Sema4 is proud to be a trusted partner to Biopharma across diverse projects, including our five-year collaborative asthma study with Sanofi. To further showcase our innovative solutions, services, and support for the evolving Biopharma landscape, we have now launched new content on our website.
Bringing a drug to market is an arduous task; it takes on average at least ten years and close to $3 billion for a drug to move from the lab to the shelf. Sema4 has a proven track record of success in working with Biopharma to deliver comprehensive insights to accelerate the drug discovery, development, and commercialization lifecycle, powered by Centrellis™, our AI-driven health intelligence platform.
We offer an extensive portfolio of health information solutions, providing analytics for actionable insights, pre-clinical and clinical trial support, and advanced sequencing services. As drug development becomes increasingly complex, Sema4 is a partner trusted to deliver innovation and value to the Biopharma industry.Continue reading “Sema4 Unveils Additional Information on its Biopharma Solutions”
Predictive network modeling can more accurately stratify multiple myeloma patients by prognosis than conventional methods, according to a recent project led by scientists at Sema4. The publication reporting their results — from lead author Yu Liu, senior author Jun Zhu, and collaborators — recently came out in the journal Cancers.
Building sophisticated network models is a core strength for the Sema4 team, and we were eager to apply it for this important oncology study. We focused on multiple myeloma, which is among the most common hematological cancers and has recently seen rapid growth in the number of treatments available and in the number of gene expression signatures used for establishing a patient’s prognosis. Still, median survival for multiple myeloma patients is only seven years. “While the therapeutic options available are increasing,” Liu et al. point out in the paper, “it remains critical to identify high risk patients early and develop personalized treatment options for them to improve outcomes.”
As data scientists and biologists, one of the things that struck us about the current landscape of multiple myeloma is that many of the published biomarkers do not overlap each other. Was it really the case that these biomarkers are isolated and acting alone? Or was there a unifying theme that would tie them all together and offer a more cohesive explanation?
To answer those questions, we analyzed more than 300 gene expression profiles, more than 250 copy number variation profiles, and relevant clinical data obtained from a Multiple Myeloma Research Consortium data set. That information allowed us to build a multiple myeloma molecular causal network, which enabled us to distinguish whether gene-gene correlations were driven by biological regulation or genomic co-localization. The resulting multiple myeloma network successfully unified eight previously published prognostic gene signatures that appeared to have few genes in common. The result was a prognostic subnetwork composed of 178 genes, many of them involved in cell cycle activities. Our network also helped to explain different drug responses across patients.Continue reading “Multiple Myeloma Study Shows Need for Sophisticated Network Modeling”
Genetic counselors are part of the backbone of Sema4. We currently employ more than 40 counselors, around half of whom were hired within the last year. We rely on these highly trained professionals to translate genetic testing results into powerful, understandable insights that can shape a patient’s health trajectory.
Recognizing the value of this profession to our field, we also want to ensure that the most capable individuals can enter genetic counseling training programs, regardless of their socioeconomic background. We are therefore proud to announce that we have, for the second year running, provided a tuition scholarship to the Master of Science in Genetic Counseling Program at the Icahn School of Medicine at Mount Sinai (ISMMS).
This year’s scholarship has been awarded to Madison Maertens, a first-year student whose undergraduate training in Biology at the University of Washington convinced her that she wanted to pursue a career in genetic counseling. Earlier this year, she was delighted to find out that she had been accepted to ISMMS’s prestigious training program. She was then even happier to find out that she was the deserving recipient of the Sema4 scholarship. “I knew that I wanted to attend Mount Sinai’s program because of its reputation and the mix of academic and clinical training,” said Madison recently between classes. “However, I was concerned about the cost of studying in New York City. So, when I heard that I’d received the scholarship, it was a huge weight off my mind.”Continue reading “Sema4 announces scholarship to support trainee genetic counselors”
But along with this technological shift have come many important clinical and ethical questions. Those were the focus of a carrier screening session at the recent Association for Molecular Pathology Annual Meeting & Expo in Baltimore, where Sema4’s Chief Diagnostics Officer Lisa Edelmann spoke about identifying ethnicity, residual risk, and more.
