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Figure showing the peak RAM consumption (kB) and the running time (s) of the programs tested. Panel A) running time as function of total number of reads for the 540 samples when programs are run using 1 core with a 1 thread for bowtie2 for STRAIN and SRST2. On the right, boxplots of the running time for each program; Panel B) running time on 7 samples selected to have the maximum number of reads among the samples of the same species when STRAIN is run setting 7 cores and 7 threads for bowtie2 and SRST2 set with 48 threads for bowtie2. On the right, boxplots of the running times per program in the seven selected biggest samples; Panel C) peak RAM consumption as function of the total number of reads of the 540 samples and boxtplots for each program. (DOCX 21 kb)

File describing the running setup of the programs and the specifications of the computational resource used to run the programs. (PDF 3690 kb)

Background: The Meningococcal Antigen Typing System (MATS) was developed to identify meningococcus group B strains with a high likelihood of being covered by the 4CMenB vaccine, but is limited by the requirement for viable isolates from culture-confirmed cases. We examined if antigen genotyping could complement MATS in predicting strain coverage by the 4CMenB vaccine. Methods: From a panel of 3912 MATS-typed invasive meningococcal disease isolates collected in England and Wales in 2007-2008, 2014-2015 and 2015-2016, and in 16 other countries in 2000-2015, 3481 isolates were also characterized by antigen genotyping. Individual associations between antigen genotypes and MATS coverage for each 4CMenB component were used to define a genetic MATS (gMATS). gMATS estimates were compared with England and Wales human complement serum bactericidal assay (hSBA) data and vaccine effectiveness (VE) data from England. Results: Overall, 81% of the strain panel had genetically predictable MATS coverage, with 92% accuracy and highly concordant results across national panels (Lin’s accuracy coefficient, 0.98; root-mean square deviation, 6%). England and Wales strain coverage estimates were 72-73% by genotyping (66-73% by MATS), underestimating hSBA values after four vaccine doses (88%) and VE after two doses (83%). The gMATS predicted strain coverage in other countries was 58-88%. Conclusions: gMATS can replace MATS in predicting 4CMenB strain coverage in four out of five cases, without requiring a cultivable isolate, and is open to further improvement. Both methods underestimated VE in England. Strain coverage predictions in other countries matched or exceeded England and Wales estimates. (C) 2019 GlaxoSmithKline Biologicals SA. Published by Elsevier Ltd.

Figure showing the peak RAM consumption (kB) and the running time (s) of the programs tested. Panel A) running time as function of total number of reads for the 540 samples when programs are run using 1 core with a 1 thread for bowtie2 for STRAIN and SRST2. On the right, boxplots of the running time for each program; Panel B) running time on 7 samples selected to have the maximum number of reads among the samples of the same species when STRAIN is run setting 7 cores and 7 threads for bowtie2 and SRST2 set with 48 threads for bowtie2. On the right, boxplots of the running times per program in the seven selected biggest samples; Panel C) peak RAM consumption as function of the total number of reads of the 540 samples and boxtplots for each program. (DOCX 21 kb)

Acute Myeloid Leukaemia (AML) is a cancer of the myeloid lineage of blood cells characterised by rapid growth of undifferentiated myeloid precursors that accumulate in the bone marrow and suppress normal haematopoiesis. It is the most common adult leukaemia with an estimated number of more than 60?000 new cases for the US in 2016. Despite the high rates of complete remissions achieved after treatment (60-80% in young adults), the number of patient that will result cured after induction and consolidation therapy is very low (~12%). The molecular basis of relapsing disease is still unclear and the small number of identified predictive factors has small predictive power. To date, chemotherapy induction treatment is similar for all patients and consists in the administration of mainly three drugs (fludarabine, cytarabine, and idarubicin). Prediction markers for the outcome of chemotherapy would instead reduce useless treatments and direct research through new possible therapeutic targets that would enhance AML treatment. In three successive studies, Ding et al., Corces-Zimmerman et al. and Kr�nke et al., described four possible behaviours for relapse patients: the return of the first leukaemia (dominant clone or a subclone), with or without additional evolution, or the emergence of ancestral clones, also in this case, with or without additional mutations. In this thesis, endowed of the NGS technologies advancement, we decided to delineate the possible process of relapse formation in order to be able in the future to predict which patients are more susceptible to relapse. Our experimental plan includes the whole exome analysis of 30 pairs of primary/relapsed AML samples using NGS to identify relapse-specific mutations, the bioinformatics analysis of the clonal evolution of the disease and the identification of pathways that correlate with the relapsing disease.?The methods for the analysis of NGS data, at present, are still in a refinement phase, especially for the high level analysis (detection of variants and definition of their role in the pathogenesis). We broadly analysed the existing methods for the treatment of NGS data (aligners, mutation callers, CNV callers and methods to reconstruct clonal composition) in order to determine those better fitting to our cohort of patients and purposes: occasionally, we had the possibility to choose the best tool meeting our investigative needs, discovering that other methods were valuable as well, in other cases we verified that more improvements are needed to obtain reliable results. Our analysis shows that the genomic landscapes of primary and relapse AMLs are similar and in the majority of the patients (76%) some relapse clones were already present in the primary tumour and reappeared after chemotherapy at similar or augmented cellular frequencies. We also identified some functional gene categories (DNA methylation pathway, cohesin complex and chromatin modifiers) more prone to resistance and peculiar genes (e.g. ASXL1, TET2) presenting growing VAFs at relapse. In 4 out of 29 patients (14%) we were able to identify driver mutations in the blood sample of the complete remission at low frequency; we hypothesise that more sophisticated diagnostic tools, based on NGS analysis, would help in driving the treatment to obtain better outcomes for patients.

Supplementary methods. (DOCX 64 kb)

The analyses carried out using 2 different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 acute myeloid leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these 2 variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and 1 secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of next-generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient.