Dr. David Rimm is a Professor in the Department of Pathology at the Yale University School of Medicine. He completed an MD-PhD at Johns Hopkins University Medical School followed by a Pathology Residency at Yale and a Cytopathology Fellowship at the Medical College of Virginia. Dr. Rimm is the Director of Translational Pathology and the Director of Yale Pathology Tissue Services. His lab group (15 researchers) focuses on quantitative pathology using the AQUA® technology invented in his lab with projects related to predicting response to therapy in breast and lung cancer and predicting recurrence or metastasis in breast and lung cancer. The technology has also been used in a series of efforts related to biospecimen science. He also has a group working on primary tumor culture using the conditionally reprogrammed cell method. He is a member of a number of correlative science committees for multi-institutional breast cancer clinical trials including SWOG, ALLTO, and TEACH. He also serves on the Molecular Oncology committee for the College of American Pathologists (CAP). He is currently supported by grants from both public and private sources. He is an author of over 280 peer-reviewed papers and 8 patents. He has served on advisory boards for Genentech, Novaritis, BMS, Perkin Elmer, Dako, ACD, Avida and Genoptix. He was a scientific co-founder of HistoRx, a digital pathology company (sold to Genoptix in 2012) and Metamark Genetics, a prognostic determinant company. For more details about 1st international antibody validation forum please check on http://www.stjohnslabs.com/ac_cms/blog

1. Use and Abuse of
Antibodies in the Lab and in
the Clinic
David L. Rimm M.D., Ph.D
Professor, Dept. of Pathology
Director of Translational Pathology
Yale University School of Medicine

2. Disclosures:
• Dr. Rimm has served as a consultant for
Genoptix, BMS, Amgen, ACD Inc., OptraScan,
Avida, and Perkin Elmer
• Dr. Rimm has research support from Gilead
Pharmaceuticals and Kolltan
• Dr. Rimm is a scientific co-founder of, and
shareholder in Metamark Genetics.

4. Between 2002-2012, Amgen was not able to reproduce
47of 53 seminal publications. These were publications
that reported something completely “new” (not “binary”
publications)
Only 6 of 53 “landmark” studies could be reproduced.
While there are many potential reasons for non-reproducibility,
the lack of antibody validation is clearly
an issue.

5. Outline:
• Examples of antibody abuse or failure of
validation
• Validation for scientifically rigorous and
reproducible data
– Quantitative analysis
– Sensitivity
– Specificity
– Reproducibility (across platforms, operators,
and labs)
• Who’s is responsible for validation

6. Antibody Binding Curve
Ab Signal
Good Ab Noise (Low)
OK Ab Noise (Moderate)
Bad Ab Noise (High)
Incubation Time
100
Percentage of Epitope Bound
50
0
Short time Long time
Higher antibody concentrations (low titer) push curves to the left
Antibody binding affinity and kinetics determine these curves.

7. Mishaps of Antibody Validation
or Omission of Validaiton
• Erbitux/ EGFR
• HER3?
• Cytoplasmic Estrogen Receptor Alpha
• Estrogen Receptor Beta
• MetMab????
• PD-L1???

8. CAP Survey Results for EGFR
In 2005 only 4 of 10 cases showed 90%
concordance (in 70 labs)
One colon cancer case in 2005 was
nearly 50:50
In 2006 only 6 of 10 cases showed 90%
concordance (in 71 labs)
In 2007, 8 of 10 cases showed 90%
concordance, but colon carcinoma was
removed from the test set
At least 5 different antibodies were used
for EGFR
Antigen Retrieval methods varied, some
did not include protease.
Survey discontinued in 2010

9. EGFR antibodies tested
Jena Giltnane and Elsa Anagnostou
Clone Antigen Sourc
e
Retrieval Dilutio
n
Supplier
H11 Extra-cellular Mouse
Mono
Standard
citrate
1:100 Dako
2-18C9
PharmDx
EC-subdomain
2
Mouse
Mono
Proteinase K neat Dako
15F8 Near C-term Rabbit
Mono
Standard
citrate
1:50 CST
2232 Around Tyr
1068
Rabbit
Poly
Standard
citrate
CST
31G7 Extra-cellular Mouse
Mono
Proteinase K 1:500 Zymed

