Transcript Challenges for the Pathologist in the era of Personalized

Challenges for the Pathologist
in the era of
Personalized Medicine
Prof Keith M Kerr
Department of Pathology
Aberdeen University Medical School
Aberdeen, UK
Disclosures
Acted as Speaker and/or Consultant for:
AstraZeneca, Roche, Boehringer Ingeheim,
Bristol-Myers-Squibb, Clovis, Eli Lilly, MSD,
Novartis, Pfizer
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
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
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%
It is worthwhile finding an
Actionable genetic alteration in Lung cancer
Driver detected – Targeted Rx
Kris MG et al. JAMA 2014; 311, 1998-2006
Kerr KM. J Clin Pathol 2013;66:832–838
ROS1 fusion genes
1.1-2.6% Adenocarcinomas
Fusion correlates with protein (IHC)
Sensitive to crizotinib
BRAF mutations
2.5-4% Adenocarcinomas
Incl V600E other V600
Smoking vs non-smoking
Vemurafenib
KRAS mutations
NTRK1 fusion
MPRIP-NTRK1 and CD74-NTRK1
3.3% of ‘onco-negative’ adenocarcinomas
Trk inhibitors exist
HER2 mutations
1-2% Adenocarcinomas
Mutually exclusive of EGFR, KRAS
Traztuzamab?
RET fusion genes
Vaishnavi A et al. Nat Med 2013; 19, 1469-72
~1% Adenocarcinomas
Vandetanib & others
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
25-35% Adenocarcinomas
MEK inhibition?
MET upregulation
4% amplification, ~50% overexpression
Biomarker issues
Failed trials
FGFR1 amplification
Biomarker issues
Definition of amplification
20% may be overestimate?
Ponatinib – FGFR1 inhibitor
Clin Can Res 2012;18, 2443-51
Wynes MW et al. Clin Cancer Res 2014;20:3299-3309
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
EGFR
Inhibitors exist
So far no success
TKI vs MoAb
Mutations – rarity (vIII – 8%)
Targeting the receptor
IGFR1
Figitumumab
Some effect in squamous
Toxicity
Panel of 54 genes with potentially druggable alterations
Govindan et al. Cell. 2012 Sep 14;150:1121-34
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
Methods of biomarker analysis
Change in
DNA sequence
Change
in Gene copy #
mRNA transcript
Transcription
Methods of biomarker analysis
Change in
DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Microarray
FISH/CISH
mRNA transcript
Transcription
RT-PCR
Methods of biomarker analysis
Change in
DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Microarray
FISH/CISH
mRNA transcript
Transcription
RT-PCR
Next-generation
sequencing
Methods of biomarker analysis
Change in
DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Microarray
FISH/CISH
mRNA transcript
RT-PCR
Transcription
Protein
Translation
Next-generation
sequencing
Methods of biomarker analysis
Change in
DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Microarray
FISH/CISH
mRNA transcript
Next-generation
sequencing
RT-PCR
Transcription
Protein
Translation
Immunohistochemistry
Methods of biomarker analysis
Change in
DNA sequence
Change
in Gene copy #
DNA mutational
analysis
Microarray
FISH/CISH
mRNA transcript
Next-generation
sequencing
RT-PCR
Transcription
Protein
Immunohistochemistry
Translation
Biological Activity
Oncogenesis
Drug target
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’
Methods of ALK biomarker analysis
Break-apart FISH
Change in
DNA sequence
Change
in Gene copy #
NGS
PCR for
ALK fusion gene
transcripts
mRNA transcript
Transcription
IHC for ALK protein
Protein
Translation
Biological Activity
Oncogenesis
Drug target
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
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
Biomarkers for Immunotherapy?
PD-L1 Negative
PD-L1 Positive (predictive of response)
Less response
1%
More response
5%
10%
?
50% cell positive
Intensity of staining?
Immune cell staining?
Several therapeutics
Several companion diagnostics…………
Gandhi L, et al. AACR 2014. Abstract CT105.
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
•
Dako automated IHC assay (28-8
Ab)3,4
•
Surface expression of
PD-L1 on tumour specimen1,2
•
Surface expression of
PD-L1 on tumour cells3,4
•
Ph I: Fresh or archival tissue1,2
•
Archival or fresh tissue3,4
Definition of Positivity†
Sample Source and
Collection
PD-L1
Assay
•
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)
MPDL3280A
Roche/Genentech
Nivolumab
Bristol-Myers Squibb
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
•
Central laboratory IHC assay6
•
•
MEDI4736
AstraZeneca
•
Ventana automated IHC
(BenchMark ULTRA using
Ventana PD-L1 (SP263) clone)8,9
Surface expression of
PD-L1 on TILs or tumour cells6,7
•
Surface expression of PD-L1 on
tumour cells8,9
Archival or fresh tissue6
•
Unknown
IHC Staining Intensity
IHC Staining Intensity:
(0, 1, 2, 3):
• Not presented to date8–10
• 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)
Tumour PD-L1 expression (all
doses):8
• PD-L1+: 34% (20/58)
• PD-L1-: 50% (29/58)
†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);
Ab, antibody;
4. Brahmer JR et al. Poster 293 presented at ASCO 2014 (abstr. 8112^); 5. http://www.clinicaltrials.gov/ct2/show/NCT02041533 Accessed January 2015;
IHC, immunohistochemistry6 . 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).
The Challenge: Delivery
•
•
•
•
•
Reliable
Accurate
Timely
Relevant
Cost
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%
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: 10-20% ALK tests challenged
RNA PCR: ~20% failed in recent ETOP study (ALK)
IHC: antibody specificity, non-specific staining
Biomarker Heterogeneity
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
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?
The Challenge: Delivery
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•
•
•
•
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
Challenges for the Pathologist
in the era of
Personalized Medicine
•
•
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Morphological assessment
Range of Biomarkers
Molecular diversity
Testing complexity
Sample limitations
Service delivery