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--- |
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license: mit |
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datasets: |
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- tahoebio/Tahoe-100M |
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tags: |
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- tahoe-deepdive |
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--- |
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# Frameshift Team Submission – Tahoe-DeepDive Hackathon 2025 |
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## Team Name |
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**Frameshift** |
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## Members |
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- Jesus Gonzalez Ferrer, UCSC — [@JesusGF1](https://github.com/JesusGF1) |
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- Carlota Pereda, UCSF — [@carlotapereda](https://github.com/carlotapereda) |
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- Laura Almonte, UCSF — [@almonteloya](https://github.com/almonteloya) |
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- Aidan Winters, Arc Institute/UCSF — [@aidanwinters](https://github.com/aidanwinters) |
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- Michael Kosicki, LBL — [@lotard](https://github.com/lotard) |
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--- |
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## Project |
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The project code can be found in the following github Repo: [frameshift](https://github.com/almonteloya/frameshift) |
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The slideshow can be found in the following google slides: [slideshow](https://docs.google.com/presentation/d/1u6BGhs_5Xd9IMpzrzD3wI1aiH-8z9j0V80ukDE08NoY/edit?usp=sharing) |
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### Title |
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**Defining context-specific responses to drug perturbations in Tahoe 100M dataset** |
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### Overview |
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Personalized (i.e. context-specific) treatments lead to better cancer outcomes. |
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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. |
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We define context-specificity as genotype-, cell line-, tissue-of-origin-, and patient-specific effects on gene expression. |
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### Motivation |
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Drugs don't work the same way for everyone. Oncotherapies sometimes lack efficacy and tend to be indiscriminate and toxic. |
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Broad-acting chemotherapies are effective but are limited by patient side effects. |
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We need better ways of stratifying patients, selecting adequate treatments, and simulating adverse effects before they happen. |
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--- |
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## Methods |
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### Data Selection |
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We applied an array of methods to a subset of the Tahoe-100M dataset. |
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We focused on cell lines with **KRAS gain-of-function mutations**, especially **G12C**. |
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Selected drugs included known KRAS inhibitors, positive controls, and negative controls. |
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### E-distance |
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- Used precomputed `scVi` embeddings from Tahoe-100M. |
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- Calculated distances to plate-paired `DMSO_TF` for each drug and cell line. |
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- Visualized results. |
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### MSE |
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- Applied similar steps as E-distance. |
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- Started from **pseudobulk samples** provided in the dataset. |
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### Augur |
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- A **scRNA classifier** to quantify separability between control and perturbed groups. |
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- Score of 1 indicates high separability. |
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- Applied across all cell lines and drug perturbations. |
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### CellCap |
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- A **generative model** for perturbation data. |
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- Models correspondence between basal state and measured perturbation. |
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- Learns interpretable response programs as weighted gene sets. |
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--- |
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## Results |
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- **E-distance** and **MSE** failed to detect context-specific drug effects across selected KRAS cell lines. |
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- **Augur** and **CellCap**: |
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- Detected strong responses in **KRAS-G12C** lines. |
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- Captured cell-specific gene expression programs linked to KRAS mutations. |
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--- |
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## Discussion |
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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. |
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- **Augur** and **CellCap** succeeded in detecting KRAS-inhibitor effects in KRAS-G12C cell lines. |
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- **E-distance** and **MSE** failed to differentiate responses. |
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We hypothesize that the success of Augur and CellCap lies in their ability to utilize **local, pathway-level expression** rather than global transcriptomic changes. |
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Preliminary results highlight genes associated with the **Ras-Raf pathway**, suggesting a targeted effect by the drugs. |
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### Future Directions |
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We aim to: |
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- Scale our approach to all cell lines and drugs in Tahoe-100M. |
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- Identify potential **cell-type specific drugs**. |
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- Propose **candidates for clinical development**. |
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