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X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20221215T130000
DTEND;TZID=America/Los_Angeles:20221215T133000
DTSTAMP:20260517T064005
CREATED:20221128T171205Z
LAST-MODIFIED:20221215T225915Z
UID:22840-1671109200-1671111000@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Roni Haas (Boutros)\, Postdoc in Human Genetics
DESCRIPTION:TITLE: “Proteogenomic characterization of the molecular determinants of prostate cancer radioresistance.” \nABSTRACT: Prostate Cancer (PC)\, the second most common cause of cancer death in men\, is frequently treated using radiotherapy with curative intent. Despite its effectiveness\, radiotherapy often results in aggressive PC relapse characterized by radioresistance. The diversity in therapeutic response to radiotherapy\, and the molecular processes underlining radioresistance are not fully understood. Here we integrated genomic\, transcriptomic\, and proteomic investigations to comprehensively characterize the molecular determinants of PC radioresistance. To model radioresistance\, we have created Conventional-Fractionation (CF) and Hypo-Fractionated (HF) isogenic radioresistant cells that represent different therapeutic regimens: the traditional CF radiotherapy with daily small radiation doses over weeks\, and the relatively new HF radiotherapy with high radiation doses across several single treatments. We discovered that CF radioresistant cells gained twice the number of somatic single-nucleotide variants than HF. Nevertheless\, the gained mutations\, irrespective of the treatment schedule\, converged on mutational signatures associated with mismatch DNA repair. CF radioresistant cells demonstrated strong and exclusive dysregulation of driver and hallmark-cancer genes in RNA abundance profiles. The differences in protein abundance display cell-fraction-dependent clusters\, foremost of which are elevated levels of DNA repair proteins in CF radioresistant cell nuclei. Collectively\, we observed a far more aggressive phenotype in CF radioresistant cells compared to HF. The clinical relevance of our top potential therapeutic targets was assessed using a cohort of 380 PC patients. Our study provides a platform for the development of therapies for radio-recurrent PC. Ongoing proteogenomic integration will help understand the relationships amongst radioresistance-associated changes at the DNA\, RNA\, and protein levels. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/11/Roni-Haas.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-roni-haas-boutros-postdoc-in-human-genetics/
LOCATION:Boyer Hall 159
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/11/RoniHaas1-scaled.jpg
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DTSTART;TZID=America/Los_Angeles:20221215T133000
DTEND;TZID=America/Los_Angeles:20221215T140000
DTSTAMP:20260517T064005
CREATED:20221125T154144Z
LAST-MODIFIED:20221215T225950Z
UID:22826-1671111000-1671112800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Zhiqian Zhai (Li JJ)\, Graduate Student in Statistics
DESCRIPTION:TITLE: “Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data.” \nABSTRACT: Recently\, various computational methods have been developed to analyze the scRNAseq data\, such as clustering and visualization. However\, current visualization methods\, including t-SNE and UMAP\, are challenged by the limited accuracy of rendering the geometric relationship of populations with distinct functional states. Most visualization methods are unsupervised\, leaving out information from the clustering results or given labels. This leads to the inaccurate depiction of the distances between the bona fide functional states. In particular\, UMAP and t-SNE are not optimal to preserve the global geometric structure. They may result in a contradiction that clusters with near distance in the embedded dimensions are in fact further away in the original dimensions. Besides\, UMAP and t-SNE cannot track the variance of clusters. Through the embedding of t-SNE and UMAP\, the variance of a cluster is not only associated with the true variance but also is proportional to the sample size. Here\, we present supCPM\, a robust supervised visualization method\, which separates different clusters\, preserves the global structure and tracks the cluster variance. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2022/11/Zhiqian-Zhai-edited.mp4\n 
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-zhiqian-zhai-li-jj-graduate-student-in-statistics/
LOCATION:Boyer Hall 159
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/11/Zhiqian-Zhai-picture.png
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