Category Archives: Genome

Yes, these are lipids. But there are so many more inside your body; and they do more than store fat!

Annual Lipids Meeting in La Jolla California

The 2015 meeting on Lipids—focusing on their impact in cancer, metabolic, and inflammatory diseases—took place on Tuesday and Wednesday, May 12 and 13 at the Scripps Seaside Forum at UCSD’s Scripps Institute of Oceanography (SIO). With a beautiful venue and superb facilities, what more can you ask for? How about some really interesting science.

Lipids are generally thought of as fats. But in a biological system, they are much more. They include chemokines and other signaling molecules involved in signal transduction to and from the cell membrane. Metabolically, lipids also play an important role. Innovation in technology allow the study all the lipids in an organism (yeast, bacteria, or animal), leading to a new field of study: lipidomics. Once again, UCSD is on the cutting edge, with an established program and website in the field.

ocean, palm trees, La Jolla pennisula, green lawn with white chairs; breakfast view for lipids conference

View from the Scripps Seaside Forum at UCSD’s Scripps Institution of Oceanography.

Michael Snyder, the keynote speaker, has subjected himself to a battery of “omic” studies including his personal genome, exosome, microbiome, epigenome, proteome, metabolome, transcriptome, auto-antibody-ome, as well as cytokines. Data from these samples comprise the “Snyerdome”. All this was done in the interest of personalized medicine. These studies were done not only at one time point, but over a range of times, making it longitudinal.

Mike sees the data providing insights into how to managing healthcare in healthy individuals to predict risk, diagnose, monitor, and treat the patient, in this case, himself.

“He has also combined different state-of–the-art “omics” technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine” (from lab website).

in the future, Mike sees genomes being sequenced before birth and all this information being channeled through your smart phone. Patients will also bear more responsibility for maintaining their health with all the information they have; they will need to learn to maintain a balanced life.

The next speaker, David Wishart, discussed how to link lipidomics to laboratory medicine. He noted that in the rationalization of translating basic research to something of value in the clinic, researcher often cite the possibility of developing a new:          

  • surgical technique
  • invent a new medical device
  • drug
  • drug target
  • medically important gene
  • biomarker

All these are good outcomes; some are more likely than others. Practitioners of lipidomics are most likely to have the best luck in developing new biomarkers; not many are surgeon and drug development has about a 0.001% success rate from basic science to the prescription bottle.

lipids, lipids, lipids

Slide from David Wishart’s talk listing the number of FDA approved clinical tests from omic data

Discovery of new biomarkers is a realm where omics, specifically lipidomics, will meet a great chance of success. For this comparison, David recommends using the statistical ROC test, which is routinely used to evaluate medical test. This test gives a good sense of a medical test’s specificity and sensitivity by plotting the true positive rate over the false positive rate.

Example of ROC curve with an assessment of the area under the curve. The PSA referred to here is the amount of Prostate-Specific Antigen test; phi refers to a different, more specific calculation with less false positives than the PSA test alone.

 

ROC curve used to show predictive value of a test for prostate cancer using two different methods.

ROC curve used to show predictive value of a test for prostate cancer using two different methods.

Or you can just know that an ROC of 0.5 is worthless, while 1.0 is perfect.

Thus, from the graph above, using the PSA test alone to determine the risk of prostate cancer is poor. A better method is to use the phi method.

Work done looking at 3 to 5 biomarkers can have great ROC results. For example, predicting congenital heart defects by looking at the level of 3 carotenes, yields a ROC of 0.98. Other areas of success with high ROC scores include endometrial cancer, prostate cancer, and chronic fatigue syndrome.

David urged participants to become more quantitative to move their research into the clinic; using the website www.roccet.ca to generate ROC curves for your data is a great place to begin.

The numerous other speakers all gave fantastic talks.

In this smaller conference, I was able to browse through the all posters, read all the titles and talk to the presenters. Large conventions tend to lack the sense of intimacy and fraternity found in this lipidomics meeting. Kudos to the organizers for a successful event. A convivial group, I would highly recommend this meeting.

