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April 17 — 22, 2026 SAN DIEGO, CA

Booth #2337

Product Theater Session

Customer Reception

31 Poster Presentations

1 Oral Presentation

AACR Annual Meeting 2026*

Join Tempus as we showcase our latest scientific and clinical research findings and discover how our AI-enabled technology is helping to advance precision medicine.

*Not affiliated with or endorsed by AACR
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Tempus Evening Reception

Step aboard the USS Midway to learn firsthand how Tempus is advancing precision medicine through the practical application of artificial intelligence in healthcare. Network while enjoying food and refreshments for an extraordinary evening of innovation. RSVP is required.

Monday, April 20, 2026
6:00 pm - 8:00 pm PST

USS Midway Museum
910 N Harbor Dr,
San Diego, CA 92101

RSVP here
Exhibitor Spotlight Theater*
April 20, 2026
Time
10:00 am – 11:00 am PST

Location
Sails Pavilion, Theater B
Presenters

Neil Bence, PhD

Neil Bence, PhD
Senior Vice President, Protein Homeostasis Thematic Research Center
Bristol Myers Squibb


Kate Sasser, PhD

Kate Sasser, PhD
Chief Scientific Officer
Tempus

Beyond the Pilot: Scaling Multimodal AI and Lab-in-the-Loop for Breakthrough R&D

High-performance oncology research requires data that is as deep as it is broad. While many discuss AI’s potential, Tempus and Bristol Myers Squibb have spent the last two years inverting the standard R&D model. We’ll discuss how tethering deep molecular insights to high-velocity Lab-in-the-Loop screening helps de-risk clinical decisions and accelerate life-saving breakthroughs. Experience a live demonstration of Tempus’ analytical tools in action, showcasing how to transform week-long research projects into insights in minutes.

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*This Exhibitor Spotlight Theater is a promotional activity and is not approved for continuing education credit. The content of this Exhibitor Spotlight Theater are the opinions of the presenter and do not represent the position or the opinion of the American Association for Cancer Research; (AACR®) or its members.

Tempus AI & Technology
April 19, 2026 - April 22, 2026
Time
Exhibit Hall Hours

Location
Exhibit Hall, Booth #2337

AI-enabled technologies

Stop by our booth to experience live demos of our latest technologies and discover how we are leveraging Al to advance precision medicine.

Tempus Lens

Quickly find, access, and analyze multimodal de-identified data records and uncover critical insights to accelerate research and innovation. Leverage our Al assistant, Tempus One, to quickly define detailed patient cohorts using natural language processing and transform complex criteria into actionable datasets in seconds. Explore unstructured clinical data and extract nuanced insights.

With Lens, the power of Tempus data is at your fingertips.

Oral Presentation
April 20, 2026
live session
Time
2:30 – 4:30pm PT

Location
Room 14-Mezzanine Level - Convention Center
Presenters
Bruno Bockorny, MD (Beth Israel Deaconess Medical Center), et al.

Real-world evidence of KMT2C mutation as a biomarker of sensitivity to platinum-based therapy in solid cancers

Researchers leveraged Tempus Lens to define a real-world cohort of 143,961 patients with solid tumors from the Tempus multimodal database. They sought to determine if KMT2C mutations are a biomarker for sensitivity to platinum-based chemotherapy (PBC). Their analysis revealed that patients with KMT2C mutations had significantly improved real-world overall survival (rwOS) after PBC treatment compared to patients without the mutation (19.6 vs. 16.7 months). The survival benefit was most significant in colorectal cancer (CRC), where patients with the mutation had a median rwOS of 51.0 months versus 25.3 months for those without. These findings, validated in the AACR Genie dataset, support KMT2C as a predictive biomarker for platinum response—particularly in CRC and GI cancers—with laboratory studies underway to clarify mechanisms.

Poster Highlights
April 19, 2026
Time
2:00 – 5:00pm PT

Location
Section 21
Presenters
Stephanie Thiede, PhD (Tempus AI, Inc.), et al.

