Immune Gene Expression Signature Research Articles

Abstract PR017: Anti-metastatic immunotherapy discovery using ex vivo lung tissue cultures and high-throughput single-cell chemical transcriptomics

Abstract Introduction: Tumors systemically remodel the immune system during metastasis. Developing anti-metastatic immunotherapies that target these tumor-immune interactions to make the metastatic niche hostile colonization would represent a paradigm shift in cancer treatment. However, traditional high-throughput screening (HTS) platforms that use simple read-outs and contrived in vitro systems are ill-suited to identify such therapies. In contrast, HTS platforms that accurately model the metastatic niche and use high-dimensional read-outs such as multiplexed single-cell RNA-sequencing (scRNA-seq) can accurately profile nuanced perturbation responses in individual immune cell types and therefore have the potential to discover anti-metastatic immunotherapies. In this study, we describe the development of an ex vivo lung tissue culture HTS platform that preserves immune gene expression signatures observed in the in vivo metastatic niche and is amenable to single-cell chemical transcriptomic analysis using MULTI- seq (McGinnis et al., Nature Methods, 2019). Inspired by our recent description of myeloid cell TLR-NFκB inflammation during breast cancer lung metastasis (McGinnis et al., Cancer Cell, 2024), we then performed the first anti-metastatic immunotherapy drug screen and identified TLR-NFκB inhibitors that effectively operate on all desired myeloid cell types in the lung metastatic niche. Methods and Results: Building upon existing techniques for culturing precision-cut lung slices (Wu et al., Journal of Experimental Medicine, 2024) and patient- derived tumor fragments (Voabil et al., Nature Medicine, 2021), we established and optimized an ex vivo lung tissue culture system that enables 48-hour culturing of lung slices isolated from 4T1 tumor-bearing mice. scRNA-seq profiling revealed successful capture of all relevant immune cell types and retention of metastasis-associated gene expression programs (e.g., myeloid TLR-NFκB inflammation and neutrophil degranulation) following culture. We then scaled our platform to address how myeloid cells in the lung metastatic niche respond to perturbations targeting every component of the canonical TLR-NFκB signaling cascade. Cell-type-specific perturbation modeling and quantification of TLR-NFκB signaling inhibition identified compounds that optimally perturb all intended myeloid cell types (e.g., tissue-resident macrophages, neutrophils, and monocytes) and represent prioritized candidates for future in vivo validation studies to assess impact on metastatic disease progression. Conclusions: In this study, we describe the development and application of a novel HTS platform that couples ex vivo lung tissue cultures with single-cell chemical transcriptomics to identify anti-metastatic immunotherapy candidates. We demonstrate how our platform can identify TLR-NFκB signaling inhibitors that optimally operate in the lung metastatic niche, paving the way for future interrogation of additional metastasis-associated immune cell signaling pathways in different disease and tissue backgrounds. Citation Format: Chris McGinnis, Winnie Yao, Ansuman Satpathy. Anti-metastatic immunotherapy discovery using ex vivo lung tissue cultures and high-throughput single-cell chemical transcriptomics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr PR017.

Germline polygenic risk scores are associated with immune gene expression signature and immune cell infiltration in breast cancer

The tumor immune microenvironment (TIME) plays key roles in tumor progression and response to immunotherapy. Previous studies have identified individual germline variants associated with differences in TIME. Here, we hypothesize that common variants associated with breast cancer risk or cancer-related traits, represented by polygenic risk scores (PRSs), may jointly influence immune features in TIME. We derived 154 immune traits from bulk gene expression profiles of 764 breast tumors and 598 adjacent normal tissue samples from 825 individuals with breast cancer in the Nurses' Health Study (NHS) and NHSII. Immunohistochemical staining of four immune cell markers were available for a subset of 205 individuals. Germline PRSs were calculated for 16 different traits including breast cancer, autoimmune diseases, type 2 diabetes, ages at menarche and menopause, body mass index (BMI), BMI-adjusted waist-to-hip ratio, alcohol intake, and tobacco smoking. Overall, we identified 44 associations between germline PRSs and immune traits at false discovery rate q<0.25, including 3 associations with q<0.05. We observed consistent inverse associations of inflammatory bowel disease (IBD) and Crohn disease (CD) PRSs with interferon signaling and STAT1 scores in breast tumor and adjacent normal tissue; these associations were replicated in a Norwegian cohort. Inverse associations were also consistently observed for IBD PRS and B cell abundance in normal tissue. We also observed positive associations between CD PRS and endothelial cell abundance in tumor. Our findings suggest that the genetic mechanisms that influence immune-related diseases are also associated with TIME in breast cancer.

Combined Transcriptome and Circulating Tumor DNA Longitudinal Biomarker Analysis Associates With Clinical Outcomes in Advanced Solid Tumors Treated With Pembrolizumab.

