Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

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

Human leukocyte antigens and genetic susceptibility to lymphoma

Familial aggregation, coupled with ethnic variation in incidence, suggests that inherited susceptibility plays a role in the development of lymphoma, and the search for genetic risk factors has highlighted the contribution of the human leukocyte antigen (HLA) complex. In a landmark study published almost 50 years ago, Hodgkin lymphoma (HL) was the first disease to be associated with HLA variation. It is now clear that Epstein-Barr virus (EBV)-positive and -negative HL are strongly associated with specific HLA polymorphisms but these differ by EBV status of the tumours. HLA class I alleles are consistently associated with EBV-positive HL while a polymorphism in HLA class II is the strongest predictor of risk of EBV-negative HL. Recent investigations, particularly genome-wide association studies (GWAS), have also revealed associations between HLA and common types of non-Hodgkin lymphoma (NHL). Follicular lymphoma is strongly associated with two distinct haplotypes in HLA class II whereas diffuse large B-cell lymphoma is most strongly associated with HLA-B*08. Although chronic lymphocytic leukaemia is associated with variation in HLA class II, the strongest signals in GWAS are from non-HLA polymorphisms, suggesting that inherited susceptibility is explained by co-inheritance of multiple low risk variants. Associations between B-cell derived lymphoma and HLA variation suggest that antigen presentation, or lack of, plays an important role in disease pathogenesis but the precise mechanisms have yet to be elucidated

Artificial Intelligence Analysis of the Gene Expression of Follicular Lymphoma Predicted the Overall Survival and Correlated with the Immune Microenvironment Response Signatures

Follicular lymphoma (FL) is the second most common lymphoma in Western countries. FL is characterized by being incurable, usually having an indolent clinical course with frequent relapses, and an eventual patient’s death or transformation to Diffuse Large B-cell Lymphoma. The immune response and the tumoral immune microenvironment, including FOXP3+Tregs, PD-1+TFH cells, TNFRSF14 (HVEM), and BTLA play a role in the pathogenesis. We aimed to analyze the gene expression of FL by Artificial Intelligence (machine learning, deep learning), to identify genes associated with the prognosis of the patients and with the microenvironment in terms of overall survival (OS). A series of 184 cases of the GSE16131 dataset was analyzed by multilayer perceptron (MLP) and radial basis function (RBF) neural networks. In the analysis, MLP and RBF had a synergistic effect. From an initial set of 22,215 genes probes, a final set of 43 genes was highlighted. These 43 genes predicted the OS and correlated with the immune microenvironment: in a multivariate Cox analysis, 18 genes were associated with a poor prognosis (namely, MED8, KRT19, CDC40, SLC24A2, PRB1, KIAA0100, EVA1B, KLK10, TMEM70, BTN2A3P, TRPM4, MED6, FRYL, CBFA2T2, RANBP9, BNIP2, PTP4A2 and ALDH1L1) and 25 genes were associated with a good prognosis of the patients. Gene set enrichment analysis (GSEA) confirmed these findings and showed a typical sinusoidal-like shape. Some of the most relevant genes for poor OS were EVA1B, KRT19, BTN2A3P, KLK10, TRPM4, TMEM70, and SLC24A2 (hazard risk = from 1.7 to 4.3, p < 0.005) and for good OS, these were TDRD12 and ZNF230 (HR = 0.34 and 0.28, p < 0.001). EVA1B, KRT19, BTN2AP3, KLK10, and TRPM4 also associated with M2-like macrophage markers including CD163, MRC1 (CD206), and IL10 in the core enrichment for dead OS outcome by GSEA and to poor OS by Kaplan–Meier with Log rank test. The scientific literature showed that some of these genes also play a role in other types of cancer. In conclusion, by Artificial Intelligence, we have identified new biomarkers with prognostic relevance in FL

mRNA in exosomas as a liquid biopsy in non-Hodgkin Lymphoma: a multicentric study by the Spanish Lymphoma Oncology Group

