Welcome to the Onconaut project, a pioneering project that bridges the gap between genomics and targeted therapies. Our mission is to empower patients and medical professionals with insights into potential treatment options based on individual genomic profiles.

Project Overview

Our project revolves around the crucial endeavor of using biomarkers to match patients with approved therapies and ongoing clinical trials that align with their individual needs, as determined by their tumor genome profiles. Our goal is two-fold: to quantify the percentage of patients who stand to benefit from targeted therapies and to provide patients with expanded treatment options, contributing to more informed medical choices.

Calculating Patient Eligibility

To assess patient eligibility, we matched patient data from the GENIE AACR project with the extensive CIViC dataset. A patient qualifies as eligible if they have at least one potential drug therapy option. A drug is considered a potential therapy for a patient if the patient’s genomic profile does not indicate any variant that is resistant to the drug.

Results

Overall Patients' Eligibility Percentage

In the following plots, we present two scenarios for assessing patient eligibility. The first plot illustrates patient eligibility based on matching the patient's biomarkers with those in the CIViC dataset. In the second plot, we expand the assessment by considering both the patient's biomarker data and their specific cancer type, matching them with the corresponding information in the CIViC dataset.

From the visualizations, it's evident that when we broaden the eligibility criteria to include both the patient's biomarker and cancer type, the percentage of eligible patients experiences a significant decrease.

In the subsequent results, we will focus exclusively on showcasing the outcomes derived from matching patients with the CIViC dataset while considering only the biomarker information.

Eligible Patients' Cancer Type Distribution

Now, let's explore the distribution of cancer types among eligible patients. This visualization offers insights into the prevalence of different cancer types among eligible individuals, focusing on the top 15 most represented cancer types.

Eligible Patients' Distribution by Evidence Rating

Furthermore, we delve into the reliability of evidence supporting the potential drug therapies matched for eligible patients. Each patient's matched drug corresponds to an evidence rating that ranges from 1 to 5, reflecting the reliability of the evidence.

The Evidence Rating depends on factors including study size, design, orthogonal validation, and reproducibility. It assesses the quality of the extracted evidence in isolation, rather than rating the overall publication. Evidence Ratings are categorized as follows:

5 Stars

Strong, well supported evidence from a lab or journal with respected academic standing. Experiments are well controlled, and results are clean and reproducible across multiple replicates. Evidence confirmed using independent methods. The study is statistically well powered.

4 Stars

Strong, well supported evidence. Experiments are well controlled, results are convincing, and any discrepancies from expected results are well-explained and not concerning.

3 Stars

Evidence is convincing, but not supported by a breadth of experiments. May include smaller scale projects or novel results without extensive follow-up, with discrepancies explained and not concerning.

2 Stars

Evidence is not well supported by experimental data, and little follow-up data is available. Publication is from a journal with low academic impact. Experiments may lack proper controls, have small sample size, or are not statistically convincing.

1 Star

Claim is not supported well by experimental evidence. Results are not reproducible, or have very small sample size. No follow-up is done to validate novel claims.

Now, let's explore the insight provided by this plot. It offers a perspective on the distribution of evidence ratings among eligible patients, shedding light on the quality of potential therapy options.