NCI Data Jamboree (Project Abstract Submission): Submission #55

serial: '55'
sid: '189171'
uuid: ad721e80-a348-48e0-95c9-ea7bf25e45b6
uri: /nci/datajamboree/abstractsubmission
created: '1785180246'
completed: '1785182527'
changed: '1785182527'
in_draft: '0'
current_page: ''
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langcode: en
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data:
  list_of_additional_authors:
    - add_author_letters: ''
      affiliation: 'Dana-Farber Cancer Institute'
      first_name: Tyler
      last_name: Aprati
    - add_author_letters: ''
      affiliation: 'Dana-Farber Cancer Institute'
      first_name: Bojan
      last_name: Karlas
    - add_author_letters: ''
      affiliation: 'Dana-Farber Cancer Institute'
      first_name: Paulina
      last_name: Koehler
    - add_author_letters: ''
      affiliation: 'Dana-Farber Cancer Institute'
      first_name: Hyeon-Tae
      last_name: Hwang
    - add_author_letters: ''
      affiliation: 'Dana-Farber Cancer Institute'
      first_name: Cathering
      last_name: Feng
    - add_author_letters: ''
      affiliation: 'Dana-Farber Cancer Institute'
      first_name: 'Xuelu '
      last_name: Liu
  category: 'Developing, refining, or validating tools, methods, algorithms, and pipelines'
  degree_s_: Ph.D.
  email: giuseppe_tarantino@dfci.harvard.edu
  first_name: Giuseppe
  keywords_abstracts: 'computational pathology; whole-slide imaging; unsupervised tile clustering; tumor cell states; '
  last_name: Tarantino
  middle_initial: ''
  organization: 'Dana-Farber Cancer Institute'
  organization_address:
    address: ''
    address_2: ''
    city: Boston
    country: ''
    postal_code: ''
    state_province: ''
  summary: |-
    Motivation: Transcriptomics has uncovered different biological tumor states that are often associated with disease outcomes and response to therapy; however, transcriptomic sequencing is rarely available in clinical practice. 
    On the other hand, whole-slide images (WSIs) such as H&E are routinely performed, but are not currently used to derive tumor state information. Linking morphological patterns from WSIs with existing transcriptional programs would allow these tumor states to be identified in routine clinical practice, integrating transcriptomic findings into the precision medicine pipeline.
    We aim to develop a general, end-to-end pipeline and an interactive application that quantifies and tests associations between morphological features and features including any transcriptional signature or patient survival.

    Approach: For WSIs, the pipeline will embed and group images into morphological clusters to summarize each slide as a vector. For transcriptomic samples, gene expression signature scores are computed directly. We will then develop a predictive model (adjusted for clinical and genomic confounders) associating these morphological features and signature scores from the matched H&E and bulk RNA-seq samples available in TCGA.
    Downstream, cluster presence will be linked to outcomes such as overall survival, therapy response. The approach is cohort- and tumor-type agnostic, providing a pipeline from histomorphology of clinical samples to associations with tumor states and survival for hypothesis generation or publication.

    Implementation: The goal is to deliver this application as a simple web app. Users paste a gene list or upload a signature file. The tool computes expression scores and returns per cluster results (survival statistics, visual summaries, etc). Interchangeable configurations (slide type, cluster number, alignment, threshold) are supported, and a precomputed library of signatures is browsable alongside user-defined ones.
  title: 'Instructor in Medicine'
  ttile: 'A pipeline and interactive tool for identifying histomorphological tile clusters associated with tumor transcriptional states'