Category Archives: cancer

WuXi NextCODE Takes on Cancer: Breakthroughs and Innovation in Sequencing using TCGA and AI

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Hannes Smarason NextCODE TCGA cancer

Sequencing reads of a sample prepared by the traditional whole-genome sequencing workflow for fresh-frozen samples and data generated using the SeqPlus whole-genome FFPE method. The green and purple indicate reads sequenced in the forward and reverse directions, respectively, and yellow represents bases with non-reference sequence. The center of the image shows a C to A mutation in each of the tumor samples.

Cancer is one of the most active fields in genomics, spurring mountains of research papers and scores of clinical trials. WuXi NextCODE (WXNC) is committed to pushing this field forward and so we had a special “Genomes for Breakfast” session devoted to this topic at the recent ASHG17 event. Featured talks addressed our pathbreaking work in how to extract impactful findings from the renowned TCGA dataset; get better sequencing results from FFPE samples; and apply deep learning to drug discovery, drug repurposing, and identifying subtypes for diagnostics and clinical trials.

The Cancer Genome Atlas (TCGA) is one of the most useful public genomic cancer databases available and has already led to numerous critical discoveries, including entirely new drug targets as well as better insights into tumor origination, development, and spread. It includes data from approximately 11,000 patients and covers 33 cancer types. Data types include WES, RNA-Seq, mi RNA, CNV, Methylation array, and clinical sample data. The data is big and complex, and can include multiple samples from one patient, which is crucial to know when doing analyses.

During his ASHG talk, Jim Lund, WXNC’s Director of Tumor Product Development, shared some insights into how we put this rich data source to work in concert with our own unique data and analytical tools, in a process he dubs “multiomics analysis.” He described how we specially process the data and use our unique analytical platform to help scientists find just what they are looking for. Researchers can search the data by cancer type, age of diagnosis, sex, ethnicity, year of diagnosis, sample type (e.g. metastatic, new primary), and more.

Multiple pivotal studies using this dataset have already been published, including some examining the prevalence of specific mutations across human cancer types as well as in-depth profiling of specific tumors, such as breast cancer and lung adenocarcinoma. Layering different types of data, such as reads from DNA and RNA, allows much more accurate detection of features such as variants with allele-specific effects on gene expression. The user-friendly but sophisticated data interface makes it easier to see such findings. Over the years, our own database and our capabilities have both grown exponentially, creating a powerful tool for multiomics cancer research. You can see Jim putting the portal through its paces in a recent webinar.

In his talk, Shannon Bailey described how Whole Genome Sequencing (WGS) can be applied to formalin-fixed paraffin-embedded (FFPE) tumor samples, which are stored by the hundreds of thousands in repositories around the world. Shannon is the Associate Director of our Cancer Genetics division. He pointed out that while these samples are abundant and often paired with extensive clinical and outcome data, there are specific hurdles to using these for the type of large-scale retrospective studies many groups are eager to carry out.

For one thing the genetic material in such samples can be degraded, crosslinked, or in low quantities. Of all these problems, the biggest issue is getting sufficient quantity of quality DNA for sequencing. Numerous studies have found that these types of samples are difficult to work with and often provide very low success rates for gene sequencing studies. Clearly, fresh frozen samples provide much better results, but they are also much harder to obtain.

In response, our team has developed the WXNC SeqPlus FFPE extraction method, which provides substantially improved coverage compared to traditional methods and even approximates the results obtained with fresh frozen samples at 10X depth, with similar numbers of heterozygous and homozygous calls.

We tested SeqPlus in a study that comprised 516 tumor-normal pairs (i.e., 1,032 samples) that had been stored for 3 to 6 years. The targeted sequencing depth was 30X for the normal tissue and 70X for tumor tissue. The starting amount of DNA was 400 ng. The results were excellent, with SeqPlus delivering a coverage analysis just about 1% below what the fresh frozen control samples achieved. Further, a comparison of our analyses to results from the TCGA, using fresh frozen samples, showed striking similarity. These study results give us confidence that SeqPlus is a new “power tool” for FFPE sequencing studies. This webinar describes the process.

Sequencing reads of a sample prepared by the traditional whole-genome sequencing workflow for fresh-frozen samples and data generated using the SeqPlus whole-genome FFPE method. The green and purple indicate reads sequenced in the forward and reverse directions, respectively, and yellow represents bases with non-reference sequence. The center of the image shows a C to A mutation in each of the tumor samples.

Another area of great interest at WXNC is artificial intelligence (AI). We have been pioneers in AI for pulling novel insights out of massive multiple datasets. Leading this effort is Tom Chittenden, our Vice President of Statistical Sciences, Founding Director of the Advanced AI Research Labs, and a Lecturer on Pediatrics and Biological Engineering at Harvard Medical School and MIT. He also spoke at the breakfast series.

Our AI capabilities improve the tools we have and expand their capabilities. For example, using our AI tools, we can improve functional annotation of missense variants to an accuracy of >99%, integrate multiple types of data to discover new genes and elaborate pathways, and improve tumor subtype and drug-response classification accuracy by combining DNA- and RNA-seq, among other data types. These tools can be used for such varied purposes as target discovery, drug repurposing, and defining responders and non-responders in clinical trials.

We’ve already helped to develop breakthrough results, such as identifying an intriguing new target for both cardiovascular and cancer drug discovery. We’ve also classified breast and lung cancer subtypes with 97% to 100% accuracy, classified 8,200 tumors of 22 TCGA cancer types with >99% accuracy, and discovered a completely novel pan-cancer molecular survival signature.

The power of our deepCODE AI tools is in part thanks to a novel, causal statistical-learning method and deep-learning classification strategy. But another advantage is that they were built on our global platform for genomic data, which underpins the majority of the world’s largest genomics efforts and includes all major global reference databases. Our database stores, manages, and integrates any type of genomic data and correlates it with phenotype, ‘omics’, biology, outcome, and virtually any other type of data that may be relevant to a particular medical challenge.

If you want to know more, I recently gave an interview to WXpress outlining WXNC’s AI strategy. As we continue to deepen our commitment to this field, I’m sure we’ll have more exciting results to share.

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New Breast Cancer Study Underscores the Need for More Sequencing

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Gene sequencing for breast cancer. More than the usual suspects at play.

Ever since actress Angelina Jolie’s highly publicized preventive mastectomy ignited discussion about BRCA 1 and BRCA2, almost everyone has heard about these genes and how they can increase risk of breast cancer.  Some people even refer to them as “the breast cancer genes.” But how genes cause this disease is much more complicated than just through the most well known BRCA mutations, as a recent study in JAMA of Ashkenazi Jewish women has demonstrated. http://jamanetwork.com/journals/jamaoncology/fullarticle/2644652

This intriguing study raises a crucial question: How much sequencing is enough when diagnosing breast cancer in the age of targeted therapies? The number of these therapies keeps growing, as does our knowledge of the links between what drugs work for women with particular mutations. But at what point should we say we have uncovered enough mutations to make a proper diagnosis? And in a field in which we know there’s a lot we don’t know, is there such a thing as enough?

The good thing is that sequencing costs are going down. “It used to be that just testing for a single gene cost several thousand dollars,” says Jim Lund, Director of Tumor Product Development at WuXi NextCODE.  “Now a panel of genes costs that and whole exome sequencing is slightly more.” At the same time, the number of mutations that are discovered and studied is increasing – in the genomes of patients and the genomes of their tumors.

The data here has a message about data itself: in principle, we should be generating as much sequencing data as possible. By generating it, storing it for vast numbers of patients and their healthy relatives, creating more comprehensive databases of all disease-linked variants, and then reanalyzing patient and tumor samples as more is learned, we can improve the risk assessment and the speed and accuracy of diagnosis for patients everywhere. Since we can do this, the question isn’t whether we can afford to do more sequencing, but why anyone would argue that we can afford not to.

