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February 24, 2019
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Consumer-focused healthcare can save lives by focusing on changing behavior

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Everything we do in the $3 trillion healthcare market today only affects 10% of outcomes to premature death.

You read that right. All of that, for just 10% of outcomes:

That 10% exists for a reason. Genetic predisposition is hard to change. So, unfortunately, are social circumstances and environmental behavior. But that 40% of behavioral patterns — why can’t we tackle that? This is what real prevention would look like: nothing comes even close to mattering as much towards whether you will die prematurely as your behavior does.

We can do better than simply focusing on that small 10% slice of the pie; in fact, we’re looking in the wrong place. Doctors, entrepreneurs and founders need to be thinking (and treating with) lifestyle as medicine. Because behavioral change is the best and most powerful way to impact that whopping 40% slice.

Too often we think of this as the “just eat right and exercise” problem. As we know very well, that platitude will not solve our healthcare problem. The true problem is the difficulty of modifying behavior. We know this, because the platitude doesn’t work. We like to eat what we want, to exercise or not exercise if we choose. In short, humans like our patterns. They’re hard to change.

Tech, on the other hand, modifies behavior very well. Just look at the phone you’re probably reading this on, which has foundationally changed the way we communicate — along with huge other swaths of human behavior, in both positive and negative ways — from the ability to call a ride service in practically any city at any time to tracking your health to screen addiction. We know technology modifies behavior; we live this every day. So the question is, how can we target this superpower ability of tech to have 4x the ability to impact that the $3 trillion healthcare budget does?

How does it work?

Let’s think about why technology actually does work for modifying behavior. For one, it’s always there, thanks to the leap in mobile tech, whether that be phones or fitness trackers. Second, technology’s ability to do constant A/B testing essentially enables RCTs, or Randomized Clinical Trials, every moment that technology is present and being used. These RCTs are invaluable laboratories for learning about what is effective therapeutic behavior modification, or improving efficacy — and it’s not toxic. Most medical products are released and then rarely get updated (think about how old the stethoscope is!). Rolling out new versions of products has been difficult and expensive. But that no longer has to be true. The same kind of A/B testing that Amazon does, for example, to optimize ecommerce — everything from the look of the website to the flow of the experience to the nature of the shipping that you get — can be now applied to behavior modification for health. Comparing the immediate efficacy of two algorithms for lifestyle behavior modification on two different populations can happen not just over years or months — as a RCT would have to be — but over weeks and even days, improving our responses and lifestyles that much faster.

Second, applying Machine Learning to vast amounts of new data is identifying all kinds of nuances of human behavior that we aren’t nearly as good, as humans, at noticing. For example, correlating patterns with data like where you shop, when you eat lunch, what activities do you do, what shows you watch, what your exercise routine has been, how much you sleep, even perhaps whether you remember to charge your phone. Identifying the clues in our behavior that eventually add up to significant lifestyle risk is the first step towards changing and improving that behavior. Like it or not, we live our lifestyles now through our phones — ML allows us to learn from it.

And last, technology allows us to scale existing therapies in new orders of magnitude.  Programs which have proven extremely effective at behavior modification through personal interaction — such as Diabetes Prevention Program for Type 2 Diabetes — have been by definition hard to scale; computation can extend their reach into the billions. Or take for another example depression, a complex disease where the molecules involved are poorly understood: drug therapies have been challenging, but therapy, specifically CBT, has a very strong track record, and computational CBT — ie, CBT scaled with technology — the strongest.

Even conditions as mysterious and difficult as cognitive decline can be treated much more effectively with technology. This is another fascinating example where the biology is so complex at the molecular level that breakthroughs have been far and few between. On the other hand, cognitive is painfully clear at the behavior level. And it is also very clear that behavioral treatment in the form of cognitive stimulation helps significantly. In this study, for example, the auditory memory and attention capability of patients who received cognitive stimulation training 1 hour per day, 5 days per week, for 8 weeks improvement was significantly greater than those who did not.

These are big challenges to meet. Behavior is the result of thousands of small decisions at every moment of every day: do I sit or do I stand? Do I drink this beer? Even, do I take regular deep breaths? One of the biggest challenges to face is how we ‘read’ this behavior and turn it into reliable data. There’s also the issue of small sample sizes: in order to narrow down to a meaningful experiment, you need, at the moment, to have very clear definitions of behavior, which often means small sample sizes of people who always do X in Y conditions. The science of behavior and decision making itself is complex, debatable, and often evolving. And there’s the company building practicalities: to build a company in this space, you need to find people who understand clinical science, data science, experimentation approaches, behavioral science *and* product and UI.

