Eytan Ruppin, University of Maryland - Stanford Medicine Big Data | Precision Health 2016 | Summary and Q&A
TL;DR
The speaker discusses their approach of using synthetic lethal and synthetic rescue genetic interactions to personalize cancer treatment and mitigate resistance, potentially increasing patient lifetimes threefold.
Key Insights
- 🍃 Precision-based oncology has limited coverage and leaves many patients without treatment options.
- ♋ Synthetic lethal and synthetic rescue interactions offer a new way to personalize cancer treatment based on the entire genome, not just cancer-related genes.
- 🌥️ Analyzing large patient datasets can help identify synthetic lethal and synthetic rescue interactions, potentially increasing treatment efficiency and addressing resistance.
- 🛀 The speaker's approach has shown promise in increasing patient lifetimes by threefold and reducing the chance of resistance.
- ⚾ Combinational therapies based on synthetic rescue interactions can help mitigate resistance.
- 😃 The use of big data allows for the identification of resistance signatures and potential targets for combinational therapies.
- 💦 The speaker's approach is still in the early stages, but initial results are promising and more work is needed in this field.
Transcript
thanks a lot for the kind invitation and for you to being here so the previous speaker are mentioned that we are in the future we will have a universal platform that will enable us to diagnose and personalized treatment in cancer and I want in the 12 minutes that I have to convince you that the future is here ok that's my little humble mission and ... Read More
Questions & Answers
Q: What is the main issue with the current precision-based oncology approach?
The major problem with the current approach is its limited coverage, as it focuses on sequencing only a few hundred cancer driver genes, leaving many patients without treatment options.
Q: How does the speaker's approach differ from traditional methods?
The speaker's approach looks at the whole genome and prioritizes treatments based on synthetic lethal interactions, which are pairs of genes that, when knocked out together, cause cell death. This allows for a personalized treatment approach based on the individual patient's genetic makeup.
Q: How does the speaker's team identify synthetic lethal interactions?
The team mines large patient datasets and looks for pairs of genes that are never lost together, indicating a possible synthetic lethal interaction. This approach takes advantage of big data to analyze genetic interactions on a larger scale.
Q: How does the speaker address the issue of resistance in cancer treatment?
The speaker's team has identified synthetic rescue interactions, where the knockout of one gene puts cancer cells under stress, but an alteration in another gene rescues the cells. By analyzing large datasets, the team can identify pathways of resistance and recommend combinational therapies to mitigate resistance.
Summary & Key Takeaways
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Most precision-based oncology focuses on sequencing hundreds of cancer driver genes, but this approach has limited coverage and cannot help many patients.
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The speaker's approach looks at the whole genome and uses synthetic lethal interactions to prioritize treatments based on the state of all 20,000 genes, not just cancer genes.
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By analyzing large patient datasets, the speaker's team has developed a pipeline to identify synthetic lethal and synthetic rescue interactions, increasing treatment efficiency and addressing resistance.