Omics of breast cancer ?

John Martens

Waarom analyses in het bloed in de toekomst onderdeel van de moleculaire diagnostiek gaat worden

John Martens

1

Disclosure of speaker’s interest

Relations that could be relevant

for the meeting

Company names

• Sponsorship or research funds

• Payment or other (financial)

renumeration

• Shareholder

• Other relation, (collaboration)

Veridex, Sanofi, Philips research, Boreal,

Therawis, Thermofisher

None

None

Cytotrack & Olink

Disclosure(s)

Outline of the presentation

• Summary of genomics research in mBC

• Why liquid biopsies?

• Type of methods for detecting nucleic acids in blood

• dPCR, targetted NGS, NPT, FASTseq)

• Application in breast cancer

• Conclusions and outlook

Primary versus metastatic breast cancer –driver genes

Primary (TCGA & BASIS)

Metastatic (CPCT)

• MBC is largely driving by the same drivers as primary BC

• TP53, ESR1, PTEN, NF1, KMT2C and AKT1 are enriched in ER+/HER2-

• And acquired drivers are mutually exclusive CPCT02

Robinson DR, Nat Genet. 2013 (12): 1446–1451.

Razavi et al., Cancer Cell (2018) 34: 427–438

Angus et al Nat Genet. 2019

Mismatch

BRCA1/2Age APOBEC (25-30%)

Mutational signatures (primary versus metastatic BC)

Primary disease cases (BASIS cohort; n=560)

Metastatic disease cases (CPCT02-BC cohort; n=492)Nu

mb

er

of m

uta

tio

ns

Nu

mb

er

of m

uta

tio

ns

CPCT02

BASISAPOBEC

Angus et al, Nat. Genet. 2019

What is different in mBC

• Metastases disseminate late from primary breast tumors (most drivers are kept)

• Double the amount SNVs, indels and SVs in mBC

• A dozen of gene enriched in mBC; One/two additional driver genes per cancer

• APOBEC cancers are (more therapy resistant) are more prominent in mBC

• Mutations in ESR1/MAPK/Myc are mutually exclusively enriched due to endocrine treatment

• Chemotherapy can damage the genome (5-FU and SBS17; platinum and SBSI)

• Subclones are common and cause resistance and form (lymph node) metastasis

Center for Personalised Treatment in NL

• All participating patients and their families

• Participating hospitals, physicians and research staff

Lindsay Angus, Saskia M. Wilting, Job van Riet, Marcel Smid, Tessa G. Steenbruggen,

Vivianne C.G. Tjan- Heijnen, Mariette Labots, Johanna M.G.H. van Riel, Haiko J.

Bloemendal, Neeltje Steeghs, Harmen J.G. van de Werken, Martijn P. Lolkema, Emile E.

Voest, Agnes Jager, Edwin Cuppen, Stefan Sleijfer, John W.M. Martens

Liquid biopsies: Why?Most cancer deaths are caused by metastases

Primary tumor and metastases are often different (acquired changes):

in genetic make-up (just summarized)

in receptor status (which thus has clinical implications)

Thus, predictive markers be best analysed on metastatic tissue

However, metastatic tissue is often not accessible

In our view CTCs and ctDNA are: A good options to repeatedly study metastatic tissue from patients;

A good method to monitor changes during treatment and disease progression

Sensitive tumor Resistant tumor subclone(s)

wtESR1

mtESR1

Inhibition of Aromatase

CTC detection

Circulating tumor cells (CTCs)

From peripheral blood

CellSearch System

Median 1-9 cells / 7,5 mL

CD45- CK+ DAPI+

Reading copy

number changes

Reading epigegenetics

MeD-seq

CIRCULATING TUMOR DNA

Oncomine

BC/CRC

NEB-next

mCRPC

Exome-Lung

Reading multiple

SNVs (NGS)Reading single SNVs

(ddPCR)

Fast-Seqs;

Plasma-seq (NPT assays)

CTCs vs cell-free DNA (cf-DNA)

CTCs cf-DNA

Intact, living tumor cells cell-free

DNA, RNA and protein DNA only (histones?)

