Molecular Profiling of elderly patients receiving frontline fludarabine-based chemo-immunotherapy in the ALLG CLL5 study

Abstract Details: Chemoimmunotherapy with fludarabine,
cyclophosphamide and rituximab (FCR) is currently
the most effective frontline therapy for younger patients
with chronic lymphocytic leukemia (CLL). The
Australasian Leukemia and Lymphoma Group (ALLG)
CLL5 Study demonstrated that FCR is also safe, tolerable
and effective first therapy for elderly patients (Mulligan
et al., iwCLL 2015). We attempted to determine whether
mutations in CLL-associated genes could predict
responses and progression free survival in these elderly
CLL patients. Methods: 120 patients were enrolled in the
ALLG CLL5 study from November 2008 to July 2012.
Study protocol received IRB approval and all patients
approved to study participation and sample collection.
Patients received up to 6 cycles of oral fludarabine-based therapy (FR5, FCR3 or FCR5). Study procedures and
patient consent were in accordance with the declaration
of Helsinki. Fluorescent in-situ hybridization: FISH was
performed using the Vysis CLL panel (Abbott Molecular)
with the following probes and cut-off values: D13S319-
del(13q14.3), 4%; CEP12 (12p11.1-q11), 4%; ATMdel(
11q22.3), 5% and TP53-del(17p13.1), 7%. DNA purification:
DNA was purified from all samples. We
designed a DNA sequencing custom amplicon panel
using Illumina design studio to identify recurrent mutations
associated with CLL (prognostic panel). Genes
included TP53, ATM, NOTCH1, SF3B1, MYD88, XPO1 and
BIRC3. Other genes were included as part of an exploratory
panel but not included in this analysis. Samples
were sequenced utilizing Illumina MiSeq sequencer
according to manufacturer’s instructions. Genomic variants
were reported if present in 20 reads with an error
probability 1:1000 (Q30). Gene mutations were confirmed
by Sanger sequencing. Data was analyzed using
Illumina proprietary software, annotated using web
ANNOVAR software and compared to COSMIC and
other genomic mutation databases. Results: Clinical
trial outcome measures (responses, PFS and OS) were
analyzable for 116 patients. FISH data was available for
92 patients (79%) and DNA sequencing data available for
79 patients (68%). The overall response rate (ORR) was
96% and the bone marrow confirmed complete
response (CR) rate was 39% (Table 1). There was no
statistically significant difference in CR, OS or PFS when
comparing patients according to FISH hierarchy. When
comparing patients with mutations of genes in the
prognostic panel, patients with XPO1 mutations had a
significantly shorter PFS by univariable analysis
(p¼0.007). There was a trend to shorter PFS for patients
with TP53 mutations but this did not reach statistical
significance (p¼0.131, Figure 1b). There was no other
statistical association between genes tested and PFS although this was limited by the small number of
samples for each mutation. Conclusions: We had limited
power to identify statistical associations to response, OS
or PFS due to our small sample size and short follow-up.
Nevertheless molecular tools may provide further stratification
to identify patients with variable benefit from
chemo-immunotherapy.