Frameshift Team Submission – Tahoe-DeepDive Hackathon 2025

Team Name

Frameshift

Members

Jesus Gonzalez Ferrer, UCSC — @JesusGF1
Carlota Pereda, UCSF — @carlotapereda
Laura Almonte, UCSF — @almonteloya
Aidan Winters, Arc Institute/UCSF — @aidanwinters
Michael Kosicki, LBL — @lotard

Project

The project code can be found in the following github Repo: frameshift

The slideshow can be found in the following google slides: slideshow

Title

Defining context-specific responses to drug perturbations in Tahoe 100M dataset

Overview

Personalized (i.e. context-specific) treatments lead to better cancer outcomes.
We want to develop a framework that measures how drugs affect cells differently based on their genetic context, and explains the genetic programs that cells use to respond.
We define context-specificity as genotype-, cell line-, tissue-of-origin-, and patient-specific effects on gene expression.

Motivation

Drugs don't work the same way for everyone. Oncotherapies sometimes lack efficacy and tend to be indiscriminate and toxic.
Broad-acting chemotherapies are effective but are limited by patient side effects.
We need better ways of stratifying patients, selecting adequate treatments, and simulating adverse effects before they happen.

Methods

Data Selection

We applied an array of methods to a subset of the Tahoe-100M dataset.
We focused on cell lines with KRAS gain-of-function mutations, especially G12C.
Selected drugs included known KRAS inhibitors, positive controls, and negative controls.

E-distance

Used precomputed scVi embeddings from Tahoe-100M.
Calculated distances to plate-paired DMSO_TF for each drug and cell line.
Visualized results.

MSE

Applied similar steps as E-distance.
Started from pseudobulk samples provided in the dataset.

Augur

A scRNA classifier to quantify separability between control and perturbed groups.
Score of 1 indicates high separability.
Applied across all cell lines and drug perturbations.

CellCap

A generative model for perturbation data.
Models correspondence between basal state and measured perturbation.
Learns interpretable response programs as weighted gene sets.

Results

E-distance and MSE failed to detect context-specific drug effects across selected KRAS cell lines.
Augur and CellCap:
- Detected strong responses in KRAS-G12C lines.
- Captured cell-specific gene expression programs linked to KRAS mutations.

Discussion

The discovery of novel cancer therapies is limited by the lack of generalizable experimental and computational workflows. In a proof-of-concept analysis, we tested four computational methods on the Tahoe-100M dataset for identifying context-specific responses to KRAS inhibitors.

Augur and CellCap succeeded in detecting KRAS-inhibitor effects in KRAS-G12C cell lines.
E-distance and MSE failed to differentiate responses.

We hypothesize that the success of Augur and CellCap lies in their ability to utilize local, pathway-level expression rather than global transcriptomic changes.

Preliminary results highlight genes associated with the Ras-Raf pathway, suggesting a targeted effect by the drugs.

Future Directions

We aim to:

Scale our approach to all cell lines and drugs in Tahoe-100M.
Identify potential cell-type specific drugs.
Propose candidates for clinical development.

Jesusgf23
/

frameshift