SITC 2024

Alveolar soft part sarcoma (ASPS) is defined by a translocation that fuses the TFE3 gene with ASPSCR1, resulting in two distinct fusion isoforms, type 1 and type 2. While ASPS has shown promising responses to immune checkpoint inhibitors (ICIs), the differences in tumor immune microenvironment (TIME) and ICI responses between these fusion isoforms remain unclear. The research team conducted a multi-omic analysis and performed RT-PCR on paired biopsy samples to differentiate between the type 1 and type 2 fusion isoforms. The findings indicated that type 1 fusions are likely to respond better to ICIs compared to type 2 fusions. Further research is being conducted to explore the functional implications of targeting these fusion isoforms in ASPS.

Poster will be displayed November 7-8.

For this study, the research team trained and evaluated various computer-vision based cancer models for the identification of driver mutations at single-organoid resolution. Following the sequencing of DNA from tumor organoid (TO) lines leveraging the Tempus xT assay, lines were cultured, regularly imaged via brightfield confocal microscopy, and then segmented and processed with three feature extractors. Results suggest that models utilizing human-interpretable features generally outperformed other extractors in predicting mutation status in TOs. The study indicates this approach could be utilized for rapid assessment of heterogeneous clonality in progenitor cell populations in the context of high-throughput screenings.

This study aimed to predict patient outcomes to immune checkpoint inhibitors (ICI) by developing an integrated DNA/RNA generalizable 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.

This study investigated the significance of POLE/POLD1 mutations as potential predictive biomarkers for immunotherapy response across solid tumors. Utilizing Tempus' multimodal real-world database, the research analyzed a cohort of approximately 93,000 patients to understand the prevalence of these mutations and their association with real-world patient outcomes when treated with immunotherapy, particularly in patients with high tumor mutation burden (TMB) or microsatellite instability (MSI). Results suggest that POLE/POLD1 mutations may serve as valuable predictive biomarkers to identify patients likely to respond to immunotherapy beyond those with TMB-high and MSI-high statuses. These insights could inform clinical decision-making, offering a more personalized approach to immunotherapy treatment and providing a new perspective for pre-clinical and clinical research.

This study examined the relationship between humoral immune features and patient survival following any immune checkpoint blockade (ICB) therapy in metastatic lung adenocarcinoma across different metastatic sites. Using the Tempus database, researchers analyzed RNA sequencing profiles from patients treated with ICB, identifying variations in immune trait correlations by tissue origin. The findings suggest that some immune features may have tissue-dependent associations with patient survival, and may support the development of more targeted ICB therapies and may improve prognostic assessments in metastatic lung cancer.

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 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.