Solving Big Problems

I frequently get asked why I joined Bone Health Technologies so I thought it would be helpful to share my story about that here.  We’re maturing as a company and as we’re getting closer to bringing our product to market it seemed like the time to begin a more frequent conversation with our prospective patients, clinicians, and all those who care about improving bone health so time to start blogging! I’m really excited about our work so check this page often as I plan to post more in the next few weeks.

For my entire career, I have been thrilled by the power of technology to improve daily life, not just for a few people but at scale. I used to love getting emails from users at SugarSync when our sync and share product, novel at the time, helped them get a job or even save a wedding. Introducing browser tabs at Netscape, new Yahoo email features…the knowledge that millions of people had our technology on their devices was both professionally satisfying and personally inspiring.  

The idea that we can apply scalable technology solutions to healthcare has motivated me to refocus my energies of late into the health technology field. While there is something truly sacred about the one-to-one doctor-patient relationship, the idea of technology-style leverage – being able to create a product that touches millions of people is special in its own right. And these two modalities are synergistic – we aim to get our technology into the toolbox (or doctor’s bag) of hundreds of thousands of primary care doctors everywhere.

Read more on the Bone Health Technologies site here

The Evolution and Importance of Hereditary Breast and Ovarian Cancer (HBOC) Genetic Testing

This topic has a lot of meaning for me personally, having undergone the testing and knowing many people who have been impacted by HBOC. I wanted include on my personal blog this post, pasted below, that I wrote as part of my consulting work with Fabric Genomics on October 25, 2021.

Breast cancer is the most common cancer for women (other than skin cancer), with over 300k cases diagnosed this year in the U.S.  Ovarian cancer, is less common, approximately 20k cases per year, but is far more deadly.  In both cases, early diagnosis is critical to achieving positive outcomes. Mammography is an effective test for breast cancer but still misses approximately 20% of cancers, and there is no effective screening for ovarian cancer.

In 1996, screening for pathogenic variants in the BRCA1 and BRCA2 genes, both associated with hereditary breast and ovarian cancer (HBOC) syndrome, became the first genetic test offered clinically for cancer risk assessment.  Pathogenic variants in the BRCA genes are associated with high rates of cancer relative to those without the variants. Pathogenic variants in BRCA1 confer a greatly elevated risk of breast cancer and ovarian cancer and a small but significant elevated risk of pancreatic cancer. There is also limited evidence for association with prostate cancer. Pathogenic variants in BRCA2 confer an elevated risk of breast cancer (especially ER+ cancer) and ovarian cancer and a small but significant elevated risk of pancreatic and prostate cancers. There is also limited evidence for association with melanoma and leukemia.1

Researchers estimate that somewhere between 1 in 300 and 1 in 800 people have a pathogenic variant in the BRCA1 or BRCA2 genes. Some ethnic groups, however, are more likely to have mutations in these genes. Approximately 1 in 40 people of Ashkenazi Jewish descent have a BRCA mutation.  Because HBOC is an inherited syndrome, a family history of breast, ovarian, and certain other cancers is a significant risk factor. If one parent has a BRCAmutation, their children will have a 50% chance of inheriting the mutation.

As the cost of performing BRCA testing has declined over the last 25 years, the recommended population of those for whom screening testing is indicated has expanded broadly – initially from those with a strong family history of breast and ovarian cancer to those at risk by virtue of being of Ashkenazi Jewish descent to potentially even broader segments of the population.2  The knowledge gained from risk screening for HBOC can be life-saving – knowledge of one’s BRCA status can allow for risk reduction measures, including more frequent and extensive radiological testing and prophylactic oophorectomy, hysterectomy, and mastectomy.  Angelina Jolie’s sharing of her personal decision to undergo prophylactic surgeries, as well as broader awareness of the potential benefits has empowered many women to take greater control of their cancer risk.

