Christina Curtis

TL;DR

Researchers are using big data, specifically genomic and other omic data, to better classify cancer patients, understand prognosis, and determine personalized treatment plans.

Transcript

thank you so much for joining me this afternoon it's a pleasure to be here thanks for having me I was reading through your biography and it struck me that you are almost a kind of a cancer detective and I wanted to know how you use big data to inform your work sure so we view big data in sort of various forms shapes and sizes and we're really inter... Read More

Key Insights

• 😃 Big data, including various omic data, is being used to better classify cancer patients and understand their prognosis and treatment responses.
• ❓ Heterogeneity within tumors challenges the traditional understanding of clonality and offers opportunities for studying tumor behavior and evolution.
• 😫 Computational tools and statistical approaches are being applied to analyze diverse data sets and model the behavior of cancer cells.
• 👨‍🔬 The translation of research findings to clinical practice may take time and require testing on larger populations.

Questions & Answers

Q: How does the speaker use big data in their cancer research?

The speaker utilizes various types of data, including genomic, transcriptomic, proteomic, and metabolomic data, to classify patients, understand prognosis, and determine treatment responses.

Q: What is the significance of studying the heterogeneity within tumors?

By studying heterogeneity, researchers can understand the future behavior of a tumor in an individual patient and potentially leverage it to develop personalized treatment plans.

Q: Are tumors composed of clonal cells?

While it was previously believed that tumors consist of clonal cells, recent research has shown that different regions of a tumor and even individual cells within a tumor exhibit heterogeneity and are not identical.

Q: When can we expect to see these computational approaches in clinical practice?

Although progress is being made due to advancements in genome sequencing and computational technologies, it will likely take a few more years to implement these approaches in clinical practice and scale them up to larger populations.

Summary & Key Takeaways

• The speaker uses big data, including genomic, transcriptomic, proteomic, and metabolomic data, to classify patients and understand their prognosis and response to therapies.

• They aim to integrate diverse signals from different data sets to stratify patients and study the heterogeneity within individual tumors.

• The diversity within tumors challenges the long-standing belief of clonality and opens up possibilities for exploiting this heterogeneity to understand tumor behavior and evolution.