Manuel Salto-Tellez, MD (LMS), FRCPath, FRCPI

Professor and Chair of Molecular Pathology

Clinical Director, Molecular Diagnostics

Deputy Director, Centre for Cancer Research and Cell Biology

WORKFLOWS IN TISSUE AND CELLULAR PATHOLOGY

SAMPLE

C L I N I C A L D I A G N O S I S

SAMPLE PREPARATION AUTOMATED H+E IMAGING SCANNING

AUTOMATED IHCAUTOM. FISH

AUT NA EXTR

HIGH THROUGHPUT TESTINGLOW THROUGHPUT TESTING

MORPHOLOGICAL OR DIGITAL EVALUATION

MANUAL OR DIGITAL SCORING

MANUAL OR DIGITAL ANNOTATION

INTEGRATED PATHOLOGY WORKFLOWS:MORPHOMOLECULAR

Building a 'Repository of Science': the importance of integrating Biobanks within molecular Pathology programmes. EJC 2016, ACCEPTED

Northern Ireland Molecular Pathology Laboratory (NI-MPL)End to End Diagnostic and Research Service

Cutting Across Technologies and Infrastructures

Molecular biomarkers used in clinical standard-of-care decision making in colorectal cancer

Biomarker Purpose

DiagnosticAPC mutation detection

MMR protein expression (MSH2, MLH1, MSH6,

PMS2)

MSI (microsatellite instability) analysis

MMR mutation detection (MSH2, MLH1, MSH6,

PMS2)

BRAF mutation detection

MYH mutation detection

LKB1, SMAD4, BMPR1A, PTEN mutation detection

Diagnosis of FAP

Diagnosis of HNPCC

Diagnosis of MYH-associated polyposis

Diagnosis of harmartomatous polyp syndromes

Prognostic/PredictiveKRAS mutation analysis

BRAF mutation analysis

Thymidylate synthase protein expression

MSI

Gene expression signature

Molecular stratification for treatment with epidermal

growth factor receptor (EGFR) inhibitors

Identification of response to 5-FU

Identification of response to 5-FU

Prognostication

Van Schaeybroeck S, et al (Salto-Tellez M). Abeloff's Clinical Oncology. 5th edition; in press.

Molecular testing in breast cancer

American Society of Clinical Oncology/College

of American Pathologists Guideline

Recommendations for Human Epidermal Growth

Factor Receptor 2 Testing in Breast

Cancer - Antonio C. Wolff AC et al.

JCO Jan 1 2007: 118-145.

Boyle DP, et al. (Salto-Tellez M). Biochim Biophys Acta 2013;1835:230–42.

AKT1 mut in 2% of patients AKT1 inhibitor AZD5363

Activating ERBB2 mut in 3% of patients plasmaMATCH

BRCA1 and BRCA2 mutPARP inhibitors.

ESR1 mutOral SERDs.

Molecular classification of lung adenocarcinoma

Pao W & Hutchinson KE.Nat Med 2012;18:349–51.

Lindeman NI et al. J Mol Diagn. 2013 Jul;15(4):415-53.

Molecular testing in sarcomas &lymphomas

Molecular testing in malignant melanomas

GLIOMA TESTING

DNA QPCR

1P

19Q

PTEN

EGFR Amplification

DNA Sanger Sequencing

IDH 1 & 2

DNA Methylation

RNA QPCR

BRAF Translocations

EGFR v.3

NI-MPL Validation Strategy

Selected target therapeutics in clinical oncology practice

Salto-Tellez M. In: Tan & Lynch’s Principles of Molecular Diagnostics and Personalized Cancer Therapy, Lippincott Williams & Wilkins, 2012.

16

Revolution in OncologyNew Drugs for Targeted Therapy

Discovery Medicine 2014

32

M O L E C U L A R D I A G N O S T I C C Y T O P A T H O L O G Y

(i) that almost any molecular test that could be applied to FFPEmaterial could also be applied to any cytology sample, providedthat due diligence in the pre-analytical aspects of the test wasexercised and that the test was specifically optimised for cytologysamples as well; and

(ii) that the adoption of a genotypic dimension to phenotypicdiagnostic routine would change the way that many of us operated, becoming both a challenge, but also an opportunity for all practising pathologists.

Salto-Tellez M, Koay ES.Cytopathology. 2004 Oct;15(5):252-5

M O L E C U L A R D I A G N O S T I C C Y T O P A T H O L O G Y

Salto-Tellez M, Koay ES.Cytopathology. 2004 Oct;15(5):252-5

2004

Salto-Tellez M.Cytopathology. 2015 Oct; in press

2015

DIGITAL PATHOLOGY IN INTEGRATEDMOLECULAR PATHOLOGY WORKFLOWS

PRIMARY DIGITAL MORPHO-EVALUATION

Healthcare EconomicsIs the digital approach cost-effective?

Can a digital image substitute the microscope for routine

morphology-based diagnostics?

