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At the 2025 American College of Medical Genetics and Genomics (ACMG) annual meeting in Los Angeles, we hosted a focused session on the role of exome and whole genome sequencing (WGS) in clinical and research settings. The goal was to spark conversation about available technologies, implementation challenges, and future strategies. Four expert speakers shared insights on clinical utility, followed by a lively audience Q&A.
This blog captures key takeaways from the event, including when and why broader testing is preferred over targeted panels, how to optimize virtual panels, and reimbursement realities. Whether you’re a lab director, clinician, or genetic counselor, these insights offer timely guidance.
Historically, targeted panels have been the cornerstone of many diagnostic workflows. However, as our understanding of gene-disease associations evolves, so too must our approach to testing.
Exome sequencing can adapt to the dynamic nature of clinical genetics. Take carrier screening, for example. As Mahmoud Aarabi, Medical Director of UPMC Cytogenetics Laboratories, explained, the list of genes recommended for autosomal recessive and X-linked carrier screening by ACMG1 is continuously updated based on new phenotypic and population data. Rather than continually revising panel content, exome sequencing provides a flexible, future-ready alternative. Phenotyping also plays a critical role. In prenatal testing, complete phenotyping can boost exome diagnostic yield by 7–10%.2
Jeanette McCarthy, Principal Consultant at Zifo, emphasized how labs can maximize efficiency using virtual panels (slice panels) to analyze exome data. With this approach, labs validate the wet lab component once and can then revise gene content as needed without extensive revalidation.
But designing virtual panels well requires careful forethought. She recommended selecting only genes with robust disease-gene validity, accounting for technically challenging targets (e.g., SMN1, PMS2), and avoiding copy number variation (CNV) analysis for genes that lack sufficient evidence of a loss-of-function mechanism. Additionally, genes should be excluded when the only relevant variant types cannot be reliably detected by exome sequencing - for example, including ATXN7 is unhelpful due to exomes’ inability to detect repeat expansions.
Exome sequencing consistently delivers higher diagnostic yield and cost savings compared to traditional approaches
Joe Jacher, Genomenon and trained genetic counselor, highlighted the clinical and economic case. “You can save $20,000 by skipping from microarray straight to exome,” he noted, citing peer-reviewed research.3 Indeed, the literature supports exomes/genomes as first- or second-tier tests for congenital anomalies or intellectual disability.4 For neurodevelopmental disorders (NDDs), exome sequencing outperforms chromosomal microarray analysis in both diagnostic yield and cost-effectiveness when used early.4,5
One of the largest challenges to broader adoption of exome and genome sequencing in clinical settings is insurance coverage. Despite proven utility, reimbursement remains inconsistent and often favors exome over genome sequencing, which is often restricted to research use.
In pediatric oncology, for example, current guidelines may still prioritize legacy tests like karyotyping and FISH over broader sequencing approaches, even when those legacy tests fall short of delivering a diagnosis.
To navigate this, some labs are adopting hybrid models. At Stanford Medicine, clinical panels are run on a genome backbone, enabling targeted reporting first, with the option for expanded analysis later if required. It also positions the lab for a future where broader genome analysis becomes more widely accepted and reimbursed.
If insurers continue to favor exomes, exome-on-genome workflows may be a practical interim solution to futureproof workflows and streamline reanalysis as new insights emerge.
WGS offers some clear technical advantages. It covers both coding and non-coding regions, provides more uniform coverage than exomes, and captures structural variants and repeat expansions with greater accuracy.
Jennefer Carter, Senior Genetic Counselor and part of the Stanford Undiagnosed Diseases Network (UDN), described how WGS delivered diagnoses in cases where exome sequencing would have failed. Among 283 Stanford UDN patients, WGS revealed diagnoses in cases involving CNVs/structural variants, repeat expansions, and non-coding variants – challenging variant types often missed by exomes.
Genome sequencing is where we’re headed - but exomes are the most practical, reimbursable, and clinically validated tool we have right now
Audience members agreed. One noted that "genome + RNASeq is the way forward," pointing to savings from eliminating multiple legacy tests. But RNASeq has its own limitations, such as with conditions affecting tissues where genes are not expressed in the blood.
Moreover, there were warnings of variability in commercial genome testing. Some labs restrict genome interpretation to just 10 bp into introns unless another variant prompts deeper review. Transparency and education are essential to ensure providers understand what their patients are receiving.
Despite historical limitations, some institutions are already shifting toward genome-first approaches. A genetic counselor from Children's Hospital Los Angeles noted that their team now defaults to WGS for most send-outs. Encouragingly, insurer coverage is improving, and third-party labs have been able to cover costs to build evidence for future reimbursement.
So, what can clinical labs and providers do today to prepare for an exome- and genome-enabled future?
While WGS promises broader insights, exome sequencing remains the most practical and reimbursable tool today. It balances diagnostic yield, cost, and flexibility, making it a strategic choice for many clinical settings.
As infrastructure, interpretation tools, and reimbursement models continue to evolve, WGS will play a growing role in routine care. But for now, optimizing the use of exomes while laying the groundwork for a genome-based future, offers the best of both worlds.
The discussion at ACMG 2025 made clear: the path to better patient outcomes lies in making high-quality genomic testing more accessible, informed, and actionable.
Visit our Rare Disorders page to learn more about SOPHiA DDM™ exome and genome solutions.
References
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.
