Next generation histopathological diagnosis for precision medicine in solid cancers

Next generation histopathological diagnosis for precision medicine in solid cancers from genomics to clinical application Aldo Scarpa ARC-NET Applied Research on Cancer Department of Pathology and Diagnostics University of Verona, Italy

Outline 1. Cancer molecular heterogeneity and clonal evolution 2. Next-generation molecular technologies 3. Next-generation pathological diagnostics

Emerging Molecular Taxonomy Molecular Heterogeneity Prevalence of Phenotypes of cancers

Emerging Molecular Taxonomy Molecular Heterogeneity Prevalence of Phenotypes of cancers Cowley MJ et al. J Hepatobiliary Pancreat Sci 2013 [Epub ahead of print] Copyright (2013) by permission of Wiley

Same morphology, different cancers Lung adenocarcinoma Reprinted by permission from Macmillan Publishers Ltd: Pao W & Hutchinson KE. Nat Med 2012;18(3):349-351 (2012)

Lung adenocarcinoma mutually exclusive molecular types Cancer Genome Atlas Research Network. Nature 2014;511(7511):543-550 (2014) Creative Commons license

The era of targeted therapy 1 KIT mutation 78% GIST 2 EGFR mutation 20% Lung adenocarcinoma 3 BRAF mutation 60% Melanoma 4 RAS mutations 40% Colorectal cancer Scarpa A. et al., unpublished data

Targeted therapy issues Targeted therapy based on single gene targeting has variable success, and development of resistance is the rule

Each cancer has multiple mutations Whole exome sequencing of 99 cases of pancreatic cancer Each case has an average of 60 mutations KRAS (93%), TP53 (42%), SMAD4 (20%) MLL3 (8%) PCDH15 (7%) TGFBR2 (6%) SF3B1 (5%) ARID1A (5%) ATM (5%) CDKN2A (4%) Reprinted by permission from Macmillan Publishers Ltd: Biankin AV et al. Nature 2012;491(7424):399-405 (2012)

Core signalling pathways are affected Global genomic analysis has revealed that several core signalling pathways are affected in each single cancer Pancreatic Cancer Pancreatic cancer: 14 cell signalling pathways altered in 60-100% of cases 100% Cowley MJ et al. J Hepatobiliary Pancreat Sci 2013 [Epub ahead of print] Copyright (2013) by permission of Wiley

Significantly mutated pathways in pancreatic adenocarcinomas MAPK pathway: (95%) KRAS TGFβ pathway: (40%) SMAD4 TGFBR2 ACVR2A TGFBR1 DNA damage repair: (20%) BBBB BRCA2 BRCA1 XXXX YYYY ZZZZ AAAA Nonsense, Indel Missense Homozygous deletion Germline mutation Scarpa A. et al., unpublished data

Significantly mutated pathways in lung adenocarcinomas RTK=90% MAPK=60% DNA repair=63% PI3K-mTOR=37% Reprinted by permission from Macmillan Publishers Ltd: Ding L et al. Nature 2008;455(7216):1069-1075 (2008)

Significantly mutated pathways in lung squamous cell carcinomas PI3K-mTOR=67% RTK=53% Cancer Genome Atlas Research Network. Nature 2013;489(7417):519-525 (2013) Creative Commons license

Significantly mutated pathways in colorectal carcinomas Cancer Genome Atlas Network. Nature 2012;487(7407):330-337 (2012) Creative Commons license

Cancer molecular heterogeneity and clonal evolution is an ongoing process Primary and metastasis clonal heterogeneity Reprinted by permission from Macmillan Publishers Ltd: Marusyk A et al. Nat Rev Cancer 2012;12(5):323-334 (2012)

Primary and metastasis clonal heterogeneity Genetic evolution of pancreatic cancer by comparative lesion sequencing: Progressor mutation Founder mutation Reprinted by permission from Macmillan Publishers Ltd: Yachida S et al. Nature. 2010;467(7319):1114-1117 (2010)

Primary and metastasis clonal heterogeneity Genetic evolution of pancreatic cancer by comparative lesion sequencing: Geographic mapping of primary carcinoma reveals metastatic clones Sub-clones metastasising to lung and liver Sub-clones metastasising to peritoneum Reprinted by permission from Macmillan Publishers Ltd: Yachida S et al. Nature. 2010;467(7319):1114-1117 (2010)

Primary and metastasis clonal heterogeneity Genetic evolution of pancreatic cancer by comparative lesion sequencing: FOUNDER PROGRESSOR Ongoing clonal evolution within primary carcinoma Reprinted by permission from Macmillan Publishers Ltd: Yachida S et al. Nature. 2010;467(7319):1114-1117 (2010)

Implications for therapy and companion diagnostics Primary tumours and metastasis are heterogeneous Treat by targeting founder mutations Core signalling pathways are affected Target circuitry rather than a single molecule Identification and quantification of cancer heterogeneity is crucial Molecular diagnostics is the tool

Clonal heterogeneity and therapy Molecular Analysis Therapy Drug Yes Anti red Yes Anti gray First line Normal Red gene mutated Gray gene mutated Anti red Second line Scarpa A. et al., unpublished data

Outline 1. Cancer molecular heterogeneity and clonal evolution 2. Next-generation molecular technologies 3. Next-generation pathological diagnostics

Sanger sequencing single gene fragment for one patient Primer design PCR Product Clean up 350 bp per PCR (1 gene fragment) Sequence reaction 4 hours - 1 hour Hands On Scarpa A. et al., unpublished data

Next generation sequencing multiple genes amplified simultaneously Primer design Multiplex PCR Primers Removal Up to 1Mb per multiplex PCR (~500 genes) Adaptors ligation Adapter clean-up 6 hours - 1 hour Hands On Scarpa A. et al., unpublished data

Next generation sequencing Starting DNA Emulsion PCR amplification - one DNA molecule per bead Clonal amplification of thousands copies Starting DNA Solid phase bridge PCR - one DNA molecule per cluster Clonal amplification of thousands copies Scarpa A. et al., unpublished data

Next Generation Sequencing data are quantitative Variant frequency 100 90 80 70 60 50 40 30 20 10 0 Mutation Allelic silent SNP Solid Allelic pseudopapillary silent tumour SNP Mutation 80% neoplastic cells ß catenin mutation Each cell has two alleles CTNNB1 FGFR3 APC PIK3CA SPT27 SPT30 ß catenin mutation is heterozygous (one allele) We should find 40% of alleles mutated Scarpa A. et al., unpublished data

Next Generation Sequencing data are quantitative and reveal clonal composition 20% 40% 40% Normal CTNNB1 CTNNB1 + PIK3CA Scarpa A. et al., unpublished data

Next Generation Sequencing: simultaneous analysis of multiple genes Primer design Multiplex PCR Primers Removal Up to 1Mb per multiplex PCR (~500 genes) Adaptors ligation Different Barcodes = multiple patients Scarpa A. et al., unpublished data

Next Generation Sequencing: simultaneous analysis of multiple patients BLOOD FFPE Primer design Multiplex PCR Primers Removal Genomic DNA A B C D Adaptors ligation B C D A Scarpa A. et al., unpublished data Different Barcodes = multiple patients

Next Generation Sequencing: simultaneous analysis of multiple genes 190 PCR in one tube 46 genes, 930 mutations Colorectal cancer KRAS BRAF EGFR TP53 PIK3CA CSF1R JAK2 NRAS PTPN11 ERBB2 SRC FGFR3 NPM1 CDKN2A RET HNF1A SMAD4 GNAS PDGFRA MPL ABL1 PTEN FLT3 STK11 SMARCB1 KIT MET NOTCH1 FGFR2 RB1 JAK3 VHL KDR SMO 20µl HRAS AKT1 ALK MLH1 FBXW7 ERBB4 ATM CDH1 IDH1 CTNNB1 APC FGFR1 Scarpa A. et al., unpublished data