Ethnicity matters in carrier screening for a few reasons. First, Edelmann pointed out, many diseases are understudied in certain ethnic groups. This means that guiding people to carrier screening panels developed for a particular ethnic group might miss diseases that are relevant to that group but have not been studied extensively enough within it. Second, patients often misreport their own ethnicity — sometimes because they are not aware of their genetic ancestry and sometimes because the “check-one box” forms fail to provide consumers from admixed populations the chance to fully represent their ethnicity, Edelmann told attendees.
For both of these situations, expanded carrier screening can offer a better option. By testing as many variants as possible, this kind of screening test gives people a greater likelihood of detecting any red flags than a smaller panel focused on a single ethnic population.
Ethnicity also makes a difference for calculating residual risk from negative results in a carrier screen, Edelmann said. This calculation requires understanding carrier frequency within an ethnic population and the detection rate of the screening test. Ethnicity influences both. Historically, residual risk scores were established based on literature searches, Edelmann noted, but now there are better methods. “Currently we can use a data-driven approach for these residual risk calculations [to move] towards a more accurate representation of risk,” she said.Continue reading “At AMP 2019, Carrier Screening Session Highlights the Importance of Ethnicity”
At Sema4, transforming healthcare is our primary mission. We believe that more information, deeper analysis, and increased engagement will improve the diagnosis, treatment, and prevention of disease. Our work is based on building dynamic models of human health using Centrellis, our innovative health intelligence platform, to generate a more complete understanding of disease and wellness.
These concepts were central to the ASRM panel discussion, which included Jaime Shamonki, MD, from California Cryobank Life Sciences; August Calhoun, PhD, from Change Healthcare; and Lisa Edelmann, PhD, Alan Copperman, MD, and Kareem Saad from Sema4. The speakers had a lively discussion about big data, variant interpretation, and helping patients all along the reproductive health journey.
Saad, who moderated the panel, set the stage for a conversation about big data in medicine. “There’s a lot of value locked in information that isn’t currently being optimized today,” he said. “Using that information to drive better clinical outcomes — better economic outcomes, even — is clearly the opportunity we have in the industry.”
Big data, said Calhoun, powers new insights that are not obvious without massive amounts of information. “Those insights could be driving early intervention, those insights could be driving understanding risks in pregnancy, those insights could drive better medical decisions,” he told attendees.Continue reading “ASRM 2019: Big Data, Expanded Carrier Screening, and the Patient Journey”
An improved strategy for drug screening may soon pave the way for novel medications for diseases that have previously seen little therapeutic innovation. A team of researchers, including scientists from Sema4, found that patient-derived cells offer a more effective approach for assessing drug response than conventional models, demonstrating the value of a precision medicine approach to drug screening.
The scientists from Icahn School of Medicine at Mount Sinai, Sema4, and Eli Lilly focused their study on schizophrenia, a severe neuropsychiatric disorder with a strong genetic component. Symptoms of schizophrenia can, in around one-third of individuals, be controlled with drugs that modulate dopamine activity. The remaining two-thirds of patients, however, do not respond to or have only a partial response to these medications, and drug discovery has been limited due to a lack of useful models for screening candidate treatments.
The team reasoned that, as drug response is thought to be a heritable component of schizophrenia, screening needs to take into account neuropsychiatric genetics and be carried out in a biologically-relevant model. At the outset of the study, the researchers used in silico prioritization to select drugs with predicted or demonstrated interactions with schizophrenia-related biology, allowing them to focus on a smaller, more refined set of drugs than is typically used in high-throughput screens. The resulting 135 candidate drugs were then used to treat neural progenitor cells from 12 schizophrenia patients and 12 healthy controls, along with eight generic cancer cell lines.
Following drug treatment, the scientists searched for drug-induced gene expression changes and found differential responses in schizophrenia biology-associated genes in the patient-derived cells compared with the control-derived cells and cancer cell lines. In some cases, certain drugs reversed the gene expression signatures associated with schizophrenia. The proof-of-concept study, published in Nature Communications, demonstrates that patient-derived cells yield more disease-relevant information than generic cell lines and establishes the feasibility of expression-based drug screens of patient-derived neural cells.
“This study is one of the first instances of transcriptomic drug screening, whereby we profiled the global response of neural cells following treatment,” said Dr. Kristen Brennand, Associate Professor of Neuroscience, Psychiatry, and Genetics and Genomic Sciences at Mount Sinai and senior author of the paper. “Our approach portends an entirely new way of screening drugs that doesn’t rely on synaptic assays, which are technically difficult to conduct, and demonstrates that there is tremendous value in gene expression-based drug screening using patient-derived cells because it can generate results that are more reflective of disease biology.”
Even with the initial in silico drug prioritization, the study was complex, requiring the use of an innovative high-throughput gene expression profiling technology from Genometry to optimize the number of expression signatures generated. Interpretation of the resulting massive dataset required a multinational effort, led by first author Benjamin Readhead in collaboration with Sinai colleague Gabriel Hoffman and Brian Eastwood of Eli Lilly in the United Kingdom.
“Collaborations are critical in the field of therapeutics discovery and development for the integration and holistic interpretation of increasingly granular and diverse data,” said senior author Dr. Radoslav Savić, Director of Scientific Collaborations at Sema4, Associate Professor of Genetics and Genomic Sciences at Mount Sinai and corresponding author of the paper. “This study demonstrates how unmet research needs can be addressed by connecting complementary forces, from experts based in academia, health systems, and industry, to the individuals who contribute their cells and without whom the work would not be possible.”
The results of the study show that neural cells from patients can respond differently to drugs than those from healthy controls, and should have immediate value in improving drug discovery, not only for schizophrenia but also for other diseases currently lacking biologically relevant screening models. “The next step is to expand the work into neurons, the cell type relevant to schizophrenia,” says Dr. Brennand. “It will be important to gather data such as this across more cell donors and more cell types, so we can discover the diversity of drug responses and refine our precision medicine approach further.”
If you could find out whether your newborn baby is at risk of developing treatable early-onset genetic diseases, would you want to know? A recent poll, commissioned by Sema4 and conducted online by Harris Poll, found that nine out of ten Americans would want this knowledge. Similarly, 87% of Americans said that they would likely request a supplemental non-invasive DNA test if their state’s newborn screening panel test did not cover many of the treatable conditions that could affect a child in their first years of life. I recently sat down with two new moms who did just that, ordering Sema4 Natalis, our new supplemental newborn screening test which screens for 193 early onset genetic diseases – around five times the number tested for on a typical state-mandated “heel prick” screen – all of which are treatable. The test also includes a genetic analysis of how a child is likely to respond to 38 medications commonly prescribed during childhood. This pharmacogenetic information can help pediatricians personalize treatment for a child and avoid adverse effects or incorrect doses of drugs, including antibiotics.
Kathryn Keho, a mom of three, used Natalis to screen her infant son, Asher, when he was a month old. Likewise, Michelle Harrison tested her daughter Eva at the same age. Both moms were savvy about the genetic testing options available before and during pregnancy, having opted for carrier and prenatal screening, and were eager to take advantage of postnatal testing. “It’s nice to have information to rule out things that they don’t normally test for,” Kathryn said. “I was hoping it would give me peace of mind, and it did.” Michelle, a wealth planner from Massachusetts, expressed a similar motivation for using Natalis: “I wanted to do this test as I will always opt for more information than less. My eldest daughter is seven, and the testing on offer has certainly changed since she was born. We’ve had additional testing with each child as it became standard, as we’ve always wanted to go down the route of knowledge.”
The advantage of knowing can be life-changing for children who test positive, as all 193 of the diseases covered by Natalis have a treatment or other intervention currently available. This actionability was the deciding factor for Michelle: “If there hadn’t been a positive action available for all the diseases, I don’t think we would have wanted that to hang over her,” she said. “I’m a planner, so if there’s no way to plan for it, I won’t find it useful.”