Predictive analytics refers to using statistical algorithms, machine learning techniques, and historical data to forecast future events. In clinical trials, this means integrating diverse data sources – such as, past clinical trials, patient records, and real-world evidence (RWE) – to provide more accurate predictions about trial outcomes, patient responses, and potential risks3. By analyzing patterns from this data, predictive analytics offers a powerful tool for improving the efficiency, accuracy, and safety of clinical trials1.
Traditionally, clinical trials have been labor-intensive and costly, often taking years to yield results4. Researchers had to rely on historical outcomes, guesswork, and incomplete data to design trials and predict success. Predictive analytics changes this paradigm by enabling data-driven decision-making5. By analyzing data from clinical trials, and real-world data (RWD) – including but not limited to patient demographics, electronic health records (EHRs), and claims data-, predictive models can help physicians and researchers make informed decisions about trial design, patient selection, and potential treatment outcomes.
Predictive analytics is particularly valuable because it can integrate multiple data modalities, such as clinical, biological, genomic, biomarker, and imaging data. The ability to combine and analyze this wealth of information is central to predictive analytics’ potential to revolutionize the clinical trial process.
As the healthcare landscape continues to evolve, predictive analytics will play an increasingly central role in the clinical trial process, driving innovation and improving patient outcomes6.
As with any emerging technology, the adoption of predictive analytics in clinical trials requires collaboration between pharmaceutical companies, regulatory agencies, and healthcare providers. However, the potential benefits are too great to ignore. With predictive analytics at the helm, the future of clinical trials looks promising, offering a path to faster, safer, and more effective drug development.
By leveraging on machine learning, SOPHiA DDM™ facilitates the integration and standardization of diverse data modalities – including but not limited to clinical, biological, radiomics, genomics, and digital pathology data – generating powerful insights to support you in accelerating drug development.
Our multimodal AI data analytics helps you optimize your clinical trial and enhance your post-launch access strategy, by predicting patient response to treatment, disease progression, risk of developing adverse events, and supporting treatment decision-making.
A great example of how SOPHiA GENETICS is spearheading innovation in cancer research by applying predictive analytics is the collaboration with UroCCR, the French Kidney Cancer Research Network, to develop a multimodal machine-learning model for predicting post-operative outcomes for individuals facing renal cell carcinoma (RCC). Using real-world prospective data from the UroCCR network, one of the world’s largest collaborative kidney cancer databases, this study showed that the AI model co-constructed by SOPHiA GENETICS and UroCCR provided a strong prediction for postoperative outcomes, outperforming the predictive performance of most usual risk scores. The results of this study have recently been published in npj Precision Oncology.
With a global network of 780+ institutions, across 70+ countries, and over 1.8 million genomics profiles analyzed to date, the SOPHiA DDM™ Platform accelerates the advances in the field of precision medicine. To learn more about SOPHiA DDM™ for Multimodal and our flagship programs visit our page.
Ensuring that the clinical trial population reflects the diversity of the population for whom the new drug is being developed has been a longstanding challenge for the Pharma industry. Historically, many clinical trials predominantly enrolled white, male participants, which overlooked important physiological and biological differences that exist across age, gender, ethnicity, and race4,5.
With the increasing focus from regulatory authorities, achieving a balanced representation of different populations is not only desirable but crucial, which will ultimately lead to more equitable healthcare solutions.
Despite the well-documented importance of data diversity, many clinical trials continue to struggle with the underrepresentation of minority groups. There are several key challenges that pharmaceutical companies face in achieving diversity in their clinical trials, including inappropriate clinical trial design for the safe participation of certain groups (e.g., excluding children, elderly, and pregnant women), lack of awareness, lack of community engagement, mistrust in the medical system, logistical and financial barriers, and cultural, generational and linguistic barriers 4,5,7.
Addressing these challenges requires targeted efforts from pharmaceutical companies, healthcare professionals, and policymakers alike.
Recognizing the importance of data diversity, regulatory bodies such as the Food and Drug Administration (FDA) in the USA and European Medicines Agency (EMA) in the EU, have implemented new guidelines to encourage more representative clinical trials. The FDA has a long history of issuing guidelines that improve clinical trial diversity emphasizing the importance of including participants from underrepresented populations (Figure 1)8-13. Similarly, the European Medicines Agency (EMA)14 and other international regulatory bodies are pushing for trials to better reflect the diversity of patient populations.
To align with these new regulatory standards and address the need for more equitable healthcare, pharmaceutical companies are implementing several strategies to meet diversity goals4,15:
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| Community Engagement and Partnerships | Decentralized Trials | Inclusive Eligibility Criteria | Adaptive Trial Design | Real-World Data Informed Trials |
| Partnering with local healthcare providers and advocacy organizations to build trust and educate underrepresented populations about the importance of clinical trials, can improve their participation. | Adopting decentralized clinical trials allows patients to participate remotely, and removes geographical barriers that often prevent minority groups participation. | Ensuring eligibility criteria are representative of the population for whom the drug is being developed, and eliminating or modifying uneccessary exclusion criteria. | Using an adaptive clinical trial design can allow for pre-specified trial design chances during the trial when data becomes available, including altering the trial population. | Using RWD to model the effect of specific eligibility criteria on the eligible population and trial endpoints (e.g., overall survival) can enable a more inclusive trial design. |
Achieving data diversity in clinical trials is not just a regulatory requirement but a moral imperative. By addressing health disparities, enriching clinical trial results, and following new regulatory guidelines, pharmaceutical companies have an unprecedented opportunity to create a more equitable and effective healthcare system.