Immune gene expression signatures are emerging as potential biomarkers for immunotherapy (IO). VIGex is a 12-gene expression classifier developed in both nCounter (Nanostring) and RNA sequencing (RNA-seq) assays and analytically validated across laboratories. VIGex classifies tumor samples into hot, intermediate-cold (I-Cold), and cold subgroups. VIGex-Hot has been associated with better IO treatment outcomes. Here, we investigated the performance of VIGex and other IO biomarkers in an independent data set of patients treated with pembrolizumab in the INSPIRE phase II clinical trial (ClinicalTrials.gov identifier: NCT02644369). Patients with advanced solid tumors were treated with pembrolizumab 200 mg IV once every 3 weeks. Tumor RNA-seq data from baseline tumor samples were classified by the VIGex algorithm. Circulating tumor DNA (ctDNA) was measured at baseline and start of cycle 3 using the bespoke Signatera assay. VIGex-Hot was compared with VIGex I-Cold + Cold and four groups were defined on the basis of the combination of VIGex subgroups and the change in ctDNA at cycle 3 from baseline (ΔctDNA). Seventy-six patients were enrolled, including 16 ovarian, 12 breast, 12 head and neck cancers, 10 melanoma, and 26 other tumor types. Objective response rate was 24% in VIGex-Hot and 10% in I-Cold/Cold. VIGex-Hot subgroup was associated with higher overall survival (OS) and progression-free survival (PFS) when included in a multivariable model adjusted for tumor type, tumor mutation burden, and PD-L1 immunohistochemistry. The addition of ΔctDNA improved the predictive performance of the baseline VIGex classification for both OS and PFS. Our data indicate that the addition of ΔctDNA to baseline VIGex may refine prediction for IO.

Evaluating tumor inflammation and PD-L1 expression in sequential biopsies of real-world primary and metastatic breast cancer.

e13035 Background: Biomarkers for measuring immunogenicity of tumors such as PD-L1 immunohistochemistry (IHC), tumor mutational burden (TMB), and immune gene expression have been studied in many cancers including breast cancer. How these biomarkers change over time and how their detection differs with sequential testing in the real-world clinical setting has been studied to a lesser extent but is an important consideration for optimal patient care. Methods: We performed an analysis of 30 patients with primary and metastatic breast cancer sequentially tested by comprehensive genomic and immune profiling (CGIP) at two time points during routine clinical care to observe temporal changes in immunotherapy markers and immune gene expression. Genomic variants and TMB were assessed with DNA-seq, while immune gene expression was assessed with RNA-seq. PD-L1 expression was determined using IHC. Immune gene expression signatures characterizing the tumor microenvironment (TME) were calculated, including tumor inflammation (TIGS), cell proliferation (CP), and cancer testis antigen burden (CTAB). Differences between sequential tests were tested for using linear mixed effect models. Results: Time between biopsies for sequential tests ranged from the same day to 4.5 years, with 40% of specimens being collected within 30 days of the first. Sequential specimens were collected from breast (27%), non-breast (40%), or a mix of breast and non-breast (33%) tissue. Non-breast tissue types included sites from lymph node (18%), GI tract (15%), skin (10%), thorax (7%), and musculoskeletal sites (7%). Overall, TMB, PD-L1 IHC, and gene expression signatures were relatively stable between sequential tests (P &gt; 0.05); however, trends were noted for second tests detecting lower tumor inflammation (TIGS score) and PD-L1 IHC (P = 0.1). Further, second tests detected significantly lower levels of tumor inflammation when both were performed on non-breast tissue (P = 0.04), but not when performed on all breast (P = 0.7) or a mix of breast and non-breast (P = 0.6) tissues. An opposite trend was noted for PD-L1 where second tests detected lower levels of PD-L1 IHC when both tests were performed on breast tissue (P = 0.1), but not when performed on all non-breast (P = 0.4) or a mix of breast and non-breast (P = 0.5) tissue. Tissue-dependent trends were still present even when excluding lymph node specimens (P = 0.1). Conclusions: Changes in immunotherapy markers and immune gene expression signatures, on average, remained relatively stable over time, except for tumor inflammation and PD-L1 IHC when sequentially testing all non-breast or breast tissue, respectively. These observations highlight the utility of clinical CGIP testing in revealing temporal changes of the TME, which may help inform clinical trial or treatment strategies occurring over time for primary and metastatic breast cancer.

Biomarker analysis and updated clinical outcomes: Neoadjuvant systemic treatment (NST) with nivolumab (nivo) and relatlimab (rela) in surgically resectable melanoma.

9587 Background: NST with immune checkpoint blockade (ICB) improves clinical outcomes over upfront surgery followed by adjuvant treatment (tx) in patients (pts) with stage III, surgically resectable melanoma. The NST platform also provides an opportunity for deep translational analysis to identify baseline (BL) biomarkers predicting pathologic response and to gain insights into resistance. BL biomarkers, including gene expression signatures (GES) such as IFN-γ/ tumor mutational burden, tumor infiltrating lymphocytes, and presence of tertiary lymphoid structures, have been associated with pathologic response to NST with anti-PD1 and/or anti-CTLA4 antibodies. These studies have not evaluated the newest ICB combination, nivo (anti-PD1) + rela (anti-LAG3), which achieved a major pathologic response rate (MPR) (≤10% viable tumor) of 63% in a Ph II study (NCT02519322). Here we report GES analysis from BL and longitudinal tumor tissues obtained on this study and correlations with outcomes. Methods: Longitudinal tissue was collected from 30 pts treated on the clinical trial and available RNA analyzed using the NanoString nCounter PanCancer IO360 panel. Clinical outcomes, including recurrence and event-free survival, were also updated with longer follow-up. Pts were grouped into MPR and non- major pathologic responders (non MPR). Normalization, differential expression / GES evaluation was performed using nSolver Advanced Analysis software. Differential expression is fit on a per signatures basis using a linear model for analysis. P-values were adjusted within each analysis and on the grouping variable level difference t-test using the Benjamini and Yekutieli False Discovery Rate adjustment to account for correlations amongst the tests. BL analysis was grouped based on pathologic response, MPR vs non-MPR. Results: Of the 30 pts (median follow up 44 months), 53 RNA samples from 26 patients (9 NR and 17 R) were included in the full analysis. Immune GES with significantly higher (adjusted p&lt; 0.05) differences at BL in MPR vs non-MPR included: B cells, CD45, CD-8+ T cells, TIGIT, IDO1, and IFN-γ. Significantly higher B7-H3 GES at BL was associated with non MPR. Additional analyses (i.e., cell population changes over time) and association with event outcomes are underway. Conclusions: To our knowledge, this is the first longitudinal RNA analysis reported from pts treated with nivo+rela. BL immune features associated with major pathologic response include higher GES of B cells, CD45+ cells, CD8 T cells, and increased expression of TIGIT, IDO and IFN-y. Additionally, higher B7-H3 in non MPR pts may indicate a clinically actionable strategy for further evaluation. Ongoing analyses of longitudinal samples and correlations with clinical outcomes are underway and will be presented. Clinical trial information: NCT02519322 .

Interaction between VEGF-A and immune checkpoint targets in triple-negative breast cancer and mechanisms of immune evasion and tumor progression.

1096 Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment triple negative breast cancer (TNBC). However, not all patients with TNBC benefit from the addition of ICIs due to factors such as intrinsic and acquired resistance to ICIs, and complex interplay between tumor and immune cells in the tumor microenvironment (TME). Vascular endothelial growth factor (VEGF) promotes angiogenesis and potentially modulates tumor immune evasion. A phase I clinical trial has shown that a bispecific antibody to VEGF-A and PD-L1 produces encouraging tumor responses in advanced TNBC. In this study, we investigated the interaction between VEGF and various immune gene expression signatures including PD-L1 in a real-world breast cancer patient population. Methods: Comprehensive immune profiling, including the RNA-seq based gene expression assessment of 395 immune-associated genes, was performed on 120 formalin-fixed paraffin-embedded breast cancer samples. PD-L1 expression (Combined Positive Score, CPS ≥ 10%) was measured by immunohistochemistry (IHC; 22C3). Clinicopathologic variables were collected. Statistical associations between quantitative biomarkers, including 28 immune checkpoint genes, were calculated using Pearson correlations (r), and comparisons of quantitative biomarkers between groups were performed using the proportions test. For statistical significance, p&lt;0.05 was required. Results: The study cohort included 29 patients (24.2%) with TNBC and 91 (75.2%) with non-TNBC (including ER+/HER2- and HER2+). Most patients had metastatic tumors (n=92, 76.7%) and invasive ductal carcinoma (n=83, 69.2%). Among the TNBC group, 21 patients had PD-L1 positive tumors (72.4%), defined by CPS ≥ 10.In non-TNBC, VEGF has significant, though weak (r&lt;0.35) correlations with the expression of three checkpoint genes: AKT1 (r=0.35, p=0.00063), ICOS (r=0.34, p=0.00099), and TIM3 (r=0.29, p=0.0061). In TNBC, VEGF has significant, strong (r&gt;0.47) correlations with the expression of three other checkpoint genes: PD-1 (r=0.47, p=0.0094), PD-L1 (r=0.57, p=0.0012), and PIK3CA (r=0.6, p=0.00051) However, in TNBC, VEGF gene expression does not have a statistical association with PD-L1 IHC (CPS ≥/&lt; 10 (p=0.9)). Conclusions: Our findings suggest that there is an interplay between angiogenesis and an immunosuppressive tumor immune microenvironment in TNBC to a greater degree than in non-TNBCs. This supports the notion that angiogenesis mediators such as VEGF-A may enhance the expression of immunosuppressive checkpoint molecules, leading to immune evasion and tumor progression. Combination treatments of ICIs, especially anti-PD-1 therapy, with VEGF antibodies may be promising approaches to enhance the immune responses in TNBC, especially in tumors with CPS&lt;10, where the current frontline treatment is cytotoxic chemotherapy.

A Phase II, Open-Label, Randomized Trial of Durvalumab With Olaparib or Cediranib in Patients With Mismatch Repair—Proficient Colorectal or Pancreatic Cancer

BackgroundThe use of immunotherapy in mismatch repair proficient colorectal cancer (pMMR-CRC) or pancreatic adenocarcinoma (PDAC) is associated with limited efficacy. DAPPER (NCT03851614) is a phase 2, basket study randomizing patients with pMMR CRC or PDAC to durvalumab with olaparib (durvalumab + olaparib) or durvalumab with cediranib (durvalumab + cediranib). MethodsPDAC or pMMR-CRC patients were randomized to either durvalumab+olaparib (arm A), or durvalumab + cediranib (arm B). Co-primary endpoints included pharmacodynamic immune changes in the tumor microenvironment (TME) and safety. Objective response rate, progression-free survival (PFS) and overall survival (OS) were determined. Paired tumor samples were analyzed by multiplexed immunohistochemistry and RNA-sequencing. ResultsA total of 31 metastatic pMMR-CRC patients were randomized to arm A (n = 16) or B (n = 15). In 28 evaluable patients, 3 patients had stable disease (SD) (2 patients treated with durvalumab + olaparib and 1 patient treated with durvalumab + cediranib) while 25 had progressive disease (PD). Among patients with PDAC (n = 19), 9 patients were randomized to arm A and 10 patients were randomized to arm B. In 18 evaluable patients, 1 patient had a partial response (unconfirmed) with durvalumab + cediranib, 1 patient had SD with durvalumab + olaparib while 16 had PD. Safety profile was manageable and no grade 4-5 treatment-related adverse events were observed in either arm A or B. No significant changes were observed for CD3+/CD8+ immune infiltration in on-treatment biopsies as compared to baseline for pMMR-CRC and PDAC independent of treatment arms. Increased tumor-infiltrating lymphocytes at baseline, low baseline CD68+ cells and different immune gene expression signatures at baseline were associated with outcomes. ConclusionsIn patients with pMMR-CRC or PDAC, durvalumab + olaparib and durvalumab + cediranib showed limited antitumor activity. Different immune components of the TME were associated with treatment outcomes.