Purpose: To determine the feasibility of mRNAs (C-MYC, BCL-XL, BCL-6, NF-κβ, PTEN and AKT) in exosomes of plasma as a liquid biopsy method for monitoring and prognostic evolution in B-cell lymphomas. Patients and Methods: Exosomes were isolated from 98 patients with B-cell Lymphoma and 68 healthy controls. mRNAs were analyzed by quantitative PCR. An additional 31 post-treatment samples were also studied. Results: In the general and follicular lymphoma series, the presence of AKT mRNA was associated with poor response to rituximab-based treatment. Patients with first relapse or disease progression showed a lower percentage of PTEN and BCL-XL mRNA. The presence of BCL-6 mRNA was associated with a high death rate. The absence of PTEN mRNA in the general series, and presence of C-MYC mRNA in follicular lymphomas, were associated with short progression-free survival. BCL-6 and C-MYC mRNA were independent prognostic variables of overall survival. C-MYC mRNA may provide prognostic information with respect to overall survival. BCL-XL mRNA and increase of BCL-6 mRNA in post-treatment samples could serve as molecular monitoring markers. Conclusions: This is the first large study to evaluate the prognostic and predictive values of pretreatment tumor-associated mRNA in exosomes. BCL-6 and C-MYC mRNA positivity in pretreatment samples were predictors of worse PFS compared to patients with mRNA negativity. C-MYC mRNA positivity was also a statistically significant predictor of inability to obtain complete response with first-line therapyThis study was supported by grants FIS-PI08/0862, and SAF2010-20750. During this study, V. García received Fundación AECC and RTICC-RD2012/0036/0006 fellowship

Biological predictors of survival in limphoma and mechanisms underlying follicular lymphoma transformaion into diffuse large B cell lymphoma (vol I e II)