The tech-agnostic, decentralized and powered by AI SOPHiA DDM™ Platform is breaking data silos by standardizing complex multimodal datasets to unlock actionable insights.
Leveraging a global network of 780+ institutions, and a growing data diversity of more than 1.8 million genomic profiles, SOPHiA DDM™ provides on-demand access to a wealth of accurate global genomics insights and real-world data that support you in optimizing your clinical trial and enriching your market access strategy.
Through strategic innovation and a genuine commitment to diversity and inclusion, SOPHiA GENETICS is actively addressing health inequalities in comprehensive cancer care. The ongoing collaboration with AstraZeneca and Memorial Sloan Kettering Cancer Center (MSK) is looking to bring quality and comprehensive cancer testing worldwide, including in underserved regions where access to testing remains scarce. This project is accelerating the deployment of MSK-ACCESS® powered with SOPHiA DDM™ to healthcare institutions, leveraging the global SOPHiA GENETICS network. In a recent press release, we announced a new milestone in this collaboration.
In a continued effort to revolutionize cancer research and embrace diversity, SOPHiA GENETICS has launched SOPHiA UNITY, a global collective intelligence network of best-in-class healthcare institutions fueling the next wave of innovation in oncology by making real-world data available for research.
By uniting a critical mass of real-world data and expertise in data analytics, SOPHiA UNITY is one of the most robust sources of diverse data available in the market to drive breakthroughs and improve patient outcomes worldwide.
With the increased requirements for more data-informed clinical trials and drug development, SOPHiA GENETICS empowers Pharma companies to take their oncology clinical trials to the next level, driving the global movement towards health equity.
To learn more about SOPHiA UNITY, visit our page and read our recent press release for an update on healthcare institutions that have joined the consortium.
In the era of precision oncology, it has become increasingly common for patients diagnosed with cancer to undergo tumor sequencing. Identifying the mutations that make up a tumor’s genomic landscape can help guide selection of targeted therapies and inform prognosis. Despite the recognized value of tumor-only sequencing, labs performing this type of testing face a number of technical challenges that, if not properly addressed, can render the results uninformative or even misleading.
Although there are a variety of inherent challenges in tumor-only sequencing, all ultimately impact the ability to accurately distinguish somatic mutations driving tumorigenesis from germline variants associated with cancer predisposition. In fact, it has been estimated that as many as one third of mutations identified by tumor-only sequencing may be false-positive germline changes, including in potentially actionable genes1. Having an accurate picture of a tumor’s genomic makeup and contextual genetic environment is crucial to an accurate clinical assessment, which impacts therapeutic recommendations and represents the patient’s best chance for successful treatment.
In this blog we explore different strategies for enriching tumor analysis for somatic mutations and discuss why matched tumor-normal sequencing has become the preferred method.
One approach is to use variants present in large population databases as a filter to remove likely germline variants from a tumor sample2. While this practice is generally effective, it will also remove true somatic variants that happen to be identical to germline variants, resulting in a false negative. Database-driven approaches can also overlook any rare germline variants missing from large population databases due to underrepresentation of non-White individuals. These variants will remain in the sequencing data and can result in false-positive germline findings.
Taking allele frequency into consideration can help. This strategy is based on the premise that an allele frequency of 50% is consistent with a heterozygous germline variant, and an allele frequency of ~100% is consistent with a homozygous germline variant1. It then stands to reason that focusing on variants with a lower allele frequency increases the likelihood of somatic origin.
While this is true, such an approach can be complicated by many factors including contamination of the tumor sample with normal tissue, tumor heterogeneity, sequencing artifacts, difficulty mapping reads in regions of high homology, high level mosaic variants that arose early in differentiation, as well as changes in allele fraction due to copy number changes or loss of heterozygosity (LOH). Any of which can lead to inaccurate attribution of origin.
Matched tumor-normal sequencing that pairs analysis of a tumor sample with that of a comparable, normal sample – most often from the same individual – has been shown to be a more effective strategy, yielding more reliable identification of the somatic changes specific to a tumor1,3,4. As the name suggests, variants in the matched normal sample are determined to be germline in origin, or of alternate origin unrelated to the current tumorigenicity. When used as a filter against the tumor sample, somatic variants relevant to the cancer at hand can be identified with a high degree of confidence. Variants found at low frequencies in the normal sample can be confidently classified as false positives if they are not significantly enriched in the tumor.
While the most important function of matched tumor-normal sequencing is to identify and retain somatic mutations, it also serves other important functions.
At the most simplistic level, biological samples can exhibit variability due to factors such as environmental influences, biological processes and sample handling. Matched-tumor normal sequencing provides a built-in baseline of background noise resulting from these factors, or from introduction of sequencing artifacts, that can be filtered out.
In the case of FFPE samples, extracted DNA is often fragmented and of a lower quality than fresh tissue samples. Matched tumor-normal sequencing provides a comparison that helps distinguish true alterations from noise resulting from degradation of the DNA, enhancing sensitivity.