Pan-cancer assessment of an RNA-based signature of HRD

To address the unmet clinical need for robust, pan-cancer biomarkers of homologous recombination deficiency (HRD), researchers developed a 1,660-gene expression-based logistic regression signature using the Tempus xR platform. This RNA-based approach was trained on a massive dataset of approximately 150,000 samples to predict HRD status across various solid tumors, demonstrating high sensitivity in ovarian (84%) and other BRCA-related cancers (82%).

Time
2:00 – 5:00pm PT

Location
Section 2
Presenters
Tian Kang, PhD (Tempus AI, Inc.), et al.

An agentic AI workflow for automated, high-fidelity curation of cancer diagnosis and staging from unstructured patient records

Investigators validated a hybrid, multi-agent AI workflow designed to autonomously extract cancer diagnoses and tumor characterization from unstructured patient records in order to address the challenges of manual clinical document abstraction. Utilizing a three-stage design—including pre-screening by two Natural Language Processing (NLP) finetuned on clinical notes and extraction with large language models (LLMs)—the system was benchmarked against manual abstraction on a cohort of 1,497 patients from the Tempus real-world database.

Time
2:00 – 5:00pm PT

Location
Section 42
Presenters
Padmapriya Swaminathan, MS (Avera Cancer Institute), et al.

Comparison of tissue and liquid biopsy CGP in advanced solid tumors: Insights from a community cancer center

This study evaluated the concordance between solid tissue and liquid biopsy in a cohort of 86 patients with advanced solid tumors from a community cancer center. Using the Tempus xF+ and a commercially available DNA sequencing tissue-based assay, the research demonstrated concordance of 59% and 100% concordance for MSI status. These findings suggest that tissue and liquid biopsy each provide complementary actionable information, supporting a dual CGP strategy to optimize therapy selection in advanced solid tumors.

Time
2:00 – 5:00pm PT

Location
Section 46
Presenters
Jesenia Marie Perez, PhD (University of Minnesota), et al.

BRAF copy number alterations and ultralow BRAF mRNA expression are prognostic for poor overall survival in prostate cancer

This study characterized the largest cohort of whole-exome and -transcriptome sequenced BRAF-altered prostate cancers to date identifying key molecular features associated with poor clinical outcomes. The research team utilized Tempus Lens to establish a cohort of 485 patients, the analysis of which revealed that metastatic PC patients with BRAF copy number (CN) gain or ultralow BRAF mRNA expression exhibited significantly worse overall survival compared to those with wildtype BRAF.

Time
2:00 – 5:00pm PT

Location
Section 41
Presenters
Abraham Apfel, PhD (Eisai US), et al.

Integrating genomics and real-world data to predict fam-trastuzumab deruxtecan response in metastatic breast cancer across HER2 subtypes

Integrating real-world data from the Tempus database, this study evaluated associations between somatic mutations and clinical outcomes in 124 patients with metastatic breast cancer (MBC) treated with fam-trastuzumab deruxtecan-nxki (T-DXd). The research team identified that PIK3CA mutations were correlated with improved outcomes in HER2-low, but not HER2+ subtypes.

Time
2:00 – 5:00pm PT

Location
Section 2
Presenters
Patrycja Krawczuk, MS (Tempus AI, Inc.), et al.

Reasoning-guided retrieval improves oncology trial eligibility matching from clinical notes

To improve the efficiency of oncology trial screening, this study evaluated an agentic retrieval strategy designed to help large language models (LLMs) autonomously search and synthesize complex eligibility evidence from unstructured clinical notes. Using Gemini 2.5 Pro, the agentic approach was benchmarked against a standard retrieval-augmented generation (RAG) system across 618 patient-level eligibility assessments and 148 biomarker extraction tasks.

Time
2:00 – 5:00pm PT

Location
Section 20
Presenters
Scott Kulm, PhD (Tempus AI, Inc.), et al.

Co-occurrence of gene fusions and microsatellite instability (MSI) defines a clinically distinct subtype of colorectal cancer

Investigation into the co-occurrence of gene fusions and microsatellite instability (MSI) in 30,884,099 colorectal cancer patients revealed that clinically relevant fusions are significantly enriched in MSI versus MSS tumors (6.2% vs. 2.2%). These findings suggest that the clinical impact of gene fusions is mediated by MSI status, defining a distinct molecular subtype of colorectal cancer that may influence therapeutic stratification.

