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

Submission information
Submission Number: 55
Submission ID: 189171
Submission UUID: ad721e80-a348-48e0-95c9-ea7bf25e45b6

Created: Mon, 07/27/2026 - 15:24
Completed: Mon, 07/27/2026 - 16:02
Changed: Mon, 07/27/2026 - 16:02

Remote IP address: 10.208.28.116
Submitted by: Anonymous
Language: English

Is draft: No
First Name Giuseppe
Middle Initial
Last Name Tarantino
Degree(s) Ph.D.
Position/Title/Career Status Instructor in Medicine
Organization Dana-Farber Cancer Institute
Organization Address Boston
Email giuseppe_tarantino@dfci.harvard.edu
List of Additional Authors
  • First Name: Tyler
    Last Name: Aprati
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Bojan
    Last Name: Karlas
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Paulina
    Last Name: Koehler
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Hyeon-Tae
    Last Name: Hwang
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Cathering
    Last Name: Feng
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Xuelu
    Last Name: Liu
    Affiliation: Dana-Farber Cancer Institute
Abstract Category Developing, refining, or validating tools, methods, algorithms, and pipelines
Abstract Keywords computational pathology; whole-slide imaging; unsupervised tile clustering; tumor cell states;
Abstract Title A pipeline and interactive tool for identifying histomorphological tile clusters associated with tumor transcriptional states
Abstract 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.