Gene expression-based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma

Targeting tumor-associated macrophages to reverse antitumor drug resistance

Currently, antitumor drugs show limited clinical outcomes, mainly due to adaptive resistance. Clinical evidence has highlighted the importance of the tumor microenvironment (TME) and tumor-associated macrophages (TAMs) in tumor response to conventional antitumor drugs. Preclinical studies show that TAMs following antitumor agent can be reprogrammed to an immunosuppressive phenotype and proangiogenic activities through different mechanisms, mediating drug resistance and poor prognosis. Potential extrinsic inhibitors targeting TAMs repolarize to an M1-like phenotype or downregulate proangiogenic function, enhancing therapeutic efficacy of anti-tumor therapy. Moreover, pharmacological modulation of macrophages that restore the immune stimulatory characteristics is useful to reshaping the tumor microenvironment, thus further limiting tumor growth. This review aims to introduce macrophage response in tumor therapy and provide a potential therapeutic combination strategy of TAM-targeting immunomodulation with conventional antitumor drugs.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.

Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes in Relapsed/Refractory Hodgkin Lymphoma

PURPOSE

About a third of patients with relapsed or refractory classic Hodgkin lymphoma (r/r CHL) succumb to their disease after high-dose chemotherapy followed by autologous stem-cell transplantation (HDC/ASCT). Here, we aimed to describe spatially resolved tumor microenvironment (TME) ecosystems to establish novel biomarkers associated with treatment failure in r/r CHL.

PATIENTS AND METHODS

We performed imaging mass cytometry (IMC) on 71 paired primary diagnostic and relapse biopsies using a marker panel specific to CHL biology. For each cell type in the TME, we calculated a spatial score measuring the distance of nearest neighbor cells to the malignant Hodgkin Reed Sternberg cells within the close interaction range. Spatial scores were used as features in prognostic model development for post-ASCT outcomes.

RESULTS

Highly multiplexed IMC data revealed shared TME patterns in paired diagnostic and early r/r CHL samples, whereas TME patterns were more divergent in pairs of diagnostic and late relapse samples. Integrated analysis of IMC and single-cell RNA sequencing data identified unique architecture defined by CXCR5+ Hodgkin and Reed Sternberg (HRS) cells and their strong spatial relationship with CXCL13+ macrophages in the TME. We developed a prognostic assay (RHL4S) using four spatially resolved parameters, CXCR5+ HRS cells, PD1+CD4+ T cells, CD68+ tumor-associated macrophages, and CXCR5+ B cells, which effectively separated patients into high-risk versus low-risk groups with significantly different post-ASCT outcomes. The RHL4S assay was validated in an independent r/r CHL cohort using a multicolor immunofluorescence assay.

CONCLUSION

We identified the interaction of CXCR5+ HRS cells with ligand-expressing CXCL13+ macrophages as a prominent crosstalk axis in relapsed CHL. Harnessing this TME biology, we developed a novel prognostic model applicable to r/r CHL biopsies, RHL4S, opening new avenues for spatial biomarker development.

Epstein-Barr virus-based prognostic model in nodular sclerosis classic Hodgkin lymphoma

The role of Epstein-Barr virus (EBV) in lymphoma cells of nodular sclerosis classic Hodgkin lymphoma (NScHL) is controversial. Our aim was to explore this and establish a clinically feasible model for risk stratification. We interrogated data from 542 consecutive subjects with NScHL receiving ABVD therapy and demonstrated EBV-infection in their lymphoma cells with EBV-encoded small RNAs (EBERs) in situ hybridization. Subjects were divided into training and validation datasets. As data from the training dataset suggested EBERs-positivity was the only independent prognostic factor for both progression-free survival (PFS) and overall survival (OS), we developed corresponding prognostic models based on it. Our models showed excellent performance in both training and validation cohort. These data indicate the close association of EBV infection and the outcomes of persons with NScHL receiving ABVD. Additionally, our newly developed models should help physicians estimate prognosis and select individualized therapy.

Immune and stromal transcriptional patterns that influence the outcome of classic Hodgkin lymphoma

Classic Hodgkin lymphoma (cHL) is characterized by a rich immune microenvironment as the main tumor component. It involves a broad range of cell populations, which are largely unexplored, even though they are known to be essential for growth and survival of Hodgkin and Reed-Sternberg cells. We profiled the gene expression of 25 FFPE cHL samples using NanoString technology and resolved their microenvironment compositions using cell-deconvolution tools, thereby generating patient-specific signatures. The results confirm individual immune fingerprints and recognize multiple clusters enriched in refractory patients, highlighting the relevance of: (1) the composition of immune cells and their functional status, including myeloid cell populations (M1-like, M2-like, plasmacytoid dendritic cells, myeloid-derived suppressor cells, etc.), CD4-positive T cells (exhausted, regulatory, Th17, etc.), cytotoxic CD8 T and natural killer cells; (2) the balance between inflammatory signatures (such as IL6, TNF, IFN-γ/TGF-β) and MHC-I/MHC-II molecules; and (3) several cells, pathways and genes related to the stroma and extracellular matrix remodeling. A validation model combining relevant immune and stromal signatures identifies patients with unfavorable outcomes, producing the same results in an independent cHL series. Our results reveal the heterogeneity of immune responses among patients, confirm previous findings, and identify new functional phenotypes of prognostic and predictive utility.

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.

On the challenges of predicting treatment response in Hodgkin's Lymphoma using transcriptomic data

BACKGROUND

Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin's Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies.

METHODS

We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin's Lymphoma patients, obtained through the NanoString's nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules.

RESULTS

Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models (e.g. Decision Trees show an improvement of 20pp in both precision and recall).

CONCLUSIONS

Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice.

Novel insights into Hodgkin lymphoma biology by single-cell analysis

The emergence and rapid development of single-cell technologies mark a paradigm shift in cancer research. Various technology implementations represent powerful tools to understand cellular heterogeneity, identify minor cell populations that were previously hard to detect and define, and make inferences about cell-to-cell interactions at single-cell resolution. Applied to lymphoma, recent advances in single-cell RNA sequencing have broadened opportunities to delineate previously underappreciated heterogeneity of malignant cell differentiation states and presumed cell of origin, and to describe the composition and cellular subsets in the ecosystem of the tumor microenvironment (TME). Clinical deployment of an expanding armamentarium of immunotherapy options that rely on targets and immune cell interactions in the TME emphasizes the requirement for a deeper understanding of immune biology in lymphoma. In particular, classic Hodgkin lymphoma (CHL) can serve as a study paradigm because of its unique TME, featuring infrequent tumor cells among numerous nonmalignant immune cells with significant interpatient and intrapatient variability. Synergistic to advances in single-cell sequencing, multiplexed imaging techniques have added a new dimension to describing cellular cross talk in various lymphoma entities. Here, we comprehensively review recent progress using novel single-cell technologies with an emphasis on the TME biology of CHL as an application field. The described technologies, which are applicable to peripheral blood, fresh tissues, and formalin-fixed samples, hold the promise to accelerate biomarker discovery for novel immunotherapeutic approaches and to serve as future assay platforms for biomarker-informed treatment selection, including immunotherapies.

