Genomic data integration in chronic lymphocytic leukemia

Extracellular vesicles from type-2 macrophages increase the survival of chronic lymphocytic leukemia cells ex vivo

The resistance of Chronic Lymphocytic Leukemia (CLL) B-cells to cell death is mainly attributed to interactions within their microenvironment, where they interact with various types of cells. Within this microenvironment, CLL-B-cells produce and bind cytokines, growth factors, and extracellular vesicles (EVs). In the present study, EVs purified from nurse-like cells and M2-polarized THP1 cell (M2-THP1) cultures were added to CLL-B-cells cultures. EVs were rapidly internalized by B-cells, leading to a decrease in apoptosis (P = 0.0162 and 0.0469, respectively) and an increased proliferation (P = 0.0335 and 0.0109). Additionally, they induced an increase in the resistance of CLL-B-cells to Ibrutinib, the Bruton kinase inhibitor in vitro (P = 0.0344). A transcriptomic analysis showed an increase in the expression of anti-apoptotic gene BCL-2 (P = 0.0286) but not MCL-1 and an increase in the expression of proliferation-inducing gene APRIL (P = 0.0286) following treatment with EVs. Meanwhile, an analysis of apoptotic protein markers revealed increased amounts of IGFBP-2 (P = 0.0338), CD40 (P = 0.0338), p53 (P = 0.0219) and BCL-2 (P = 0.0338). Finally, exploration of EVs protein content by mass spectrometry revealed they carry various proteins involved in known oncogenic pathways and the RNAseq analysis of CLL-B-cells treated or not with NLCs EVs show various differentially expressed genes.

Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI

Cancer is known as a heterogeneous disease. Cancer driver genes (CDGs) need to be inferred for understanding tumor heterogeneity in cancer. However, the existing computational methods have identified many common CDGs. A key challenge exploring cancer progression is to infer cancer subtype-specific driver genes (CSDGs), which provides guidane for the diagnosis, treatment and prognosis of cancer. The significant advancements in single-cell RNA-sequencing (scRNA-seq) technologies have opened up new possibilities for studying human cancers at the individual cell level. In this study, we develop a novel unsupervised method, CSDGI (Cancer Subtype-specific Driver Gene Inference), which applies Encoder-Decoder-Framework consisting of low-rank residual neural networks to inferring driver genes corresponding to potential cancer subtypes at the single-cell level. To infer CSDGs, we apply CSDGI to the tumor single-cell transcriptomics data. To filter the redundant genes before driver gene inference, we perform the differential expression genes (DEGs). The experimental results demonstrate CSDGI is effective to infer driver genes that are cancer subtype-specific. Functional and disease enrichment analysis shows these inferred CSDGs indicate the key biological processes and disease pathways. CSDGI is the first method to explore cancer driver genes at the cancer subtype level. We believe that it can be a useful method to understand the mechanisms of cell transformation driving tumours.

Myeloid/Lymphoid Neoplasms with ETV6::PDGFRB Fusion Gene: A Rare Case of Poor Response to Imatinib and Possible Transformation Mechanisms from Myeloid Neoplasms of Bone Marrow to T-Cell Lymphoblastic Lymphoma Invasion in Lymph Nodes

The ETV6::PDGFRB fusion gene is commonly reported in chronic myelomonocytic leukemia with eosinophilia, yet patients with ETV6::PDGFRB presenting myeloid and lymphoid neoplasms successively have not been reported. Here, we report the first case of a 35-year-old man with myeloid and lymphoid neoplasms harboring an ETV6::PDGFRB fusion gene who demonstrated poor response to imatinib. The patient was diagnosed with an ETV6::PDGFRB fusion gene myeloid neoplasm on initial diagnosis at our hospital. After 5 months of treatment with imatinib, he was diagnosed with T-cell lymphoblastic lymphoma. ETV6::PDGFRB turned negative after increasing the dose of imatinib, but enlarged superficial lymph nodes reappeared the following year. Notably, the patient exhibited a worse response to imatinib treatment. This study describes this rare case and speculates on a possible mechanism.

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19

BACKGROUND

To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection.

METHODS

We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them.

RESULTS

The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities.

CONCLUSIONS

Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

Overexpression of c-Myc-dependent heterogeneous nuclear ribonucleoprotein A1 promotes proliferation and inhibits apoptosis in NOTCH1-mutated chronic lymphocytic leukemia cells

BACKGROUND

NOTCH1 mutation is an essential molecular biologic aberration in chronic lymphocytic leukemia (CLL). CLL patients with NOTCH1 mutation have shown an unfavorable survival and a poor response to chemoimmunotherapy. This study aims to present the mechanisms of adverse prognosis caused by NOTCH1 mutation from the perspective of the splicing factor heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1).

