Research Integrity, The Importance of Data Acquisition and

Report
Research Integrity, The
Importance of Data
Acquisition and
Management
Ralph H. Hruban, M.D.
Monday, February 13, 2011
Conflict of Interest
• I receive royalty
payments from Myriad
Genetics for the PalB2
invention
I think what happened is that you are
betting on football, and what’s after
football is basketball, and then the
NCAA tournament. The next thing that
follows is betting on baseball…
I wish I could take it all back.
Pete Rose
Aristotle
“We become just
by performing just
actions, temperate
by performing
temperate actions,
brave by performing
brave actions”
Nicomachean Ethics
http://www.gap-system.org/~history/PictDisplay/Aristotle.htm
Henry L. Mencken
“Science, at
bottom, is really
anti-intellectual. It
always distrusts
pure reason, and
demands the
production of
objective fact.”
http://www.toptenz.net
“The [pirate] code is more
what you’d call ‘guidelines’
than actual rules”
Barbossa,
Pirates of
the
Caribbean
Screenrant.com
PHS 42 C.F.R. 93
The PHS regulation (42 C.F.R. 93) defines research misconduct as
fabrication, falsification, or plagiarism in proposing, performing, or
reviewing research, or in reporting research results.
(a) Fabrication is making up data or results and
recording or reporting them.
(b) Falsification is manipulating research materials,
equipment, or processes, or changing or omitting data or
results such that the research is not accurately
represented in the research record.
(c) Plagiarism is the appropriation of another person's
ideas, processes, results, or words without giving
appropriate credit.
(d) Research misconduct does not include honest error or
differences of opinion.
Data Integrity
1. How common is data fraud?
2. Fraud harms patients, the institution
and the investigator
3. Examples of inappropriately
published data
4. How can you prevent fraud in your
own lab?
Fraud is More
Common Than
You Think
Fraud in High School
ORI is pleased to have high school
students, Michael Moorin and Tyler
Smith, present at the Quest for Research
Excellence 2011 Conference. Moorin
and Smith made headlines in the news
media, such as The Washington Post,
when they found over 60% of high school
students reported that they had falsified
or fabricated the data in their science fair
projects
http://ori.hhs.gov/blog/category/researchmisconduct/
Web Sites
http://retractionwatch.wordpress.com/
and
http://abnormalscienceblog.wordpress.com/
Yet another young scientist starting postgrad
Desires their CV to be better, a tad.
Such a wonderful gel!
I must publish in Cell!
The controls I can fix on my iPad.
Retractionwatch.wordpress.com/
How Common is Misconduct?
• Meta-analysis of surveys of
scientific misconduct
• 2% of scientists admitted to
have fabricated, falsified or
modified data or results at
least once
Fanelli PLoS One 2009; 4:e5738
Fanelli PLoS One 2009; 4:e5738
Many More Were Aware of
Misconduct by Others!
Fanelli PLoS One 2009; 4:e5738
Nature 478, 26-28 (2011)
Nature 478, 26-28 (2011)
Data Integrity
1. How common is data fraud?
2. Fraud harms patients, the institution
and the investigator
3. Examples of inappropriately
published data
4. How can you prevent fraud in your
own lab?
Fraud Harms Patients
• Analyzed 180 retracted articles that involved human
subjects or “freshly derived human material,” along with
851 published studies citing that research
• The retracted papers were cited over 5,000 times
• According to Steen, 6,573 patients received treatment in
studies eventually retracted because of fraud. One study
alone, published in 2001, included 2,161 women being
treated for postpartum bleeding
• The downstream studies included more than 400,000
subjects, with 70,501 receiving treatment
R. Grant Steen, Journal of
Medical Ethics, 2011
Deception at Duke
Scott Pelley reports on a
Duke University oncologist
whose supervisor says he
manipulated the data in his
study of a breakthrough
cancer therapy
http://www.cbsnews.com/8301-18560_162-57376073/deception-at-duke
Fraud Harms the Institution
and the Investigator
“But the research at Duke turned
out to be wrong. Its gene-based
tests proved worthless, and the
research behind them was
discredited. Ms. Jacobs died a
few months after treatment, and
her husband and other patients’
relatives have retained lawyers.”
Anil Potti, MD
Gina Kolata, on Anil Potti, New York times, July 7, 2011
Potti Scandal
The defendants named in the suits are:
• Duke University
• Duke University Health System, Inc.
• Private Diagnostics Clinic PLLC
• Joseph Nevins, PhD
• Anil Potti, MD
• Michael Cuff, MD
• Sally Kornbluth, MD
• John M. Harrelson, MD
• Cancer Diagnostics, Inc.
