April 5 — 10, 2024 SAN DIEGO, CA

Booth #1705 & 1611
Exhibitor Spotlight Theater
Customer Reception
18 Poster Presentations

AACR Annual Meeting 2024

Tempus is advancing precision medicine through the practical application of artificial intelligence in healthcare. We are pleased to share our latest scientific and clinical research findings during AACR 2024.

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Tempus Customer Reception

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Tempus Exhibitor Spotlight Theater
Poster Presentations

Tempus Exhibitor Spotlight Theater

April 8, 2024

live session

Time
10:00–11:00am PT

Location
Spotlight Theater: B

Presenters
Iker Huerga, Executive Vice President of Life Sciences Strategy and Real World Data (Tempus)

Cristian Massacesi, Chief Medical Officer and Oncology Chief Development Officer (AstraZeneca)

AI Powered Precision Oncology: Leveraging Diagnostics, Data and AI to Drive Next-Generation Drug Development

Precision oncology is rapidly evolving with the integration of artificial intelligence, intelligent diagnostics, and vast amounts of data. This session will delve into the transformative power of AI in precision oncology and its pivotal role in propelling drug development into a new era. Attendees will gain insights into how to embrace this revolution, harnessing the synergy of cutting-edge genomic assays, multimodal real-world data, and AI-driven insights to expedite the discovery and development of novel therapeutics.

*This Exhibitor Spotlight Theater is a promotional activity and is not approved for continuing education credit. The content of this Exhibitor Spotlight Theater and opinions expressed by presenters are those of the sponsor or presenter and are not of the American Association for Cancer.

Poster Presentations

April 7, 2024

Time
1:30pm–5:00pm PT

Location
Section 9

Integration of patient-derived tumor organoids and patient clinical multimodal data to investigate the role of organoids in predicting treatment response

To compare patient response to drugs ex vivo, the research team sequenced and analyzed 38 patient-derived organoids (PDOs) with paired patient clinical data across nine different cancer types. Analysis of the standard-of-care panel indicated a correlation between treatment response in PDOs and patient response to the corresponding drugs, highlighting the valuable role of PDOs in predicting patient response to treatment.

April 8, 2024

Time
9:00am–12:30pm PT

Location
Section 35

A tumor-intrinsic signature involving immunosuppression via MIF-CD74 signaling is associated with overall survival in ICT-treated lung adenocarcinoma

The research team conducted single-cell RNA-sequencing (scRNAseq) on samples from lung adenocarcinoma (LUAD) patients to improve RNA-based predictions of patient response to immune checkpoint therapies (ICT). They identified a tumor-intrinsic immune suppression signature significantly associated with decreased real-world overall survival (rwOS) and upregulation of the MIF-CD74 interaction between neoplastic cells and macrophages in multiple tumor samples, indicating these predictions can help identify patients likely to experience reduced benefit from ICT and MIF-CD74 blockade may be a promising target to enhance anti-tumor immune responses in LUAD.

Time
9:00am–12:30pm PT

Location
Section 31

Genetic ancestry associations with prostate adenocarcinoma mutational profiles: New insights from a diverse 5,959-patient real-world cohort

To identify associations of mutational profiles in prostate adenocarcinoma (PRAD) with NGS-inferred genetic ancestry, the research team profiled tumors from a large, diverse real-world cohort of patients and confirmed known associations between somatic alterations in PRAD cancer genes and race/ethnicity. They also identified novel associations that could help understand disparities in disease outcomes within understudied populations, such as Black and Hispanic men.

Time
9:00am–12:30pm PT

Location
Section 31

Genetic ancestry associations with pancreatic cancer mutational profiles from a diverse 9,274-patient real-world cohort

Associations between genetic ancestry or race and ethnicity categories with mutational profiles, such as tumor mutational burden (TMB) or changes in receptor tyrosine kinase (RTK) genes, could identify genomic differences that may help explain documented racial and ethnic disparities in pancreatic cancer. The research team profiled tumors from a real-world diverse pancreatic cancer cohort and found modest, non-significant associations upon correcting for multiple hypotheses, suggesting somatic mutation differences may not largely drive differential outcomes in pancreatic cancer by race and ethnicity.

Time
9:00am–12:30pm PT

Location
Section 43

Identification of poor responders to Trastuzumab-Deruxtecan with a multi-modal HER2-status predictor

HER2 status from IHC and FISH correlates with RNA expression and can help determine overall survival (OS) for patients on trastuzumab deruxtecan (TDXD), a treatment for HER2-low or -positive metastatic breast cancer. Relying on a training set of 1,275 breast cancer samples, the research team developed a 12-gene linear model based on RNA expression and DNA copy number to predict HER2-positivity by IHC and FISH. In the validation cohort (n=397), they found that adding DNA to an RNA-based linear model improved its prediction of HER2 status, and the predicted score positively correlated with OS for TDXD-treated patients.