METHODS

The microarray data in Gene Expression Omnibus datasets were analyzed by bioinformatics and the function of hnRNPA1 was checked by testing the proliferation and apoptosis of CLL-like cell lines. Afterward, quantitative reverse transcription-polymerase chain reaction and Western blotting were applied to explore the relationship among NOTCH1, c-Myc, and hnRNPA1.

RESULTS

RNA splicing was found to play a vital part in NOTCH1-mutated CLL cells; hence, hnRNPA1 was selected as the focus of this study. Higher expression of hnRNPA1 validated in primary NOTCH1-mutated CLL samples could promote proliferation and inhibit apoptosis in CLL. The expression of hnRNPA1 increased when NOTCH1 signaling was activated by transfection with NOTCH1 intracellular domain (NICD)-overexpressed adenovirus vector and declined after NOTCH1 signaling was inhibited by NOTCH1-shRNA. Higher expression of c-Myc was observed in NICD-overexpressed cells and hnRNPA1 expression was downregulated after applying c-Myc inhibitor 10058-F4. Moreover, in NICD-overexpressed cells, hnRNPA1 expression decreased through c-Myc inhibition.

CONCLUSION

Overexpression of c-Myc-dependent hnRNPA1 could promote proliferation and inhibit apoptosis in NOTCH1-mutated CLL cells, which might partly account for the poor prognosis of patients with NOTCH1 mutation.

RPL32 Promotes Lung Cancer Progression by Facilitating p53 Degradation

Lung cancer is the leading cause of cancer death worldwide, and the overall survival rate of advanced lung cancer patients is unsatisfactory. Ribosomal proteins (RPs) play important roles in carcinogenesis. However, the role of RPL32 in lung cancer has not been demonstrated. Here, we report that RPL32 is aberrantly, highly expressed in lung cancer tissues and that the overexpression of RPL32 is correlated with the poor prognosis of these patients. RPL32 silencing significantly inhibited the proliferation of lung cancer cells, with an observed p53 accumulation and cell-cycle arrest. Mechanistically, knockdown of RPL32 resulted in ribosomal stress and affected rRNA maturation. RPL5 and RPL11 sensed stress and translocated from the nucleus to the nucleoplasm, where they bound to murine double minute 2 (MDM2), an important p53 E3 ubiquitin ligase, which resulted in p53 accumulation and inhibition of cancer cell proliferation. As lung cancer cells usually express high levels of Toll-like receptor 9 (TLR9), we conjugated RPL32 small interfering RNA (siRNA) to the TLR9 ligand CpG to generate CpG-RPL32 siRNA, which could stabilize and guide RPL32 siRNA to lung cancer cells. Excitingly, CpG-RPL32 siRNA displayed strong anticancer abilities in lung cancer xenografts. Therefore, RPL32 is expected to be a potential target for lung cancer treatment.

Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer

Background

Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize the defective pathways in phenotype prediction problems, such as the Fisher’s ratio sampler, the Holdout sampler and the Random sampler, and apply each one to the analysis of genetic pathways involved in tumor behavior and outcomes of triple negative breast cancers (TNBC). Altered biological pathways are identified using the most frequently sampled genes and are compared to those obtained via Bayesian Networks (BNs).

Results

Random, Fisher’s ratio and Holdout samplers were more accurate and robust than BNs, while providing comparable insights about disease genomics.

Conclusions

The three samplers tested are good alternatives to Bayesian Networks since they are less computationally demanding algorithms. Importantly, this analysis confirms the concept of “biological invariance” since the altered pathways should be independent of the sampling methodology and the classifier used for their inference. Nevertheless, still some modifications are needed in the Bayesian networks to be able to sample correctly the uncertainty space in phenotype prediction problems, since the probabilistic parameterization of the uncertainty space is not unique and the use of the optimum network might falsify the pathways analysis.

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches

BACKGROUND

Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

METHOD

We carried out a retrospective analysis of two different microarray datasets, using machine learning techniques to understand the defective pathways involved in this disease. We have modeled two data sets that are publicly accessible. The first was used to establish the list of most discriminatory genes; whereas, the second one was utilized for validation purposes.

RESULTS

The analysis provided a list of high discriminatory genes with predictive cross-validation accuracy higher than 95%, both in learning and in blind validation. The results were confirmed via the holdout sampler. The most discriminatory genes were related to the production of Hemoglobin. The biological processes involved were related to T-cell Receptor Signaling and co-stimulation, Interferon-Gamma Signaling and Antigen Processing and Presentation. Drug repositioning via CMAP methodologies highlighted the importance of Trichostatin A and other HDAC inhibitors.

CONCLUSIONS

The defective pathways suggest viral or bacterial infections as plausible mechanisms involved in MS development. The pathway analysis also confirmed coincidences with Epstein-Barr virus, Influenza A, Toxoplasmosis, Tuberculosis and Staphylococcus Aureus infections. Th17 Cell differentiation, and CD28 co-stimulation seemed to be crucial in the development of this disease. Furthermore, the additional knowledge provided by this analysis helps to identify new therapeutic targets.

Study of gene expressions' correlation structures in subgroups of Chronic Lymphocytic Leukemia Patients

In chronic lymphocytic leukemia (CLL) the interaction of leukemic cells with the microenvironment ultimately affects patient outcome. CLL cases can be divided in two subgroups with different clinical course based on the mutational status of the immunoglobulin heavy variable (IGHV) genes: mutated CLL (M-CLL) and unmutated CLL (U-CLL). Since in CLL, the differentiated relation of genes between the two subgroups is of greater importance than the individual gene behavior, this paper investigates the differences between the groups' gene interactions, by comparing their correlation structures. Fisher's test and Zou's confidence intervals are employed to detect differences of correlation coefficients. Afterwards, networks created by the genes participating in most differences are estimated with the use of structural equation models (SEM). The analysis is enhanced with graph modeling in order to visualize the between group differences in the gene structures of the two subgroups. The applied methodology revealed stronger correlations between genes in U-CLL patients, a finding in line with related biomedical literature. Using SEM for multigroup analysis, different gene structures between the two groups of patients were confirmed. The study of correlation structures can facilitate the detection of differential gene expression profiles in CLL subgroups, with potential applications in other diseases. Comparison of correlations can be very useful in understanding the complex internal structural differences which signify the variations of a disease.

Machine Learning and Integrative Analysis of Biomedical Big Data

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Chimeric antigen receptor-modified T cell therapy in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL), a common type of B cell chronic lymphoproliferative disorder in adults, has witnessed enormous development in its treatment in recent years. New drugs such as ibrutinib, idelalisib, and venetoclax have achieved great success in treating relapsed and refractory (R/R) CLL. In addition, with the development of immunotherapy, chimeric antigen receptor-engineered T cells (CAR-T) therapy, a novel adoptive immune treatment, has also become more and more important in treating R/R CLL. It combines the advantages of T cells and B cells via ex vivo gene transfer technology and is able to bind targets recognized by specific antibodies without antigen presentation, thus breaking the restriction of major histocompatibility complex. So far, there have been lots of studies exploring the application of CAR-T therapy in CLL. In this review, we describe the structure of chimeric antigen receptor, the preclinical, and clinical results of CAR-T therapy against CLL, along with its adverse events and advances in efficacy.

NOTCH1 mutation and its prognostic significance in Chinese chronic lymphocytic leukemia: a retrospective study of 317 cases

The proto-oncogene NOTCH1 is frequently mutated in around 10% of patients with chronic lymphocytic leukemia (CLL). This study analyzed NOTCH1 mutation status of 317 Chinese patients with CLL by Sanger sequencing. The frequencies of NOTCH1 mutation in the PEST (proline (P), glutamic acid (E), serine (S), threonine (T)-rich protein sequence) domain and the 3' untranslated regions (UTR) were 8.2% and 0.9%, with the most frequent mutation being c.7541_7542delCT and c.*371A>G, respectively. Clinical and biological associations were determined including NOTCH1 mutations with advanced stage (Binet stage, P = 0.010), unmutated immunoglobulin heavy-chain variable region (IGHV) gene (P < 0.001) and trisomy 12 (+12) (P = 0.014). NOTCH1-mutated patients had lower CD20 expression intensity than NOTCH1-unmutated patients (P = 0.029). In addition, NOTCH1-mutated patients had shorter overall survival (OS) (P = 0.002) and treatment-free survival (TFS) (P = 0.002) than NOTCH1-unmutated patients, especially for patients with NOTCH1 c.7541_7542delCT and/or c.*371A>G mutations. Patients with both mutated NOTCH1 and unmutated IGHV had shorter OS (P < 0.001) and TFS (P < 0.001) than those with unmutated NOTCH1 or mutated IGHV. These data provide a comprehensive view of the clinical relevance and prognostic impact of NOTCH1 mutations on Chinese patients with CLL.