Immune infiltration could predict the efficacy of short-term radiotherapy in patients with cervical cancer

Radiotherapy is the main treatment for cervical cancer. It is usually applied alone or in combination with surgery and/or chemotherapy. To explore the association between immune microenvironment of cervical cancer and radiotherapy response, we collected 20 paired cervical cancer tumor samples before and after radiotherapy and partial clinical information. With paired-end RNA-seq, we quantified the immune infiltration and tumor purity of these samples, and obtained 6350 differentially expressed genes before and after radiotherapy. With the help of R language, the function enrichment analysis and 22 immune cells infiltration analysis were carried out. Moreover, we built a random forest model based on the immune microenvironment to predict the short-term efficacy of radiotherapy. We found that the effect of radiotherapy on the immune microenvironment of stage III and IV cervical cancer patients was weaker than that of stage I and II cervical cancer patients. Radiotherapy can significantly reduce the tumor purity and increase immune infiltration. The proportions of the immune infiltrating cells are predictive of the radiotherapy efficacy. In addition, the local mucositis caused by radiotherapy can improve the curative effect of radiotherapy.

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.

Identification of genomic determinants contributing to cytokine release in immunotherapies and human diseases

Background

Cytokine release syndrome (CRS) is a strong immune system response that can occur as a result of the reaction of a cellular immunotherapy with malignant cells. While the frequency and management of CRS in CAR T-cell therapy has been well documented, there is emerging interest in pre-emptive treatment to reduce CRS severity and improve overall outcomes. Accordingly, identification of genomic determinants that contribute to cytokine release may lead to the development of targeted therapies to prevent or abrogate the severity of CRS.

Methods

Forty three clinical CD22 CAR T-cell products were collected for RNA extraction. 100 ng of mRNA was used for Nanostring assay analysis which is based on the nCounter platform. Several public datasets were used for validation purposes.

Results

We found the expression of the PFKFB4 gene and glycolytic pathway activity were upregulated in CD22 CAR T-cells given to patients who developed CRS compared to those who did not experience CRS. Moreover, these results were further validated in cohorts with COVID-19, influenza infections and autoimmune diseases, and in tumor tissues. The findings were similar, except that glycolytic pathway activity was not increased in patients with influenza infections and systemic lupus erythematosus (SLE).

Conclusion

Our data strongly suggests that PFKFB4 acts as a driving factor in mediating cytokine release in vivo by regulating glycolytic activity. Our results suggest that it would beneficial to develop drugs targeting PFKFB4 and the glycolytic pathway for the treatment of CRS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03531-3.

A new prognostic model including platelet/lymphocyte ratio and International Prognostic Score 3 for freedom from progression in patients with previously untreated advanced classical Hodgkin lymphoma

PURPOSE

We aimed to develop a new risk stratification tool to predict freedom from progression (FFP) for newly diagnosed advanced classical Hodgkin lymphoma (cHL).

METHODS

We collected data from 386 patients with advanced cHL diagnosed between December 8, 2000 and October 29, 2018, and treated with curative intent with ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or an ABVD-equivalent regimen. Cases were randomly divided into training and validation cohorts at a ratio of 7:3. The new model was constructed based on the results of Cox proportional hazards model in the training cohort. Comparisons of discrimination between the new model and other models in the training and validation cohorts for FFP prediction were measured by time-dependent area under curve (tAUC) and Harrell's C-index. Calibration plots were constructed to compare the consistency between the predicted and observed estimates of survival probability for the new model in the training and validation cohorts.

RESULTS

The new model (IPSPLR) composed of International Prognostic Score (IPS)-3 and platelet/lymphocyte ratio (PLR) provided four distinct risk groups. The IPSPLR showed better discriminative ability when compared with IPS-3 and IPS-7. The AUC of IPSPLR was consistently higher than that of IPS-3 and IPS-7 between 12 and 120 months. The C-index of the IPSPLR was higher than that of IPS-7 and IPS-3. The calibration plots showed an excellent agreement between the IPSPLR-predicted and observed estimates of 5-year FFP.

CONCLUSION

The IPSPLR is an easily used tool for FFP prediction for newly diagnosed advanced cHL. Validation of this tool in other large datasets is needed.

RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data

The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.

Prognostic role of red blood cell distribution width and platelet/lymphocyte ratio in early-stage classical Hodgkin lymphoma

Background: To investigate the prognostic role of red blood cell distribution width (RDW) and platelet/lymphocyte ratio (PLR) in early-stage classical Hodgkin lymphoma (cHL). Materials & methods: Data from 402 patients with newly diagnosed early-stage cHL were retrospectively collected. The impact of factors on complete response (CR) rate and freedom from progression (FFP) was analyzed. Results: High PLR was associated with lower CR, but high RDW was not. The univariate analysis showed that RDW and PLR were predictive of FFP. On multivariate analysis, high PLR was an independent risk factor for inferior FFP. Subgroup analysis and a prognostic model for FFP based on PLR validated the prognostic role of PLR. Conclusion: PLR was a robust prognostic factor for newly diagnosed early-stage cHL.

A gene expression-based model predicts outcome in children with intermediate-risk classical Hodgkin lymphoma

Classical Hodgkin lymphoma (cHL) is a common malignancy in children and adolescents. Although cHL is highly curable, treatment with chemotherapy and radiation often come at the cost of long-term toxicity and morbidity. Effective risk-stratification tools are needed to tailor therapy. Here, we used gene expression profiling (GEP) to investigate tumor microenvironment (TME) biology, to determine molecular correlates of treatment failure, and to develop an outcome model prognostic for pediatric cHL. A total of 246 formalin-fixed, paraffin-embedded tissue biopsies from patients enrolled in the Children's Oncology Group trial AHOD0031 were used for GEP and compared with adult cHL data. Eosinophil, B-cell, and mast cell signatures were enriched in children, whereas macrophage and stromal signatures were more prominent in adults. Concordantly, a previously published model for overall survival prediction in adult cHL did not validate in pediatric cHL. Therefore, we developed a 9-cellular component model reflecting TME composition to predict event-free survival (EFS). In an independent validation cohort, we observed a significant difference in weighted 5-year EFS between high-risk and low-risk groups (75.2% vs 90.3%; log-rank P = .0138) independent of interim response, stage, fever, and albumin. We demonstrate unique disease biology in children and adolescents that can be harnessed for risk-stratification at diagnosis. This trial was registered at www.clinicaltrials.gov as #NCT00025259.

