On November 8, 2024 Tempus AI, Inc. (NASDAQ: TEM), a technology company leading the adoption of AI to advance precision medicine and patient care, reported nine abstracts were accepted for presentation at the Society for Immunotherapy of Cancer (SITC) (Free SITC Whitepaper)’s (SITC) (Free SITC Whitepaper) 39th Annual Meeting, which convenes in Houston, Texas, from November 6-10, 2024 (Press release, Tempus, NOV 8, 2024, View Source [SID1234648052]).

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"Presenting our latest research is a vital opportunity to showcase the advancements Tempus is making in harnessing the power of data and AI to drive immunotherapy innovation," said Ezra Cohen, MD, Chief Medical Officer of Oncology at Tempus. "We are committed to working with the oncology community to share insights that can accelerate the development of more effective, personalized therapies and contribute to the improvement of outcomes for patients."

This year, highlights of Tempus’ poster presentations include:

Poster Presentation (#188): Clinical validation of a novel multi-omic algorithm for stratifying outcomes in a real-world cohort of metastatic solid cancer patients treated with immune checkpoint inhibitors
Session Date & Time: Saturday, November 9, 2024; 9:00 a.m. – 8:30 p.m. CT
Location: George R. Brown Convention Center; Exhibit Halls AB

Overview: This study aimed to predict patient outcomes to immune checkpoint inhibitors (ICI) by developing an integrated DNA/RNA ICI biomarker. A de-identified pan-cancer cohort from the Tempus multimodal real-world database was utilized to develop and validate the Immune Profile Score (IPS) algorithm that leverages Tempus xT (DNA sequencing) and xR (RNA sequencing). The researchers found that IPS status can be used to stratify patient cohorts and prognosticate ICI-treatment response.

Poster Presentation (#1352): Impact of timing of real-world CT imaging on cost-effectiveness of a molecular biomarker for treatment response monitoring of immunotherapy
Session Date & Time: Saturday, November 9, 2024; 9:00 a.m. – 8:30 p.m. CT
Location: George R. Brown Convention Center; Exhibit Halls AB

Overview: The research team sought to model the impact of Computed Tomography (CT) imaging patterns on the clinical utility and cost-effectiveness of a molecular biomarker for treatment response monitoring (TRM) compared to imaging. The team analyzed real-world imaging patterns from a cohort of 4,147 advanced cancer patients treated with immune checkpoint inhibitors (ICI) across five solid tumor types. The study found significant variability in CT scan intervals between cancer types and treatments. Incorporating these patterns into a microsimulation model, the team demonstrated that using the molecular biomarker in conjunction with CT imaging provided substantial cost savings and reduced inappropriate therapy compared to imaging alone, with the most benefit observed in small cell lung cancer (SCLC) treated with ICI-chemotherapy.