Clinical Implication of Targeting of Cancer Stem Cells

The existence of cancer stem cells (CSCs) is receiving increasing interest particularly due to its potential ability to enter clinical routine. Rapid advances in the CSC field have provided evidence for the development of more reliable anticancer therapies in the future. CSCs typically only constitute a small fraction of the total tumor burden; however, they harbor self-renewal capacity and appear to be relatively resistant to conventional therapies. Recent therapeutic approaches aim to eliminate or differentiate CSCs or to disrupt the niches in which they reside. Better understanding of the biological characteristics of CSCs as well as improved preclinical and clinical trials targeting CSCs may revolutionize the treatment of many cancers. Copyright (c) 2012 S. Karger AG, Base

Neural regulation of cancer: from mechanobiology to inflammation.

Despite recent progress in cancer research, the exact nature of malignant transformation and its progression is still not fully understood. Particularly metastasis, which accounts for most cancer death, is a very complex process, and new treatment strategies require a more comprehensive understanding of underlying regulatory mechanisms. Recently, the sympathetic nervous system (SNS) has been implicated in cancer progression and beta-blockers have been identified as a novel strategy to limit metastasis. This review discusses evidence that SNS signaling regulates metastasis by modulating the physical characteristics of tumor cells, tumor-associated immune cells and the extracellular matrix (ECM). Altered mechanotype is an emerging hallmark of cancer cells that is linked to invasive phenotype and treatment resistance. Mechanotype also influences crosstalk between tumor cells and their environment, and may thus have a critical role in cancer progression. First, we discuss how neural signaling regulates metastasis and how SNS signaling regulates both biochemical and mechanical properties of tumor cells, immune cells and the ECM. We then review our current knowledge of the mechanobiology of cancer with a focus on metastasis. Next, we discuss links between SNS activity and tumor-associated inflammation, the mechanical properties of immune cells, and how the physical properties of the ECM regulate cancer and metastasis. Finally, we discuss the potential for clinical translation of our knowledge of cancer mechanobiology to improve diagnosis and treatment

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

A Single Gene Expression Set Derived from Artificial Intelligence Predicted the Prognosis of Several Lymphoma Subtypes; and High Immunohistochemical Expression of TNFAIP8 Associated with Poor Prognosis in Diffuse Large B-Cell Lymphoma

OBJECTIVES: We have recently identified using multilayer perceptron analysis (artificial intelligence) a set of 25 genes with prognostic relevance in diffuse large B-cell lymphoma (DLBCL), but the importance of this set in other hematological neoplasia remains unknown. METHODS AND RESULTS: We tested this set of genes (i.e., ALDOB, ARHGAP19, ARMH3, ATF6B, CACNA1B, DIP2A, EMC9, ENO3, GGA3, KIF23, LPXN, MESD, METTL21A, POLR3H, RAB7A, RPS23, SERPINB8, SFTPC, SNN, SPACA9, SWSAP1, SZRD1, TNFAIP8, WDCP and ZSCAN12) in a large series of gene expression comprised of 2029 cases, selected from available databases, that included chronic lymphocytic leukemia (CLL, n = 308), mantle cell lymphoma (MCL, n = 92), follicular lymphoma (FL, n = 180), DLBCL (n = 741), multiple myeloma (MM, n = 559) and acute myeloid leukemia (AML, n = 149). Using a risk-score formula we could predict the overall survival of the patients: the hazard-ratio of high-risk versus low-risk groups for all the cases was 3.2 and per disease subtype were as follows: CLL (4.3), MCL (5.2), FL (3.0), DLBCL not otherwise specified (NOS) (4.5), multiple myeloma (MM) (5.3) and AML (3.7) (all p values 60 years, high serum levels of soluble IL2RA, a non-GCB phenotype (cell-of-origin Hans classifier), moderately higher MYC and Ki67 (proliferation index), and high infiltration of the immune microenvironment by CD163-positive tumor associated macrophages (CD163+TAMs). CONCLUSIONS: It is possible to predict the prognosis of several hematological neoplasia using a single gene-set derived from neural network analysis. High expression of TNFAIP8 is associated with poor prognosis of the patients in DLBCL

Application of mathematical modelling to describe and predict treatment dynamics in patients with NPM1-mutated Acute Myeloid Leukaemia (AML)

Background: Acute myeloid leukaemia (AML) is a severe form of blood cancer, which in many cases can not be cured. Although chemotherapeutic treatment is effective in most cases, often the disease relapses. To monitor the course of disease, as well as to early identify a relapse, the leukaemic cell burden in the bone marrow is measured. In the genome of these cells certain mutations can be found, which lead to the occurrence of leukaemia. One of those mutations is in the neucleophosmin 1 (NPM1) gene. This mutation is found in about one third of all AML patients. The burden of leukaemic cells can be derived from the proportion of NPM1 transcripts carrying this mutation in a bone marrow sample. These values are measured routinely at specific time points during treatment and are then used to categorise the patients into defined risk groups. In the studies, the data for this work originates from, the NPM1 burden was measured beyond the treatment period. That leads to a more comprehensive picture of the molecular course of disease of the patients. Hypothesis: My hypothesis is that the risk group categorisation can be improved by taking into account the dynamic time course information of the patients. Another hypothesis of this work is that with the help of statistical methods and computer models the time course data can be used to describe the course of disease of AML patients and assess whether they will experience a relapse or not. Materials and Methods: For these investigations I was provided with a dataset consisting of quantitative NPM1 time course measurements of 340 AML patients (with a median of 6 mea- surements per patient). To analyse this data I used statistical methods, such as correlation, logistic regression and survival time analysis. For a better understanding of the course of disease I developed a mechanistic model describing the dynamics of the cell numbers in the bone marrow of an AML patient. This model can be fitted to the measurements of a patient by adjusting two parameters, which represent the individual severity of disease. To predict a possible relapse within 2 years after beginning of treatment, I used data that was generated using the mechanistic model (synthetic data). For the prediction three different methods were compared: the mechanistic model, a recurrent neural network (RNN) and a generalised linear model (GLM). Both, the RNN and the GLM were trained and tuned on part of the synthetic data. Afterwards all three methods were tested using the so far unseen part of the data set (test data). Results: Following the analysis of the data I found that the decreasing slope of NPM1 burden during primary treatment as well as the absolute burden after the treatment harbour information about the further course of disease. Specifically, I found that a faster decrease of NPM1 burden and a lower final burden lead to a better prognosis. Further, I could show that the developed simple mechanistic model is able to describe the course of disease of most patients. When I divided the patients into two different risk groups using the fitted parameters from the model I could show that the patients in those groups show distinct relapse-free survival times. The categorisation using the parameters lead to a better distinction of groups than using current categorisation by the WHO. Further, I tried to predict a 2-year relapse using synthetic data and three different prediction methods. I could show that it had nearly no impact at all which method I used. Much more important, however, was the quality of data. Especially the sparseness of data, which we find in the time courses of AML patients, has a considerable negative effect on the predictability of relapse. Using a synthetic data set with measurement times oriented on the times of chemotherapy I could show that a sophisticated measurement scheme could improve the relapse predictability. Conclusions: In conclusion, I suggest to include the dynamic molecular course of the NPM1 burden of AML patients in clinical routine, as this harbours additional information about the course of disease. The involvement of a mechanistic model to asses the risk of AML patients can help to make more accurate predictions about their general prognosis. An accurate prediction of the time of relapse is not possible. All three used methods (mechanistic model, statistical model and neural network) are in general suitable to predict relapse of AML patients. For reliable predictions, however, the quality of the data needs to be drastically improved

How to predict relapse in leukemia using time series data: A comparative in silico study

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients’ time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients

Automatic recognition of different types of acute leukaemia using peripheral blood cell images

[eng] Clinical pathologists have learned to identify morphological qualitative features to characterise the different normal cells, as well as the abnormal cell types whose presence in peripheral blood is the evidence of serious haematological diseases. A drawback of visual morphological analysis is that is time consuming, requires well-trained personnel and is prone to intra-observer variability, which is particularly true when dealing with blast cells. Indeed, subtle interclass morphological differences exist for leukaemia types, which turns into low specificity scores in the routine screening. They are well-known the difficulties that clinical pathologists have in the discrimination among different blasts and the subjectivity associated with their morphological recognition. The general objective of this thesis is the automatic recognition of different types of blast cells circulating in peripheral blood in acute leukaemia using digital image processing and machine learning techniques. In order to accomplish this objective, this thesis starts with a discrimination among normal mononuclear cells, reactive lymphocytes and three types of leukemic cells using traditional machine learning techniques and hand-crafted features obtained from cell segmentation. In the second part of the thesis, a new predictive system designed with two serially connected convolutional neural networks is developed for the diagnosis of acute leukaemia. This system was proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage. Furthermore, it was evaluated for its integration in a real-clinical setting. This thesis also contributes in advancing the state of the art of the automatic recognition of acute leukaemia by providing a more realistic approach which reflects the real-life complexity of acute leukaemia diagnosis

Intelligent techniques using molecular data analysis in leukaemia: an opportunity for personalized medicine support system

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.Haneen Banjar, David Adelson, Fred Brown, and Naeem Chaudhr

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine

Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS

Publisher Copyright: ©2021 American Association for Cancer Research.In myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN), bone marrow (BM) histopathology is assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, other morphologic findings may elude the human eye. We used convolutional neural networks to extract morphologic features from 236 MDS, 87 MDS/MPN, and 11 control BM biopsies. These features predicted genetic and cytogenetic aberrations, prognosis, age, and gender in multivariate regression models. Highest prediction accuracy was found for TET2 [area under the receiver operating curve (AUROC) = 0.94] and spliceosome mutations (0.89) and chromosome 7 monosomy (0.89). Mutation prediction probability correlated with variant allele frequency and number of affected genes per pathway, demonstrating the algorithms' ability to identify relevant morphologic patterns. By converting regression models to texture and cellular composition, we reproduced the classical del(5q) MDS morphology consisting of hypolobulated megakaryocytes. In summary, this study highlights the potential of linking deep BM histopathology with genetics and clinical variables. SIGNIFICANCE: Histopathology is elementary in the diagnostics of patients with MDS, but its high-dimensional data are underused. By elucidating the association of morphologic features with clinical variables and molecular genetics, this study highlights the vast potential of convolutional neural networks in understanding MDS pathology and how genetics is reflected in BM morphology.See related commentary by Elemento, p. 195.Peer reviewe