Abstract PO2-01-06: Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in HER2+ early-breast cancer (EBC)

Abstract Title Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in HER2+ early-breast cancer (EBC). Background In HER2+ EBC, neoadjuvant trastuzumab-based therapy is the standard of care, with adjuvant therapy determined by whether residual disease (RD) is present. Patients (pts) with RD have significantly higher relapse rates than pts with pathologic complete response. Differences in tumor and immune biology between pre-treated and post-treated tumors after HER2-blockade have been described, but little is known about how these changes can affect long-term outcomes or can be leveraged to tailor adjuvant treatment. Here we evaluate the biology of RD using gene expression analyses of pre- and post-treated HER2+ tumors, and their prognostic value to predict event-free survival (EFS) in a pooled analysis of 4 neoadjuvant studies in which single-agent trastuzumab or combined with lapatinib was explored, with and without chemotherapy: CALGB 40601 (Alliance), PAMELA, NeoALTTO, and NSABP B-41. Methods Gene expression profiling by RNA sequencing was assessed on 497 pre-treated samples from pts with RD, and on pre-treated and RD in 171 tumor pairs. Intrinsic subtypes and 22 tumor and immune gene expression signatures (GESs) were evaluated. We studied and compared the association of gene expression biomarkers with EFS in different cohorts and time points, including when GESs were calculated at baseline (N=497), in the 171 paired baseline and RD samples, and the nature of biomarker changes during neoadjuvant therapy. Association between GESs and EFS was explored by uni- and multivariable Cox regression models adjusted by clinical parameters (i.e., study, treatment arm, hormone receptor status, clinical tumor size, and clinical node involvement), and the performance of the models was assessed by the c-index. Results In 497 baseline untreated samples from pts with RD after neoadjuvant therapy (N=497), intrinsic subtypes were significantly associated with EFS (log-rank [LR] P 0.007), with Luminal A showing the best prognosis and Basal-like the worst. Among 171 paired tumors, HER2-Enriched tumors were more common in baseline (50.3%) with change to Normal-like (49.7%) and Luminal A (18.7%) in RD (chi-square P &amp;lt; 0.001). This luminal phenotypic change was also confirmed by a significant reduction in the correlation to the HER2-Enriched centroid and a significant increase in the correlation to the Luminal A centroid between pre- and post-treated samples; a significant decrease in ERBB2 gene expression and the HER2 amplicon gene expression signature was also observed (Wilcoxon P &amp;lt; 0.001). At an immune GES level, we found a significant increase in B cell, CD8+ T cell, and NK cell signatures in RD specimens. In contrast, T reg GES was significantly downregulated (Wilcoxon P &amp;lt; 0.05). In uni- and multivariable models, immune GESs provided more prognostic information when assessed in RD samples. In contrast, subtype-related biomarkers provided more prognostic information at baseline. From all the multivariable models, the best (c-index= 0.78) included the IgG signature assessed in RD (adjusted hazard ratio 0.46, 95% CI 0.30-0.70, adjusted P 0.005). IgG signature levels (tertiles) in RD could also stratify pts into different groups based on prognostic information (EFS at 7 years: 69% in IgG low vs. 91% in IgG high, LR P 0.02). Conclusions In pts with HER2+ EBC and RD after neoadjuvant treatment with HER2-blockade, tumor-related biomarkers seem to provide more prognostic information when calculated at baseline, whereas immune biomarkers in the RD perform better for EFS prognostication. This finding could be relevant for implementing RD biomarkers in the clinic to stratify pts into different prognostic groups and tailor the post-operative treatment strategy. Supported in part by U10 CA180821, U10 CA180882, R01-CA229409 https://acknowledgments.alliancefound.org Citation Format: Aranzazu Fernandez-Martinez, Maki Tanioka, Sung Gwe Ahn, Paola Zagami, Tomás Pascual, Mattia Rediti, Gong Tang, Katherine A Hoadley, David Venet, Naim U. Rashid, Patty Spears, Judith Bliss, Jens Huober, Priya Rastogi, Md N. Islam, Javier Cortés, Antonio Llombart-Cussac, Sandra Swain, Christos Sotiriou, Aleix Prat, Charles M Perou, Lisa Carey. Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in HER2+ early-breast cancer (EBC) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-06.

Abstract 578: Prediction of PD-1 treatment outcome in mismatch repair deficient metastatic colorectal cancer using a multi-omic approach

