Using a third-party sequencing service is an attractive option to institutions for whom the up-front capital investment of starting up a sequencing lab is an obstacle. There are many benefits to having access to a sequencing service, such as having predictable expenses and turn-around-times. However, in most cases there are significant challenges that remain in terms of data access and interpretation. Third party sequencing services often provide either a report for each sample, or just raw data. If a report is the only deliverable, institutions do not get access to the raw data and won’t be able to share it, archive it, or reanalyze it later. If raw data alone is delivered by the sequencing service, then institutions are on their own to find and test suitable secondary and tertiary analysis tools.
Gaining access to the raw data enables labs to perform data analysis in-house, for further analysis or to be aggregated with other samples for cohort-level investigation. Doing the analysis in house also gives the institution visibility into all variants present and detected within the sample, rather than just the reported variants. This visibility can lead to greater genomic discovery and increase knowledge of potentially relevant variants.
Doing data analysis in house can also help to support better decision making. Integrating the analysis platform into your institution’s existing health data management systems will allow decision makers to take a more comprehensive view of all the individual’s data which could help them consider more variants that might otherwise not be reported. Keeping interpretation in house will also allow institutions to consider the individual’s history. Breaking these data silos through integrations with native systems and bringing genomic analysis in house can support better decision making by focusing on the most relevant variants.
There are also cost benefits to doing the genomic analysis in house. Many sequencing services charge for the sequencing and the interpretation of the data. Doing the interpretation in house removed the associated costs, requiring expenses only for sequencing.
Decentralizing the analysis of genomic sequencing data can help institutions further adopt data driven medicine through ownership of their data. SOPHiA Integrated Solutions can help institutions bring genomic data analysis in house. With SOPHiA Integrated Solutions, samples are sent to one of our partner sequencing labs and the raw data is transferred back to your SOPHiA DDM account for analysis. This allows you to harness the analytical power of SOPHiA DDM to identify and report the relevant variants.
In its very nature, cancer grows and evolves. Luckily, so does science. In order to combat one of the leading causes of death worldwide, doctors, data scientists, engineers, and countless other medical professionals have worked to discover new strategies to improve patient outcomes. Thanks to recent advancements in technology, patients are now receiving more accurate and uniquely personalized care through Precision Medicine. This requires the combination of all available health data in novel ways that doctors can interpret for new actionable insights for their patients. Leading this revolution of “multi-omics” in Data-Driven Medicine is the groundbreaking field of Radiomics.
You can learn more about Radiomics from our experts at RSNA.
Radiomics is the science of “converting digital medical images such as PET or CT examinations and MR imaging, into mineable high-dimensional data,” (1) for medical professionals to use in clinical settings. These are routine examinations that doctors are already performing, which means Radiomics maximizes upon information that’s already being collected. The difference is how that info is used.
By extracting valuable quantifiable data from images that goes beyond what human eyes can detect, SOPHiA GENETIC’s smart algorithms (AI) equip clinical researchers with more detailed and accurate information from their data, including tumor characteristics or clues about the changes in growth following treatment. This goes beyond the traditional RECIST or PERCIST criteria. This offers experts predictive models based on sophisticated computer algorithms.
While Radiomics has become rapidly utilized within the field of oncology, this technology is applicable to all disease domains. With rapid growth in Radiomics technology, there are several immediate benefits and challenges to tackle as it becomes fully integrated into clinical settings.
Here are some of the things you need to know about Radiomics right now:
1. Tumor segmentation is tricky
2. Radiomics improves workflows
3. Standardizing biomarkers is necessary
4. Radiomics improves patient outcomes
5. Sharing knowledge saves lives
Tumor segmentation is one of the most challenging aspects of Radiomics. This is the actual “capturing” of imaging data in which imaging technologies must go beyond simply scanning the diameter of a lesion. This can be quite time consuming and there is a need for a more simplified process that could be achieved through automation.
More intricate details and biomarkers are required to enhance research outcomes. Some experts have defined these new details as “habitats”. Habitats are the area related to a given tumor, including its’ distinct volumes such as blood flow, cell density, etc. Habitats refer to all of the various parts inside and around the tumor. The details of and differences between these habitats can give specific insights into treatment response, such as pseudoprogression (1, 3). The analysis of the distribution of habitats can eventually indicate which tumors will progress more aggressively than others.
