Robust Biomarkers Research Articles

Integration of metalloproteome and immunoproteome reveals a tight link of iron-related proteins with COVID-19 pathogenesis and immunity

Increasing clinical data show that the imbalance of host metallome is closely associated with different kinds of disease, however, the intrinsic mechanisms of action of metals in immunity and pathogenesis of disease remain largely undefined. There is lack of multiplexed profiling system to integrate the metalloproteome-immunoproteome information at systemic level for exploring the roles of metals in immunity and disease pathogenesis. In this study, we build up a metal-coding assisted multiplexed proteome assay platform for serum metalloproteomic and immunoproteomic profiling. By taking COVID-19 as a showcase, we unbiasedly uncovered the most evident modulation of iron-related proteins, i.e., Ft and Tf, in serum of severe COVID-19 patients, and the value of Ft/Tf could work as a robust biomarker for COVID-19 severity stratification, which overtakes the well-established clinical risk factors (cytokines). We further uncovered a tight association of transferrin with inflammation mediator IL-10 in COVID-19 patients, which was proved to be mainly governed by the monocyte/macrophage of liver, shedding light on new pathophysiological and immune regulatory mechanisms of COVID-19 disease. We finally validated the beneficial effects of iron chelators as anti-viral agents in SARS-CoV-2-infected K18-hACE2 mice through modulation of iron dyshomeostasis and alleviating inflammation response. Our findings highlight the critical role of liver-mediated iron dysregulation in COVID-19 disease severity, providing solid evidence on the involvement of iron-related proteins in COVID-19 pathophysiology and immunity.

TET1 is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Papillary Thyroid Cancer.

To investigate the function of ten-eleven translocation 1 (TET1) and its underlying mechanism in papillary thyroid cancer (PTC). Using the RNA-Seq data based on GDC TCGA, we analyzed the gene expression pattern of TET1 in PTC. Immunohistochemistry was carried out to assess the TET1 protein level. Then, its diagnostic and prognostic functions were determined by various bioinformatics approaches. Enrichment analysis was performed to explore the potential pathways in which TET1 is mainly involved. Finally, the immune cell infiltration analysis was conducted and the association of TET1 mRNA expression with the expression levels of immune checkpoints, tumor mutation burden (TMB) score, microsatellite instability (MSI) score, and cancer stem cells (CSC) score was examined. TET1 expression was lower in PTC tissues compared with that in normal tissues (P < 0.01). Besides, TET1 had a certain value in diagnosing PTC, and low-TET1 mRNA expression led to favorable disease-specific survival (DSS) (P < 0.01). The enrichment analysis revealed autoimmune thyroid disease and cytokine-cytokine receptor interaction were the consistent pathways in which TET1 participated. TET1 was negatively correlated with the Stromal score and Immune score. The different proportions of immune cell subtypes were observed between high- and low-TET1 expression groups. Interestingly, TET1 mRNA expression was inversely related to the expression levels of immune checkpoints, and TMB, MSI, and CSC scores. TET1 might be a robust diagnostic and prognostic biomarker for PTC. TET1 affected the DSS of PTC patients possibly through the regulation of immune-related pathways and tumor immunity.

Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs.

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

A Novel Nucleic Acid Sensing-related Genes Signature for Predicting Immunotherapy Efficacy and Prognosis of Lung Adenocarcinoma.

