RNA-sequencing analysis in B-cell acute lymphoblastic leukemia reveals aberrant gene expression and splicing alterations

Background: B-cell acute lymphoblastic leukemia (B-ALL) is a neoplasm of immature lymphoid progenitors and is the leading cause of cancer-related death in children. The majority of B-ALL cases are characterized by recurring structural chromosomal rearrangements that are crucial for triggering leukemogenesis, but do not explain all incidences of disease. Therefore, other molecular mechanisms, such as alternative splicing and epigenetic regulation may alter expression of transcripts that are associated with the development of B-ALL. To determine differentially expressed and spliced RNA transcripts in precursor B-cell acute lymphoblastic leukemia patients a high throughput RNA-seq analysis was performed. Methods: Eight B-ALL patients and eight healthy donors were analyzed by RNA-seq analysis. Statistical testing was performed in edgeR. Each annotated gene was mapped to its corresponding gene object in the Ingenuity KB. Analysis of RNA-seq data for splicing alterations in B-ALL patients and healthy donors was performed with custom Perl script. Results: Using edgeR analysis, 3877 DE genes between B-ALL patients and healthy donors based on TMM (trimmed mean of M-values) normalization method and false discovery rate, FDR less than 0.01, logarithmically transformed fold changes, logFC greater than 2) were identified. IPA revealed abnormal activation of ERBB2, TGFB1 and IL2 transcriptional factors that are crucial for maintaining proliferation and survival potential of leukemic 26 cells. B-ALL specific isoforms were observed for genes with roles in important canonical signaling pathways, such as oxidative phosphorylation and mitochondrial dysfunction. A mechanistic study with the Nalm 6 cell line revealed that some of these gene isoforms significantly change their expression upon 5-Aza treatment, suggesting that they may be epigenetically regulated in B-ALL. Conclusion: Our data provide new insights and perspectives on the regulation of the transcriptome in B-ALL. In addition, we identified transcript isoforms and pathways that may play key roles in the pathogenesis of B-ALL. These results further our understanding of the transcriptional regulation associated with B-ALL development and will contribute to the development of novel strategies aimed towards improving diagnosis and managing patients with B-ALL. Keywords: B-ALL, RNA-sequencing, differential gene expression, alternative splicing

Identification of immune-related gene signatures to evaluate immunotherapeutic response in cancer patients using exploratory subgroup discovery

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients[trademark] heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50 percent of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments or chemotherapy. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although, matching patient subgroup to treatment option that can improve patients[trademark] health outcome remains a challenging task. We extend our exploratory subgroup discovery algorithm to identify patient subpopulations that can potentially benefit from immuno-targeted combination therapies or chemoimmunotherapy in five cancer types: Head and Neck Squamous Carcinoma (HNSC), Lung Adenocarcinoma (LUAD), Lung Squamous Carcinoma (LUSC), Skin Cutaneous Melanoma (SKCM) and Triple-Negative Breast Cancer (TNBC). We employ various regression models to identify immune-related gene signatures and drug targets that increase the likelihood of partial remission on combination therapies, either immunotargeted regimen or chemoimmunotherapy. Moreover, our pipelines can pinpoint adverse drug effects associated with predicted drug combinations. In addition, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients that differentiate patients with partial remission from patients with progressive disease after chemoimmunotherapy. Finally, we incorporate our methodological developments on Mutational Forks Formalism that enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease. Our suit of methods can help to better select responders for combination therapies and improve health outcome for cancer patients with limited treatment options.Includes bibliographical references

Identification of Immuno-Targeted Combination Therapies Using Explanatory Subgroup Discovery for Cancer Patients with EGFR Wild-Type Gene

(1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients’ health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets—FCGR2B, IGF1R, and KIT—substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients’ outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution