Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, ...focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.
Tumor‐associated macrophages (TAMs) are associated with a poor prognosis of diffuse large B‐cell lymphoma (DLBCL). As macrophages are heterogeneous, the immune polarization and their pathological ...role warrant further study. We characterized the microenvironment of DLBCL by immunohistochemistry in a training set of 132 cases, which included 10 Epstein–Barr virus‐encoded small RNA (EBER)‐positive and five high‐grade B‐cell lymphomas, with gene expression profiling in a representative subset of 37 cases. Diffuse large B‐cell lymphoma had a differential infiltration of TAMs. The high infiltration of CD68 (pan‐macrophages), CD16 (M1‐like), CD163, pentraxin 3 (PTX3), and interleukin (IL)‐10‐positive macrophages (M2c‐like) and low infiltration of FOXP3‐positive regulatory T lymphocytes (Tregs) correlated with poor survival. Activated B cell‐like DLBCL was associated with high CD16, CD163, PTX3, and IL‐10, and EBER‐positive DLBCL with high CD163 and PTX3. Programmed cell death‐ligand 1 positively correlated with CD16, CD163, IL‐10, and RGS1. In a multivariate analysis of overall survival, PTX3 and International Prognostic Index were identified as the most relevant variables. The gene expression analysis showed upregulation of genes involved in innate and adaptive immune responses and macrophage and Toll‐like receptor pathways in high PTX3 cases. The prognostic relevance of PTX3 was confirmed in a validation set of 159 cases. Finally, in a series from Europe and North America (GSE10846, R‐CHOP‐like treatment, n = 233) high gene expression of PTX3 correlated with poor survival, and moderately with CSF1R, CD16, MITF, CD163, MYC, and RGS1. Therefore, the high infiltration of M2c‐like immune regulatory macrophages and low infiltration of FOXP3‐positive Tregs is associated with a poor prognosis in DLBCL, for which PTX3 is a new prognostic biomarker.
This research focused on the analysis of several macrophage and regulatory T lymphocyte markers in diffuse large b‐cell lymphoma. We found that high PTX3 expression correlated with poor prognosis of the patients.
Tumor microenvironment influences the behavior of follicular lymphoma (FL), although the specific cell subsets involved are not well known. The aim of this study was to determine the impact of ...programmed cell death 1 (PD-1) -positive inhibitory immunoregulatory lymphoid cells in the clinicobiologic features and outcome of patients with FL.
We examined samples from 100 patients (53 men and 47 women; median age, 54 years) at diagnosis, as well as in 32 patients at first relapse, with a recently generated monoclonal antibody against PD-1. The cells were quantified using computerized image analysis. Additional analysis consisted of double immunofluorescence and flow cytometry.
PD-1 expression was alternative to FOXP3 in lymphoid cells from both reactive tonsils and FL. At diagnosis, the median percentage of PD-1-positive cells was 14% (range, 0.1% to 74%). Patients with grade 3 FL, poor performance status, and high serum lactate dehydrogenase showed lower numbers of PD-1-positive cells. After a median follow-up of 6.2 years, patients with PD-1-positive cells <or= 5% (n = 25), 6% to 33% (n = 50), and more than 33% (n = 25) had a 5-year progression-free survival rate of 20%, 46%, and 48% (P = .038) and overall survival (OS) of 50%, 77%, and 95% (P = .004), respectively. PD-1 and FL International Prognostic Index maintained prognostic value for OS in multivariate analysis. Patients with PD-1-positive cells <or= 5% showed a higher risk of histologic transformation. At that time, transformed diffuse large B-cell lymphomas had lower percentage of PD-1-positive cells than FL.
A high content of PD-1-positive cells predicted favorable outcome of FL patients, whereas a marked reduction is observed in transformation.
Purpose: The microenvironment influences outcome in follicular lymphoma. Our hypothesis was that several immune cell subsets are important
for disease outcome and their individual prognostic ...importance should be demonstrable in the same analysis and in competition
with clinical factors.
Experimental Design: Seventy follicular lymphoma patients with extreme clinical outcome (“poor” and “good” cases) were selected in a population-based
cohort of 197. None of the 37 good-outcome patients died from lymphoma, whereas all the 33 poor-outcome patients succumbed
in ≤5 years. Furthermore, the good-outcome patients were followed for a long time and needed no or little treatment. A tissue
microarray was constructed from diagnostic, pretreatment biopsies. Cellular subsets were quantified after immunostaining,
using computerized image analysis, separating cells inside and outside the follicles (follicular and interfollicular compartments).
Flow cytometry data from the same samples were also used.
