Spatially-resolved transcriptomics reveal macrophage heterogeneity and prognostic significance in diffuse large B-cell lymphoma.

Macrophages are abundant immune cells in the microenvironment of diffuse large B-cell lymphoma (DLBCL). Macrophage estimation by immunohistochemistry shows varying prognostic significance across studies in DLBCL, and does not provide a comprehensive analysis of macrophage subtypes. Here, using digital spatial profiling with whole transcriptome analysis of CD68+ cells, we characterize macrophages in distinct spatial niches of reactive lymphoid tissues (RLTs) and DLBCL. We reveal transcriptomic differences between macrophages within RLTs (light zone /dark zone, germinal center/ interfollicular), and between disease states (RLTs/ DLBCL), which we then use to generate six spatially-derived macrophage signatures (MacroSigs). We proceed to interrogate these MacroSigs in macrophage and DLBCL single-cell RNA-sequencing datasets, and in gene-expression data from multiple DLBCL cohorts. We show that specific MacroSigs are associated with cell-of-origin subtypes and overall survival in DLBCL. This study provides a spatially-resolved whole-transcriptome atlas of macrophages in reactive and malignant lymphoid tissues, showing biological and clinical significance.

Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma.

Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6- (M+2+6-) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6- percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6- unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance. SIGNIFICANCE: Using single-cell-resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer. This article is highlighted in the In This Issue feature, p. 1027.

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial.

OBJECTIVE: To date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery. DESIGN: The primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort. RESULTS: From the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022). CONCLUSION: Using machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit. TRIAL REGISTRATION NUMBER: UMIN Clinical Trials Registry: C000000082 (SAMIT); ClinicalTrials.gov identifier, 02628951 (South Korean trial).

Profiling of gastric cancer cell-surface markers to achieve tumour-normal discrimination.

BACKGROUND: Differentiating between malignant and normal cells within tissue samples is vital for molecular profiling of cancer using advances in genomics and transcriptomics. Cell-surface markers of tumour-normal discrimination have additional value in terms of translatability to diagnostic and therapeutic strategies. In gastric cancer (GC), previous studies have identified individual genes or proteins that are upregulated in cancer. However, a systematic analysis of cell-surface markers and development of a composite panel involving multiple candidates to differentiate tumour from normal has not been previously reported. METHODS: Whole transcriptome sequencing (WTS) of GC and matched normal samples from the Singapore Gastric Cancer Consortium (SGCC) was used as a discovery cohort to identify upregulated putative cell-surface proteins. Matched WTS data from the The Cancer Genome Atlas (TCGA) was used as a validation cohort. Promising candidates from this analysis were validated orthogonally using multispectral immunohistochemistry (mIHC) with automated quantitative analysis using the Vectra platform. mIHC was performed on a tissue microarray containing matched normal, marginal and tumour tissues. The receiver-operating characteristic (ROC) curves were analysed to identify markers with the highest diagnostic validity independently and in combination. RESULTS: Analysis of putative membrane protein transcripts from the SGCC discovery cohort WTS data (n=15 matched tumour and normal pairs) identified several differentially and highly expressed candidates in tumour compared with normal tissues. After validation with TCGA data (n=29 matched tumour and normal pairs), the following proteins were selected for mIHC analysis: CEACAM5, CEACAM6, CLDN4, CLDN7, and EpCAM. These were compared with established glycoprotein markers in GC, namely CA19-9 and CA72-4. Individual ROC curves yielded the best performance for CEACAM5 (area under the ROC curve (AUC)=0.80), CEACAM6 (AUC=0.82), EpCAM (AUC=0.83), and CA72-4 (AUC=0.76). Combined multiplexed imaging of these four markers revealed improved specificity and sensitivity for detection of tumour from normal tissue (AUC of 4-plex=0.91). CONCLUSION: CEAMCAM5, CEACAM6, EpCAM, and CA72-4 form a versatile set of markers for robust discrimination of GC from adjacent normal tissue. As cell-surface markers, they are compatible with both IHC and live imaging approaches. These candidates may be exploited to improve automated identification of tumour tissue in GC.

PRDM15 is a key regulator of metabolism critical to sustain B-cell lymphomagenesis.

PRDM (PRDI-BF1 and RIZ homology domain containing) family members are sequence-specific transcriptional regulators involved in cell identity and fate determination, often dysregulated in cancer. The PRDM15 gene is of particular interest, given its low expression in adult tissues and its overexpression in B-cell lymphomas. Despite its well characterized role in stem cell biology and during early development, the role of PRDM15 in cancer remains obscure. Herein, we demonstrate that while PRDM15 is largely dispensable for mouse adult somatic cell homeostasis in vivo, it plays a critical role in B-cell lymphomagenesis. Mechanistically, PRDM15 regulates a transcriptional program that sustains the activity of the PI3K/AKT/mTOR pathway and glycolysis in B-cell lymphomas. Abrogation of PRDM15 induces a metabolic crisis and selective death of lymphoma cells. Collectively, our data demonstrate that PRDM15 fuels the metabolic requirement of B-cell lymphomas and validate it as an attractive and previously unrecognized target in oncology.