10. EGFR Antibodies appear visually
similar and appropriately localized
Jena Giltnane

11. Elsa Anagnostou
Comparison of different
EGFR antibodies on
over 600 breast cancer
spots on a TMA shows
dramatic discordance

12. Zymed 31G7 vs. Dako H11
Discordance as a function of
antigen retrieval method
31G7 vs H11
R2 = 0.2919
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0 0.5 1 1.5 2 2.5
31G7 vs H11
R2 = 0.7268
2.5
2
1.5
1
0.5
0
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Zymed: Incubation with Proteinase K
for 5 min at RT
Zymed: Incubation with Proteinase K
for 15 min at 37C + 5min at RT
Eirini Pectacides and Elsa Anagnostou

13. Different outcomes with different Abs to the
H11-Median H11-1st Tertile
A B 31G7-Median 31G7-1st Tertile
p=0.63 p=0.015
E F G H
p=0.86 p=0.79
C D
p=0.06 p=0.12
15F8-Median 15F8-1st Tertile 2-18C9-Median 2-18C9-1st Tertile
p=0.89 p=0.95
Low
High
same protein (EGFR)
Elsa Anagnostou

14. Five Commercially available Her 3 antibodies
are largely completely unrelated to each other
R2 = 0.0244
3.1
2.7
2.3
1.9
1.5
1 1.2 1.4 1.6 1.8
RTJ1 clone
C17 polyclonal
R2 = 0.5155
3.1
2.7
2.3
1.9
1.5
1.7 1.9 2.1 2.3 2.5
SGP1 clone
C17 polyclonal
R2 = 0.0244
3.1
2.7
2.3
1.9
0.1 0.3 0.5 0.7 0.9
M7297 clone
C17 polyclonal
R2 = 0.2332
3.1
2.7
2.3
1.9
1.9 2.1 2.3 2.5 2.7
RB-9211 polyclonal
C17 polyclonal
R2 = 0.3693
2.5
2.2
1.9
0.15 0.35 0.55 0.75
M7297 clone
RB9211 polyclonal
R2 = 0.0641
2.5
2.2
1.9
1 1.2 1.4 1.6 1.8
RTJ1 clone
RB9211 polyclonal
R2 = 0.2561
2.5
2.3
2.1
1.9
1.8 2 2.2 2.4
SGP1 clone
RB9211 polyclonal
R2 = 0.1093
3
2.8
2.6
2.5
2.3
2.1
1.9
0.15 0.35 0.55 0.75
M7297 clone
SGP1 clone
R2 = 0.0184
2.5
2.3
2.1
1.9
1.7
1 1.2 1.4 1.6 1.8
RTJ1 clone
SGP1 clone
R2 = 0.00001
1
0.7
0.4
0.1
1 1.2 1.4 1.6 1.8
RTJ1 clone
M7297 clone
R2 = 0.1854
2.4
2 2.2 2.4
RTJ2 clone
C17 polyclonal
HER3
C17
Elsa Anagnostou

15. Antibodies to Estrogen Receptor are highly reproducible
Alley Welsh

16. Literature summary for Estrogen Receptor Beta
Summary of over 40 papers selected using PubMed
Reasons for this discrepancy:
• Small sample sizes
• Disregard for different splice variants
• Non-quantitative, non-standardized methods
• Various nonspecific antibodies
Hallie Wimberly

19. Summary of IHC Antibody
Validation
• Sensitivity: Test signal to noise on FFPE
test TMAs at multiple titers
• Specificity in vitro: Western blot, IP or
knock-down
• Specificity on the slide: linearity in FFPE
cell line series or siRNA knock-down
• Reproducibility: regressions of multiple lot,
multiple day testing on serial TMA sections
(may include a 2nd ab)

20. Sensitivity:
• Does the signal localize to the biologically
correct compartment (subcellular or
architectural)
• Use a quantitative method to optimize the
assay for signal to noise