Both women remarked on the ease of the testing process. Kathryn, a marketing director living in California, does all her shopping online and was pleased that she could initiate medical testing in the same manner. She also appreciated not having to carve out time to visit a doctor. Each Natalis order requires physician approval, to ensure suitability of the test for the child, but this approval is performed remotely by a physician from the PWN network. “I just hit ‘order,’” said Kathryn. “I didn’t have to set up an appointment or deal with anyone else. But there was a support phone number, and you do have the included genetic counselor access as well, so I could talk to someone if I needed to.”
Once a physician has approved the order on the basis of answers to a brief medical survey, the Natalis kit is sent out in the mail so that DNA samples can be collected at home. A gentle cheek swab is used to obtain the DNA, a procedure that’s easy on the babies and the parents. “Eva didn’t mind; we didn’t mind. It was a very easy process,” said Michelle. “I’ve never done an at-home DNA test, so it was totally foreign, but it couldn’t have been easier. The instructions were very clear.” Kathryn’s son, Asher, was also completely onboard with the genetic sampling going on in his mouth: “He thought it was a pacifier,” she joked.
It takes around two to three weeks to receive the results of the genetic analysis. Neither mom found the wait stressful. “I wasn’t incredibly worried,” said Kathryn. “Asher was in the NICU for 26 days, so this was nothing. There was a level of curiosity, wondering when the results are going to get in. But I didn’t feel like we were going to find anything scary.” Michelle said she put it out of her mind because the outcome was something she could not control. As soon as an email popped up telling them that results were available, the women logged into the Sema4 patient portal on their phones. Both Eva and Asher tested negative for all 193 diseases tested. “I felt reassured about his health and glad that I’d taken the proactive step to screen him,” said Kathryn. Michelle’s immediate feeling was one of relief: “Even though they’re all treatable, it’s still a treatment. I’d always opt for good health.”
While neither baby’s disease screen indicated the need for further action, the results of the accompanying pharmacogenetic screen gave the moms useful, actionable information on how their children would respond to a variety of medications commonly prescribed during the early years. The medication report provides descriptions of what the different drugs are and how they are used, outlines how the child is likely to respond to each, and includes a summary that a parent can print out to give to their child’s pediatrician, as well as keeping in his or her wallet.Continue reading “The advantage of early insight: Two new Moms talk about their experience with Sema4 Natalis”
Going back to the earliest artwork and texts in history, humans have been fascinated with infertility. Old legends and myths tell stories of miraculous births. Hippocrates wrote of infertility, and instead of attributing the issue to magic, he strove to understand the anatomy and even began to formulate treatment options. When von Leeuwenhoek invented the microscope, and then in 1677 discovered sperm, a more modern understanding of infertility started to emerge. Soon, we began to understand cells and then organ systems, and we medically and surgically had our first success stories in treating and curing infertility. By the 1980s, the era of endoscopic surgery on the uterus and fallopian tubes had emerged.
In 1978, doctors Patrick Steptoe and Robert Edwards would change everything, and the first in vitro fertilization (IVF) baby, Louise Brown, was born. Millions of IVF births later, we began to face new problems and find new opportunities. All too often, in an attempt to increase pregnancy success rates, multiple embryos were being transferred, resulting in twins and even triplets. These multiple pregnancies frequently put both mom and the babies at risk.
We needed a way to identify the single best embryo for transfer to maximize the chance of a healthy pregnancy and minimize the possibility of a failed cycle, a miscarriage, or an unhealthy pregnancy. We began to realize that the physical appearance of an embryo is not the only predictor of its viability. Two identical looking embryos might produce very different outcomes if one of them carries genetic abnormalities. So, we needed a way to reliably and affordably test their genomic makeup to ensure that we only transferred the embryos with the best chance of success.
The genomic revolution now allows us to biopsy just a few cells from a blastocyst (early embryo) and get tens of thousands of points of information on every single chromosome, and nearly a million points of data on each embryo. Now we can tell whether the embryo is chromosomally normal or abnormal: Whether it is “euploid,” and contains 46 chromosomes, or “aneuploid” due to monosomy or trisomy of a chromosome or chromosomes. Today’s analytics are so sophisticated that we can even tell if small pieces of DNA are duplicated or deleted (segmental aneuploidy). We have now progressed from the organ to the cell to the subcellular to the genomic, and we’re able to identify the healthiest embryo. I think that this scientific progress is going to continue, and that it is going to continue to improve outcomes, as well as raise ethical issues, of which we must be both aware and respectful.
