Hayssam Soueidan

Clinical and genomic analysis of a randomised phase II study evaluating anastrozole and fulvestrant in postmenopausal patients treated for large operable or locally advanced hormone-receptor-positive breast cancer

Background:The aim of this study was to assess the efficacy of neoadjuvant anastrozole and fulvestrant treatment of large operable or locally advanced hormone-receptor-positive breast cancer not eligible for initial breast-conserving surgery, and to identify genomic changes occurring after treatment.Methods:One hundred and twenty post-menopausal patients were randomised to receive 1 mg anastrozole (61 patients) or 500 mg fulvestrant (59 patients) for 6 months. Genomic DNA copy number profiles were generated for a subgroup of 20 patients before and after treatment.Results:A total of 108 patients were evaluable for efficacy and 118 for toxicity. The objective response rate determined by clinical palpation was 58.9% (95% CI=45.0–71.9) in the anastrozole arm and 53.8% (95% CI=39.5–67.8) in the fulvestrant arm. The breast-conserving surgery rate was 58.9% (95% CI=45.0–71.9) in the anastrozole arm and 50.0% (95% CI=35.8–64.2) in the fulvestrant arm. Pathological responses >50% occurred in 24 patients (42.9%) in the anastrozole arm and 13 (25.0%) in the fulvestrant arm. The Ki-67 score fell after treatment but there was no significant difference between the reduction in the two arms (anastrozole 16.7% (95% CI=13.3–21.0) before, 3.2% (95% CI=1.9–5.5) after, n=43; fulvestrant 17.1% (95%CI=13.1–22.5) before, 3.2% (95% CI=1.8–5.7) after, n=38) or between the reduction in Ki-67 in clinical responders and non-responders. Genomic analysis appeared to show a reduction of clonal diversity following treatment with selection of some clones with simpler copy number profiles.Conclusions:Both anastrozole and fulvestrant were effective and well-tolerated, enabling breast-conserving surgery in over 50% of patients. Clonal changes consistent with clonal selection by the treatment were seen in a subgroup of patients.

Finding and identifying the viral needle in the metagenomic haystack: trends and challenges

Collectively, viruses have the greatest genetic diversity on Earth, occupy extremely varied niches and are likely able to infect all living organisms. Viral infections are an important issue for human health and cause considerable economic losses when agriculturally important crops or husbandry animals are infected. The advent of metagenomics has provided a precious tool to study viruses by sampling them in natural environments and identifying the genomic composition of a sample. However, reaching a clear recognition and taxonomic assignment of the identified viruses has been hampered by the computational difficulty of these problems. In this perspective paper we examine the trends in current research for the identification of viral sequences in a metagenomic sample, pinpoint the intrinsic computational difficulties for the identification of novel viral sequences within metagenomic samples, and suggest possible avenues to overcome them.

MYB–GATA1 fusion promotes basophilic leukaemia: involvement of interleukin-33 and nerve growth factor receptors

Acute basophilic leukaemia (ABL) is a rare subtype of acute myeloblastic leukaemia. We previously described a recurrent t(X;6)(p11;q23) translocation generating an MYB–GATA1 fusion gene in male infants with ABL. To better understand its role, the chimeric MYB–GATA1 transcription factor was expressed in CD34‐positive haematopoietic progenitors, which were transplanted into immunodeficient mice. Cells expressing MYB–GATA1 showed increased expression of markers of immaturity (CD34), of granulocytic lineage (CD33 and CD117), and of basophilic differentiation (CD203c and FcϵRI). UT‐7 cells also showed basophilic differentiation after MYB–GATA1 transfection. A transcriptomic study identified nine genes deregulated by both MYB–GATA1 and basophilic differentiation. Induction of three of these genes (CCL23, IL1RL1, and NTRK1) was confirmed in MYB–GATA1‐expressing CD34‐positive cells by reverse transcription quantitative polymerase chain reaction. Interleukin (IL)‐33 and nerve growth factor (NGF), the ligands of IL‐1 receptor‐like 1 (IL1RL1) and neurotrophic receptor tyrosine kinase 1 (NTRK1), respectively, enhanced the basophilic differentiation of MYB–GATA1‐expressing UT‐7 cells, thus demonstrating the importance of this pathway in the basophilic differentiation of leukaemic cells and CD34‐positive primary cells. Finally, gene reporter assays confirmed that MYB and MYB–GATA1 directly activated NTRK1 and IL1RL1 transcription, leading to basophilic skewing of the blasts. MYB–GATA1 is more efficient than MYB, because of better stability. Our results highlight the role of IL‐33 and NGF receptors in the basophilic differentiation of normal and leukaemic cells. Copyright

MICADo - Looking for mutations in targeted PacBio cancer data: an alignment-free method

Targeted sequencing is commonly used in clinical application of NGS technology since it enables generation of sufficient sequencing depth in the targeted genes of interest and thus ensures the best possible downstream analysis. This notwithstanding, the accurate discovery and annotation of disease causing mutations remains a challenging problem even in such favorable context. The difficulty is particularly salient in the case of third generation sequencing technology, such as PacBio. We present MICADo, a de Bruijn graph based method, implemented in python, that makes possible to distinguish between patient specific mutations and other alterations for targeted sequencing of a cohort of patients. MICADo analyses NGS reads for each sample within the context of the data of the whole cohort in order to capture the differences between specificities of the sample with respect to the cohort. MICADo is particularly suitable for sequencing data from highly heterogeneous samples, especially when it involves high rates of non-uniform sequencing errors. It was validated on PacBio sequencing datasets from several cohorts of patients. The comparison with two widely used available tools, namely VarScan and GATK, shows that MICADo is more accurate, especially when true mutations have frequencies close to backgound noise. The source code is available at http://github.com/cbib/MICADo.