Research
Misconduct Harms
Patients, the
Investigator and
the Institution
Data Integrity
1. How common is data fraud?
2. Fraud harms patients, the institution
and the investigator
3. Examples of inappropriately
published data
4. How can you prevent fraud in your
own lab?
http://www.pioneerinstitute.org
Examples
•
•
•
•
Fabricated data
Falsified data
Selective reporting of data
Image manipulation
Example 1:
Trial of 3 Drugs- Actual Results
1.2
1
0.8
0.6
0.4
0.2
0
Drug 1
Drug 2
Drug 3
Trial of 3 Drugs- Results Reported
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Data Fabrication
Fabrication is making up data
or results and recording or
reporting them
Jon Sudbø- Fabrication
• Medical researcher at
the Radium Hospital,
Oslo, Norway
• 2005 article in the
Lancet suggested that
Ibuprofen reduces oral
cancer in smokers
The Lancet, 366 (9494): 1359–1366;
http://www.vg.no/nyheter/innenriks/artikkel.php
Jon Sudbø- Fabrication
• Suspicion aroused because the data were
supposedly from a cancer patient database
which had not yet opened
• Of the 908 subjects in the Lancet study 250
had the same date of birth
• Sudbø later acknowledged that he used
fictional data in at least two more papers,
published in the New England Journal of
Medicine and Journal of Clinical Oncology
http://en.m.wikipedia.org/wiki/Jon_Sudb%C3%B8#cite_note-2
Jon Sudbø- Fabrication
• Independent commission investigated and
also criticized the co-authors of Sudbø's
papers
• Dr. Atle Klovning, a leading European
authority, said that Sudbø's co-authors had
probably not lived up to their responsibilities
according to the rules of authorship
• You think they would have noticed the
database wasn’t open yet!
http://en.m.wikipedia.org/wiki/Jon_Sudb%C3%B8#cite_note-2
International Committee of
Medical Journal Editors
• An “author” is generally considered to be someone
who has made substantive intellectual contributions to
a published study…. An author must take
responsibility for at least one component of the work,
should be able to identify who is responsible for each
other component, and should ideally be confident in
their co-authors’ ability and integrity.
• When a large, multicenter group has conducted the
work, the group should identify the individuals who
accept direct responsibility for the manuscript
http://www.icmje.org/ethical_1author.html
Example 2:
Trial of 3 Drugs- Actual Results
3.5
3
2.5
2
Drug 1
Drug 2
Drug 3
1.5
1
0.5
0
Time 1
Time 2
Time 3
Time 4
Trial of 3 Drugs- Results
Reported
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Data Falsification
Falsification is manipulating research
materials, equipment, or processes, or
changing or omitting data or results such
that the research is not accurately
represented in the research record
Suggested that microarray data from cell lines (NCI 60) could be used
to define drug response signatures, and these signatures could in
turn be used to guide therapy
The results of
this study had
therapeutic
implications
Nature Medicine,
2006
Woodward and Bernstein of Bioinformatics
Keith Baggerly
Time.com
Kevin Coombes
Forensic bioinformatics
http://videolectures.net/cancerbioinfor
matics2010_baggerly_irrh/
http://www.cbsnews.com/8301-18560_162-57376073/deception-at-duke/
Baggerly and Coombes Investigate
Potti
Baggerly
Reported
Genes
http://www.jhsph.edu/cct/videos/
Potti’s paper suffers from a “frameshift mutation”
(Off by one error for all of the genes caused by an
extra column)
Potti
Baggerly
Potti et al submit erratum with updated gene lists
NOT the end of the saga…….
http://www.jhsph.edu/cct/videos
Sensitive and Resistant
Switched for some Drugs
http://www.jhsph.edu/cct/videos/
Keith Baggerly and Coombes
Letter to the Editor
(Nature Medicine) –
one page letter, 149
pages of
Supplementary Data
November 2007
http://www.jhsph.edu/cct/videos
For cisplatin, U133A arrays were
used for training.
ERCC1, ERCC4 and DNA repair
genes are identified as “important”
Journal of Clinical Oncology, 2007
http://www.jhsph.edu/cct/videos
Four Genes Didn’t Match
The four that couldn’t be matched
were the genes that were touted to be
functionally important
http://www.jhsph.edu/cct/videos
Based directly on the Potti and Nevins publications,
despite concerns raised by Baggerly and Combes, Duke
Initiates three Clinical trials in 2007
Adjuvant Cisplatin With Either Genomic-Guided Vinorelbine or
Pemetrexed for Early Stage Non-Small-Cell Lung Cancer
(TOP0703)
Study Using a Genomic Predictor of Platinum Resistance to Guide
Therapy in Stage IIIB/IV Non-Small Cell Lung Cancer (TOP0602)
Phase II Study Evaluating The Safety And Response To
Neoadjuvant Dasatinib In Early Stage Non-Small Cell Lung Cancer
(TOP0706)
O, what a tangled web we weave;
When first we practice to deceive!
