With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.

Background: Improved usage of the repertoires of pancreatic ductal adenocarcinoma (PDAC) profiles is crucially needed to guide the development of predictive and prognostic tools that could inform the selection of treatment options. Methods: Using publicly available mRNA abundance datasets, we performed a large retrospective meta-analysis on 466 PDAC patients to discover prognostic gene signatures. These signatures were trained on two clinical cohorts (n = 70), and validated on four independent clinical cohorts (n = 246). Further validation of the identified gene signature was performed using quantitative real-time RT-PCR. Results: We identified 225 candidate prognostic genes. Using these, a 36-gene signature was discovered and validated on fully independent clinical cohorts (hazard ratio (HR) = 2.06, 95% confidence interval (CI) = 1.51 to 2.81, P = 3.62 × 10⁻⁶, n = 246). This signature serves as a good alternative prognostic stratification marker compared to tumour grade (HR = 2.05, 95% CI = 1.45 to 2.88, P = 3.18 × 10⁻⁵) and tumour node metastasis (TNM) stage (HR = 1.13, 95% CI = 0.66 to 1.94, P = 0.67). Upon multivariate analysis with adjustment for TNM stage and tumour grade, the 36-gene signature remained an independent prognostic predictor of clinical outcome (HR = 2.21, 95% CI = 1.17 to 4.16, P = 0.01). Univariate assessment revealed higher expression of ITGA5, SEMA3A, KIF4A, IL20RB, SLC20A1, CDC45, PXN, SSX3 and TMEM26 was correlated with shorter survival while B3GNT1, NOSTRIN and CADPS down-regulation was associated with poor outcome. Conclusions: Our 36-gene classifier is able to prognosticate PDAC independent of patient cohort and microarray platforms. Further work on the functional roles, downstream events and interactions of the signature genes is likely to reveal true molecular candidates for PDAC therapeutics.

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rankp < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

The inclusion of familial myeloid malignancies as a separate disease entity in the revised WHO classification has renewed efforts to improve the recognition and management of this group of at risk individuals. Here we report a cohort of 86 acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) families with 49 harboring germline variants in 16 previously defined loci (57%). Whole exome sequencing in a further 37 uncharacterized families (43%) allowed us to rationalize 65 new candidate loci, including genes mutated in rare hematological syndromes (ADA,GP6, IL17RA, PRF1andSEC23B), reported in prior MDS/AML or inherited bone marrow failure series (DNAH9,NAPRT1and SH2B3) or variants at novel loci (DHX34) that appear specific to inherited forms of myeloid malignancies. Altogether, our series of MDS/AML families offer novel insights into the etiology of myeloid malignancies and provide a framework to prioritize variants for inclusion into routine diagnostics and patient management.

While most myelodysplastic syndrome/acute myeloid leukemia cases are sporadic, rare familial cases occur and provide some insight into leukemogenesis. The most clearly defined familial cases result from inherited mutations in RUNX1 or CEBPA. Recently, novel germline mutations in GATA2 have been reported. We, therefore, investigated individuals from families with one or more first-degree relatives with myelodysplastic syndrome/acute myeloid leukemia with wild-type RUNX1 and CEBPA, for GATA2 mutations. Screening for other recurrent mutations was also performed. A GATA2 p.Thr354Met mutation was observed in a pedigree in which 2 first-degree cousins developed high-risk myelodys-plastic syndrome with monosomy 7. They were also observed to have acquired identical somatic ASXL1 mutations and both died despite stem cell transplantation. These findings confirm that germline GATA2 mutations predispose to familial myelodysplastic syndrome/acute myeloid leukemia, and that monosomy 7 and ASXL1 mutations may be recurrent secondary genetic abnormalities triggering overt malignancy in these families.

Acute myeloid leukemia (AML) disrupts the generation of normal blood cells, predisposing patients to hemorrhage, anemia, and infections. Differentiation and proliferation of residual normal hematopoietic stem and progenitor cells (HSPCs) are impeded in AML-infiltrated bone marrow (BM). The underlying mechanisms and interactions of residual hematopoietic stem cells (HSCs) within the leukemic niche are poorly understood, especially in the human context. To mimic AML infiltration and dissect the cellular crosstalk in human BM, we established humanized ex vivo and in vivo niche models comprising AML cells, normal HSPCs, and mesenchymal stromal cells (MSCs). Both models replicated the suppression of phenotypically defined HSPC differentiation without affecting their viability. As occurs in AML patients, the majority of HSPCs were quiescent and showed enrichment of functional HSCs. HSPC suppression was largely dependent on secreted factors produced by transcriptionally remodeled MSCs. Secretome analysis and functional validation revealed MSC-derived stanniocalcin 1 (STC1) and its transcriptional regulator HIF-1α as limiting factors for HSPC proliferation. Abrogation of either STC1 or HIF-1α alleviated HSPC suppression by AML. This study provides a humanized model to study the crosstalk among HSPCs, leukemia, and their MSC niche, and a molecular mechanism whereby AML impairs normal hematopoiesis by remodeling the mesenchymal niche.