Abstract
Eric Schadt, Founder and CEO, Sema4
Sema4 is a health intelligence company composed of scientists, data engineers, and clinicians committed to pioneering the future of healthcare. It is founded on the belief that the best way to optimize wellness is to understand humans holistically as complex information networks, from the molecular to the cellular, the tissue and organ, system and ecosystem scales. Sema4 applies AI-based algorithms to these networks to derive powerful insights that drive personalized clinical care solutions.
Our biggest success has been the rapid development of an intelligence platform that has enabled advanced, information-driven genomic tests and drug discovery partnerships. Sema4 started as a project incubated within the Icahn School of Medicine at Mount Sinai, and then spun out to accelerate the development and global growth of our information-driven solutions for diagnostics, disease modeling, therapeutic target identification and validation, and risk assessment. Since then, we have made great strides in developing algorithms to quickly search through oceans of high-dimensional molecular, literature, imaging, and clinical data to extract knowledge that can be used to define individualized patient health trajectories.
Sema4 offers a comprehensive selection of advanced clinical solutions and services that enable more informed decision-making for physicians and patients. We offer a range of reproductive health solutions, including expanded carrier screening to help couples make informed decisions and pursue advanced IVF treatments to avoid passing catastrophic disease on to their offspring. In oncology, our somatic testing solutions provide targeted identification of clinically actionable mutations in solid tumor cancers; our informatics-led approach allows us to identify appropriate targeted therapies and match patients to clinical trials. Similarly, our pharmacogenomic panels provide personalized information that can empower a healthcare provider to avoid negative or suboptimal drug responses and select the drug most likely to be effective.
Our expertise in network modeling, clinical data, pan-omics data, and clinical genomics provides us with a remarkable opportunity to transform healthcare. Centrellis™, our proprietary health intelligence platform, is enabling us to generate a more complete understanding of disease and wellness and to extract individualized insights into human health. We can also follow patients longitudinally, allowing us to generate and acquire multi-dimensional data at population scale to more accurately model personalized health trajectories. We are uniquely positioned to change how healthcare systems view precision medicine and, by promoting data sharing, to fundamentally shift how patients take control of their own health—to the benefit of clinicians, researchers, and patients alike.
We are developing innovative tools based on Centrellis that serve a number of applications, including predictive causal modeling of disease and wellness, better assessment of patient risk across a spectrum of diseases, personalized treatment strategies, and informed decision-making across the drug discovery pipeline, from the best points of therapeutic intervention to identification of the most appropriate patients for clinical trials. Other outputs include the delivery of novel test content and interpretations for state-of-the-art clinical testing. Our tools enable us to define and explore cohorts of patients across a broad spectrum of diseases, including cancer, from our curated datasets and advanced predictive network models, enabling physicians to make data-driven decisions about patient care. We are also developing a timeline visualization of the patient’s health journey, including treatment history and model-driven interpretive insights to help physicians evaluate a patient’s diagnosis and treatment options. Finally, we are leveraging advances in NGS technologies (such as low pass whole genome sequencing, high-coverage whole exome sequencing (WES), and whole transcriptome sequencing (WTS)) to generate more expansive data on patients in a cost-effective way to deliver high-value insights across both somatic and germline mutations. Following initial WES/WTS, we can test in silico longitudinally as the patient’s clinical needs evolve, allowing us to guide and optimize their individualized health course trajectory.
