Using Biosensors for Early Detection of Lung Cancer

Photo by Harlie Raethel on Unsplash

Lung cancer is the leading cause of cancer deaths among men and women, making up about 25% of all deaths caused by cancer. Every year, more people die of lung cancer than colon cancer, prostate cancer, and breast cancer combined.

Cancer is a disease where mutated cells divide uncontrollably and start to destroy healthy cells. For lung cancer, that means that these tumors form in the lungs and if left untreated, can spread throughout the body in a process called metastasis.

If lung cancers were detected early, we could keep cancer localized, preventing metastasis and increasing the patient’s chance for survival. Unfortunately, only 16% of lung cancer cases are found early.

Current detection methods are expensive and take time to process results. For example, a common screening procedure is a CT scan. On average, it costs about $3,275 for the procedure which isn’t accessible for most people. Other options like biopsies, blood tests, etc. are invasive procedures that require collecting samples and sending them to a lab to get results.

Additionally, the early stages of lung cancer often have NO symptoms, delaying diagnosis and treatment. As the disease progresses, symptoms such as chronic cough, coughing up blood, and chest pain appear. If we were able to detect lung cancer early, it’s estimated that we could save 12,000 lives in high-risk populations.

In contrast, our solution entails a biosensor that is able to detect all 6 biomarkers in parallel, using only the patient’s saliva. Not only would this be non-invasive, but it provides an alternative method that is low-cost, highly sensitive, and saves time.

Biosensors

Biosensors are chemical sensors that use biorecognition elements to recognize a target. A target could be a pathogen, an enzyme, pollutants, etc. found in a sample that we want to measure. In our case, we would be measuring biomarkers, a metric/substance that can be used as an indicator of a particular disease state in an organism. In lung cancer, there are six key biomarkers: CEA, CYFRA 21–1, NSE, CA 19–9, CA 125, and Ferritin that when detected in patients have an 80% sensitivity rate for the disease.

image made by Andrea Durham

Simply, a biosensor works by first detecting its target (analyte) using a bioreceptor. When the target binds to the bioreceptor, it generates an electrical signal that can be amplified and measured by the electronic microchip. Afterward, the data can be sent to a computer for further analysis.

image source: https://www.nature.com/articles/s41557-019-0366-y

Following a similar process, our solution is inspired by a research paper that utilizes a gene-circuit design for detecting multiple antibiotic resistance genes in parallel. For our solution, we modified their model to fit our purpose of detecting lung cancer biomarkers.

First, a saliva sample would be collected from the patient (as all 6 biomarkers can be found in saliva) and run through the device. The process from here on out is similar to taking the train.

When you are taking the train, you first need to buy a ticket. At a ticket machine, you insert your credit card. In the same way, the biomarker is the ‘credit card’ that inserts into your bioreceptor (toehold switch).

From there, the toehold switch will unwind, exposing two key sites for transcription and translation: the ribosome-binding site (RBS) and the promoter. When this happens, you can think of the ticket machine preparing to print your ticket.

As your ticket is printing, the ink shows up one line at a time. In the same way, the ribosome acts as a printer, reading each codon and creating its respective amino acid.

When the process is complete, you’re left with your ticket, and the cell is left with the target gene expressed. In our case, a restriction enzyme would be created.

When you board your train, you are sometimes met with a ticket master who cuts your ticket to validate that you paid. Similarly, in the cellular environment of the chip, there are DNA strands floating around in the environment. Acting as scissors, restriction enzymes will come in and cut the DNA in half.

Afterward, what’s left is a single strand of DNA connected to a redox reporter. The redox molecule at the end can be thought of as your train conductor and the DNA strand as your train. The end goal of the redox molecule is to direct the strand towards its final destination.

The final destination of the strand is a compatible receptor located on the microchip. When it ‘arrives’ at its station, instead of hearing a robotic voice alerting you that you’ve made it to your stop, an electrochemical signal is generated. Once this signal is measured, we can detect the presence of the specific biomarkers and at what levels.

Closing

Given the theoretical nature of this idea, there are still some barriers in the way of developing this technology and implementing it as an early detection diagnostic tool (for the sake of this overview, we won’t be going over them).

Believe it or not, we are using biosensors today (i.e. apple watches, fit bits, etc.) that measure your heart rate and sleep patterns. In the same way, creating consumer-accessible early detection tools provides the opportunity for such tests to be available to those who truly need them and save lives by detecting cancers earlier.

In the past, we used our biological features to sense the world around us and create an understanding of the environments we inhabited. This information helped us figure out how to respond and survive in the face of adversity. Today, we can take advantage of technology to measure nanoscale systems in our own bodies, to figure out how to help our cells respond and survive in the face of disease.

This article was made in collaboration with my amazing teammates Andrea Durham, Jeffrey Huynh, and Samantha Hatcher! Feel free to connect with us below:

Andrea Durham: Medium, Linkedin

Jeffrey Huynh: Medium, Linkedin

Samantha Hatcher: Medium, Linkedin

A 17-year-old who knows less about life than she thought she did. Fascinated by biology, specifically biocomputing, synthetic biology, and bioinformatics.