Gene expression-based outcome prediction in advanced stage classical Hodgkin lymphoma treated with BEACOPP

T-cell diversity and exclusion of blood-derived T-cells in the tumor microenvironment of classical Hodgkin Lymphoma

The Tumor Microenvironment (TME) in classical Hodgkin Lymphoma (HL) contains abundant immune cells and only few neoplastic Hodgkin and Reed-Sternberg cells (HRSC). We analyzed the T-cell receptor (TCR) repertoire to detect T-cell expansion in the TME and blood. In contrast to solid cancer tissue, T-cells in the TME of HL are highly polyclonal at first diagnosis and show only minor clonal expansion during anti-PD1 immune checkpoint blockade (ICB). At relapse and during ICB, pre-amplified T-cell populations increase in the TME of solid cancers but to a much lesser extent in HL. In contrast, T-cell populations in the peripheral blood of HL patients display higher clonality than healthy controls reaching clonality levels comparable to solid cancer. However, pre-amplified blood T-cells in HL patients show only minor additional clonal expansion during ICB. Moreover, blood-derived T-cells do not repopulate the TME of HL to the same extent as observed in solid cancers. Thus, the T-cell repertoire in the TME of HL appears unique by a relatively low clonal T-cell content and the exclusion of clonally expanded T-cells from the peripheral blood. Exclusion of clonally expanded tumor-specific T-cells from the TME may present a novel mechanism of immune evasion in HL.

Human leukocyte antigen (HLA) class I expression on Hodgkin-Reed-Sternberg cells is an EBV-independent major determinant of microenvironment composition in classic Hodgkin lymphoma

Hodgkin-Reed-Sternberg cells (HRSCs) in classic Hodgkin Lymphoma (HL) frequently lack expression of human leukocyte antigen class I (HLA-I), considered to hamper activation of cytotoxic T cells in the tumor microenvironment (TME). Here, we demonstrate HLA-I expression on HRSCs to be a strong determinant of TME composition whereas expression of HLA-II was associated with only minor differential gene expression in the TME. In HLA-I-positive HL the HRSC content and expression of CCL17/TARC in HRSCs are low, independent of the presence of Epstein-Barr virus in HRSCs. Additionally, HLA-I-positive HL shows a high content of CD8+ cytotoxic T cells. However, an increased expression of the inhibitory immune checkpoint LAG3 on CD8+ T cells in close proximity to HRSCs is observed. Suggesting interference with cytotoxic activity, we observed an absence of clonally expanded T cells in the TME. While HLA-I-positive HL is not associated with an unfavorable clinical course in our cohorts, they share features with the recently described H2 subtype of HL. Given the major differences in TME composition, immune checkpoint inhibitors may differ in their mechanism of action in HLA-I-positive compared to HLA-I-negative HL.

Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma

PURPOSE

Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS).

PATIENTS AND METHODS

This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort).

RESULTS

In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis.

CONCLUSION

The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma

The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to approximately 1,000× median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7, novel stop gain mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. As a further application of our exome sequencing, we attempted to identify expressed somatic single-nucleotide variants (SNV) in single-nuclei RNA sequencing (snRNA-seq) data generated from a patient in our cohort. Our snRNA analysis identified a clear cluster of cells containing a somatic SNV identified in our deep exome data. This cluster has differentially expressed genes that are consistent with genes known to be dysregulated in HRS cells (e.g., PIM1 and PIM3). The cluster also contains cells with an expanded B-cell clonotype further supporting a malignant phenotype. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells and demonstrates the feasibility of snRNA-seq in the context of cHL. These studies provide the foundation for the further analysis of genomic variants in large cohorts of patients with cHL.

SIGNIFICANCE

Our data demonstrate the utility of ultra-deep exome sequencing in uncovering somatic variants in Hodgkin lymphoma, creating new opportunities to define the genes that are recurrently mutated in this disease. We also show for the first time the successful application of snRNA-seq in Hodgkin lymphoma and describe the expression profile of a putative cluster of HRS cells in a single patient.

