Publications

Alternative polyadenylation (APA) results in messenger RNA molecules with different 3' untranslated regions (3' UTRs), affecting the molecules' stability, localization, and translation. APA is pervasive and implicated in cancer. Earlier reports on APA focused on 3' UTR length modifications and commonly characterized APA events as 3' UTR shortening or lengthening. However, such characterization oversimplifies the processing of 3' ends of transcripts and fails to adequately describe the various scenarios we observe.

Effective analysis of protein samples by mass spectrometry (MS) requires careful selection and optimization of a range of experimental parameters. As the output from the primary detection device, the "raw" MS data file can be used to gauge the success of a given sample analysis. However, the closed-source nature of the standard raw MS file can complicate effective parsing of the data contained within. To ease and increase the range of analyses possible, the RawQuant tool was developed to enable parsing of raw MS files derived from Thermo Orbitrap instruments to yield meta and scan data in an openly readable text format. RawQuant can be commanded to export user-friendly files containing MS{{sup}}1{{/sup}}, MS{{sup}}2{{/sup}}, and MS{{sup}}3{{/sup}} metadata as well as matrices of quantification values based on isobaric tagging approaches. In this study, the utility of RawQuant is demonstrated in several scenarios: (1) reanalysis of shotgun proteomics data for the identification of the human proteome, (2) reanalysis of experiments utilizing isobaric tagging for whole-proteome quantification, and (3) analysis of a novel bacterial proteome and synthetic peptide mixture for assessing quantification accuracy when using isobaric tags. Together, these analyses successfully demonstrate RawQuant for the efficient parsing and quantification of data from raw Thermo Orbitrap MS files acquired in a range of common proteomics experiments. In addition, the individual analyses using RawQuant highlights parametric considerations in the different experimental sets and suggests targetable areas to improve depth of coverage in identification-focused studies and quantification accuracy when using isobaric tags.

Expression of miR-143 and miR-145 is reduced in hematopoietic stem/progenitor cells (HSPCs) of myelodysplastic syndrome patients with a deletion in the long arm of chromosome 5. Here we show that mice lacking miR-143/145 have impaired HSPC activity with depletion of functional hematopoietic stem cells (HSCs), but activation of progenitor cells (HPCs). We identify components of the transforming growth factor β (TGFβ) pathway as key targets of miR-143/145. Enforced expression of the TGFβ adaptor protein and miR-145 target, Disabled-2 (DAB2), recapitulates the HSC defect seen in miR-143/145{{sup}}-/-{{/sup}} mice. Despite reduced HSC activity, older miR-143/145{{sup}}-/-{{/sup}} and DAB2-expressing mice show elevated leukocyte counts associated with increased HPC activity. A subset of mice develop a serially transplantable myeloid malignancy, associated with expansion of HPC. Thus, miR-143/145 play a cell context-dependent role in HSPC function through regulation of TGFβ/DAB2 activation, and loss of these miRNAs creates a preleukemic state.

Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

It is estimated that there are 370 million indigenous peoples in 90 countries globally. Indigenous peoples generally face substantial disadvantage and poorer health status compared with nonindigenous peoples. Population-level cancer surveillance provides data to set priorities, inform policies, and monitor progress over time. Measuring the cancer burden of vulnerable subpopulations, particularly indigenous peoples, is problematic. There are a number of practical and methodological issues potentially resulting in substantial underestimation of cancer incidence and mortality rates, and biased survival rates, among indigenous peoples. This, in turn, may result in a deprioritization of cancer-related programs and policies among these populations. This commentary describes key issues relating to cancer surveillance among indigenous populations including 1) suboptimal identification of indigenous populations, 2) numerator-denominator bias, 3) problems with data linkage in survival analysis, and 4) statistical analytic considerations. We suggest solutions that can be implemented to strengthen the visibility of indigenous peoples around the world. These include acknowledgment of the central importance of full engagement of indigenous peoples with all data-related processes, encouraging the use of indigenous identifiers in national and regional data sets and mitigation and/or careful assessment of biases inherent in cancer surveillance methods for indigenous peoples.

Maximizing the clinical utility of information obtained in longitudinal precision medicine programs would benefit from robust comparative analyses to known information to assess biological features of patient material toward identifying the underlying features driving their disease phenotype. Herein, the potential for utilizing publically deposited mass-spectrometry-based proteomics data to perform inter-study comparisons of cell-line or tumor-tissue materials is investigated.

