TeloView Measures Genomic Stability To Predict Disease Aggressiveness

MedicalResearch.com Interview with:

3D SignaturesJason Flowerday, CEO
Director of 3D Signatures 

MedicalResearch.com: What is the background for 3D Signatures?

Response: 3D Signatures, and its clinical lab tests, which incorporate its proprietary TeloViewTM software analytics, is the culmination of over 20 years of ground-breaking research conducted by Dr. Sabine Mai and her colleagues. It is the only technology in the world that quantifies genomic instability, which is the hallmark of cancer and other proliferative diseases at the whole-cell level.

By measuring the degree of genomic instability from different tissues, TeloViewTM has produced clinically actionable distinctions in the stage of disease, rate of progression of disease, drug efficacy, and drug toxicity. The technology is well developed and supported by 22 clinical studies on over 2,000 patients on 13 different cancers including Alzheimer’s disease. The results have been exceptional and represent a universal biomarker platform across all disease areas that the company has investigated to date.

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Frequency of Retinal Screening in Diabetes May Be Tailored to Individual

MedicalResearch.com Interview with:
John M. Lachin, Sc.D.
Research Professor of Biostatistics and of Epidemiology, and of Statistics
The George Washington University Biostatistics Center and
David Matthew Nathan, M.D.
Professor of Medicine, Diabetes Unit
Massachusetts General Hospital 

MedicalResearch.com: What is the background for this study?

Response: Traditional guidelines for screening for retinopathy, based on indirect evidence, call for annual examinations. The automatic annual screening for retinopathy, without considering potential risk factors for progression,  appears excessive based on the slow rate of progression through sub-clinical states of retinopathy.

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Precision Therapy In Early Stages For Triple Negative Breast Cancer

MedicalResearch.com Interview with:
Eran Andrechek, PhD

Eran Andrechek, PhD Associate Professor Department of Physiology Michigan State University East Lansing, MI

Associate Professor
Department of Physiology
Michigan State University
East Lansing, MI 

MedicalResearch.com: What is the background for this study?

Response: Of the various types of breast cancer, triple negative breast cancer (lacking estrogen receptor, progesterone receptor and HER2) has the worst outcome and is largely limited to chemotherapy for treatment.  Other types can be treated with personalized medicine, resulting in better outcome.  For instance, a HER2+ve breast cancer can be treated with Herceptin, which targets HER2 itself.  The fact that triple negative breast cancer lacks these sort of targeted treatments presents a clear need in breast cancer therapy.

The goal of this study was to bring together our computational work using large databases from breast cancer with research into therapeutic options.  Essentially we wanted to ask if we could use patterns in what genes were being expressed to predict optimal therapy for triple negative breast cancer.  Continue reading

Genomic Testing Can Improve Confidence in Prostate Cancer Treatment Strategy

MedicalResearch.com Interview with:

Dr. John L. Gore, MD Associate Professor Adjunct Associate Professor-Surgery Department of Urology University of Washington

Dr. John Gore

Dr. John L. Gore, MD
Associate Professor
Adjunct Associate Professor-Surgery
Department of Urology
University of Washington

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: The rationale for our study derives from the uncertainty that both patients and clinicians confront when trying to make decisions about adjuvant therapy for prostate cancers found to have aggressive pathologic features at the time of radical prostatectomy. There is level 1 evidence in support of adjuvant radiation therapy in this setting, but several factors restrain providers from recommending adjuvant radiation. We found that interjecting a genomic test that predicts the risk of clinical metastases 5 years after surgery impacts the treatment recommended and helps men and clinicians feel more confident in the decision they are making or recommending.

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AI plus Genetic Database Drives Personalized Cancer Treatment

MedicalResearch.com Interview with:

Dr. Kai Wang Zilkha Neurogenetic Institute, University of Southern California Institute for Genomic Medicine, Columbia University

Dr. Kai Wang

Dr. Kai Wang
Zilkha Neurogenetic Institute, University of Southern California
Institute for Genomic Medicine, Columbia University

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Cancer is a genetic disease caused by a small number of “driver mutations” in the cancer genome that drive disease initiation and progression. To understand such mechanism, there are increasing community efforts in interrogating cancer genomes, transcriptomes and proteomes by high-throughput technologies, generating huge amounts of data. For example, The Cancer Genome Atlas (TCGA) project has already made public 2.5 petabytes of data describing tumor and normal tissues from more than 11,000 patients. We were interested in using such publicly available genomics data to predict cancer driver genes/variants for individual patients, and design an “electronic brain” called iCAGES that learns from such information to provide personalized cancer diagnosis and treatment.

iCAGES is composed of three consecutive layers, to infer driver variants, driver genes and drug treatment, respectively. Unlike most other existing tools that infer driver genes from a cohort of patients with similar cancer, iCAGES attempts to predict drivers for individual patient based on his/her genomic profile.

What we have found is that iCAGES outperforms other tools in identifying driver variants and driver genes for individual patients. More importantly, a retrospective analysis on TCGA data shows that iCAGES predicts whether patients respond to drug treatment and predicts long-term survival. For example, we analyzed two groups of patients and found that using iCAGES recommend drugs can increase patients’ survival probability by 66%. These results suggest that whole-genome information, together with transcriptome and proteome information, may benefit patients in getting optimal and precise treatment. Continue reading

Supermagnetized Crystals May Deliver Precision Drugs Directly To Medical Targets

MedicalResearch.com Interview with:
Dr. Kezheng Chen

Lab of Functional and Biomedical Nanomaterials
College of Materials Science and Engineering
Qingdao University of Science and Technology
Qingdao China

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: From a view point of practical applications, superparamagnetism is usually highly desirable, because it can prevent the magnetic particles from irreversible aggregation and ensure an excellent dispersity once the applied magnetic field is removed.

Up to now, the largest size of reported superparamagnetic clusters is around several hundreds of nanometers, which are composed of numerous nanocrystals, and hence exhibiting polycrystalline nature. In this sense, how to realize well pronounced superparamagnetism in large-size single crystals is of great interest to the general public.

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Penn Reports Successful Pilot Study of Liquid Biopsy To Monitor Advanced Lung Cancer

MedicalResearch.com Interview with:

Erica L. Carpenter, MBA, PhD Research Assistant Professor, Department of Medicine Director, Circulating Tumor Material Laboratory Division of Hematology/Oncology Abramson Cancer Center Perelman School of Medicine at the University of Pennsylvania

Dr. Erica Carpenter

Erica L. Carpenter, MBA, PhD
Research Assistant Professor, Department of Medicine
Director, Circulating Tumor Material Laboratory
Division of Hematology/Oncology
Abramson Cancer Center
Perelman School of Medicine at the University of Pennsylvania

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: The advent of precision medicine practices for cancer patients, including the use of drugs that target specific tumor mutations, has necessitated improved diagnostics with real-time molecular monitoring of patients’ tumor burden. While biopsy material, obtained surgically or through fine needle aspirate, can provide tissue for next generation sequencing (NGS) and mutation detection, this requires an invasive often painful procedure for the patient. In many cases, especially in more advanced disease when multiple metastases are present, such tissue cannot be obtained or can only be obtained from a single tumor site, thus limiting the sensitivity of tissue-based biopsy.

Here we report on a prospective cohort of 102 consecutively enrolled patients with advanced non-small lung cancer (NSCLC) for whom a non-invasive liquid biopsy was used for real-time detection of therapeutically targetable mutations. Tissue samples were only obtainable for 50 of the 102 patients, and these tissue biopsies were analyzed using a 47-gene Next Generation Sequencing (NGS) panel at Penn’s Center for Personalized Diagnostics. Concordance of results for the 50 patients who received both tests was close to 100% when the samples were obtained concurrently.

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Biomarker May Predict Response to Common Leukemia Treatment

MedicalResearch.com Interview with:

Dr Laura Eadie PhD Post Doctoral Researcher Affiliate Lecturer Discipline of Medicine University of Adelaide

Dr. Laura Eadie

Dr Laura Eadie PhD
Post Doctoral Researcher
Affiliate Lecturer Discipline of Medicine
University of Adelaide

Summary: Researchers based at SAHMRI (South Australian Health and Medical Research Institute) in Adelaide, South Australia have recently demonstrated the significance of early increases in the expression of ABCB1 in predicting long-term response to imatinib therapy. Lead researcher, Dr Laura Eadie, has recently had these findings published in the journal Leukemia and says that she hopes “the evidence provided by the study could be used to inform better patient treatment in the future”.

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: ABCB1 (p-glycoprotein) is a membrane transporter known to be involved in the efflux of the tyrosine kinase inhibitors (TKIs) that are used to treat chronic myeloid leukaemia (CML). Overexpression of ABCB1 has also been demonstrated to cause resistance to the TKIs imatinib, nilotinib and dasatinib in vitro. Although studied previously in CML patients, the predictive value of ABCB1 in determining a patient’s long-term response to imatinib had not been realized … until now.

Previous studies investigating ABCB1 as a predictive biomarker focused on expression levels of ABCB1 at one time point in isolation. For our study, we have measured the levels of ABCB1 at two separate time points specified in the TIDEL II trial protocol: day 1 (prior to the start of imatinib therapy) and day 22 (three weeks on imatinib). We then calculated the fold rise in ABCB1 expression levels at day 22 compared with day 1 and grouped patients about the median into high and low fold rise. When we compared molecular outcomes for patients within these two ABCB1 expression groups we noticed a striking difference in outcome to imatinib therapy.

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Panel of Nine Proteins Improves Risk Assessment in Patients With Stable Coronary Artery Disease

MedicalResearch.com Interview with:

Peter Ganz, MD Chief, Division of Cardiology Director, Center of Excellence in Vascular Research Zuckerberg San Francisco General Hospital Maurice Eliaser Distinguished Professor of Medicine University of California, San Francisco

Dr. Peter Ganz

Peter Ganz, MD
Chief, Division of Cardiology
Director, Center of Excellence in Vascular Research
Zuckerberg San Francisco General Hospital
Maurice Eliaser Distinguished Professor of Medicine
University of California, San Francisco

MedicalResearch.com: What is the background for this study? What are the main findings?

Dr. Ganz:  The research described in the JAMA paper involved measuring 1,130 different proteins in nearly 2000 individuals with apparently stable coronary heart disease, who were followed up to 11 years. Initially, two hundred different proteins were identified whose blood levels could be related to the risk of heart attacks, strokes, heart failure and death, and ultimately a combination of nine proteins was selected for a risk prediction model, based on their combined accuracy and sensitivity.

Application of these findings to samples of patients with stable coronary heart disease demonstrated that some of those who were deemed clinically stable instead had a high risk of adverse cardiovascular outcomes, while other patients with the same clinical diagnosis had a very low risk. Thus, individuals who all carried the same clinical diagnosis of stable coronary heart disease had a risk of an adverse cardiovascular event that varied by as much as 10-fold, as revealed by analysis of the levels of the nine proteins in their blood. Given such large differences in risk and outcomes, patients could reasonably opt to be treated differently, depending on their level of risk. We hope that in the future, management of patients with stable angina will at least in part rely on risk assessment based on levels of blood proteins.

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Biomarker Based Chemotherapy Approach Improved Outcomes

MedicalResearch.com Interview with:

Maria Schwaederle PharmD Clinical Research Scientist Center for Personalized Cancer Therapy UCSD Moores Cancer Center La Jolla, CA 92093

Dr. Maria Schwaederle

Maria Schwaederle PharmD
Clinical Research Scientist
Center for Personalized Cancer Therapy
UCSD Moores Cancer Center
La Jolla, CA 92093

MedicalResearch.com: What is the background for this study? What are the main findings?

Dr. Schwaederle: We performed this analysis with experts in the field, including but not limited to Drs Schilsky, Lee, Mendelsohn and Kurzrock, all known for their experience in the area of precision/personalized medicine.
Historically, phase I trials (which are often first in human or highly experimental in other ways) were believed to be examining only toxicity. Our meta-analysis of 13,203 patients shows that in the era of precision medicine, this historical belief needs to be discarded. Second, it is the use of precision medicine that makes this belief outdated.
Indeed, Phase I trials that utilized a biomarker-driven approach that is the essence of precision medicine had a median response rate of about 31%, which is higher than many FDA approved drugs, and this is in spite of the fact that phase I patients are a highly refractory group having failed multiple lines of conventional therapy.

Importantly, however, it was not the use of targeted agents alone that was important. It was the biomarker-based approach where patients are matched to drugs. Without matching, response rates were dismal—about 5%.

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