Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction.

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, to directly address the effects of variable normal tissue contamination in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer

AHNAK and inflammatory markers predict poor survival in laryngeal carcinoma.

AHNAK/Desmoyokin is a giant protein which has been recently linked to reorganization of the actin cytoskeleton, cellular migration and invasion. Here, we investigated the role of AHNAK in the pathophysiology of larynx carcinoma-one of the major subtypes of head and neck cancer. To this end, we analysed AHNAK expression in tumor tissues from 83 larynx carcinoma patients in relation to overall survival. We found that tumoral AHNAK overexpression significantly associated with poor survival of these patients both in univariate and multivariate analysis. In further studies, we combined the prognostic value of AHNAK with selected markers of inflammation, such as macrophage migration inhibitory factor (MIF) and tumor-infiltrating neutrophils (CD66b-positive cells). Both MIF and neutrophils have been linked to enhanced tumoral migration and poor clinical outcome in patients with orohypopharynx carcinoma-another major subtype of head and neck cancer. Interestingly, we found that synchronous high levels of AHNAK and MIF or AHNAK and neutrophils, respectively, were stronger predictors of poor survival than AHNAK alone. Synchronous high levels of all three markers were the strongest predictors of poor survival in our patient cohort. Taken together, our findings propose novel strategies for an accurate prognosis in larynx carcinoma and suggest potential mechanisms of inflammation-mediated tumor progression

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Transcriptional landscape of neuronal and cancer stem cells

Tumor mass is composed by heterogeneous cell population including a subset of “cancer stem cells” (CSC). Oncogenic signals foster CSC by transforming tissue stem cells or by reprogramming progenitor/differentiated cells towards stemness. Thus, CSC share features with cancer and stem cells (e.g. self-renewal, hierarchical developmental program leading to differentiated cells, epithelial/mesenchimal transition) and these latter are maintained by the constitutive activation of stemness-promoting signals. CSC could trigger tumor formation, drive to resistance to conventional therapeutics and underlie patients’ relapse. Indeed, stem cell signatures have been associated with poor prognosis in various. This background makes the identification of CSC molecular features mandatory to highlight the survival inner working and to design novel CSC specific therapeutic strategies. Medulloblastoma (MB) is the most common childhood malignant brain tumor and a leading cause of cancerrelated morbidity and mortality. Current multimodal therapies are effective in about 50% of patients but often cause long-term side effects, i.e. developmental, neurological, neuroendocrine and psychosocial deficits (Northcott PA Nature Rev cancer 2012). For many years, MB treated as a single tumor entity despite the divergent tumor histology, patients’ outcome and drug sensitivity, and also by the diversity of the stem cell of origin. Very recently the scenario of human MB has dramatically changed since its heterogeneous biology has been addressed by high-throughput gene expression analysis (oligonucleotide microarrays) or by the powerful genomic next-generation sequencing. These led to the identification of four tumor subgroups (WNT, SHH, Group 3 and Group 4) uncovering the existence of a highly diverse mutational spectra and gene expression. However a quantitative approach has not yet been applied to the transcriptional landscape of Medulloblastoma stem cells (MbSC) through RNA Next Generation Sequencing (RNA-Seq) technology. This is a relevant issue, since RNA-Seq is able to interrogate the genome wide global transcriptome including new transcripts, alternative spliced isoforms and non-coding RNAs. Lower rhombic lip progenitors of the dorsal brainstem are considered the trigger cells in WNT tumors; in SHH subgroup initiation cells are Prominin1+ CD15+ stem cells from the subventricular zone requiring the commitment to Math1+ granule cell progenitors [GCP] of the external granule cell layer [EGL]; while Math1+ or Math1- EGL-GCP or Prominin1+/lineage-negative stem cells sustain the MYC driven Group 3. MbSC derived from SHH tumors and postnatal normal cerebellar stem cells (NcSC) have been reported to share several features. A key signal for both of them is Hedgehog. Furthermore, both NcSC and MbSC display up-regulation of stemness genes (e.g Sox2, Nestin, Nanog, Prom1). Finally, constitutive activation of the Shh pathway by conditional deletion of Ptch1 inhibitory receptor in NcSC, promote medulloblastoma in vivo, producing a mouse model of the human SHH tumor. Acquisition of stemness features may therefore represent the first step of oncogenic conversion. Cooperation with additional oncogenic signals is however needed to enhance MbSC tumorigenicity. In order to understand the MbSCs transcriptional programs, we analyze by RNA-Seq, MbSC derived from Ptch1+/- tumors (Ptch1+/- MbSC). This choice, of a genetically determined model of MB, has allowed us to work with Ptch1+/- MbSC together with appropriate NcSC counterpart, and to analyze biological replicates doing statistical analysis. We identify a number of transcripts, annotated ones, novel isoforms, and long non-coding RNAs, characterizing MbSC and/or NcSC. Some of these genes control stemness or are cancer related and conserved in human medulloblastomas. Interestingly a subset of them, belonging to cell stress response, are of prognostic relevance being significantly related to clinical outcome. Correlation of genes expression characterizing MbSC with survival information from our human medulloblastomas database further demonstrates the significance of these findings. Our data suggest that the modulation of normal and cancer stem cell functions observed in vitro is effective in dissecting the transcriptional programs underlying the in vivo behavior of human medulloblastomas