Traditional radiology reports are not always able to integrate multimodal biomarkers or capture such a detailed analysis. It’s the combination of the different data sources that unlocks new potential in how we analyze and track the evolution of diseases in ways that experts were never able to before.
Every industry is looking to be more efficient, but in the medical community, efficiency can remove so much of the horrible stress patients face and ultimately save lives. Seeking a more efficient workflow in the clinic continues to be the driving factor behind the development and adoption of Radiomics.
As a necessary pillar of work performed in the lab, medical imaging is routine in cancer diagnosis and determining a patients’ prognosis. Most, if not all, cancer patients will undergo various, standard examinations including CT, PET and MRI at some point early on in their diagnostic odyssey. The beauty of Radiomics is that it doesn’t require any additional expensive or invasive tests to work. It’s easily integrated into the workflow of medical imaging.
Currently, radiologists are overburdened by the ever-increasing demand of medical imaging and administrative charges (2). Reaching well beyond the capacities of dated computer-aided detection and diagnosis (CAD) systems of routine clinical work, Radiomics automates these mundane tasks and reduces the massive workload of radiologists and clinicians. It doesn’t replace their expertise, rather, as clinicians combine their expertise with the technology available, Radiomics will continue to help investigators transform digital images to uncover hidden patterns or specific information elusive to even the most deliberately trained and experienced eyes.
With Radiomics, clinical researchers will have extra time for more cognitively challenging tasks and be better supported to make the best decisions about their patients’ care.
Accuracy, repeatability, reproducibility – these are the main challenges radiologists and oncologists face while analyzing patients’ tumor progression or response. Without Standardization, it can often feel like researchers are speaking different languages when cross-referencing examinations. In some cases, they are literally speaking different languages in their labs, so without standardization of data, things can become confusing.
Efforts like the Image Biomarker Standardisation Initiative (IBSI) are being made to better standardize image acquisition and data extraction. Going through an image slice by slice with manual segmentation results in regions of greater uncertainty compared to the more modern tools available (3). However, as new tools are developed, they come with more methods that are being used to extract features, thus resulting in more room for variables and bias.
There is ongoing debate about automated tools and how to anticipate potential error and risk with their use. It is necessary to continue to perform adequate risk assessment and validation studies in order to make sure the tools in doctors' hands are not only easy to use, but safe. It’s a tale as old as time. As technology evolves, so must its accuracy.
As no technology has ever replaced a doctor's final judgement, expertise or clinical skills, Radiomics simply provides doctors with more tools in their standards of care to treat their patients. Equipped with more actionable insights, doctors can better understand the underlying pathophysiology and choose the right therapeutic strategy for each given patient.
Every day there are new and exciting therapeutics developing in the field of oncology, such as immunotherapy. While the treatments are promising, new innovative tools are required to continue to comprehensively track and manage patients’ care. Radiomics provides clinicians with more dimensions of reliable and valuable data in order to chart out a path to better patient care and treatments that are best for the individual.
With Radiomics, clinicians can identify biomarkers in vivo over time, such as following tumor changes in volume, texture, and many other characteristics that define a response to treatment. As more medical professionals integrate this new technology into their clinic, Radiomics offers an exciting reality of democratized big-data – a future where clinicians can connect their unique patients to other complex cases or to clinical trials around the world.
Responsible data sharing is the greatest challenge for the fast-growing field of Radiomics. There is a huge need to share information across institutions and clinics globally to connect patients to clinical trials or to support clinical decision-making. For AI to evolve, it’s also essential that algorithms are trained on new sets of data all the time. This takes collaboration.
For a dataset to be statistically relevant, a general rule-of-thumb is to have at least ten times more samples than the parameters you’re modeling (5). So, more data can mean better paths to accuracy. However, with the advancement of radiomic technologies, more and more unsupervised, unlabeled, or poorly curated datasets are becoming available. In some cases across the field, we've seen unreliable datasets being used to train automated tools, resulting in ineffective models (6). This is why SOPHiA GENETICS experts work to collect only the highest quality data, constantly examining the efficacy of what we analyze and share on the platform. We ensure that data is only shared in compliance with the applicable laws and requirements, adhering to the most up-to-date international standards.
