Cancer proteogenomics : connecting genotype to molecular phenotype

The central dogma of molecular biology describes the one-way road from DNA to RNA and finally to protein. Yet, how this flow of information encoded in DNA as genes (genotype) is regulated in order to produce the observable traits of an individual (phenotype) remains unanswered. Recent advances in high-throughput data, i.e., ‘omics’, have allowed the quantification of DNA, RNA and protein levels leading to integrative analyses that essentially probe the central dogma along all of its constituent molecules. Evidence from these analyses suggest that mRNA abundances are at best a moderate proxy for proteins which are the main functional units of cells and thus closer to the phenotype. Cancer proteogenomic studies consider the ensemble of proteins, the so-called proteome, as the readout of the functional molecular phenotype to investigate its influence by upstream events, for example DNA copy number alterations. In typical proteogenomic studies, however, the identified proteome is a simplification of its actual composition, as they methodologically disregard events such as splicing, proteolytic cleavage and post-translational modifications that generate unique protein species – proteoforms. The scope of this thesis is to study the proteome diversity in terms of: a) the complex genetic background of three tumor types, i.e. breast cancer, childhood acute lymphoblastic leukemia and lung cancer, and b) the proteoform composition, describing a computational method for detecting protein species based on their distinct quantitative profiles. In Paper I, we present a proteogenomic landscape of 45 breast cancer samples representative of the five PAM50 intrinsic subtypes. We studied the effect of copy number alterations (CNA) on mRNA and protein levels, overlaying a public dataset of drug- perturbed protein degradation. In Paper II, we describe a proteogenomic analysis of 27 B-cell precursor acute lymphoblastic leukemia clinical samples that compares high hyperdiploid versus ETV6/RUNX1-positive cases. We examined the impact of the amplified chromosomes on mRNA and protein abundance, specifically the linear trend between the amplification level and the dosage effect. Moreover, we investigated mRNA-protein quantitative discrepancies with regard to post-transcriptional and post-translational effects such as mRNA/protein stability and miRNA targeting. In Paper III, we describe a proteogenomic cohort of 141 non-small cell lung cancer clinical samples. We used clustering methods to identify six distinct proteome-based subtypes. We integrated the protein abundances in pathways using protein-protein correlation networks, bioinformatically deconvoluted the immune composition and characterized the neoantigen burden. In Paper IV, we developed a pipeline for proteoform detection from bottom-up mass- spectrometry-based proteomics. Using an in-depth proteomics dataset of 18 cancer cell lines, we identified proteoforms related to splice variant peptides supported by RNA-seq data. This thesis adds on the previous literature of proteogenomic studies by analyzing the tumor proteome and its regulation along the flow of the central dogma of molecular biology. It is anticipated that some of these findings would lead to novel insights about tumor biology and set the stage for clinical applications to improve the current cancer patient care

CLIP and complementary methods

RNA molecules start assembling into ribonucleoprotein (RNP) complexes during transcription. Dynamic RNP assembly, largely directed by cis-acting elements on the RNA, coordinates all processes in which the RNA is involved. To identify the sites bound by a specific RNA-binding protein on endogenous RNAs, cross-linking and immunoprecipitation (CLIP) and complementary, proximity-based methods have been developed. In this Primer, we discuss the main variants of these protein-centric methods and the strategies for their optimization and quality assessment, as well as RNA-centric methods that identify the protein partners of a specific RNA. We summarize the main challenges of computational CLIP data analysis, how to handle various sources of background and how to identify functionally relevant binding regions. We outline the various applications of CLIP and available databases for data sharing. We discuss the prospect of integrating data obtained by CLIP with complementary methods to gain a comprehensive view of RNP assembly and remodelling, unravel the spatial and temporal dynamics of RNPs in specific cell types and subcellular compartments and understand how defects in RNPs can lead to disease. Finally, we present open questions in the field and give directions for further development and applications

A Higher-Order Generalized Singular Value Decomposition for Comparison of Global mRNA Expression from Multiple Organisms

The number of high-dimensional datasets recording multiple aspects of a single phenomenon is increasing in many areas of science, accompanied by a need for mathematical frameworks that can compare multiple large-scale matrices with different row dimensions. The only such framework to date, the generalized singular value decomposition (GSVD), is limited to two matrices. We mathematically define a higher-order GSVD (HO GSVD) for N≥2 matrices , each with full column rank. Each matrix is exactly factored as Di = UiΣiVT, where V, identical in all factorizations, is obtained from the eigensystem SV = VΛ of the arithmetic mean S of all pairwise quotients of the matrices , i≠j. We prove that this decomposition extends to higher orders almost all of the mathematical properties of the GSVD. The matrix S is nondefective with V and Λ real. Its eigenvalues satisfy λk≥1. Equality holds if and only if the corresponding eigenvector vk is a right basis vector of equal significance in all matrices Di and Dj, that is σi,k/σj,k = 1 for all i and j, and the corresponding left basis vector ui,k is orthogonal to all other vectors in Ui for all i. The eigenvalues λk = 1, therefore, define the “common HO GSVD subspace.” We illustrate the HO GSVD with a comparison of genome-scale cell-cycle mRNA expression from S. pombe, S. cerevisiae and human. Unlike existing algorithms, a mapping among the genes of these disparate organisms is not required. We find that the approximately common HO GSVD subspace represents the cell-cycle mRNA expression oscillations, which are similar among the datasets. Simultaneous reconstruction in the common subspace, therefore, removes the experimental artifacts, which are dissimilar, from the datasets. In the simultaneous sequence-independent classification of the genes of the three organisms in this common subspace, genes of highly conserved sequences but significantly different cell-cycle peak times are correctly classified

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Background: RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence and secondary structure specificities is challenging for both predicting RBP binding sites and accurate sequence and structure motifs detection. Results: In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, to enable subsequent convolution operations. To reveal the hidden binding knowledge from the observed sequences, the CNNs are applied to learn the abstract features. Considering the close relationship between sequence and predicted structures, we use the BLSTM to capture possible long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets. The results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage compared to other methods is that iDeepS can automatically extract both binding sequence and structure motifs, which will improve our understanding of the mechanisms of binding specificities of RBPs. Conclusion: Our study shows that the iDeepS method identifies the sequence and structure motifs to accurately predict RBP binding sites. iDeepS is available at https://github.com/xypan1232/iDeepS