The genetic landscape of high-risk CLL

Transcription

1 The genetic landscape of high-risk CLL Jonathan Strefford PhD Reader in Molecular Haematology Cancer Genomics

2 Treatment development 2010 Further development of Small molecules for Stratified CLL treatment What is CLL and why is it importance Recent advanced in CLL genetics 1971 CLL B-cell disorder 1975 Rai Staging 1977 Chlorambucil 1975 Binet Staging 1980 Trisomy 12 Clinical heterogeneity Need for sensitive prognostication 1985 BCL1 BCL q Chromosomes As biomarkers 1997 Fludarabine p q IGHV 1999 ATM 2000 FISH Model 2002 Anti-CD FC 2002 FC+R Anti-CD BCR Inhibitors 2011 Four CLL genomes sequenced 2011 NOTCH CLL exomes sequenced 2011 SF3B1 CLL History HapMap Sanger Microarray 2003 Restriction BACS sequencing Complete Maps HGS 1971 Banding 1975 Southern Blotting 1980 FISH 1983 PCR 1990 Launch HGP 2000 Draft HGS st Cancer Genome sequences 2011 Chromothrypsis 2010 ICGC DNA History

3 What is CLL 2-5 new cases per 100,000 (men 5.0, women 2.5) 30% of new leukaemia cases Age at diagnosis (men 70yrs, women 74yrs) Gradual accumulation of mature, but poorly functioning lymphocytes 70-80% of new CLL cases are chance findings 40% of patients are free of symptoms at diagnosis weakness, fatigue, night sweats and repeat infections, enlarged lymph nodes, enlarged liver and/or spleen Incurable with a extremely heterogeneous clinical course Some patients have indolent disease, Others can show aggressive disease Survival ranges from 6 months to >20 years Normal B cells CLL B cells

4 A diagnosis of CLL Clinically diagnosed by lymphocytosis (>5x10-9/L) Mature lymphocytes Distinguish from other mature B-cell malignancies CLL confined to lymph node is small lymphocytic lymphoma (SLL) Similar cell morphology below cut-off is monoclonal B cell lymphocytosis

5 Clinical heterogeneity Clinical stage IGHV status ZAP-70/ CD38 TP53 defect 11q del Genome complexity No markers accurately predict

6 Prognostic markers Classical markers Clinical stage Blood lymphocyte count Morphology Lymphocyte doubling time Bone marrow infiltration degree Biological markers (established) Serum markers (thymidine kinase) IgVH mutational status V3-21 gene usage FISH and Cytogenetics CD38 expression ZAP-70 expression Biological markers (preliminary) Genome complexity Chromosomal translocations NOTCH1 mutations SF3B1 mutations CLLU1 expression MicroRNA signature MCL -1 expression Bcl-2/Bax ratio Telomere length and telomerase activity Functional analysis of p53/atm pathways

7 Chromosomal abnormalities 17p 32 months 11q 79 months 13q 133 months Total Frequency Sub-group Frequency

8 Our approach and the patients Gene discovery Clinical Utility Biological importance Our genomic work Patient cohorts Mouse models n=340 n=25 (80) Clinical trials CLL4 [n=500] ARCTIC/ADMIRE [n=400] BCRs and small molecule inhibitors Early stage and sequential cohorts [n=500] Survival Treatment Response MRD Confirmation and characterization Screening Functional analysis Re-sequencing mrna expression Protein expression DNA Melt analysis Re-sequencing Biochemical production of mutant proteins Cellular knockdown/knock-in with Nucleofection Pathway interrogation with chemical inhibitors Published data

9 Deletion of 13q14 ( 60%) Fitchett et al (1987) Cancer Genet Cytogenet 24(1): Interstitial deletions (80%) Remaining translocations 70% are heterozygous Deletion size is highly variable Associated with a good prognosis as a sole change CDR DLEU2, DLEU1 and mir15a/16-1 CLL cases without the MDR MDRs are not the whole story

10 The analysis of minimally deleted regions, definitely not the whole story... Deletion of 11q23 ( 20%) MDRs have been critical historical signposts Simple, amenable to technology However, huge simplification of deletion gene content Deletions are larger in patients Multiple overlapping MDRs One target is ATM Mutated in 40% of cases Mutations can occur without deletion Other genes on 11q DNA damage response is maintained in non-mutated cases status predicts poor outcome independent of deletion

11 10 11p MDR TRIM44 TRA F6 RAG1 &2 LLRC4 C 30 11q MDR 1 ZFP91 CNTF LOC q MDR 2 MARK2 STIP1 FERMT3 Genomic position (Mb) 70 11q MDR 3 GAB2 THRSP NARS q MDR 4 CCDC83 EED FZD4 110 ACAT1 EXPH q23 MDR NPAT ATM C11orf65 KDELC2 More likely to be ATM mutated

12 EED H2AFX MRE11A HDAC1 ATM CNTF VHL p53 IRAKs, SPOP TRAF6 MYD88 UBC NOTCH1 ZAP70 KPNA2 RAG2 KPNA GAB2 RAG1 HMGB2 HMGB1

13 Deletion size as a driver of proliferation MRE11A ATM H2AFX % 11q FISH Clone size Number of CNA Number of CNA P = P = P = 0.02 P = P < P = ns ATM MRE11A H2AFX Increased cell proliferation in H2AFX Deleted CLL cells Non del(11q)/(17p) del(11q) del(17p) Elevated genomic complexity in 11q and 17p deleted CLL MRE11A ATM H2AFX Elevated complexity in large 11q deletions

14 High-throughput Sequencing

15 Novel genes in CLL emerging from NGS studies Gene % Role NOTCH NOTCH Signalling SF3B mrna splicing POT1 5 Telomere maintenance CHD2 5 Epigenetic regulation of gene expression LRP1B 5 TSG in myeloid disease XPO1 3 Nuclear export DDX3X 2 mrna splicing FBXW7 2 NOTCH signalling MAPK1 2 Inflammatory pathways KLHL6 2 Germinal centre formation

16 Extended screen Whole genome, exome and paired end sequencing 4 patients WGS 40x of >98% Exome 90x of >99% > 3 million variants Complex filtering dbsnp 1000 genomes Acquired Protein altering >1000 per tumour

17 10-17% of CLL Critical component of the Spliceosome Controls appropriate mrna gene splicing Mutations are conserved and within the HEAT domains Associated with reduced survival and established biomarkers Aberrant splicing of putative Cancer genes Reduced normal splicing of target genes

18 Homogeneous,not heterogeneous cohorts Serendipitous date of diagnosis and protracted disease Treatment heterogeneity Prognostic relevance must be ascertained in clinical trials With stringent entry criteria Response to treatment MRD Time to treatment Progression free survival Overall survival

19 Univariate associations SF3B1 in CLL4 at time of treatment 415 CLL4 patients for SF3B1 exon (HRM and sequencing) 20% prevalence Overall survival 86 vs 60mths Progression free 36 vs 29mths Variable OR P N Stage 1 ns 415 CD ZAP ns 309 IGHV U 1.85 ns 379 TP ns 387 ATM 0.85 ns Multivariate Cox Analysis Variable HR P Cum Survival OS UM SF3B Age at Diagnosis 1.05 < IGHV U 2.13 < TP < ATM P < M Time in months

20 Univariate associations NOTCH1 in CLL4 at time of treatment 458 CLL4 patients for NOTCH1 exon 34 (HRM and sequencing) 9% prevalence Overall survival 81 vs 65mths Progression free 36 vs 27mths Variable OR P N Stage 0.8 ns 458 CD < ZAP IGHV U TP ns 418 ATM 0.65 ns Multivariate Cox Analysis Variable HR P Cum Survival OS UM NOTCH Age at Diagnosis 1.06 < IGHV U 2.27 < TP < ATM P = M Time in months

21 The take-home message Cum Survival OS TP53 NOTCH1 SF3B1 UM Time in months Both genes associated with reduced survival Only SF3B1 is independent in a clinical trial of untreated patients TP53 remains the strongest indicator of survival SF3B1 identified an additional 20% of patients with poor outcome similar to 11q loss

22 Conclusions - value of genomic prognostic markers In treatment context Other established biomarkers TP53 remains THE most important Deletion & MUTATIONS 11q loss uncertain with immunotherapy May identify patient for PARP inhibitors Mutations of ATM NOTCH1 identifies patients with poor outcome Not independent in our clinical trial SF3B1 completely novel pathway in cancer Different mutational targets and outcome in myeloid versis lymphoid disease Novel prognostic marker

23 Molecular diagnostics 8x60K platform Optimized for CLL Established and novel regions, deletion size CLL, a copy number disease Processed 70 samples with clinically relevant copy number changes at varying clonal levels (10-95%) All detected Promising preliminary data from array features to detect recurrent somatic mutations

24 Conclusions General Novel genomic technologies allow unprecedented resolution Permits cytogenetic changes to be linked to the genome sequence Data can be correlated to expression and epigenetic alterations Help identify and characterize clinically relevant aberrations Identify novel genes and mechanisms Ultimately expanded our understanding of cancer pathogenesis

25 Acknowledgements Cancer Genomic Group, University of Southampton Dr Matthew Rose-Zerilli Dr Helen Parker Anton Parker (RBH) Jade Forster Human Genetics, University of Southampton Professor Andrew Collins Dr Jane Gibson Royal Bournemouth Hospital Professor David Oscier Dr Hazel Robinson Anne Gardiner UK CLL Biobank and Trials Professor Andy Pettitt Professor Peter Hillmen Professor Christine Harrison Professor Bryan Young Professor Tanja Stankovic Professor Daniel Catovsky Dr David Gonzalez de Castro Professor Mark Cragg Professor Mel Greaves Professor Graham Packham Professor Paul Moss Dr Anna Schuh Professor Martin Dyer Dr Tracy Chaplin Doug Hurd