On the Clock Charting a Pathway Eyeing Epigenetics in Cancer It's in the Immune Markers
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VOL. 5, ISSUE NO. 4 JULY/AUGUST 2018
On the Clock
Charting a Pathway
Eyeing Epigenetics
in Cancer
It’s in the Immune
Markers
Nobel Laureate Aziz Sancar
Delves into Cancer’s Circadian Rhythm
VOL. 5, Issue No. 4 July/August 2018
Seeking All CDxes
20 As Pharma Companies Develop More Immuno-
Oncology Therapies, Diagnostics Employ New
Classes of Biomarkers
Eyeing Epigenetic Markers
26 Identifying the Methylation Patterns of cfDNA Aids in
Identifying Cancer’s Origins
3 | NEWS 30| DATA & INFORMATICS
New Research Expands Human Charting a Pathway: Philips,
Genome by Nearly 5K Genes Dana-Farber Join Forces for
Cancer Treatment Decision
10 | FROM THE EDITOR Support
Finding Inspiration in Innovation
34 | IN THE LAB
It’s in the Immune Markers: Israeli Company MeMed
11 | OP-ED
Seeks to Provide Fast Bacterial POC Diagnostics
What’s Next for the Single Cell Space?
39 | NEW PRODUCTS
12 | FEATURE
On the Clock: Can Transcriptomics Help Find the Right 40 | FEATURE
Time to Administer Chemotherapy? Five Innovative Technologies
44 | PRECISION MEDICINE
16 | DIAGNOSTICS
The House Oncomine Built: Thermo Fisher Opens Precision
Forging a New Course: Cancer Genetics Inc. Refocuses after
Medicine Center to Help Expand Use of its Cancer CDx
Departure of Long-Time CEO
48 | INDUSTRY EVENTS
Cover: Max Englund; (top right) Foundation Medicine; (top left) jamesbenet /Getty Images; (middle left) Yagi Studio /
Getty Images; (middle right) Philips. @ClinicalOMICs
www.clinicalomics.com July/August 2018 Clinical OMICs 1
biomaterial that allows human cells to live and grow in the ing on the ink’s viscosity, and features both pneumatic and
same way they would inside the human body. The company inkjet extrusion. The printbed, too, is carefully temperature
says it was the first to have a universal 3D printable bioink. controlled, which allows the bioinks to be cooled immedi-
Boasting nearly 20 tissue-specific bioinks available—with ately upon printing to maintain their 3D form.
more on the drawing board—the future is now, says com- While CELLINK has shot out of the pack in the 3D bio-
pany CEO Gatenholm. “This has always been my vision and printing world, and its technology is groundbreaking, it is
dream—to be part of something that creates the future,” he still early days, especially if you consider the Holy Grail
says. “At CELLINK, that is exactly what we do. We create of 3D bioprinting—the creation of entire human organs—
the future of medicine.” which is many years away. Today, CELLINK’s customers
The company’s workhorse is its versatile 3D printer range from biomedical research institutions and pharma-
BIOX, featuring a patented “clean printing technology”—a ceutical companies to cosmetic companies. With such a
small positive pressure chamber on board the printer that printing technology at their fingertips, scientists already
creates a clean printing environment for the biomaterial and have developed methods for printing 3D models of cancer-
thus eliminates the need for a cleanroom environment. The ous tumors—for research to better understand the tumor
system features three interchangeable print heads that can microenvironment—and organ tissues, among others, all
either heat or cool the bioink for optimal printing, depend- intended to speed development of new drugs.
Moon, Rare Disease Diagnosis
Diploid, Leuven, Belgium
When Peter Schols, founder and CEO of Belgian variant
interpretation software company Diploid, first began provid-
ing services to labs and hospitals in Europe and the United
States shortly after its founding in 2014, he was struck by
the lack of efficiency and manual processes required to try to
pinpoint the causal variants of rare disease. “We wondered
why can’t software just figure this out by itself? Why do we
sometimes need to manually go through 50, 100, or 200 vari-
ants before solving a case?” Schols asks.
