Molecular genotyping finds genetic changes in cancer cells that can be treated with certain drugs. For people with advanced non-small cell lung cancer (NSCLC), which is a common cause of cancer deaths in the U.S., finding these genetic changes is very important to choose the right treatment. The National Comprehensive Cancer Network (NCCN) says doctors should test for specific genes linked to treatment options. Knowing the genetic profile helps doctors pick better drugs or clinical trials to improve patient results.
Tissue biopsy is still the main method for molecular profiling. It involves taking a sample directly from the tumor. This method shows detailed information and has been the most trusted way to find cancer-related genetic mutations. Studies show that tissue comprehensive genomic profiling (CGP) found important NCCN biomarkers in about 66% of patients when both tissue and liquid biopsies were done.
But tissue biopsy has some problems. Sometimes, it is hard to get enough tissue because the process is invasive. Also, tissue biopsies take time, which can delay diagnosis and treatment. Because of this, there is interest in finding other or additional testing methods.
Liquid biopsy looks for tumor DNA that is floating in the blood. It only needs a simple blood draw. This method is less invasive and gives quicker results. The “plasma-first” approach means starting with liquid biopsy before tissue testing, especially when tissue sample is not available or quick results are needed.
New evidence shows that liquid biopsy can add to tissue genotyping by finding extra genetic changes when tissue tests are limited. For example, in patients with advanced NSCLC who had limited tissue testing (up to five genes), liquid biopsy found important NCCN biomarkers in 18% of cases. Some of these were changes in RET, MET, or ERBB2 genes that tissue biopsy did not catch. This means liquid biopsy can find mutations missed if tissue testing is incomplete.
However, when detailed tissue CGP is done, liquid biopsy does not find many new mutations beyond what tissue testing finds. Tissue CGP is better at identifying key biomarkers and found markers in 23% of patients that liquid biopsy missed. So, liquid biopsy should be used together with tissue testing, not as a replacement.
Using liquid biopsy in the U.S. can help speed up cancer care. Patients with advanced NSCLC often wait weeks for tissue biopsy results. Liquid biopsy can give faster results, allowing doctors to start treatment sooner.
This also helps more patients get access to targeted treatments and clinical trials. It works well when tissue samples are not available. For medical administrators and IT managers, liquid biopsy is easier to manage because it is less invasive and can be done in outpatient or office settings. It also needs less special equipment than tissue biopsy.
Medical practices should think about using both liquid biopsy and tissue CGP. Starting with liquid biopsy when suitable and then adding tissue CGP can find more mutations and improve patient care. This matches the NCCN guidelines.
A study with more than 500 patients with advanced nonsquamous NSCLC looked at how much adding liquid biopsy CGP to tissue testing helps. Tissue CGP found NCCN biomarkers in 66% of patients. Liquid biopsy found them in 43%. But liquid biopsy did not find any extra cases beyond those found by tissue CGP. On the other hand, tissue CGP found biomarkers in 23% of patients when liquid biopsy missed them.
In patients who had limited tissue testing, liquid biopsy found 18% more cases with actionable markers that were not detected by tissue tests limited to five genes. This shows blood-based testing is helpful when tissue biopsy is partial or incomplete.
Doctors like Dr. Lee S. Schwartzberg and Dr. Ariel B. Bourla say that liquid biopsy and tissue genotyping together give a fuller molecular profile needed for precision medicine. Dr. Geoffrey R. Oxnard’s work also shows that tissue CGP is important after a negative liquid biopsy to avoid missing mutations.
AI and automation help make cancer diagnostics and patient care smoother. AI can analyze complicated genetic data from both tissue and liquid biopsies. It can find mutation patterns, suggest treatments, and help with clinical trial matching faster and more accurately than people can.
Medical practices that add AI to electronic health records (EHR) can work more efficiently. Workflow automation can help with scheduling sample collection, tracking lab results, and sharing molecular data with doctors on time. These tools reduce paperwork and help healthcare teams make fast decisions based on data.
Companies are creating AI systems to help with patient communication and scheduling tests like liquid biopsy. This makes it easier for oncology centers that see many patients to run smoothly and use resources well.
Also, machine learning keeps improving the way we understand genomic data by connecting mutation profiles with how patients respond to treatments. This helps doctors make better treatment plans for individual cancer patients.
Running cancer care services in the U.S. needs a balance of quality, access, and cost. Using liquid biopsy with tissue genotyping improves mutation detection and cuts delays before starting treatment. Administrators and owners should look at their molecular testing plans, make sure both test types are available, and teach staff when to use each method.
IT managers are important for setting up technology that links genetic labs, EHR systems, and oncologists smoothly. AI tools can help handle lots of patient data, create useful reports, and alert doctors about possible clinical trials for patients.
Money matters too. It is important to check if adding liquid biopsy early in testing gives good value. Even though faster results help patients, the cost effects of a “plasma-first” method are not fully known and need more study. Careful planning and working with payers and labs will help practices use these tools in a cost-effective way.
Using liquid biopsy with tissue testing and AI-driven workflow automation can improve cancer care in the U.S. It lets practices do full molecular profiling, find actionable mutations faster, and use resources better in busy cancer care settings. Knowing about these updates helps healthcare leaders and tech teams support better patient care and run their programs well.
AI accelerates the discovery of novel targets, predicts treatment effectiveness, identifies life-saving clinical trials, and diagnoses multiple diseases earlier, enhancing personalized patient care through advanced data analysis and algorithmic insights.
Tempus provides an AI-enabled assistant that helps physicians make more informed treatment decisions by analyzing multimodal real-world data and identifying personalized therapy options.
Tempus supports pharmaceutical and biotech companies with AI-driven drug development, leveraging extensive molecular profiling, clinical data integration, and algorithmic models to optimize therapeutic strategies.
The xT Platform combines molecular profiling with clinical data to identify targeted therapies and clinical trials, outperforming tumor-only DNA panel tests by using paired tumor/normal plus transcriptome sequencing.
It uses neural-network-based, high-throughput drug assays with light-microscopy to predict patient-specific drug response heterogeneity across various solid cancers, improving treatment personalization.
Liquid biopsy assays complement tissue genotyping by detecting actionable variants that might be missed otherwise, providing a more comprehensive molecular and clinical profiling for patients.
~65% of US Academic Medical Centers and over 50% of US oncologists are connected to Tempus, enabling wide adoption of AI-powered sequencing, clinical trial matching, and research partnerships.
Tempus One is an AI-enabled clinical assistant integrated into the Electronic Health Record (EHR) system, allowing custom query agents to maximize workflow efficiency and streamline access to patient data.
xM is a liquid biopsy assay designed to monitor molecular response to immune-checkpoint inhibitor therapy in advanced solid tumors, offering real-time treatment response assessment.
Fuses combines Tempus’ proprietary datasets and machine learning to build the largest diagnostic platform, generating AI-driven insights and providing physicians a comprehensive suite of algorithmic tests for precision medicine.