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\u2019s body.<\/p>\n
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.<\/p>\n
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\u2019s unique biology. It increases the chance of success and lowers the risk of giving treatments that won\u2019t work.<\/p>\n
Artificial intelligence is very important in combining large amounts of molecular data that would be hard for people to understand quickly. BostonGene\u2019s AI platform uses machine learning on combined data sets. It finds patterns and creates \u201cresponder scores.\u201d These scores show how likely a patient is to benefit from treatments like immune checkpoint inhibitors (ICIs) or VEGF-targeted TKIs.<\/p>\n
The AI models are not \u201cblack boxes.\u201d 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.<\/p>\n
Also, BostonGene\u2019s 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.<\/p>\n
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.<\/p>\n
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\u2019s 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.<\/p>\n
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.<\/p>\n
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.<\/p>\n
Tempus\u2019 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.<\/p>\n
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.<\/p>\n
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.<\/p>\n
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