Time
9:00am–12:30pm PT

Location
Section 37

Leveraging a comprehensive genomic data library for detecting clonal hematopoiesis in liquid biopsy

To more efficiently filter out clonal hematopoiesis (CH) variants from tumor-derived variants, the team trained a random forest classifier on advanced, pan-solid tumor cancer samples sequenced using liquid biopsy and solid-tumor NGS with matched buffy coat assays (n=660). On a held-out validation set of samples (n=661), the classifier could reliably distinguish between CH and other tumor-derived variants with high accuracy, sensitivity, and specificity using only liquid biopsy data for predictions, providing an operationally simpler alternative to combined liquid and solid-based methodologies.

Time
9:00am–12:30pm PT

Location
Section 45

Real-world validation of the PurIST classifier demonstrates enhanced therapy selection for Pancreatic Ductal Adenocarcinoma (PDAC) patients

Using the gene expression-based PurIST algorithm, the research team ran a post-hoc analysis study on 258 patients with advanced pancreatic ductal adenocarcinoma to determine whether PurIST subtypes can distinguish likely to respond to FOLFIRINOX (FFX) versus gemcitabine + nab-paclitaxel (GnP). 67% of patients (n=173) were labeled as classical by the PurIST algorithm, with 35% of these having low ECOG scores. Among those, patients receiving FFX as first-line therapy (1L) had significantly longer overall survival (OS) than patients receiving GnP as 1L (HR (95% CI) = 2.32 (1.29-4.17)).

Time
9:00am–12:30pm PT

Location
Section 36

Deep learning-enabled dynamic infiltration and response to NK therapies in solid tumor organoids

Deep learning-based image analysis uses brightfield images to co-localize tumor organoids (TOs) with effector cells, enabling label-free measurement of TO-specific responses to novel candidate immunotherapies. Using this approach, the research team quantified immune cell infiltration across confocal microscopy images of TOs co-cultured with NK cells at increasing target-to-effector cell concentrations and found a high correlation between immune cell infiltration and TO death. This scalable methodology potentially enables high throughput screening of many therapeutic candidates across dozens to hundreds of unique TO-models.

Time
1:30pm–5:00pm PT

Location
Section 41

A circulating tumor fraction DNA biomarker response stratified by ESR1 mutation status correlates with overall survival in patients with HR+ HER2- metastatic breast cancer

Tempus xM used for treatment response monitoring (TRM) is an algorithm that quantifies changes in circulating DNA tumor fraction (TF) and can be simultaneously used to detect the emergence of ESR1 mutation (ESR1m) variants. In a heterogeneous real-world cohort of ER+ HER2- metastatic breast cancer patients, we showed that the combined effect of molecular response and ESR1 mutation status, a mutation associated with resistance to aromatase inhibitors (AIs), was associated with real-world overall survival outcomes. These preliminary findings suggest that xM used for TRM can identify patients with ESR1m and poor response on AI who may benefit from switching therapy.

Time
1:30pm–5:00pm PT

Location
Section 34

A novel combination of tissue-informed, comprehensive genomic profiling (CGP) and non-bespoke blood-based profiling for quantifying circulating tumor DNA (ctDNA)

Quantitative circulating tumor DNA tumor fraction (ctDNA TF) can be challenging to estimate. The research team showed that sensitive and specific ctDNA TF estimation can be achieved by combining tissue-informed comprehensive genomic profiling (CGP) with non-bespoke, blood-based liquid biopsy panel sequencing. The method showed increased linearity after sequencing advanced pan-solid tumor samples with larger liquid biopsy panels and more somatic variants, illustrating the potential for a tumor-informed, non-bespoke approach for estimating ctDNA TF to improve sensitivity beyond existing methods in circulating tumor fraction estimation.

April 9, 2024

Time
9:00am–12:30pm PT

Location
Section 42

Multimodal real-world data reveals immunogenomic drivers of acquired and primary resistance to immune checkpoint blockade

The research team compared clinical features and immunogenomic drivers of acquired and primary resistance to immune checkpoint blockade (ICB) immunotherapy across major cancer types. Post-ICB, the tumor microenvironment (TME) in acquired resistant patients with NSCLC and HNC was significantly more inflamed than in primary resistant ones, with higher infiltration of T cells and myeloid cells, increased interferon-gamma (IFNg) signaling, and in NSCLC stronger selection for mutations implicated in immunomodulatory pathways.