RESUMO: Os biomarcadores tumorais permitem identificar os doentes com maior risco de recorrência da doença, predizer a resposta tumoral à terapêutica e, finalmente, definir candidatos a novos alvos terapêuticos. Novos biomarcadores são especialmente necessários na abordagem clínica dos linfomas. Actualmente, esses tumores são diagnosticados através de uma combinação de características morfológicas, fenotípicas e moleculares, mas o prognóstico e o planeamento terapêutico estão quase exclusivamente dependentes de características clínicas. Estes factores clínicos são, na maioria dos linfomas, insuficientes numa proporção significativa dos doentes, em particular, aqueles com pior prognóstico. O linfoma folicular (LF) é, globalmente, o segundo subtipo mais comum de linfoma. É tipicamente uma doença indolente com uma sobrevida média entre os 8 e 12 anos, mas é geralmente fatal quando se transforma num linfoma agressivo de alto grau, habitualmente o linfoma difuso de grandes células B (LDGCB). Morfologicamente e funcionalmente, as células do LF recapitulam as células normais do centro germinativo na sua dependência de sobrevivência do microambiente não-tumoral, especialmente das células do sistema imunológico. Biomarcadores preditivos de transformação não existem pelo que um melhor conhecimento da biologia intrínseca de progressão do LF poderá revelar novos candidatos. Nesta tese descrevo duas abordagens distintas para a descoberta de novos biomarcadores. A primeira, o estudo da expressão global de genes ('genomics') obtidos por técnicas de alto rendimento que analisam todo o genoma humano sequenciado, permitindo identificar novas anomalias genéticas que possam representar mecanismos biológicos importantes de transformação. São descritos novos genes e alterações genómicas associados à transformação do LF, sendo especialmente relevantes as relacionadas com os eventos iniciais de transformação em LDGCB. A segunda, baseou-se em várias hipóteses centradas no microambiente do LF, rico em vários tipos de células nãomalignas. Os estudos imunoarquitectural de macrófagos, células T regulatórias e densidade de microvasos efectuado em biopsias de diagnóstico de doentes com LF tratados uniformemente correlacionaram-se significativamente, e independentemente dos critérios clínicos, com a evolução clínica e, mais importante, com o risco de transformação em LDGCB. Nesta tese, foram preferencialmente utilizadas (e optimizadas) técnicas que permitam o uso de amostras fixadas em parafina e formalina (FFPET). Estas são facilmente acessíveis a partir das biopsias de diagnóstico de rotina presentes nos arquivos de todos os departamentos de patologia, facilitando uma transição rápida dos novos marcadores para a prática clínica. Embora o FL fosse o tema principal da tese, os novos achados permitiram estender facilmente hipóteses semelhantes a outros subtipos de linfoma. Assim, são propostos e validados vários biomarcadores promissores e relacionados com o microambiente não tumoral, sobretudo dependentes das células do sistema imunológico, como contribuintes importantes para a biologia dos linfomas. Estes sugerem novas opções para a abordagem clínica destas doenças e, eventualmente, novos alvos terapêuticos.------------- ABSTRACT: Cancer biomarkers provide an opportunity to identify those patients most at risk for disease recurrence, predict which tumours will respond to different therapeutic approaches and ultimately define candidate biomarkers that may serve as targets for personalized therapy. New biomarkers are especially needed in the management of lymphoid cancers. At present, these tumours are diagnosed using a combination of morphologic, phenotypic and molecular features but prognosis and overall survival are mostly dependent on clinical characteristics. In most lymphoma types, these imprecisely assess a significant proportion of patients, in particular, those with very poor outcomes. Follicular lymphoma (FL) is the second most common lymphoma subtype worldwide. It is typically an indolent disease with current median survivals in the range of 8-12 years, but is usually fatal when it transforms into an aggressive high-grade lymphoma, characteristically Diffuse Large B Cell Lymphoma (DLBCL). Morphologically and functionally it recapitulates the normal cells of the germinal center with its survival dependency on non-malignant immune and immunerelated cells. Informative markers of transformation related to the intrinsic biology of FL progression are needed. Within this thesis two separate approaches to biomarker discovery were employed. The first was to study the global expression of genes (‘genomics’) obtained using high-throughput, wholegenome-wide approaches that offered the possibility for discovery of new genetic abnormalities that might represent the important biological mechanisms of transformation. Gene signatures associated with early events of transformation were found. Another approach relied on hypothesis-driven concepts focusing upon the microenvironment, rich in several non-malignant cell types. The immunoarchitectural studies of macrophages, regulatory T cells and microvessel density on diagnostic biopsies of uniformly treated FL patients significantly predicted clinical outcome and, importantly, also informed on the risk of transformation. Techniques that enabled the use of routine formalin fixed paraffin embedded diagnostic specimens from the pathology department archives were preferentially used in this thesis with the goal of fulfilling a rapid bench-to-beside” translation for these new findings. Although FL was the main subject of the thesis the new findings and hypotheses allowed easy transition into other lymphoma types. Several promising biomarkers were proposed and validated including the implication of several non-neoplastic immune cells as important contributors to lymphoma biology, opening new options for better treatment planning and eventually new therapeutic targets and candidate therapeutics

Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

Tumor-associated macrophages (TAMs) of the immune microenvironment play an important role in the Diffuse Large B-cell Lymphoma (DLBCL) pathogenesis. This research aimed to characterize the expression of macrophage colony-stimulating factor 1 receptor (CSF1R) at the gene and protein level in correlation with survival. First, the immunohistochemical expression of CSF1R was analyzed in a series of 198 cases from Tokai University Hospital and two patterns of histological expression were found, a TAMs, and a diffuse B-lymphocytes pattern. The clinicopathological correlations showed that the CSF1R + TAMs pattern associated with a poor progression-free survival of the patients, disease progression, higher MYC proto-oncogene expression, lower MDM2 expression, BCL2 translocation, and a MYD88 L265P mutation. Conversely, a diffuse CSF1R + B-cells pattern was associated with a favorable progression-free survival. Second, the histological expression of CSF1R was also correlated with 10 CSF1R-related markers including CSF1, STAT3, NFKB1, Ki67, MYC, PD-L1, TNFAIP8, IKAROS, CD163, and CD68. CSF1R moderately correlated with STAT3, TNFAIP8, CD68, and CD163 in the cases with the CSF1R + TAMs pattern. In addition, machine learning modeling predicted the CSF1R immunohistochemical expression with high accuracy using regression, generalized linear, an artificial intelligence neural network (multilayer perceptron), and support vector machine (SVM) analyses. Finally, a multilayer perceptron analysis predicted the genes associated with the CSF1R gene expression using the GEO GSE10846 DLBCL series of the Lymphoma/Leukemia Molecular Profiling Project (LLMPP), with correlation to the whole set of 20,683 genes as well as with an immuno-oncology cancer panel of 1790 genes. In addition, CSF1R positively correlated with SIRPA and inversely with CD47. In conclusion, the CSF1R histological pattern correlated with the progression-free survival of the patients of the Tokai series, and predictive analytics is a feasible strategy in DLBCL