Cell-free DNA (cfDNA) samples, also known as liquid biopsy samples, contain DNA from tumor cells, but they also contain a significant amount of DNA from white blood cells. In many individuals, especially those who are older, these phenotypically normal blood cells contain acquired mutations subsequently increased in relative frequency due to clonal expansion. These clonal hematopoiesis of indeterminate potential (CHIP) variants often, but not always, occur in the same genes associated with blood cancers like leukemia. However, while they are indicative of an increased risk of developing a blood cancer in the future, they are not likely to be relevant to the tumor being analyzed.
Simultaneously sequencing matched white blood cells as a normal control can successfully distinguish somatic mutations that are relevant to driving tumorigenesis from somatic mutations arising from the normal process of clonal hematopoiesis4. This is such an important consideration that both ESMO and AMP guidelines specify that matched white blood cell sequencing should be used for interpretation of somatic variants in cfDNA testing5,6.
Removal of false positives arising from CHIP variants is not only important for accurate cfDNA analysis, but also FFPE analysis. In a study by Memorial Sloan Kettering Cancer Center (MSK) investigators, matched tumor-normal sequencing results showed that 5.2% (912/17,469) of patients with advanced cancer would have had at least 1 clonal hematopoietic (CH)-associated mutation erroneously called as tumor-derived in the absence of matched blood sequencing7. Of these CH variants, 49.7% of them were classified as oncogenic or likely oncogenic based on OncoKB™, and 3.2% were associated with approved or investigational therapies (e.g. mutations in IDH1/2). Failure to recognize such mutations as blood-derived may result in inaccurate precision therapy recommendations.
The ability to distinguish between somatic and germline variants has the additional benefit of streamlining analysis of germline variants which have additional implication for a patient’s clinical care. Notably providing information about future disease risk which can be managed in part through surveillance as well as allowing for testing of family members who may also be at risk for disease.
It is for the reasons discussed here that MSK-ACCESS® powered with SOPHiA DDM™ for liquid biopsy and MSK-IMPACT® powered with SOPHiA DDM™ for comprehensive genomic profiling (CGP) utilize the matched tumor-normal analysis strategy to accurately delineate somatic variants from germline and CHIP variants.
Contact us to learn more about adopting advanced liquid biopsy and CGP technology in your laboratory.
References
Unleashing the power of healthcare data with the New Generation SOPHiA DDM™ Platform.
As the burden of cancer and rare diseases continues to grow globally, the complexity of the diseases demands more sophisticated solutions. Researchers and clinicians are constantly striving to develop novel, more effective therapies, and diagnostic tools to improve patient outcomes and resolve the biggest unmet needs in global healthcare. At the core of these efforts, there is one key element: Data.
From diagnosis to therapy selection and drug development, data is now indispensable for diagnosis and personalized treatment. The rise of precision medicine highlights the critical need for cutting-edge solutions that can harness and analyze vast amounts of healthcare data, driving advanced decision-making to improve patient outcomes at scale.
In response to this pressing need, platforms like SOPHiA DDM™ have emerged as revolutionary solutions in advancing data-driven medicine. Since its initial release in 2015, SOPHiA DDM™ has pioneered how healthcare professionals use data, having analyzed over 1.8 million genomic profiles to date and accelerating the practice of precision medicine worldwide. With nearly 30,000 analyses per month, the SOPHiA DDM™ Platform has proven itself to be a vital tool in the fight against cancer, rare and inherited diseases. Yet, as healthcare evolves, so too must the tools and technologies that support it.
In recent years, the healthcare landscape has witnessed a dramatic increase in both the volume and complexity of data. Genomic, radiomic, and clinical data have become integral to understanding diseases on a deeper level. However, the ability to process, integrate, and analyze these diverse data sources remains a significant challenge for clinicians and researchers. This challenge highlights an unmet need in global healthcare: the necessity for platforms that can break silos within and among healthcare institutions, and bridge the gap between data generation and actionable insights, allowing for more accurate diagnoses and personalized treatment strategies.
In response to this emerging need, SOPHiA GENETICS has just revealed the New Generation SOPHiA DDM™ Platform, aiming to stay at the forefront of precision medicine and address today the healthcare needs of tomorrow.
The New Generation SOPHiA DDM™ Platform not only enhances the speed and efficiency of data processing but also offers a powerful, web-based architecture designed to meet the evolving demands of clinical research. By leveraging advanced technologies like cloud computing and GPUs from world-class industry partners such as NVIDIA and Microsoft, SOPHiA DDM™ is set to revolutionize how healthcare professionals manage and interpret complex datasets to make informed decisions.
Leveraging the groundbreaking capabilities of the SOPHiA DDM™ Platform, healthcare professionals benefit from significantly reduced turnaround times, enabling quicker insights from data upload to final analysis.
Moreover, the platform’s enhanced computing capabilities allow it to process larger and more complex datasets, paving the way for new applications such as Whole Genome Sequencing (WGS), Minimal Residual Disease (MRD), Liquid Biopsy, and more, providing deeper insights into the genetic underpinnings of diseases, helping clinicians tailor therapies to individual patients with greater precision.
In addition to genomics, the platform offers advanced multimodal analytics, which are essential for understanding diseases like cancer, where multiple data types (genomic, radiomic, and clinical) need to be integrated for a more comprehensive view of the patient’s condition and unique biology. This multimodal approach allows for the analysis and interpretation of diverse data across different modalities, leading to more accurate predictions and personalized treatment plans.
One of the most significant advancements in the New Generation SOPHiA DDM™ Platform is its ability to offer genomic, radiomic, and multimodal analyses within a single, integrated workspace. This unified approach empowers healthcare providers to select the tools and applications that best suit their needs, whether they are focused on identifying genetic mutations, analyzing medical images, or integrating various data sources for predictive modeling.