Sir Walter Scott
July 16, 2010
Retraction watch November 2010
Improving Validation Practices in
“Omics” Research
• Routine replication, public data
and protocol availability, funding
incentives, reproducibility
rewards or penalties, and
targeted repeatability checks
Ioannidis, et al., Science December 2
2011: Vol. 334: 1230-1232
Example 3:
Trial of 3 Drugs-Actual Results
Results
Statistically
Significant
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Trial of 3 Drugs-Reported Results
(Results still Significant)
7
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
It is Still Data
Falsification
Falsification is manipulating research
materials, equipment, or processes, or
changing or omitting data or results such
that the research is not accurately
represented in the research record
(No qualifier here that falsification is ok so
long as the results were originally
statistically significant)
Example 4:
Trial of 3 Drugs-Actual Results:
Results
Statistically
Significant
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
The PI Tells the Post-Doc
“These two data
points seems off. I
would expect there
to be a greater
difference”
Trial of 3 Drugs-Reported Results
(Results still Significant)
7
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
It is found out later
that the Post-Doc
Changed the data in
question
Does the P.I. have
any responsibility for
what happened?
Dipak K. Das
•
The University of Connecticut
report alleges Dr. Das
“defunded” the work of a
student in his lab because
she did not produce results
that he wanted
•
The investigation of Dr. Das’s
work began in January 2009,
two weeks after the
university received an
anonymous allegation about
research irregularities in his
laboratory
NY Times, January 11, 2012 and
retractionwatch.wordpress.com
Allegations of misconduct
often come from a whistle
blower inside the group, such
as a postdoc or graduate
student who does not agree
with the PI's tendencies of
glossing over data or blatant
misconduct
We All Have a Responsibility to
Maintain Integrity
What should we do when we
“suspect” another PI is
falsifying data?
What if the other PI is a
competitor?
Example 5:
5-Month Trial of 3 Drugs-Actual
Results
of
a
5-Month
Design
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Time 5
Trial of 3 Drugs-Results
Reported
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Could be
Falsification
Falsification is manipulating research
materials, equipment, or processes, or
changing or omitting data or results
such that the research is not accurately
represented in the research record
OK Only if Clearly
Documented in the
Paper
“…not accurately represented in
the research record”
Example 6:
Results (4 Day Expt., but Technician
ran the Experiment too long)
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Time 5
Trial of 3 Drugs-Results
Reported
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Probably OK if the
study design was
shorter, but it
ought to get you
thinking!
The most exciting phrase
to hear in science, the one
that heralds new
discoveries, is not
‘Eureka,’ but ‘That’s
funny…’
Isaac Asimov
Example 7:
Trial of 3 Drugs- Results First Run
3.5
3
2.5
2
Drug 1
Drug 2
Drug 3
1.5
1
0.5
0
Time 1
Time 2
Time 3
Time 4
Results Second Run
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Time 5
Results Third Run
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Reported (The Third Run)
6
5
4
Drug 1
Drug 2
Drug 3
3
2
1
0
Time 1
Time 2
Time 3
Time 4
Likely Falsification
Falsification is manipulating research
materials, equipment, or processes, or
changing or omitting data or results
such that the research is not accurately
represented in the research record
“…not accurately represented in
the research record”
If there were genuine
reasons the first two runs
didn’t work you ought to
document why, fix them
th
and repeat the study a 4
time!
Example 8: Mouse Model
• Genetically engineered mouse
model suggests that protein X
promotes metastases
• Scientist shares an antibody to
human protein X with the
collaborating pathologist
studying human disease
• The antibody doesn’t label any
human metastases, in fact, it
is only expressed in nonmetastatic lesions
http://www.buzzfeed.com
Mouse Model
• Manuscript published reads
“Protein X Promotes
Metastases”
• Is this selective reporting of
data?
Mouse Model
• Manuscript published reads
“Protein X Promotes
Metastases in a Mouse
Model”
• Is this selective reporting of
data?
Mouse Model
• What was suggested in the
paper?
• Was the discussion always
focused on mouse models or
did it stray into suggesting that
protein X is important in
humans?
Example 9: A New Drug to
Cure Depression
• The P.I. develops a new drug to treat
depression
• It works on 100 of 103 patients
• The investigators go back and review the
charts on the three patients on whom the drug
didn’t work and on re-review it is clear that the
3 patients have manic depressive illness
• The drug is reported to be effective in 100% of
patients with depression
Dangers of Re-Review of
Selected Data
…such that the research is not
accurately represented in the
research record
Example 10: PowerPoint
Presentation Within Hopkins
Falsified data are presented
at a meeting within the
Hopkins community. The
data are not published. Is
this research misconduct?
Yes, it is research misconduct
even if the data are not
published
Image Manipulation
Falsification is manipulating
research materials, equipment, or
processes, or changing or omitting
data or results such that the
research is not accurately
represented in the research
record
It‘s so easy to add and subtract with Photoshop
M. Rossner
and K.