Radiomics has the potential to offer a democratized data solution for both clinical and research purposes, bolstering a community of experts. Doctors rely on their years of experience and professional networks to decide which treatment will work best for a patient. With all of the targeted therapies available, Radiomics can help define, standardize, and cultivate big datasets for clinicians to tap into for each specific case, eventually connecting patients with similar profiles for treatments or clinical trials all over the world.
CLICK HERE TO CONTACT A SOPHiA GENETICS EXPERT TODAY
CLICK HERE TO CONTACT A GE HEALTHCARE EXPERT TODAY
The information included in this presentation has been prepared for and is intended for viewing by a global audience. This blog post contains information about products which may or may not be available in different countries and if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please consult local sales representatives. All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.
SOPHiA GENETICS' Radiomics products are for Research Use Only and are not intended for purposes other than research. They are not for diagnostic, therapeutic, or treatment purposes.
References:
Data Driven Medicine
Did you know that the “DDM” in SOPHiA DDMTM stands for Data Driven Medicine? You may have heard this phrase before. It’s become a common phrase across the industry as medical research evolves in the digital age. DDM is a term used widely these days, but one that SOPHiA GENETICS has championed from the start. Data Driven Medicine is clinical research that can expand the way we approach traditional medicine, powered by deeper data analysis.
The diagnostic journey can be filled with many unknowns for both patients and physicians. These unknowns are hiding amongst a massive expanse of medical data within each of us, waiting to be discovered, to further inform our personal healthcare. Having the most comprehensive amount of medical data properly analyzed and pre-classified in one single space, allows clinicians to arm themselves with the most pertinent information for their patients, ultimately aiding in the fight against concerning diseases.
SOPHiA DDMTM plays a crucial role in advancing medical research. It is an artificial intelligence-powered software as a service (SaaS) platform that’s currently used in hundreds of labs around the world to compile large sets of varying medical data from multiple sources.
Data optimization
Many larger hospital networks are already using instruments that produce these large amounts of medical research data. But many of those tests being run in the labs are becoming more in-depth, more precise, which means professionals are dealing with incredible amounts of new data each day at increasing rates. For this reason, fast and accurate analysis is essential in order to process more and more relevant information for patient research. This level of efficiency can also allow smaller labs to take on much more comprehensive testing, utilizing fewer resources than traditionally required.
Clinical research depends on accurate, reproducible results. What’s often overlooked is how much more productivity can be enabled within a lab when experiments produce consistent levels of accuracy with faster turn-around-times. This takes a lot of fine-tuning of clinical instruments, which can be yet another taxing and time-consuming process. The simple solution is optimized workflows that can quickly discover what users are searching for among their medical data. With SOPHiA DDMTM, variants of interest are discovered by elite algorithms and preclassified for the user so that they don’t have to go digging and sifting through all of the available data. Think of it like trying to find a treasure buried on a beach. It would be a lot easier to use a metal detector than to pick through each grain of sand.
The SOPHiA GENETICS factor
Since our beginnings, SOPHiA GENETICS has taken a global approach to supporting medical research. This means we designed our technology to be able to support many forms of medical data input, making us the universal platform of choice for more than 780 healthcare institutions worldwide. With Radiomics, we’ve taken our first steps toward true multimodal research.
If you’d like to learn more about the SOPHiA DDMTM platform, you’re in luck! You can book a demo right here through our website, or you could come and see us at the upcoming HLTH conference in Boston from October 17-20th.
Instead, it can accelerate and validate researchers’ discoveries for faster application of their hard work to improve patient care. In healthcare, we see a promising step toward better patient outcomes thanks to advancements in machine learning.
AI and NGS
There’s a reason why it took so long for the first human genome to be sequenced. It’s not an easy thing to accomplish without a “roadmap.” When researchers analyze genomic samples, they’re often overwhelmed with countless panels to run, limited time and resources, and a whole ton of data that gets pulled from every single sample. With artificial intelligence, that first “roadmap” discovered in the Human Genome Project isn’t such a long journey anymore. Algorithms can help quickly identify biomarkers within a person’s genomic data that can assist clinicians shed light on many medical mysteries.
Artificial intelligence has been adopted more widely for clinical trials and patient care in recent years thanks to the evolution of NGS (Next Generation Sequencing). Even when it’s not being utilized as the key player to gather important new insights, machine learning applied to Next-Generation Sequencing can better organize and identify variants of interest hidden among background noise. Algorithms trained on specific data points discover and flag biomarkers in new, more efficient ways, down to the exon level. More in-depth output supports better informed decisions.
AI in radiology
Imaging is one of the first tests performed when a patient begins the diagnostic journey. Traditionally, an expert is trained and educated to specifically search for and identify concerning areas or segments of a given medical image. Artificial intelligence in radiology goes well beyond what the human eye can see by analyzing the available data, not just the image.
Through 3D segmentation and visualization, AI can pinpoint concerning features in medical scans faster and more efficiently than the human eye. Recent studies even suggest that AI for radiomics could be used in preliminary interpretations of chest radiographs to address the scarcity of resources, improve accuracy, and reduce the cost of care.
Democratizing AI-powered data for all
Trustworthy AI requires data volume and diversity. The more unique data that the algorithms can train upon, the more accurate they become in searching for relevant biomarkers within a sample. This is why SOPHiA GENETICS created a universal platform that can adapt and evolve with its users. A global community of more than 780 health care institutions have already supplied the SOPHiA DDM™ platform with relevant data, analyzing more than 770,000 genomic profiles. Standardized, pseudonymized, and aggregated data improve our machine learning algorithms to deliver variant detection, analysis, and interpretation that empowers researchers beyond manual investigation. Learn more about SOPHiA DDMTM, combining data from AI in Genomics and Radiomics with our technology by clicking here.
Let’s say you have just had an amazing dessert prepared by a friend. You’d love to recreate this experience yourself. But instead of giving you a recipe with standard measurements you’re able to recreate in your own kitchen, they give you instructions like pouring a handful of sugar, half a glass of water, and three fork lengths of butter together in a bowl. Get the portions wrong, and the result may not be so sweet. Baking can be boiled down to a science if most recipes and ingredient measurements are standardized. The same concept can be applied in your genomic research labs.
Breakthrough research depends on replicable laboratory procedures and reliable analysis. Testing should follow universally embraced protocols in sample collection for the resulting data to be considered valid. The analysis and interpretation of data, no matter the lab of origin, must follow common metrics.
Standardization helps find variants
To measure results from one individual against those of a control group and those with similar genomic profiles, data analysis must be unconditionally repeatable. Standardized data enables variant detection and identification, batch analysis and genetic assessment. Valuable insight has a positive impact for researchers today and clinicians and patients tomorrow.
It’s hard to avoid normalization when talking standardization. Normalization, also known as min-max scaling, sets data values between 0 and 1. You normalize data if features that need to be compared have different origins. Normalization, however, does not account for outliers. And in genomic research, outliers are the very data points researchers are looking for.
Standardization, also called Z-Score Normalization, scales data to have a mean of zero and a predetermined standard deviation. Standardized data is not bound to a specific scale and ensures that, if various datasets are compared, they’re measured by the same deviation without compromising reporting quality to better identify relevant variants.
Standardization supports universal learning
Consider what’s happening inside all of us at a molecular level. It took decades to compile data for the Human Genome Project. In the past decade we’ve started to see how genomic ingredients create a unique recipe for each person.
To understand cancer, inherited diseases, or even COVID-19, we must understand the recipes, but also where they are mutating. We’ve seen medical research advance rapidly, especially with the efficiency required for COVID-19 response. Labs use different machines for analysis, different sample sources and different algorithms to discover new variants. But if they standardize data, research can produce desperately needed solutions in record time by peers working in labs across the globe.
Standardization is the future of Data-Driven Medicine
In order to advance the research being conducted for disease prevention, detectability, or the spread of a virus, data must be standardized so that scientists working in a European lab could easily share their data with other scientists in Asia or North America, while observing applicable laws. Without standardization of data, language barriers would be the least of our concerns when it comes to understanding the work performed by our international peers. Data standardization can be like a universal language that connects all research with one comprehensive purpose – to eliminate data corruption or “bad data” and preserve the work of thousands to be built upon for the future.
If you’d like to learn more about how SOPHiA GENETICS can help you create a more efficient workflow in your lab, you can contact us today.
How data accuracy can be improved with better algorithms
Your favorite song is playing on the car radio, but as you drive along, the frequency seems to hit a snag as hisses and pops infiltrate the music. The same song could sound much clearer on a slightly different radio frequency. It just might take some fine tuning. The process of tuning into a specific “clean” frequency is not unique to music on the radio alone. Medical research must be reproducible without the static.
How do we “tune” into data accuracy?
Some of the most concerning diseases of our time can now be studied in ways that scientists had only dreamed of decades ago. Through the evolution of Next Generation Sequencing, data becomes the lifeblood of new clinical research capabilities.
If you think of data accuracy like that radio signal, what you’re trying to do is tune into the right frequency, searching for your intended disease-causing variant. Ideally, your end result is to hear a perfect signal coming through a mess of static. It’s that signal that gives you the most accurate reading for what you’re searching for among the messy noise that’s naturally present in any given sample you may be testing. In order to finetune the end result, you must eliminate what is known as background noise. For NGS, this is a combination of the biases inherit to design. It looks like peaks of signals when visualized. Background noise could come from any outliers and excess datapoints that don’t apply to the research.
How can data be made better?
The new age of Next Generation Sequencing comes with massive amounts of data being analyzed each day at record levels. The amount of background noise in those datasets also increases on a major scale, making it more difficult to reach levels of accuracy that support your research.
Every single step of an experiment can introduce noise to the mix. Luckily, when data is muddied with irregularities captured throughout the analysis, it can also be cleaned. With advanced algorithms and exceptional analytical performance, it’s easier to identify variants of interest or to overcome any corruption of the data quality/accuracy. This is thanks to the ability to look past the “static” of background noise and zoom in on variants of interest with a higher resolution, sometimes down to 2-5 exons.
How can we further data analysis? It’s clear that the initial data capture is far from the final step in your research. In addition to the existing interpretation functionalities such as ACMG automated variant classification, virtual gene panels, and cascading filters as part of our platform, SOPHiA DDMTM for use with KAPA HyperExome offers extremely accurate detection of biomarkers in a single workflow. The solution and our platform include the Familial Variant Analysis (trio analysis) to automatically filter variants based on different inheritance modes. If you’d like to learn more about what we offer, contact us today.
My one inspiration in life has always been my family. Each and every one of them has given me so much, but it’s my uncle, in particular, who helped me discover and pursue my passion for science. He’s a very talented scientist and his success story in the field really inspired me to try and follow in his footsteps. Our discussions were always revolving around nourishing and exciting topics such as string theory, black holes, supersymmetry, etc - and the reason why my first aspiration was to become a theoretical physicist.
I quickly realized that I needed to study and work in a field that is more exciting and stimulating on a daily basis that theoretical physics. I have always been passionate about mathematics and computers ever since I was a kid, so when the time to choose arrived, I went on to study Computer Science at KNU, in Kyiv, Ukraine. My other passion in life has always been traveling, opening to the world, and discovering other cultures; that’s why after winning a scholarship for COPERNIC program I have moved to Paris, France to study Business Administration at Sciences Po.
Being particularly drawn to the field of technology in general, I was then looking for a job that could combine this passion with my education and an exciting environment for me to work in. I heard of SOPHiA GENETICS, and it seemed like the kind of company I wanted to be a part of. Health, data, scalable services, cloud computing, web, opensource, innovation… all of these words reflect my personal interest and are at the core of what the company does every day.
I now live in the beautiful French Basque country with my family, working as a Senior Software Development Engineer.
My main focus of work is building new platform services powering data transfer that will be at the core of the new SOPHiA DDM™ platform. On the day-to-day, it means that I am solving challenging problems of data transfer. It is very rewarding, and it makes me grow as an engineer a little more every day. I am part of a great team - the so-called Plancha team - and we all lift each other up in the most challenging way. We deal with scaling problems, performance bottlenecks, internals of algorithms and protocols for distributed systems, micro-service architectures patterns, failures at scale etc, while also regularly meeting with customers. When a new customer is using the platform, we have onboarding call to help them with automating uploads by using the uploader CLI tool. This helps us understand their needs and better prioritize the products we build.
It’s important for my team, and for the whole company, to have this proximity with our customers; it gives us important insights and directions to help us fulfil our mission: helping healthcare institutions to improve people’s health across the globe. It’s incredible to work for a company that is full of great people at every level, working together toward the same goal.
If I had to sum up what it’s like to work at SOPHiA GENETICS, I would say it just combines all the elements you could possibly be looking for in a job. First of all, I am lucky enough to be working in our French R&D office in Bidart which really is a dream location, close to the sea. Also, the job in itself is stimulating. I’m surrounded with a brilliant team, and we solve challenging technical and scientific issues; it never gets boring! But what is truly amazing is the bond we, as colleagues, all share. We come from all over the world, from different backgrounds, we don’t speak the same language and don’t have the same culture, but we all learn from one another and grow together, united by the same exceptional company mission. I may not have become an astrophysicist working on black holes, but with the SOPHiA team around me, I am for sure - we are all - reaching for the stars!
Sometimes referred to as the father of modern computing, Alan Turing planted the roots of artificial intelligence and machine learning. The British mathematician deciphered cryptic messages during World War II, helping the Allies win. Yet, he was considered a criminal for his homosexuality. And because much of his work was classified, he did not receive due praise from his peers.
The vision
According to his biographer, Alan Turing envisioned machines “turned to any well-defined task by being supplied with the appropriate program.” The theories in Turing’s seminal paper, “On Computable Numbers, with an Application to the Entscheidungsproblem,” supported his invention of the universal Turing Machine, an abstract computing machine that provoked the creation of digital computers.
His digital program storage concepts set the framework for machine learning. Today, Turing’s ripple effect echoes through many industries including healthcare. Thanks to Turing’s first steps, computers categorize molecular data from biological samples to determine the root cause of disease.
The early days of machine learning
Turing’s design for the Automatic Computing Engine was based in his belief that humans and machines could think and learn similarly. He saw the cerebral cortex of an infant as an unorganized machine that became more organized through education. He noted that computing is learned and that both machines and humans could be trained to solve mathematical equations. He also thought that technology would one day embody intelligence in an artificial capacity.
Persecution
Turing’s greatest tragedy seemed to come just after he had reached acclaim. His wartime work to decode Nazi transmissions earned him an accolade as Officer of Most Excellent Order of the British Empire. In 1951 he was elected fellow of the Royal Society of London. But in 1952, Turing admitted he was in a relationship with a man. He was convicted of gross indecency and sentenced to 12 months of hormone therapy.
Turing was discovered dead in his bed, poisoned by cyanide in 1954. Officially ruled a suicide, his death is often attributed to an altered state of mind brought on by the hormone treatment. He had been publishing groundbreaking theories on patterns in living organisms — work still inspiring pattern recognition by machines today.
A continuing contribution
In 2009, Prime Minister Gordon Brown apologized for the British government’s treatment of Turing. In 2013, his work was declassified, and Queen Elizabeth II granted him a Royal Pardon. Turing’s theories were radical for his time but truly visionary.
Turing couldn’t have predicted the impact his theories on computing data would have. The Human Genome Project and the advanced computing abilities on which it relied applied Turing’s work in modern algorithms. Now, his concepts — far more than theories — help inform Data-Driven Medicine. To learn more about the patented algorithms of SOPHiA GENETICS applied to genomic and radiomic data, visit https://www.sophiagenetics.com/technology/.
More than six people die every hour in the US from a blood cancer. Solutions can’t come fast enough for those who suffer with these cancers all around the world. Fortunately, researchers studying blood diseases have experienced rapid advances in their capabilities to develop and test effective therapies with some extremely significant advancements.
Some of the most difficult limitations of molecular profiling for hematological cancer disorders include accurate detection of mutations in GC-rich gene regions and insertions or deletions in challenging genes. Data analysis on NGS DNA samples identifies complex variants to accurately identify myeloid malignancies. This validation of targeted mutations has encouraged many medical centers to order NGS testing for every acute myeloid leukemia case.
Faster, more efficient NGS analysis can drive better hematological cancer research outcomes to potentially improve care for patients with blood cancers and diagnosis of new cases.
International guidelines for hematological cancer diagnosis and treatment are continuously evolving and create the need for laboratories’ fast adaptation. Those evidence-based guidelines by physician commissions contribute to improving the clinical standard of care. The World Health Organization, European Hematology Association, European LeukemiaNet, College of American Pathologists and the American Society of Hematology call for increased use of NGS testing for initial diagnostic workup of blood cancers.
Detection of the relevant biomarkers for myeloid malignancies by NGS, per international guidelines, helps to ensure optimal clinical trial enrollment, therapy validation, dose protocols and other research benefits. A solution that can be constantly updated and inform based on those guidelines ensures that the research is always current.
The accurate assessment of biomarkers and the validity of resulting research findings depend on reliable DNA and RNA fusion panels and easily reproducible results. Data analysis and reporting in a comprehensive platform eliminates silos of valuable data and maximizes its application.
