The role of combined tumor/normal plus transcriptome sequencing platforms in identifying targeted cancer therapies and improving clinical trial matching

Traditional genomic profiling often involves sequencing only the tumor DNA to find mutations. But this method has limits. It cannot tell apart somatic (tumor-specific) mutations from germline (inherited) variants well. It also may have more false-positive results. A better way is to sequence tumor DNA along with normal DNA from the same patient, often taken from blood or healthy tissue. This tumor/normal matched sequencing makes mutation detection more accurate by removing inherited genetic changes not related to cancer.

Besides DNA sequencing, transcriptome sequencing (called RNA sequencing or RNA-seq) looks at RNA molecules to check gene expression patterns. This helps find gene fusions—when parts of two different genes join—which are important for choosing targeted treatments but can be missed by DNA sequencing alone. RNA-seq also helps classify cancer types by showing tumor-specific expression profiles.

When combined, tumor/normal DNA sequencing plus transcriptome sequencing gives a more complete picture of a cancer patient’s molecular profile. This lets doctors find more usable genomic changes, offer more therapy choices, and increase the chances that patients join clinical trials.

The Impact on Targeted Cancer Therapy Identification

The Tempus xT platform uses this combined sequencing in clinical practice. Studies on hundreds of patient samples with various tumor types show that combining tumor-normal DNA sequencing with transcriptome data improves finding precise treatments.

For example, a study with 500 cancer patients tested by Tempus xT shows:

• Adding RNA-seq to DNA sequencing increased detection of clinically useful gene fusions by 29%.
• Using only tumor DNA sequencing matched 29.6% of patients to precision therapies with strong clinical proof.
• Combining DNA-seq, RNA-seq, and immunotherapy biomarker data raised the therapy matching rate to 43.4%.
• Tumor-normal matched sequencing lowered false-positive mutation calls by 28%, leading to more confident treatment choices.

This detailed profiling finds somatic mutations, gene fusions, and immune-related biomarkers needed for picking FDA-approved targeted drugs or clinical trial candidates.

Also, combining solid tumor testing with liquid biopsy testing (using circulating tumor DNA from blood) reveals unique actionable variants in about 9% of metastatic cancer patients. These would be missed with only solid tumor testing. This mixed method expands therapy options, especially for advanced cancers.

Improving Clinical Trial Enrollment in the United States

One main benefit of advanced genomic profiling is linking patients to the right clinical trials. Trials give patients access to new treatments, especially when standard options have limited effect. But matching patients to trials is hard since eligibility often depends on specific genetic changes.

Tempus reports that when molecular profiling is combined with clinical data, about 76.8% of patients match at least one relevant clinical trial based on tumor biomarkers. Across a larger network, over 40,000 patients were found for possible trial enrollment with Tempus.

This is important for medical practice administrators and owners in the U.S. who run oncology departments. Having fast and precise molecular data helps providers improve patient results by enrolling suitable patients in trials, giving more treatment choices beyond usual therapies.

This matching success comes from using large databases with millions of anonymous genomic and clinical records, algorithmic models, and AI clinical helpers that match patient data with thousands of open studies.

Case Study: Application in Advanced Biliary Tract Cancer

A study of 124 patients with advanced biliary tract cancers (BTCs) showed benefits of combining tumor/normal DNA and transcriptome sequencing. Researchers found actionable or possibly actionable genomic changes in 63.7% of these patients. Those who got targeted therapies matched by sequencing results lived a median of 28.1 months. This was much longer than patients who did not receive matched therapies (13.3 months) or lacked actionable mutations (13.9 months).

This supports using broad genomic profiling for different solid cancers beyond well-known types like lung or breast cancer. It shows how sequencing-guided therapy choice can lead to better survival.

Addressing Workflow Efficiencies: AI and Automation in Genomic Reporting

Adding complex genomic data into clinical work is not easy. Medical administrators and IT managers need to think about how automated tools and artificial intelligence (AI) can help make decisions and improve efficiency.

Tempus offers AI clinical assistants like Tempus One that work with Electronic Health Records (EHR) systems. These assistants help with:

• Real-time querying of patient molecular and clinical data.
• Creating custom clinical reports to meet provider needs.
• Finding and closing care gaps using tools like NEXT.
• Better understanding complex data through AI algorithms.

These features reduce the paperwork for oncologists and care teams by automating data analysis, report writing, and clinical trial screening. They let providers spend more time on patient care instead of reviewing records by hand.

Also, AI-driven platforms can:

• Update clinical insights as new research and trials come out.
• Combine molecular profiles with clinical guidelines such as those from the NCCN (National Comprehensive Cancer Network).
• Send automatic alerts for patients who are eligible for certain therapies or trials.

For IT managers, adding these AI tools means enabling smooth data sharing between lab systems, EHRs, and third-party platforms without interrupting patient care.

The Scale and Network Impact in U.S. Healthcare

The use of combined tumor/normal plus transcriptome sequencing is growing fast. About 65% of all Academic Medical Centers in the U.S. are connected to AI-enabled precision medicine platforms like Tempus. More than half of U.S. oncologists use these tools for sequencing, trial matching, and research.

Tempus also works with over 95% of the top 20 pharmaceutical oncology companies. This shows how the industry relies on large sequencing data to speed up drug development. This combined approach links clinical work, research, and new drug findings.

Large-scale data connectivity and access to over 40 million anonymous research records plus more than 350 petabytes of clinical and molecular data provide a big resource for evidence-based recommendations and precision cancer care progress.

Practical Considerations for Medical Practice Administrators and Owners

For administrators and healthcare leaders managing oncology services, several points should be kept in mind when adopting these sequencing technologies:

• Cost and Reimbursement: Although sequencing costs have dropped, tests are still costly. Using platforms with financial help and simple billing can lower money issues for patients.
• Integration with Existing Systems: It is important that genomic data outputs work well with EHRs and practice management systems. AI reporting tools make it easier to understand data and document findings.
• Training and Education: Oncology teams—including doctors and nurses—need ongoing learning about advances in genomic profiling and computational medicine to use results in care decisions effectively.
• Patient Engagement: Better molecular profiling offers patients more personal care, which might improve satisfaction and outcomes. Programs should include clear communication to patients about genomic testing and its purpose.
• Collaboration with Biopharma and Research Networks: Working with companies like Tempus increases access to newer diagnostics and possible trial opportunities, benefiting patients and practices.

The Future Outlook

Sequencing technology will keep improving. Bigger molecular databases and better AI analysis will make personalized cancer care in the U.S. even more precise. Sequencing many people combined with transcriptome data gives a clear molecular picture. This helps pick the right therapies and improves access to clinical trials.

Healthcare decision-makers who invest in platforms that use tumor-normal plus RNA sequencing with AI workflow automation take a step toward more effective, data-driven cancer care. This fits with the move toward value-based care by targeting therapies better, avoiding ineffective treatments, and supporting advanced clinical research.

Summary for Medical Practice Stakeholders

• Combined tumor/normal plus transcriptome sequencing improves finding targeted cancer treatments.
• Adding RNA sequencing helps detect gene fusions and better defines cancer types.
• Integrating genomic and clinical data matches about 77% of patients to clinical trials, giving more treatment options.
• AI tools like Tempus One make clinical workflows easier by automating data analysis and decision support.
• Many U.S. academic medical centers and oncologists use these platforms, showing their growing usefulness in care.
• Practices should focus on integrating genomic data with EHR systems, educating patients, and working with research groups for best care.

The move to combined molecular profiling with transcriptomic analysis, backed by AI, is shaping cancer care in the U.S. By using these methods, medical administrators and IT managers can help clinical teams deliver precision medicine that better handles cancer treatment and clinical trial enrollment.