A clinical trial summary.

1. 3 Cell ???, ??MONTH?? ??DATE??, 200? ©200? Elsevier Inc. DOI XXXXXXXXX See online version for ??????.
SnapShot:
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AUTHOR
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AFFILIATION
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Interventional
trials
Real-world
data
Pretrial
line(s)
of
therapy
Post-trial
line(s)
of
therapy
Randomized
controlled
trial
Diagnosis
and
staging
Inclusion/exclusion
screen
Data
Collection
Trial
line
of
therapy
Trial
patients
Overall
clinical
outcome
(e.g.,
survival)
Patients
to
screen
A
Patients
to
screen
Single
arm
trial
Pretrial
line(s)
of
therapy
Post-trial
line(s)
of
therapy
Diagnosis
and
staging
Inclusion/exclusion
screen
Data
collection
Trial
line
of
therapy
Trial
patients
Biomarker
screening
A
Patients
to
screen
Data
collection
Trial
line
of
therapy
Prospective
observational
trial
Pretrial
line(s)
of
therapy
Post-trial
line(s)
of
therapy
Diagnosis
and
staging
Inclusion/exclusion
screen
Trial
patients
A
Biomarker
screening
Retrospective
observational
trial
Pretrial
line(s)
of
therapy
Post-trial
line(s)
of
therapy
Diagnosis
and
staging
Protocol-defined
review
Data
collection
Trial
line
of
therapy
Charts
to
screen
Trial
patients
Biomarker
testing
Medical
record
review
Previous
line(s)
of
therapy
Line(s)
of
therapy
after
Diagnosis
and
staging
Chart
review
criteria
Data
collection
Review
line
of
therapy
Patients
Biomarker
testing
Charts
to
screen
Overall
clinical
outcome
(e.g.,
survival)
Overall
clinical
outcome
(e.g.,
survival)
Overall
clinical
outcome
(e.g.,
survival)
Overall
clinical
outcome
(e.g.,
survival)
Master
interventional
trials
Basket
trial
Platform
trial
Master
observational
trial
Pretrial
line(s)
of
therapy
Post-trial
line(s)
of
therapy
Diagnosis
and
staging
Inclusion/exclusion
screen
Data
collection
Trial
line
of
therapy
Overall
clinical
outcome
(e.g.,
survival)
Patients
to
screen
All
with
similar
disease
but
different
molecular
alterations
Umbrella
trial
Pretrial
line(s)
of
therapy
Post-trial
line(s)
of
therapy
Inclusion/exclusion
screen
Data
collection
Trial
line
of
therapy
Overall
clinical
outcome
(e.g.,
survival)
Diagnosis
staging
Biomarker
screening
Different
disease
histologies
but
similar
molecular
alterations
Patients
to
screen
Note:
This
is
a
hypothetical
example
of
the
organization
and
evolution
of
a
platform
trial.
The
master
protocol
governing
the
trial
adds,
removes,
or
replaces
arms
based
on
predetermined
events
such
as
pre-planned
analysis
or
introduction
of
new
interventions.
Data
collection
generally
focuses
on
the
intervention
and
its
associated
outcome
for
the
line
of
therapy
and
only
limited
information
from
pre-
and
post-trial
events.
Analysis
(Stop)
(New
standard
of
care)
(Stop)
Analysis
(Stop)
Time
(At
beginning
of
trial)
(New
biomarker
introduced)
Broad
inclusion
criteria
Data
collection
Every
line
of
therapy
Overall
clinical
outcome
(e.g.,
survival)
All
patients
All
treatments
1
1
2
2
3
3
4
4
x
x
A
1
1
1
1
1
1
1
1
4
1
1
1
3
1
2
3
3
4
2
5
B
C
?
?
?
A
R
R
No
actionable
biomarker
2
X
C
3
3
4
4
x
X
1
1
A
2
2
Data
collection:
detailed
clinical
and
related
data
(yellow
box)
and
limited
data
collected
(orange
line)
Randomization
of
patients
R
Limited
toxicity:
variable
reporting
of
toxicity
or
limited
toxicity
data
Biomarker
testing
method:
using
a
specific
technology
and
instrument
“A,”
“B,”
etc.
A
Biomarker
testing
method:
using
any
technology
or
instrument
to
find
a
biomarker
?
Specific
biomarker(s)
absent:
any
biomarker(s)
tested
and
not
present
Detailed
toxicity:
determining
side
affects
of
a
given
treatment
in
a
standardized
fashion
Response
determination:
disease
response
quantified
(e.g.,
overall
response
rate)
Response
time:
time
of
disease
response
to
a
treatment
(e.g.,
progression
free
survival)
Therapeutic
intervention:
protocol-determined
intervention:
“1,”
“2,”
etc.
1
Specific
biomarker(s)
present:
any
specific
biomarker(s)
tested
and
present;
“1,”
“2,”
etc.
1
Standard
intervention:
treatment
determined
by
provider
for
given
patient
scenario:
“1,”
“2,”
etc.
1
1
2
X
Data
collection
R
4
Biomarker
screening
A
B
All
biomarkers
present
All
testing
details
Diagnosis
and
staging
All
biomarkers
missing
Biomarker
screening
SnapShot:
Trial
Types
in
Precision
Medicine
Dane
Dickson,
1,2
Jennifer
Johnson,
3
Raymond
Bergan,
1
Rebecca
Owens,
2
Vivek
Subbiah,
4
and
Razelle
Kurzrock
5
¹Knight
Cancer
Institute,
Oregon
Health
and
Science
University,
Portland,
OR,
USA;
²Taproot
Health,
Salt
Lake
City,
UT,
USA;
³Sidney
Kimmel
Cancer
Center,
Thomas
Jefferson
University,
Philadelphia,
PA,
USA;
4
University
of
Texas
MD
Anderson
Cancer
Center,
Houston,
TX,
USA;
5
Moores
Cancer
Center,
University
of
California
at
San
Diego,
San
Diego,
CA,
USA
See online version for
legends and references
208 Cell 181, April 2, 2020 ©2020 Elsevier Inc. DOI: 10.1016/j.cell.2020.02.032

2. 208.e1 Cell 181, April 2, 2020 ©2020 Elsevier Inc. DOI: 10.1016/j.cell.2020.02.032
SnapShot: Trial Types in
Precision Medicine
Dane Dickson,1,2
Jennifer Johnson,3
Raymond Bergan,1
Rebecca Owens,2
Vivek Subbiah,4
and Razelle Kurzrock5
¹Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA; ²Taproot Health, Salt Lake City, UT, USA; ³Sidney
Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA; 4
University of Texas MD Anderson Cancer Center, Houston,
TX, USA; 5
Moores Cancer Center, University of California at San Diego, San Diego, CA, USA
Clinical Data Collection in the Molecular Era
Evidenced-based medicine has been revolutionized by molecular biology, allowing for the development of deeper questions, more refined hypotheses, detailed diagnostics,
targeted cellular processes, and more comprehensive data analyses. Yet, we have only begun to scratch the surface of how biomarkers and cellular activity affect diagnosis,
treatment, and outcomes in most disease states. What we know about clinical data tied to molecular signatures comes through varying research methods. Data originate from
interventional or real-world sources through a specific trial or data collection effort. Each are equipped to address specific sets of questions with associated strengths and
weaknesses (Sherman et al., 2016).
Clinical Data Collection Methods (Non-master Protocols)
The addition of molecular testing has provided more detail related to diagnosis, prognosis, and treatment of patients. However, it has not dramatically altered most trial
designs. Interventional trials generally focus on determining how patients with a given biomarker identified by a specified test respond to a particular drug (i.e., one test, one
biomarker, one treatment). Observational or chart review efforts can be used to identify potential molecularly based testing or treatment strategies but generally do not focus
on a specific type of testing and rarely lead to a change in standard of care.
Interventional Master Protocols (Umbrella, Basket, and Platform)
Master protocols address some of the challenges introduced by molecular medicine. Through shared infrastructure and screening methods, a more efficient data collec-
tion method is created. Like other interventional trials, the interventional master protocols generally focus on a specific treatment tied to a specific biomarker but have multiple
adaptable arms (or disease histologies) running in a parallel fashion (i.e., one or more tests with each linked to an associated biomarker, treatment, and study arm) (Woodcock
and Lavange, 2017).