Abstract Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of patients with deficient DNA mismatch repair (d-MMR) cancers, yet less than half of patients with metastatic colorectal cancer (mCRC) receive clinical benefit. We analyzed heterogeneity within d-MMR tumors using a multi-omics approach inclusive of percent unstable microsatellite loci (MS). Methods: We studied consecutive patients with d-MMR mCRC with available primary tumors (N=32) that were treated with an anti-PD-1 antibody at Mayo Clinic (2015-2022). Tumors were profiled using a whole exome and transcriptome platform (ImmunoID NeXT®). MSIsensor was used to calculate percent unstable microsatellite loci from whole exome sequencing data. CIBERSORT and ssGSEA were used to analyze immune gene expression in relationship to percent unstable microsatellite loci. An immune checkpoint gene expression signature was calculated as the mean expression of CD274, CTLA-4, HAVCR2, LAG-3, PDCD1, PDCD1LG2, TIGIT. Aforementioned variables were analyzed in relationship to objective tumor response (RECIST version 1.1). A Cox proportional hazards model was fit to predict progression-free survival (PFS). Results: Among 32 patients with d-MMR mCRC treated with PD-1 blockade, 16 (50%) received first-line anti-PD-1 therapy; others had &amp;gt; 1 prior chemotherapy regimen. Eighteen (56.3%) tumors harbored BRAFV600E, 4 (12.5%) had mutant KRAS, and median PFS was 24.6 months (95% CI: 8.4, NR) with 18 events. Neither tumor mutation burden (TMB) nor PD-L1 mRNA expression predicted PFS. Median percent unstable MS loci was 14.38 (IQR: 2.6-23.2). Patients whose tumors had higher percent unstable MS loci had significantly better PFS (Q2-4 vs Q1; HR: 0.22, 95%CI: 0.06, 0.62; P=0.003), and were associated with increased objective response (P=0.049). Tumors with higher unstable loci also had higher neoantigen clonality (R= 0.64; P= 0.0003) and increased M0 (resting) macrophages (P=0.02). Nonresponders (vs responders) to PD-1 blockade had an increased immune checkpoint expression signature (P= 0.005). Conclusion: A higher percentage of unstable microsatellite loci, associated with higher neoantigen clonality and resting macrophages, was predictive of anti-PD-1 efficacy. Furthermore, an immune checkpoint gene expression signature was associated with objective response. Citation Format: Bahar Saberzadeh-Ardestani, Oluwadunni E. Emiloju, Rondell P. Graham, Jason T. Lewis, Charles W. Abbott, Sean M. Boyle, Frank A. Sinicrope. Prediction of PD-1 treatment outcome in mismatch repair deficient metastatic colorectal cancer using a multi-omic approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 578.

Abstract 5182: Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response

Abstract Immune checkpoint inhibitors (ICI) have become the standard of care as a first- or second-line systemic treatment for patients with various cancer types. Unfortunately, many patients do not respond to this treatment and the occurrence of immune-related adverse events varies widely. There is an urgent need for robust predictive biomarkers for ICI response to identify patients with likely clinical benefit from this costly treatment. Several biomarkers, aimed at predicting response to ICI, have been proposed, including PD-L1 expression, tumor mutation burden (TMB), infiltration of cytotoxic T-cells in the tumor, Microsatellite Instability (MSI) and expression of various immune gene signatures. However, these individual biomarkers have suboptimal performance and multiple complementary omics technologies are required to properly quantify them. Integration of these multi-omic biomarkers has proven valuable in increasing the accuracy and robustness of ICI response prediction.We have developed a computational pipeline that determines the expressed mutation burden (eTMB), MSI status, fraction of infiltrating immune cells and various immune gene expression signatures directly from the RNA-sequencing profile of the tumor. Each biomarker is quantified using a dedicated algorithm that applies machine learning (eTMB and MSI) or computational deconvolution (infiltrating immune cells) and integrates external data sources to maximize performance. Algorithm performance has been validated on large cohorts of tumor RNA-sequencing data with matching gold standard quantification of said biomarkers. As a proof of concept, we applied our pipeline to tumor RNA-sequencing data of 45 gastric cancer patients treated with pembrolizumab. Responders showed significantly higher eTMB scores and were enriched among the MSI-H patients. Fractions of CD8 T-cells and M1 Macrophages, together with interferon gamma and cytotoxic T-cell gene expression signatures, were significantly increased in responders. Notably, integrating these biomarkers into a single prediction model improved prediction of response to checkpoint inhibition therapy compared to predictions based on eTMB or MSI alone. Taken together, our approach enables the quantification of various ICI response biomarkers from a single omics layer (RNA-sequencing) and can contribute to ICI response prediction. Citation Format: Pieter Mestdagh, Pieter-Jan Van Dam, Frederick De Baene, Carolina Fierro, Elise Van Hoof, Emmanuel Rivière, Hanne Dangreau, Reindert Van Cauwenberge, Jo Vandesompele. Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5182.

Construction of an immune gene expression meta signature to assess the prognostic risk of colorectal cancer patients.

Despite recent advancements in colorectal cancer (CRC) treatment, particularly with the introduction of immunotherapy and checkpoint inhibitors, the efficacy of these therapies remains limited to a subset of patients. To address this challenge, our study aimed to develop a prognostic biomarker based on immune-related genes to predict better outcomes in CRC patients and aid in treatment decision-making. We comprehensively analysed immune gene expression signatures associated with CRC prognosis to construct an immune meta-signature with prognostic potential. Utilising data from The Cancer Genome Atlas (TCGA), we employed Cox regression to identify immune-related genes with prognostic significance from multiple studies. Subsequently, we compared the expression levels of immune genes, levels of immune cell infiltration, and various immune-related molecules between high-risk and low-risk patient groups. Functional analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses provided insights into the biological pathways associated with the identified prognostic genes. Finally, we validated our findings using a separate CRC cohort from the Gene Expression Omnibus (GEO). Integration of the prognostic genes revealed significant disparities in survival outcomes. Differential expression analysis identified a set of immune-associated genes, which were further refined using LASSO penalisation and Cox regression. Univariate Cox regression analyses confirmed the autonomy of the gene signature as a prognostic indicator for CRC patient survival. Our risk prediction model effectively stratified CRC patients based on their prognosis, with the high-risk group showing enrichment in pro-oncogenic terms and pathways. Immune infiltration analysis revealed an augmented presence of certain immunosuppressive subsets in the high-risk group. Finally, we validated the performance of our prognostic model by applying the risk score equation to a different CRC patient dataset, confirming its prognostic potential in this new cohort. Overall, our study presents a novel immune-related gene signature with promising implications for predicting cancer progression and prognosis, thereby enabling more personalised management strategies for CRC patients.