Time
9:00am–12:30pm PT

Location
Section 41

Robust single sample consensus molecular subtype classification for primary and metastatic colorectal cancer

The research team developed a Tempus Consensus Molecular Subtypes (CMS) algorithm to improve single-sample classification of both primary and metastatic colorectal cancer (CRC) tumors based on gene expression. Upon testing the classifier on a large CRC cohort of samples from primary and metastatic sites (n=5,090), they found a recapitulation of known biology based on the expected enrichment of molecular markers across all tissue groups, demonstrating the classifier can support clinical studies requiring robust molecular stratification.

Time
9:00am–12:30pm PT

Location
Section 44

Association of a ctDNA biomarker of treatment response with clinical outcomes in a real-world pan-cancer cohort treated with tyrosine kinase inhibitors

By analyzing changes in circulating DNA tumor fraction (ctDNA TF) in a real-world pan-cancer cohort of patients treated with Tyrosine kinase inhibitors (TKIs), the research team found that patients classified as molecular responders (defined as ≥ 50% in ctDNA tumor fraction) had significantly improved real-world overall survival and progression-free survival compared to molecular non-responders. Based on these findings, xM used for TRM may be used to optimize treatment decision making for patients treated with TKIs, sparing patients who do not respond to TKIs from ineffective therapy.

Time
9:00am–12:30pm PT

Location
Section 41

Profiling the splicing landscape in solid tumors in a large, real-world dataset

Identifying patients who may benefit from splice-targeted therapies (STTs) is challenging because promising biomarkers, such as mutations in splicing factors (SFs), are rare. The research team identified and characterized splicing patterns (SPs) in a large, multi-cancer cohort and found multiple clinically and molecularly distinct SPs associated with significant differences in real-world overall survival (rwOS), many of which were enriched for samples from a single cancer type, biopsy, or histology—suggesting the need for additional biomarker characterization to contextualize STT responses.

Time
9:00am–12:30pm PT

Location
Section 41

Molecular characterization of microsatellite stable (MSS) colorectal cancer (CRC) patients with a BRAF V600E mutation

The clinicopathology of microsatellite stable (MSS) colorectal cancer (CRC) BRAF^V600E sub-populations has not been comprehensively characterized. The research team retrospectively analyzed data from 8,419 MSS CRC patients and found higher immune activation in the BRAF^V600E group (n=485) compared to the BRAF^V600EWT group, evidenced by an enriched CMS1 signature, elevated PD-L1 expression, and CD8+ T-cell infiltration, supporting the investigation of novel immune-based therapeutic avenues in MSS BRAF^V600E CRC as an immunologically distinct sub-population of MSS CRC.

Time
9:00am–12:30pm PT

Location
Section 44

Analysis of HER2 prevalence by RNA expression across solid tumors

Utilizing de-identified records from 3,898 NGS-tested locally advanced or metastatic breast and gastric cancer patients, ERBB2 RNA expression thresholds were defined to correspond to HER2 IHC/ISH based classifications of HER2-zero, -low, and -positive. By applying these thresholds to 4,726 patients with other cancer types (NSCLC, HNSCC, ovarian and endometrial cancers) , the research team found that RNA expressions may be used to identify additional patients corresponding to HER2 IHC≥1+. These findings open up potential therapeutic routes for HER2-directed antibody-drug conjugates in several tumor types.

Time
1:30pm–5:00pm PT

Location
Section 45

Genomic characterization of vulvar squamous cell carcinoma

Molecular profiling of vulvar squamous cell carcinoma (vSCC), a rare cancer, can inform new therapeutic routes to satisfy an unmet need for first-line treatments. The research team conducted a whole-transcriptome multimodal molecular subtyping analysis on a cohort of vSCC samples and identified three molecular subtypes, some of which are similar to SCC tumor types and may be responsive to existing SCC therapeutics.

Time
1:30pm–5:00pm PT

Location
Section 43

Early-stage endometrioid endometrial cancer recurrence risk stratification using a machine learning RNA-seq gene expression signature

To improve existing molecular risk stratification approaches for predicting distant recurrence risk in early-stage endometrioid endometrial cancer (EEC) patients, specifically those at high intermediate risk (HIR), the research team developed a 24-gene RNA-seq-based gene expression profiler (GEP) using a machine-learning pipeline with TCGA data that classifies these patients as molecular risk (MR) high or low. In a separate case-controlled evaluation cohort of 109 early-stage EEC patients from a single institution with documented recurrence status at 4 years, the GEP signature was prognostic for distant recurrence for the entire cohort and for those with high-intermediate risk. Furthermore, within the context of the newly-defined TCGA molecular subtypes, the signature was also prognostic within those within the subgroup with no specific molecular profile (NSMP), implying the test could potentially be applied in clinical adjuvant therapy management, pending further validation.

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