The SOPHiA DDM™ platform enables the upload of multimodal data from any environment to one of the world’s largest networks of connected labs. Data remains the property of the healthcare institution, but pseudonymized and pooled with like data, it can propel research and ultimately treatment forward with the goal of improved patient care.
Learn more about the capabilities of SOPHiA DDM™ platform for myeloid biomarker detection and more by contacting us today.
My educational and professional journey is a pretty straightforward one, and would have probably been entirely pursued in my native Canada if it wasn’t for a sudden move that happened during my PhD. Unexpectedly, my PhD professor joined the University of Miami and I decided to follow him. This wasn’t planned but it’s probably the catalyst for my current career path.
In Florida, I completed a PhD in Biochemistry and Molecular Biology, investigating the expression and function of long noncoding RNA in amyloid formation, and in that pursuit, I quickly had to involve bioinformatics in my focus. The main motivation for that is a pretty classic tale: as the molecules we discovered in the lab were not well-characterized, it pushed us to develop our own unique NGS and bioinformatics workflow to look at expression and function. While the learning curve was steep (I had never touched a terminal before that), the process was rewarding and it opened up the possibility of pursuing opportunities outside the lab.
I graduated in 2018 and started looking for a job in the field of biology that could fulfill my interests in NGS, bioinformatics and client services.
In early 2019, I heard of SOPHiA GENETICS, a foreign company recently installed in the US and looking to grow a team here. I was up to the challenge and applied to a bioinformatician position. I joined the company on April 1, 2019, as one of the first members of the Data Science team in Boston, MA. Data Science being at the heart of what the company does, the team was already pretty big in Europe, but here on the other side of the Atlantic, everything was to be built from scratch.
Now, two years after I joined, I manage the bioinformatics services team for North America. We are a team of four people and growing, looking to hire more talents. Our focus is quite broad as we support a wide range of products, meaning that our work is never dull. All in all, the inherent nature of what we do allows us to be excited to go to work every day. I have no doubt there are still other elements to discover in the human genome related to human health; that’s what makes me so passionate about my job. The growing adoption of NGS and the discovery of other next generation sequencing technologies are key elements in elevating standards of care for various diseases. Knowing that what I do on a daily basis can impact people as an end benefit and improve their quality of life is immensely rewarding.
When you work for a growing company like SOPHiA GENETICS, another interesting aspect is that it allows you to grow with and within the company. Our core business being so innovative and evolving, there are plenty of learning opportunities That’s why I could quickly evolve in my role and now be in charge of my own team after less than two years with the company.
At SOPHiA GENETICS, the way we work is simple: we focus on the finish line. When you know you can contribute to something greater than yourself that can benefit other people, that’s when you can thrive to be at your best level. We push boundaries by being always at the forefront of innovation with our solutions, while being responsive to the needs of our users. But all of this wouldn’t be possible without the help and mutual support of a great team. People, colleagues, teammates, are a big part of who we are as a company. I’m surrounded with supportive, motivated, hard-working and passionate people at all levels; this creates an authentic company culture that boosts all of us, every day.
SOPHiA GENETICS products are for Research Use Only and not for use in diagnostic procedures unless specified otherwise.
SOPHiA DDM™ Dx Hereditary Cancer Solution, SOPHiA DDM™ Dx RNAtarget Oncology Solution and SOPHiA DDM™ Dx Homologous Recombination Deficiency Solution are available as CE-IVD products for In Vitro Diagnostic Use in the European Economic Area (EEA), the United Kingdom and Switzerland. SOPHiA DDM™ Dx Myeloid Solution and SOPHiA DDM™ Dx Solid Tumor Solution are available as CE-IVD products for In Vitro Diagnostic Use in the EEA, the United Kingdom, Switzerland, and Israel. Information about products that may or may not be available in different countries and if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please contact us at [email protected] to obtain the appropriate product information for your country of residence.
All third-party trademarks listed by SOPHiA GENETICS remain the property of their respective owners. Unless specifically identified as such, SOPHiA GENETICS’ use of third-party trademarks does not indicate any relationship, sponsorship, or endorsement between SOPHiA GENETICS and the owners of these trademarks. Any references by SOPHiA GENETICS to third-party trademarks is to identify the corresponding third-party goods and/or services and shall be considered nominative fair use under the trademark law.