But that’s exactly the opportunity. These things are coming; we understanding more about behavior every day, as devices enter our daily lives and health data becomes more and more fine-grained. New conceptions of roles that blend behavioral science and product design are clearly emerging. All of these means are not exclusive and can be combined into powerful ways of modifying behavior for health. Those that can connect all these dots have the ability to build companies that can take a giant bite out of that 40% — and have tremendous impact on mortality for huge swaths of the population.

There’s an old joke that plumbers have saved more lives than doctors, because improving sewers and sanitation (and eradicating the disease that went along with that) was so impactful on longevity for humans. By cleaning up the modern day ‘sewers’ of our lifestyles — not through magical drugs, complex procedures, or platitudes about prevention — but through a real infrastructure of technology that is being built right now — technology will bring an analogous impact.

News Source = techcrunch.com

Healthcare by 2028 will be doctor-directed, patient-owned and powered by visual technologies

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Visual assessment is critical to healthcare – whether that is a doctor peering down your throat as you say “ahhh” or an MRI of your brain. Since the X-ray was invented in 1895, medical imaging has evolved into many modalities that empower clinicians to see into and assess the human body.  Recent advances in visual sensors, computer vision and compute power are currently powering a new wave of innovation in legacy visual technologies(like the X-Ray and MRI) and sparking entirely new realms of medical practice, such as genomics.

Over the next 10 years, healthcare workflows will become mostly digitized, with wide swaths of personal data captured and computer vision, along with artificial intelligence, automating the analysis of that data for precision care. Much of the digitized data across healthcare will be visual and the technologies that capture and analyze it are visual technologies.

These visual technologies traverse a patient’s journey from diagnosis, to treatment, to continuing care and prevention.They capture, analyze, process, filter and manage any visual data from images, videos, thermal, x-ray’s, ultrasound, MRI, CT scans, 3D, and more. Computer vision and artificial intelligence are core to the journey.

Three powerful trends — including miniaturization of diagnostic imaging devices, next generation imaging to for the earliest stages of disease detection and virtual medicine — are shaping the ways in which visual technologies are poised to improve healthcare over the next decade.

Miniaturization of Hardware Along with Computer Vision and AI will allow Diagnostic Imaging to be Mobile

Medical imaging is dominated by large incumbents that are slow to innovate. Most imaging devices (e.g. MRI machines) have not changed substantially since the 1980s and still have major limitations:

  • Complex workflows: large, expensive machines that require expert operators and have limited compatibility in hospitals.

  • Strict patient requirements: such as lying still or holding their breath (a problem for cases such as pediatrics or elderly patients).

  • Expensive solutions: limited to large hospitals and imaging facilities.

But thanks to innovations in visual sensors and AI algorithms, “modern medical imaging is in the midst of a paradigm shift, from large carefully-calibrated machines to flexible, self-correcting, multi-sensor devices” says Daniel K. Sodickson, MD, PhD, NYU School of Medicine, Department of Radiology.

MRI glove-shaped detector proved capable of capturing images of moving fingers.  ©NYU Langone Health

Visual data capture will be done with smaller, easier to use devices, allowing imaging to move out of the radiology department and into the operating room, the pharmacy and your living room.

Smaller sensors and computer vision-enabled image capture will lead to imaging devices that are being redesigned a fraction of the size with:

  • Simpler imaging process: with quicker workflows and lower costs.

  • Lower expertise requirements: less complexity will move imaging from the radiology department to anywhere the patient is.

  • Live imaging via ingestible cameras: innovation includes powering ingestibles via stomach acid, using bacteria for chemical detection and will be feasible in a wider range of cases.

“The use of synthetic neural network-based implementations of human perceptual learning enables an entire class of low-cost imaging hardware and can accelerate and improve existing technologies,” says Matthew Rosen, PhD, MGH/Martinos Center at Harvard Medical School.

©Matthew Rosen and his colleagues at the Martinos Center for Biomedical Imaging in Boston want liberate the MRI.

Next Generation Sequencing, Phenotyping and Molecular Imaging Will Diagnose Disease Before Symptoms are Presented

Genomics, the sequencing of DNA, has grown at a 200% CAGR since 2015, propelled by Next Generation Sequencing (NGS) which uses optical signals to read DNA, like our LDV portfolio company Geniachip which was acquired by Roche. These techniques are helping genomics become a mainstream tool for practitioners, and will hopefully make carrier screening part of routine patient care by 2028.

