Detecting Residual Cancer Using Whole-Genome Sequencing and Artificial Intelligence
Industry Insight Apr 15, 2021
Credit: C2i Genomics
When a tumor is surgically removed, small amounts of residual cancer can remain at a level that is undetectable with current imaging or blood-based approaches. This can result in patients being needlessly overtreated, or in some instances undertreated, providing residual cancer cells the ability to silently progress and metastasize to other parts of the body.
In this interview, Technology Networks spoke with Asaf Zviran, co-founder and CEO of C2i Genomics to find out more about the company’s C2-Intelligence Platform for the detection of trace amounts of cancer.
Laura Lansdowne (LL): Could you comment on the importance of monitoring tumor recurrence in post-resection cancer patients? What limitations do traditional monitoring technologies have?
Asaf Zviran (AZ): Monitoring for tumor resurgence in many cases is just as important as removing the tumor itself. Currently, when a patient gets a tumor removed, doctors don't know if a tiny amount of cancer remains in their body or not. This leads to incredibly expensive and painful problems of overtreatment (in which patients unnecessarily go through toxic radiation/chemotherapy as a precaution, even when their cancer is eliminated) and undertreatment (in which patients have trace amounts of cancer that go undetected and untreated after surgery, which can metastasize and become deadly).
This problem exists primarily because of limitations in current imaging and blood-based screening methods. Imaging-based screening can’t detect cancer until it grows to > 1 billion cells, meaning it takes more than a year to detect recurrence after surgery and by that time the disease is already spread throughout the body. Current blood-based screening only looks at 0.02% of the genome, whereas we look at the entire genome to allow for more sensitive and accurate tumor monitoring.
LL: How is C2i Genomics combining whole-genome sequencing and artificial intelligence approaches to recognize patient-specific cancer patterns?
AZ: Whole-genome sequencing (WGS) gives us an incredible amount of data; the problem is that it is difficult to pick up faint signals and indications of cancer in all that data.
I spent the early part of my career developing integrated radar systems for the Israeli defense sector that used signal processing algorithms to detect threats. After being diagnosed with cancer myself, and losing my father as well as both my wife’s parents to the disease, I decided to go back to school to try and solve this deadly disease. After receiving my Ph.D., I partnered with Dr Dan Landau at Cornell University on a project that used signal processing techniques, pattern recognition and WGS to detect cancer.
By combining advanced AI-based pattern recognition applied to the massive amount of data provided by WGS, we can find previously undetectable cancer traces in the blood.
Kate Robinson (KR): Can you elaborate on the C2-Intelligence Platform and how it can be used to identify previously undetectable traces of cancer?
AZ: By providing 100x more sensitive cancer detection with the C2-Intelligence Platform, physicians can know much sooner if a patient is cancer-free, if their tumor burden is shrinking and responding to therapy, or if it’s growing and they need a new treatment regimen.
Cancer treatment in the US costs nearly $150 billion per year. By having more accurate and informed testing, we can prevent unnecessary treatments and treat resurgence earlier – all while reducing cost and saving lives.
KR: Will this platform be accessible to cancer laboratories globally?
AZ: Our cloud-based technology can be deployed globally at scale to provide actionable insights into the progression of any patient’s cancer as rapidly as in one week, enabling informed and timely treatment decisions. C2i’s cloud platform is GDPR and HIPAA compliant and ISO certified and is already being used in multiple sites in the US, Europe and Singapore. Our partner labs can process and sequence their own samples, upload the raw sequencing data to a server that C2i provides in their own geography and receive a report within days with their patient’s cancer detection and treatment monitoring.
KR: Is there scope to further enhance the platform’s detection capabilities or to include a prognostic element in the future?
AZ: C2i’s WGS protocol generates very rich data that includes the entire genome-wide mutational signature of the patient’s cancer and its evolution over treatment and during follow up. The company is committed to using its growing genomic and clinical database to develop and devise new genome-wide prognostic signatures that will allow better stratification of patients to different treatments from chemotherapy to targeted and immunotherapy. C2i’s vision is to bring a comprehensive cancer intelligence platform that will support clinical decision-making for treatment stratification and monitoring in an integrated manner.
Asaf Zviran was speaking to Laura Lansdowne, Managing Editor and Kate Robinson, Editorial Assistant for Technology Networks.