Precision Medicine for Blood Cancer Patients
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Precision Medicine for Blood
Cancer Patients
How to Improve Outcome for Patients with
Blood Cancer
Professor Stefan K Bohlander, MD
Marijana Kumerich Chair in Leukaemia and Lymphoma Research
Leukaemia & Blood Cancer Research Unit
Department of Molecular Medicine and Pathology
Faculty of Medical and Health Sciences
The University of Auckland
Auckland, New Zealand
NEXT Federation Webinar
Auckland, 23/6/2020
In 1847, Rudolf Virchow coined the term “weißes Blut”
white blood
leukaemia
Normal
Blood Leukemia λευκος αίμα
Plasma
>30%
White white
Blood Cancer: Acute Myeloid Leukemia
(AML)
AML-M0: Blast cells in peripheral blood AML:
extremely aggressive disease of myeloid cells.
Usually fatal within a few days to weeks after
diagnosis without treatment.
Case History:
• 35 year old woman
• Intractable back pain
• Diagnosis of AML after 6 weeks
• Chemotherapy -> remission
• Bone marrow transplant 4 months later
• Recovery
• Relapse after 11 months
ACUTE MYELOID LEUKEMIA, M0, BLOOD. Acute minimally
differentiated myeloid leukemia (AML-M0) is characterized by lack of • Chemotherapy with 2nd BMT (14 months)
obvious myeloid differentiation by routine histologic examination and
presence of myeloperoxidase in
Survival of Patients with AML
Percent Survival
AMLCG 86 < 60YRS :N=725 (Cens .279)
Years since Start of Therapy
Only Incremental Progress in AML
Treatment Outcome in the Last 30 Years!
Sauerland, AMLCG study group, July 2014
Origin of Blood Cancer
Bone marrow cell Blood Cancer
Spelling Mistakes
in Genetic Code
Well behaved cell Misbehaving Cell Daughter Cells inherit
Blood Cancer Cell spelling mistake and
bad behaviour
It takes about 3 to 6 one letter spelling mistakes
among billions of letters to initiate blood cancer
Survival of AML Patients:
According to Cytogenetic Subgroups
100
t(8;21)
Favourable N=278
t(15;17)
75
inv(16)
50
Intermediate N=725
25 Normal and other abnormalities
Unfavourable N=222
0
0 1 2 3 4 5 6 7 Years from diagnosis
Complex aberrant, other unfavourable, 11q/MLL
(From Schoch et al., 2004)
Genetics of AML
Translocations
Cytogenetics
PCR, Sequencing
Molecular
Genetics
Routine Molecular Testing in AML only for
FLT3, NPM1 and CEBPA Mutations
Two kinds of mutation testing
NPM1
• Hotspot testing ATG STOP FLT3
• Whole gene sequencing CEBPA
ATG STOP
Next Generation Sequencing (NGS) Technology Allows for More Comprehensive Molecular Testing
The Next-Generation
Sequencing Revolution 1 human
genome in
1 day!
Log 10 scale daily machine output
1 billion
fold
difference
1000 base pairs
Year1990 2014
200 million fold
http://www.ocf.berkeley.
edu/~edy/genome/sanger
.jpg
600 bases per day 120,000,000,000 bases per day
270 000 years per human genome 1 day per human genomeProgress in Genome Analysis Technologies in the
last 10 Years
In 1817, Karl Drais invented the “bicycle”
(called “Laufmaschine”)
10 fold increase in
speed
http://1.bp.blogspot.com/-moSRSPtZ-
Gk/UT6kBGS-
DfI/AAAAAAAAARE/vjq_k5q1lwo/s1600/
25 km/h Mercedes+Benz+cars10.jpg
40 million fold
increase in speed 250 km/h
http://l.yimg.com/bt/api/res/1.2/E8PeVEXa.exSFcGmWs
9iNA--/YXBwaWQ9eW5ld3M7cT04NQ--
/http://media.zenfs.com/en/blogs/technews/fva-630-
star-trek-warp-drive-enterprise-credit-nbc.jpg