Gene expression signature as a surrogate marker of microvascular invasion on routine hepatocellular carcinoma biopsies

BACKGROUND & AIMS

Microvascular invasion (MVI), a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma (HCC), is only detectable by microscopic examination of the surgical specimen. We aimed to define a transcriptomic signature associated with MVI in HCC than can be applied to formalin-fixed paraffin-embedded (FFPE) biopsies for use in clinical practice.

METHODS

To identify a gene expression signature related to MVI by using NanoString technology, we selected a set of 200 genes according to the literature and RNA-sequencing data obtained from a cohort of 150 frozen HCC samples previously published. We used 178 FFPE-archived HCC samples, including 109 surgical samples for the training set and 69 paired pre-operative biopsies for the validation set. In 14 cases of the training set, a paired biopsy was available and was also analyzed.

RESULTS

We identified a 6-gene signature (ROS1, UGT2B7, FAS, ANGPTL7, GMNN, MKI67) strongly associated with MVI in the training set of FFPE surgical HCC samples, with 82% accuracy (sensitivity 82%, specificity 81%, AUC 0.82). The NanoString gene expression was highly correlated in 14 paired surgical/biopsy HCC samples (mean R: 0.97). In the validation set of 69 FFPE HCC biopsies, the 6-gene NanoString signature predicted MVI with 74% accuracy (sensitivity 73%, specificity 76%, AUC 0.74). Moreover, on multivariate analysis, the MVI signature was associated with overall survival in both sets (hazard ratio 2.29; 95% CI 1.03-5.07; p = 0.041).

CONCLUSION

We defined a 6-gene signature that can accurately predict MVI in FFPE HCC biopsy samples, which is also associated with overall survival, although its survival impact must be confirmed by extensive study with further clinical data.

LAY SUMMARY

Microvascular invasion, a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma, is only detectable by microscopic examination of a surgical specimen. In this study, we defined a relevant surrogate signature of microvascular invasion in hepatocellular carcinoma that may be applied in clinical practice with routine tumor biopsy and integrated into the therapeutic strategy.

Gene Expression Profiling of FFPE Samples: A Titration Test

The gene expression analysis of formalin-fixed paraffin-embedded (FFPE) tissues is often hampered by poor RNA quality, which results from the oxidation, cross-linking and other chemical modifications induced by the inclusion in paraffin. Yet, FFPE samples are a valuable source for molecular studies and can provide great insights into disease progression and prognosis. With the advancement of genomic technologies, new methods have been established that offer reliable and accurate gene expression workflows on samples of poor quality. NanoString is a probe-based technology that allows the direct counting of the mRNA transcripts and can be applied to degraded samples. Here, we have tested 2 RNA extraction methods for FFPE samples, and we have performed a titration experiment to evaluate the impact of RNA degradation and RNA input on the gene expression profiles assessed using the NanoString IO360 panel. We have selected FFPE samples of different DV200 values and assessed them on the nCounter platform with 2 different amounts of input RNA. This study concludes that the nCounter is a robust and reliable platform to assess the gene expression of RNA samples with DV200 > 30%; its robustness and ease of use could be of particular benefit to clinical settings.

Survival by age in paediatric and adolescent patients with Hodgkin lymphoma: a retrospective pooled analysis of children's oncology group trials

BACKGROUND

Adolescents with Hodgkin lymphoma have worse disease outcomes than children. Whether these differences persist within clinical trials is unknown. We examined survival, by age, in patients receiving response-adapted therapy for Hodgkin lymphoma on Children's Oncology Group (COG) trials.

METHODS

Patients (aged 1-21 years) diagnosed with classical Hodgkin lymphoma and enrolled between Sept 23, 2002, and Jan 19, 2012, on one of three phase 3 COG trials in the USA and Canada were eligible for inclusion. The three COG trials were defined by risk group according to Ann Arbor stage, B-symptoms, and bulk (AHOD0431 [low risk; NCT00302003], AHOD0031 [intermediate risk; NCT00025259], or AHOD0831 [high risk; NCT01026220]). The outcomes of this study were event-free survival (death, relapse, or subsequent neoplasm) and overall survival. Cox proportional hazards models estimated survival, adjusting for disease and treatment factors both overall and in patients with mixed cellularity or non-mixed cellularity (nodular sclerosing and not-otherwise-specified) disease.

FINDINGS

Of 2155 patients enrolled on the three trials, 1907 (88·4%; 968 [50·8%] male and 939 [49·2%] female; 1227 [64·3%] non-Hispanic White) were included in this analysis. After a median follow-up of 7·4 years (IQR 4·3-10·2), older patients (aged ≥15 years) had worse unadjusted 5-year event-free survival (80% [95% CI 78-83]) than did younger patients (aged <15 years; 86% [83-88]; HR 1·38 [1·11-1·71]; p=0·0038). Older patients also had worse unadjusted 5-year overall survival than did younger patients (96% [95% CI 95-97] vs 99% [98-99]; HR 2·50 [1·41-4·45]; p=0·0012). In patients with non-mixed cellularity histology, older patients had a significantly increased risk of having an event than did younger patients with the same histology (HR 1·32 [1·03-1·68]; p=0·027). Older patients with mixed cellularity had significantly worse 5-year event-free survival than did younger patients in unadjusted (77% [95% CI 65-86] for older patients vs 94% [88-97] for younger patients; HR 2·93 [1·37-6·29]; p=0·0039) and multivariable models (HR 3·72 [1·56-8·91]; p=0·0032). Overall, older patients were more likely to die than younger patients (HR 3·08 [1·49-6·39]; p=0·0025).

INTERPRETATION

Adolescents (≥15 years) treated on COG Hodgkin lymphoma trials had worse event-free survival and increased risk of death compared with children (<15 years). Our findings highlight the need for prospective studies to examine tumour and host biology, and to test novel therapies across the age spectrum.

FUNDING

National Institutes of Health, St Baldrick's Foundation, and Lymphoma Research Foundation.

Checkpoint protein expression in the tumor microenvironment defines the outcome of classical Hodgkin lymphoma patients

Emerging evidence indicates a major impact for the tumor microenvironment (TME) and immune escape in the pathogenesis and clinical course of classical Hodgkin lymphoma (cHL). We used gene expression profiling (n=88), CIBERSORT, and multiplex immunohistochemistry (n=131) to characterize the immunoprofile of cHL TME, and correlated the findings with survival. Gene expression analysis divided tumors into subgroups with T cell-inflamed and non-inflamed TME. Several macrophage-related genes were upregulated in samples with the non-T cell-inflamed TME, and based on the immune cell proportions, the samples clustered according to the content of T cells and macrophages. A cluster with high proportions of checkpoint protein (PD-1, PD-L1, IDO-1, LAG-3, and TIM-3) positive immune cells translated to unfavorable overall survival (OS) (5-year OS 76% vs. 96%, P=0.010), and remained as an independent prognostic factor for OS in multivariable analysis (HR 4.34, 95% CI 1.05-17.91, P=0.043). cHLs with high proportions of checkpoint proteins overexpressed genes coding for cytolytic factors, proposing paradoxically that they were immunologically active. This checkpoint molecule gene signature translated to inferior survival in a validation cohort of 290 diagnostic cHL samples (P<0.001) and in an expansion cohort of 84 cHL relapse samples (P=0.048). Our findings demonstrate the impact of T cell- and macrophage-mediated checkpoint system on the survival of patients with cHL.