By integrating these diverse data types, the New Generation SOPHiA DDM™ empowers clinicians to make better-informed decisions, improving the precision of diagnosis and treatment in oncology, rare and inherited diseases.
One of the key challenges in modern healthcare is the fragmentation of data. In many systems, vital information is siloed across different platforms and institutions, limiting the ability to generate a comprehensive understanding of a patient’s condition. SOPHiA GENETICS addresses this issue by promoting a decentralized, technology-agnostic, global platform where data can be securely shared among users, breaking down barriers to knowledge and experience exchange.
As Dr. Zhenyu Xu, Chief Scientific Officer at SOPHiA GENETICS, explains, “Our decentralized, multimodal analytics platform supports customers and helps break data silos by creating a global community where knowledge is safely and securely shared amongst users. The new generation of our SOPHiA DDM™ Platform is revolutionizing the user experience by blending our powerful AI algorithms with multimodal data to produce meaningful insights to further the field of precision medicine.”
As cancer therapies and data technologies continue to evolve, platforms like SOPHiA DDM™ will play a central role in shaping the future of precision medicine. The need for innovative, data-driven solutions is more urgent than ever, as healthcare providers strive to keep pace with the complexities of modern diseases.
Abhimanyu Verma, Chief Technology Officer at SOPHiA GENETICS, reflects on the broader impact of these advancements: “We pride ourselves on adapting our technology to meet our customers’ needs. As the technology infrastructure at most healthcare organizations worldwide has evolved, we are thrilled to continue to provide best-in-class technology and set them up for success. This new generation of our platform will allow us the flexibility to respond quickly to our customer’s evolving needs and introduce new features faster, and more efficiently.”
With its innovative architecture and advanced analytics capabilities, the new SOPHiA DDM™ Platform represents a major leap forward in precision medicine. By addressing the unmet needs in global healthcare data analysis, SOPHiA GENETICS is helping to pioneer a future where data-driven insights lead to more personalized, effective, and timely care for patients around the world.
Learn more about the New Generation SOPHiA DDM™ Platform here. Interested in getting a free demo of the Platform? Book it here!
The transition from the In Vitro Diagnostic Directive (IVDD) to the In Vitro Diagnostic Regulation (IVDR) in the European Union marks an important advancement in regulatory standards for genetic testing and analysis. The new standards promote transparency and traceability throughout genomic analysis processes, helping to ensure the reliability and accuracy of diagnostic results and ultimately patient safety.
Everyone wants to ensure that genomic analysis is safe for patients. However, healthcare institutions face real challenges in transitioning from IVDD to IVDR. Particularly with the use of complex software solutions for genomic data analysis, many of which are designated as research use only (RUO).
One of the biggest changes for healthcare institutions is that IVDR specifically regulates in-house manufactured tests. With few exceptions, healthcare institutions utilizing in-house manufactured tests must now meet the same requirements and proof of conformity with IVDR as manufacturers. These requirements extend to the software that is used for the analysis, interpretation and reporting of NGS data. Software developed in-house, including from public domain materials, must meet many of the same requirements and proof of conformity as commercial software solutions.
Here we answer some of the most pressing questions about what IVDR compliance entails for healthcare institutions performing genomic analysis and how CE-IVD certified software solutions can help.
Q: How does IVDR impact the analysis, interpretation, and reporting of NGS data?
A: Software used to support the analysis, interpretation and reporting of NGS data from genetic testing must conform to IVDR’s general safety and performance requirements (GSPR) to ensure reliability and safety.
Q: What are the key requirements for meeting IVDR compliance?
A: Genetic tests and their analytical software require that a healthcare institution perform the following to ensure IVDR compliance:
It is also important to ensure and demonstrate that suppliers are complying with applicable regulatory requirements.
After launch, post-market surveillance is required to monitor the safety and clinical performance of the test. Genetic tests require regulatory management of updates as well as reporting of any serious incidents, with the corrective actions taken.
Q: When does IVDR take effect?
A: IVDR replaced IVDD in 2022, with timelines for compliance differing for devices with new, versus legacy, status and class. Genetic tests and analytical software that are Class C devices, for example, must comply with IVDR by 2028, and in-house manufactured devices must comply with all relevant IVDR requirements by 2030 at the latest (see timeline).
Q: How can commercial software solutions help with the IVDR transition?
A: When using a CE-IVD commercial software solution, the manufacturer’s CE-IVD certification and their existing technical documentation provide proof of compliance with current regulations, helping to lessen the burden for the healthcare institute.
Q: Can research use only (RUO) solutions be used to meet IVDR requirements?
A: RUO solutions are not intended or validated for clinical diagnostic use. RUO solutions will not be considered compliant without additional testing and validation as part of an in-house manufactured device.
Q: What are the most important considerations when evaluating commercial CE-IVD analytical software solutions?
A: When evaluating analytical software solutions, regulatory compliance is a must. However, it is also important to consider analytical and clinical validity. Does the solution provide reliable data processing and variant calling with high sensitivity and specificity? What variant types does it cover? Is it able to interpret variants according to established guidelines? Looking ahead to the future, can the solution scale with growing genomic analysis volumes?