As a novel pillar for lung adenocarcinoma (LUAD) treatment, immunotherapy has limited efficiency in LUAD patients. The nucleic acid sensing (NAS) pathways are critical in the anti-tumor immune response, but their role in LUAD remains controversial. The study aims to develop a classification system to identify immune subtypes of LUAD based on nucleic acid sensing-related genes so that it can help screen patients who may respond to immunotherapy. We performed a comprehensive bioinformatics analysis of the NAS molecule expression profiles across multiple public datasets. Using qRT-PCR to verify the NAS genes in multiple lung cancer cell lines. Molecular docking was performed to screen drug candidates. The NAS-activated subgroup and NAS-suppressed subgroup were validated based on the different patterns of gene expression and pathways enrichment. The NAS-activated subgroup displayed a stronger immune infiltration and better prognosis of patients. Moreover, we constructed a seven nucleic acid sensing-related risk score (NASRS) model for the convenience of clinical application. The predictive values of NASRS in prognosis and immunotherapy were subsequently fully validated in the lung adenocarcinoma dataset and the uroepithelial carcinoma dataset. Additionally, five potential drugs binding to the core target of the NAS signature were predicted through molecular docking. We found a significant correlation between nucleic acid sensing function and the immune treatment efficiency in LUAD. The NASRS can be used as a robust biomarker for the predicting of prognosis and immunotherapy efficiency and may help in clinical decisions for LUAD patients.

The Role of the Thymus Gland in Atopic Dermatitis

The thymus gland is a vital organ for maintaining a vigilant immune system. It develops at the perinatal stage, maturing during childhood and involutes during adolescence. The thymus secretes numerous cells, chemokines and lymphokines to sustain its function. The Thymus and activation-regulated chemokine (TARC/CCL17) is a member of the T-helper 2 chemokine that aids in immunosurveillance in allergic disease. The TARC is a robust objective biomarker involved in the pathogenesis of Atopic Dermatitis, enabling the monitoring of many aspects of the disease. Arguably, it is currently the most sensitive clinical biomarker of Atopic Dermatitis. TARC has enabled atopic dermatologic management to be precise and personal. The regeneration of the thymus gland will be favourable to conserving many of its essential tasks in the body.

MicroRNAs in Pancreatic Cancer: Advances in Biomarker Discovery and Therapeutic Implications.

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a case study using public datasets and address commonly identified research discrepancies, challenges, and limitations. Lastly, we discuss analytical approaches that integrate multimodal analyses incorporating clinical and molecular features, presenting new insights into identifying robust miRNA-centric biomarkers.

Neurofilament Light Chain Levels Interact with Neurodegenerative Patterns and Motor Neuron Dysfunction in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease involving rapid motor neuron degeneration leading to brain, primarily precentral, atrophy. Neurofilament light chains are a robust prognostic biomarker highly specific to ALS, yet associations between neurofilament light chains and MR imaging outcomes are not well-understood. We investigated the role of neurofilament light chains as mediators among neuroradiologic assessments, precentral neurodegeneration, and disability in ALS. We retrospectively analyzed a prospective cohort of 29 patients with ALS (mean age, 56 [SD, 12] years; 18 men) and 36 controls (mean age, 49 [SD, 11] years; 18 men). Patients underwent 3T (n = 19) or 7T (n = 10) MR imaging, serum (n = 23) and CSF (n = 15) neurofilament light chains, and clinical (n = 29) and electrophysiologic (n = 27) assessments. The control group had equivalent 3T (n = 25) or 7T (n = 11) MR imaging. Two trained neuroradiologists performed blinded qualitative assessments of MR imaging anomalies (n = 29 patients, n = 36 controls). Associations between precentral cortical thickness and neurofilament light chains and clinical and electrophysiologic data were analyzed. We observed extensive cortical thinning in patients compared with controls. MR imaging analyses showed significant associations between precentral cortical thickness and bulbar or arm impairment following distributions corresponding to the motor homunculus. Finally, uncorrected results showed positive interactions among precentral cortical thickness, serum neurofilament light chains, and electrophysiologic outcomes. Qualitative MR imaging anomalies including global atrophy (P = .003) and FLAIR corticospinal tract hypersignal anomalies (P = .033), correlated positively with serum neurofilament light chains. Serum neurofilament light chains may be an important mediator between clinical symptoms and neuronal loss according to cortical thickness. Furthermore, MR imaging anomalies might have underestimated prognostic value because they seem to indicate higher serum neurofilament light chain levels.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer.