April 20, 2026
Time
9:00am – 12:00pm PT

Location
Section 5
Presenters
Swati Kaushik, PhD (Tempus AI, Inc.), et al.

Multimodal AI for patient subtype discovery in LUSC using real-world data

To address the molecular complexity and therapeutic challenges of lung squamous cell carcinoma (LUSC), researchers developed a multimodal AI autoencoder to integrate gene expression, copy number variation, and mutation data from 4,973 tumors. This approach identified seven distinct molecular subtypes with significant differences in real-world overall survival.

Time
9:00am – 12:00pm PT

Location
Section 40
Presenters
Natalie Vokes, MD (MD Anderson Cancer Center), et al.

High PRMT5 expression is associated with decreased immune infiltrate and worse outcomes to immune checkpoint inhibitors in non-small cell lung cancer

Analyzing a cohort of 3,676 patients with non-small cell lung cancer (NSCLC), this study investigated the impact of PRMT5 mRNA expression and MTAP deletions on immune infiltration and clinical outcomes. Utilizing Tempus Lens, the research team found that tumors with high PRMT5 expression were characterized by a significantly decreased infiltrate of multiple immune cell proportions.

Time
9:00am – 12:00pm PT

Location
Section 4
Presenters
Akul Singhania, PhD (Tempus AI, Inc.), et al.

Lauren subtype classification in gastric cancer using deep learning on real-world H&E images

The research team developed a deep learning classifier to automate Lauren subtype assignment in gastric cancer, addressing the inter-observer variability and scalability challenges of manual pathologist review. By analyzing de-identified H&E-stained images from 637 patient samples in the Tempus real-world database, the model achieved robust performance.

Time
9:00am – 12:00pm PT

Location
Section 43
Presenters
Elizabeth Swisher, MD (University of Washington), et al.

Prospective organoid drug profiling and clinical response correlation for patients with primary or recurrent ovarian carcinoma (OC) in the PROSPERITY study

The research team evaluated the prospective drug sensitivity profiling of patient-derived organoids (PDOs) from 42 patients with primary or recurrent ovarian carcinoma (OC) enrolled in the PROSPERITY study. Utilizing the Tempus platform, the study successfully generated drug profiles for 86% of samples, demonstrating that clinical response to carboplatin-based therapy was concordant with PDO predictions in 77.8% of pre-chemotherapy cases.

Time
2:00 – 5:00pm PT

Location
Section 49
Presenters
Gagandeep Brar, MD (City of Hope), et al.

Identifying biomarkers of response in BRAF p.V600E mutant colorectal cancers (mCRC)

This study utilized the Tempus multimodal database to explore genomic and transcriptomic biomarkers of response in 274 patients with BRAF V600E-mutant metastatic colorectal cancer (mCRC) treated with first-line systemic therapy. Researchers leveraged Tempus Lens to establish the patient cohort, finding responders demonstrated significantly longer 90-day landmark real-world overall survival.

Time
2:00 – 5:00pm PT

Location
Section 10
Presenters
Daniel Rabe, PhD (Tempus AI, Inc.), et al.

A patient-derived fragmented tumor assay for detailed evaluation of drug response in the tumor microenvironment (TME) using scRNAseq

To overcome the limitations of conventional models in recapitulating the complex cellular interactions of the tumor microenvironment (TME), investigators developed a fragmented tumor assay that preserves physiological cell ratios and secreted factors. Fresh patient tumor specimens were processed into drug-penetrable fragments and exposed to various therapies followed by analysis via single-cell RNAseq.

April 21, 2026
Time
9:00am – 12:00pm PT

Location
Section 46
Presenters
Matt Maxwell, PhD (Tempus AI, Inc.), et al.

MSI colon adenocarcinoma transcriptomic subtypes are biomarkers of response to immunotherapy

To address the clinical need for more accurate predictors of immunotherapy response in metastatic MSI-high colon adenocarcinoma (COAD), investigators identified two novel transcriptomic subtypes—"Immune/Stromal" (C1) and "Goblet/Enterocyte" (C2)—using a real-world cohort of 793 patients.