Statistical assessment of somatic mutations and genomic variability using DNA sequence data

The development of new DNA sequencing techniques have made it possible to generate high-resolution genomic data at an unprecedented pace. However, the high dimensionality in combination with the substantial levels of technical errors and biological variability make the analysis challenging. Tailored statistical methods need therefore to be developed and applied in order to facilitate correct biological interpretation. The first two papers in this thesis are focused on finding tumor-specific (somatic) mutations in cancer, while in the third paper a new method to assess genomic variability in microbial communities is developed. In paper I, the aim was to characterize somatic mutations in pheochromocytoma/paraganglioma, and to identify mutations that contribute to malignancy. Statistical analysis of exome sequencing data from nine replicated paired normal--tumor samples revealed 225 unique somatic mutations. A significantly higher rate of mutations was found in malignant compared to benign tumors. In addition, three genes with recurrent somatic mutations, exclusively located in malignant tumors, were identified. In paper II, exome sequencing data was used to detect somatic mutations in 17 patients with acute myeloid leukemia. The identified mutations were evaluated as markers in a more sensitive analysis of remaining cancer cell levels after treatment. All but one of the studied patients were found to have potential markers in their somatic mutation profiles. In paper III, a hierarchical Bayesian model for detecting genetic differences on nucleotide level between groups of microbial communities is proposed. The model is based on a Dirichlet-multinomial distribution and takes both within- and between-sample variability into account. The evaluation of the performance show that the model has a high sensitivity and maintains a low false positive rate even when the between-sample variability is high. The thesis demonstrates the importance of dedicated statistical analysis and understanding of the error structure in DNA sequence data, in order to assure accurate identification of mutations and differences in genomic variability

UCN-01 enhances cytotoxicity of irinotecan in colorectal cancer stem-like cells by impairing DNA damage response

Colorectal cancer (CRC) is one of the most common and lethal cancers worldwide. Despite recent progress, the prognosis of advanced stage CRC remains poor, mainly because of cancer recurrence and metastasis. The high morbidity and mortality of CRC has been recently ascribed to a small population of tumor cells that hold the potential of tumor initiation, i.e. cancer stem cells (CSCs), which play a pivotal role in cancer recurrence and metastasis and are not eradicated by current therapy. We screened CRC-SCs in vitro with a library of protein kinase inhibitors and showed that CRC-SCs are resistant to specific inhibition of the major signaling pathways involved in cell survival and proliferation. Nonetheless, broad-spectrum inhibition by the staurosporin derivative UCN-01 blocks CRC-SC growth and potentiates the activity of irinotecan in vitro and in vivo CRC-SC-derived models. Reverse-Phase Protein Microarrays (RPPA) revealed that, albeit CRC-SCs display individual phospho-proteomic profiles, sensitivity of CRC-SCs to UCN-01 relies on the interference with the DNA damage response mediated by Chk1. Combination of LY2603618, a specific Chk1/2 inhibitor, with irinotecan resulted in a significant reduction of CRC-SC growth in vivo, confirming that irinotecan treatment coupled to inhibition of Chk1 represents a potentially effective therapeutic approach for CRC treatment

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes