The Maher lab interests include bioinformatics, computational biology and cancer genomics.
Long non-coding RNAs (lncRNAs)
Our lab is currently integrating genomics, bioinformatics, and molecular biology approaches to characterize RNA species, elucidate their function, and assessing their clinical applicability.
Understanding the role of long non-coding RNAs in lung cancer
In our recently published study in Genome Biology (PMID: 25116943) we leveraged three large publically available data collections comprising ~550 lung cancer patients that enable us to detect 111 lung cancer associated lncRNAs, which we refer to as LCALs. We experimentally validated a subset of LCALs in cell lines and an independent tissue cohort as well as confirming their full-length transcripts. A meta-analysis across human cancers revealed that most LCALs that have lung cancer specific expression and therefore may represent putative biomarkers. However, a few LCALs are altered in multiple solid tumors suggesting they may have a common oncogenic role. Furthermore, we have found that the expression of multiple LCALs is associated with poor overall survival in a cohort of 409 stage I/II lung cancer patients suggesting their clinical potential. Lastly, to demonstrate that LCALs may contribute to lung tumorigenesis we silenced and over expressed the most commonly up-regulated lncRNA across lung subtypes, LCAL1, highlighting its role in regulating cellular proliferation. Overall, transcriptomic analysis highlights the lncRNA landscape that may contribute to lung cancer and lays the framework for future studies exploring their mechanism in lung tumorigenesis and potential use as biomarkers. Our lab is currently addressing the challenge of understanding how lncRNAs function using high-throughput assays (i.e., RIP-Seq) within subsets of lung cancer patients with the intent of identifying novel therapeutic strategies.
Understanding the biological and clinical significant of Prostate Cancer Associated Transcripts (PCATs)
Each year, over 180,000 men are diagnosed with prostate cancer in the United States, and over 29,000 will die from this disease. To date prostate cancer research has primarily focused on the deregulation of protein-coding genes to identify oncogenes and tumor suppressors as potential diagnostic and therapeutic targets. We leveraged the unbiased approach of transcriptome sequencing to identify over 1,800 unannotated lncRNAs, with 121 found to be involved in prostate tumorigenesis, referred to as prostate cancer associated transcripts (PCATs). Although originally regarded as transcriptional noise, several well-described examples indicate that lncRNAs may be master regulators in cancer biology, typically facilitating epigenetic gene repression through chromatin-modifying complexes. However, of the thousands of lncRNAs discovered to date, only a few have been functionally characterized in human cancers. Interestingly, PCAT-1 was found to have mutually exclusive expression with EZH2, a core PRC2 protein and prostate cancer biomarker. Subsequent in vitro and in vivo studies demonstrated that PCAT-1 regulates cellular proliferation through an unknown mechanism. Through RNA immunoprecipitation we found that PCAT-1 associates with PRC2, which in turn can bind to the PCAT-1 promoter. Taken together, these results suggest that PCAT-1 biology may exhibit two distinct modalities: one in which PRC2 represses PCAT-1 and a second in which active PCAT-1 promotes cell proliferation. Building upon this, our lab is currently focusing on understanding how additional PCATs promote to prostate cancer and placing them in clinical context (as part of the projects described in the 'Prostate Cancer Genomics' section).
Understanding the contribution of long non-coding RNAs to metastasis
As part of a Team Science Award, we are exploring the differences in the genome, transcriptome, and epigenome between primary and metastatic colorectal cancer with the intent of revealing novel pathways critical for tumor metastases, identify potential diagnostic/prognostic markers, and unmask new targets for therapeutic intervention. Interestingly, a gene set enrichment analysis of the differentially expressed protein-coding genes from our patient cohort revealed an enrichment of Polycomb Repressive Complex 2 (PRC2) target genes during the progression of mCRC. Consistent with earlier reports, our subsequent preliminary studies demonstrate that ~20% of the known lncRNAs associate with the PRC2, which represses the transcription of pro-differentiation and anti-proliferative genes leading to poorly differentiated and aggressive human tumors. Therefore, we are optimizing novel techniques to systematically identify how lncRNAs interact with PRC2 to modulate chromatin states.