Routine H&E

5 years219,000 biopsies/year

$12.4M savings in productivity/consolidation

1 year40,000 biopsies/year

£286,380 savings in productivity/consolidation

DIGITAL IHC / ISH SCORING

Molecular Diagnostics

Tissue Hybridization-basedTesting

Extracted Nucleic Acid-basedTesting

Digital pathology is not FDA approved for primary diagnosis.However, several manufacturers have received 510(k) clearances or specific uses.

https://digitalpathologyassociation.org/healthcare-faqs

WHY IS DIGITAL PATHOLOGY NOT GENERALLY ADOPTED AS THE TOOL OF CHOICE FOR

TISSUE-BASED PERSONALISED MEDICINE TESTING?

1.DOES DIGITAL PATHOLOGY NEED TO BE AS GOOD

AS THE HUMAN EYE?OR DOES IT NEED TO BE BETTER?

2.IS IHC THE BEST WAY OF QUANTITATING EXPRESSION IN

TISSUE HYBRIDIZATION STUDIES?

WHY IS DIGITAL PATHOLOGY NOT GENERALLY ADOPTED AS THE TOOL OF CHOICE FOR

TISSUE-BASED PERSONALISED MEDICINE TESTING?

1.DOES DIGITAL PATHOLOGY NEED TO BE AS GOOD

AS THE HUMAN EYE?OR DOES IT NEED TO BE BETTER?

2.IS IHC THE BEST WAY OF QUANTITATING EXPRESSION IN

TISSUE HYBRIDIZATION STUDIES?

Ong CW, (Salto-Tellez M). Histopathology. 2010 Mar;56(4):523-9.

?

Ong CW, (Salto-Tellez M). Histopathology. 2010 Mar;56(4):523-9.

Ong CW, (Salto-Tellez M). Histopathology. 2010 Mar;56(4):523-9.

https://qupath.github.ioBankhead P et al (Salto-Tellez & Hamilton), 2016

Bankhead P et al (Salto-Tellez & Hamilton), 2016 Submitted

WHY IS DIGITAL PATHOLOGY NOT GENERALLY ADOPTED AS THE TOOL OF CHOICE FOR

TISSUE-BASED PERSONALISED MEDICINE TESTING?

1.DOES DIGITAL PATHOLOGY NEED TO BE AS GOOD

AS THE HUMAN EYE?OR DOES IT NEED TO BE BETTER?

2.IS IHC THE BEST WAY OF QUANTITATING EXPRESSION IN

TISSUE HYBRIDIZATION STUDIES?

Maxwell P, Salto-Tellez M. Cancer Cytopathol. 2016 Aug;124(8):540-5

Bingham V, (Salto-Tellez M) McQuaid S. Human Pathology. 2015, In Press

Bingham V, (Salto-Tellez M) McQuaid S. Human Pathology. 2015, In Press

Bingham V, (Salto-Tellez M) McQuaid S. Human Pathology. 2015, In Press

Gland-to-gland &Intragland heterogeneity

QUANTITATION - ABSOLUTE AND RELATIVE(HOUSE-KEEPING GENE)

DIGITAL ANNOTATION FOR MACRODISSECTION

Transforming how we practice pathology….

Molecular Diagnostics

Tissue Hybridization-basedTesting

Extracted Nucleic Acid-basedTesting

EGFRKRASBRAFNRASCMETMMR

Oncotype DxMammaprint

Foundation OneClinical Sequencing

Molecular testing, FFPE and H&E Review

Sample

FFPE

Tumour Markup

TumourSufficiency

Macrodissection

DNA Extraction

DNA Quantification

Platform

Molecular Assay Output

Sanger

QPCR

NGS

Pre-Analytical

Analytical

Op

era

tor

Var

iab

ility

NGS Failure Rates

Quantity of DNAQuality of DNA

SMP2

ALL CANCERSWGS

FAILURE RATE = 60%

LUNG CANCERSNGS TARGETED PANELFAILURE RATE = 30%

Ion Torrent Illumina iScan Affymetrix Illumina MiSeq Illumina NextSeq

MOLECULAR PATHOLOGY PROGRAMME – GENOMICS

Hamilton P (Salto-Tellez M). Oncotarget 2015 (advanced publication)

% Tumour cells is criticalTotal number of cells is critical

Tumour cells ? Tumour cells ?

Pre-analytical Pipeline - Cellularity

0 %

20 %

40 %

60 %

80 %

100 %

Very low <100 Very low-low 100-700

Low ~1,000 Low-intermediate1,500-4,000

Intermediate4,000-10,000

High >10,000 Very high >50,000

QC fail result at varying sample cellularity

QC Pass

QC Fail

n=9 n=37 n=98 n=73 n=59 n=115 n=26

Sample size: 417

0 %

20 %

40 %

60 %

80 %

100 %

Very low <100 Very low-low 100-700

Low ~1,000 Low-intermediate1,500-4,000

Intermediate4,000-10,000

High >10,000 Very high >50,000

Number of genes failed at varying sample cellularity (samples run on NGS)

