Discussion of national policy issues relevant for cancer research. Consent, data access, privacy.

1. Opportuni)es
for
Posi)vely
Impac)ng
Cancer
Care
–
an
informa)cs
perspec)ve
Warren
A.
Kibbe,
PhD
warren.kibbe@nih.gov
Center
for
Biomedical
Informa)cs
and
Informa)on
Technology
Na)onal
Cancer
Ins)tute
hHp://wiki.bioinforma)cs.northwestern.edu/index.php/Warren_Kibbe

2. Three
policy
issues
• Informed
Consent
–
what
should
it
enable?
Does
it?
• Iden)fica)on
of
specimens
and
data.
What
is
privacy?
How
do
we
share
appropriately?
Is
that
a
consent
issue?
• Open
access
to
data
–
how
can
we
respect
the
desire
of
pa)ents
to
share
their
specimens
and
data
to
make
truly
transforma)ve
inference
and
observa)ons?

3. Outline
Disrup)ve
technologies
GeQng
social
What
is
big
data?
Open
access
to
data

4. Disrup2ve
Technologies
• Printing
Access
to
knowledge
–
democra)za)on
of
learning

5. Disrup2ve
Technologies
• Printing
• Steam power
Move
from
human
power
or
animal
power
to
hundreds
of
horsepower
per
person

6. Disrup2ve
Technologies
• Printing
• Steam power
• Transportation
Easy
distribu)on
of
goods

7. Disrup2ve
Technologies
•
•
•
•
Printing
Steam power
Transportation
Electricity
Easy
distribu)on
of
energy

8. Disrup2ve
Technologies
•
•
•
•
•
Printing
Steam power
Transportation
Electricity
Antibiotics
Reduced
the
impact
of
secondary
infec)ons.
Huge
change
in
life
expectancy

9. Disrup2ve
Technologies
•
•
•
•
•
•
Printing
Steam power
Transportation
Electricity
Antibiotics
Semiconductors &VLSI design
Digital
Compu)ng
Density
of
devices

10. Disrup2ve
Technologies
•
•
•
•
•
•
•
Printing
Steam power
Transportation
Electricity
Antibiotics
Semiconductors &VLSI design
http
Hyperlinking!

11. Disrup2ve
Technologies
•
•
•
•
•
•
•
•
Printing
Steam power
Transportation
Electricity
Antibiotics
Semiconductors &VLSI design
http
High throughput biology
Systems
view
-­‐
end
of
reduc)onism?

12. Disrup2ve
Technologies
• Printing
• Steam power
• Transportation
• Electricity
• Antibiotics
6.6B
ac2ve
mobile
contracts
1.9B
smart
phone
contracts
• Semiconductors &VLSI design
1.1B
land
lines
US:
• http
345M
ac2ve
mobile
contracts
287M
smart
phone
contracts
• High throughput biology
Everyone
is
a
data
provider
• Ubiquitous computing
Data
immersion

13. GeIng
Social
• Measuring behavior across a population
• Understanding behavior – can we provide better
risk estimates for individuals?
• Social media is a big data opportunity – what are
the ethics of big data?
• Synergize with the energy and immediacy of
patient advocates
• Patients want more data sharing – how can we
facilitate that appropriately?
This
changes
trial
design
–
sta)s)cs
un)l
now
has
been
focused
on
how
to
design
an
appropriate
sample
so
that
the
sample
can
be
generalized
to
the
popula)on
–
what
happens
when
we
measure
the
ENTIRE
popula)on
??

14. Big
Data
• To
me,
Big
Data
is
about
emergent
proper)es
• Big
Data
with
social
media
changes
the
sta)s)cal
paradigm
–
rather
than
modeling
if
a
given
sample
is
representa)ve
of
the
popula)on,
you
have
all
the
data
from
the
popula)on!!
• To
accelerate
solving
real
problems
in
cancer
we
must
combine
systems
biology,
social
data
(behavior
and
exposure)
with
clinical
care
and
outcomes
from
healthcare
providers

15. The
future
• Elastic computing ‘clouds’
• Social networks
• Big Data analytics
• Precision medicine
• Measuring health
• Practicing protective medicine
Seman)c
and
synop)c
data
Intervening
before
health
is
compromised
Learning systems that enable
learning from every cancer patient

16. Open
Data
Access
• We
need
to
provide
data
access
to
people
outside
of
biomedicine
who
have
the
skills
and
training
to
mine
and
analyze
data
• More
access
will
mean
more
innova2on

17. Precision
Oncology
• The
era
of
precision
medicine
and
precision
oncology
is
predicated
on
the
integra)on
of
research,
care,
and
molecular
medicine
and
the
availability
of
data
for
modeling,
risk
analysis,
and
op)mal
care
How
do
we
re-­‐engineer
transla8onal
research
policies
that
will
enable
a
true
learning
healthcare
system?

18. Consent
• In
a
learning
healthcare
system,
we
‘learn’
from
every
pa)ent
who
comes
in
for
treatment.
What
is
consent
in
this
model?
What
is
research?
• What
role
is
there
for
standardized
consent?
• Are
there
ways
to
reimagine
transla)onal
research
without
consent?
Would
that
help
us?

19. Iden2fying
informa2on
• Equa)ng
genomic
data
with
a
fingerprint
is
appropriate
• Privacy
needs
to
be
respected
• If
a
pa)ent
consents
to
release
genomic
data,
how
can
we
lower
the
barriers
to
accessing
and
analyzing
their
data
and
genomes?

20. Data
access
• How
do
we
lower
the
barriers
for
accessing
research
data,
including
molecular
informa)on?
• Much
clinical
data
belongs
to
the
pa)ent,
but
pa)ents
should
have
the
right
to
provide
data
and
specimens
for
the
public
good.
How
can
we
honor
that
request?
Is
this
a
way
to
promote
appropriate,
low
barrier
data
access?
If
we
can
provide
pa)ents
with
the
ability
to
change
their
level
of
approval
over
)me,
how
does
that
impact
consent?

21. Thank
You!
• Ques)ons?
Warren
A.
Kibbe
warren.kibbe@nih.gov

22. Ques2ons
• Are
there
beHer
models
for
standardized
consent?
Are
there
ways
to
reimagine
transla)onal
research
without
consent
• If
pa)ents
consent
to
release
genomic
data,
how
can
we
lower
the
barriers
to
accessing
and
analyzing
their
data
and
genomes?
These
data
are
inherently
iden)fying.
• How
do
we
lower
the
barriers
for
accessing
research
data?
Access
to
individual-­‐level
data
is
cri)cal
for
precision
medicine,
but
is
mired
in
regula)ons
even
with
appropriate
consents
are
in
place.