As important as the new IVDR standards are to ensuring patient safety, it is clear that the transition poses a number of challenges to healthcare institutions performing genomic analysis. At SOPHiA GENETICS we’re proud to help simplify the transition, offering fast, reliable CE-IVD oncology applications powered by SOPHiA DDM™.
To learn more, explore our current portfolio and stay tuned for further updates.
Multimodal healthcare datasets synergistically integrate diverse data modalities such as genomic, clinical, radiomic, proteomic, and biological data, to provide comprehensive insights into human biology and medical conditions. Multimodal datasets have the potential to predict outcomes more accurately and informatively than the sum of their parts (Fig. 1).
Figure 1.Multimodal healthcare data integrated and analyzed by artificial intelligence (AI)/machine learning can provide useful information for healthcare professionals to use to improve patient care.
Genomics data.
Radiomics data include x-rays, CT scans, MRI scans, ultrasound images, and mammograms.
Clinical and biological data from electronic health records include patient histories, demographics, notes, diagnosis codes, procedure codes, laboratory results, and vital signs.
Proteomics data.
Digital pathology data.
Patient-reported data includes questionnaires and health journals, as well as data from wearable devices monitoring heart rate, sleep patterns, and activity levels, and implantable devices such as pacemakers, insulin pumps, and continuous blood glucose monitors.
Environmental data includes air quality and location data.
New data-driven technologies powered by novel ways of linking and analyzing patient data are set to transform the way that healthcare is delivered.1 Healthcare professionals routinely make use of multiple sources of data to arrive at a diagnosis and to decide on patient management.2 However, a significant level of expertise is required for an in-depth understanding of even a single data type (e.g. radiological images) such that it is unfeasible for individual healthcare professionals to master all areas. AI/machine learning technologies can be leveraged to bring together and analyze multimodal healthcare data, breaking data silos and creating robust and accurate predictive models.3 With the appropriate guidance around decision-making and communication, the valuable insights gained from these predictive models have the potential to support healthcare professionals to improve patient care.
Machine learning technologies can integrate data from disparate multimodal sources to provide a holistic understanding of patients’ health and medical conditions. Data are combined from multiple modalities with the aim of extracting complementary information to power predictive models that can find relationships between different variables/features that are not clearly visible or known by healthcare professionals. Indeed, multimodal data fusion models have consistently shown to provide increased accuracy (1.2-27.7% higher) and performance (AUC 0.02-0.16 higher) than models that utilize data from single modalities for the same task.4
Oncology is one of the medical specialties that most commonly leverages multimodal methods for clinical decision support.5 Machine learning technologies have the potential to explore complex and diverse data to support healthcare professionals from screening to treatment (including relapse).6 Identification of risk factors can support non-invasive patient screening and preventive care.3 Detection of patterns in easily accessible data can help identify diagnostic or prognostic biomarkers to improve patient risk stratification or selection for clinical trials. Identification of predictive signatures of risk factors, adverse treatment reactions, treatment responses, or treatment benefit, can guide decisions around patient management.
Figure 2. The number of PubMed articles published on multimodal oncology data has dramatically increased in recent years.
PubMed search for ((multimodal) AND (oncology)) OR ((multimodal) AND (cancer)).
*2023 analysis includes data available at time of writing (January-September).
With data privacy and security paramount, multimodal healthcare data can also be leveraged to accelerate advances in medical research, such as the discovery of novel biomarkers and therapeutic targets for drug development, as well as supporting population health management by providing a comprehensive view of health trends and outcomes. The rapid increase in peer-reviewed publications on the topic over the last 13 years demonstrates that the extraordinary value of multimodal oncology data is already recognized by the scientific and medical communities (Fig. 2). Leveraging machine learning to collate and analyze the vast diversity of multimodal data for data-driven precision medicine is on track to drive the next revolution in healthcare.
SOPHiA DDM™ multimodal healthcare analytics will have the potential to break data silos by streamlining the integration of longitudinal oncology data from multiple sources and modalities – including but not limited to genomic, radiomic, digital pathology, biological, and clinical data. The SOPHiA DDM™ Platform uses machine learning-powered analytics to assemble, standardize, and transform multimodal data into accessible data-driven insights, facilitating the identification of multimodal predictive signatures, as well as treatment response patterns and trends. To learn more and get in touch, visit the webpage.
Product in development – Technology and concepts in development. May not be available for sale.
Area under the ROC curve (AUC) – A ROC (receiver operating characteristic) curve is a graph that plots true and false positive rates to demonstrate the performance of a model. AUC measures the area underneath the ROC curve to provide an aggregate measure of performance. AUC values range between 0 and 1, with a score of 0 meaning that all predictions are wrong, and a score of 1 meaning that all predictions are 100% correct. Essentially, AUC represents the probability that a positive result is truly positive and a negative result is truly negative.
Omics data – Large-scale information related to the biology of organisms.
Digital pathology images – Scanned images of tissue samples on glass slides.
What is liquid biopsy?
Liquid biopsies enable analysis of biofluids, typically blood, to examine biomarkers shed by solid tumors into circulation1. They can detect actionable genomic alterations in a non-invasive way, providing valuable insights to facilitate early cancer detection and disease monitoring2.
Tumor-derived biomarkers that are a source for liquid biopsy analysis include (Fig. 1):
CTCs are initially released from primary tumors in the tissue, travel through the bloodstream, and account for the development of metastatic tumors at distant sites in the body. As they are live cells, they have the potential to be used for functional analysis such as therapy sensitivity/resistance evaluation3. However, CTCs are rare events in the blood, which makes them difficult to identify and characterize in routine clinical practice1.
