1. Challenges for the Pathologist in the era of Personalized Medicine
Prof Keith M Kerr
Department of Pathology
Aberdeen University Medical School
Aberdeen, UK

2. Disclosures
Acted as Speaker and/or Consultant for: AstraZeneca, Roche, Boehringer Ingeheim, Bristol-Myers-Squibb, Clovis, Eli Lilly, MSD, Novartis, Pfizer

3. Personalised approach to treating cancer
Understanding the molecular basis of cancer has identified specific drivers and allowed for sub-classification of the disease, revealing the potential for targeted agents
‘ONE SIZE FITS ALL’
Drug X
PERSONALISED THERAPY
Drug X Y Z
Personalised treatment consists of three essential components :
Oncogenic target that drives cancer growth
Predictive biomarker that detects presence of the target
Well conducted clinical studies that confirm treatment efficacy in the identified patient group

4. Diagnostic steps in lung cancer
Morphological diagnosis of lung cancer
Separate SCLC from NSCLC
Subtype NSCLC where possible
Predictive Immunohistochemistry
Only when needed
Identification of
Actionable genetic alterations

5. The Challenge: Subtyping non-small cell carcinomas?
Squamous (Cis/Gem) vs Non-squamous (Cis/Pem) cytotoxic chemotherapy
Contraindication of anti-angiogenic therapy for squamous carcinomas
Triage for molecular testing
Simple immunohistochemistry has solved this problem (TTF1, p63, p40, CK5/6)
NSCLC-NOS rate should be <10%

6. It is worthwhile finding an
Actionable genetic alteration in Lung cancer
Driver detected – Targeted Rx
Kris MG et al. JAMA 2014; 311,

7. ROS1 fusion genes BRAF mutations KRAS mutations HER2 mutations
% Adenocarcinomas
Fusion correlates with protein (IHC)
Sensitive to crizotinib
Kerr KM. J Clin Pathol 2013;66:832–838
BRAF mutations
2.5-4% Adenocarcinomas
Incl V600E other V600
Smoking vs non-smoking
Vemurafenib
KRAS mutations
25-35% Adenocarcinomas
MEK inhibition?
HER2 mutations
1-2% Adenocarcinomas
Mutually exclusive of EGFR, KRAS
Traztuzamab?
NTRK1 fusion
MPRIP-NTRK1 and CD74-NTRK1
3.3% of ‘onco-negative’ adenocarcinomas
Trk inhibitors exist
Vaishnavi A et al. Nat Med 2013; 19,
CD74-NRG1 fusion
Search in ‘onco-negative’ adenocarcinomas
ERBB3 and PI3K-AKT pathway activation
Mucinous adenocarcinomas
Potential therapeutic target
Fernandez-Cuesta L et al. Can Disc 2014; jan30 epub
RET fusion genes
~1% Adenocarcinomas
Vandetanib & others
MET upregulation
4% amplification, ~50% overexpression
Biomarker issues
Failed trials

8. Discoid Domain Receptor 2 mutation
FGFR1 amplification
Biomarker issues
Definition of amplification
20% may be overestimate?
Ponatinib – FGFR1 inhibitor
Wynes MW et al. Clin Cancer Res 2014;20:
Clin Can Res 2012;18,
Discoid Domain Receptor 2 mutation
Prevalence 3.8%
Good in vitro target – miRNA & Dasatinib
Limited clinical evidence
Hammerman PS et al. Cancer Discov 2011
PI3Kinase
~30% amplification
~6% mutations – addictive??
Inhibitors exist
MET
Inhibitors exist
So far no success
EGFR
TKI vs MoAb
Mutations – rarity (vIII – 8%)
Targeting the receptor
IGFR1
Figitumumab
Some effect in squamous
Toxicity

9. Panel of 54 genes with potentially druggable alterations
Govindan et al. Cell. 2012 Sep 14;150:

10. The Challenge: Lots of targets – Lots of drugs?
Practical reality
EGFR mutation
ALK gene fusion
‘Nearly routine’?
BRAF mutation
ROS1 gene fusion
Many more potential drugs with biomarkers