Overcoming drug resistance and targeting leukemic stem cells (LSCs) remain major challenges in curing BCR-ABL{{sup}}+{{/sup}} human leukemia. Using an advanced drug/proliferation screen, we have uncovered a prosurvival role for protein phosphatase 2A (PP2A) in tyrosine kinase inhibitor (TKI)-insensitive leukemic cells, regulated by an Abelson helper integration site-1-mediated PP2A-β-catenin-BCR-ABL-JAK2 protein complex. Genetic and pharmacological inhibition of PP2A impairs survival of TKI nonresponder cells and sensitizes them to TKIs in vitro, inducing a dramatic loss of several key proteins, including β-catenin. We also demonstrate that the clinically validated PP2A inhibitors LB100 and LB102, in combination with TKIs, selectively eliminate treatment-naïve TKI-insensitive stem and progenitor cells, while sparing healthy counterparts. In addition, PP2A inhibitors and TKIs act synergistically to inhibit the growth of TKI-insensitive cells, as assessed by combination index analysis. The combination eliminates infiltrated BCR-ABL{{sup}}+{{/sup}} blast cells and drug-insensitive LSCs and confers a survival advantage in preclinical xenotransplant models. Thus, dual PP2A and BCR-ABL inhibition may be a valuable therapeutic strategy to synergistically target drug-insensitive LSCs that maintain minimal residual disease in patients.

Diffuse Large B-Cell Lymphoma (DLBCL) is an aggressive hematological cancer for which mitochondrial metabolism may play an important role. Mitochondrial DNA (mtDNA) encodes crucial mitochondrial proteins, yet the relationship between mtDNA and DLBCL remains unclear. We analyzed the functional consequences and mutational spectra of mtDNA somatic mutations and private constitutional variants in 40 DLBCL tumour-normal pairs. While private constitutional variants occurred frequently in the D-Loop, somatic mutations were randomly distributed across the mitochondrial genome. Heteroplasmic constitutional variants showed a trend towards loss of heteroplasmy in the corresponding tumour regardless of whether the reference or variant allele was being lost, suggesting that these variants are selectively neutral. The mtDNA mutational spectrum showed minimal support for ROS damage and revealed strand asymmetry with increased C > T and A > G transitions on the heavy strand, consistent with a replication-associated mode of mutagenesis. These heavy strand transitions carried higher proportions of amino acid changes - which were also more pathogenic - than equivalent substitutions on the light strand. Taken together, endogenous replication-associated events underlie mtDNA mutagenesis in DLBCL and preferentially generate functionally consequential mutations. Yet mtDNA somatic mutations remain selectively neutral, suggesting that mtDNA-encoded mitochondrial functions may not play an important role in DLBCL.

Autophagy is an evolutionarily conserved lysosome-mediated degradation and recycling process, which functions in cellular homeostasis and stress adaptation. The process is highly dynamic and involves autophagosome synthesis, cargo recognition and transport, autophagosome-lysosome fusion, and cargo degradation. The multistep nature of autophagy makes it challenging to quantify, and it is important to consider not only the number of autophagosomes within a cell but also the autophagic degradative activity. The rate at which cargos are recognized, segregated, and degraded through the autophagy pathway is defined as autophagic flux. In practice, methods to measure autophagic flux typically evaluate the lysosome-mediated cargo degradation step by leveraging known autophagy markers such as MAP1LC3B (microtubule-associated proteins 1A/1B light chain 3 beta) or lysosome-dependent fluorescent agents. In this review, we summarize the tools and methods used in mammalian cultured cells pertaining to these two approaches, and highlight innovations that have led to their evolution in recent years. We also discuss the potential limitations of these approaches and recommend using a combination of strategies and multiple different autophagy markers to reliably evaluate autophagic flux in mammalian cells.

Pancreatic neuroendocrine tumors (PNETs) are a genomically and clinically heterogeneous group of pancreatic neoplasms often diagnosed with distant metastases. Recurrent somatic mutations, chromosomal aberrations, and gene expression signatures in PNETs have been described, but the clinical significance of these molecular changes is still poorly understood, and the clinical outcomes of PNET patients remain highly variable. To help identify the molecular factors that contribute to PNET progression and metastasis, and as part of an ongoing clinical trial at the BC Cancer Agency (clinicaltrials.gov ID: NCT02155621), the genomic and transcriptomic profiles of liver metastases from five patients (four PNETs and one neuroendocrine carcinoma) were analyzed. In four of the five cases, we identified biallelic loss of and as well as recurrent regions with loss of heterozygosity. Several novel findings were observed, including focal amplification of concomitant with loss of and in one sample with wild-type and Transcriptome analyses revealed up-regulation of target genes in this sample, confirming a -driven gene expression signature. We also identified a germline fusion event in one sample that resulted in a striking C>T mutation signature profile not previously reported in PNETs. These varying molecular alterations suggest different cellular pathways may contribute to PNET progression, consistent with the heterogeneous clinical nature of this disease. Furthermore, genomic profiles appeared to correlate well with treatment response, lending support to the role of prospective genotyping efforts to guide therapy in PNETs.