EVs are membrane-enclosed structures containing proteins, genetic material, and lipids that can provide biological information on the cell of origin4. Due to their role in pathological processes, EVs are an attractive analyte for liquid biopsy, but their isolation and purification is technically challenging1.
cfDNA refers to DNA fragments that are freely circulating in the bloodstream, primarily originating from normal cells5. Circulating tumor DNA (ctDNA) is the small portion of cfDNAthat derives from tumor cells or CTCs undergoing cell death (i.e. apoptosis or necrosis)5. There are well-established methods for isolating cfDNA, and for analyzing it using methods such as PCR and next-generation sequencing (NGS)-based tests6, making it an ideal and feasible substrate for routine genomic analysis.
Figure 1. Blood-based cancer biomarkers in liquid biopsy7. RBC, red blood cell.
The analysis of cell-free DNA is a promising method for guiding clinical decisions and can complement current standard-of-care practices8.
What are the clinical applications of liquid biopsy?
In the era of precision medicine, tumor molecular profiling is a critical tool to identify targetable alterations and guide treatment decision-making9. Tissue biopsy is currently the gold standard for tumor profiling8; however, there are limitations associated with this approach:
Liquid biopsy has the potential to be a transformative tool in clinical oncology, showing promise for applications in many stages of cancer management (Fig. 2):
Figure 2. The advantages and clinical utility of liquid biopsy in the cancer care journey10–15.
Innovations in liquid biopsy analysis over the past decade have led to the regulatory approvals of blood-based tests to guide treatment for NSCLC, prostate, breast, and ovarian cancers16. Clinical guidelines have also provided expert recommendations for its use in specific clinical scenarios8,15. Despite great advances in technology and its increasing utility in clinical practice, there are still challenges to overcome when using liquid biopsy to identify clinically relevant information.
Overcoming “fisherman’s luck” in liquid biopsy
One challenging aspect of liquid biopsy analysis is that ctDNA concentration varies greatly across cancer types and between patients17. In patients with cancer, the quantity of ctDNA in the blood can be impacted by several factors, including histology, tumor site, clinical factors (age, sex, treatment history, etc.), and ctDNA fragmentation17. Therefore, it is important to have a robust test to detect clinically relevant variants, even at low ctDNA concentrations against a cfDNA background.
Another factor that may impact liquid biopsy analysis is the presence of clonal hematopoiesis of indeterminate potential (CHIP). In healthy individuals, the majority of cfDNA arises from hematopoietic cells (i.e. stem cells in the bone marrow that give rise to other blood cells)18. Normal hematopoietic cells accumulate somatic mutations during aging, known as CHIP, which are technically indistinguishable to tumor-specific mutations in NGS assays18,19. It is important that the biological noise caused by CHIP signals are removed in liquid biopsy analysis to eliminate false positive variant calls and give an accurate representation of disease burden19,20.
These biological confounders can make “fishing” for clinically relevant information in cfDNA a challenge (Fig. 3). For example, if a patient has high disease burden, there is likely more ctDNA available to analyze, which makes it easier to “catch” what you are looking for. However, if there is less ctDNA and more biological noise, you may need to modify your tools and approach to improve your yield.
Figure 3. “Fishing” for clinically relevant information in liquid biopsy can be complicated by biological confounders17,18.
Highly precise and sensitive liquid biopsy technologies are needed to overcome “fisherman’s luck” and detect rare, causative variants and disease burden in cfDNA. Guidelines issued by the ESMO Precision Medicine Working Group on the use of cfDNA assays in clinical practice discuss the need for advanced techniques capable of capturing spatial and temporal tumor heterogeneity and reducing rates of false negatives8.
Pioneer innovation with SOPHiA DDM™ for Liquid Biopsy
SOPHiA GENETICS is at the forefront of innovation in liquid biopsy technology for tumor profiling. The advanced proprietary algorithms of the SOPHiA DDMTM Platform empower clinical researchers to reveal deep genomic insights from cell-free DNA samples.
With a streamlined, sample-to-report NGS workflow, you can:
In addition, we are excited to be collaborating with Memorial Sloan Kettering Cancer Center (MSK) to decentralize MSK-ACCESS® for liquid biopsy, designed to provide a maximum coverage of cancer disease variants in ctDNA20. By combining MSK’s clinical expertise in cancer genomics, the predictive algorithms of the SOPHiA DDM™ Platform, and the power of the global SOPHiA GENETICS community, the collaboration aims to expand access to precision cancer analysis capabilities worldwide.
Read more about how you can enhance your analytical capabilities and advance your clinical research here.
What is cascade testing?
Cascade testing is the practice of offering genetic testing to relatives of known carriers of pathogenic variants associated with autosomal dominant conditions. In oncology, cascade testing is performed in families affected by hereditary cancer syndromes1. The most common include hereditary breast and ovarian cancer syndrome (HBOC), Lynch syndrome (LS), familial adenomatous polyposis syndrome, hereditary pancreatic cancer syndrome and gastric cancer syndrome2,3. Testing for variants associated with HBOC and LS belongs to the so-called Tier 1 testing, i.e., genomic applications, the implementation of which is supported by robust evidence4.