But what if we are not talking about the risk of potential cancer in a healthy individual but rather the value of genetic testing in patients with a diagnosis of breast or ovarian cancer?  In these cases, BRCA testing can still provide important information. First, a patient with a BRCAmutation is at increased risk of developing additional cancers. Testing provides potentially life-saving information to other family members who may also have inherited the pathogenic variant. Finally, there is compelling research showing that adjuvant treatment with the PARP inhibitor olaparib (Merck/AstraZeneca’s Lynparza) significantly delayed cancer progression in germline BRCA1/2-mutated, early-stage, HER2-negative breast cancer patients.3  A benefit was also seen specifically in the reduction of breast cancer recurrence in the PARP inhibitor group – 59 patients died, versus 86 in the placebo group.  The data from this and other studies are proving the importance of all breast and ovarian cancer patients and their clinicians knowing their BRCA status.

With BRCA tests being prescribed by more doctors to more people, there is a parallel rise in the desire by local molecular laboratories to be able to offer these tests.  Lower cost NGS sequencers fit in the budgets of regional labs, and tools and services such as those from Fabric Genomics enable these labs to quickly and confidently start and scale up their HBOC testing making BRCA testing more readily available for all patients.   

  1. https://ask2me.org/resources.php.  Also Oncol Lett. 2019 Feb; 17(2): 1986–1995.PMCID: PMC6341769 doi: 10.3892/ol.2018.9770.  BRCA1 mutation in breast cancer patients: Analysis of prognostic factors and survival Joanna HusznoZofia Kołosza, and Ewa Grzybowska
  2.  JAMA Netw Open, 2020;3(10):e2022874 October 29, 2020 Cost-effectiveness of Population-Wide Genomic Screening for Hereditary Breast and Ovarian Cancer in the United States, Gregory F. Guzauskas, MSPH, PhD1; et al.. doi:10.1001/jamanetworkopen.2020.22874
  3.  N Engl J Med 2021; 384:2394-2405, Adjuvant Olaparib for Patients with BRCA1- or BRCA2-Mutated Breast Cancer Andrew N.J. Tutt, M.B., et al June 24, 2021  DOI: 10.1056/NEJMoa2105215

Genetic Testing for Inherited Cardiac Risk

Originally posted on April 29, 2021 as part of my consulting at Fabric Genomics

Heart disease is the leading cause of death for men and women in the United States.1  Many different types of heart disease can be inherited. Some common conditions such as high blood pressure or coronary artery disease run in families but are probably due to multiple genetic variations (polygenic). Each variation individually confers a small increased risk and likely works together in a complex manner to cause disease. Genetic testing is at the early stage of development for these cases (for instance polygenic risk analysis for hypertension is in development), however, there are a number of inherited heart diseases that are caused by just one or a few genetic changes.  Testing protocols for these diseases are well established.  These genes are both highly penetrant and deleterious and therefore are strongly linked to serious disease. Examples of these conditions include:

  1. Cardiomyopathies, such as hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and arrhythmogenic right ventricular cardiomyopathy (ARVC) – these are conditions that affect the heart muscle and can lead to heart failure.
  2. Inherited arrhythmias – these are also inherited heart conditions that affect the electrical system of the heart, causing abnormal heart rhythms called arrhythmias. Examples include Long QT syndrome and Brugada syndrome. These arrhythmias can be deadly and may require changes in lifestyle or medical therapy. 
  3. Aortic dysfunctions (including aneurysms and dissections), are genetic conditions that affect the main artery, the aorta. Bulging of the wall of the aorta (aneurism) can lead to rupture of the vessel, while tears in the aorta can lead to separation of the inner and middle layers of the aorta (dissection). Both can be life-threatening.  Examples include Marfan syndrome, Loeys Dietz syndrome, Ehler Danlos syndrome. 
  4. Familial hypercholesterolemia is a condition with extremely high LDL cholesterol levels that greatly increase the risk of heart attack

In the past, knowledge of one of these inherited conditions has tragically come about from a sudden, often fatal, cardiac incident, frequently impacting young people at the prime of their lives.  Fortunately, with advances in Next Generation Sequencing (NGS) technology, it is now possible to test for these conditions.  Comprehensive cardiac risk panels cover all of these conditions.  Importantly, knowledge of the condition may significantly affect the treatment and management of the patient.  Specific medications, devices such as implantable defibrillators, and exercise and lifestyle interventions may be called for and can be very effective in extending lives.  