Present in 65% of MBC Present in 82% of MBC

More complex processing

(EpCAM-based enrichment)

Relatively easy processing

(plasma isolation)

Both: Present in extreme low frequency

Calls fo sensitive and specific assays

Distribute cfDNA into multiple

reaction wells20,000 wells * chip

• Single molecules are amplified by PCR

• in separate reactions

• one or no copies of the sequence of interest

• Wild type or mutant molecules are counted

Digital PCR

EDTA tubes

LIMITATIONS

Low Volume of cfDNA (<20 uL)

Low Concentration of cfDNA (<1 ng/uL)

LIMITATION

Only 200 uL of plasma available

QIAamp Kit cfDNA

Workflow

Workflow

Without With

cfDNA input: 7.8 uL (maximum) cfDNA input: 2 uL (maximum)

Conclusions and DiscussionESR1 mutations more frequently observed in cf-DNA than in CTCs

Much higher variant allele frequencies in cf-DNA

Problem of leukocyte background in CellSearch-enriched CTCs

ESR1 mutations rarely present in patients starting first-line endocrine therapy, but enriched after endocrine therapies

Despite previous literature, not only after AI exposure

ESR1 mutations are not mutually exclusive

Sieuwerts, Vitale et al 2017; Schiavon Sci Transl Med. 2015

Uni-molecular barcode sequencing

PulmonaryMedicineCase: cfDNAmutationsat baseline andat progressivedisease

Mutation detection methods

Single molecule

evaluation

Unique Molecule Identifiers

Agena

Detection 0.1% variant:

20 ng input

~ 6000 haploïd genomes

~ 6000 templates

25000x coverage

6000 unique molecules

0,1% = 6 variant molecules

Relationship DNA-input, limit of detection (LOD), and sequence coverage

Overview NGS platforms & panels

IonTorrent (Thermo Scientific):

Ampliseq-panels (Customized):

• 45 genes panel (<3000 amplicons)

• 21-genes CRC-panel (~1100 amplicons)

Oncomine cfDNA-panels (<40 amplicons, ~ 160 hotspot mutations):

• Lung (ALK, BRAF, EGFR, KRAS, MAP2K1, MET, NRAS, PIK3CA, ROS1, and TP53)

• Breast (AKT1, EGFR, ERBB2, ERBB3, ESR1, FBXW7, KRAS, PIK3CA, SF3B1, and TP53)

• Colon (APC, AKT1, BRAF, CTNNB1, FBXW7, GNAS, KRAS, MAP2K1, NRAS, PIK3CA, SMAD4, and TP53)

MiSeq/Hiseq (Illumina):

WES (GATC; 120x coverage, paired end; 125bp; UMI WES; Broad institute)

NebNext Direct Cancer HotSpot Panel (Bioke; ~190 hotspot mutations in 50 genes; ~150bp)

NebNext Direct custom Panel (50 genes; ~150bp)

Qiaseq breast (93 gene panel)

OnTarget (Boreal; 96 hotspot mutations in 9 genes:

(BRAF, CTNNB1, EGFR, KRAS, FOXL2, GNAS, NRAS, PIK3CA, TP53)

TrueSight170 (Illumina; 170 genes for DNA and RNA: SNV, CNV, fusion)

Aims

• To identify genomic ctDNA alterations (tumor load, heterogeneity, specific variants)

associated with outcome to

- first-line non-steroidal aromatase inhibitors

- treatment with everolimus/exemestane

• To identify mechanisms associated to resistance to non-steroidal aromatase

inhibitors

Balselga et al. N Eng J Med. 2012;366(6):520-9.

Study outline

First line non-steroidal aromatase

inhibitor (NSAI)

ER+, HER2-

mBC

Cohort 1

Baseline ctDNA

characteristics

CTC count

Everolimus/Exemestane

Cohort 2

Baseline ctDNA

characteristics

ER+, HER2-

mBC

NSAI refractory

Workflow

UMI panel

Median 20,000 read depth

Variant annotation: coverage >500 molecules>2 unique molecules

2mL plasma

baseline

cfDNA isolation

Maxwell

cfDNA

quantification Qubit

10 ng cfDNA input

Oncomine™ Breast cfDNA

Assay

AKT1 EGFR

ERBB2 ERBB3

ESR1 FBXW7

KRAS PIK3CA

SF3B1 TP53

ctDNA characteristics in both cohorts

NSAI

baseline

N=77

EVE/EXE

baseline

N=164

P-

value

ctDNA characteristics NSAI refr.