Results: Independently of the Follicular Lymphoma International Prognostic Index, CD4 + cells were associated with poor outcome and programmed death-1–positive and CD8 + cells were associated with good outcome. The prognostic values of CD4 + and programmed death-1–positive cells were accentuated when they were follicular and that of CD8 + cells were accentuated when they were interfollicular. Follicular FOXP3 + cells were associated with good outcome and interfollicular CD68 + cells were associated with poor outcome. Additionally, high CD4/CD8 and CD4 follicular/interfollicular ratios correlated
with poor outcome.
Conclusion: There are many important immune cell subsets in the microenvironment of follicular lymphoma. Each of these is independently
associated with outcome. This is the first study showing the effect of the balance of the entire microenvironment, not only
of individual subsets. Clin Cancer Res; 16(2); 637–50.
Rheumatoid arthritis patients often develop the diffuse large B-cell lymphoma subtype of methotrexate-associated lymphoproliferative disorder (DLBCL). We characterized the genomic profile and ...pathologic characteristics of 20 biopsies using an integrative approach. DLBCL was associated with extranodal involvement, a high/high-intermediate international prognostic index in 53% of cases, and responded to MTX withdrawal. The phenotype was nongerminal center B-cell in 85% of samples and Epstein-Barr encoding region positive (EBER) in 65%, with a high proliferation index and intermediate MYC expression levels. The immune microenvironment showed high numbers of CD8 cytotoxic T lymphocytes and CD163 M2 macrophages with an (CD163/CD68) M2 ratio of 3.6. Its genomic profile was characterized by 3p12.1-q25.31, 6p25.3, 8q23.1-q24.3, and 12p13.33-q24.33 gains, 6q22.31-q24.1 and 13q21.33-q34 losses, and 1p36.11-p35.3 copy neutral loss-of-heterozygosity. This profile was closer to nongerminal center B-cell DLBCL not-otherwise-specified, but with characteristic 3q, 12q, and 20p gains and lower 9p losses (P<0.05). We successfully verified array results using fluorescent DNA in situ hybridization on PLOD2, MYC, WNT1, and BCL2. Protein immunohistochemistry revealed that DLBCL expressed high IRF4 (6p25.3) and SELPLG (12q24.11) levels, intermediate TNFRSF14 (1p36.32; the exons 1 to 3 were unmutated), BTLA (3q13.2), PLOD2 (3q24), KLHL6 (3q27.1), and MYC (8q24.21) levels, and low AICDA (12p13.31) and EFNB2 (13q33.3) levels. The correlation between the DNA copy number and protein immunohistochemistry was confirmed for BTLA, PLOD2, and EFNB2. The characteristics of EBER versus EBER cases were similar, with the exception of specific changesEBER cases had higher numbers of CD163 M2 macrophages and FOXP3 regulatory T lymphocytes, high programmed cell death 1 ligand 1 expression levels, slightly fewer genomic changes, and 3q and 4p focal gains. In conclusion, DLBCL has a characteristic genomic profile with 3q and 12 gains, 13q loss, different expression levels of relevant pathogenic biomarkers, and a microenvironment with high numbers of cytotoxic T lymphocytes and M2 macrophages.
NOTCH signaling suppresses tumor growth and proliferation in several types of stratified epithelia. Here, we show that missense mutations in NOTCH1 and NOTCH2 found in human bladder cancers result in ...loss of function. In murine models, genetic ablation of the NOTCH pathway accelerated bladder tumorigenesis and promoted the formation of squamous cell carcinomas, with areas of mesenchymal features. Using bladder cancer cells, we determined that the NOTCH pathway stabilizes the epithelial phenotype through its effector HES1 and, consequently, loss of NOTCH activity favors the process of epithelial-mesenchymal transition. Evaluation of human bladder cancer samples revealed that tumors with low levels of HES1 present mesenchymal features and are more aggressive. Together, our results indicate that NOTCH serves as a tumor suppressor in the bladder and that loss of this pathway promotes mesenchymal and invasive features.
Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphomas. We used artificial neural networks (multilayer perceptron and radial basis function), machine ...learning, and conventional bioinformatics to predict the overall survival and molecular subtypes of DLBCL. The series included 106 cases and 730 genes of a pancancer immune-oncology panel (nCounter) as predictors. The multilayer perceptron predicted the outcome with high accuracy, with an area under the curve (AUC) of 0.98, and ranked all the genes according to their importance. In a multivariate analysis,
,
,
, and
correlated with favorable survival (hazard risks: 0.3-0.5), and
,
, and
, with poor survival (hazard risks = 1.0-2.1). Gene set enrichment analysis (GSEA) showed enrichment toward poor prognosis. These high-risk genes were also associated with the gene expression of M2-like tumor-associated macrophages (
), and
expression. The prognostic relevance of this set of 7 genes was also confirmed within the IPI and
translocation strata, the EBER-negative cases, the DLBCL not-otherwise specified (NOS) (High-grade B-cell lymphoma with
and
and/or
rearrangements excluded), and an independent series of 414 cases of DLBCL in Europe and North America (GSE10846). The perceptron analysis also predicted molecular subtypes (based on the Lymph2Cx assay) with high accuracy (AUC = 1).