Time
9:00am – 12:00pm PT

Location
Section 46
Presenters
Sana Parveen (Medical College of Wisconsin), et al.

Analysis of RNA expression of 47 cell surface proteins in real-world small cell lung cancer patients

The research team performed an exploratory analysis of 56 cell surface proteins (CSPs) in 1,353 small cell lung cancer (SCLC) patients to identify differential expression patterns across subtypes, disease stages, and treatment statuses. Leveraging Tempus xT DNA-seq and xR RNA-seq data, the study identified subtype-specific expression.

Time
9:00am – 12:00pm PT

Location
Section 44
Presenters
Sunyoung S. Lee, MD, PhD (MD Anderson Cancer Center), et al.

Molecular and immune landscape of HER2-amplified colorectal cancer

The research team utilized the Tempus multimodal database and Lens platform to characterize the molecular and immune landscape of HER2-amplified colorectal cancer (CRC) in 16,394 patients. The study identified that HER2-amplified tumors (2.7%) exhibit a coordinated ERBB2 activation axis across DNA, RNA, and protein levels, with significant concordance (88%) to external IHC/ISH testing.

Time
9:00am – 12:00pm PT

Location
Section 36
Presenters
Kyle D. Klingbeil, MD, PhD, MS (UCLA Health), et al.

Integrated clinicopathologic, genomic and transcriptomic characterization of gastric cancer by race/ethnicity in a national multi-institutional cohort

The research team utilized the Tempus multimodal database and Lens platform to conduct a comprehensive clinico-genomic analysis of 1,842 gastric adenocarcinoma patients, evaluating differences in molecular landscapes and survival outcomes by race/ethnicity.

Time
9:00am – 12:00pm PT

Location
Section 46
Presenters
Carlos Ronchi, PhD (Tempus AI, Inc.), et al.

Evaluating prognostic biomarkers in advanced epithelial ovarian carcinoma using machine learning on real-world data

By leveraging the Tempus multimodal real-world database, this study applied machine learning models to identify clinical prognostic biomarkers for real-world progression-free survival (rwPFS) in 3,016 patients with advanced epithelial ovarian carcinoma (EOC) and primary peritoneal carcinoma (PPC).

Time
9:00am – 12:00pm PT

Location
Section 49
Presenters
Lisa Gai (Tempus AI, Inc.), et al.

Detection of rare oncogenic fusions through concurrent DNA and RNA next-generation sequencing in a pan-cancer clinical setting

By retrospectively analyzing de-identified records from 74,182 patients with advanced cancer, this study quantified the clinical benefit of concurrent DNA and RNA testing for identifying rare oncogenic fusions.

Time
9:00am – 12:00pm PT

Location
Section 31
Presenters
Wafaa Bzeih, MD (Brigham and Women's Hospital), et al.

Sarcomatoid transformation rewires the immune spatial landscape and checkpoint regulation in chromophobe renal cell carcinoma

The research team performed spatial analysis and bulk RNA-seq (Tempus xR) to better understand the immune landscape of sarcomatoid chromophobe renal cell carcinoma (ChRCC), a particularly aggressive subtype. The analysis revealed that sarcomatoid tumors have an immune-infiltrated microenvironment.

Time
9:00am – 12:00pm PT

Location
Section 46
Presenters
Kevin Lu (UC San Diego), et al.

Profiling membrane antigen expression of select antibody-drug conjugate (ADC) targets in EGFR-altered non-small cell lung cancer treated with osimertinib

Analyzing membrane antigen expression in 583 patients with classical EGFR-altered non-small cell lung cancer (NSCLC), researchers identified high mRNA levels of Antibody-Drug Conjugate (ADC) targets including ERBB2, MET, and TACSTD2 (TROP2).

Time
2:00 – 5:00pm PT

Location
Section 31
Presenters
Yajas Shah, PhD (Tempus AI, Inc.), et al.

