Artificial intelligence methods to estimate overall mortality and non-relapse mortality following allogeneic HCT in the modern era: an EBMT-TCWP study

Allogeneic haematopoietic cell transplantation (alloHCT) has curative potential counterbalanced by its toxicity. Prognostic scores fail to include current era patients and alternative donors. We examined adult patients from the EBMT registry who underwent alloHCT between 2010 and 2019 for oncohaematological disease. Our primary objective was to develop a new prognostic score for overall mortality (OM), with a secondary objective of predicting non-relapse mortality (NRM) using the OM score. AI techniques were employed. The model for OM was trained, optimized, and validated using 70%, 15%, and 15% of the data set, respectively. The top models, "gradient boosting" for OM (AUC = 0.64) and "elasticnet" for NRM (AUC = 0.62), were selected. The analysis included 33,927 patients. In the final prognostic model, patients with the lowest score had a 2-year OM and NRM of 18 and 13%, respectively, while those with the highest score had a 2-year OM and NRM of 82 and 93%, respectively. The results were consistent in the subset of the haploidentical cohort (n = 4386). Our score effectively stratifies the risk of OM and NRM in the current era but do not significantly improve mortality prediction. Future prognostic scores can benefit from identifying biological or dynamic markers post alloHCT.

Improving Species Level-taxonomic Assignment from 16S rRNA Sequencing Technologies

Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depends on the quality of the database. For instance, Greengenes and RDP have not been updated since 2013 and 2016, respectively. In addition, the nature of 16S sequencing technologies (short reads) focuses mainly on the V3-V4 hypervariable region sequencing and hinders the species assignment, in contrast to whole shotgun metagenome sequencing. Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology-based methods, and we propose a new re-annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3). This new re-annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm. Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment. Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database. Basic Protocol 4: Definitive selection of lineages among the three methods.