Real-World Master Observational Protocols
The master observational trial (MOT) is a new construct to bridge the gap that exists between the specificity of the interventional trials and the broad nature of the actual
practice of medicine. By hybridizing the scientific methods of the interventional trials with restricting data collection to the most clinically relevant elements, broader data col-
lection can take place. Precise classification of molecular testing tied to longer-term outcomes across broad study populations allows for evaluation of higher-complexity care
models. The versatility of the MOT type allows for easy synergy with other trials and provides the flexibility of adding new elements or arms to address specific questions.
Pros and Cons
The quality of data tends to be proportional to the cost and complexity of collecting it. Investigational trials are the gold standard of evidence generation methods but are
usually only used for drug development at only one point in time in a patients’ treatment history. Real-world data efforts allow for much broader exploration, but the “many
drugs in many patients harboring many biomarkers” approach can make finding an unbiased benefit challenging. Theoretically, questions regarding benefits of continually
advancing molecular testing, of treating rare alterations or combinations of alterations, of sequencing or combining treatments, and of using treatments not approved by the
FDA or specialty societies can be answered using real-world datasets. However, these answers require data of sufficient quality and quantity to mitigate bias. Lack of patient
consent limits ability to reach back to a specific patient to verify benefits. In all trials of molecular medicine, interventional or real world, there need to be enough patients
screened and enrolled to identify rare or complex associations and benefits.
Challenges
We are beginning to understand the clinical implication of the foundational layer of molecular medicine: genomics. The deeper layers of transcriptomics, proteomics,
metabolomics, epigenomics, cellular metabolism, microenvironment, host factors, immune factors, gut biome, and other factors steadily increase the complexity of clinical
data collection. Ideally, we need to use every data collection method available to aggregate information on exponentially greater numbers of patients in order to truly under-
stand personalized medicine.
DECLARATION OF INTERESTS
D.D., stock and other equity interests (cofounder, CEO, and shareholder of Taproot Health, which is the sponsor of ROOT), speaker’s fee (Novartis); J.J., consulting or advisory role
(Foundation Medicine); R.B., stock and other equity interests (co-owner of Third Coast Therapeutics, which has an option to license patents on which he is an inventor and that relate
to experimental drugs for the treatment of cancer); R.O., stock and other equity interests (cofounder, CCO, and shareholder of Taproot Health); V.S., research funding/grant support
for clinical trials (Novartis, Bayer, GlaxoSmithKline, Nanocarrier, Vegenics, Celgene, Northwest Biotherapeutics, Berghealth, Incyte, Fujifilm, Pharmamar, D3, Pfizer, Multivir, Amgen,
Abbvie, Alfa-sigma, Agensys, Boston Biomedical, Idera Pharm a, Inhibrx, Exelixis, Blueprint medicines, Loxo oncology, Medimmune, Altum, Dragonfly therapeutics, Takeda and
Roche/Genentech, National Comprehensive Cancer Network, NCI-CTEP, and UT MD Anderson Cancer Center), travel (Novartis, Pharmamar, ASCO, ESMO, Helsinn, Incyte, US-
FDA), consultancy/advisory board (Helsinn, LOXO Oncology/Eli Lilly, R-Pharma US, INCYTE, Medimmune, Novartis); R.K.: stock and other equity interests (IDbyDNA, CureMatch,
and Soluventis), consulting or advisory role (Gaido, LOXO, X-Biotech, Actuate Therapeutics, Roche, NeoMed, Soluventis, and Pfizer), speaker’s fee (Roche), research funding (Incyte,
Genentech, Merck Serono, Pfizer, Sequenom, Foundation Medicine, Guardant Health, Grifols, Konica Minolta, DeBiopharm, Boerhringer Ingelheim, and OmniSeq [all institutional]),
board member (CureMatch). J.J., R.B., R.K., and S.S. are principal investigators of the ROOT trial. These are uncompensated, volunteer roles.
REFERENCES
Sherman, R.E., Anderson, S.A., Dal Pan, G.J., Gray, G.W., Gross, T., Hunter, N.L., LaVange, L., Marinac-Dabic, D., Marks, P.W., Robb, M.A., et al. (2016). Real-world evidence — what
is it and what can it tell us? N. Engl. J. Med. 375, 2293–2297.
Woodcock, J., and LaVange, L.M. (2017). Master protocols to study multiple therapies, multiple diseases, or both. N. Engl. J. Med. 377, 62–70.