Could We Detect Breast Cancer with a Fingerprint? | Simona Francese | TED

Overview

This talk explores a groundbreaking approach to breast cancer detection using molecular analysis of fingerprints. Chemist and professor Simona Francese introduces a non-invasive, painless, and accessible alternative to mammograms, leveraging advanced mass spectrometry techniques to identify biomarkers of breast cancer.

Notable Quotes

- Do you think a test that bypasses NHS backlogs, is painless, radiation-free, and increases accessibility isn’t worth the investment? I think it is. - Simona Francese, on the potential impact of fingerprint-based breast cancer screening.

- Let that sink in. Fingertip smears contain proteins that might be able to tell whether a woman is affected by breast cancer or not. - Simona Francese, on the discovery of breast cancer biomarkers in fingerprints.

🩺 Breast Cancer Statistics and Challenges

- Breast cancer affects one in eight women globally, with 670,000 deaths in 2022 and cases projected to rise significantly by 2040.

- Death rates have fallen by 44% due to early detection, better screening, and improved treatments.

- Mammography remains the gold standard for screening, but uptake is low in certain areas due to NHS backlogs, underfunding, accessibility issues, and cultural barriers.

- Surveys reveal that 21% of women avoid mammograms due to embarrassment or fear of pain.

🔬 Fingerprints as a Diagnostic Tool

- Fingerprints are molecular patterns containing sweat and other substances produced or metabolized by the body.

- Using mass spectrometry (MALDI), Simona Francese and her team analyze these molecular patterns to identify proteins unique to individuals.

- Serendipitous findings revealed that certain proteins in fingerprints are biomarkers for breast cancer, enabling detection with 97.8% accuracy.

💡 Advantages of Fingerprint-Based Screening

- Non-invasive, painless, and radiation-free, addressing common barriers to mammogram compliance.

- Can be conducted at GP practices or even at home, improving accessibility for underserved populations.

- Reduces emotional distress for patients and families while alleviating NHS resource constraints.

- Potential to save billions in healthcare costs associated with breast cancer interventions and treatments.

🚀 Development and Future Potential

- The concept took six years to prove, with initial funding of £45,000 supporting a study of 15 women and 135 fingerprint samples.

- Machine learning algorithms trained on protein patterns achieved high accuracy in distinguishing early, benign, and metastatic breast cancer cases.

- The vision includes widespread adoption of this test as a pre-screening tool, ensuring only positive cases proceed to mammograms, streamlining healthcare processes and saving lives.