Next-Generation Sequencing-Based Mutations Scanning Strategy of the BCR-ABL Kinase Domain in Patients with PhiladelPhia-Chromosome Positive Leukemias Treated with Tyrosine Kinase Inhibitors

In chronic myeloid leukemia and Philadelphia-positive acute lymphoblastic leukemia patients resistant to tyrosine kinase inhibitors (TKIs), BCR-ABL kinase domain mutation status is an essential component of the therapeutic decision algorithm. The recent development of Ultra-Deep Sequencing approach (UDS) has opened the way to a more accurate characterization of the mutant clones surviving TKIs conjugating assay sensitivity and throughput. We decided to set-up and validated an UDS-based for BCR-ABL KD mutation screening in order to i) resolve qualitatively and quantitatively the complexity and the clonal structure of mutated populations surviving TKIs, ii) study the dynamic of expansion of mutated clones in relation to TKIs therapy, iii) assess whether UDS may allow more sensitive detection of emerging clones, harboring critical 2GTKIs-resistant mutations predicting for an impending relapse, earlier than SS. UDS was performed on a Roche GS Junior instrument, according to an amplicon sequencing design and protocol set up and validated in the framework of the IRON-II (Interlaboratory Robustness of Next-Generation Sequencing) International consortium.Samples from CML and Ph+ ALL patients who had developed resistance to one or multiple TKIs and collected at regular time-points during treatment were selected for this study. Our results indicate the technical feasibility, accuracy and robustness of our UDS-based BCR-ABL KD mutation screening approach. UDS was found to provide a more accurate picture of BCR-ABL KD mutation status, both in terms of presence/absence of mutations and in terms of clonal complexity and showed that BCR-ABL KD mutations detected by SS are only the “tip of iceberg”. In addition UDS may reliably pick 2GTKIs-resistant mutations earlier than SS in a significantly greater proportion of patients.The enhanced sensitivity as well as the possibility to identify low level mutations point the UDS-based approach as an ideal alternative to conventional sequencing for BCR-ABL KD mutation screening in TKIs-resistant Ph+ leukemia patients

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.