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

LncRNA GAS6-AS1 contributes to 5-fluorouracil resistance in colorectal cancer by facilitating the binding of PCBP1 with MCM3

5-Fluorouracil (5-FU) resistance has always been a formidable obstacle in the adjuvant treatment of advanced colorectal cancer (CRC). In recent years, long non-coding RNAs have emerged as key regulators in various pathophysiological processes including 5-FU resistance. TRG is a postoperative pathological score of the chemotherapy effectiveness for CRC, of which TRG 0–1 is classified as chemotherapy sensitivity and TRG 3 as chemotherapy resistance. Here, RNA-seq combined with weighted gene correlation network analysis confirmed the close association of GAS6-AS1 with TRG. GAS6-AS1 expression was positively correlated with advanced clinicopathological features and poor prognosis in CRC. GAS6-AS1 increased the 50% inhibiting concentration of 5-FU, enhanced cell proliferation and accelerated G1/S transition, both with and without 5-FU, both in vitro and in vivo. Mechanistically, GAS6-AS1 enhanced the stability of MCM3 mRNA by recruiting PCBP1, consequently increasing MCM3 expression. Furthermore, PCBP1 and MCM3 counteracted the effects of GAS6-AS1 on 5-FU resistance. Notably, the PDX model indicated that combining chemotherapeutic drugs with GAS6-AS1 knockdown yielded superior outcomes in vivo. Together, our findings elucidate that GAS6-AS1 directly binds to PCBP1, enhancing MCM3 expression and thereby promoting 5-FU resistance. GAS6-AS1 may serve as a robust biomarker and potential therapeutic target for combination therapy in CRC.

Abstract 2993: TmiS: A prognostic indicator for prostate cancer survival based on total tumor cell miRNA levels

Abstract Background: MicroRNAs (miRNAs) are small noncoding RNAs that bind mRNA and inhibit translation or encourage degradation. MiRNAs play a significant regulatory role, with approximately 60% of protein-coding genes harboring miRNA binding sites. MiRNA dysregulation is associated with tumor onset, growth, and metastasis. These important regulatory RNAs have been studied as potential cancer biomarkers, yet the widespread adoption of any miRNA signature has been hindered by the variable nature of miRNA targeting, which is highly context specific. Total tumor cell mRNA content, or TmS, has been shown to have potential as a pan-cancer prognostic indicator. Here we expand the TmS framework using deconvolved miRNA expression to calculate TmiS, an estimate of total tumor-cell miRNA content and investigate the utility of TmiS as a prognostic marker in prostate cancer. Methods: Whole exome sequencing and miRNA sequencing, along with clinical annotations were downloaded for prostate cancer patient samples from The Cancer Genome Atlas (TCGA). Sample purity and ploidy were estimated from exomes using ASACT and ABSOLUTE. Tumor miRNA proportion was estimated using our previously developed miRNA deconvolution method DeMixMir, and TmiS, the total miRNA content per haploid genome per tumor cell, was calculated for each sample. Patients were separated by Gleason category and were binarized into High-TmiS and Low-TmiS within samples with Gleason 7 and Gleason 8+ scores separately. Results: We find Low-TmiS patients to have significantly better prognosis in both Gleason 7 (P = 0.04) and Gleason 8+ (P = 0.01) prostate cancer. We confirm that TmiS is indeed an aggregate measure of total miRNA levels, with no strong correlations observed with any one miRNA. We identify differentially expressed miRNAs and mRNAs between high and low TmiS, and find distinct enriched pathways between Gleason 7 and Gleason 8+ samples, and that several of the differentially expressed miRNAs are well-characterized cancer-related transcripts. Conclusions: In spite of their important regulatory function, the variable and context specific nature of miRNA targeting has been a barrier to their implementation as a robust biomarker. We find TmiS to be a measure of total miRNA dysregulation, and that patients with high total miRNA content show poorer survival within prostate samples graded at Gleason 7 (as well as Gleason 8+), which has been a challenging group for further risk classification. In summary, while the consequences of miRNA dysregulation are subtype specific, we find that by measuring total tumor cell miRNA, TmiS has potential as a robust prognostic indicator independent cellular context. Citation Format: Matthew Montierth, Kinga Németh, George A. Calin, Wenyi Wang. TmiS: A prognostic indicator for prostate cancer survival based on total tumor cell miRNA levels [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 2993.

Abstract 1195: Prospective study for the identification of nivolumab biomarkers via analyses of pre-treatment plasma exosome mRNAs from head and neck cancer patients (BIONEXT)

Abstract Nivolumab paved a new way in the treatment of recurrent or metastatic (RM) head and neck squamous cell carcinoma of (HNSCC). However, the limited rates of long-term survivors (&amp;lt; 20%) demand a robust prognostic biomarker. This nationwide multi-centric prospective study aimed to identify a plasma exosome (PEX) mRNA signature, which serves as a companion diagnostic of nivolumab and as well provides a biological clue to develop effective therapies for a majority of non-survivors. Pre-treatment plasmas (N = 104) were subjected to comprehensive PEX mRNA analyses for prognostic marker discovery and validation. In parallel, paired treatment naïve tumor and plasma samples (N = 20) were assayed to elucidate biological implications of the PEX mRNA signature. A combination of 6 candidate PEX mRNAs plus neutrophil-to-lymphocyte ratio precisely (P = 0.000124) distinguished non-survivors from &amp;gt;2-yr survivor (0% vs 57.7%) with a high hazard ratio (2.878; 95% confidence interval 1.639-5.055; P = 0.0002348). In paired samples, high PEX HLA-E mRNA (a non-survivor-predicting marker) reflected the increased HLA-E protein expression (P = 0.0191) and the dense population of tumor-infiltrating NK cells (P = 0.024) in the corresponding tumor. It is estimated that the effects of PD-1blockade is canceled by the HLA-E-NKG2A immune checkpoint in patients with high PEX HLA-E mRNA. The combination of NKG2A antibody (i.e., monalizumab) and nivolumab may be a promising strategy for non-survivors predicted by a RT-qPCR-based pre-treatment measurement of PEX mRNAs. A candidate companion diagnostic of nivolumab that indicates a possible treatment option is identified. Citation Format: Muneyuki Masuda, Satoshi Toh, Mioko Matsuo, Masashi Sugasawa, Keisuke Yamazaki, Ueki Yushi, Torahiko Nakashima, Hideoki Uryu, Takeharu Ono, Tsutomu Ueda, Hirohito Umeno, Satoshi Kano, Kiyoaki Tsukahara. Prospective study for the identification of nivolumab biomarkers via analyses of pre-treatment plasma exosome mRNAs from head and neck cancer patients (BIONEXT) [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 1195.

Abstract 2908: EDB+FN is an attractive therapeutic target in oncology: Insights from protein expression analysis of solid tumors