This testing is now at the point where it is cost-effective and makes sense from a public health point of view.  Guidelines are emerging and the latest version from the American Heart Association is described here. But all the major societies (including the American College of Cardiology, the American Heart Association, and the Heart Failure Society of America) recommend testing for those individuals with established or suspected clinical conditions.  Assays for these panel tests are widely available. Fabric Genomics provides software that facilitates the analysis of the sequencing results.  It provides a seamless workflow, taking the output from sequencing machines and carrying out secondary and tertiary analyses.  Given the significant growth in the volume of these tests, it is critical that interpretation can be done accurately yet efficiently.  AI-based software such as Fabric ACE is an example of such a tool while being fully ACMG compliant.  Fabric ACE has long been used for Hereditary Breast and Ovarian Cancer screening and a number of other hereditary risk gene groups and we are now seeing rapid growth in the cardiac space – exciting due to the large potential clinical impact.  Given the seriousness of these conditions, the actionability of a diagnosis, and the declining cost of the testing we expect to see significant growth in this testing and, hopefully, improved health and lifespan for those with these conditions.

  1. Centers for Disease Control and Prevention. Underlying Cause of Death, 1999–2018. CDC WONDER Online Database. Atlanta, GA: Centers for Disease Control and Prevention; 2018. Accessed March 12, 2020.

Where Pareto Doesn’t Apply – The NICU

One of my favorite posts I did as part of my consulting at Fabric Genomics. Originally posted on December 3, 2020

The use of AI in healthcare is gaining increased attention with the significant advances and widening clinical use in radiology and pathology and now increasingly in genomics. In all of these cases, there are vast quantities of data to consider that should, in fact, be considered as they could be clinically significant.

Beyond the technology in use, we do see certain diagnostic situations that have a need for AI interpretation assistance. To illustrate this, we provide two different diagnostic scenarios. For instance, in adult critical care the doctor’s diagnostic process supplemented by well-researched rubrics has proven resilient. These cases are rarely primarily genetic in nature and are much more impacted by natural aging, environment, infectious agents, and lifestyle. There is a fairly high concentration of causation and a Pareto-like distribution. In contrast, the NICU pattern is quite different, with much more likelihood of a direct genetic cause. NICU genetic conditions are frequently rare and require unique considerations.

Let’s take a prototypical case – an elderly man presents in the ER with shortness of breath. In this case, there are a few highly likely and perhaps 200 possible causes and the doctor has them roughly mentally ranked in order of frequency. The clinician reranks likely causes real-time based on information as it is revealed – history, demographics, test results, etc. The top few causes make up over 95% of the cases and can be selected with reasonable confidence and confirmed via additional testing. Perhaps a few additional low-probability but high-risk causes are tested for, and a diagnosis will be confirmed in the vast majority of cases. A large but manageable dataset is analyzed and iteratively reanalyzed by the doctor in what is essentially a human Bayesian process – adjusting the prior probability based on real-time data. How likely is it heart failure (as opposed to an infection) given that there is no fever? How does the likelihood change given a specific test result? And for the vast majority of diagnoses, the doctor has confirmed and managed that specific diagnosis many times before in their career.

By contrast, let us consider the case of a critically ill child in the NICU. While there are a few common causative elements such as preterm birth, in over 30% of the cases the child’s condition has a genetic basis. The fact that there is a high chance of a genetic cause immediately brings us into a different diagnostic equation implying many thousands of potential causes. The vast majority of genetic causes will not be identified by standard newborn assay screening as they are not on the standard diagnostic panels (and those take weeks). Even the most experienced pediatricians will have only seen and be personally familiar with a tiny minority of those diseases and, on the off chance that they are familiar with a particular genetic disease, the phenotypic presentations are often not fully expressed in the newborn.