Next Generation Sequencing: simultaneous analysis of multiple genes in multiple patients Variant type (frequency) Sample CRC1 CRC2 CRC3 CRC4 CRC5 CRC6 CRC7 CRC8 CRC9 Neoplastic cells 90% 80% 85% 60% 40% 85% 55% 70% 50% Subtype MSS MSS MSS MSS MSS MSS MSS MSS MSS 61Q/H 12G/D KRAS (23%) (23%) 271E/K 245G/R R209del 278P/S 175R/H TP53 (20%) (73%) (46%) (35%) (22%) 482C/R KDR (50%) SMAD4 361R/H ATM ERBB2 CTNNB1 FBXW7 FGFR3 MET CDH1 410V/A (50%) 777V/L (43%) 45S/F (43%) 452E/Q (56%) 375N/S (59%) 72D/N (13%) (24%) 604P/S (64%) 776G/V (39%) 384F/L (61%) 375N/S (45%) Scarpa A. et al., unpublished data

Next Generation Sequencing data are quantitative Variant type (frequency) Sample CRC1 CRC2 CRC3 CRC4 CRC5 CRC6 CRC7 CRC8 CRC9 Neoplastic cells 90% 80% 85% 60% 10% 85% 55% 70% 50% Subtype MSS MSS MSS MSS MSS MSS MSS MSS MSS 61Q/H 12G/D KRAS (23%) (23%) 271E/K 245G/R R209del 278P/S 175R/H TP53 (20%) (73%) (46%) (35%) (22%) 482C/R KDR (50%) SMAD4 361R/H ATM ERBB2 CTNNB1 FBXW7 FGFR3 MET CDH1 410V/A (50%) 777V/L (43%) 45S/F (43%) 452E/Q (56%) 375N/S (59%) 72D/N (13%) (24%) 604P/S (64%) 776G/V (39%) 384F/L (61%) 375N/S (45%) Scarpa A. et al., unpublished data

Next Generation Sequencing data are quantitative A B C TP53 exon 8 (wild type) TP53 exon 8 (Arg282Trp; 20%) TP53 exon 8 (Val274Phe; 73%) Mafficini A et al. PLoS One 2014;9(8):e104979 2014 Creative Commons License

Outline 1. Cancer molecular heterogeneity and clonal evolution 2. Next-generation molecular technologies 3. Next-generation pathological diagnostics

Liver carcinosarcoma TP53 (F109C) TP53 (F109C) TP53 (F109C) TP53 (F109C) TP53 (F109C) Luchini C et al. J Clin Oncol 2014;32(17):e63-e66. Reprinted with permission (2014) American Society of Clinical Oncology. All rights reserved

Liver carcinosarcoma TP53 (F109C) Carcinosarcoma is monoclonal Report of clonal heterogeneity Targets for therapy TP53 (F109C) TP53 (F109C) TP53 (F109C) TP53 (F109C) Luchini C et al. J Clin Oncol 2014;32(17):e63-e66. Reprinted with permission (2014) American Society of Clinical Oncology. All rights reserved

Quantification of mutated clones Materials 100 cancers Method Sensitivity (MT/WT) Sanger sequencing 20% ARMS PCR 1% Result Sanger Result ARMS Interpretation Positive Positive High abundance Negative Positive Low abundance Negative Negative Wild Type Zhou Q et al. J Clin Oncol 2011;29(24):3316-3321

Quantification of mutated clones Progression-free survival (PFS) Overall survival (OS) High Low WT High Low WT Zhou Q et al. J Clin Oncol 2011;29(24):3316-3321. Reprinted with permission (2011) American Society of Clinical Oncology. All rights reserved

Quantification of mutated clones Materials Methods Zhou Q et al. J Clin Oncol 2011;29(24):3316-3321

Quantification of mutated clones Normal EGFR positive EGFR negative Zhou Q et al. J Clin Oncol 2011;29(24):3316-3321 Sequencing ARMS-PCR Therapy 60% + + YES 2% - + NO 2% - + YES Scarpa A. et al., unpublished data

Molecular Heterogeneity Lung adenocarcinoma Coexistent EGFR and TP53 mutations Scarpa A. et al., unpublished data

Molecular Heterogeneity Lung adenocarcinoma Response to gefitinib of EGFR mutant cancers Scarpa A. et al., unpublished data

Molecular Heterogeneity Lung adenocarcinoma Response to gefitinib of EGFR mutant cancers * * PMA = Proportion of mutated alleles Scarpa A. et al., unpublished data

Theranostics tomorrow Analysis of genomic data from 12 tumour types Breast, colon, lung, bladder, ovary, brain.. Ciriello G et al. Nat Genet 2013;45:1127-1133 Hoadley KA et al. Cell 2014; 158: 929 944

Map of actionable alterations across 12 tumour types RTK MTOR AURKA PARPi Ciriello G et al. Nat Genet 2013;45(10):1127-1133 (2013) Creative Commons license

Map of actionable alterations across 12 tumour types 100 Samples (%) 50 0 0 1 2 3 4 Number of pathways altered Ciriello G et al. Nat Genet 2013;45(10):1127-1133 (2013) Creative Commons license

Challenge in molecular diagnostics 60-80% of cancer patients: - Advanced stages - Poor performance status Cytology Limited quantity of cancer sample NGS & cytology Scarpa A. et al., unpublished data

Challenge in molecular diagnostics Molecular typing of lung adenocarcinoma on cytological samples using a multigene next generation sequencing panel 36/38 (95%) adequate libraries EGFR KRAS TP53 PIK3CA BRAF STK11 6/36 (16%) 10/36 (28%) 7/36 (18%) 3/36 (8%) 2/36 (5%) 1/36 (3%) 24/36 (67%) at least one 9/36 (25%) multiple Scarpa A et al. PLoS One 2013;8(11):e80478 2013 Creative Commons License

Challenge in molecular diagnostics Scarpa A et al. PLoS One 2013;8(11):e80478 2013 Creative Commons License

The future in molecular diagnostics Highly selective testing Stepwise, single-gene testing algorithms tailored to specific cancers Multiplex testing Simultaneous multigene and multiplexed approach Unbiased testing Global and unbiased whole-genome approach EGFR mutation DNA RNA DNA RNA KRAS mutation Multiplexed mutation testing Multiplexed RNA profiling and fusion transcript detection Whole-exome (or genome) sequencing Whole-transcriptome sequencing (including paired ends) ALK fusion Genomic copy number profiling Present Future Taylor BS and Ladanyi M. J Pathol 2011;223(2):318-326

The future in molecular diagnostics Whole Genome Sequencing Single nucleotide variations Copy number variations Epigenetic changes Structural alterations: Courtesy of Biankin A.

The future in molecular diagnostics Next Generation Histopathological diagnosis Patient XY Scarpa A. et al., unpublished data

The future in molecular diagnostics Liquid biopsy: Circulating Tumour Cells and DNA Reprinted by permission from Macmillan Publishers Ltd: Crowley E et al. Nat Rev Clin Oncol 2013;10(8):472-484 (2013)

The future in molecular diagnostics Liquid biopsy: Digital PCR Partition PCR Quantification Scarpa A. et al., unpublished data

The future in molecular diagnostics Liquid biopsy: Digital PCR KRAS G12R 1/10.000 MUT MUT+WT Mut Mut+wt background wt WT wt Scarpa A. et al., unpublished data

Take home messages Cancers of the same histopathological category differ for their molecular anomalies; i.e. even the most frequent cancer types are a collection of rare diseases from the molecular standpoint Each cancer has multiple gene alterations that affect the function of several signalling pathways, that may be peculiar to a cancer type or similar among different tumour types Molecular heterogeneity is an ongoing process that is responsible for the clonal evolution of cancers in both primary sites and metastasis

Take home messages cont. Next-generation sequencing is a cost effective technology for the identification and quantification of molecular heterogeneity; it allows sequencing of multiple genes simultaneously from routine materials, such as small biopsies and cytological samples Further technological and bio-informatic improvements are needed for the introduction into routine use of liquid biopsies (circulating DNA) to evaluate tumour load and monitor response to therapy, and whole genome/transcriptome sequencing to assess the molecular landscape of individual cancers.

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