Case Study in Action: Predicting Radiotherapy Response with TP53

Case Study in Action: Predicting Radiotherapy Response with TP53

The capabilities described above are not just theoretical. We recently applied the PrecisionRad platform to a critical clinical question using real-world data, demonstrating its power to move from a complex dataset to a clear, actionable insight.

The Challenge: A Common Clinical Dilemma

We started with a foundational question in lung cancer treatment: **Why do some patients respond well to radiation therapy, while others don't?** We hypothesized that the answer might lie in the DNA of their tumors, specifically in mutations of the TP53 gene, a master regulator of cell growth and death.

The Platform in Action: From Messy Data to Clear Insight

Using data from the TCGA Pan-Cancer Atlas, we tasked the platform with analyzing the TP53 status for hundreds of patients and correlating it with their survival after radiation.

This process was a true test of our `analyze_single_variant` capability:

1. A Sophisticated AI Expert: Instead of relying on a single score, our platform used a multi-stage AI system to assess each mutation. It first sieved out catastrophic mutations (like those that prematurely stop a protein from being made) and then used a deep learning model to evaluate the more subtle "missense" mutations. This mimics the workflow of a human genetics expert, providing a more reliable classification.

2. Ensuring Scientific Validity: The analysis initially failed, not because of a code bug, but because of a subtle data integrity issue—our AI was "reading from a different textbook" than the one used to record the original patient mutations. The platform's transparency allowed us to diagnose this, find the correct "textbook" (the canonical TP53 protein sequence, NP_000546.6), and relaunch the analysis. This was a critical step in ensuring our final results were scientifically sound.

3. Robustly Handling Complexity: As seen in our final run, the system correctly processed the missense mutations it was designed for, while intelligently and safely rejecting complex variants (like splices and deletions) that were outside the scope of the current model. This ability to "know what it doesn't know" is a hallmark of a mature, clinical-grade platform.

The Discovery: A Clear Biomarker Emerges

The final output was the survival plot shown above. The story it tells is unambiguous:
* Patients with **pathogenic TP53 mutations** who received radiation had the **worst survival outcomes**.
* Their outcomes were even worse than patients with the same mutations who received **no radiation at all**.

This is a powerful, data-driven insight. It strongly suggests that for this patient group, TP53 status is a predictive biomarker. It provides a compelling, biological reason *why* radiation might be ineffective and helps identify patients who could be spared a difficult treatment that is unlikely to help them.

The Impact: Making the Vision Real

This case study directly demonstrates the value of CrisPRO outlined in this document:
* Personalizes Treatment: by identifying a genomic biomarker that predicts response.
* Treatment Outcome Prediction: by moving beyond population averages to individual genetic risk.
* Enhance Patient Counseling, allowing for more informed conversations about the risks and benefits of a given treatment plan.
* Knowledge Integration & Research Support , turning a raw dataset into a novel scientific hypothesis in a fraction of the time of traditional research.