Integrated multi-omic profiling identifies molecular subtypes and predicts outcomes in newly diagnosed peripheral T-cell lymphomas Free

Introduction Peripheral T-cell lymphomas (PTCLs) comprise a heterogeneous group of aggressive hematological malignancies, accounting for 10–15% of all Non-Hodgkin Lymphomas (NHLs). Despite therapeutic advances, prognosis remains poor for most patients. In this context, the PTCL-13 study was designed to evaluate the addition of Romidepsin to anthracycline-based chemotherapy (Ro-CHOEP) followed by autologous stem cell transplantation (SCT) (NCT02223208). Notably, the clinical analysis of this cohort found no significant improvement by adding romidepsin (Chiappella et al. Leukemia, 2023), highlighting the need for new drugs as well as molecular profiling to guide patient stratification and possibly therapeutic decision-making. To address this, we performed an integrative multi-omics analysis of patients enrolled in the PTCL13 clinical trial with the aim of identifying molecular signatures associated with outcomes.

Material and Methods Formalin-fixed, paraffin-embedded (FFPE) tissue and plasma were prospectively collected at baseline from 73 newly diagnosed PTCL patients with different histologies [nodal T-follicular helper lymphoma (nTFHL;n=27); Peripheral T-cell lymphoma not otherwise specified (PTCL-NOS;n=29); Anaplastic Lymphoma Kinase-negative Anaplastic large-cell lymphoma (ALK^neg ALCL;n=17)]. Targeted sequencing was performed on paired FFPE and plasma samples (Illumina NextSeq 550) using a panel of 60 genes (Roche) to identify somatic mutations. RNA from FFPE was used to perform gene expression profiling using the nCounter 780-gene panel (NanoString) and bulk RNA-sequencing (Illumina NextSeq 550 platform). Clustering analysis of multi-omic datasets was performed on R (version 4.5.0).

Results Unsupervised clustering of transcriptomic data identified three distinct clusters (Cluster A, Cluster B, and Cluster C). Cluster A had significantly higher proportion of nTFHL patients (58%; p<0.0001), but also included some PTCL-NOS (35%) and ALK^neg ALCL (7%) patients. Consistent with this, genes associated with T-follicular helper (TFH) and B-cells (TCF7, CD22, MS4A1) (adjusted p<0.01) were significantly upregulated in Cluster A. Interestingly, there was also a significant downregulation of genes associated with myeloid cells, including CD14, CD163, and CD33 (adjusted p<0.05) in Cluster A. Conversely, genes related to M2 macrophages (CCL13, CCL17) (adjusted p<0.05) and exhausted T cells (PDL2, CD276) (adjusted p<0.05) were significantly upregulated in Clusters B and C, respectively. Notably, patients in Cluster A had superior progression-free survival (PFS) as compared to patients in Clusters B and C (p<0.05). To better define the molecular landscape of PTCLs, an integrated multi-omic analysis combining genomic and transcriptomic profiles was conducted. The Cluster-of-Clusters analysis (COCA) approach identified two principal clusters (Cluster 1 and Cluster 2). Cluster 1 closely mirrored the transcriptional and histological profile of Cluster A. Furthermore, there was a higher frequency of gene mutations in epigenetic regulators, including TET2 (36%), RHOA (18%), IKZF2 (14%), and DNMT3A (14%), and a significant co-occurrence of RHOA and TET2 in this group (p<0.01). In contrast, Cluster 2 closely resembled Clusters B and C in terms of gene expression profile and histological subgrouping. Additionally, mutations in oncogenic drivers such as CARD11 (7%), TP63 (4%) and MYC (4%) with significant co-occurrence between CARD11 and NOTCH1 genes (p<0.01) were found to be associated with Cluster 2. Patients in Cluster 1 had significantly higher complete response (CR) rates to Ro-CHOEP as compared to Cluster 2 (46% CR in Cluster 1 vs. 18% CR in Cluster 2; p<0.05). Additionally, Cluster 1 patients demonstrated a trend toward prolonged PFS (p=0.06), and significantly improved overall survival (p<0.05).

Conclusions Multi-omic clustering identified a subset of patients likely to benefit from Ro-CHOEP treatment, characterized by a less immunosuppressive tumor microenvironment and higher frequency of mutation in epigenetic regulators. Collectively, our findings underscore the potential of integrative molecular profiling as a tool to guide risk stratification and provide biological insights beyond conventional histological sub-classification. Ongoing studies integrating bulk RNA sequencing and whole genome sequencing data into this model will be presented, allowing us to further refine molecular characterization of PTCLs.