau EJ, Tognazzi K, Perruzzi CA, Dvorak HF, Senger DR: Osteopontin expression and distribution in human carcinomas. The American journal of pathology 1994, 145(3):610-623.

8.            Loo L, Capobianco JA, Wu W, Gao X, Shih WY, Shih WH, Pourrezaei K, Robinson MK, Adams GP: Highly sensitive detection of HER2 extracellular domain in the serum of breast cancer patients by piezoelectric microcantilevers. Analytical chemistry 2011, 83(9):3392-3397.

Other posts from this site on Biomarkers, Cancer, and Nanotechnology include:

Stanniocalcin: A Cancer Biomarker.

Mesothelin: An early detection biomarker for cancer (By Jack Andraka)

Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker

PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy

Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Early Biomarker for Pancreatic Cancer Identified

In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission

Prostate Cancer Molecular Diagnostic Market – the Players are: SRI Int’l, Genomic Health w/Cleveland Clinic, Myriad Genetics w/UCSF, GenomeDx and BioTheranostics

Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline

A Blood Test to Identify Aggressive Prostate Cancer: a Discovery @ SRI International, Menlo Park, CA

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition

Prostate Cancer and Nanotecnology

 

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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

and

  • 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

TGEN/US
Oncology

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

Foundation Medicine

Novartis

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

Clarient, Inc.
(GE Healthcare)

Acorn
Research

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

GenomeQuest

Beth Israel Deaconess
Medical Center

Whole genome analysis and to guide patient management
Outcomes Data Mining

AstraZeneca

WellPoint

Evaluate comparative effectiveness of selected marketed therapies

23andMe

NIH

Leverage information linking drug response and CYP2C9/CYP2C19 variation

Pfizer

Medco

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

IBM

WellPoint

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

Oracle

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

Cernostics

Geisinger
Health

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

CardioDx

GE Healthcare

Integration of genomics with imaging data in CVD

Implications

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

http://www.chm.bris.ac.uk/motm/herceptin/index_files/Page450.htm

 

REFERENCES

The Cost Burden of Disease: U.S. and Michigan CHRT Brief. January 2010.
@www.chrt.org

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

http://www.pnas.org/content/109/37/15012

 

Other related articles published in http://pharmaceuticalintelligence.com include the following:

 

  • The Automated Second Opinion Generator

Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2012/08/13/the-automated-second-opinion-generator/

 

  • The electronic health record: How far we have travelled and where is journeys end

Larry H Bernstein, MD, FCAP

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

 

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

Larry H Bernstein, MD, FCAP

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

 

  • Clinical Decision Support Systems for Management Decision Making of Cardiovascular Diseases

Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/05/04/cardiovascular-diseases-decision-support-systems-for-disease-management-decision-making/

 

  • Demonstration of a diagnostic clinical laboratory neural network applied to three laboratory data conditioning problems

Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2012/08/13/demonstration-of-a-diagnostic-clinical-laboratory-neural-network-agent-applied-to-three-laboratory-data-conditioning-problems/

 

  • CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics

Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2014/08/30/cracking-the-code-of-human-life-the-birth-of-bioinformatics-computational-genomics/

 

  • Genetics of conduction disease atrioventricular AV conduction disease block gene mutations transcription excitability and energy homeostasis

Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/04/28/genetics-of-conduction-disease-atrioventricular-av-conduction-disease-block-gene-mutations-transcription-excitability-and-energy-homeostasis/

 

  • Identification of biomarkers that are related to the actin cytoskeleton

Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/

 

  • Regression: A richly textured method for comparison of predictor variables

Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2012/08/14/regression-a-richly-textured-method-for-comparison-and-classification-of-predictor-variables/

 

  • Diagnostic evaluation of SIRS by immature granulocytes

Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2012/08/02/diagnostic-evaluation-of-sirs-by-immature-granulocytes/

 

  • Big data in genomic medicine

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https://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/

 

  • Automated inferential diagnosis of SIRS, sepsis, septic shock

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https://pharmaceuticalintelligence.com/2012/08/01/automated-inferential-diagnosis-of-sirs-sepsis-septic-shock/