Nanostring-Based Identification of the Gene Expression Profile in Trigger Finger Samples

Trigger finger is a common yet vastly understudied fibroproliferative hand pathology, severely affecting patients' quality of life. Consistent trauma due to inadequate positioning within the afflicted finger's tendon/pulley system leads to cellular dysregulation and eventual fibrosis. While the genetic characteristics of the fibrotic tissue in the trigger finger have been studied, the pathways that govern the initiation and propagation of fibrosis are still unknown. The complete gene expression profile of the trigger finger has never been explored. Our study has used the Nanostring nCounter gene expression assay to investigate the molecular signaling involved in trigger finger pathogenesis. We collected samples from patients undergoing trigger finger (n = 4) release surgery and compared the gene expression to carpal tunnel tissue (n = 4). Nanostring nCounter analysis identified 165 genes that were differentially regulated; 145 of these genes were upregulated, whereas 20 genes were downregulated. We found that several collagen genes were significantly upregulated, and a regulatory matrix metalloproteinase (MMP), MMP-3, was downregulated. Bioinformatic analysis revealed that several known signaling pathways were dysregulated, such as the TGF-β1 and Wnt signaling pathways. We also found several novel signaling pathways (e.g., PI3K, MAPK, JAK-STAT, and Notch) differentially regulated in trigger finger. The outcome of our study helps in understanding the molecular signaling pathway involved in the pathogenesis of the trigger finger.

Single-cell profiling reveals the importance of CXCL13/CXCR5 axis biology in lymphocyte-rich classic Hodgkin lymphoma

Lymphocyte-rich classic Hodgkin lymphoma (LR-CHL) is a rare subtype of Hodgkin lymphoma. Recent technical advances have allowed for the characterization of specific cross-talk mechanisms between malignant Hodgkin Reed-Sternberg (HRS) cells and different normal immune cells in the tumor microenvironment (TME) of CHL. However, the TME of LR-CHL has not yet been characterized at single-cell resolution. Here, using single-cell RNA sequencing (scRNA-seq), we examined the immune cell profile of 8 cell suspension samples of LR-CHL in comparison to 20 samples of the mixed cellularity (MC, 9 cases) and nodular sclerosis (NS, 11 cases) subtypes of CHL, as well as 5 reactive lymph node controls. We also performed multicolor immunofluorescence (MC-IF) on tissue microarrays from the same patients and an independent validation cohort of 31 pretreatment LR-CHL samples. ScRNA-seq analysis identified a unique CD4⁺ helper T cell subset in LR-CHL characterized by high expression of Chemokine C-X-C motif ligand 13 (CXCL13) and PD-1. PD-1⁺CXCL13⁺ T cells were significantly enriched in LR-CHL compared to other CHL subtypes, and spatial analyses revealed that in 46% of the LR-CHL cases these cells formed rosettes surrounding HRS cells. MC-IF analysis revealed CXCR5⁺ normal B cells in close proximity to CXCL13⁺ T cells at significantly higher levels in LR-CHL. Moreover, the abundance of PD-1⁺CXCL13⁺ T cells in the TME was significantly associated with shorter progression-free survival in LR-CHL (P = 0.032). Taken together, our findings strongly suggest the pathogenic importance of the CXCL13/CXCR5 axis and PD-1⁺CXCL13⁺ T cells as a treatment target in LR-CHL.

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.

Optimizing Second-Line Therapy for Hodgkin Lymphoma: A Work in Progress

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice.

Comparison of the Lymph2Cx Assay and Hans Algorithm in Determining the Cell-of-Origin of Diffuse Large B-Cell Lymphomas, Not Otherwise Specified

In the era of precision medicine, accurate and reproducible assignment of cell-of-origin (COO) in diffuse large B-cell lymphoma patients has become important. The Lymph2Cx assay is accurately determining COO by analyzing RNA expression of 20 selected genes while the Hans algorithm based on immunohistochemistry is the most popular method for routine daily diagnosis. However, there are discrepancies between the 2 methods, which need to be evaluated for better correlation. We prospectively analyzed 156 cases of diffuse large B-cell lymphoma, not otherwise specified to analyze the characteristics of discrepancy groups of COO determined by Lymph2Cx and Hans algorithm. We investigated the pattern and cause of discrepancy of COO assigned by the 2 methods. Hans algorithm classified 50 cases (32%) as germinal-center B-cell-like (GCB) type and 106 cases (68%) as non-GCB type. Lymph2Cx assay assigned 43 cases (28%) as GCB type, 94 cases (60%) as activated B-cell-like type, and 19 cases (12%) as intermediate/unclassified type. The agreement rate was 86% after exclusion of unclassified type. With regard to the clinicopathologic factors related with discrepancy between Hans algorithm and Lymph2Cx assay, endoscopic biopsy of the gastrointestinal tract (4/11, 36%) showed higher discrepancy rate (P=0.052). Immunophenotypically, CD10/BCL6/MUM1 GCB type and CD10/BCL6//MUM1 (=30%, low level expression) non-GCB type exhibited a significantly higher discrepancy rate (6/13, 46%; 4/13, 31%) (P=0.0001). Activated B-cell-like subgroup via Lymph2Cx assay predicted poor progression-free survival (mean survival duration 28.6 mo, P=0.049) compared with the GCB and unclassified type. Hans algorithm revealed no significant difference in progression-free survival and overall survival (P=0.122 and 0.121). These results suggest that when assigning COO via Hans algorithm, CD10/BCL6/MUM1 GCB type and CD10/BCL6/MUM1 (=30%, low level) non-GCB type require careful interpretation, especially if the MUM1 staining is weak and heterogeneous in the biopsied specimen.

Predicting the clinical outcome of oral potentially malignant disorders using transcriptomic-based molecular pathology

BACKGROUND

This study was undertaken to develop and validate a gene expression signature that characterises oral potentially malignant disorders (OPMD) with a high risk of undergoing malignant transformation.

METHODS

Patients with oral epithelial dysplasia at one hospital were selected as the 'training set' (n = 56) whilst those at another hospital were selected for the 'test set' (n = 66). RNA was extracted from formalin-fixed paraffin-embedded (FFPE) diagnostic biopsies and analysed using the NanoString nCounter platform. A targeted panel of 42 genes selected on their association with oral carcinogenesis was used to develop a prognostic gene signature. Following data normalisation, uni- and multivariable analysis, as well as prognostic modelling, were employed to develop and validate the gene signature.

RESULTS

A prognostic classifier composed of 11 genes was developed using the training set. The multivariable prognostic model was used to predict patient risk scores in the test set. The prognostic gene signature was an independent predictor of malignant transformation when assessed in the test set, with the high-risk group showing worse prognosis [Hazard ratio = 12.65, p = 0.0003].

CONCLUSIONS

This study demonstrates proof of principle that RNA extracted from FFPE diagnostic biopsies of OPMD, when analysed on the NanoString nCounter platform, can be used to generate a molecular classifier that stratifies the risk of malignant transformation with promising clinical utility.

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Objective

To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice.

Methods

For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model.

Results

Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles.

Conclusions

We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.

Whole-slide image analysis of the tumor microenvironment identifies low B-cell content as a predictor of adverse outcome in patients with advanced-stage classical Hodgkin lymphoma treated with BEACOPP

Asubset of patients with advanced-stage classical Hodgkin lymphoma (cHL) relapse or progress following standard treatment. Given their dismal prognosis, identifying this group of patients upfront represents an important medical need. While prior research has identified characteristics of the tumor microenvironment, which are associated with cHL outcomes, biomarkers that are developed and validated in this high-risk group are still lacking. Here, we applied wholeslide image analysis (WSI), a quantitative, large-scale assessment of tumor composition that utilizes conventional histopathology slides. We conducted WSI on pre-treatment biopsies from 340 patients with advanced-stage cHL enrolled in the HD12 and HD15 trials of the German Hodgkin Study Group (GHSG), and tested our results in a validation cohort of 147 advanced-stage cHL patients within the GHSG HD18 trial. All patients were treated with BEACOPP-based regimens. By quantifying T cells, B cells, Hodgkin and Reed-Sternberg cells and macrophages with WSI, 80% of all cells in the tumor tissue were identified. Crucially, low B-cell count was associated with significantly reduced progression-free survival and overall survival, while the content of T cells, macrophages and Hodgkin and Reed-Sternberg cells was not associated with the risk of progression or relapse in the study cohort. We further validated low Bcell content as a prognostic factor for progression-free survival and overall survival in the validation cohort and demonstrated the good interobserver agreement of WSI. WSI may represent a key tool for risk stratification of advanced-stage cHL and can easily be added to the standard diagnostic histopathology work-up.

Easier and more explanatory indices by integrating leukocyte lymphocyte ratio (LLR) and prognostic nutritional index (PNI) to IPS systems in cases with classical Hodgkin lymphoma

The aim of this study is to determine the power of he international prognostic scoring systems (IPS-7 and IPS-3) and to obtain indices by integrating leukocyte lymphocyte ratio (LLR) and prognostic nutritional index (PNI) factors as prognostic indicators in cases with classical Hodgkin lymphoma (cHL). 1012 patients with cHL were evaluated with 2 different IPS-4 scores with four parameters: stage, age, hemoglobin level, and either LLR or PNI. Statistical package SPSS v 22.0 was used. Two different Cox regression models were obtained for OS and PFS. Model 1 showed LLR ≥ 5,8 as the highest risk for OS and anemia as the highest risk for PFS. Model 2 showed PNI ≤ 45,2 as the highest risk for OS and anemia as the highest risk for PFS. IPS-4 scores obtained by integrating either LLR or PNI to IPS-3 integration of a biologic parameter either LLR or PNI need to be determined with clinical risk scoring parameters.

Characteristics of Infiltrating Immune Cells and a Predictive Immune Model for Cervical Cancer

The role of infiltrating immune cells within the tumor microenvironment has received considerable attention, but their function in cervical cancer remains to be elucidated; thus, comprehensive evaluation of their predictive value is needed. Using cervical cancer samples from 406 patients, immune cell infiltration was evaluated via immunohistochemistry. CD3+, CD4+, CD8+, CD20+, CD57+, CD68+, and CD163+ cell infiltration was compared in samples from adjacent tissues and the tumor center. The associations between immune cell distributions in the tumor center, clinicopathological features, and prognosis were correlated among immune cell types. Using three immune features, an immune model was constructed based on a Cox regression analysis with the least absolute shrinkage and selection operator (lasso) penalty to derive immune risk scores.

Immune cells that infiltrated the tumor center correlated with clinicopathological characteristics and prognosis. The immune risk scores were an independent prognostic indicator and were found to predict cervical cancer prognosis as well as the effects of chemoradiotherapy. We classified patients into either high- or low-risk subgroups (namely CD4+^highCD163+^highCD57+^low and CD4+^lowCD163+^lowCD57+^high, respectively) based on their immune scores. Significant differences were found in the 3-year overall survival of patients with high- and low-risk scores (83.0% vs. 96.6%; P < 0.001). High immune risk scores resulted in decreased overall survival for patients in stages IB1+IIA1, IB2+IIA2, and IIB-IV (P = 0.001, P = 0.008, and P = 0.044, respectively). Overall survival was significantly worse following chemoradiotherapy in high-scoring patients in stages IB1+IIA1 and IB2+IIA2 (P = 0.001 and P=0.008, respectively). Moreover, overall survival was significantly worse after radiotherapy or chemotherapy in high-scoring patients in stage IB1+IIA1 (P = 0.03). Our work reveals that the distribution of infiltrating immune cells affects their function in cervical cancer. Our tumor center-centric immune model effectively predicted survival, suggesting its potential use in identifying suitable candidates for chemoradiotherapy.

An immune-related signature that to improve prognosis prediction of breast cancer

Although the classic molecular subtype of breast cancer (BRCA) has been widely used in clinical diagnosis, as a highly heterogeneous malignant tumor, the classic scheme is not enough to accurately predict the prognosis of breast cancer patients. Immune cells in the tumor microenvironment (TME) are thought to play a paramount role in tumor development and driving poor prognosis. In this study, we aimed to develop a TME-associated, immune-related signature to improve prognosis prediction of BRCA. BRCA_OURS enriched transcriptomic RNA sequencing (RNA-seq) of tumor tissue was acquired from 43 breast cancer patients before any treatment. On the immune gene profiles of 43 patients from BRCA_OURS and 932 BRCA patients from The Cancer Genome Atlas (TCGA), we identified a robust immune-related signature including one positive coefficients gene (IL-10) and other 9 genes (C14orf79, C1orf168, C1orf226, CELSR2, FABP7, FGFBP1, KLRB1, PLEKHO1, and RAC2), of which the negative coefficients suggesting higher expression were correlated with better prognosis. Based on the expression of these genes, patients were grouped into the high- and low-risk group with significant overall survival (OS) (P<0.0001). The high-risk group was likely to have inferior outcomes related to several important cancer-associated pathways, including mobilizing more Golgi vesicle-mediated transport and intensive DNA double-strand breaking, which are closely related to the infiltration of immune cells and holds the key for further growing and metastasizing. Collectively, our results highlight that the immunological value within BRCA is an essential determinant of prognostic factor. Our signature may provide an effective risk stratification tool for clinical prognosis assessment of patients with BRCA.