The recent Baby Bear study1 led by Rady Children’s Hospital clearly showed the prevalence of rare genetic diseases in NICU cases. Per Appendix A of the study, “Thirty-five of the diagnosed genetic diseases are rare conditions with an incidence of less than one in one million births.Sixty-five of the 71 primary genetic diseases were diagnosed just once in the Baby Bear population.” Of course, given the rarity of these conditions, it is beyond the likely human clinical experience.

We also know that the choice of treatment matters greatly. Genetic, metabolic, and neurological disorders are highly specific, and the wrong or delayed intervention can have life-long consequences.

In the NICU, we ideally would aggressively seek all reasonably accessible diagnostic information and immediately explore all possible genetic causes rather than work our way slowly along a curve that lacks the steep Pareto shape. Time is often the enemy in these cases. Damage from seizures, nutrition acting as a metabolic poison, or invasive procedures can take place that could be avoided with an early, specific diagnosis.

Just as we treat a critically ill febrile patient with broad-spectrum antibiotics to not lose time with narrow therapeutic shots in the dark, we need a broad but fast approach to diagnosis.

Fortunately, with technology lowering the cost of NGS and with the support of AI algorithms such as Fabric GEM, this approach is coming into use. Multiple peer-reviewed studies2including Farnaes et al 2018 show significant clinical efficacy of this approach and even cost savings.3 The technology is here, it’s available and even economical. Now is the time to insist on its use for the NICU babies that depend on us.

References

1. https://radygenomics.org/wp-content/uploads/2020/06/PBB-Final-Report_06.16.20.pdf

2. https://www.nature.com/articles/s41525-018-0049-4

3. https://health.ucdavis.edu/health-news/newsroom/project-baby-bear-shows-genomic-sequencing-for-infants-in-intensive-care-yields-life-changing-benefits-and-medical-cost-savings-/2020/06

Bringing the Promise of Precision Medicine to Rare Disease

My latest post as part of my work at Fabric Genomics.

Precision medicine is a form of medicine that uses information about a person’s own genes or proteins to prevent, diagnose, or treat disease.1 When we think of precision medicine, we often focus on cancer and the recent efforts to specifically target treatment based on the genetic composition of a tumor (somatic) or the person’s germline. While it is early days, the health gains of this approach are quite significant with better outcomes shown in multiple studies2 and even data showing lower overall health costs.3 However, the point of precision medicine is not only to optimize treatment for cancer, but also to target health interventions generally for all conditions. In fact, we see the application of precision medicine to high volume diseases such as diabetes and heart disease and even lifestyle interventions such as diet and exercise.

Nowhere is this more challenging than for patients suffering from a rare disease. Ironically it is common to have a rare disease. More than 25 million Americans and more than 400 million people worldwide suffer from one of over 7000 rare conditions, defined as those conditions having an incidence of 1 in 200,000 or less. Precision medicine has focused on big data approaches to studying more common conditions, thereby leaving out those with rare conditions. Lacking a diagnosis, most of these patients and their families are on a “diagnostic odyssey.” These patients typically spend more than five years seeking accurate diagnosis and might see up to eight doctors, often receiving many misdiagnoses and differing opinions on their journey. Along the way, they may be exposed to harmful treatments and invasive testing. It is clear that comprehensive genetic testing gives the best possibility of getting to the exact diagnosis efficiently. Still, historically, genetic data have been available to a minority of patients: only those referred to a clinical geneticist for testing.