The next chapter in the genomic story will, I believe, open with whole exome sequencing and transition into whole genome and transcriptome sequencing. We’re going to pick up microdeletions and microduplications, and maybe no longer need genomic-based prenatal testing or chorionic villus sampling. I think that we are likely to be using gene panels to figure out whether an embryo is predisposed to become a child with early childhood cancer. It certainly is likely that, over the next few years, we will be able to use these technologies to prevent more and more diseases.
Another path to preventing disease may be the use of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats), which has captured my imagination recently. Instead of just diagnosing a patient or an embryo which is at risk of being unhealthy, what if we could take it that extra mile and fix something before it’s broken? Indeed, CRISPR has already been used to fix disease genes in viable human embryos, although these modified embryos were not allowed to develop beyond an early stage. The science of DNA editing is still in its infancy, but I can imagine a future where we could tell a pancreatic cell to make insulin just by repairing a broken gene or help a couple that is at risk of not being able to have a healthy baby for a variety of reasons. You can also see how DNA editing could stop a baby from expressing certain diseases. Another technique, mitochondrial replacement therapy, has already been approved in the UK. By replacing defective mitochondrial DNA (mtDNA) with healthy mtDNA from a donor egg, it may be possible to prevent babies from inheriting severe metabolic disorders. This year, we can routinely screen embryos with next-generation sequencing. Next year, perhaps we might be able to routinely fix an embryo.
Addressing the ethics accompanying these advances is, of course, of paramount importance. For the first time, humans can change the germline, and we can potentially alter evolution. The power of this technology is massive, but our responsibility, therefore, is also great. Groups that are doing groundbreaking research in this area need to have bioethicists as part of their team, and patients must always be informed.
I think that scientifically, within the next two to three years, we’re going to be able to rank an embryo and not just ask whether it is morphologically attractive and if it has 46 chromosomes. We will also learn about its implantation potential, and possibly even about the health and wellness of the future individual – an ethical slippery slope. Every one of these decisions is loaded – what to test for, how to test, and how to counsel a patient – and we must have deep information about the accuracy, precision, and potential consequences of using such information. I could foresee a future in which testing and embryo selection decisions are made with the patient and the reproductive endocrinologist, as well as the geneticist, the data scientist, and maybe even the bioethicist. The data scientist is crucial as informatics has massive potential to revolutionize fertility outcomes and will usher in the next, post-genomic stage in the evolution of reproductive medicine – a topic I will explore further in my next post.
Alan B. Copperman, M.D.
Chief Medical Officer
Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer, with a median survival time of just one year following diagnosis. Treatment is complicated by the considerable variability in GBM tumors – what works for one tumor often fails to work for another. Selecting the most appropriate treatment may soon, however, be easier, thanks to an improved GBM classification system, developed by Mount Sinai and Sema4 scientists and published in Cancer Research.
A team of researchers, led by Sema4’s Head of Data Sciences Jun Zhu, PhD, reasoned that an improved GBM classification system could help clinicians to select the most pertinent therapy – a case of “know your enemy”. Some GBM tumors are dependent on the mitotic spindle checkpoint molecule BUB1B for their survival, so his team mined complex datasets to produce an innovative computational method to classify tumors based on their BUB1B dependency. In doing so, they uncovered new tumor subtypes and found that while BUB1B-sensitive tumors had a significantly worse prognosis, they were also predicted to be more responsive to many of the cancer drugs already in clinical use.
The molecular subtypes identified in this new study appear to provide a more accurate estimate of prognosis and therapeutic response than existing classifications. One reason that previous classifications have failed to lead to effective personalized treatments is the high degree of intratumoral heterogeneity in GBM. Cells from different parts of the tumor may belong to different molecular subtypes and, therefore, subtype-specific therapies fail to eradicate all the cancerous cells. The BUB1B classification system, however, does not suffer the same defect.
