Serum profiling identifies ibrutinib as a treatment option for young adults with B‐cell acute lymphoblastic leukaemia

Acute lymphoblastic leukaemia (ALL) is a haematological malignancy that is characterized by the uncontrolled proliferation of immature lymphocytes. 80% of cases occur in children where ALL is well understood and treated. However it has a devastating affects on adults, where multi-agent chemotherapy is the standard of care with allogeneic stem cell transplantation for those who are eligible. New treatments are required to extend remission and prevent relapse to improve patient survival rates. We used serum profiling to compare samples from presentation adult B-ALL patients with age- and sex-matched healthy volunteer (HV) sera and identified 69 differentially recognised antigens (P ≤ 0·02). BMX, DCTPP1 and VGLL4 showed no differences in transcription between patients and healthy donors but were each found to be present at higher levels in B-ALL patient samples than HVs by ICC. BMX plays a crucial role in the Bruton's Tyrosine Kinase (BTK) pathway which is bound by the BTK inhibitor, ibrutinib, suggesting adult B-ALL would also be a worthy target patient group for future clinical trials. We have shown the utility of proto-array analysis of B-ALL patient sera, predominantly from young adults, to help characterise the B-ALL immunome and identified a new target patient population for existing small molecule therapy

Downregulation of 26S proteasome catalytic activity promotes epithelial-mesenchymal transition.

The epithelial-mesenchymal transition (EMT) endows carcinoma cells with phenotypic plasticity that can facilitate the formation of cancer stem cells (CSCs) and contribute to the metastatic cascade. While there is substantial support for the role of EMT in driving cancer cell dissemination, less is known about the intracellular molecular mechanisms that govern formation of CSCs via EMT. Here we show that β2 and β5 proteasome subunit activity is downregulated during EMT in immortalized human mammary epithelial cells. Moreover, selective proteasome inhibition enabled mammary epithelial cells to acquire certain morphologic and functional characteristics reminiscent of cancer stem cells, including CD44 expression, self-renewal, and tumor formation. Transcriptomic analyses suggested that proteasome-inhibited cells share gene expression signatures with cells that have undergone EMT, in part, through modulation of the TGF-β signaling pathway. These findings suggest that selective downregulation of proteasome activity in mammary epithelial cells can initiate the EMT program and acquisition of a cancer stem cell-like phenotype. As proteasome inhibitors become increasingly used in cancer treatment, our findings highlight a potential risk of these therapeutic strategies and suggest a possible mechanism by which carcinoma cells may escape from proteasome inhibitor-based therapy

Application of the pMHC array to characterise tumour antigen specific T cell populations in leukaemia patients at disease diagnosis

Immunotherapy treatments for cancer are becoming increasingly successful, however to further improve our understanding of the T-cell recognition involved in effective responses and to encourage moves towards the development of personalised treatments for leukaemia immunotherapy, precise antigenic targets in individual patients have been identified. Cellular arrays using peptide-MHC (pMHC) tetramers allow the simultaneous detection of different antigen specific T-cell populations naturally circulating in patients and normal donors. We have developed the pMHC array to detect CD8+ T-cell populations in leukaemia patients that recognise epitopes within viral antigens (cytomegalovirus (CMV) and influenza (Flu)) and leukaemia antigens (including Per Arnt Sim domain 1 (PASD1), MelanA, Wilms’ Tumour (WT1) and tyrosinase). We show that the pMHC array is at least as sensitive as flow cytometry and has the potential to rapidly identify more than 40 specific T-cell populations in a small sample of T-cells (0.8–1.4 x 106). Fourteen of the twenty-six acute myeloid leukaemia (AML) patients analysed had T cells that recognised tumour antigen epitopes, and eight of these recognised PASD1 epitopes. Other tumour epitopes recognised were MelanA (n = 3), tyrosinase (n = 3) and WT1126-134 (n = 1). One of the seven acute lymphocytic leukaemia (ALL) patients analysed had T cells that recognised the MUC1950-958 epitope. In the future the pMHC array may be used provide point of care T-cell analyses, predict patient response to conventional therapy and direct personalised immunotherapy for patients

New targets for therapy: antigen identification in adults with B-cell acute lymphoblastic leukaemia

Acute lymphoblastic leukaemia (ALL) in adults is a rare and difficult-to-treat cancer that is characterised by excess lymphoblasts in the bone marrow. Although many patients achieve remission with chemotherapy, relapse rates are high and the associated impact on survival devastating. Most patients receive chemotherapy and for those whose overall fitness supports it, the most effective treatment to date is allogeneic stem cell transplant that can improve overall survival rates in part due to a ‘graft-versus-leukaemia’ effect. However, due to the rarity of this disease, and the availability of mature B-cell antigens on the cell surface, few new cancer antigens have been identified in adult B-ALL that could act as targets to remove residual disease in first remission or provide alternative targets for escape variants if and when current immunotherapy strategies fail. We have used RT-PCR analysis, literature searches, antibody-specific profiling and gene expression microarray analysis to identify and prioritise antigens as novel targets for the treatment of adult B-ALL

IL-13 Augments Compressive Stress–Induced Tissue Factor Expression in Human Airway Epithelial Cells

Tissue factor (TF) is best known as a cellular initiator of coagulation, but it is also a multifunctional protein that has been implicated in multiple pathophysiologic conditions, including asthma. In the lung, airway epithelial cells express TF, but it is unknown how TF expression is regulated by asthma-associated mediators. We investigated the role of IL-13, a type 2 cytokine, alone and in combination with compressive stress, which mimics asthmatic bronchoconstriction, on TF expression and release of TF-positive extracellular vesicles from primary normal human bronchial epithelial cells. Well-differentiated normal human bronchial epithelial cells were treated with IL-13 and compressive stress, alone and in combination. TF mRNA, protein and activity were measured in the cells and conditioned media. TF was also measured in the bronchoalveolar lavage (BAL) fluid of allergen-challenged mice and patients with asthma. IL-13 and compressive stress increased TF expression, but only compressive stress induced TF-positive extracellular vesicle release. Pretreatment with IL-13 augmented compressive stress–induced TF expression and release. TF protein and activity in BAL fluid were increased in allergen-sensitized and -challenged mice. TF was elevated in the BAL fluid of patients with mild asthma after an allergen challenge. Our in vitro and in vivo data indicate close cooperation between mechanical and inflammatory stimuli on TF expression and release of TF-positive extracellular vesicles in the lungs, which may contribute to pathophysiology of asthma

Kawasaki Disease Patient Stratification and Pathway Analysis Based on Host Transcriptomic and Proteomic Profiles

The aetiology of Kawasaki disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host ‘omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple ‘omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering, and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infections at the transcriptomic and proteomic levels through comparison of ‘omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both ‘omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both ‘omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition

Tumor immune infiltration estimated from gene expression profiles predicts colorectal cancer relapse

A substantial fraction of patients with stage I-III colorectal adenocarcinoma (CRC) experience disease relapse after surgery with curative intent. However, biomarkers for predicting the likelihood of CRC relapse have not been fully explored. Therefore, we assessed the association between tumor infiltration by a broad array of innate and adaptive immune cell types and CRC relapse risk. We implemented a discovery-validation design including a discovery dataset from Moffitt Cancer Center (MCC; Tampa, FL) and three independent validation datasets: (1) GSE41258 (2) the Molecular Epidemiology of Colorectal Cancer (MECC) study, and (3) GSE39582. Infiltration by 22 immune cell types was inferred from tumor gene expression data, and the association between immune infiltration by each cell type and relapse-free survival was assessed using Cox proportional hazards regression. Within each of the four independent cohorts, CD4+ memory activated T cell (HR: 0.93, 95% CI: 0.90-0.96; FDR = 0.0001) infiltration was associated with longer time to disease relapse, independent of stage, microsatellite instability, and adjuvant therapy. Based on our meta-analysis across the four datasets, 10 innate and adaptive immune cell types associated with disease relapse of which 2 were internally validated using multiplex immunofluorescence. Moreover, immune cell type infiltration was a better predictors of disease relapse than Consensus Molecular Subtype (CMS) and other expression-based biomarkers (Immune-AICMCC:238.1-238.9; CMS-AICMCC: 241.0). These data suggest that transcriptome-derived immune profiles are prognostic indicators of CRC relapse and quantification of both innate and adaptive immune cell types may serve as candidate biomarkers for predicting prognosis and guiding frequency and modality of disease surveillance

Feature selection using Haar wavelet power spectrum

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem [1]. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special softwares or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design