22. Sensitivity:
• Does the signal localize to the biologically
correct compartment (subcellular or
architectural)
• Use a quantitative method to optimize the
assay for signal to noise

23. y = 0.9809x
R² = 0.9948
100
90
80
70
60
50
40
30
20
10
15000
10000
5000
0
0 5000 10000 15000
Nuclear AQUA Score (User 2)
Nuclear AQUA Score (User 1)
y = 0.9086x
R² = 0.9629
0
0 10 20 30 40 50 60 70 80 90 100
Percent Positive Nuclei (User 2)
Percent Positive Nuclei (User 1)
Comparison
Between
Methods
(reproducibility)
y = 0.9211x
R² = 0.914
100
90
80
70
60
50
40
30
20
10
0
Pathologist vs
Pathologist
0 10 20 30 40 50 60 70 80 90 100
Percent Positive Nuclei (Path. 2)
Percent Positive Nuclei (Path. 1)
DAB semi vs DAB
semi
QIF (AQUA) vs
QIF (AQUA)
Elizabeth Zarrella and Veronique Neumeister

24. 100
80
60
40
20
0
0 20 40 60 80 100
Percent Pos. Nuclei (Aperio)
Percent Pos. Nuclei (Pathologist)
14000
12000
10000
8000
6000
4000
2000
0
0 20 40 60 80 100
Nuclear AQUA Score
Percent Pos. Nuclei (Pathologist)
Comparison
Between
Methods
14000
12000
10000
8000
6000
4000
2000
0
0 20 40 60 80 100
Nuclear AQUA Score
Percent Positive Nuclei (Aperio)
Pathologist vs
DAB semi
DAB semi vs
QIF(AQUA)
Pathologist vs
QIF(AQUA)
Elizabeth Zarrella and Veronique Neumeister

25. Different Intensities of HER2 IHC Staining
Observed Within HER2(+) Patient
Population
Intensities of HER2 IHC Staining Observed in HER2+ Patients From
the HERA Trial
2+ 3+ 3+ 3+ 3+
Although there was a high proportion of positive staining tumor cells in all of
the 3+ samples the range of staining intensity varied.
Slide provided by Mitch Dowsett
Zabaglo L, et al. Presented at 33rd SABCS; December 8-12, 2010; San Antonio, TX. Abstract PD10-01.

26. Regression of IHC vs QIF scores for
CB11 and SP3 in YTMA 263
CB11 SP3
Daniel Carvajal

27. Titer optimization of Cofilin
•Staining on YTMA279-3
•1:10
•1:50
•1:100
•1:500
•1:1000
•1:5000
•No Primary
1:1000 was chosen as the optimal dilution for Cofilin on YTMA279-3
John McGuire and Veronique Neumeister

28. Specificity
• Western blot (a good start, but neither
necessary nor sufficient)
• Progressive expression in a cell line series
• Over expression in negative cell lines or
knock-down or knock-out
• Confirmation by reproducibility with a
second antibody

29. Example
of
Estrogen
Receptor
Ab
Validation
Failure
(Santa Cruz MC-20)
Alley Welsh

30. Standardized Index Array
Alley Welsh
ER antibody used is 1D5

32. Western blot
may show other
bands that may
be responsible
for IHC/IF signal
Elsa Anagnostou and Liz Killiam

33. When cell lines express different levels of protein as a
function of growth conditions and are difficult to
standardize, try siRNA knockdown to validate for IHC/IF
Maria Baquero and Jason Hanna

34. Antibodies that did validate
Hallie Wimberly and Kurt Schalper

35. YTMA 140-1 L3N and J2J
Regression ?? Validation?
4500
4000
3500
3000
2500
2000
1500
1000
500
0
0 1000 2000 3000 4000 5000 6000 7000
L3N 1:1600
J2J 1:5000 Joe McLaughlin and Kurt Schalper

36. Comparing ECD and ICD PD-L1 antibodies
extracellular
PD-L1 J2J
intracellular
extracellular
PD-L1
intracellular
L3N
J2J + + - -
L3N + - + -
N 53 57 11 110
% 22.9 24.7 4.8 47.6
Nathan Fons