The impact of molecular alterations and the immune microenvironment on the natural history of Follicular Lymphoma including transformation to Diffuse Large B cell Lymphoma

PhDThe natural history of follicular lymphoma is heterogeneous with numerous relapses and remissions over many years. A substantial number of patients suffer an aggressive disease course with death due to disease within 5 years of diagnosis. The prognosis is significantly worse in patients (10-68%) who transform to an aggressive histology. The clinical parameters used to stratify patients with this disease have limited discriminative power and new prognostic biomarkers are required. Insights into the biology of the disease, with identification of potential therapeutic targets are also required. Analysis of paraffin embedded diagnostic FL biopsies from populations of patients at the extremes of overall survival (15 years) demonstrated that expression of CD4 T lymphocytes and a perifollicular location of forkhead box protein P3 were significantly more common in diagnostic biopsies from patients who lived >15 years. Patients with high numbers of intrafollicular CD4 T lymphocytes and higher numbers of CD68 positive macrophages were more likely to undergo rapid transformation to diffuse large B cell lymphoma (DLBCL). Analysis of sequential biopsies pre-and post-transformation from patients with FL who subsequently transformed to DLBCL demonstrated high numbers of CD68 positive macrophages in the majority of cases. The overall survival from transformation was reduced in patients in whom the number of FOXP3 positive T cells decreased/remained low compared to patients in whom the number of FOXP3 positive T cells increased/remained high. Analysis of biopsies pre-and post-transformation from patients with FL who subsequently transformed, identified mutation of TP53 in 28% of cases suggesting a limited role in the process of transformation. The immunocytochemical expression of MDM2, the TP53 regulator, was significantly higher on transformation. The phenotype of transformed FL was confirmed immunocytochemically as Germinal Centre type and two potential drug targets, Aurora Kinase B and nm23, were confirmed as being up-regulated on transformation

Genetic heterogeneity in follicular lymphoma

The genetic underpinnings of follicular lymphoma (FL) are now better understood through sequencing efforts of the last decade. Epigenetic deregulation, particularly through mutations in chromatin-modifying enzymes, is recognised as a pivotal hallmark that occurs alongside the t(14;18) chromosomal translocation, together with mutations in genes that affect a number of secondary biological pathways including mTORC1, JAK-STAT, NF-kB signalling and immune evasion. In recent years, the functional relevance of these genetic aberrations has been independently deciphered. The protracted nature of FL has provided an excellent model to chart the heterogeneity and evolution of the genetic features of the lymphomas both temporally and spatially. These studies have pointed to the early and late genetic drivers of the disease and the existence of a putative reservoir population that is difficult to eradicate with conventional treatment and most likely contributes to the relapsing-remitting nature of FL. Additionally, these sequencing studies have identified similarities and distinct differences in the genetic profiles of FL compared to related histological entities. In this review, we aim to summarise the current state of our understanding of the genetic landscape and heterogeneity, its contribution to the spectrum of clinical phenotypes in FL and related entities and finally, the ongoing efforts to utilise biology to provide lines of sight to the clinic