“It was a pleasant surprise to us that our subtype is stable for heterogeneous tumor cells within a GBM tumor and, thus, it is possible to kill all tumor cells instead of just a subgroup,” says Dr. Zhu. “Preliminary results indicate that the stability is associated with certain genomic features, but more data are needed to understand why. More importantly, we also need to work out how to leverage the subtype information to develop mechanism-specific therapies.”
“These findings underscore the significant potential we see to improve patient outcomes by investing in predictive modeling of even the most complex types of cancer,” explains Eric Schadt, PhD, Sema4’s CEO and Dean for Precision Medicine at Icahn School of Medicine at Mount Sinai.
The study was the result of a multidisciplinary collaboration between computational scientists and clinicians – a characteristic of many Sema4 research projects. Information generated from our integrative studies – such as the GBM project and a recent examination of lung cancer mutations – is the first step towards designing improved diagnostic tests and optimizing personalized cancer therapies. Currently, Sema4 offers the Oncology Hotspot Panel, which provides information on over 200 mutational hotspots associated with a range of cancers. As our knowledge of cancer genomics increases so too will our ability to expand this number, leading to improved diagnosis, treatment, and survival rates for cancers including glioblastoma. “We look forward to building on this collaborative project and moving toward development of a diagnostic test that could help physicians better understand and treat their patients’ glioblastoma cases,” says Dr. Schadt.
Nearly quarter of a million Americans will be diagnosed with lung cancer in 2018, according to new estimates published by the American Cancer Society, and lung cancer will be responsible for more deaths than the next three most common cancers (breast, prostate, colon) combined.
Cancer arises due to genetic mutations – changes in the patient’s DNA. By figuring out precisely which genetic variants instructed their patient’s lung cells to change from normal to cancerous, doctors can select personalized therapies targeted to these mutations and minimize non-specific side-effects.
Sema4 and Mount Sinai scientists recently profiled the range of mutations found in non-small cell lung cancer (NSCLC), the most common form of lung cancer. They examined DNA from 932 NSCLC tumors and found nearly 3,000 mutations in cancer-associated genes, demonstrating the clinical utility of targeted next-generation sequencing with a focused oncology hotspot panel in NSCLC. The study, published in Genome Medicine, provides a comprehensive overview of not only the genetics but also potential treatment options for NSCLC, as actionable mutations were present in 65% of the patients.
In addition to detecting known mutations, the Sema4 team also identified novel lung cancer mutations by combing through the large volume of sequencing data generated during panel analysis. An activating somatic mutation was detected in the JAK2 gene in one in every 100 patients. This genetic variant would be expected to cause over-activity of the JAK-STAT cell signaling pathway, so the Sema4 researchers trawled publicly available pharmacogenomic data to investigate potential therapies, and found that the mutation could confer sensitivity to both JAK inhibitors and anti-PD1 immunotherapy. They also detected activating germline mutations in the JAK3 gene, in 7% of patients, that would be predicted to render tumors susceptible to anti-PD1 treatment.
“These novel mutations define a unique disease mechanism that has not been previously described in lung cancer, therefore facilitating the development of personalized treatments for patients with these variants,” says lead author Dan Li, PhD. “Going forward, we will continue to perform integrative analyses of cancer genetic data and patient clinical data to discover novel predictive biomarkers for treatment response. We will also develop and implement improved cancer genetic testing platforms for diagnosis.” Sema4’s current testing platform, the Oncology Hotspot Panel, analyzes 207 mutational hotspots across the genome that are known to be associated with cancer.
The clinical implications of the novel mutations were identified through an integrative analysis of genetic, genomic, and pharmacogenomic data. Mining large, multidisciplinary data sets is a central element of Sema4’s research ethos and one that successfully uncovers information that can directly impact clinical decision-making, as shown by this study and another recent study on glioblastoma. Our team is committed to using this approach to improve diagnosis, treatment, and outcomes for cancer patients, including the one in 16 Americans who will be diagnosed with lung cancer in their lifetime.