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1. Machine learning empowers phosphoproteome prediction in cancers

   https://doi.org/10.1093/bioinformatics/btz639  

   Li, Hongyang ; Guan, Yuanfang ; Wren, ed., Jonathan ( August 2019 , Bioinformatics)

   Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic and genomic data.

   Here, we present a winning algorithm in the 2017 NCI-CPTAC DREAM Proteogenomics Challenge for predicting phosphorylation levels of the proteome across cancer patients. We integrate four components into our algorithm, including (i) baseline correlations between protein and phosphoprotein abundances, (ii) universal protein–protein interactions, (iii) shareable regulatory information across cancer tissues and (iv) associations among multi-phosphorylation sites of the same protein. When tested on a large held-out testing dataset of 108 breast and 62 ovarian cancer samples, our method ranked first in both cancer tissues, demonstrating its robustness and generalization ability.

   Our code and reproducible results are freely available on GitHub: https://github.com/GuanLab/phosphoproteome_prediction.

   Supplementary data are available at Bioinformatics online.

    

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2. SpaceX: gene co-expression network estimation for spatial transcriptomics

   https://doi.org/10.1093/bioinformatics/btac645  

   Acharyya, Satwik ; Zhou, Xiang ; Baladandayuthapani, Veerabhadran ; Kendziorski, ed., Christina ( September 2022 , Bioinformatics)

   The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene–gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.

   We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

   The SpaceX R-package is available at github.com/bayesrx/SpaceX.

   Supplementary data are available at Bioinformatics online.

    

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3. Longitudinal Position and Cancer Risk in the United States Revisited

   https://doi.org/10.1158/2767-9764.CRC-23-0503  

   Niu, Jin ; Brown, Charlotte ; Law, Michael ; Colacino, Justin A. ; Ritov, Ya'acov ( February 2024 , Cancer Research Communications)

   In this article, we investigate the relation between the epidemiology of cancer incidence in the United States and time zone–related longitudinal positions. Our results differ from previous research, which were based on a subset of our data, and show that the time zone effect on cancer incidence rate is not significant. Our research provides implications on the implementation of DST by suggesting that there is no cancer-risk associated reason to prefer one time over the other. Our study also uses regression discontinuity design using natural splines, a more advanced statistical method, to increase robustness of our result. Our findings challenge prior research, revealing numerous inconsistencies. These disparities urge a reconsideration of the potential disparities in human health associated with DST and standard time. They offer insights contribute to the ongoing discussion surrounding the retention or abandonment of DST.

    

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4. Personalized Integrated Network Modeling of the Cancer Proteome Atlas

   https://doi.org/10.1038/s41598-018-32682-x  

   Ha, Min Jin ; Banerjee, Sayantan ; Akbani, Rehan ; Liang, Han ; Mills, Gordon B. ; Do, Kim-Anh ; Baladandayuthapani, Veerabhadran ( October 2018 , Scientific Reports)

   Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-drivende novocausal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings (https://mjha.shinyapps.io/PRECISE/).

    

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5. Oncogenic KRAS alters splicing factor phosphorylation and alternative splicing in lung cancer

   https://doi.org/10.1186/s12885-022-10311-1  

   Lo, April ; McSharry, Maria ; Berger, Alice H. ( December 2022 , BMC Cancer)

   Alternative RNA splicing is widely dysregulated in cancers including lung adenocarcinoma, where aberrant splicing events are frequently caused by somatic splice site mutations or somatic mutations of splicing factor genes. However, the majority of mis-splicing in cancers is unexplained by these known mechanisms. We hypothesize that the aberrant Ras signaling characteristic of lung cancers plays a role in promoting the alternative splicing observed in tumors.

   We recently performed transcriptome and proteome profiling of human lung epithelial cells ectopically expressing oncogenic KRAS and another cancer-associated Ras GTPase, RIT1. Unbiased analysis of phosphoproteome data identified altered splicing factor phosphorylation in KRAS-mutant cells, so we performed differential alternative splicing analysis using rMATS to identify significantly altered isoforms in lung epithelial cells. To determine whether these isoforms were uniquely regulated by KRAS, we performed a large-scale splicing screen in which we generated over 300 unique RNA sequencing profiles of isogenic A549 lung adenocarcinoma cells ectopically expressing 75 different wild-type or variant alleles across 28 genes implicated in lung cancer.

   Mass spectrometry data showed widespread downregulation of splicing factor phosphorylation in lung epithelial cells expressing mutant KRAS compared to cells expressing wild-type KRAS. We observed alternative splicing in the same cells, with 2196 and 2416 skipped exon events in KRAS^G12Vand KRAS^Q61Hcells, respectively, 997 of which were shared (p < 0.001 by hypergeometric test). In the high-throughput splicing screen, mutant KRAS induced the greatest number of differential alternative splicing events, second only to the RNA binding protein RBM45 and its variant RBM45^M126I. We identified ten high confidence cassette exon events across multiple KRAS variants and cell lines. These included differential splicing of the Myc Associated Zinc Finger (MAZ). As MAZ regulates expression of KRAS, this splice variant may be a mechanism for the cell to modulate wild-type KRAS levels in the presence of oncogenic KRAS.

   Proteomic and transcriptomic profiling of lung epithelial cells uncovered splicing factor phosphorylation and mRNA splicing events regulated by oncogenic KRAS. These data suggest that in addition to widespread transcriptional changes, the Ras signaling pathway can promote post-transcriptional splicing changes that may contribute to oncogenic processes.

    

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