The researchers who led the recent JAMA study used multiplex genomic sequencing on breast tumor samples from 1007 patients. They tested for a total of 23 known and candidate genes.  It has been well documented that women of Ashkenazi descent have a higher risk of breast and ovarian cancer, and that is at least in part because of three particular BRCA1 and BRCA2 mutations. These are called founder mutations, because they probably originated among some of the earliest members of this ethnic group, and have been propagated because of a strong history of marriage within the same community.

But the researchers working on this study wanted to know if there were mutations in other genes besides BRCA that made it more likely these particular women would develop breast cancer. The patients were from 12 major cancer centers; had a first diagnosis of invasive breast cancer; self-identified as having Ashkenazi Jewish ancestry; and had all participated in the New York Breast Cancer Study (NYBCS).

Surprisingly, only 104 of the patients were carrying one of the infamous founder alleles. Seven patients had non-founder mutations in BRCA1 or BRCA2, and 31 had mutations in other genes linked to increased risk of breast cancer, including CHEK2. The vast majority of these women carried none of the mutations that are “obvious suspects” for breast cancer. “We do not know why those women got breast cancer,” says Shannon T. Bailey, Associate Director of Cancer Genetics at WuXi NextCODE.

It’s important to note that thousands of different cancer-predisposing mutations have been found in BRCA1 and BRCA2 alone. Every population studied to date includes people with such mutations.  The three founder mutations that have been established as being common among Ashkenazis are estimated to account for about 10% of breast cancers in this group. The rest of BRCA1 and BRCA2 mutations are considered extremely rare under any circumstances.

“If you look at the genes on the panel used in this study, it looks as if they are mostly associated with DNA damage and there are no cell cycle regulating genes included,” says Bailey. “But there are all kinds of mutations that cause breast cancer, even in noncoding regulatory zones.” As a result, even the best designed panel may fall short.

That’s why this study is so important. It tells us that even with founder mutations, family history matters but it doesn’t yet always tell you everything you’d like to know. Of the women with the founder BRCA mutations, only about half had a mother or sister with breast or ovarian cancer.  It’s also already well known that just carrying a BRCA1 or BRCA2 mutation is no guarantee the patient will get cancer. For reasons we don’t yet understand, these mutations raise overall risk, but not everyone who carries one will develop the disease. So while BRCA mutations are important, we need lots more information about other genes too.

The authors of this JAMA report suggest that Ashkenazi patients with breast cancers should have “comprehensive sequencing,” including, perhaps, complete sequencing of BRCA1 and BRCA2 and possibly testing for other breast cancer genes as well.

And what about other patients?  WuXi NextCODE’s Lund points out that even the most highly regarded recommendations for breast cancer testing do not cite specific panels. Those recommendations come from the U.S. Government Task Force [https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/brca-related-cancer-risk-assessment-genetic-counseling-and-genetic-testing] and the NCCN Clinical Practice Guidelines. Women with a family history will likely get more comprehensive testing, but beyond that it is not clear exactly how to proceed in every case.

At WuXi NextCODE we believe that this is clear evidence pointing to the value of doing more sequencing across all ethnic groups – for healthy individuals, patients, and their tumors, and pushing towards sequencing as standard of care. This would expand our knowledge of the genetic risk factors for breast and other cancers; provide vast new cohorts for research; and deliver the most actionable insights to patients, from risk assessment through diagnosis and then ongoing as new discoveries are made.

All of the participants in this JAMA study consented to have their sequence data used to advance research. They are already helping to do that, and this is just one study of thousands that are now underway and that are helping us to expand our data- and knowledgebases with the ultimate aim of delivering even better outcomes for all people and patients everywhere.

Let’s Speed the Genomic Revolution, UK CMO Says

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Sally Davies genomics

Whatever path various societies take to tap the power of the genome to improve human health, a recent report from England’s Chief Medical Officer, Dame Sally Davies, calls out key elements for realizing that future sooner rather than later.

England’s Chief Medical Officer wants to build on the success of Genomics England’s 100,000 Genomes Project and take her country swiftly into the age of precision medicine. The goal is to get patients optimal treatment more quickly and with fewer side effects. That means using genomics to more accurately guide prescribing—initially for cancer, infections, and rare diseases—but increasingly for all conditions and overall wellness and prevention.

Dame Sally Davies’ vision is anchored in the work that Genomics England is engaged in today and to which WuXi NextCODE and other leading genomics organizations have contributed. It’s a rallying cry that many voices are joining and underpins our work not only in England, but also similar efforts we are helping to advance in countries near and far, from Ireland to Singapore.

Her call is particularly forceful in three areas that she rightly singles out as critical to realizing the potential of precision medicine to revolutionize healthcare:

  • Industrial scale: Genomics has in many ways been treated and developed as a “cottage industry,” yielding important advances. But the need is massive scale in the era of population health (e.g., whole-genome sequencing, or WGS).
  • Privacy AND data sharing: Dame Sally wants to provide and ensure high standards of privacy protection for genomic data but is adamant that this should not come at the price of stifling the data sharing and large-scale collaboration that will transform medical care and many patients’ lives. She wants to move beyond “genetic exceptionalism,” which holds that genomic data is fundamentally different or more valuable than other data. Like other sensitive data, we can protect genomic data well and use it for public benefit.
  • Public engagement: She calls for a new “social contract” in which we, as individuals and members of society, recognize that all of us will benefit if we allow data about our genomes to be studied. That holds whether we are talking within our own countries or globally.

In England, as elsewhere, these shifts require the input of political leaders, regulators, and a range of healthcare professionals, including researchers as well as care providers. Crucially, such a transformation also requires a level of commitment on the part of patients throughout the National Health Service (NHS) and citizenry in general. If England takes this bold step forward, it could have tremendous effects. But “NHS must act fast to keep its place at the forefront of global science,” said Davies. “This technology has the potential to change medicine forever.”

To date, more than 30,000 people have had their genomes sequenced as part of the 100,000 Genomes Project. But there are 55 million people in the UK, and Dame Sally would like to see genomic testing become as normal as blood tests and biopsies for cancer patients: She wants to “democratize” genomic medicine, making it available to every patient that needs it.

We share and are, indeed, taking part in helping to realize much of Dame Sally’s vision as we work to accelerate Genomics England’s work and engage with our partners globally. As we know, different societies have different models of healthcare and different approaches to research and care delivery. But the ability for people anywhere to tap into the power of the genome to improve their health is at the very core of our own mission as an organization, and we applaud Dame Sally for calling out some of the key elements for realizing that future sooner rather than later.

Whatever path different societies choose to follow toward precision medicine, her recent report provides one enlightening view of a starting point for making the leap.

As Cancer Databases Grow, A Global Platform Leaps the Big Data Hurdle

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cancer databases

As massive cancer databases like The Cancer Genome Atlas (TCGA) proliferate and expand worldwide, WuXi NextCODE expects to see—and to drive—a boom in discoveries of cancer biomarkers that will advance our ability to treat cancer and improve outcomes for patients.

One of the fastest-growing areas in medicine today is the creation of massive cancer databases. Their aim is to provide the scale of data required to unravel the complexity and heterogeneity of cancer—the key to getting patients more precise diagnoses faster, and to getting them the best treatments for their particular disease.

In short, this data has the potential to save lives.

Such databases are not new, but they are now proliferating and expanding at an unprecedented pace. Driven by governments, hospitals, and pharmaceutical companies, they catalogue a growing range of genetic data and biomarkers together with clinical information about their effects on disease, therapy, and outcomes.

Only with such data can we answer the key questions: Does a certain marker suggest that a cancer will be especially aggressive? Does it signal that the tumor responds best to particular treatments? Are there new pathways involved in particular cancers that we can target to develop new drugs?

It’s the cutting edge of oncology, but to be powered to answer these questions, these databases have to be very, very big. They have to bring together whole-genome sequence data on patients and their tumors as well as a host of other ‘omics and biological data. One of the biggest challenges to realizing this potential is to manage and analyze datasets of that scale around the world. It’s one we are addressing in a unique manner through our global platform.

One of the most renowned and widely used of these is The Cancer Genome Atlas, a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI). TCGA data is freely available to those who qualify, and there is a lot of it. It already comprises 2.5 petabytes of data describing tumor tissue and matched normal tissues for 33 tumor types from more than 11,000 patients. Researchers all over the world can apply to use this data for their own studies, and many have.

Yet asking questions of TCGA alone can take months for most groups and requires sophisticated tools. At Boston’s recent Bio-IT World conference, WuXi NextCODE’s director of tumor product development, Jim Lund, explained how we have put TCGA on our global platform—providing a turnkey solution with integrated analytics to transform the data into valuable findings.

Jim and his team have imported into WuXi NextCODE’s cloud platform virtually all key TCGA data: raw whole exome sequence data from patients and tumors, as well as variant calls using MuTect2 and Varscan2; RNA and microRNA sequence and expression data; and data on copy number variation, methylation arrays, and some 150 different clinical attributes. But this data isn’t just hosted in the cloud: it can all now be queried directly and at high speed online, enabling researchers to quickly ask and answer highly complex questions without having to download any data or provide their own bioinformatics software.

To demonstrate the power of this approach, Jim’s team decided to run the same queries in a recent published study that looked at sequence data from the exons of 173 genes in 2,433 primary breast tumors (Pereira et al., Nature 2016). They were specifically looking for driver mutations of cancer’s spread and growth. In a matter of minutes, rather than months, they were able to replicate key mutations identified in the study. That analysis was then extended to all cancer genes, and additional driver genes were found. More important, because they were able to correlate these mutations with clinical outcomes data, they were also able to begin systematically matching specific mutation patterns to patient outcomes.

Next, Jim’s team looked at the genomics of lung adenocarcinoma, the leading cause of death from cancer worldwide. Following up on the findings in another published study (Collison et al., Nature 2014), they profiled the 230 samples examined in the paper and immediately made several observations. Eighteen genes were mutated in a significant number of samples; EGFR mutations (which are well known) were more common in samples from women; and RBM10 mutations were more common in samples from men. These results were extended to 613 samples and shown to be robust. But because they had a wide range of data including mRNA, microRNA, DNA sequencing, and methylation, Jim’s team was further able to suggest some actual biological processes that may be fueling the origin and growth of lung adenocarcinomas.

What’s making this type of research possible? It’s our global platform for genomic data. The platform spans everything required to make the genome useful for helping patients around the world, from CLIA/CAP sequencing to the world’s most widely used system for organizing, mining, and sharing large genomic datasets. At its heart is our database—the Genomically Ordered Relational database (GORdb). Because it references sequence data according to its position on the genome, it makes queries of tens of thousands of samples computationally efficient, enabling the fast, online mining of vast datasets stored in multiple locations.

That’s how we are making the TCGA—and every major reference dataset in the world—available and directly minable by any researcher using our platform. Those users can combine all that data with their own to conduct original research at massive scale.

These breast and lung cancer studies are just two of more than a thousand that have been carried out so far on TCGA data. As more such datasets become available, we expect to see—and to drive—a boom in discoveries of cancer markers that will advance our ability to treat cancer and improve outcomes for patients. For those who want to go further still, our proprietary DeepCODE AI tools offer a means of layering in even more datasets to drive insights even deeper into the biology of cancer and other diseases. And that’s a topic I’ll return to in the weeks ahead.

Bringing Artificial Intelligence to Cardiovascular Medicine and Cancer: Genomics in Action

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WuXi NextCODE Nature

A Yale research team, with contributions from WuXi NextCODE’s artificial intelligence (AI) and sequencing teams, has discovered a novel mechanism regulating how blood vessels grow.

Artificial Intelligence (AI) can already catch a criminal and identify the right patients for certain types of surgery. But those challenges involve relatively few parameters compared to number of parameters or features involved in linking the 3 billion bases in the human genome with other ‘omics data and all the complexity of human biology. For that very reason, the promise of AI in genomics is as necessary as it is enticing, and WuXi NextCODE is committed to pushing the frontier of this emerging field.

This week, I am encouraged by results from a study published in the latest edition of Nature, which describes how a well-known growth factor may play a previously unknown role in some important diseases. That report, led by Yale University scientist Michael Simons, investigates blood vessel growth regulation—a crucial process in some very common conditions, including cardiovascular disease and cancer. Our Shanghai team provided RNA sequencing for this study. Our Cambridge AI team applied some of the most advanced statistic in their toolset to take the data analysis to the next level.

Simons’ team studied knock out mice, whose fibroblast growth factor (FGF) receptor genes were turned off. The scientists were able to prove, for the first time, that FGFs have a key role in blood vessel growth, uncovering some metabolic processes that were “a complete surprise,” according to scientists on the team. Further, they mapped out pathways that could help provide new drug leads.

It’s inspiring to see scientists from around the world using top-notch technology to collaborate on pivotal research questions. This study involved scientists in six different countries.

This FGF study also comes on the heels of our recent announcement about how our deepCODE approach classified 27 different tumor types with greater than 95% accuracy when applied across approximately 9,000 human tumors from The Cancer Genome Atlas (TCGA) collection. [LINK: https://www.wuxinextcode.com/highlights/posters-at-aacr/#/brief–using-ai]

With the rapid rate of progress, it’s not surprising that AI is finding success in genomics. Today’s informatics capabilities allow for assimilating larger and larger datasets with AI applications, and the field is evolving at a rapid pace. Google alone published more than 200 papers on AI in 2016. Like us, they use a deep learning approach.

From facial recognition to genomic solutions
Each AI problem has a different scale. In facial recognition, AI applications analyze relatively few features in the human face (about two dozen). Digital scans of the human eye that use AI techniques are able to segment patients before eye surgeries, and this entails algorithms that consider hundreds of features.

Genomics, of course, involves looking at any number of feature sets among billions of possibilities. It’s an immense challenge, but I think it’s perfectly suited to AI. And with deep-learning tools, we can fish out many more insights than with traditional analyses.

Our goal is to see how AI can help researchers achieve better results in identifying and evaluating new medicines, pinpointing risk factors and disease drivers, finding new combination therapies that work better than single drugs, and more. Our deepCODE tools comprise a novel, multinomial statistical-learning method and deep learning classification strategy. It’s an advanced approach to AI.

This week’s Nature paper is another encouraging sign.

Many of the stickiest problems in medicine are longstanding. The role of FGFs in blood vessel development was poorly understood until now. This group’s findings may help open new avenues of research.

Our team is always seeking to tackle problems with the latest approaches and technologies. Now, in the age of big data, it makes sense to start letting computers do more and more of the work, even some of the actual thinking. Certainly, we pick the questions and frame them. But then, let’s load the data and let the machines help us find the answers. If we can polish this process, and apply it to a growing number of problems, new answers and insights are sure to come.

Marking Progress in Genomics: Reflections and Prospects

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Progress in Genomics WuXi NextCODEAs leaders of our field gather in Vancouver for the annual American Society of Human Genetics Meeting (ASHG 2016), it is an excellent time to take stock of the past and clarify our perspectives for the future. For the field of genomics, this is an opportunity both to reflect on our accomplishments over the last few years and to consider what we can achieve in the years ahead.

Indeed, our accomplishments have been numerous and our goals are ambitious, yet achievable. Here, I would like to summarize five significant ways in which our work in genomics has been revolutionizing medicine and improving patient outcomes.  In addition, I would like to share my thoughts about five areas in which I believe our field can drive meaningful change over the next few years.

What We Have Achieved
1. Improvements in Sequencing Technology and Analytical Tools
The ever-increasing volume of genomic data is testimony to the dramatic increases in sequencing speed and efficiency over recent years.  At the same time, novel methods of analysis, like the powerful genomics platform employed here at WuXi NextCODE, have considerably advanced our understanding of genetic variations and their clinical significance.