Estrogen-receptor-positive breast cancer exhibits highly variable prognoses, histological growth patterns and treatment outcomes. The observation that estrogen receptor positive breast cancer growth is stimulated by estrogen led to clinical trials utilizing anti-estrogen therapies prior to surgery. To date multiple clinical trials have focused on aromatase inhibitors, a form of anti-estrogen therapy that blocks the enzyme aromatase from synthesizing estrogen. Despite the successes of using neoadjuvant hormone therapy, only a subset of patients will respond to treatment. Therefore, our lab is focusing on understanding the role of lncRNAs in therapy sensitivity.
Prostate Cancer Genomics
Our lab also has a long-standing interest in translating genome-based discoveries that to improve prostate cancer treatment. This can be exemplified by two ongoing projects.
Identifying Molecular Biomarkers of Long-Term Response to Androgen Deprivation Therapy and Radiation in Lethal Prostate Cancer
Of the 29,480 men who died from prostate cancer last year, over 80% of these patients presented with localized disease. Thus, a majority of lethal prostate cancers are diagnosed at a potentially curable stage. This presents two critical challenges: determining which patients will benefit from intensified treatment with earlier initiation of anti-androgen therapy, and conversely, identifying which patients will develop resistance and proceed to lethal disease. Therefore, we our lab is using comprehensive genomic approaches to discover biomarkers predictive of treatment resistance and lethal disease. To accomplish this, we will study 771 patients enrolled in a phase III clinical trial. This study is highly unique as these patients were randomized to receive common treatments and have long-term clinical follow-up (>10-years). Therefore, this represents the first study of its kind to discover predictors of prostate cancer cure and death. Successful completion of this study will benefit men fighting metastatic prostate cancer and their families by discovering markers that can predict who will benefit from treatment and who will become resistant resulting in more personalized patient care. This in turn will also reduce putting patients at risk of side effects unnecessarily. The discovery of predictive biomarkers will also help shape the design of future clinical trials and significantly impact the direction of prostate cancer therapy.
Molecular predictors of indolent and aggressive disease
A critical goal in prostate cancer research is determining the molecular underpinnings of aggressive and indolent disease and its subsequent application to patient management and prognosis. At present, one of the best indications of disease aggressiveness is Gleason grade, an indicator of cellular differentiation level, assigned by histopathologic examination of carcinoma in prostate tissue. Tumors with Gleason grade 4 are clinically more aggressive than Gleason grade 3. Therefore, the nucleotide-level characterization of isolated Gleason grade 3 and 4 tumor foci from the same patient are of particular interest in understanding the genomic relatedness of foci and the genetic underpinnings of disease progression. In an effort to better understand the genetic progression of the disease, we will sequence Gleason grade 3 and 4 foci as well as grade 3 and 5 foci from the same patients to permit this comparison. To isolate carcinoma cells of different grades we will use laser capture microdissection, then we will use the small amount of DNA isolated from the cells to produce a whole genome sequence data set from the microdissected-isolated cells. The resulting data will provide a comprehensive comparison of genome-wide somatic alterations in the progression from indolent to aggressive disease, and illustrate the genomic relationship of different tumor grades within the same tumor. Our goal is to identify hallmarks of prostate cancer progression and illustrate the genomic relatedness between shared tumor foci by comparing the mutational landscapes of Gleason grade 3 and 4 cells and Gleason grade 3 and 5 cells.
Over the last few years, our group has focused on developing methods to discover gene fusions using transcriptome sequencing (RNA-Seq) and understand their biological and clinical significance. This has led to numerous discoveries including a recurrent RNA chimera SLC45A3-ELK4 and a novel class of RAF fusions in prostate cancer (PMID: 20526349), MAST kinase and NOTCH fusions in triple negative breast cancer (PMID: 22101766), ESR1 fusions in ER+ breast cancer (PMID: 24055055), ALK and ROS fusions in lung cancer (PMID: 22980976), and a recurrent RNA chimera in chronic lymphocytic leukemia (PMID: 23382248). To enable other labs to make similar discoveries we have developed and released open source software, ChimeraScan, to detect gene fusions using transcriptome sequencing data. Despite our successes, we are still faced with the critical challenge of ensuring that a casual gene fusion is not only detected, but that it can be prioritized accordingly amongst the increasing number of chimeric mutations in a cancer transcriptome. To address this, our lab is continuing to optimize computational and experimental methods for discovering and prioritizing gene fusions.
Our group is also actively participating in:
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