 

  • A Software Agent for Diagnosis of ACUTE MYOCARDIAL INFARCTION

Isaac E. Mayzlin, Ph.D., David Mayzlin and Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2012/08/12/1815/

 

  • Artificial Vision: Cornell and Stanford Researchers crack Retinal Code

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https://pharmaceuticalintelligence.com/2012/08/15/1946/

 

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https://pharmaceuticalintelligence.com/2013/05/13/vinod-khosla-20-doctor-included-speculations-musings-of-a-technology-optimist-or-technology-will-replace-80-of-what-doctors-do/

 

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CD47: Target Therapy for Cancer

Author/Curator: Tilda Barliya

“A research team from Stanford University’s School of Medicine is now one step closer to uncovering a cancer treatment that could be applicable across the board in killing every kind of cancer tumor” (1). It appeared that their antibody-drug against the CD47 protein, enabled the shrinking of all tumor cells. After completing their animal studies the researchers now move into a human phase clinical trials. CD47 has been previously studied and evaluated for its role in multiple cells, some of this data however, is somewhat controversy. So where do we stand?

CD47

CD47 (originally named integrin-associated protein (IAP)) is a cell surface protein of the immunoglobulin (Ig) superfamily, which is heavily glycosylated and expressed by virtually all cells in the body and overexpressed in many types of cancer  including breast, ovarian, colon, prostate and others (3). CD47 was first recognized as a 50 kDa protein associated and copurified with the  Alpha-v-Beta-3 integrin in placenta and neutrophil granulocytes and later shown to have the capacity to regulate integrin function and the responsiveness of leukocytes to RGD-containing extracellular matrix proteins. CD47 has also been shown to be identical to the OA-3/OVTL3 antigen highly expressed on most ovarian carcinomas (4,5).

CD47 consists of an extracellular IgV domain, a five times transmembrane-spanning domain, and a short alternatively spliced cytoplasmic tail. In both humans and mice, the cytoplasmic tail can be found as four different splice isoforms ranging from 4 to 36 amino acids, showing different tissue expression patterns (3).

CD47 interactions (3, 6):

  • Thrombospondin-1 (TSP-1) – a secreted glycoprotein that plays a role in vascular development and angiogenesis. Binding of TSP-1 to CD47 influences several fundamental cellular functions including cell migration and adhesion, cell proliferation or apoptosis, and plays a role in the regulation of angiogenesis and inflammation.
  • Signal-regulatory protein-alpha (SIRPα) – an inhibitory transmembrane receptor present on myeloid cells. The CD47/SIRPα interaction leads to bidirectional signaling, resulting in different cell-to-cell responses including inhibition of phagocytosis, stimulation of cell-cell fusion, and T-cell activation.
  • Integrins – several membrane integrins, most commonly integrin avb3. These interactions result in CD47/integrin complexes that effect a range of cell functions including adhesion, spreading and migration

These interactions with multiple proteins and cells types create several important functions, which include:

  • Cell proliferation – cell proliferation is heavily dependent on cell type as both activation and loss of CD47 can result in enhanced proliferation. For example, activation of CD47 with TSP-1 in wild-type cells inhibits proliferation and reduces expression of stem cell transcription factors. In cancer cells however, activation of CD47 with TSP-1 increases proliferation of human U87 and U373 astrocytoma. it is likely that CD47 promotes proliferation via the PI3K/Akt pathway in cancerous cells but not normal cells (7).  Loss of CD47 allows sustained proliferation of primary murine endothelial cells and enables these cells to spontaneously reprogram to form multipotent embryoid body-like clusters (8).
  • Apoptosis – Ligation of CD47 by anti-CD47 mAbs was found to induce apoptosis in a number of different cell types (3). For example: Of the two SIRP-family members known to bind the CD47 IgV domain (SIRPα and SIRPγ), SIRPα as a soluble Fc-fusion protein does not induce CD47-dependent apoptosis, hile SIRPα or SIRPγ bound onto the surface of beads induces apoptosis through CD47 in Jurkat T cells and the myelomonocytic cell line U937.
  • Migration – CD47  role on cell migration was first demonstrated in neutrophils, these effects were shown to be dependent on avb3 integrins, which interact with and are activated by CD47 at the plasma membrane. In cancer, Blocking CD47 function has been shown to inhibit migration and metastasis in a variety of tumor models. Blockade of CD47 by neutralizing antibodies reduced migration and chemotaxis in response to collagen IV in melanomaprostate cancer and ovarian cancer-derived cells (9).
  • Angiogenesis – The mechanism of the anti-angiogenic activity of CD47 is not fully understood, but introduction of CD47 antibodies and TSP-1 have been shown to inhibit nitric oxide (NO)-stimulated responses in both endothelial and vascular smooth muscle cells (10). More so, CD47 signaling influences the SDF-1 chemokine pathway, which plays a role in angiogenesis (11). (12)
  • Inflammatory response – Interactions between endothelial cell CD47 and leukocyte SIRPγ regulate T cell transendothelial migration (TEM) at sites of inflammation. CD47 also functions as a marker of self on murine red blood cells which allows RBC to avoid phagocytosis. Tumor cells can also evade macrophage phagocytosis through the expression of CD47 (2, 13).

It appears that CD47 ligation induce different responses, depending on cell type and partner for ligation.

Therapeutic and clinical aspect of CD47 in human cancer:

CD47 is overexpressed in many types of human cancers  and its known function as a “don’t eat me” signal, suggests the potential for targeting the CD47-SIRPα pathway as a common therapy for human malignancies (2,13). Upregulation of CD47 expression in human cancers also appears to influence tumor growth and dissemination. First, increased expression of CD47 in several hematologic malignancies was found to be associated with a worse clinical prognosis, and in ALL to predict refractoriness to standard chemotherapies (13, 14-16). Second, CD47 was demonstrated to regulate tumor metastasis and dissemination in both MM and NHL (13, 17).

Efforts have been made to develop therapies inhibiting the CD47-SIRPα pathway, principally through blocking monoclonal antibodies directed against CD47, but also possibly with a recombinant SIRPα protein that can also bind and block CD47.

Figure 2

Chao MP et al. 2012 Combination strategies targeting CD47 in cancer

While monotherapies targeting CD47 were efficacious in several pre-clinical tumor models, combination strategies involving inhibition of the CD47-SIRPα pathway offer even greater therapeutic potential. Specifically, antibodies targeting CD47-SIRPα can be included in combination therapies with other therapeutic antibodies, macrophage-enhancing agents, chemo-radiation therapy, or as an adjuvant therapy to inhibit metastasis (13).

For example, anti-SIRPα antibody was found to potentiate  antibody-dependent cellular cytotoxicity (ADCC) mediated by the anti-Her2/Neu antibody trastuzumab against breast cancer cells (18).  CD47–SIRPα interactions and SIRPα signaling negatively regulate trastuzumab-mediated ADCC in vitro and antibody-dependent elimination of tumor cells in vivo

More so, chemo-radiation therapy-mediated upregulation of cell surface calreticulin may potentially augment the activity of anti-CD47 antibody. However, this approach may also lead to increased toxicity as cell surface calreticulin is expressed on non-cancerous cells undergoing apoptosis, a principle effect of chemo-radiation therapy (19).

Highlights:

  • Phagocytic cells, macrophages, regulate tumor growth through phagocytic clearance
  • CD47 binds SIRPα on phagocytes which delivers an inhibitory signal for phagocytosis
  • A blocking anti-CD47 antibody enabled phagocytic clearance of many human cancers
  • Phagocytosis depends on a balance of anti-(CD47) and pro-(calreticulin) signals
  • Anti-CD47 antibody synergized with an FcR-engaging antibody, such as rituximab

Summary

Evasion of immune recognition is a major mechanism by which cancers establish and propagate disease. Recent data has demonstrated that the innate immune system plays a key role in modulating tumor phagocytosis through the CD47-SIRPα pathway. Careful development of reagents that can block the CD47/SIRPα interaction may indeed be useful to treat many forms of cancer without having too much of a negative side effect in terms of inducing clearance of host cells. Therapeutic approaches inhibiting this pathway have demonstrated significant efficacy, leading to the reduction and elimination of multiple tumor types.

Dr. Weissman says: “We are now hopeful that the first human clinical trials of anti-CD47 antibody will take place at Stanford in mid-2014, if all goes wellClinical trials may also be done in the United Kingdom”. These clinical trials must be designed so that the data they generate will produce a valid scientific result!!!

REFERENCES

1. By Sara Gates:  Cancer Drug That Shrinks All Tumors Set To Begin Human Clinical Trials. http://www.huffingtonpost.com/2013/03/28/cancer-drug-shrinks-tumors_n_2972708.html

2. Willingham SB, Volkmer JP, Gentles AJ, Sahoo D, Dalerba P, Mitra SS, Wang J, Contreras-Trujillo H, Martin R, Cohen JD, Lovelace P, Scheeren FA, Chao MP, Weiskopf K, Tang C, Volkmer AK, Naik TJ, Storm TA, Mosley AR, Edris B, Schmid SM, Sun CK, Chua MS, Murillo O, Rajendran P, Cha AC, Chin RK, Kim D, Adorno M, Raveh T, Tseng D, Jaiswal S, Enger PØ, Steinberg GK, Li G, So SK, Majeti R, Harsh GR, van de Rijn M, Teng NN, Sunwoo JB, Alizadeh AA, Clarke MF, Weissman IL. The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Proc Natl Acad Sci U S A. 2012 Apr 24;109(17):6662-6667. http://www.pnas.org/content/early/2012/03/20/1121623109

3. Oldenborg PL. CD47: A Cell Surface Glycoprotein Which Regulates Multiple Functions of Hematopoietic Cells in Health and Disease. ISRN Hematology Volume 2013 (2013), Article ID 614619, 19 pages.  http://www.hindawi.com/isrn/hematology/2013/614619/

4. G. Campbell, P. S. Freemont, W. Foulkes, and J. Trowsdale, “An ovarian tumor marker with homology to vaccinia virus contains an IgV- like region and multiple transmembrane domains,”Cancer Research, vol. 52, no. 19, pp. 5416–5420, 1992. http://cancerres.aacrjournals.org/content/52/19/5416.long

5. L. G. Poels, D. Peters, Y. van Megen et al., “Monoclonal antibody against human ovarian tumor-associated antigens,” Journal of the National Cancer Institute, vol. 76, no. 5, pp. 781–791, 1986. http://www.ncbi.nlm.nih.gov/pubmed/3517452

6. CD47. Wikipedia. http://en.wikipedia.org/wiki/CD47

7. Sick E, Boukhari A, Deramaudt T, Rondé P, Bucher B, André P, Gies JP, Takeda K (February 2011). “Activation of CD47 receptors causes proliferation of human astrocytoma but not normal astrocytes via an Akt-dependent pathway”. Glia 59 (2): 308–319. http://www.ncbi.nlm.nih.gov/pubmed/21125662

8. Kaur S, Soto-Pantoja DR, Stein EV, Liu C, Elkahloun AG, Pendrak ML, Nicolae A, Singh SP, Nie Z, Levens D, Isenberg JS, Roberts DD.  “Thrombospondin-1 Signaling through CD47 Inhibits Self-renewal by Regulating c-Myc and Other Stem Cell Transcription Factors”Sci Rep 2013: 3: 1673. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628113/

9. Shahan TA, Fawzi A, Bellon G, Monboisse JC, Kefalides NA. “Regulation of tumor cell chemotaxis by type IV collagen is mediated by a Ca(2+)-dependent mechanism requiring CD47 and the integrin alpha(V)beta(3)”. J. Biol. Chem 2000. 275 (7): 4796–4802. http://www.jbc.org/content/275/7/4796

10. Isenberg JS, Ridnour LA, Dimitry J, Frazier WA, Wink DA, Roberts DD. “CD47 is necessary for inhibition of nitric oxide-stimulated vascular cell responses by thrombospondin-1”. J. Biol. Chem  2006. 281 (36): 26069–26080.  http://www.jbc.org/content/281/36/26069

11. Smadja DM, d’Audigier C, Bièche I, Evrard S, Mauge L, Dias JV, Labreuche J, Laurendeau I, Marsac B, Dizier B, Wagner-Ballon O, Boisson-Vidal C, Morandi V, Duong-Van-Huyen JP, Bruneval P, Dignat-George F, Emmerich J, Gaussem P. “Thrombospondin-1 is a plasmatic marker of peripheral arterial disease that modulates endothelial progenitor cell angiogenic properties”. Arterioscler. Thromb. Vasc. Biol  2011. 31 (3): 551–559. http://atvb.ahajournals.org/content/31/3/551

12. G. D. Grossfeld, D. A. Ginsberg, J. P. Stein et al., “Thrombospondin-1 expression in bladder cancer: association with p53 alterations, tumor angiogenesis, and tumor progression,” Journal of the National Cancer Institute 1997 vol. 89, no. 3, pp. 219–227. http://www.scopus.com/record/display.url?eid=2-s2.0-18744423089&origin=inward&txGid=9C86356DDB0B6816ACCBF90F9CA44E92.WlW7NKKC52nnQNxjqAQrlA%3a2

13. Chao MP, Weissman IL, Majeti R. “The CD47-SIRPα pathway in cancer immune evasion and potential therapeutic implications”Curr. Opin. Immunol 2012. 24 (2): 225–32. http://www.sciencedirect.com/science/article/pii/S095279151200012Xhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3319521/

14. Majeti R, Chao MP, Alizadeh AA, Pang WW, Jaiswal S, Gibbs KD, Jr, van Rooijen N, Weissman IL. Cd47 is an adverse prognostic factor and therapeutic antibody target on human acute myeloid leukemia stem cells. Cell. 2009;138(2):286–299. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2726837/

15. Chao MP, Alizadeh AA, Tang C, Jan M, Weissman-Tsukamoto R, Zhao F, Park CY, Weissman IL, Majeti R. Therapeutic antibody targeting of cd47 eliminates human acute lymphoblastic leukemia.Cancer Res. 2011;71 (4):1374–1384. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041855/

16. Chao MP, Alizadeh AA, Tang C, Myklebust JH, Varghese B, Gill S, Jan M, Cha AC, Chan CK, Tan BT, Park CY, et al. Anti-cd47 antibody synergizes with rituximab to promote phagocytosis and eradicate non-hodgkin lymphoma. Cell. 2010;142(5):699–713. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943345/

17. Chao MP, Tang C, Pachynski RK, Chin R, Majeti R, Weissman IL. Extranodal dissemination of non-hodgkin lymphoma requires cd47 and is inhibited by anti-cd47 antibody therapy. Blood.2011;118(18):4890–4901. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3208297/

18. Zhao XW, van Beek EM, Schornagel K, Van der Maaden H, Van Houdt M, Otten MA, Finetti P, Van Egmond M, Matozaki T, Kraal G, Birnbaum D, et al. Cd47-signal regulatory protein-alpha (sirpalpha) interactions form a barrier for antibody-mediated tumor cell destruction. Proc Natl Acad Sci U S A.2011;108(45):18342–18347. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3215076/

19. Obeid M, Tesniere A, Ghiringhelli F, Fimia GM, Apetoh L, Perfettini JL, Castedo M, Mignot G, Panaretakis T, Casares N, Metivier D, et al. Calreticulin exposure dictates the immunogenicity of cancer cell death. Nat Med. 2007;13(1):54–61. http://www.ncbi.nlm.nih.gov/pubmed/17187072

Other related articles on this Open Access Online Scientific Journal include the following:

I. By: Larry Bernstein MD. Treatment for Metastatic HER2 Breast Cancer https://pharmaceuticalintelligence.com/2013/03/03/treatment-for-metastatic-her2-breast-cancer/

II. By: Tilda Barliya PhD. Colon Cancer.  https://pharmaceuticalintelligence.com/2013/04/30/colon-cancer/

III. By: Ritu Saxena PhD. In focus: Triple Negative Breast Cancer. https://pharmaceuticalintelligence.com/2013/01/29/in-focus-triple-negative-breast-cancer/

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Treatment for Metastatic HER2 Breast Cancer

Reporter: Larry H Bernstein, MD, FCAP
Leaders in Pharmaceutical Innovation
https://pharmaceuticalintelligence.com/2013/03/03/9680/Treatment for Metastatic HER2 Breast Cancer 

FDA Approves New Treatment for Metastatic HER2 Breast Cancer (antibody-drug conjugate)
T-DM1 is indicated for patients who were previously treated with the anti-HER2 therapy trastuzumab (Herceptin, Genentech) and a taxane chemotherapy.

The US Food and Drug Administration (FDA) today approved ado-trastuzumab emtansine (Kadcyla, Genentech), also known as T-DM1, for the treatment of patients with HER2-positive metastatic breast cancer.
T-DM1 is indicated for patients who were previously treated with

  • the anti-HER2 therapy trastuzumab (Herceptin, Genentech) and a taxane chemotherapy.

This product offers a new twist on an older product; it is an antibody–drug conjugate in which the

  • HER2-targeted antibody trastuzumab
  • is chemically linked to the cytotoxin mertansine (DM1).

The antibody homes in on HER2 breast cancer cells, delivering the chemotherapy directly to the tumor, which reduces the risk for toxicity.  According to Richard Pazdur, MD, at the FDA Center for Drug Evaluation and Research, T-DM1 carries the drug-conjugate

  • directly to the cancer site
  • to shrink the tumor,
  • slow disease progression, and
  • prolong survival .

It is the fourth drug approved that targets the HER2 protein. Apart from lapatinib, which is marketed by GlaxoSmithKline, all the other HER2-targeted products have been developed and are marketed by Genentech/Roche. For T-DM1, the proprietary technology involved in the DM1 portion of the product was developed by ImmunoGen, working in collaboration with Genentech/Roche.

In the pivotal phase 3 EMILIA study, patients receiving T-DM1 survived nearly 6 months longer than patients receiving the standard therapy of

  • lapatinib (Tykerb) plus capecitabine (Xeloda) (median overall survival, 30.9 vs 25.1 months).

There were fewer grade 3 or higher (severe) adverse events with TDM-1 than with standard therapy

  • 43.1% vs. 59.2%)

The approval represents a “momentous” day in breast cancer, said Kathy Miller, MD, from Indiana University in Indianapolis, in her Miller on Oncology Medscape blog.

  • HER2-positive patients with metastatic disease have a therapy that offers prolonged disease control with less toxicity

 T-DM1 was more effective in EMILIA than standard therapy on every outcome:

  • overall response rate,
  • disease-free survival,
  • progression-free survival, and
  • overall survival.
Herceptin Fab (antibody) - light and heavy chains

Herceptin Fab (antibody) – light and heavy chains (Photo credit: Wikipedia)

Ribbon diagram of the Fab fragment of , a , bo...

Ribbon diagram of the Fab fragment of , a , bound to the extracellular domain of HER2. Created using Accelrys DS Visualizer Pro 1.6 and . ; Legend Trastuzumab Fab fragment, Trastuzumab Fab fragment, HER2, extracellular domain (Photo credit: Wikipedia)

Breast cancer (Infiltrating ductal carcinoma o...

Breast cancer (Infiltrating ductal carcinoma of the breast) assayed with anti HER-2 (ErbB2) antibody. (Photo credit: Wikipedia)

English: Breast cancer incidence by age in wom...

English: Breast cancer incidence by age in women in the United Kingdom 2006-2008. Reference: Excel chart for Figure 1.1: Breast Cancer (C50), Average Number of New Cases per Year and Age-Specific Incidence Rates, UK, 2006-2008 at Breast cancer – UK incidence statistics at Cancer Research UK. Section updated 18/07/11. (Photo credit: Wikipedia)

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