2. Transformations in Cancer Treatment
As I have discussed here, the expanding use of genomic data to guide treatment decisions in oncology is transforming the way clinicians approach cancer treatment.  In addition, our growing understanding of genetic predispositions for certain cancers is helping high-risk individuals make informed choices about preventive care.

3. Progress in Rare Diseases
Genomic data has brought new hope to families struggling with rare diseases by shortening diagnostic odysseys, guiding treatment, and building communities.  I provide examples of the game-changing power of genomics in the diagnosis of rare diseases here.

4. Empowerment of Patients and Consumers
Patients and consumers are increasingly informed about the innovative and meaningful ways in which genomic data can guide healthcare decisions.  The successes in our field are empowering individuals to pursue personalized medicine and generating interest in direct-to-consumer testing.  I offer my thoughts about DTC genetic testing here.

5. Innovations in Cloud-Based Analysis
The vast and ever-growing quantity of genomic data and related information necessitates new approaches to storage and analysis.  As I have previously discussed, cloud-based computing is essential to continued success in genomics.  WuXi NextCODE’s Exchange is at the forefront of the accelerated research made possible by real-time collaboration and analysis in the cloud.

What We Can Achieve in the Years Ahead

1. Effective Communication and Collaboration
Realizing the full potential of big data and cloud-based computing will require new efforts to dismantle “data silos.”  I am encouraged by recent initiatives to facilitate collaboration in cancer research, and – as I have recently discussed – call upon researchers and clinicians throughout the field of genomics to improve communication among all stakeholders.

2. Policies for Research with Patient Data
Our field derives its greatest power from careful analysis of genomic data, and access to data is critical to effecting meaningful change in healthcare.  In order to gather this game-changing data – from patients, from consumers, and from population-wide studies – we need to develop and embrace policies that lead to consider the ‘biorights’ of patients.  Individuals who wish to contribute information for research should have the opportunity to do so, and all parties should clearly communicate the purposes and extent of data-sharing.

3. Integration for Clinical Trials
I perceive significant movement toward the development of clinical trials that test the efficacy of treatments tailored to specific genetic anomalies – and use genetic information to screen participants.  This is an area in which genomics will dramatically accelerate the development of personalized therapies that will surely improve patient outcomes.

4. Actionable Information from Population-Wide Genomic Studies
I believe that in the near future we will reap significant rewards from projects that gather population-wide genomic information.  Analysis of the data we are collecting around the world, which I describe here, is an essential step to reshaping healthcare practices worldwide.

5. Globalization of Genomic Products: ‘Think Globally, Act Locally’
The power of genomic information is now known throughout the globe, and can be applied in a multitude of positive ways.  With such widespread potential, individual countries and cultures will choose to advance and roll-out genomics in their own distinct ways for the benefit of their citizens.  Companies that develop genomic products will need to adapt and design their products for use in specific markets.  At WuXi NextCODE, the first focus of our product portfolio for individual patients and families is in China, where we are delivering three offerings: population-optimized diagnostics, carrier screening, and whole-genomic wellness scans.

Together these initiatives build upon our recent accomplishments and further the creation of data and analysis necessary for meaningful change in healthcare.

The genomic revolution in medicine that we envisage will be achieved through applied use of research and development that is:

  • Fueled by big data, including data provided by informed consumers and patients and data derived from population-wide studies;
  • Supported by clinical trials crafted to assess the safety and efficacy of treatments tailored to individual characteristics; and
  • Enabled by collaborative work and effective communication.

At WuXi NextCODE, we are energized by the prospects for genomics in the years to come. We are proud to be at the cutting edge, providing the tools and resources that researchers and clinicians need to harness the transformative power of genomic data. And—importantly—we are confident that our field will continue to drive meaningful changes in healthcare that improve patient outcomes.

Genomics in Cancer: Continuing to Push the Leading Edge

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genomics in cancer - hannes smarason

Genomics is helping to prevent and treat cancer at an accelerating rate, supporting the goal of oncologists to dramatically improve cancer patient outcomes.

The progress in the use of genomics to help prevent and treat cancer continues to grow at a pace that is impressive. Indeed, there is expanded use of genomics to drive patient care and improve outcomes across an ever-expanding number of cancers by a growing number of oncologists.

Genomic Knowledge Can Clearly Drive Better Care

Applying genomics to cancer treatment is a powerful clinical application, as genomics can provide a window into how to best treat a patient’s particular cancer as it:

  1. may help better understand the genetics of the tumor itself, and
  2. can provide insight into how cancerous tumors may grow and spread over time.

With a genomic-based approach to cancer care, oncologists can more personally tailor anti-cancer treatments to an individual tumor’s mutations, thus molecularly targeting the specific cancer’s Achilles heel. Already, there are well-documented successes of molecularly targeted anti-cancer agents, such as cancer drugs that target certain genes—HER2, EFGR, ALK, and others.

In 2015, the pace of adoption of genomics in clinical oncology has advanced significantly. Recent evidence of the accelerating use of genomics to help fight cancer includes:

  • Evolving from ‘why’ to ‘how’ to use genomics at leading cancer centers. At the top cancer care facilities, genomics has become part of the programmatic approach to provide certain cancer patients with optimal care—care that is fundamentally designed to lead to the best outcomes. The question for leading medical centers globally has evolved over the last few years from “do we need genomics?” to “for which cancer types and at what stages of cancer treatment and diagnosis can we best use genomic sequencing and analysis?”—an evolution from “why?” to “how?” at a very fundamental level. The accelerating use and deployment of genomics by leading medical facilities validates that they are deriving significant value from genomics, and that value is resulting ultimately in meaningfully advancing better care for cancer patients.
  • Expanding potential applications of genomics within different types of cancers, broadening the types of cancers and tumors that can potentially benefit from genomics. Researchers and clinicians continue to publish a wealth of information validating the potential of genomics to improve outcomes in certain types of cancer patients. In 2015 alone, highlights of these advancements include certain prostate cancers, brain cancers, rare types of pediatric kidney cancers, and even potential targets in certain non-small cell lung cancers.
  • Broadening acceptance in cancer prevention. Driven in part by the education of oncologists and physicians generally and in part by the empowerment of knowledgeable patients, people are seeking and benefiting from genetic tests that reveal their personal risk for certain tumors (such as BRCA for breast or ovarian cancers). The idea of using genomic analysis to predict an individual’s cancer risk by comparing their genome with databases of confirmed genetic mutations linked to disease is—for certain individuals with specific family histories and genetics—driving appropriate medical decisions for patients who may be at high risk for certain cancers.
  • Powering clinical trials with genomics. The use of genomics in cancer clinical trials – whether for inclusion in data-gathering or even screening of patients—has gone from rare to commonplace over recent years, and is improving knowledge around the safety and efficacy of drugs in cancer and beyond. Two large-scale cancer trials have been initiated in 2015 with the bold goal of substantially advancing the understanding and use of genomics in cancer care. The anti-cancer treatments being tested in both trials were selected for their activity on a specific molecular target, independent of tumor location and histology. The two trials are actively enrolling and are (1) an American Society of Clinical Oncology (ASCO)-sponsored study, called TAPUR (Targeted Agent and Profiling Utilization Registry) and National Cancer Institute (NCI) and is called NCI-MATCH (Molecular Analysis for Therapy Choice). These trials and any subsequent follow-on trials will doubtless provide insightful information to drive the growing use of genomics in improving cancer care.

In summary, genomics is helping to prevent and treat cancer at an accelerating rate, supporting the goal of oncologists to dramatically improve cancer patient outcomes. There are at least four frontiers where we can see substantial progress in the use of genomics in cancer care, including expanded use in leading medical centers, increased potential applications within cancer, widespread acceptance in cancer prevention, and an increase in the use of genomics within clinical trials. I am personally committed to continue to drive and accelerate this genomic revolution to continue to bring true progress in improving cancer care to patients in need globally.