So Schols and his team looked to tackle this problem, via
an in-house development project considered a moonshot at
the time—creating software that scours 4.5 million variants
and picks the one variant, or the small handful of causal
variants, responsible for a patient’s disease. When the proj-
ect was launched, some at Diploid wondered whether it was
even possible to remove the geneticist and manual interpre-
tation from the equation. What was once an internal code
name for a project some thought was not possible, has now
become the product Moon, software that can take a patient’s artificial intelligence to filter and rank genetic variants and
phenotypic data and genomic data and provide a disease provide autonomous interpretation of a patient’s genome.
diagnosis in five minutes. Moon continually updates its knowledgebase using natural
Battle tested by Stephen Kingsmore, M.D., in the NICU language processing technology to “read” an average of 45
at Rady Children’s Hospital, which holds the world record new publications each week on human genetics and rare
for the fastest genetic diagnosis, Moon has been deployed diseases. Not content to reduce the time to answer to five
worldwide at such prestigious institutions as the Antwerp minutes, Moon squeezes even more time out of the process
University Hospital, Belgium, The Swiss Foundation for via natural language generation (NLG) technology to auto-
People with Rare Diseases, and at the National Institutes matically write a first draft of the diagnostic report.
for Health in the U.S. The technology significantly leans on
(continued on next page)
www.clinicalomics.com July/August 2018 Clinical OMICs 41
(continued from previous page)
DeepVariant, Visual Genetic
Variant Calling
Google AI, Mountain View, CA
It’s no secret that error rates in variant calling are a
long-standing issue in the sequencing world. While the error
rates are small in percentage, when you consider the 3 bil-
lion base pairs comprising the human genome, they can add
up quickly. To the folks working at Google AI, they posited
there might be a new approach they could take to poten- Institute. In a nutshell, in order to employ visual analysis
tially improve variant calling by moving it from statistical to sequencing data, the Google AI team assigned different
and mathematical approaches to a visual approach, enabled colors to three classes of data: each of the four base pairs, the
by existing artificial intelligence and machine learning tools. quality of the sequencing at a given location, and on which
“As we started thinking of how deep learning technolo- strand the base pair was located. Using the color-coded
gies like TensorFlow could be used for genomics problems, sequencing images, DeepVariant was trained using the
it made sense to try to reframe variant calling as computer GIAB reference genome, using tens of millions of replicates.
vision problems to leverage these tools,” says Pi-Chuan While Google says the calling of DeepVariant is more
Chang, a software engineer at Google AI. “Intuitively, given accurate than existing, widely used statistical methods, it
that well-trained bioinformaticians can examine their data still has one more hurdle to clear if it is to become the go-to
with visualization tools like Integrative Genomics Viewer method among the scientific community: speed. The visual
when troubleshooting, it seemed possible that a visual interpretation takes significantly more computing power
approach would work.” than existing methods and takes about twice as long for
Thus was born DeepVariant, an open-source visual vari- results. Nevertheless, as computing power improves, adop-
ant calling tool that was released to Google Cloud at the tion of the tool should continue to surge.
end of last year. The team that created DeepVariant were “We’re aware of several organizations that are incorporat-
no genomics neophytes, counting among its leaders Mark ing DeepVariant into their clinical sequencing workflows,”
DePristo and Ryan Poplin, both of whom helped create the adds Chang. “We’re particularly excited about clinical users,
variant discovery tool GATK while both were at The Broad because that’s where accuracy is really critical.”
Flongle, a Flow Cell Dongle for MinION
and GridION
Oxford Nanopore Technologies, Oxford, England
As long-read nanopore sequencing continues to improve
its accuracy and read lengths, and adoption of the method
spreads, companies have now added the creation of tech-
nology platforms for specific applications to their ongo-
ing efforts to refine the technology. Nowhere is this more
apparent than at what is perhaps the preeminent company
in the field, Oxford Nanonpore. The company, known
for its MinION portable, credit card-sized (or smaller)
sequencing device, has busily added platforms to serve
different market niches. For instance, one of its newer
entries, PromethION, is a benchtop system geared toward
those conducting large-scale sequencing projects. While it
runs the same workflow as MinION, the new system can
run 48 flow cells—each with 3,000 nanopore channels—
either concurrently or independently.
42 Clinical OMICs July/August 2018 www.clinicalomics.com
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