Identifying the genetic makeup of a disease via liquid biopsies, where blood, urine or saliva is tested for tumor DNA or RNA, are poised to take a prime role in early cancer screening. The company GRAIL, for instance, raised $1B for a cancer blood test that uses NGS and deep learning to detect circulating tumor DNA before a lesion is identified.

Phenomics, the analysis of observable traits (phenotypes) that result from interactions between genes and their environment, will also contribute to earlier disease detection. Phenotypes are expressed physiologically and most will require imaging to be detected and analyzed.

Next Generation Phenotyping (NGP) uses computer vision and deep learning to analyze physiological data, understand particular phenotype patterns, then it correlates those patterns to genes. For example, FDNA’s Face2Gene technology can identify 300-400 disorders with 90%+ accuracy using images of a patient’s face. Additional data (images or videos of hands, feet, ears, eyes) can allow NGP to detect a wide range of disorders, earlier than ever before.

Molecular imaging uses DNA nanotech probes to quantitatively visualize chemicals inside of cells, thus measuring the chemical signature of diseases. This approach may enable early detection of neurodegenerative diseases such as Alzheimer’s, Parkinson’s and dementia.

Telemedicine to Overtake Brick-and-Mortar Doctors Visits

By 2028 it will be more common to visit the doctor via video over your phone or computer than it will be to go to an office.

Telemedicine will make medical practitioners more accessible and easier to communicate with. It will create an all digitized health record of visits for a patient’s profile and it will reduce the costs of logistics and regional gaps in specific medical expertise. An example being the telemedicine services rendered for 1.9M injured in the war in Syria.4

The integration of telemedicine into ambulances has led to stroke patients being treated twice as fast.  Doctors will increasingly call in their colleagues and specialists in real time.

Screening technologies will be integrated into telemedicine so it won’t just be about video calling a doctor. Pre-screening your vitals via remote cameras will deliver extensive efficiencies and hopefully health benefits.

“The biggest opportunity in visual technology in telemedicine is in solving specific use cases. Whether it be detecting your pulse, blood pressure or eye problems, visual technology will be key to collecting data,” says Jeff Nadler, Teldoc health.

Remote patient monitoring (RPM) will be a major factor in the growth of telemedicine and the overall personalization of care. RPM devices, like we are seeing with the Apple Watch, will be a primary source of real-time patient data used to make medical decisions that take into account everyday health and lifestyle factors. This personal data will be collected and owned by patients themselves and provided to doctors.

Visual Tech Will Power the Transformation of Healthcare Over the Next Decade

Visual technologies have deep implications for the future of personalized healthcare and will hopefully improve the health of people worldwide. It represents unique investment opportunities and we at LDV Capital have reviewed over 100 research papers from BCC Research, CBInsights, Frost & Sullivan, McKinsey, Wired, IEEE Spectrum and many more to compile our 2018 LDV Capital Insights report. This report highlights the sectors that power to improve healthcare based on the transformative nature of the technology in the sector, projected growth and business opportunity.

There are tremendous investment opportunities in visual technologies across diagnosis, treatment and continuing care & prevention that will help make people healthier across the globe.

News Source = techcrunch.com

Healthcare wearables level up with new moves from Apple and Alphabet

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Announcements that Apple has partnered with Aetna health insurance on a new app leveraging data from its Apple Watch and reports that Verily — one of the health-focused subsidiaries of Google‘s parent company — Alphabet, is developing a shoe that can detect weight and movement, indicate increasing momentum around using data from wearables for clinical health applications and treatments.

For venture capital investors, the movea from Apple and Alphabet to show new applications for wearable devices is a step in the right direction — and something that’s been long overdue.

“As a healthcare provider, we talk a lot about the important of preventative medicine, but the US healthcare system doesn’t have the right incentives in place to pay for it,” writes Cameron Sepah, an entrepreneur in residence at Trinity Ventures. “Since large employers largely pay for health care (outside of Medicaid and Medicare), they usually aren’t incentivized to pay for prevention, since employees don’t stay long enough for them to incur the long-term costs of health behaviors. So most startups in this space end up becoming an expendable wellness perk for companies. However, if an insurer like Aetna keeps its members long enough, there’s better alignment for disseminating this app.”

Sepah sees broader implications for the tie ups between health insurers and the tech companies making all sorts of devices to detect and diagnose conditions.

“Most patients relationship with their insurer is just getting paper bills/notifications in the mail, with terrible customer satisfaction (NPS) across the board,” Sepah wrote in an email. “But when there’s a way to build a closer relationship through a device that sits on your wrist, it opens possibilities to partner with other health tech startups that can notify patients when they are having mental health issues before they even recognize it (e.g. Mindstrong); or when they should get treatment for hypertension or sleep apnea (e.g. Cardiogram); or leverage their data into a digital chronic disease treatment program (e.g. Omada Health).”

Aetna isn’t the first insurer to tie Apple Watch data to their policies. In September 2018, John Hancock launched the Vitality program, which also gave users discounts on the latest Apple Watch if they linked it with John Hancock’s app. The company also gave out rewards if users changed their behavior around diet and exercise.

In a study conducted by Rand Europe of 400,000 people in the U.S., the U.K., and South Africa, research showed that users who wore an Apple Watch and participated in the Vitality benefits program averaged a 34 percent increase in physical activity compared to patients without the Apple Watch. It equated to roughly 5 extra days of working out per month.

“[It will] be interesting to see how CVS/Apple deal unfolds. Personalized health guidance based on a combination of individual medical records and real time wearable data is a huge and worthy goal,” wrote Greg Yap, a partner at the venture capital firm, Menlo Ventures . But, Yap wrote,I’m skeptical their first generation app will have enough data or training to deliver value to a broad population, but we’re likely to see some anecdotal benefits, and I find that worthwhile.”

Meanwhile the types of devices that record consumer health information are proliferating — thanks in no small part to Verily.

With the company reportedly working to co-develop shoes with sensors that monitor users’ movement and weight, according to CNBC, Verily is expanding its portfolio of connected devices for health monitoring and management. The company already has a watch that monitors certain patient data — including an FDA approved electrocardiogram — and is developing technologies to track diabetes-related eye disease in patients alongside smart lenses for cataract recovery.

It’s part of a broader push from technology companies to tie themselves closer to consumer health as they look to seize a part of the nearly $3 trillion healthcare industry.

If more data can be collected from wearable devices (or consumer behavior) and then monitored in a consistent fashion, tech companies ideally could suggest interventions faster and provide lower cost treatments to help avoid the need for urgent or emergency care.

These “top of the funnel” communications and monitoring services from tech companies could conceivably divert users and future healthcare patients into an alternative system that is potentially lower-cost with more of a focus on outcomes than on the volume of care and number of treatments prescribed.

Not all physicians are convinced that the use of persistent monitoring will result in better care. Dr. John Ioannidis, a celebrated professor from Stanford University, is skeptical about the utility of monitoring without a better understanding of what the data actually reveals.

“Information is good for you provided you know what it means. For much of that information we have no clue what it means. We have absolutely no idea what to do with it other than creating more anxiety,” Dr. Ioannidis said

The goal is to provide personalized guidance where machine learning can be used to identify problems and come up in concert with established therapeutic practices, according to investors who back life sciences starups.

“I think startups like Omada, Livongo, Lark, Vida, Virta, and others, can work and are already working on this overall vision of combining real time and personal historical data to deliver personalized guidance. But to be successful, startups need to be more narrowly focused and deliver improved outcomes and financial benefits right away,” according to Yap.

 

News Source = techcrunch.com

Up to $818 million deal between J&J and Locus Biosciences points to a new path for CRISPR therapies

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The up to $818 million deal between Locus Biosciences and Janssen Pharmaceuticals (a division of Johnson & Johnson) that was announced yesterday points toward a new path for CRISPR gene editing technologies and (potentially) the whole field of microbiome-targeted therapies.

Based in Research Triangle Park, N.C., Locus is commercializing research initially developed by scientists at North Carolina State University that focused on Cas3 proteins, which devour DNA Pac-Man-style, rather than edit it like the more well-known Cas9-based CRISPR technologies being used by companies like Caribou Biosciences, Editas Medicine, Synthego, Intellia Therapeutics, CRISPR Therapeutics and Beam Therapeutics.

While the Cas9 CRISPR technologies can edit targeted DNA — either deleting specific genetic material or replacing it with different genetic code — Cas3 simply removes DNA strains. “Its purpose is the destruction of invading DNA,” says Locus chief executive, Paul Garofolo.