For the complete post see https://fabricgenomics.com/2020/12/bringing-the-promise-of-precision-medicine-to-rare-disease/

Improving Health Outcomes for Infants with Pompe Disease

Fourth post as part of my work at Fabric Genomics

By Martin Reese & Laura Yecies

It was terrific to see this paper by our long time collaborator Arindam Bhattacharjeeon the use of NGS as a second-tier test for Pompe Disease (PD). This is part of an important diagnostic trend of earlier (even to the point of first-line for selected infants) use of NGS as a diagnostic tool that can dramatically improve the newborn’s projected health outcome. Pompe Disease is a perfect example of how this can work.

Background

PD is one of several glycogen storage diseases with variable timing of onset and rates of progressions. According to NORD, “Pompe disease is a rare multisystem disorder caused by pathogenic variations in the GAA gene containing the information for production and function of a protein called acid alpha-glucosidase (GAA). Because of the shortage of this protein (an enzyme), a complex sugar named ‘glycogen’ cannot be degraded to a simple sugar like glucose. This causes the glycogen to accumulate in all kinds of tissues, but primarily in skeletal muscle, smooth muscle, and cardiac muscle, where it causes damage to tissue structure and function. Pompe disease is inherited as an autosomal recessive genetic trait.” Early diagnosis and initiation of treatment are of paramount importance at no later than two weeks of age to minimize muscle damage and avoid significant negative impact on the quality of life.

For the full post please see Fabric’s site here

Enabling NGS Testing and Precision Medicine with Fabric AI Technologies

I was invited to write about NGS on Xifin’s blog…

Next-Generation Sequencing (NGS) testing is experiencing tremendous growth driven at a high level by the promise of precision medicine and the life-changing power of applications in preventive genetic screening, somatic testing, and rare disease diagnosis. In all of these use cases, we see important clinical advances. Preventive genetic screening for risk factors such as BRCA mutations allows people to take preventative measures that save lives every day. Somatic mutation analysis allows for highly targeted therapies, and rare disease diagnosis is improving outcomes for babies in the NICU and providing hope for the 400 million people worldwide suffering from a rare disease.

For the full article please continue on Xifin’s site here

Accuracy is the New Speed

Second post as part of my work at Fabric Genomics

By Martin Reese & Laura Yecies

When caring for a critically ill child, two simultaneous thoughts are competing – the urgent need for a diagnosis to optimize treatment and the need for thoroughness – to carefully review all the possibilities.  Don’t jump to a conclusion but don’t get lost in the weeds keeping the patient, and the others behind them, in limbo.  We commonly see accuracy and speed as a dichotomy.  This has certainly been true in the past in genomics – how many variants to review? Review variants from less likely parts of the genome? Use a more restrictive filtering rule?

We had been operating in a world where deciding to use some of the heuristic shortcuts or to time limit review meant settling for less than optimal accuracy. Time-saving techniques left some diagnoses on the cutting room floor.  These simple Pareto prioritizations that are highly effective in dealing with everyday clinical situations are inherently problematic in the rare disease world.  We cannot eliminate the zebras when we know it’s unlikely to be a horse

Read more on the Fabric website here 

Delivering Better Care at a Lower Cost – a Case Study of Project Baby Bear at Rady’s Children’s Hospital

My first post as part of my work with http://www.FabricGenomics.com

By Martin Reese & Laura Yecies

The power and cost-effectiveness of AI are calling into question many of our assumptions about healthcare.  The most important dichotomy proving to be false is that providing the latest and most thorough diagnostic technology to optimize clinical outcomes is more expensive.  When we use AI to more comprehensively analyze cases we benefit from Moore’s law rapidly and continuously reducing costs.  By contrast, hospital-based care, especially when in an intensive setting such as the NICU is continuously increasing in cost. It is not surprising that when more extensive testing produces clinically actionable results that actually decrease hospital days we can accomplish the holy grail — better care and less expensive simultaneously…

Read more on the Fabric website here

 

Women’s History Month Podcast

Melinda Byerly and her team at “Stayin’ Alive in Tech” have put together a cool compendium from their various podcast interviews in honor of Women’s History Month and I’m thrilled to be included – you can see the post here

 

Women's History Month EVENT_2