Advanced molecular profiling combined with multimodal data analysis to optimize drug development and therapeutic strategies in oncology

Molecular profiling means studying a tumor’s genes, proteins, and other small parts to learn more about how cancer grows and reacts to treatment. Traditional ways mostly use images and tissue studies, but molecular profiling looks much closer at the cells.

In the US, platforms made by Tempus, Caris Life Sciences, and BostonGene use large sets of molecular data to support precision oncology. This means treatment is made to fit each patient’s unique cancer. For example, Tempus works with about 65% of US Academic Medical Centers and over 50% of oncologists. Their xT platform mixes molecular and clinical data to find the best treatments and clinical trials for patients. This is more detailed than regular tests because it studies both tumor and normal tissue as well as the complete set of RNA, which helps pick the right therapy.

Caris Life Sciences has a huge database from over 6.5 million tests with 13 quadrillion data points. They use whole exome and transcriptome sequencing with AI to understand tumors better in more than 80 cancer types. Most of their data comes from community oncology centers, where 85% of US cancer patients get care. With Ontada’s clinical data, Caris helps turn research into practical treatments faster.

BostonGene mixes gene, RNA, protein, and immune system data in one platform. Their Tumor Portrait™ test gives a full picture of tumors and immune response. This helps doctors decide when to use immunotherapy or targeted treatment.

Because of advanced molecular profiling, doctors can stop using the same treatments for everyone. Instead, they choose therapies based on each tumor’s unique features. This helps make treatments work better, lowers side effects, and gives more chances for patients to join clinical trials.

Multimodal Data Analysis in Enhancing Therapeutic Precision

Multimodal data analysis means putting together many types of data like DNA, RNA, proteins, images, and clinical information. This approach looks at how genes, proteins, immune cells, and treatments all affect cancer and patient health as a whole.

Many US research groups use this method to make drugs and adjust therapies:

• BostonGene’s AI platform mixes gene, RNA, protein, immune, and clinical outcome data. This helps find biomarkers and plan clinical trials. They shared studies on molecular targets and immunotherapy in cancers like pancreatic, sarcoma, and cervical at the ESMO Congress 2025.

• Caris and Ontada work together by combining molecular and real-world clinical data from millions of patients. This big dataset helps companies solve hard questions, improve treatments, and make drug development faster.

• In breast cancer research, a deep learning model uses gene, protein, mutation, and drug response data from 52 breast cancer lines. This model predicts drug sensitivities well, focusing on RTK signaling pathways. Researchers found it helps find strong drug candidates and fight drug resistance.

• In gastrointestinal cancer, mixing real-world data and molecular profiles is hard but shows promise. Meetings like the Translational and Precision GI-Oncology one in Freiburg, Germany, stress the need for teamwork among scientists, doctors, and companies to bring research to patients using multimodal data.

These examples show how mixing different data types gives doctors a better view of a patient’s cancer. This helps them make smarter treatment and drug testing decisions.

AI Integration and Clinical Workflow Automation: Changing Oncology Practice

Artificial intelligence (AI) and workflow automation are very important to handle all the molecular and clinical data now available. AI can quickly study complex data and find patterns human experts might miss. When combined with automation, these tools help cancer clinics run better and care for patients more efficiently.

Clinical Decision Support: Tempus has an AI assistant called Tempus One that works inside electronic health records (EHR). It helps doctors and staff search patient data, get therapy suggestions, find clinical trials, and create custom workflows to make data easier to use. This improves clinic work and lowers mistakes from manual data searching.

Drug Development and Trial Matching: AI platforms study multimodal molecular data to guess which patients will respond to certain drugs. This speeds drug repurposing and helps drug companies design better clinical trials with patients who may benefit most. BostonGene’s AI tools helped analyze gene therapy benefits in tough cancers and supported phase 2 trials.

Diagnostic Accuracy and Therapy Optimization: AI helps improve imaging and diagnosis. In theranostics, AI combines real-time imaging with treatment targeting to make both diagnosis and treatment more exact. Though new, companies like Crown Bioscience are working on combining PET, MRI, and fluorescence imaging with AI to improve cancer treatment results.

Workflow Automation for Data Management: The amount of data from molecular tests, proteins study, and clinical records is huge. Automation helps bring in, process, and report this data, cutting the work for staff and improving data quality and access.

For healthcare leaders and IT managers in the US, investing in AI tools that work with EHRs and labs will improve workflows, save resources, and raise care quality. As precision medicine grows, AI platforms will be more and more important.

Impact on Oncology Practice in the United States

Advanced molecular profiling combined with multimodal data analysis is changing cancer care across the US. More than half of US oncologists use big molecular data platforms like Tempus. Millions of patient records studied by Caris and others affect diagnosis, treatment choices, and clinical trial access.

Community oncology centers, where most cancer patients are treated, benefit a lot from these tools. Caris and Ontada’s work focuses on community data, making sure real-world care is part of research and drug creation. This helps personalize treatment without needing patients to visit only specialized centers.

Advanced molecular profiling also helps address problems like drug resistance and tumor differences. For example, breast cancer studies using multimodal data and AI target specific RTK pathways to fight resistance found through miRNA and exosome analysis. This approach may raise treatment success and lower costs from ineffective therapies.

Also, teamwork among researchers, doctors, and technology companies speeds new therapy development and use. Oncology conferences and research meetings help share results and promote using these new tools in clinics.

Recommendations for Healthcare Administrators and IT Managers

• Invest in Molecular Profiling Infrastructure: Healthcare groups should partner with companies providing molecular profiling that fits with current clinical work.

• Adopt AI-Enabled Clinical Tools: Use AI assistants and clinical decision support systems that connect with EHRs to make real-time data searching, patient sorting, and workflow smoother.

• Support Data Integration and Analytics: Build or improve IT systems that securely manage large multimodal datasets for research and clinical use.

• Facilitate Training and Change Management: Prepare clinical and administrative teams to work well with new technologies to get the best results from precision oncology.

• Enable Collaboration and Data Sharing: Encourage partnerships inside the institution and with outside groups to stay updated on oncology developments and clinical trials.

Advanced molecular profiling combined with multimodal data analysis is helping the future of cancer care by giving a bigger picture of cancer biology and patient differences. In the United States, this helps healthcare groups improve treatment choice, drug development, and clinical work. AI and automation add to these strengths, letting oncology clinics give care that is more precise, efficient, and personalized to patients.