Abstract C017: Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response

Abstract Immune checkpoint inhibitors (ICI) have become the standard of care as a first- or second-line systemic treatment for patients with various cancer types. Unfortunately, many patients do not respond to this treatment and the occurrence of immune-related adverse events varies widely. There is an urgent need for robust predictive biomarkers for ICI response to select patients with likely clinical benefit from this costly treatment. Several biomarkers, aimed at predicting response to ICI, have been proposed, including PD-L1 expression, tumor mutation burden (TMB), infiltration of cytotoxic T-cells in the tumor, Microsatellite Instability (MSI) and expression of various immune gene signatures. However, these individual biomarkers have suboptimal performance and multiple complementary omics technologies are required to properly quantify them. Integration of these multi-omic biomarkers has proven valuable in increasing the accuracy and robustness of ICI response prediction. We have developed a computational pipeline that determines the expressed mutation burden (eTMB), MSI status, fraction of infiltrating immune cells and various immune gene expression signatures directly from the RNA-sequencing profile of the tumor. Each biomarker is quantified using a dedicated algorithm that applies machine learning (eTMB and MSI) or computational deconvolution (infiltrating immune cells) and integrates external data sources to maximize performance. Algorithm performance has been validated on large cohorts of tumor RNA-sequencing data with matching gold standard quantification of said biomarkers. Moreover, integrating these biomarkers improves prediction of response to checkpoint inhibition therapy. Taken together, our approach enables the quantification of various ICI response biomarkers from a single omics layer (RNA-seq) and can contribute to ICI response prediction. Citation Format: Pieter Mestdagh. Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C017.

Mapping the immunological battlefield in gastric cancer: prognostic implications of an immune gene expression signature

BackgroundGastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance.ObjectiveThis study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses.MethodsWe integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups.ResultsWe identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions.ConclusionOur study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets.

Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment. We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.

Tau polarizes an aging transcriptional signature to excitatory neurons and glia.

Aging is a major risk factor for Alzheimer's disease (AD), and cell-type vulnerability underlies its characteristic clinical manifestations. We have performed longitudinal, single-cell RNA-sequencing in Drosophila with pan-neuronal expression of human tau, which forms AD neurofibrillary tangle pathology. Whereas tau- and aging-induced gene expression strongly overlap (93%), they differ in the affected cell types. In contrast to the broad impact of aging, tau-triggered changes are strongly polarized to excitatory neurons and glia. Further, tau can either activate or suppress innate immune gene expression signatures in a cell-type-specific manner. Integration of cellular abundance and gene expression pinpoints nuclear factor kappa B signaling in neurons as a marker for cellular vulnerability. We also highlight the conservation of cell-type-specific transcriptional patterns between Drosophila and human postmortem brain tissue. Overall, our results create a resource for dissection of dynamic, age-dependent gene expression changes at cellular resolution in a genetically tractable model of tauopathy.

Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial

We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail. We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and desert, based on the spatial distribution of CD8+ T cells in two prospective (n= 453; TNM-I trial) and retrospective (n= 481) stage I-IIIA NSCLC surgical cohorts. NanoString assays and targeted gene panel sequencing were used to evaluate the association of gene expression and mutations with immune phenotypes. Among the total of 934 patients, 24.4% of tumors were classified as inflamed, 51.3% as altered, and 24.3% as desert. There were significant associations between ML-derived immune phenotypes and adaptive immunity gene expression signatures. We identified a strong association of the nuclear factor-κB pathway and CD8+ T-cell exclusion through a positive enrichment in the desert phenotype. KEAP1 [odds ratio (OR) 0.27, Q= 0.02] and STK11 (OR 0.39, Q= 0.04) were significantly co-mutated in non-inflamed lung adenocarcinoma (LUAD) compared to the inflamed phenotype. In the retrospective cohort, the inflamed phenotype was an independent prognostic factor for prolonged disease-specific survival and time to recurrence (hazard ratio 0.61, P= 0.01 and 0.65, P= 0.02, respectively). ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. LUADs with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes.

Mycobacterium leprae and host immune transcriptomic signatures for reactional states in leprosy.

Mycobacterium leprae transcriptomic and human host immune gene expression signatures that demonstrate a plausible association with type I (T1R) and type II reactions (T2R) aid in early diagnosis, prevention of nerve damage and consequent demyelinating neuropathy in leprosy. The aim of the study is to identify M. leprae and host-associated gene-expression signatures that are associated with reactional states in leprosy. The differentially expressed genes from the whole transcriptome of M. leprae were determined using genome-wide hybridization arrays with RNA extracted from skin biopsies of 20 T1R, 20 T2R and 20 non reactional controls (NR). Additionally, human immune gene-expressions were profiled using RT2-PCR profiler arrays and real-time qPCRs. The RNA quality was optimal in 16 NR, 18 T1R and 19 T2R samples. Whole transcriptome expression array of these samples revealed significant upregulation of the genes that encode integral and intrinsic membrane proteins, hydrolases and oxidoreductases. In T1R lesional skin biopsy specimens, the top 10 significantly upregulated genes are ML2064, ML1271, ML1960, ML1220, ML2498, ML1996, ML2388, ML0429, ML2030 and ML0224 in comparison to NR. In T2R, genes ML2498, ML1526, ML0394, ML1960, ML2388, ML0429, ML0281, ML1847, ML1618 and ML1271 were significantly upregulated. We noted ML2664 was significantly upregulated in T1R and repressed in T2R. Conversely, we have not noted any genes upregulated in T2R and repressed in T1R. In both T1R and T2R, ML2388 was significantly upregulated. This gene encodes a probable membrane protein and epitope prediction using Bepipred-2.0 revealed a distinct B-cell epitope. Overexpression of ML2388 was noted consistently across the reaction samples. From the host immune gene expression profiles, genes for CXCL9, CXCL10, CXCL2, CD40LG, IL17A and CXCL11 were upregulated in T1R when compared to the NR. In T2R, CXCL10, CXCL11, CXCL9, CXCL2 and CD40LG were upregulated when compared to the NR group. A gene set signature involving bacterial genes ML2388, ML2664, and host immune genes CXCL10 and IL-17A can be transcriptomic markers for reactional states in leprosy.