AYA Considerations for Aggressive Lymphomas

PURPOSE OF REVIEW

Lymphoma is the one of the most common cancer diagnoses among adolescents and young adults (AYAs) aged 15-39. Despite significant advances in outcomes observed in older adults and younger children, improvements in AYAs have lagged behind. The reasons for this are likely multifactorial including disparities in access to health insurance, low rates of enrollment to clinical trials, potential differences in disease biology, and unique psychosocial challenges. Here we will review Hodgkin lymphoma (HL) and primary mediastinal B cell lymphoma (PMBCL), two of the most common aggressive lymphomas that occur in AYAs. We will discuss the current knowledge about disease biology in AYAs, adult and pediatric treatment strategies, novel targeted therapies, and ongoing AYA clinical trials in these lymphoma subtypes. We also will review unique considerations for treatment-related toxicities in AYAs and psychosocial issues relevant to this population.

RECENT FINDINGS

Pediatric and adult trials in HL and PMBCL have demonstrated that treatment with dose-intense chemotherapeutic regimens with or without radiation results in high cure rates but can also be associated with long-term toxicity which must be considered in this young population. Novel targeted agents such as the antibody-drug conjugate brentuximab vedotin and/or antibodies targeted against PD-1/PD-L1 have demonstrated activity in the relapsed setting and are currently being evaluated in the upfront setting, which may reduce our reliance on therapies associated with long-term toxicity. AYA-focused clinical trials are currently underway to better elucidate the optimal therapy for lymphomas in this age group. There is an urgent need for clinical trials including AYAs in order to increase the knowledge of age-specific outcomes, toxicities, disease biology, and the need to develop comprehensive AYA care models that meet the unique and complex care needs of this patient population.

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

BACKGROUND

We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature.

METHODS

A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts.

FINDINGS

In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies.

INTERPRETATION

The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen.

FUNDING

Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

PURPOSE

Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features.

EXPERIMENTAL DESIGN

Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting.

RESULTS

Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations.

CONCLUSIONS

We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.

A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study

We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565-6.628; p < 0.001), disease-free survival (DFS: HR 3.148, 95% CI 1.857-5.339; p < 0.001), and overall survival (OS: HR 3.790, 95% CI 2.237-6.423; p < 0.001) compared with low-risk patients. The prognostic accuracy of the classifier was validated in the internal testing (n = 84) and independent validation cohorts (n = 360). A prognostic nomogram consisting of five independent variables including the classifier, lactate dehydrogenase levels, ECOG-PS, central nervous system involvement, and NOTCH1/FBXW7 status showed significantly greater prognostic accuracy than each single variable alone. The addition of a five-miRNA-based signature further enhanced the accuracy of this nomogram. Furthermore, patients with a nomogram score ≥154.2 significantly benefited from the BFM protocol. In conclusion, our nomogram comprising the 11-gene-based classifier may make contributions to individual prognosis prediction and treatment decision-making.

PET-adapted approaches to primary therapy for advanced Hodgkin lymphoma

Recent results of randomized phase III studies of FDG-PET-adapted therapy for advanced Hodgkin lymphoma (HL) have clearly demonstrated benefit to alteration of treatment according to interim response, in particular regarding reducing toxicity while maintaining efficacy. However, these studies have differences in design including initial chemotherapy regimen, PET response criteria, patient populations enrolled, and inclusion of radiation, and report different results regarding efficacy and toxicities, which makes cross-trial comparisons difficult. Practitioners are presented with deciding which of these approaches will provide the optimum outcome, balancing toxicity and efficacy, and for which patient with advanced-stage HL. This review summarizes the observations reported from these trials and provides context to help guide physicians and patients in treatment decisions for advanced HL.

Localized- and advanced-stage follicular lymphomas differ in their gene expression profiles

The genetic background of follicular lymphomas (FLs) diagnosed in advanced clinical stages III/IV, and which are frequently characterized by t(14;18), has been substantially unraveled. Molecular features, as exemplified in the clinicogenetic risk model m7FLIPI, are important tools in risk stratification. In contrast, little information is available concerning localized-stage FL (clinical stages I/II), which accounts for ∼20% of newly diagnosed FL in which the detection rate of t(14;18) is only ∼50%. To investigate the genetic background of localized-stage FL, patient cohorts with advanced-stage FL or localized-stage FL, uniformly treated within phase 3 trials of the German Low-Grade Lymphoma Study Group, were comparatively analyzed. Targeted gene expression (GE) profiling of 184 genes using nCounter technology was performed in 110 localized-stage and 556 advanced-stage FL patients. By penalized Cox regression, a prognostic GE signature could not be identified in patients with advanced-stage FL, consistent with results from global tests and univariate regression. In contrast, it was possible to define robust GE signatures discriminating localized-stage and advanced-stage FL (area under the curve, 0.98) by penalized logistic regression. Of note, 3% of samples harboring an "advanced-stage signature" in the localized-stage cohort exhibited inferior failure-free survival (hazard ratio [HR], 7.1; P = .0003). Likewise, in the advanced-stage cohort, 7% of samples with a "localized-stage signature" had prolonged failure-free survival (HR, 2.3; P = .017) and overall survival (HR, 3.4; P = .072). These data support the concept of a biological difference between localized-stage and advanced-stage FL that might contribute to the superior outcome of localized FL.

An update on disease biomarkers for Hodgkin lymphoma

Introduction: Hodgkin Lymphoma (HL) carries an overall excellent prognosis for young patients treated with multimodal therapy. Predicting an individual patient's prognosis is currently heavily dependent on imaging modalities such as Positron Emission Tomography (PET).Areas covered: Potential biomarkers from serum, tissue, circulating nucleic acids and non-tumor derived cells have all been reported to be of prognostic relevance in HL. We review a range of these biomarkers and discuss the integration of new biomarkers into individualized patient care.Expert opinion: Better prognostic markers are needed to predict an individuals response to HL therapy. Interim PET-scan improves the ability to predict long-term treatment responders. However, it is our opinion that supplementation of PET results with additional biomarkers (including circulating tumor DNA, protein biomarkers, tissue genotyping and metabolic tumor volume) are likely to improve risk stratification for future patients with HL.

