Understanding the biology of CLL in the light of newer technologies Richard Rosenquist, MD, PhD Uppsala University Sweden
Genetic milestones in CLL G-banding CLL-FISH Microarrays Subclonal heterogeneity 1970 1990 2000 2011 2012 2013 Cumulative Proportion Surviving 100% 80% 60% 40% IGHV unmutated n=134 IGHV3-21 n=31 IGHV mutated n=79 BIRC3 20% 0% 0 24 48 72 96 120 144 168 192 Time/ months IGHV mutation status Novel mutations
The new golden era of next-generation sequencing
WGS vs WES vs targeted enrichment 3,000.000.000 bp 30X coverage 20.000 genes 100X coverge 20-50 genes >500X coverge
Whole-genome and whole-exome sequencing
Mountains and hills The genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. Wood et al, Science 2007
Put simply; how to find the most important peaks in the landscape Driver Driver Passenger Passenger Passenger
Puente et al, Nature 2011 Quesada et al, Nature 2011 Fabbri et al, JEM 2011
New peaks start to emerge in the CLL landscape
Landau and Wu, Genome Med 2013
Varying frequences depending on clinical aggressiveness 3490 pts Baliakas et al, Leukemia 2014
Prognostic impact of the novel mutations?
NOTCH1 mutations and survival Increased risk of: CLL progression RS transformation Short survival Associated with +12 Rossi et al, Blood 2012 Puente et al, Nature 2011 Rossi et al, Blood 2012 Del Giudice et al, Blood 2012 Villamore et al, Leukemia 2013
SF3B1 mutations in CLL Increased risk of: CLL progression Fludarabine resistance Short survival Associated with del(11q) Rossi et al, Blood 2011 Quesada et al, Nature Gen 2012 Jeromin et al, Leukemia 2014
BIRC3 mutation are associated with chemorefractoriness in CLL Rossi et al, Blood 2012
Prognostic impact of the novel mutations How to combine new and old facts?
New hierarchal classification? Rossi et al, Blood 2013
New hierarchal classification? Rossi et al, Blood 2013
NOTCH1, FBXW7, MYD88, XPO1 and TP53 in 1160 untreated CLL patients TTT OS Jeromin et al, Leukemia 2013
ERIC Novel Mutation Project 9 centers 3490 patients NOTCH1, SF3B1, BIRC3, MYD88, TP53 Frequencies Prognostic impact Guidelines SPAIN Barcelona UK Bournemouth/ Southampton GERMANY Ulm SWEDEN Uppsala CZECH REP Brno ITALY Milan & Novara GREECE Thessaloniki & Athens
Association between cytogenetic and molecular markers Baliakas et al, Leukemia 2014
Novel mutation and disease timepoint All cases Cases requiring treatment Binet A patients grouped according to the time elapsed between diagnosis and time of analysis for each mutation.
Time to first treatment (Binet stage A) Baliakas et al, Leukemia 2014
Multivariate analysis - TTFT Univariate analysis Multivariate analysis HR 95% CI P HR 95% CI p NOTCH1 2.633 1.928-3.598 <0.0001 1.305 0.923-1.847 0.128 SF3B1 2.635 1.855-3.745 <0.0001 1.644 1.134-2383 0.008 TP53ab 2.266 1.580-3.252 <0.0001 2.081 1.431-3.021 0.0001 U-CLL 4.840 3.860-6.069 <0.0001 3.701 2.833-4.434 <0.0001 Isolated del(13q) 0.524 0.413-0.663 <0.0001 0.98 0.740-1.209 0.889 del(11q) 2.924 2.229-3.849 <0.0001 1.421 1.031-1.970 0.03 Trisomy 12 1.997 1.520-2.623 <0.0001 1.338 0.972-1.841 0.07 Baliakas et al, Leukemia 2014
Prognostic impact of the novel mutations An integrated hierarchal classification is possible, but further studies must be conducted
Clonal evolution in CLL Sutton & Rosenquist, Haematologica 2014
Driver versus passenger in CLL? Landau et al, Cell 2013
Small clones matter: the case of TP53 28/309 (9%) untreated CLL (median allele frequency: 2%) Rossi et al, Blood 2014
Targeted re-sequencing
Targeted re-sequencing in CLL 9 genes, 7 previously linked to CLL prognosis 168 high risk (aggressive) CLL patients (unmutated IGHV, n=119) HaloPlex enrichment Sequenced on the Illumina 2000 HiSeq ATM BIRC3 KLHL6 MYD88 NOTCH1 POT1 SF3B1 TP53 XPO1 Sutton et al
HaloPlex targeted enrichment
Is this it? Do we have the right perspective of the landscape?
The stereotypy saga will change the perspective
Unmutated IGHV genes are associated with a more aggressive form of CLL Mutated 55% Median survival 293 months Unmutated 45% Median survival 117 months Hamblin et al (Blood, 1999) Damle et al (Blood, 1999)
IGHV3-21 usage and prognosis 100% Two-thirds of cases IGHV mutated Cumulative Proportion Surviving 80% 60% 40% 20% IGHV unmutated n=134 70 months 10% of cases 146 months IGHV mutated n=79 IGHV3-21 n=31 83 months 0% 0 24 48 72 96 120 144 168 192 Tobin et al, Blood 2003 Time/ months
IGHV3-21 CLL and stereotypy IGHV3-21 IGLV3-21 Tobin et al, Blood 2002 & 2003 Implicate antigen selection in CLL pathogenesis!
Stereotyped B cell receptors in CLL Stereotyped B cell receptors (Messmer et al, JEM 2004) Similar heavy/light chain gene use >60% CDR3 amino acid identity Up to 30% of CLL (Stamatopoulos et al, Blood 2007; Murray et al, Blood 2008; Darzentas et al, Leukemia 2010; Agathangelidis et al, Blood 2012 ) HCDR3 ARDANGMDV LCDR3 QVWDS(S/G)SDHPWV Appears to impact clinical outcome (Stamatopoulos et al, Blood 2007; Dal-Bo et al, BJH 2011)
~30% of all CLL carry stereotyped BcRs Good Poor Poor Agathangelidis et al. Blood 2012
What about genetic mutations in stereotyped subsets?
Subset-biased acquisition of novel gene mutations SF3B1 NOTCH1 BIRC3 44%!!! 170 cases Poor-prognostic subsets: #1, #2 and #8 Strefford et al, Leukemia 2013 Rossi et al, Blood 2013
Distinct frequency of TP53 gene defects in stereotyped subsets 1175 pts Malcikova et al, BJH 2014
Clinical impact of stereotypy?
Clinical impact of stereotypy n=8,593 Baliakas et al, Lancet Hematology 2014
TTFT in subsets #1 and #2 Baliakas et al, Lancet Hematology 2014
Subsets & Döhner model (TTFT) Baliakas et al, Lancet Hematology 2014
Acknowledgements Uppsala University Dept of Immunology, Genetics and Pathology Panagiotis Baliakas Sujata Bhoi Diego Cortese Viktor Ljungström Larry Mansouri Christer Sundström Lesley Sutton Emma Young Lund University Gunnar Juliusson Rebeqa Gunnarsson Copenhagen, Denmark Christian Geisler Milan, Italy Paolo Ghia Thessaloniki, Greece Kostas Stamatopoulos Anastasia Hazidimitriou Athens, Greece Chrysoula Belessi Paris, France Frederic Davi Brno, Czech Republic Sarka Pospisilova Jitka Malcikova Nikos Darzentas Southampton/Bournemouth Jonathan Strefford David Oscier Zadie Davis
Thanks for listening!