 

revolutionizing cancer treatment

Treating Cancer in the Genomic Era

Revolutionizing Cancer Treatment

by DeeAnn Visk

We have all had “ah-ha” moments.  I had one on October 15, 2013 listening to Dr. Razelle Kurzrock illustrate a new way of thinking about cancer and cancer drug development.  Historically, cancers are categorized by the organ in which they originate.  With the advent of genomic sequencing, cancers can now be grouped by the mutations they contain.  Thinking about cancer in this way will revolutionize how this disease is treated therapeutically, researched in academia, targeted by drug companies, and conceptualized in clinical trial design.

This epiphany occurred at the recent meeting of the Southern California Chapter of Women In Bio at Janssen Labs, while listening to Dr. Kurzrock, one of three excellent speakers at the meeting.

Director of Clinical Trials, Moores Cancer Research Center

Dr. Razelle Kurzrock, Director, Center for Personalized Cancer Therapy

Dr. Kurzrock pointed out that, while the light microscope was invented in 1590, it is still used today to diagnose cancer. While current cancer therapies are not quite as ancient, treatment for many cancers has not changed for up to 20 years.  This is shocking, given the enormous strides in technology that have occurred in the last two decades.  Most importantly, we need to change the paradigm of thinking of cancer as an organ-centric disease. Molecular abnormalities in cancer are not associated with the cancer’s organ of origin. Hence, we should treat cancers based on their molecular profile, not on where they originated in the body.

Now that the genomic era is upon us…

we can analyze the molecular signature of each cancer.  Clinical trials need to be redesigned to be mutation-centric, not drug-centric.  Multiple genetic markers should be employed to diagnose and classify cancers.

Generally, clinicians are entrenched in their way of thinking, which presents an obstacle to this kind of fundamental change.  To paraphrase Max Planck, science progresses one funeral at a time.  Regrettably, medicine also seems to progress this way.  Previous ways of thinking about cancer have become so ingrained that many are not even aware of their underlying assumptions.

The concept of classifying cancer by mutational profile will also impact cancer research.  How many times have you heard of a laboratory studying breast cancer, or prostate cancer, or liver cancer?  Several more times than you hear about a laboratory studying a particular mutation in a cancer biomarker like the epidermal growth factor receptor (EGFR), I’ll bet.

Further areas of inertia include applications for new drugs submitted to the Food and Drug Administration (FDA).  No application for an investigational new drug study (IND) has ever been filed based on a treatment targeted at a mutation in a cancer (of any kind), rather than treatment of a cancer in a specific organ. This situation persists despite the fact that the FDA has indicated it would be open to INDs using this approach.

Need a new paradigm for treating cancers.

Hope for new cancer treatments–turning in a new direction.

I hope the idea of classifying cancers by the mutations that drive them, not the organ in which they originate, changes how cancers are treated.  Dr. Kurzrock did an excellent job of articulating and advocating for these changes.  Employing old-school approaches to cancer is so engrained that we are often unaware of these underlying assumptions.  Rethinking cancer biology certainly has changed how I would respond to a loved one being diagnosed with cancer. I would seek out a forward-thinking doctor, willing to utilize this new paradigm from the onset, not waiting for last-ditch efforts once the cancer re-occurs.

Challenging the current methods for treatment, research, and drug development will not be easy, given with the institutional barriers that remain. Financial interests of the institutions involved will need to be realigned with this new paradigm.  Either that or we need AIDS-activist-like protests to spur on this change in thinking. In the end, as with AIDS, it may be patient advocacy groups that can best bring about this change in thinking in the medical, pharmaceutical, research, and regulatory communities.

The views expressed here are solely those of DeeAnn Visk are not necessarily those of Women in Bio, AWIS-SD, Janssen Labs, NPR, or your local NPR station. A special thanks to Nurith Amitai for her especially helpful editing.

This article was previously published in the January/February 2014 edition of the Association for Women in Science San Diego Chapter Newsletter.

DeeAnn Visk, Ph.D., is a freelance science writer, editor, and blogger. Her passions include cell culture, molecular biology, genetics, and microscopy. DeeAnn lives in the San Diego, California area with her husband, two kids, and two spoiled hens. You are welcome to contact her at deeann.v@cox.net