Cascade testing starts with first-degree relatives (parents, siblings, children) of index cases (i.e., the family member in whom a pathogenic variant was identified) and then proceeds to second- (grandparents/grandchildren, aunts/uncles, nieces/nephews, half-siblings) and third-degree relatives (great-grandparents/great-grandchildren, first cousins)1.
Most hereditary cancer syndromes follow the autosomal dominant inheritance pattern. Therefore, the first-, second- and third-degree relatives have, respectively, a 50%, 25%, and 12.5% probability of inheriting the predisposition to develop cancer (see Figure 1)1. For some pathogenic variants of genes associated with hereditary cancer syndromes, such as BRCA1/2, the penetrance is high5. Establishing accurate estimates of penetrance and relative risk for genes implicated in hereditary cancer syndromes is an ongoing task3.
Figure 1. Heritable pathogenic variants increase the risk of developing cancer at a younger age (left). Cascade genetic testing is the practice of testing the relatives of known carriers (right)1.
Why is cascade testing important?
At the level of an individual and their family, cascade testing has two important goals. The first goal is to identify relatives that carry the familial pathogenic variant and require personalized cancer risk management1. The second goal is to exclude the non-carriers from intensive cancer surveillance and prevention interventions1. The detection of pathogenic variants in individuals at a reproductive age may lead to decisions of assisted reproduction or prenatal diagnosis. In the case of actionable monogenic conditions, cascade testing may reduce adverse health outcomes in cohorts of relatives1.
At the societal level, cascade testing has important clinical and research implications for oncology. It can further our knowledge of hereditary cancers and is a cost-effective way of identifying unaffected individuals at-risk, thus, providing important information to plan long-term resources necessary to cope with hereditary cancers. Moreover, today’s testing is needed to tailor future approaches in cascade testing1.
Figure 2. Cascade testing involves genetic counseling before and after the test, risk estimation and management, and has treatment implication7.
What are the barriers to cascade testing?
Despite the advantages of cascade testing, its uptake is low. The reported rates of uptake of cascade testing in HBOC and LS equals ~50% and the underutilization of testing results in missed opportunities of cancer prevention1. In a recent Swiss study, there was a 25-50% response rate to invitations to cascade testing and at least one-in-three individuals at risk did not undergo testing. An index case possesses an average of 10 relatives eligible for testing, while the average rate of genetic tests per index case is only 1.51.
There are several barriers to cascade testing6,7. These include ineffective family communication of genetic risk information, low knowledge of cascade testing among index cases and primary care providers, and geographic barriers to receiving genetic services. Cascade testing uptake is also lower among male than female relatives and in distant compared to first-degree relatives. A facilitator of adherence to cascade testing is the parents’ desire to understand their children’s risk6. “Dear family” letters, digital chatbots (a technology-based simulated conversations), and direct contact programs have been shown to be effective in motivating cascade testing8.
Several initiatives exist to promote cascade testing. One such enterprise is the Cascade Resources Network, an independently run, non-profit organization that offers access to genetic testing, genetic counseling, variant interpretation, screening guidelines, and forums and support. It was developed by Memorial Sloan Kettering Cancer Center (MSK) fellows, Ryan Kahn and Sushmita Gordhandas. The network was created to increase the rate of genetic testing among relatives of patients with inherited cancer risk variants to help identify cancer early in families and, ultimately, to prevent future cancers. Similarly, the Swiss Cancer Genetic Predisposition Cascade Screening Consortium was established in 2016 to foster research related to the hereditary cancer predisposition. In particular, the Consortium promotes the CASCADE cohort, a family-based open-ended cohort targeting HBOC and LS variant-harboring families to elicit factors that enhance adherence to testing (NCT03124212).
Analyze genetic predisposition to cancer with the SOPHiA DDM™ Platform
Multi-gene testing is an efficient, affordable, and guideline-recommended9 approach to cascade testing as it allows for comprehensive assessment of biologically relevant hereditary cancer genes. The SOPHiA DDM™ Platform supports various next generation sequencing (NGS)-based Hereditary Cancer Applications to help clinician researchers characterize the complex mutational landscape associated with hereditary cancer disorders.
Powered by advanced analytics, users can detect challenging variants in a streamlined sample-to-report workflow, including:
Variant pathogenicity levels are assigned using machine learning complemented by guideline-driven ranking, helping to prioritize relevant variants and reduce interpretation time. Furthermore, deeper variant exploration is supported by Alamut™ Visual Plus, a full-genome browser that integrates numerous curated genomic and literature databases, guidelines, missense and splicing predictors.
To learn more about SOPHiA DDM™ for Hereditary Cancers, explore here or request a demo here.
References
1. Sarki M, et al. Cancers (Basel) 2022;14:1636.
2. Brown GR, et al. JAAPA 2020;33(12):10-16.
3. Mighton C, Lerner-Ellis JP. Genes Chromosomes Cancer 2022;61(6):356-381.
4. Dotson WD, et al. Clin Pharmacol Ther 2014;95(4):394-402.
5. Chen S, Parmigiani G. J Clin Oncol 2007;25(11):1329-1333.
6. Roberts MC, et al. Health Aff (Millwood) 2018;37(5):801-808.
7. O'Neill SC, et al. Hered Cancer Clin Pract 2021;19(1):40.
8. Campbell-Salome G, et al. (2022) Transl Behav Med 2022;12(7):800–809.
9. Daly MB, et al. J Natl Compr Canc Netw. 2021 Jan 6;19(1):77-102.
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