Up-regulation of indoleamine 2,3-dioxygenase 1 (IDO1) expression and catalytic activity is associated with immunosuppression and poor prognosis in penile squamous cell carcinoma patients

Background

Indoleamine 2,3‐dioxygenase 1 (IDO1) and tryptophan (Trp) catabolism have been demonstrated to play an important role in tumor immunosuppression. This study examined the expression and catalytic activity of IDO1 in penile squamous cell carcinoma (PSCC) and explored their clinical significance.

Methods

IDO1 expression level, serum concentrations of Trp and kynurenine (Kyn) were examined in 114 PSCC patients by immunohistonchemistry and solid‐phase extraction‐liquid chromatography‐tandem mass spectrometry. The survival was analyzed using Kaplan‐Meier method and the log‐rank test. Hazard ratio of death was analyzed via univariate and multivariate Cox regression. Immune cell types were defined by principal component analysis. The correlativity was assessed by Pearson's correlation analysis.

Results

The expression level of IDO1 in PSCC cells was positively correlated with serum Kyn concentration and Kyn/Trp radio (KTR; both P < 0.001) but negatively correlated with serum Trp concentration (P = 0.001). Additionally, IDO1 up‐regulation in cancer cells and the increase of serum KTR were significantly associated with advanced N stage (both P < 0.001) and high pathologic grade (P = 0.008 and 0.032, respectively). High expression level of IDO1 in cancer cells and serum KTR were associated with short disease‐specific survival (both P < 0.001). However, besides N stage (hazard radio [HR], 6.926; 95% confidence interval [CI], 2.458‐19.068; P < 0.001) and pathologic grade (HR, 2.194; 95% CI, 1.021‐4.529; P = 0.038), only serum KTR (HR, 2.780; 95% CI, 1.066‐7.215; P = 0.036) was an independent predictor for PSCC prognosis. IDO1 expression was positively correlated with the expression of interferon‐γ (IFNγ, P < 0.001) and immunosuppressive markers (programmed cell death protein 1, cytotoxic T‐lymphocyte‐associated protein 4 and programmed death‐ligand 1 and 2; all P < 0.05), and the infiltration of immune cells (including cytotoxic T lymphocytes, regulatory T lymphocytes, tumor‐associated macrophages, and myeloid‐derived suppressor cells; all P < 0.001) in PSCC tissues. Furthermore, the expression of IDO1 was induced by IFNγ in a dose‐dependent manner in PSCC cells.

Conclusions

IFNγ‐induced IDO1 plays a crucial role in immunoediting and immunosuppression in PSCC. Additionally, serum KTR, an indicator of IDO1 catabolic activity, can be utilized as an independent prognostic factor for PSCC.

The classic prognostic factors in advanced Hodgkin's lymphoma patients are losing their meaning at the time of Pet-guided treatments

The International Prognostic Score (IPS) is the most commonly used risk stratification tool for patients with advanced Hodgkin lymphoma (HL). It incorporates seven clinical parameters independently associated with a poorer outcome: male sex, age, stage IV, hemoglobin level, white blood cell and lymphocyte counts, and albumin level. Since the development of the IPS, there have been significant advances in therapy and supportive care. Recent studies suggest that the IPS is less discriminating due to improved outcomes with ABVD therapy. The aim of the present study was to asses if classic prognostic factors maintain their prognostic meaning at the time of response-adapted treatment based on interim PET scans. We evaluated the prognostic significance of IPS in the 520 advanced stage HL patients enrolled in the PET-guided, HD0801 trial in which PET2-positive patients underwent a more intense treatment with an early stem-cell transplantation after 2 cycles of ABVD. We observed that in these patients, the IPS completely loses its prognostic value together with all the single parameters that contribute to the IPS. Furthermore, neutrophils, monocytes, lymphocytes, and the ratio among them also no longer had any predictive value. We believe that the substantial improvement in survival outcomes in PET2-positive patients treated with early autologous transplantation could explain the complete disappearance of the residual prognostic significance of the IPS.

Validation of a simplified international prognostic score (IPS-3) in patients with advanced-stage classic Hodgkin lymphoma

A novel prognostic score (IPS-3), comprised of only three of the seven IPS-7 indicators (age ≥45, stage IV, haemoglobin <105 g/l), was recently proposed as a simplified model for advanced-stage classic Hodgkin lymphoma (cHL). We aimed to validate this model in advanced-stage cHL patients treated with doxorubicin, bleomycin, vinblastine, dacarbazine (ABVD) in British Columbia. The estimated five-year freedom from progression (FFP) for scores of 0, 1, 2 and 3 were very similar to the original report at 84%, 76%, 72% and 68% respectively. The IPS-3 score is highly reproducible in this independent dataset and its simplicity makes it appealing for everyday clinical practice.

A Gene Expression-based Model to Predict Metabolic Response After Two Courses of ABVD in Hodgkin Lymphoma Patients

PURPOSE

Early response to ABVD, assessed with interim FDG-PET (iPET), is prognostic for classical Hodgkin lymphoma (cHL) and supports the use of response adapted therapy. The aim of this study was to identify a gene-expression profile on diagnostic biopsy to predict iPET positivity (iPET⁺).

EXPERIMENTAL DESIGN

Consecutive untreated patients with stage I-IV cHL who underwent iPET after two cycles of ABVD were identified. Expression of 770 immune-related genes was analyzed by digital expression profiling (NanoString Technology). iPET was centrally reviewed according to the five-point Deauville scale (DS 1-5). An iPET⁺ predictive model was derived by multivariate regression analysis and assessed in a validation set identified using the same inclusion criteria.

RESULTS

A training set of 121 and a validation set of 117 patients were identified, with 23 iPET⁺ cases in each group. Sixty-three (52.1%), 19 (15.7%), and 39 (32.2%) patients had stage I-II, III, and IV, respectively. Diagnostic biopsy of iPET⁺ cHLs showed transcriptional profile distinct from iPET^-. Thirteen genes were stringently associated with iPET⁺. This signature comprises two functionally stromal-related nodes. Lymphocytes/monocytes ratio (LMR) was also associated to iPET⁺. In the training cohort a 5-gene/LMR integrated score predicted iPET⁺ [AUC, 0.88; 95% confidence interval (CI), 0.80-0.96]. The score achieved a 100% sensitivity to identify DS5 cases. Model performance was confirmed in the validation set (AUC, 0.68; 95% CI, 0.52-0.84). Finally, iPET score was higher in patients with event versus those without.

CONCLUSIONS

In cHL, iPET is associated with a genetic signature and can be predicted by applying an integrated gene-based model on the diagnostic biopsy.

