Cancer is a complicated disease that involves changes at the genetic, molecular, and cellular levels. Traditional ways of diagnosing and treating cancer have mostly used tumor tissue study and some genetic tests. But these methods sometimes miss how cancer behaves and interacts with its surroundings. Multimodal molecular profiling is a method that combines different types of biological data. This includes genomics, transcriptomics, proteomics, and immune microenvironment analysis to get a full picture of a tumor and the patient’s body.
For example, BostonGene, a biotechnology company in the United States, created an AI-driven multimodal molecular profiling platform. It includes genomics, transcriptomics, and immune tumor microenvironment (TME) profiling. This platform studied over 3,600 samples from patients with metastatic clear cell renal cell carcinoma (ccRCC). It found five new tumor immune subtypes. Each subtype has different immune cell patterns, genetic changes, and chances of survival. This helps doctors group patients based on who might respond well to immunotherapy or targeted tyrosine kinase inhibitors (TKIs), and who might not.
By using many layers of molecular data, doctors understand tumor biology better than just looking under a microscope or testing one biomarker. This full view helps them choose treatments that fit each person’s unique biology. It increases the chance of success and lowers the risk of giving treatments that won’t work.
Artificial intelligence is very important in combining large amounts of molecular data that would be hard for people to understand quickly. BostonGene’s AI platform uses machine learning on combined data sets. It finds patterns and creates “responder scores.” These scores show how likely a patient is to benefit from treatments like immune checkpoint inhibitors (ICIs) or VEGF-targeted TKIs.
The AI models are not “black boxes.” That means the decision-making process is clear. BostonGene proves this by testing AI predictions with methods like spatial proteomics. This shows a clear connection between patient results and tumor biology. Doctors can trust AI suggestions and use them in their work easily.
Also, BostonGene’s platform can find cancer groups in ccRCC that resist treatment. These groups have immune-desert or angiogenic signaling profiles. Knowing this guides researchers to find new therapies for these patients.
Using AI-driven molecular profiling in American healthcare needs smooth connection with systems that keep clinical data, like electronic health records (EHRs). This helps doctors see important patient details and molecular test results together. It makes decision-making easier.
This combined method also supports research and drug development. BostonGene works with the SWOG Cancer Research Network on the Phase II PRISM trial. The trial includes up to 900 patients with extensive-stage small cell lung cancer (ES-SCLC). BostonGene’s platform divides patients into molecular subtypes. These groups get targeted maintenance therapies like Durvalumab or combinations guided by biomarkers, such as PARP inhibitors. This trial tries to improve survival beyond the usual 2-3 months extension.
The AI platform helps by quickly sorting patients for trials, finding good candidates, and supporting regulations. Working closely with AI developers, research groups, and healthcare workers helps turn lab advances into better patient care.
Tempus is a major player in AI-based precision medicine. It connects about 65% of all Academic Medical Centers in the U.S. and over half of oncologists. This wide network lets Tempus use more than 8 million anonymous research records and over 350 petabytes of clinical and molecular data to improve cancer care.
Tempus’ xT platform uses both tumor and normal sequencing plus transcriptome analysis. It works better than standard tumor-only DNA tests in accuracy and coverage. Their AI helps find targeted therapies and clinical trials for cancer patients. More than 30,000 patients have been matched for possible trials across the Tempus network.
Tempus also puts its AI assistant, Tempus One, inside popular EHR systems. This helps doctors quickly access patient molecular and clinical data without extra work. Another tool, the xM assay, is a liquid biopsy test. It tracks how patients respond to immunotherapy in real time. This helps doctors adjust treatments as needed.
These tools matter especially in community hospitals and smaller cancer centers that may not have many resources. AI help lets these places offer precise cancer care once only possible in big academic hospitals.
For administrators, clinic owners, and IT managers, AI use goes beyond patient tests and treatments. AI can improve front-office and back-office work, like managing molecular profiling and clinical data. Efficient workflows reduce paperwork and save time for patient care. They also speed up important decisions.
Simbo AI, mainly for front-office phone automation, is an example of how AI eases work in healthcare. AI assistants linked with molecular profiling systems improve clinical routines, cut mistakes, and handle big data without overwhelming staff.
AI tools in oncology help with:
These AI systems can make patient care better, speed up treatment plans, and lower costs. They help clinics run smoothly while meeting the needs of modern cancer care.
When combining AI-driven molecular profiling with clinical data, healthcare groups must protect patient privacy and follow rules. They must handle data carefully according to HIPAA laws. Lab data should meet CLIA and CAP certification standards.
BostonGene and Tempus have CLIA certification and CAP accreditation. This means their molecular tests and data processes follow strict quality rules. It gives confidence to clinic leaders and IT managers that these AI tools meet legal and clinical standards.
The growth of AI in cancer care comes from teamwork among medical centers, drug companies, and research groups. Tempus works with over 200 biopharma partners, showing a growing network of data sharing and joint development in precision medicine.
These partnerships help increase data sets, improve AI algorithms, and test new biomarkers and treatments. About 95% of the top 20 pharmaceutical cancer companies use Tempus technology. This shows the industry trusts AI-based data.
These collaborations help medical practices connect to more knowledge and resources. Clinics can find clinical trial options and new treatments found through AI networks, giving patients better care choices.
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.
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