37. Specificity Failure
• Peptide competition assays do not prove
specificity
• Omission of the primary antibody does not
prove specificity
• Specificity failure is most often not
absence of target binding, but rather target
binding and also cross-reactivity to show
non-target binding

38. Reproducibility
• Reproducibility with complementary
reagent
• Reproducibility between different days,
different lots of product and different
operators

39. Validation of a Second
Extracellular Domain
Antibody for HER2
Daniel Carvajal

40. Antibody Reproducibility
Between Days Same Day
N=21
%CV Data
Same Day: 1.9
Between Days:
4.6
3.2
4.3
John McGuire and Veronique Neumeister

41. Some antibodies are not reproducible on serial sections
Elsa Anagnostou and Liz Killiam

42. Summary of QC Protocol for
Antibody Validation
• Test sensitivity with a cell line series and
with Western blot correlation
• Test on non-expressing cell line with
transfected with target or a high
expressing cell line with and without
siRNA knockdown
• Test for lot to lot and run to run
reproducibility and consider a second
antibody with to the same epitope

44. Who should validate the antibody

45. Who should validate the antibody

46. Thanks to: Rimm Group:
Veronique Neumeister
Yalai Bai
Kurt Schalper
Daniel Carvajal
Jason Brown
Susan Combs
James Smithy
Lauren Moore
Joe McLaughlin
Mehmet Altan
Nemanja Rodic
John McGuire
Joanna Hu
Vasiliki Pelakanou
Mengyao Feng
Nikita Mani
Yan Song
Maria Toki
Yale Pathology Tissue Services
Lori Charette
Sudha Kumar
Outside Yale Collaborators
David Hicks (U Rochester)
Edith Perez and NCCTG
George Fountzilas – HeCOG
Amanda Psyrri – HeCOG
Konstantine Kalogeras -HeCOG
Rimm Lab Alums:
Vamsi Velchetti
Elsa Anagnostou
Anastasios Dimou
Alley Welsh
Robert Camp
Maria Vassilikapoulou
Huan Cheng
Jennifer Bordeaux
Vamsi Velchetti
Elizabeth Zarrella
Hallie Wimberly
Yale Collaborators
Lajos Pusztai
Yuval Kluger
Leiping Chen
Work supported by grants from the NCI, DOD, BCRF, and the Susan G Komen
Foundation for the Cure

47. Rimm Lab, Fall 2014 www.tissuearray.org

49. Regression for YTMA 201, same
day staining, different autostainers
ER SP1 on YTMA 201; same day staining on Discovery Ultra at 1to30 and DAKO autostainer at
y = 7.2739x + 1114
R² = 0.5162
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
1to100 for 1 hour
0 200 400 600 800 1000 1200 1400 1600
ER SP1 DAKO
ER SP1 Discovery Ultra

50. Validate Abs by showing run to run (A) and lot to lot (B)
reproducibility
Maria Baquero and Elsa Anagnostou

51. Correlation cMet AQUA (Ventana) and DAB based
IHC outside scoring by eye
y = 0.0173x + 8.3595
R² = 0.1099
160
140
120
100
80
60
40
20
0
0 1000 2000 3000 4000 5000 6000
membrane Hscores
cMet AQUA scores
Correlation cMet AQUA (Ventana) and DAB based
IHC outside scoring by eye
y = 0.0142x + 9.5516
R² = 0.0926
120
100
80
60
40
20
0
0 1000 2000 3000 4000 5000 6000
total membrane positive score
cMet AQUA scores
y = 0.0234x + 32.376
R² = 0.1308
180
160
140
120
100
80
60
40
20
0
0 1000 2000 3000 4000 5000 6000
membrane cytoplasmic H-scor
Correlation cMet AQUA (Ventana) and DAB based
IHC outside scoring by eye
y = 0.0176x + 36.845
R² = 0.0942
140
120
100
80
60
40
20
0
0 1000 2000 3000 4000 5000 6000
total membrane cytopl. Positive
scores
Correlation cMet AQUA (Ventana) and DAB based
IHC outside scoring by eye