Elevating bulk sequencing with high-resolution spatial transcriptomics: A paired-data analysis of clinically-relevant spatial niches in CRC and NSCLC

The research team analyzed a large multimodal, real-world cohort of microsatellite stable colorectal (MSS CRC) and non-small cell lung cancer (NSCLC) tumors to explore spatial heterogeneity as it relates to bulk RNA-NGS, and targeted bulk DNA-NGS.

Time
2:00 – 5:00pm PT

Location
Section 42
Presenters
Metamia Ciampricotti, PhD (Tempus AI, Inc.), et al.

Genomic characterization of lung cancer: ERRFI1 and NKX2-1 mutations and CLU expression

By analyzing de-identified records from a Tempus Lens defined cohort of 34,362 lung cancer patients, this study evaluated the prevalence and dynamic expression of ERRFI1, NKX2-1, and CLU across different disease states and therapies.

Time
2:00 – 5:00pm PT

Location
Section 21
Presenters
Akul Singhania, PhD (Tempus AI, Inc.), et al.

Large-scale analysis reveals distinct molecular subtypes in real-world gastric cancer data

This study identified seven biologically distinct molecular subtypes of gastric cancer (GC) with significant prognostic differences (p=0.0006). By integrating real-world clinico-genomic data from the Tempus database, the analysis of 1,866 tumors revealed subtypes characterized by unique genomic alterations.

April 22, 2026
Time
9:00am – 12:00pm PT

Location
Section 46
Presenters
Sana Parveen (Medical College of Wisconsin), et al.

Machine learning predicts retinoblastoma (Rb) function in real-world small cell lung cancer patients

To challenge the long-held belief that the retinoblastoma (RB) tumor suppressor is always inactive in small cell lung cancer (SCLC), the research team developed a machine learning model to predict RB function. Using genomic (Tempus xT) and transcriptomic (Tempus xR) data from a real-world cohort of approximately 1,400 SCLC patients, the model found that nearly 30% of patients with RB1 genomic alterations still showed evidence of RB function transcriptionally.

Time
9:00am – 12:00pm PT

Location
Section 6
Presenters
Kyle A. Beauchamp, PhD (Tempus AI, Inc.), et al.

Validation of HER2, TROP2, and NECTIN4 IHC prediction algorithms for the ADC MATCH trial

To facilitate patient selection for the ADC MATCH clinical trial, investigators validated RNA-seq algorithms designed to identify patients likely to test positive for HER2, TROP2, and NECTIN4 via immunohistochemistry (IHC).

Time
9:00am – 12:00pm PT

Location
Section 42
Presenters
Young Kwang Chae, MD (Northwestern Medicine), et al.

Immune-related RNA-seq biomarker-based clustering reveals heterogeneous immunotherapy responses and guides subtype-specific strategies in metastatic NSCLC

Patients with metastatic non-small cell lung cancer (mNSCLC) respond variably to first-line immunotherapy plus chemotherapy, yet the underlying immune biology driving these differences remains poorly understood. In a real-world cohort of 2,235 mNSCLC patients from the Tempus database, RNA-seq-based unsupervised clustering using immune markers identified four biologically distinct immune subtypes.

Time
9:00am – 12:00pm PT

Location
Section 21
Presenters
Qidi Yang, MS (Tempus AI, Inc.), et al.

Beyond HLA LOH: Alternative modes of HLA loss are common and vary by cancer type

By leveraging an integrated workflow with the Tempus xT assay, researchers characterized diverse modes of Human Leukocyte Antigen (HLA) loss across 11,030 cancer samples. While loss of heterozygosity (LOH) was the dominant mechanism in most cancers, the study found that 12% of tumors without DNA-level loss still displayed RNA-level HLA loss.

Time
9:00am – 12:00pm PT

Location
Section 46
Presenters
Mikayla Bendix (Sanford School of Medicine, University of South Dakota), et al.

Prevalence and association of clonal hematopoiesis with cardiovascular health in patients of the Avera sequencing and analytics protocol (ASAP) Study

Prevalence and association of clonal hematopoiesis with cardiovascular health in patients of the Avera sequencing and analytics protocol (ASAP) Study

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