Summary The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences. Availability and implementation DeepMSPeptide is available at https://github.com/vsegurar/DeepMSPeptide. Supplementary information Supplementary data are available at Bioinformatics online.

Identification of new markers associated with long-term efficacy in patients treated with CAR T cells is a current medical need, particularly in diseases such as multiple myeloma. In this study, we address the impact of CAR density on the functionality of BCMA CAR T cells. Functional and transcriptional studies demonstrate that CAR T cells with high expression of the CAR construct show an increased tonic signaling with up-regulation of exhaustion markers and increased in vitro cytotoxicity but a decrease in in vivo BM infiltration. Characterization of gene regulatory networks using scRNA-seq identified regulons associated to activation and exhaustion up-regulated in CAR High T cells, providing mechanistic insights behind differential functionality of these cells. Last, we demonstrate that patients treated with CAR T cell products enriched in CAR High T cells show a significantly worse clinical response in several hematological malignancies. In summary, our work demonstrates that CAR density plays an important role in CAR T activity with notable impact on clinical response.

The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets allow unprecedented gene expression analyses. Here, using these datasets, we performed pan-cancer and pan-tissue identification of coding and long noncoding RNA (lncRNA) transcripts differentially expressed in tumors and preferentially expressed in healthy tissues and/or tumors. Pan-cancer comparison of mRNAs and lncRNAs showed that lncRNAs were deregulated in a more tumor-specific manner. Given that lncRNAs are more tissue-specific than mRNAs, we identified healthy tissues that preferentially express lncRNAs upregulated in tumors and found that testis, brain, the digestive tract, and blood/spleen were the most prevalent. In addition, specific tumors also upregulate lncRNAs preferentially expressed in other tissues, generating a unique signature for each tumor type. Most tumors studied downregulated lncRNAs preferentially expressed in their tissue of origin, probably as a result of dedifferentiation. However, the same lncRNAs could be upregulated in other tumors, resulting in "bimorphic" transcripts. In hepatocellular carcinoma (HCC), the upregulated genes identified were expressed at higher levels in patients with worse prognosis. Some lncRNAs upregulated in HCC and preferentially expressed in healthy testis or brain were predicted to function as oncogenes and were significantly associated with higher tumor burden, and poor prognosis, suggesting their relevance in hepatocarcinogenesis and/or tumor evolution. Taken together, therapies targeting oncogenic lncRNAs should take into consideration the healthy tissue, where the lncRNAs are preferentially expressed, to predict and decrease unwanted secondary effects and increase potency. Significance: Comprehensive analysis of coding and noncoding genes expressed in different tumors and normal tissues, which should be taken into account to predict side effects from potential coding and noncoding gene-targeting therapies.

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single cell RNA sequencing (scRNAseq) to characterize an enriched population of human hematopoietic stem and progenitor cells (HSPCs) obtained from young and elderly healthy individuals. Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain age-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and gene regulatory networks (GRN) regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2 and RUNX1 suggesting a role of these TF in the pathogenesis of the disease. In summary, we demonstrate that the combination of single cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

Identification of new markers associated with long-term efficacy in patients treated with CAR T cells is a current medical need, particularly in diseases such as multiple myeloma. In this study we address the impact of CAR density on the functionality of BCMA-CAR T cells. Functional and transcriptional studies demonstrate that CAR T cells with high expression of the CAR construct show an increased tonic signaling with upregulation of exhaustion markers, increased in vitro cytotoxicity but a decrease in in vivo BM infiltration. Characterization of Gene Regulatory Networks using scRNA-seq identified regulons associated to activation and exhaustion upregulated in CARHigh T cells, providing mechanistic insights behind differential functionality of these cells. Finally, we demonstrate that patients treated with CAR T cell products enriched in CARHigh T cells show a significantly worse clinical response in several hematological malignancies. In summary, our work demonstrates that CAR density plays an important role in CAR T activity with significant impact on clinical response.