11. Methods of biomarker analysis
Change in DNA sequence
Change
in Gene copy #
mRNA transcript
Transcription

12. Methods of biomarker analysis
Change in DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Microarray
FISH/CISH
mRNA transcript
RT-PCR
Transcription

13. Methods of biomarker analysis
Change in DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Next-generation
sequencing
Microarray
FISH/CISH
mRNA transcript
RT-PCR
Transcription

14. Methods of biomarker analysis
Change in DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Next-generation
sequencing
Microarray
FISH/CISH
mRNA transcript
RT-PCR
Transcription
Protein
Translation

15. Methods of biomarker analysis
Change in DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Next-generation
sequencing
Microarray
FISH/CISH
mRNA transcript
RT-PCR
Transcription
Protein
Immunohistochemistry
Translation

16. Methods of biomarker analysis
Change in DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Next-generation
sequencing
Microarray
FISH/CISH
mRNA transcript
RT-PCR
Transcription
Protein
Immunohistochemistry
Translation
Biological Activity
Oncogenesis
Drug target

17. The Challenge: Complex testing landscape
Biomarkers at different stages between gene and protein
Complex testing strategy
Multiplex testing offers some solutions
Can be confusing: EGFR
Sometimes unclear which approach is the best
Multiple biomarkers in the same ‘diagnostic space’

18. Methods of ALK biomarker analysis
Change in DNA sequence
Change
in Gene copy #
Break-apart FISH
NGS
PCR for
ALK fusion gene
transcripts
mRNA transcript
Transcription
IHC for ALK protein
Protein
Translation
Biological Activity
Oncogenesis
Drug target

19. Provides information on outcome with regards to
Prognostic and predictive biomarkers are used to guide treatment decisions in oncology
Prognostic
Predictive
Provides information on outcome, independent of the administered therapy
Provides information on outcome with regards to
a specific therapy
Prognostic biomarkers may help define patient’s prognosis, risk of recurrence or the duration of survival
Predictive biomarkers estimate response or survival of a specific patient on a specific therapy and can be a target for therapy
Biomarkers for NSCLC
ERCC1
Ki67/MIB1
p53
BRAF
EGFR
KRAS
MET
PD-L1
ALK
HER2
RET
ROS1

20. protein expression (IHC) may inhibit Immune response
High levels if PD1 or PDL1
protein expression (IHC) may inhibit
Immune response
Chen, et al. Clin Cancer Res 2012
Block PD1 or PDL1
Immune damage to tumour

21. Biomarkers for Immunotherapy?
PD-L1 Negative
PD-L1 Positive (predictive of response)
Less response More response
1% 5%
10% 50% cell positive ?
Intensity of staining?
Immune cell staining?
Several therapeutics
Several companion diagnostics…………
Gandhi L, et al. AACR Abstract CT105.