Abstract OT2-16-01: ONCX-NAV-G201: A phase 2, basket study of navicixizumab monotherapy or in combination with chemotherapy in patients with select advanced solid tumors: Triple-negative breast cancer cohort (trial in progress)

Abstract Objective: The objective of this Phase 2, open-label, multicenter study is to investigate the antitumor activity of navicixizumab monotherapy or in combination with chemotherapy in patients with advanced solid tumors. Cohort C will enroll triple-negative breast cancer (TNBC) patients. Background: Navicixizumab is a bispecific humanized monoclonal immunoglobulin G2 kappa antibody directed against human delta-like ligand 4, a critical ligand of the NOTCH pathway, and human vascular endothelial growth factor (VEGF). Aberrant NOTCH signaling is associated with chemotherapy resistance, tumor plasticity, enhanced metastatic potential, promotion of a cancer stem cell phenotype, and immune evasion. In TNBC, aberrant NOTCH expression is associated with poor prognosis, making targeting of this pathway an attractive therapeutic strategy. Additionally, NOTCH may mediate resistance to angiogenesis inhibition. While VEGF inhibitors are approved for the treatment of multiple solid tumors, observed anti-tumor activity is limited in breast cancer. Pre-clinical data show that co-inhibition of NOTCH and angiogenesis have superior antitumor activity compared to individual pathway targeting. We postulate that navicixizumab will demonstrate anticancer efficacy against TNBC. Methods: Eligible TNBC patients will have locally advanced or metastatic disease and have received at least 2 and no more than 4 prior lines of standard therapy for metastatic disease, including immunotherapy (for PD-L1 positive TNBC patients), and sacituzumab govitecan. TNBC is defined as ER and PR &amp;lt; 1%, and HER2-negative (IHC 0, 1+, or 2+ and negative fluorescence in situ hybridization [FISH]). Formalin-fixed paraffin-embedded tissue from an archival or a core tumor sample must be available for biomarker analysis. Samples will be tested using Oncomap™ ExTra with the Xerna TME Panel™ to classify patient samples into one of four tumor microenvironment (TME) subtypes based on angiogenic and immune gene expression signatures. Up to 30 patients will be enrolled to each TNBC cohort from approximately 8 sites in the US and will receive 3 mg/kg navicixizumab alone (Cohort C1) or in combination with paclitaxel (80 mg/m2 on Days 1, 8, and 15 of a 28-day cycle) (Cohort C2). Patients will have radiologic tumor assessments every 8 weeks and will continue to receive treatment until disease progression per RECIST v1.1 (as assessed by the investigator), unacceptable toxicity, withdrawal of consent, another protocol-defined discontinuation criterion is met, or the sponsor terminates the study, whichever occurs first. The primary endpoints of the study are objective response rate (ORR) and progression-free survival (PFS). The historical benchmarks for this patient population are estimated at an ORR of &amp;lt; 5% and a 20% PFS rate at 4 months, with targets of 15% and 40% respectively considered promising for this study. Interim efficacy assessments will be at 10-patient increments following the time patients have had at least 1 post-baseline scan. Cohort continuation and future evaluation decisions will be guided by the boundaries identified by a sequential monitoring procedure. Secondary efficacy endpoints include overall survival, time to response, disease control rate, duration of response, and the relationship between antitumor activity of navicixizumab and Xerna TME Panel™ biomarker subtypes. Citation Format: Amelia B. Zelnak, Heinz-Josef Lenz, Kerry Culm, Lukas Makris, Valerie Chamberlain Santos, Hagop Youssoufian, Colleen Mockbee, Kathy D. Miller. ONCX-NAV-G201: A phase 2, basket study of navicixizumab monotherapy or in combination with chemotherapy in patients with select advanced solid tumors: Triple-negative breast cancer cohort (trial in progress) [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT2-16-01.

Abstract PD9-06: Histopathological and molecular immune landscape and DNA damage response signatures to predict response to carboplatin and docetaxel in TNT trial TNBC cohort