Number of ctDNA positive patients N (%) 41 (53) 125 (79) <0.001

Patients categorized by ctDNA with

≥3 mutations N (%) 2 (3) 29 (18) 0.001

≥54 mutant molecules/mL plasma N (%) 20 (26) 62 (38) 0.071

Number of mutant molecules/mL plasma median 48 54 0.870

ESR1 mutation N (%) 7 (9) 65 (40) <0.001

PIK3CA mutation N (%) 22 (29) 76 (46) 0.009

TP53 mutation N (%) 17 (22) 37 (24) 0.933

AKT1 mutation N (%) 3 (4) 5 (3) 0.732

ERBB2 mutation N (%) 1 (1) 3 (2) 0.764

ERBB3 mutation N (%) 0 (0) 3 (2) 0.232

KRAS mutation N (%) 0 (0) 2 (1) 0.331

Enrichment of different ESR1 variants in endocrine resistant patients

cfDNA and CTCs as measure for tumor loadMetastaticsetting, prior to first-line treatment

ctDNA characteristics related to PFS on NSAI monotherapy

Univariate analysis

FACTOR:

Number of

samples

Number of

Events

3 or more mutations in ctDNA 29 27

High ctDNA load 62 58

ESR1 -mutant ctDNA 65 58

ESR1 - high ctDNA load 30 27

PIK3CA -mutant ctDNA 76 69

PIK3CA - high ctDNA load 43 40

TP53 -mutant ctDNA 37 36

SF3B1-mutant ctDNA 6 4

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

Hazard Ratio for PFS (95% CI)

* Remained significant in multivariate analysis with clinicopathological factors

FACTOR

Number of

samples

Detectable ctDNA 41

High ctDNA load 20

Number of mutations -

CTC count ≥5/7.5 mL of blood* 30

Heterogeneity in NSAI refractory patients

ESR1 p.Y537S significant more in PFS-T1 than PFS-T2/T3 (P=0.023)

ctDNA variants and benefit to EVE/EXE

PFS-T1Median: 2.5 months

Range: 1.0-3.9 months

PFS-T2 Median: 5.1 months

Range: 4.1-6.4 months

PFS-T3Median: 11.5 months

Range: 6.8-23.9 months

FACTOR:

Number of

samples

Number of

Events

3 or more mutations in ctDNA 29 27

High ctDNA load 62 58

ESR1 -mutant ctDNA 65 58

ESR1 - high ctDNA load 30 27

PIK3CA -mutant ctDNA 76 69

PIK3CA - high ctDNA load 43 40

TP53 -mutant ctDNA 37 36

SF3B1-mutant ctDNA 6 4

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

Hazard Ratio for PFS (95% CI)

ctDNA characteristics related to PFS on EVE/EXE

Univariate analysis

* Remained significant in multivariate analysis with clinicopathological factors

FACTOR

Number of

samples

3 or more mutations in ctDNA* 29

High ctDNA load* 62

ESR1- mutant ctDNA 65

ESR1- high ctDNA load 30

PIK3CA- mutant ctDNA 76

PIK3CA- high ctDNA load 43

TP53- mutant ctDNA 37

SF3B1 – mutant ctDNA 6

ctDNA characteristics related to PFS on EVE/EXE

Conclusions

Factors associated to outcome

• ctDNA at baseline not predictive for outcome to NSAI; CTCs numbers are!!

• ctDNA load, heterogeneity and ESR1 (p.Y537S) mutations at baseline associated

with worse outcome to EVE/EXE; SF3B1 with good outcome

Mechanisms associated to NSAI resistance

• ESR1 and PIK3CA mutations significantly enriched at PD

• ESR1 variants seem to contribute differently

• KRAS and ERBB3 mutations only detected at PD, however at low numbers

ctDNA decline for reponse monitoring

O’Leary et al nat comm. 2018

Paloma-3 study

Emerging model for therapy resistance(and/or progression)

Subclones (more

metastastic

or therapy resistant

Dominant clone in

the primary tumor

ESR1

Common

driversESR1 E380Q

Homogenus

breasttumor

Heterogenus

breasttumor

APOBEC ?

Acknowledgements

Medical Oncology Martijn Lolkema Ronald de Wit Stefan Sleijfer Agnes Jager

Medical Oncology; VUmc

Dinja Kruger

Epie Boven

Translational Cancer Genomics Maurice Jansen Manuk Bos Nick Beije Anieta Sieuwerts Jean Helmijr Silvia Vitale Jaco Kraan Mai Van Joan Bolt-de Vries Zahra Alawi Lindsay Angus Lisanne van Dessel

Molecular Pathology Winand Dinjens

Erik-Jan Dubbink

Ronald van Marion

Peggy Atmodimedjo

Oncomine - Thermo Fisher Scientific

On target – Boreal genomics

Borstkanker Onderzoek

Groep

Financial support by:

Study (Eudract 2013-

004120-11) supported by:

All the patients for participating in

this study

I. Konings

O. Hadj

M. Derksen