The exclusive deal between Janssen Pharmaceuticals and Locus gives Janssen the exclusive license to develop, manufacture and commercialize CRISPR-Cas3-enhanced products targeting bacterial pathogens for the potential treatment of respiratory and other organ infections.

Under the terms of the deal, Locus is getting $20 million in upfront payments and could receive up to $798 million in potential future development and commercial milestone payments and any royalties on potential product sales.

A former executive at Valiant Pharmaceuticals and Paytheon, Garofolo was first introduced to the technology that would form the core of Locus as an executive in residence at North Carolina State University. It was there that he met Dr. Chase Beisel and Rodolphe Barrangou, whose research into Cas3 proteins would eventually be productized by Locus.

The company spun out of NC State in 2015 and raised its first cash from the North Carolina Biotech Center a year later.

Locus is already commercializing a version of its technology with bacteriophages designed to target e coli bacteria to treat urinary tract infections. The company is on target to begin its first clinical trials in the third quarter of the year.

The focus on bacterial infection and removing harmful bacteria while ensuring that the rest of a patient’s microbiome is intact is a huge step forward for treating diseases that scientists believe could be linked to bacterial health in a body, according to Garofolo.

“Most microbiome companies are about adding probiotics to your body,” says Garofolo, representing a thesis that introducing “good” bacteria to the body can offset any harmful pathogens that have infected it.

“Things you’re exposed to are creating the groundwork for an infection or disease, or exacerbating an existing disease,” says Garofolo. And while he believes that the microbiome is the next big field for scientific discovery, the approach of adding probiotics to a system seems less targeted and effective to him.

Already, Garofolo has managed to convince investors of his approach. In addition to the initial outside investment from the North Carolina Biotech Center, Locus has attracted $25 million in financing from investors, including Artis Ventures and the venture capital arm of the Chinese internet giant, Tencent.

Meanwhile, investors have spent millions backing alternative approaches to improving human health through the manipulation of the microbiome.

Companies like Second Genome, Viome and Ubiome are all using approaches that identify bacteria in the human body and try to regulate the production of that bacteria through diet and probiotic pills. It’s an approach that allows these companies to skirt the more stringent requirements the Food and Drug Administration has put in place for drugs.

That doesn’t mean that extensive amounts of research haven’t gone into the development of these probiotics. Seed, a Los Angeles-based startup that launched last year, has recruited as its chief scientist George Reid, the leading scientist on microbial health and the microbiome.

Founded by Raja Dhir, a graduate from the University of Southern California and a leading researcher on microbiotics in his own right, and Ara Katz, the former chief marketing officer of BeachMint and an MIT Media Lab fellow, Seed focuses on developing probiotic treatments using well-established research.

“Foundational to our approach is that it’s not which microbes are present in your gut… It’s based on looking at what specific microbes can do to a healthy individual to improve that status of health independent of what is already present,” Dhir said in an interview around the company’s launch last June. “It’s a little bit less exciting from a tech perspective, but it’s hardcore grounded in basic science… The question is, does this have changes and effects in validated bio-makers in a controlled and placebo setting?”

Dhir said that a basic understanding of how different bacteria can influence health is necessary before getting into the benefits of personalization.

These things can dance between drugs and nutrition,” Dhir said. “Probacteria are an additional lever that people should pull… like diet and exercise and cessation of smoking… In every correspondence we always have been and need to be clear that this should never be seen as a replacement of therapies.”

By contrast, the tools that Locus is developing are very much therapies with potentially far-reaching implications for illnesses, from irritable bowel syndrome to gastrointestinal cancers and even neurological disorders.

“The science [around the microbiome] is early, but it is very well-known that a potentially deadly pathogen should be removed from your body,” Garofolo said.

News Source = techcrunch.com

Companies tracking mutations in cancer cells can provide a key to unlocking better therapies

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Investors and entrepreneurs are beginning to bring new diagnostic tools to market that promise better results for cancer patients through the identification of mutations in cancer cells that can create more targeted therapies.

Earlier this month, research using technology developed by the startup Mission Bio helped identify cellular mutations in acute myeloid leukemia cancer cells that could be indicators of potential relapse or recurrence of the cancer after therapy.

In the study, which was presented at the American Society for Hematology’s recent conference, a team from the MD Anderson cancer research institute in Texas, including Dr. Koishi Takahashi, sequenced more than 500,000 cells across 70 patients using Mission Bio’s “Tapestri” platform.