Abstract Stroma plays a major role in the initiation, growth, survival, and drug-resistance of solid tumors, yet few therapeutics specifically target tumor-associated stroma. Extra-domain-B fibronectin (EDB+FN) is an oncofetal splice variant of fibronectin upregulated in solid tumor stroma, which is associated with tumor growth, angiogenesis, and metastases. Here we developed an immunohistochemistry (IHC) assay to assess EDB+FN protein expression in the stroma of tumor and normal tissues to characterize the potential of EDB+FN as a therapeutic target for solid tumors with high unmet need. We validated a primary detection antibody and titrated assay conditions to achieve specificity and a broad dynamic range of EDB+FN expression levels across indications, including thyroid cancer, head and neck squamous cell carcinoma, hepatocellular carcinoma, soft tissue sarcoma, pancreatic cancer, non-small cell lung cancer, ovarian cancer, and renal cancer. Considering the stroma-specific expression pattern of EDB+FN, we developed a scoring algorithm weighting both the intensity and distribution of EDB+FN expression specifically in the stroma compartment and separately in tumor cells. Using this assay and scoring approach, we screened more than 200 tumor samples from 10 solid tumor indications and found EDB+FN to be highly differentially expressed in tumor-associated stroma with little to no expression on tumor cells and trace to undetectable levels in healthy tissue. EDB+FN was expressed across the indications evaluated with a wide range of expression both within and between indications. Stromal EDB+FN expression was not correlated with stromal density, which also varied widely within and across indications. In parallel, we developed an algorithm using digital pathology to approximate pathologist scoring of stromal density and EDB+FN expression, allowing streamlined screening of preclinical samples for in vivo studies and indication selection. The disease-specific expression pattern of EDB+FN and high incidence of expression across indications make EDB+FN an ideal target for high-unmet-need solid tumors. The specificity and dynamic range of our IHC assay for detection of EDB+FN protein expression position it as a robust potential biomarker assay for EDB+FN-targeting therapeutics. Citation Format: Sara Lewandowski, Liyang Diao, Alyssa Quigley, Marsha Crochiere, Jan Pinkas. EDB+FN is an attractive therapeutic target in oncology: Insights from protein expression analysis of solid tumors [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 2908.

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.

Abstract 7521: Tumor NLRP3 inflammasome activity mediates resistance to Anti-PD-1 immunotherapy in advanced gastroesophageal cancer

Abstract The addition of anti-PD-1 immunotherapy to the therapeutic armamentarium of advanced gastroesophageal (GE) cancer patients has resulted in modest improvements in clinical outcomes for a subset of patients. More robust predictive biomarkers to guide patient management and to select optimal combination immunotherapy strategies for the individual patient are needed. Our previous studies indicate that the tumor-intrinsic NLRP3 inflammasome-HSP70 signaling axis promotes the recruitment of granulocytic myeloid-derived suppressor cells (PMN-MDSCs) and drives resistance to anti-PD-1 immunotherapy. The KEYLargo phase II clinical trial evaluated the efficacy of pembrolizumab (pembro) in combination with capecitabine/oxaliplatin (CAPOX) in HER2-negative advanced GE cancer patients. An 8-day lead-in period of pembro monotherapy in 36 advanced GE cancer patients allowed for the acquisition of blood and tumor tissue specimens to determine which immune-mediated alterations within the tumor microenvironment correlated with resistance to anti-PD-1 immunotherapy. Baseline and on-treatment specimens were collected for HSP70 ELISA, immunofluorescence microscopy, spatial transcriptomics, NLRP3 FISH, IHC, and NLRP3-ASC proximity ligation assays (PLA) to measure NLRP3 pathway activity levels. All data was correlated with best objective response, progression-free survival (PFS), and overall survival (OS). Tumor-infiltrating CD8+ T cells and PMN-MDSCs were increased in responders and non-responders, respectively. While PD-L1 CPS failed to associate with response, elevated baseline NLRP3 PLA activity correlated with increased tumor-infiltrating PMN-MDSCs (P = 0.001) and resistance to pembro/CAPOX therapy (P = 0.01). Only non-responding tumors exhibited an increase in NLRP3 PLA activity and NLRP3 amplification. Transcriptional studies revealed tumors with enhanced NLRP3 activity to exhibit decreased expression of the NLRC5 transactivator, MHC class I antigen presentation genes, and an increase in the NK cell negative regulator, HLA-G. Baseline plasma HSP70 levels correlated with tumor NLRP3 activity (P = 0.043) and with resistance to pembro/CAPOX therapy (P = 0.001). Elevated baseline NLRP3 PLA activity and amplification were associated with diminished PFS (P = 0.008) and OS (P = 0.009). In additional studies, pharmacologic inhibition of the NLRP3 inflammasome suppressed PMN-MDSC recruitment, upregulated MHC class I, and reversed anti-PD-1 resistance in an orthotopic model of GE cancer. Together, these findings implicate tumor-intrinsic NLRP3-HSP70 signaling as an important mediator of anti-PD-1 resistance in advanced GE cancer. The NLRP3-HSP70 pathway is a promising source of therapeutic targets and biomarkers capable of improving treatment outcomes for this patient population. Additional clinical studies are warranted. Citation Format: Nagendra Yarla, Kaylee Howell, Y-Van Nguyen, Donna Niedwiecki, Hope Uronis, John Strickler, Nicholas C. DeVito, Bala Theivanthiran, Brent A. Hanks. Tumor NLRP3 inflammasome activity mediates resistance to Anti-PD-1 immunotherapy in advanced gastroesophageal cancer [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 7521.