22. Sample Source and Collection Definition of Positivity†
PD-L1 as a predictive immune biomarker: assays, sample collection and analysis in NSCLC studies
Pembrolizumab
Merck
Prototype or clinical trial IHC assay (22C3 Ab)1,2
Surface expression of PD-L1 on tumour specimen1,2
Ph I: Fresh or archival tissue1,2
IHC Staining:
Strong vs weak expression1,2
PD-L1 expression required for NSCLC for enrollment1
Note that one arm of KEYNOTE 001 trial requires PD-L1- tumours1
Tumour PD-L1 expression:1,2
≥50% PD-L1+ cut-off: 32% (41/129)
1–49% PD-L1+ cut-off: 36% (46/129)
Nivolumab
Bristol-Myers Squibb
Dako automated IHC assay (28-8 Ab)3,4
Surface expression of PD-L1 on tumour cells3,4
Archival or fresh tissue3,4
IHC Staining:
Strong vs weak expression3,4
Patients not restricted by PD-L1 status in 2nd- & 3rd-line
Ph III 1st-line trial in PD-L1+5
Tumour PD-L1 expression:
5% PD-L1+ cut-off: 59% (10/17)3
5% PD-L1+ cut-off: 49% (33/68)4
MPDL3280A
Roche/Genentech
Central laboratory IHC assay6
Surface expression of PD-L1 on TILs or tumour cells6,7
Archival or fresh tissue6
IHC Staining Intensity
(0, 1, 2, 3):
IHC 3 (≥10% PD-L1+)6,7
IHC 2,3 (≥5% PD-L1+)6,7
IHC 1,2,3 (≥1% PD-L1+)6,7
IHC 0,1,2,3 (all patients with evaluable status)6,7
PD-L1 expression required for NSCLC for enrolment in Ph II trials6 x
TIL PD-L1 expression:6
IHC 3 (≥10% PD-L1+): 11% (6/53)
PD-L1 low (IHC 1, 0): 62% (33/53)
MEDI4736
AstraZeneca
Ventana automated IHC (BenchMark ULTRA using Ventana PD-L1 (SP263) clone)8,9
Surface expression of PD-L1 on tumour cells8,9
Unknown
IHC Staining Intensity:
Not presented to date8–10
Tumour PD-L1 expression (all doses):8
PD-L1+: 34% (20/58)
PD-L1-: 50% (29/58)
PD-L1 Assay
Sample Source and Collection
Definition of Positivity†
†Definition of PD-L1 positivity differs between assay methodologies.
1. Garon EB, et al. Presented at ESMO 2014 (abstr. LBA43); 2. Rizvi NA, et al. Presented at ASCO 2014 (abstr. 8007); 3. Gettinger S et al. Poster p38 presented at ASCO 2014 (abstr. 8024); 4. Brahmer JR et al. Poster 293 presented at ASCO 2014 (abstr. 8112^); 5. Accessed January 2015; 6 . Rizvi NA et al. Poster presented at ASCO 2014 (abstr. TPS8123); 7. Soria J-C, et al. ESMO 2014 (abstr. 1322P); 8. Brahmer JR, et al. Poster presented at ASCO 2014 (abstr. 8021^); 9. Segal NH, et al. Presented at ASCO 2014 (abstr. 3002^); 10. Segal NH, et al. ESMO 2014 (abstr. 1058PD).
Ab, antibody; IHC, immunohistochemistry

23. The Challenge: Delivery
Reliable
Accurate
Timely
Relevant
Cost

24. The Challenge: Delivery
Reliable – will the test work?
Pre-analytics
Formalin fixed-Paraffin embedded tissue (FFPE)
DNA for mutations
DNA quality – fragmentation
Single test – 1.5% complete failure
Multiple tests - ?
FISH <10%
RNA PCR >10% ?
IHC <5%

25. The Challenge: Delivery
Reliable – will the test work?
Accurate – is the test outcome correct?
False positive tests – contamination
False negative test
DNA quantity
DNA ‘purity’ – how much is from tumour
Enough for multiplex testing?
FISH: % ALK tests challenged
RNA PCR: ~20% failed in recent ETOP study (ALK)
IHC: antibody specificity, non-specific staining
Biomarker Heterogeneity

26. The Challenge: Delivery
Reliable – will the test work?
Accurate – is the test outcome correct?
Timely - How quickly do you really need the results?
More tests – more time
More complexity - more time
Communication Patient-Physician-Lab-Physician-Patient

27. The Challenge: Delivery
Reliable – will the test work?
Accurate – is the test outcome correct?
Timely - How quickly do you really need the results?
Relevant
Are the biomarker tests needed?
Has the lab performed the correct test?

28. The Challenge: Delivery
Reliable – will the test work?
Accurate – is the test outcome correct?
Timely - How quickly do you really need the results?
Relevant
Cost
Technology is not cheap
Manpower is not cheap
Companion diagnostics are not cheap
Pathology and Therapy: budgets & reimbursement

29. Challenges for the Pathologist in the era of Personalized Medicine
Morphological assessment
Range of Biomarkers
Molecular diversity
Testing complexity
Sample limitations
Service delivery