“These results demonstrate the power of analyzing heterogeneity for the study and treatment of cancer patients,” said Dr. Takahashi, in a statement. “Tapestri’s ability to precisely identify cancer subclones throughout treatment and disease progression brings us closer to delivering on the promise of precision medicine.”

Increasingly, researchers are coming to the conclusion that genetic mutations of individual cancer cells can lead to the persistence of minimal residual disease and therapy resistance. Other leading cancer centers at  universities including the University of California, San Francisco, University of Pennsylvania, and Stanford University have also released papers on the viability of Mission Bio’s approach.

That research may help explain why Mission Bio was able to land $30 million in new funding from a slew of investors including Agilent Technologies, Cota Capital, LabCorp, LAM Capital, and Mayfield.

The company said it will use the cash to increase the work it’s doing in blood cancer research while expanding its business into the analysis of CRISPR applications and potential mutations that can occur through the use of that gene editing technology.

“Cancer will kill 10 million people this year alone. We can beat cancer with more effective, dynamic therapies, but we first need to precisely understand its biology, starting with the varying genetic composition of each and every cancerous cell,” explained Charlie Silver, CEO of Mission Bio. “Minimal residual disease is a major cause of cancer relapse; overlooking even one cell could put a life at risk. With the Tapestri Platform, we can track every cell, every mutation, to better guide treatments and save patient lives.”

That mutation tracking is also what brought Agilent on board as the company takes its initial steps into monitoring the intended and unintended consequences of using CRISPR technology to edit genes.

“The Tapestri platform’s unique quality control capabilities are strenghtening our CRISPR R&D programs,” remarked Darlene Solomon, Senior Vice President and Chief Technology Officer of Agilent Technologies. “Agilent’s commitment to innovation and precision medicine are well matched with Mission Bio’s Tapestri platform as it has the potential to improve patient outcomes in the fight against cancer — and that’s the most meaningful benchmark of all.”

Mission Bio isn’t the only company making strides when it comes to cancer treatments and new targeted monitoring technologies.

Cambridge Cancer Genomics is another startup company working on bringing new technologies to blood sample analysis that can better identify cancer and target personalized therapies for the disease.

The company has raised $4.5 million to build what it’s calling one of the largest datasets of longitudinal cancer in the world

Like Mission Bio, CCG is hoping that its data can help map the ways cancer cells evolve in response to treatments and suggest new therapies to doctors.

Financing the companies rollout are investors including AME Cloud Ventures, Refactor Capital, Romulus Capital and Y Combinator. Additional capital has come from the company’s early partner, the Comprehensive Blood and Cancer Center in Bakersfield, Calif. who invested not only cash but provided 4,000 clinical samples for CCG to analyze and develop their monitoring and predictive solution.

Both companies are trying to tackle the “one-size-fits-all” approach to cancer therapy that exists for most patients around the world.

First line cancer treatment fails two-thirds of all patients and the realization that treatments aren’t working can take up to six months to recognize. Like Mission Bio, CCG is also working to identify whether a patient is at risk of relapse — something the company claims it can do 7 months earlier than standard practices.

“When you drill down into the DNA changes behind cancer, you quickly find that no two tumors are the same. To apply cancer therapies more successfully to any given tumor, we need a deeper understanding of what exactly has gone wrong in each case at a molecular level,” says Dr. Harry Clifford, a co-founder and chief technology officer at Cambridge Cancer Genomics. “This starts with effective tools to capture that information. The approaches we’re developing at CCG will have widespread applications, from identifying targets for new therapy development, to deciding which personalized approach is best for a given patient.”

That echoes the thinking of companies like Mission Bio, and like Mission Bio, CCG has published results from recent trials of its technology.

The company applied its predictive technology to the outcome of different therapies in over 2,500 breast cancer patients and used its machine learning technology to identify the same kind of variants that Mission Bio is working to call out in an attempt to understand when and how relapses can occur.

 

1) Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection 

https://arxiv.org/abs/1811.11674

Summary: Differences in our DNA underlie many aspects of human health; from rare genetic diseases to cancer. In this paper, we build a new class of software for detecting DNA variants. Based on the same principles behind facial recognition, our technique can identify cancer variants with unparalleled accuracy. We hope that releasing this software for non-commercial use will lead to more successful targeted therapy and personalized cancer medicine. 

News Source = techcrunch.com

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