Genomic profiling for clinical decision making in lymphoid neoplasms

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.

Predictors of risk of relapse in classic Hodgkin lymphoma

Using multiparametric flow cytometric analysis, in a cohort of 62 patients with classic Hodgkin lymphoma having a median follow-up period of 69.5 months, we found-patients who experienced primary resistance or disease relapse (DR) had lower percentage of rosetted Hodgkin Reed-Sternberg cells (HRS-cells) as compared with patients who achieved sustained complete remission (SCR) (p=0.022); patients >35 years of age had higher percentage of HRS-cells (p=0.017) and lower percentage of B cells (p=0.017) and the nodular sclerosis subtype had higher percentage of B-cells (p=0.046) and activated B-cells (p=0.03). The proportion of SCR and DR subsets did not differ by histological subtypes, disease stage or age groups.

Current and Emerging Approaches to Study Microenvironmental Interactions and Drug Activity in Classical Hodgkin Lymphoma

Simple Summary

In classical Hodgkin Lymphoma (cHL), the tumor microenvironment (TME) plays an important role in tumor progression and treatment response, making its evaluation critical for determining prognosis, treatment strategies and predicting an increase in drug toxicity. Therefore, there is a need to utilize more complex systems to study the cHL-TME and its interplay with tumor cells. To evaluate new anticancer drugs and to find the mechanisms of drug resistance, this review summarizes emerging approaches for the analysis of the TME composition and to identify the state of the disease; the in vitro techniques used to determine the mechanisms involved in the building of an immunosuppressive and protective TME; new 3-dimensional (3D) models, the heterospheroids (HS), developed to mimic TME interactions. Here, we describe the present and likely future clinical applications indicated by the results of these studies and propose a classification for the in vitro culture methods used to study TME interactions in cHL.

Abstract

Classic Hodgkin lymphoma is characterized by a few tumor cells surrounded by a protective and immunosuppressive tumor microenvironment (TME) composed by a wide variety of noncancerous cells that are an active part of the disease. Therefore, new techniques to study the cHL-TME and new therapeutic strategies targeting specifically tumor cells, reactivating the antitumor immunity, counteracting the protective effects of the TME, were developed. Here, we describe new methods used to study the cell composition, the phenotype, and the spatial distribution of Hodgkin and Reed–Sternberg (HRS) cells and of noncancerous cells in tumor tissues. Moreover, we propose a classification, with increasing complexity, of the in vitro functional studies used to clarify the interactions leading not only to HRS cell survival, growth and drug resistance, but also to the immunosuppressive tumor education of monocytes, T lymphocytes and fibroblasts. This classification also includes new 3-dimensional (3D) models, obtained by cultivating HRS cells in extracellular matrix scaffolds or in sponge scaffolds, under non-adherent conditions with noncancerous cells to form heterospheroids (HS), implanted in developing chick eggs (ovo model). We report results obtained with these approaches and their applications in clinical setting.

OUP accepted manuscript

MOTIVATION

Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e. data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data are rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e. we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM).

RESULTS

We show in simulation studies that this approach outperforms the state of the art. Furthermore, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort.

AVAILABILITY AND IMPLEMENTATION

We provide BITES as an easy-to-use python implementation including scheduled hyper-parameter optimization (https://github.com/sschrod/BITES). The data underlying this article are available in the CRAN repository at https://rdrr.io/cran/survival/man/gbsg.html and https://rdrr.io/cran/survival/man/rotterdam.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hodgkin Lymphoma: A Special Microenvironment

Classical Hodgkin's lymphoma (cHL) is one of the most particular lymphomas for the few tumor cells surrounded by an inflammatory microenvironment. Reed-Sternberg (RS) and Hodgkin (H) cells reprogram and evade antitumor mechanisms of the normal cells present in the microenvironment. The cells of microenvironment are essential for growth and survival of the RS/H cells and are recruited through the effect of cytokines/chemokines. We summarize recent advances in gene expression profiling (GEP) analysis applied to study microenvironment component in cHL. We also describe the main therapies that target not only the neoplastic cells but also the cellular components of the background.