VOL. 5, ISSUE NO. 4 JULY/AUGUST 2018JULY/AUGUST 2018VOL. 5, ISSUE NO. 4

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

www.clinicalomics.com July/August 2018 Clinical OMICs 1

@ClinicalOMICs

30| DATA & INFORMATICS

Charting a Pathway: Philips,

Dana-Farber Join Forces for

Cancer Treatment Decision

Support

34 | IN THE LAB

It’s in the Immune Markers: Israeli Company MeMed

Seeks to Provide Fast Bacterial POC Diagnostics

39 | NEW PRODUCTS

40 | FEATURE

Five Innovative Technologies

44 | PRECISION MEDICINE

The House Oncomine Built: Thermo Fisher Opens Precision

Medicine Center to Help Expand Use of its Cancer CDx

48 | INDUSTRY EVENTS

Seeking All CDxesAs Pharma Companies Develop More Immuno-

Oncology Therapies, Diagnostics Employ New

Classes of Biomarkers

20

Eyeing Epigenetic MarkersIdentifying the Methylation Patterns of cfDNA Aids in

Identifying Cancer’s Origins

26

3 | NEWS

New Research Expands Human

Genome by Nearly 5K Genes

10 | FROM THE EDITOR

Finding Inspiration in Innovation

11 | OP-ED

What’s Next for the Single Cell Space?

12 | FEATURE

On the Clock: Can Transcriptomics Help Find the Right

Time to Administer Chemotherapy?

16 | DIAGNOSTICS

Forging a New Course: Cancer Genetics Inc. Refocuses after

Departure of Long-Time CEO

Cover: Max Englund; (top right) Foundation Medicine; (top left) jamesbenet /Getty Images; (middle left) Yagi Studio /

Getty Images; (middle right) Philips.

www.clinicalomics.com July/August 2018 Clinical OMICs 41

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

phenotypic data and genomic data and provide a disease

diagnosis in five minutes.

Battle tested by Stephen Kingsmore, M.D., in the NICU

at Rady Children’s Hospital, which holds the world record

for the fastest genetic diagnosis, Moon has been deployed

worldwide at such prestigious institutions as the Antwerp

University Hospital, Belgium, The Swiss Foundation for

People with Rare Diseases, and at the National Institutes

for Health in the U.S. The technology significantly leans on

artificial intelligence to filter and rank genetic variants and

provide autonomous interpretation of a patient’s genome.

Moon continually updates its knowledgebase using natural

language processing technology to “read” an average of 45

new publications each week on human genetics and rare

diseases. Not content to reduce the time to answer to five

minutes, Moon squeezes even more time out of the process

via natural language generation (NLG) technology to auto-

matically write a first draft of the diagnostic report.

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biomaterial that allows human cells to live and grow in the

same way they would inside the human body. The company

says it was the first to have a universal 3D printable bioink.

Boasting nearly 20 tissue-specific bioinks available—with

more on the drawing board—the future is now, says com-

pany CEO Gatenholm. “This has always been my vision and

dream—to be part of something that creates the future,” he

says. “At CELLINK, that is exactly what we do. We create

the future of medicine.”

The company’s workhorse is its versatile 3D printer

BIOX, featuring a patented “clean printing technology”—a

small positive pressure chamber on board the printer that

creates a clean printing environment for the biomaterial and

thus eliminates the need for a cleanroom environment. The

system features three interchangeable print heads that can

either heat or cool the bioink for optimal printing, depend-

ing on the ink’s viscosity, and features both pneumatic and

inkjet extrusion. The printbed, too, is carefully temperature

controlled, which allows the bioinks to be cooled immedi-

ately upon printing to maintain their 3D form.

While CELLINK has shot out of the pack in the 3D bio-

printing world, and its technology is groundbreaking, it is

still early days, especially if you consider the Holy Grail

of 3D bioprinting—the creation of entire human organs—

which is many years away. Today, CELLINK’s customers

range from biomedical research institutions and pharma-

ceutical companies to cosmetic companies. With such a

printing technology at their fingertips, scientists already

have developed methods for printing 3D models of cancer-

ous tumors—for research to better understand the tumor

microenvironment—and organ tissues, among others, all

intended to speed development of new drugs.

42 Clinical OMICs July/August 2018 www.clinicalomics.com

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.

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-

tially improve variant calling by moving it from statistical

and mathematical approaches to a visual approach, enabled

by existing artificial intelligence and machine learning tools.

“As we started thinking of how deep learning technolo-

gies like TensorFlow could be used for genomics problems,

it made sense to try to reframe variant calling as computer

vision problems to leverage these tools,” says Pi-Chuan

Chang, a software engineer at Google AI. “Intuitively, given

that well-trained bioinformaticians can examine their data

with visualization tools like Integrative Genomics Viewer

when troubleshooting, it seemed possible that a visual

approach would work.”

Thus was born DeepVariant, an open-source visual vari-

ant calling tool that was released to Google Cloud at the

end of last year. The team that created DeepVariant were

no genomics neophytes, counting among its leaders Mark

DePristo and Ryan Poplin, both of whom helped create the

variant discovery tool GATK while both were at The Broad

Institute. In a nutshell, in order to employ visual analysis

to sequencing data, the Google AI team assigned different

colors to three classes of data: each of the four base pairs, the

quality of the sequencing at a given location, and on which

strand the base pair was located. Using the color-coded

sequencing images, DeepVariant was trained using the

GIAB reference genome, using tens of millions of replicates.

While Google says the calling of DeepVariant is more

accurate than existing, widely used statistical methods, it

still has one more hurdle to clear if it is to become the go-to

method among the scientific community: speed. The visual

interpretation takes significantly more computing power

than existing methods and takes about twice as long for

results. Nevertheless, as computing power improves, adop-

tion of the tool should continue to surge.

“We’re aware of several organizations that are incorporat-

ing DeepVariant into their clinical sequencing workflows,”

adds Chang. “We’re particularly excited about clinical users,

because that’s where accuracy is really critical.”

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