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma.

Immunohistochemical (IHC) methods for the in-situ analysis of protein expression by light microscopy are a powerful tool for both research and diagnostic purposes. However, the visualization and quantification of multiple antigens in a single tissue section using conventional chromogenic IHC is challenging. Multiplexed imaging is especially relevant in lymphoma research and diagnostics, where markers have to be interpreted in the context of a complex tumor microenvironment. Here we describe a protocol for multiplexed fluorescent IHC staining to enable the quantitative assessment of multiple targets in specific cell types of interest in lymphoma.The method covers aspects of antibody validation, antibody optimization, the multiplex optimization with markers of lymphoma subtypes, the staining of tissue microarray (TMA) slides, and the scanning of the slides, followed by data analysis, with specific reference to lymphoma. Using this method, scores for both the mean intensity of a marker of interest and the percentage positivity are generated to facilitate further quantitative analysis. Multiplexing minimizes sample utilization and provides spatial information for each marker of interest.

Epstein-Barr virus-associated primary nodal T/NK-cell lymphoma shows a distinct molecular signature and copy number changes.

The molecular biology of primary nodal T- and NK-cell lymphoma and its relationship with extranodal NK/T-cell lymphoma, nasal type is poorly understood. In this study, we assessed the relationship between nodal and extranodal Epstein-Barr virus-positive T/NK-cell lymphomas using gene expression profiling and copy number aberration analyses. We performed gene expression profiling and copy number aberration analysis on 66 cases of Epstein-Barr virus-associated T/NK-cell lymphoma from nodal and extranodal sites, and correlated the molecular signatures with clinicopathological features. Three distinct molecular clusters were identified with one enriched for nodal presentation and loss of 14q11.2 (TCRA loci). T/NK-cell lymphomas with a nodal presentation (nodal-group) were significantly associated with older age, lack of nasal involvement, and T-cell lineage compared to those with an extranodal presentation (extranodal-group). On multivariate analysis, nodal presentation was an independent factor associated with short survival. Comparing the molecular signatures of the nodal and extranodal groups it was seen that the former was characterized by upregulation of PD-L1 and T-cell-related genes, including CD2 and CD8, and downregulation of CD56, consistent with the CD8+/CD56-immunophenotype. PD-L1 and CD2 protein expression levels were validated using multiplexed immunofluorescence. Interestingly, nodal group lymphomas were associated with 14q11.2 loss which correlated with loss of TCR loci and T-cell origin. Overall, our results suggest that T/NK-cell lymphoma with nodal presentation is distinct and deserves to be classified separately from T/NK-cell lymphoma with extranodal presentation. Upregulation of PD-L1 indicates that it may be possible to use anti-PD1 immunotherapy in this distinctive entity. In addition, loss of 14q11.2 may be a potentially useful diagnostic marker of T-cell lineage.

Low Levels of NDRG1 in Nerve Tissue Are Predictive of Severe Paclitaxel-Induced Neuropathy.

INTRODUCTION: Sensory peripheral neuropathy caused by paclitaxel is a common and dose limiting toxicity, for which there are currently no validated predictive biomarkers. We investigated the relationship between the Charcot-Marie-Tooth protein NDRG1 and paclitaxel-induced neuropathy. METHODS/MATERIALS: Archived mammary tissue specimen blocks of breast cancer patients who received weekly paclitaxel in a single centre were retrieved and NDRG1 immunohistochemistry was performed on normal nerve tissue found within the sample. The mean nerve NDRG1 score was defined by an algorithm based on intensity of staining and percentage of stained nerve bundles. NDRG1 scores were correlated with paclitaxel induced neuropathy. RESULTS: 111 patients were studied. 17 of 111 (15%) developed severe paclitaxel-induced neuropathy. The mean nerve NDRG1 expression score was 5.4 in patients with severe neuropathy versus 7.7 in those without severe neuropathy (p = 0.0019). A Receiver operating characteristic (ROC) curve analysis of the mean nerve NDRG1 score revealed an area under the curve of 0.74 (p = 0.0013) for the identification of severe neuropathy, with a score of 7 being most discriminative. 13/54 (24%) subjects with an NDRG1 score < = 7 developed severe neuropathy, compared to only 4/57 (7%) in those with a score >7 (p = 0.017). CONCLUSION: Low NDRG1 expression in nerve tissue present within samples of surgical resection may identify subjects at risk for severe paclitaxel-induced neuropathy. Since nerve biopsies are not routinely feasible for patients undergoing chemotherapy for early breast cancer, this promising biomarker strategy is compatible with current clinical workflow.