All genes passed

1-7 genes failed

8-14 genes failed

15-21 genes failed

22-all genes failed

n=2 n=16 n=50 n=48 n=46 n=100 n=15

Sample size: 287

32

Graph 21

Graph 22

From: Prof. David Gonzalez de Castro

25 High resolution images breast cancer

Circulate to 3 pathologists

% tumour estimates

Variation in breast % tumour cell estimates amongst pathologists

PathXL – QUB – BHSCT internal assessment, 2015

Breast Cancer % Tumour Estimates

PathXL – QUB – BHSCT internal assessment, 2015PathXL – QUB – BHSCT internal assessment, 2015

Variation in % Tumor Cell

Evaluation in Lung Cancer

Smits AJJ et al. Modern Pathology 27, 168-174 (February 2014)

Variation in % Tumor Cell

Evaluation in Colorectal Cancer

Viray, et al. Arch Pathol Lab Med. 2013;137:1545–154

Oncotarget 2015, minor revisionsOncotarget 2015, advanced publication

Figure 5. Scatterplot of tumor percentage for 136 lung cancer cases derived from two experienced pathologists, showing gross variation between estimates.

Oncotarget 2015, minor revisionsOncotarget 2015, advanced publication

Oncotarget 2015, minor revisionsOncotarget 2015, advanced publication

FIG 11. (A) COMPARISON OF AUTOMATED TUMOUR NUCLEI COUNTS AND PERCENTAGE

TUMOUR VALUES (Y-AXIS), AGAINST BENCHMARK DATA ON TUMOR % SHOWING STRONG

CORRELATION, MAPPING CLOSELY TO ACTUAL TUMOR CELL PERCENTAGE VALUES. (B) THE

SAME SCATTERPLOT AS (A) BUT SUPERIMPOSING THE RANGE OF PATHOLOGY ESTIMATES (RED

CIRCLES) AGAINST THE BENCHMARK DATA.

Oncotarget 2015, minor revisionsOncotarget 2015, advanced publication

Oncotarget 2015, minor revisionsOncotarget 2015, advanced publication

Transformational Digital Pathology

Hamilton P (Salto-Tellez M) Oncotarget 2015, advanced publication

DIGITAL PATHOLOGY: THE FUTURE

Digital Pathology: The Tipping Point

Digital Pathology: The Tipping Point

Digital Pathology: The Tipping Point

Information Integration

Cancer Immunology & Immunotherapy

PATHOLOGICAL INFORMATION

MOLECULAR DIAGNOSTIC INFORMATION(SINGLE BIOMARKERS)

MOLECULAR DIAGNOSTIC INFORMATION(MULTIPLE BIOMARKERS)

RESEARCH INFORMATION(SINGLE BIOMARKERS)

RESEARCH INFORMATION(MULTIPLE BIOMARKERS)

CLINICAL INFORMATION

DIGITAL PATHOLOGY THE FUTURE - INFORMATION MANAGEMENT

McArt, Salto-Tellez & Hamilton. Mol Oncology, 2015;9(6):1234-40

McArt, Salto-Tellez & Hamilton. Mol Oncology, 2015;9(6):1234-40

Cancer immunology is a branch of immunology that studies

interactions between the immune system and cancer cells.

It is a growing field of research (and increasingly diagnostics) that aims to discover innovative cancer immunotherapiesand immune companion diagnostics to treat and retard

progression of the disease.

DIGITAL PATHOLOGY: THE FUTURECANCER IMMUNOLOGY

$3.9M to develop Digital Pathology in the UK

Fridman (Galon). Nat Rev Cancer 2012 Galon. J Pathol 2014; 232: 199–209

http://qupath.github.io

$3.9M to develop Digital Pathology in the UK

Time

Pe

rce

nt

Surv

ival

CHEMOTHERAPY

GENOMIC TARGETED THERAPY

IMMUNE CHECKPOINT THERAPY

COMBINATION OF IMMUNE CHECKPOINT THERAPYAND GENOMIC TARGETED THERAPY

Sharma & Allison. Cell 161, April 9, 2015

Progression-free Survival in the Intention-to-Treat Population.

Reck M et al. N Engl J Med 2016. DOI: 10.1056/NEJMoa1606774

KEYNOTE – 024 Phase 3 trial305 patients

untreated advanced NSCLC with PD-L1 expression on at least 50% of tumor cells

EGFR wt & no ALK translocation

http://www.captodayonline.com/pd-l1-targeted-therapies-await-standardized-ihc/

Single IHC analysis may not be able to encapsulate all thepredictive value necessary: not ideal predictive value

C O N C L U S I O N

An Integrated Morpho-molecular Pathology workflow make the most of our talent, capacities and technical know-how

Digital Pathology can improve the workflow by: a) digital routine H&E analysis;

b) automated scoring of tissue hybridization-type tests; andc) annotation of H&E ahead of molecular testing

The paradigms that may facilitate this approach in the broader sense are the analysis of cancer adaptive immunity and immune checkpoints

Tom Simms Memorial Fund