Yamada,
JCB, 2004
Rubber Stamp to “Clean” the
Background
M. Rossner and K. Yamada,
JCB, 2004
“Your X-ray showed a broken rib, but we fixed it in Photoshop”
http://www.glasbergen.com
Image
Manipulations
M. Rossner and K. Yamada,
JCB, 2004
Woo-Suk Hwang
Science 2005
http://news.naver.com/main/read.nhn
Woo-Suk Hwang
Time, Dec. 15, 2005
Reusing Images- Potti
Augustine et al., 2009, Clin
Can Res, 15:502-10, Fig 4A.
Temozolomide, NCI-60.
Hsu et al., 2007, J Clin
Oncol, 25:4350-7, Fig 1A.
Cisplatin, Gyorffy cell lines.
http://videolectures.net/keith_baggerly/
Altering Images
If you misrepresent your data, you are
deceiving your colleagues, who expect
and assume basic scientific honesty—
that is, that each image you present is
an accurate representation of what you
actually observed. In addition, an image
usually carries information beyond the
specific point being made.
M. Rossner and K. Yamada,
JCB, 2004
Altering Images
Data must be reported directly, not through a
filter based on what you think they “should”
illustrate to your audience. For every
adjustment that you make to a digital image,
it is important to ask yourself, “Is the image
that results from this adjustment still an
accurate representation of the original data?”
If the answer to this question is “no,” your
actions may be construed as misconduct.
M. Rossner and K. Yamada,
JCB, 2004
Image Manipulation
Falsification is manipulating
research materials, equipment, or
processes, or changing or omitting
data or results such that the
research is not accurately
represented in the research
record
Data Integrity
1. Fraud in the history of science
2. How common is data fraud?
3. Fraud harms patients, the institution
and the investigator
4. Examples of inappropriately
published data
5. How can you prevent fraud in your
own lab?
http://randysright.files.wordpress.com
Prevention!!
• Establish a culture of honesty
above all in your lab
• Inform and educate
• Screen- Periodically ask to see
lab books
• Detect problems by working
closely with primary data
Establish a Culture of Honesty
• “We need this difference to be significant
or I won’t get my grant”
• “These data points don’t fit the results I
expected”
These small things can add up and can
quickly become the norm
Establish a Culture of Honesty
vs.
From day one; “All that
matters to me is that the
results you present are
100% honest”
Inform and Educate
• Dedicate some journal clubs or
lab group meetings to educating
those under you on the
importance of academic integrity
• Encourage members of your lab
to attend lectures such as this
one!
Even so we
have to screen
for problems!
We are not going to detect fraud if we
only look at PowerPoint presentations
of finished results
Picture of a PowerPoint
presentation
We need to carefully review
and question primary data
Henry L. Mencken
“Conscience is the
inner voice that
warns us somebody
may be looking”
If it is Too Good to be True
• Blind the
samples and
ask the person
to rerun the
experiment
• Have someone
else in the lab
rerun the
experiment
http://econsultancy.com/
Tools for detecting misconduct
• Anti-plagiarism software (eTBLAST,
CrossCheck, Turnitin)
• Screening images (PhotoShop)Pioneered by J Cell Biology. See M.
Rossner and K. Yamada, JCB 2004;
166:11-15- found 1% unacceptable
manipulation
• Data Review (digit preference)
Liz Wager, Council of
Scientific Editors
Conclusions
Preventing damage would save
careers from ruin
Everyone has a responsibility to
promote a culture in which
research misconduct does not
happen
Harold C. Sox, Annals of Internal Medicine
If you are the first or last author
on a paper
You are responsible:
1. For making sure all of the other authors have
read and approved the manuscript
2. For everything in the manuscript- make sure
the images included are correct, that the text
isn’t copied from somewhere else, that the data
weren’t manipulated, that you have appropriate
IRB protocols, and that the protocols were
followed
Take Home Message #1
We need to be aware that at
a place like Johns Hopkins
people may feel enormous
pressures
Take Home Message #2
Science should
be our
“touchstone”
The currency of science is the peerreviewed and peer-accepted manuscript
that is backed by a gold standard of
scientific integrity and scrupulous
honesty. Anything that tarnishes this gold
standard threatens to devalue the worth
of scientific currency. Ultimately, society
itself suffers because scientific
advancement prepares the way for social
progress
Curt Civin
Editor-in-Chief, Stem Cells
“Cloned Photomicrographs, not
cloned cells”
Panel Discussion
•
•
•
•
•
Ralph Hruban, M.D.
Bob Bollinger, M.D., M.P.H.
Curt Civin, M.D.
Anirban Maitra, M.B.B.S.
Sheila Garrity, J.D., M.P.H.,
M.B.A. – Moderator

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