Abstract Background The TNT trial (NCT00532727) showed no evidence of carboplatin (C) superiority over docetaxel (D) overall in metastatic triple negative breast cancers (TNBC), but a C benefit was observed in the pre-specified sub-group analysis in patients with a gBRCA1/2 mutation (Tutt et al, Nat Med 2018). Given only ~30% of patients have a gBRCA1/2 mutation, broader predictive biomarkers of response are needed. In this cohort we previously found that DNA Damage Response (DDR) signatures were associated with improved C response in chemotherapy (CT) naïve patients only (Tovey et al, ASCO 2020). Since DDR activities influence tumour immune-microenvironment, we explored the predictive ability of immune cell markers and performed integrative analyses on multi-omics features to identify novel TNBC subgroups. Patients and Methods Tumour infiltrating lymphocytes (TILs) were evaluated on haematoxylin and eosin stained primary tumour (PT) slides for 222/376 TNT patients. Formalin-fixed paraffin-embedded PT tissues from 186/376 TNT patients were successfully profiled using total RNA-sequencing. Matched recurrence (REC) was also sequenced for 13 patients. Twenty-five immune signatures were assessed. Logistic regression and restricted mean progression free survival (PFS) were applied to delineate the relationship of these features with treatment outcomes. Random forest clustering of multi-omics DDR and immune biology markers, including gene expression signatures and mutation/methylation status, was applied to identify subgroups. We further molecularly characterised these clusters through supervised clustering of 693 gene expression “modules” (sets of co-expressed genes), immune cell deconvolution and genomic scars. Results Immune gene expression signatures and TILs were highly correlated. Average immune infiltration based on ConsensusTME was lower in mutated/methylated tumours compared with BRCA1 wildtype tumours (p=0.04). Immune signature score markers decreased from PT to REC, demonstrating a dynamic immune microenvironment. In the overall population and when restricting to prior CT treated patients, high immune infiltration (gene expression based &amp; TILs) was associated with response to D while C response rates were not associated with immune scores (interaction p-values&amp;lt; 0.05). This did not translate to a PFS benefit. Multi-omics clustering identified 6 biological subgroups including immune enriched, immune depleted, DDR deficient and proficient clusters as well as 2 small clusters with no obvious distinguishing features. Immune enriched TNBC were predominantly basal-like immune activated with high B-cell/T-cell diversity. Immune depleted TNBC showed higher activity of proliferation and DDR pathway modules. DDR proficient tumours had low expression of immune markers and enrichment for ESR1/PGR expression, markers of extra cellular formation, cell structure, lipid metabolism and proliferation. The DDR deficient cluster was enriched for proliferation and demonstrated high number of TILs despite no apparent enrichment for gene expression-based immune modules. In the prior CT treated cohort, the immune enriched cluster had preferential response to D (62.5% (D) vs. 29.4% (C); p=0.02). The immune depleted cluster had preferential response to C (8.0% (D) vs. 40.0% (C); p=0.01). Numbers were too small to assess differential response within the other clusters or in the CT naïve cohort. Conclusions Tumours with high immune features have high response to D while those with low immune features have preferential response to C in advanced TNBC. Combining multi-omics markers of DDR deficiency and immune biology can identify clusters of patients with distinct biological profiles and differential treatment specific response rates. Citation Format: Holly Tovey, Orsolya Sipos, Katherine A Hoadley, Joel S Parker, Jelmar Quist, Sarah Kernaghan, Lucy Kilburn, Roberto Salgado, Sherene Loi, Richard D Kennedy, Ioannis Roxanis, Patrycja Gazinska, Sarah E. Pinder, Judith Bliss, Charles M. Perou, Syed Haider, Andrew Tutt, Anita Grigoriadis, Maggie Chon U Cheang. Histopathological and molecular immune landscape and DNA damage response signatures to predict response to carboplatin and docetaxel in TNT trial TNBC cohort [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD9-06.

ONCX-NAV-G201: A phase 2 basket study of navicixizumab monotherapy or in combination with chemotherapy in patients with select advanced solid tumors—Colorectal Cancer Cohort (trial in progress).

TPS263 Background: The objective of this Phase 2, open-label, multicenter study is to investigate the antitumor activity of navicixizumab monotherapy or in combination with chemotherapy in patients with advanced solid tumors (NCT05453825). Cohort A will enroll colorectal cancer (CRC) patients. Navicixizumab is a bispecific humanized monoclonal immunoglobulin G2 kappa antibody directed against human delta-like ligand 4, a critical ligand of the NOTCH pathway, and human vascular endothelial growth factor (VEGF). Aggressive CRC remains difficult to treat owing to the aberrant activation of oncogenic signaling pathways including the NOTCH pathway. Preclinical data show that co-inhibition of NOTCH and angiogenesis have superior antitumor activity compared to individual pathway inhibition. We postulate that navicixizumab will demonstrate anticancer efficacy against CRC. Methods: Eligible CRC patients will have locally advanced or metastatic disease and have received at least 2 and no more than 3 prior lines of standard therapy for metastatic disease, including an anti-VEGF monoclonal antibody. Formalin-fixed paraffin-embedded tissue from an archival or a core tumor sample must be available for biomarker analysis. Samples will be tested using Oncomap ExTra with the Xerna TME Panel where patient samples will be classified into one of four tumor microenviroment (TME) subtypes based on angiogenic and immune gene expression signatures. Up to 30 patients will be enrolled to each CRC cohort from approximately 8 sites in the US and will receive 3 mg/kg navicixizumab alone (Cohort A1) or in combination with irinotecan (180 mg/m2 on Days 1 and 15 of a 28-day cycle, Cohort A2). Patients will have radiologic tumor assessments every 8 weeks and will continue to receive treatment until disease progression per RECIST v1.1 (as assessed by the investigator), unacceptable toxicity, withdrawal of consent, another protocol-defined discontinuation criterion is met, or the sponsor terminates the study, whichever occurs first. The primary endpoints of the study are objective response rate (ORR) and progression-free survival (PFS). The historical benchmarks for this patient population are estimated at an ORR of &lt;5% and a 25% PFS rate at 4 months, with targets of 15% and 50% respectively considered promising for this study. Interim efficacy assessments will be at 10-patient increments following the time patients have had at least 1 post-baseline scan. Cohort continuation and future evaluation decisions will be guided by the boundaries identified by a sequential monitoring procedure. Secondary efficacy endpoints include overall survival, time to response, disease control rate, duration of response, and the relationship between antitumor activity of navicixizumab and Xerna TME Panel biomarker subtypes. Clinical trial information: NCT05453825 .