4 million fold increase in speed
1,079,252,849 km/hHow can we use Next Generation
Sequencing Technology to
Improve the Outcome for AML
Patients?Auckland AML Gene Panel
70 gene Auckland AML panel
(68 Metzeler genes plus CALR and RB1)
ABCB1 FBXW7 NT5C2
ABCG2 FLT3 PHF6
ADA GATA1 PTEN
ASXL1 GATA2 PTPN11
BCOR GATA3 PTPRT
BCORL1 HNRNPK RAD21
BRAF HRAS RUNX1
BRINP3 IDH1 SETBP1
36 genes mutated in ≥1% of patients
CALR IDH2 SF1
CBL IL7R SF3A1
CDA JAK1 SF3B1
CDKN2A JAK2 SMC1A
CEBPA JAK3 SMC3
CSF1R KDM6A SRSF2
CSF3R KIT STAG2
DAXX KMT2A TERT
DCK KRAS TET2
DCLK1 MPL TP53
DIS3 MYD88 U2AF1
DNMT3A NOTCH1 U2AF2
ETV6 NPM1 WAC
EZH2 NRAS WT1
ZRSR2
RB1
• N=664 AML pts
• Genes in panel: 68
Metzeler et al., Blood 2016 • Identified at least 1 driver mutation in 97% of patientsMany Spelling Mistakes in Blood Cancer
LBCRU: Leukaemia & Blood Cancer Research Unit
Patients 1 to 22 Muts in gene GCG: Grafton Clinical Genomics
Gene Sample -> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
1 ABCB1 0
2 ABCG2 0
3 ADA 0
4 ASXL1 1
5 BCOR 2
6 BCORL1 2
7 BRAF 0
8 BRINP3 0
9 CALR 0
10 CBL 0
11 CDA 0
12 CDKN2A 1
13 CEBPA 2
14 MIR-142 0
15 TERC 0
16 CSF1R 0
17 CSF3R 0
18 DAXX 0
19 DCK 0
20 DCLK1 0
From March 2019 till July 2019:
Genes 1 to 70
21 DIS3 0
22 DNMT3A 6
23 ETV6 1
24
25
EZH2
FBXW7
1
1 • 22 samples sequenced
26 FLT3 1
27
28
GATA1
GATA2
0
1
• Average of 3 somatic mutations per sample
•
29 GATA3 0
30
31
HNRNPK
HRAS
0
0
(range 1 to 7)
32 IDH1 3
33 IDH2 2
34 IL7R 0
35 JAK1 0
36
37
JAK2
JAK3
3
0 Each patient has a unique combination
38 KDM6A 0
39
40
41
KIT
KMT2A
KRAS
2
1
0
of spelling mistakes!
42 MPL 1
43 MYD88 0
44 NOTCH1 1
45
46
47
NPM1
NRAS
NT5C2
5
2
0
Each patient has a unique blood cancer!
48 PHF6 0
49 PTEN 0
50 PTPN11 2
51 PTPRT 0
52 RAD21 1
53 RB1 0
54 RUNX1 1
55 SETBP1 1
56 SF1 2
57 SF3A1 0
58 SF3B1 0
59 SMC1A 2
60 SMC3 0
61 SRSF2 2
62 STAG2 1
63 TERT 0
64 TET2 6
65 TP53 1
66 U2AF1 2
67 U2AF2 2
68 WAC 0
69 WT1 2
70 ZRSR2 1
Phenotype APML APML MDS-MLD PMF MDS PMF
Number of Muts1 4 2 4 2 5 7 3 3 3 4 4 2 2 3 3 1 1 3 2 3 3Each Patient’s Blood Cancer is
Unique!Auckland Myeloid Gene Panel
Newly diagnosed AML
78 Gene Panel
Familial Predisposition is
more frequent than
Spelling Mistake anticipated! About 8% in
our patients.
What is it?
Diagnosis Is the disease running in the family?
Familial Predisposition
What will happen? Is my treatment working?
Prognosis Minimal Residual Disease Monitoring
How can I treat?
Target Identification
Improved treatment of AML patientsSummary • Each patient’s blood cancer is unique • Next-Generation Sequencing (NGS) uncovers this uniqueness • NGS analysis improves: o Diagnosis o Prognostication o Identification of drug targets o Monitoring of disease o Uncovering familial cases (implication for bone marrow transplant donor
Thank you very much for your Support
Leukaemia & Blood Cancer
Research Unit,
University of Auckland:
Peter Browett
Andrew Wood
Purvi Kakadia
Marjan Askarian Amiri
Robyn Lints
Rhea Desai
Sarvanez Taghavi
Mandy De Silva
Leon Griner
Omid Delfi
Matthew Prouse
Jenny Chien
Alyona Oryshchuk
Alix Coysh
Maryam Saberi
Niloofar Zandvakili
Huimei Lee
Lachlan Macdonald
Jessica Chase
Christina Walker
Chloé Morin
Monash University The Family of
The Alfred
Andrew Wei
Ing Soo Tiong
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