,
, and
were associated with the germinal center B-cell (GCB) subtype, and
,
,
, and
were associated with the activated B-cell (ABC)/unspecified subtype. The GSEA had a sinusoidal-like plot with association to both molecular subtypes, and immunohistochemistry analysis confirmed the correlation of
with the GCB subtype in another series of 96 cases (notably, MAPK3 also correlated with LMO2, but not with M2-like tumor-associated macrophage markers CD163, CSF1R, TNFAIP8, CASP8, PD-L1, PTX3, and IL-10). Finally, survival and molecular subtypes were successfully modeled using other machine learning techniques including logistic regression, discriminant analysis, SVM, CHAID, C5, C&R trees, KNN algorithm, and Bayesian network. In conclusion, prognoses and molecular subtypes were predicted with high accuracy using neural networks, and relevant genes were highlighted.
The microenvironment influences the behavior of follicular lymphoma (FL) but the specific roles of the immunomodulatory BTLA and TNFRSF14 (HVEM) are unknown. Therefore, we examined their ...immunohistochemical expression in the intrafollicular, interfollicular and total histological compartments in 106 FL cases (57M/49F; median age 57-years), and in nine relapsed-FL with transformation to DLBCL (tFL). BTLA expression pattern was of follicular T-helper cells (TFH) in the intrafollicular and of T-cells in the interfollicular compartments. The mantle zones were BTLA+ in 35.6% of the cases with similar distribution of IgD. TNFRSF14 expression pattern was of neoplastic B lymphocytes (centroblasts) and “tingible body macrophages”. At diagnosis, the averages of total BTLA and TNFRSF14-positive cells were 19.2%±12.4STD (range, 0.6%-58.2%) and 46.7 cells/HPF (1-286.5), respectively. No differences were seen between low-grade vs. high-grade FL but tFL was characterized by low BTLA and high TNFRSF14 expression. High BTLA correlated with good overall survival (OS) (total-BTLA, Hazard Risk=0.479, P=0.022) and with high PD-1 and FOXP3+Tregs. High TNFRSF14 correlated with poor OS and progression-free survival (PFS) (total-TNFRSF14, HR=3.9 and 3.2, respectively, P<0.0001), with unfavorable clinical variables and higher risk of transformation (OR=5.3). Multivariate analysis including BTLA, TNFRSF14 and FLIPI showed that TNFRSF14 and FLIPI maintained prognostic value for OS and TNFRSF14 for PFS. In the GSE16131 FL series, high TNFRSF14 gene expression correlated with worse prognosis and GSEA showed that NFkB pathway was associated with the “High-TNFRSF14/dead-phenotype”.In conclusion, the BTLA-TNFRSF14 immune modulation pathway seems to play a role in the pathobiology and prognosis of FL.
Thymocyte selection-associated high-mobility group box (TOX) is a DNA-binding factor that is able to regulate transcription by modifying local chromatin structure and modulating the formation of ...multi-protein complexes. TOX has multiple roles in the development of the adaptive immune system including development of CD4 T cells, NK cells and lymph node organogenesis. However very few antibodies recognizing this molecule have been reported and no extensive study of the expression of TOX in reactive and neoplastic lymphoid tissue has been performed to date. In the present study, we have investigated TOX expression in normal and neoplastic lymphoid tissues using a novel rat monoclonal antibody that recognizes its target molecule in paraffin-embedded tissue sections. A large series of normal tissues and B- and T-cell lymphomas was studied, using whole sections and tissue microarrays. We found that the majority of precursor B/T lymphoblastic, follicular and diffuse large B-cell lymphomas, nodular lymphocyte-predominant Hodgkin lymphomas and angioimmunoblastic T-cell lymphomas strongly expressed the TOX protein. Burkitt and mantle cell lymphomas showed TOX expression in a small percentage of cases. TOX was not found in the majority of chronic lymphocytic leukemia, myelomas, marginal zone lymphomas and classical Hodgkin lymphomas. In conclusion, we describe for the first time the expression of TOX in normal and neoplastic lymphoid tissues. The co-expression of TOX and PD-1 identified in normal and neoplastic T cells is consistent with recent studies identifying TOX as a critical regulator of T-cell exhaustion and a potential immunotherapy target. Its differential expression may be of diagnostic relevance in the differential diagnosis of follicular lymphoma, the identification of the phenotype of diffuse large B-cell lymphoma and the recognition of peripheral T-cell lymphoma with a follicular helper T phenotype.
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