Five-year follow-up of SWOG S0816: limitations and values of a PET-adapted approach with stage III/IV Hodgkin lymphoma

Patients with advanced-stage Hodgkin lymphoma (HL) demonstrated excellent 2-year progression-free survival (PFS) after receiving positron emission tomography (PET)-adapted therapy on SWOG S0816. Patients received 2 cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD). Patients achieving complete response (CR) on PET scan following cycle 2 of ABVD (PET2) continued 4 additional cycles of ABVD. Patients not achieving CR on PET2 were switched to escalated bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone (eBEACOPP) for 6 cycles. After a median follow-up of 5.9 years, a subset of 331 eligible patients with central review of PET2 was analyzed. PET2 was negative in 82% and positive in 18%. For all patients, the estimated 5-year PFS and OS was 74% (95% confidence interval [CI], 69%-79%) and 94% (95% CI, 91%-96%), respectively. For PET2- and PET2+ patients, the 5-year PFS was 76% (95% CI, 70%-81%) and 66% (95% CI, 52%-76%), respectively. Seven (14%) and 6 (2%) patients reported second cancers after treatment with eBEACOPP and ABVD, respectively (P = .001). Long-term OS of HL patients treated on S0816 remains high. Nearly 25% of PET2- patients experienced relapse events, demonstrating limitations ABVD therapy and of the negative predictive value of PET2. In PET2+ patients who received eBEACOPP, PFS was favorable, but was associated with a high rate of second malignancies compared with historical controls. Our results emphasize the importance of long-term follow-up, and the need for more efficacious and less toxic therapeutic approaches for advanced-stage HL patients. This trial was registered at www.clinicaltrials.gov as #NCT00822120.

Closing the survivorship gap in children and adolescents with Hodgkin lymphoma

The treatment of Hodgkin lymphoma (HL) is one of early success. However, disease-free survival (DFS) does not reflect latent organ injury and its impact on health status and well-being beyond 5 years. In fact, we are at a crossroads, in terms of needing individualized approaches to maintain DFS, while minimizing late effects and preserving health-related quality of life (HRQoL). Premature morbidity and mortality translate to a high societal cost associated with the potential number of productive life years ahead in this population who are young at diagnosis. The discordance between short-term lymphoma-free survival and long-term health and HRQoL creates a "survivorship gap" which can be characterized for individuals and for subgroups of patients. The current review delineates contributors to compromised outcomes and health status in child and adolescent (paediatric) HL and frames the survivorship gap in terms of primary and secondary prevention. Primary prevention aims to titrate therapy. Secondary prevention entails strategies to intervene against late effects. Bridging the survivorship gap will be attained with enhanced knowledge of and attention to biology of the tumour and microenvironment, host genetic factors, HRQoL and sub-populations with disparate outcomes.

A novel lymphoma-associated macrophage interaction signature (LAMIS) provides robust risk prognostication in diffuse large B-cell lymphoma clinical trial cohorts of the DSHNHL

Diffuse large B-cell lymphoma (DLBCL) is a disease with heterogeneous outcome. Stromal signatures have been correlated to survival in DLBCL. Their use, however, is hampered by the lack of assays for formalin-fixed paraffin-embedded material (FFPE). We constructed a lymphoma-associated macrophage interaction signature (LAMIS) interrogating features of the microenvironment using a NanoString assay applicable to FFPE. The clinical impact of the signature could be validated in a cohort of 466 patients enrolled in prospective clinical trials of the German High-Grade Non-Hodgkin Lymphoma Study Group (DSHNHL). Patients with high expression of the signature (LAMIS^high) had shorter EFS, PFS, and OS. Multivariate analyses revealed independence from IPI factors in EFS (HR 1.7, 95% CI 1.2-2.4, p-value = 0.001), PFS (HR 1.8, 95% CI 1.2-2.5, p-value = 0.001) and OS (HR 1.8, 95% CI 1.3-2.7, p-value = 0.001). Multivariate analyses adjusted for the IPI factors showed the signature to be independent from COO, MYC rearrangements and double expresser status (DE). LAMIS^high and simultaneous DE status characterized a patient subgroup with dismal prognosis and early relapse. Our data underline the importance of the microenvironment in prognosis. Combined analysis of stromal features, the IPI and DE may provide a new rationale for targeted therapy.

Relationship between semiquantitative 18F-fluorodeoxyglucose positron emission tomography metrics and necrosis in classical Hodgkin lymphoma

Semiquantitative ¹⁸F-fluoro-2-deoxy-D-glucose positron emission tomography (¹⁸F-FDG PET) parameters have been proposed as prognostic markers in classical Hodgkin lymphoma (cHL). In non-Hodgkin lymphoma necrosis as assessed by ¹⁸F-FDG PET or computed tomography (CT) (necrosis^visual) correlates with an adverse prognosis. We investigated whether semiquantitative ¹⁸F-FDG PET metrics correlate with necrosis^visual, determined the incidence of necrosis^visual and explored the prognostic impact of these factors in cHL. From 87 cHL cases treated with ABVD, (escalated) BEACOPP or CHOP chemotherapy between 2010 and 2017, 71 had both a NEDPAS/EARL accredited ¹⁸F-FDG PET and a contrast enhanced CT scan. Semiquantitative ¹⁸F-FDG PET parameters were determined using Hermes Hybrid 3D software. Necrosis^visual, defined by photopenic tumor areas on ¹⁸F-FDG PET and attenuation values between 10 and 30 Hounsfield units (HUs) on CT, was assessed blinded to outcome. Univariate Cox regression survival analyses of progression free survival (PFS) were performed. Necrosis^visual was observed in 18.3% of cHL patients. Bulky disease (tumor mass >10 cm in any direction) (P = 0.002) and TLG (P = 0.041) but no other semiquantitative parameters were significantly associated with necrosis^visual. In exploratory univariate survival analysis for PFS the covariates IPS, bulky disease, MTV and TLG were prognostic, while necrosis^visual was not.

Novel Biomarker Approaches in Classic Hodgkin Lymphoma

Classic Hodgkin lymphoma (cHL) is one of the most common lymphomas in the Western world. Advances in the management of cHL have led to high cure rates exceeding 80%. Nevertheless, relapse or refractory disease in a subset of patients and treatment-related toxicity still represents unsolved clinical problems. The introduction of targeted treatments such as PD-1 blockade and the CD30 antibody drug conjugate, brentuximab vedotin, has broadened treatment options in cHL, emphasizing the critical need to identify biomarkers with the goal to provide rationales for treatment selection, increase effective drug utilization, and minimize toxicity. The unique biology of cHL featuring low abundant tumor cells and numerous nonmalignant immune cells in the tumor microenvironment can provide various types of promising biomarkers related to the tumor cells directly, tumor microenvironment cross-talk, and host immune response. Here, we comprehensively review novel biomarkers including circulating tumor DNA and gene expression-based prognostic models that might guide the ideal management of cHL in the future.

Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach

BACKGROUND

For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately.

METHODS

We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies.

RESULTS

Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3 μm2.

CONCLUSIONS

ROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.