Abstract 3652: Spatial transcriptomic study of the tumor microenvironment in HNSCC

Abstract Background: The complex and dynamic nature of the tumor-immune microenvironment (TME) presents challenges for identification of robust and predictive biomarkers in immuno-oncology (IO). Standard multiplex immunohistochemistry (mIHC) or multiplex immuno-fluorescent assays (mIF) allow for the phenotyping on the cellular level but are limited by the number of markers available and the detection systems. Gene expression or next generation sequencing (NGS) platforms provide high-plex bulk data sets about the TME, but the spatial context of the tumor and immune cell interactions is lost. The NanoString GeoMx® Digital Spatial Profiler (DSP) combines spatial context, utilizing fluorescent morphological markers, with molecular profiling capabilities. This combination allows for the quantitative analysis of high-plex analyte abundance which can be traced back to a region of interest within a sample, providing biological inference in the region of interest. Methods: Formalin-fixed paraffin-embedded (FFPE) specimens from HNSCC patients were cut in 5µm sections for all analyses. Whole section tissue analysis was previously performed utilizing the NanoString nCounter® PanCancer IO 360™ Panel. Tumor inflammation signatures (TIS), an 18-gene signature measuring the adaptive immune response within tumors, was generated for each specimen and categorized each as immune-hot or immune-cold based on predefined parameters. Four specimens were selected based on their TIS score and two sets of cut sections were processed by either Labcorp lab in Morrisville, NC or Nanostring lab in Seattle, WA for precision evaluation, using the GeoMx Human Whole Transcriptome Atlas (WTA) panel (18,000 genes) with the NanoString GeoMx digital spatial profiling technology. The TME regions of interest (ROI) were identified using a set of morphology markers consisting of panCK, CD45 and Syto13; 12 ROIs were selected from each sectioned tumor material. The same ROIs were selected at the two testing sites with matching tissue location and morphology characters. Results: GeoMx WTA panel provided an unbiased view of 18,000 protein-coding genes at specific TME regions. These counts were successfully mapped to the 12 ROIs on the tissue. A pseudo-TIS score was generated using the same 18 gene signature from the WTA panel for each ROI. The pseudo-TIS scores from ROIs from the four specimens showed consistency with the specimen-wise TIS score, yet variations were observed across different TME/ROI within each specimen. Whole transcriptome expression results showed high correlation between the two testing sites, providing confidence in the reproducibility of the technology. High resolution fluorescent staining images were able to be fed to downstream quantitative digital analysis to generate complementary spatial data. Citation Format: Caitlin Schroyer, Shanshan Zhou. Spatial transcriptomic study of the tumor microenvironment in HNSCC [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 3652.

Abstract 2491: The Oxford Classic can identify HGSOC patients who may benefit from EMT-targeting therapies

Abstract Introduction: Despite development of novel targeted therapies such as PARP inhibitors and anti-angiogenic drugs, there is a clear lack of treatment options for High Grade Serous Ovarian Cancer (HGSOC) patients who are Homologous Recombination Repair proficient (50% of cases) and those with intrinsic resistance to these drugs. In this study we demonstrate the ability of the Oxford Classic (OxC)1,2, a non-genetic classifier, to identify HGSOC patients who may benefit from EMT targeting drugs. Methods: 139 HGSOC diagnostic tumor tissue (Brescia cohort) underwent RNA sequencing. Scottish cohort3 was used for external validation and TCGA, AOCS &amp; OVCAD datasets were used for meta-analysis. Deconvolution of tumor RNAseq data, survival analyses, differential gene expression (DGE) analysis, gene pathway analyses in R and tumor immune profiling using CIBERSORT were performed. Results: Risk stratification of HGSOC using the Oxford Classic Patients with a higher OxC-EMT score had 3.6 times increased risk of death (95%CI: 1.6-8.0; p=2e-03) compared to patients with a lower OxC-EMT score by a multivariable cox regression analysis of Brescia cohort. By Kaplan-Meier survival analyses, a significant difference in overall survival in Brescia cohort (p=9e-06), Scottish cohort (p=2e-03) and a combined set of 1023 cases (p=1e-04), was observed between EMT-low risk patients (OxC-EMT score-0) and EMT-high risk patients (OxC-EMT score&amp;gt;0). Notably, 5-year median survival of EMT-low risk and EMT-high risk group was 50% and 13%, resp. (95%CI: 36.1%-69.3% vs 7.1%-23.5%) in Brescia cohort. Therapeutic options for OxC-EMT-high risk group DGE analysis of EMT-low patients and EMT-very high patients (OxC-EMT score&amp;gt;0.5) identified 404 differentially expressed genes common to the datasets. These included genes related to extracellular matrix organisation (VCAN, TGFβI), epithelial cell proliferation (RUNX2, FABP4, SERPINF1), cell chemotaxis (CCL19, DUSP1), angiogenic factors (VEGFC, CXCL12) and transmembrane kinase signalling pathways (TGFβ1/3, PDGFRα/β, IGFBP4/5/6, Wnt11). Of note, key EMT transcription factors, TWIST1/2, SNAI1/2, ZEB1/2, and stemness marker, ALDH1A3, were 2-7 times overexpressed in EMT-very high group. Furthermore, immune modulators, IL6 and IL10 were significantly upregulated and M2 macrophages were significantly more abundant in EMT-very high patients. Multiplex IHC is currently underway to confirm the abundance of TAMs and CD8-positive TRM in the two risk groups. Conclusions: 1) The Oxford Classic-based EMT is a robust prognostic biomarker of overall survival in HGSOC, that faithfully represents the complex circuitry of pathways which are a hallmark of Epithelial to Mesenchymal Transition. 2) OxC-EMT risk stratification can identify HGSOC patients who may benefit from EMT-targeting therapies. Reference: 1) Hu Z. Cancer Cell 2020 2) Hu Z. Clin Cancer Res 2021 3) Hollis R. Clin Cancer Res 2022 Citation Format: Lena Rai, Antonella Ravaggi, Eliana Bignotti, Robb Hollis, Joel Nulsen, Leticia Campo, Alistair Easton, Mara Artibani, Mike Churchman, Federico Ferrari, Christopher Yau, Charlie Gourley, Franco Odicino, Ahmed Ahmed. The Oxford Classic can identify HGSOC patients who may benefit from EMT-targeting therapies [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 2491.

Abstract 7177: Development and validation of clinical pharmacodynamic biomarkers to assess treatment response to HPK1 inhibition and guide dosing decisions

Abstract Checkpoint inhibitors represent promising cancer therapeutics as they have the potential to reignite T cell activity and reverse T cell exhaustion, which is a critical mechanism of immune evasion by tumors. Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T-cell signaling that has recently emerged as a novel target for cancer immunotherapy. Inhibition of HPK1 can restore T cell function and anti-tumor immune responses, and several HPK1 inhibitors are currently under pre-clinical development, or clinical evaluation. Identifying clinically useful pharmacodynamic (PD) biomarkers in response to HPK1 inhibition is critical to characterize exposure-response relationship in early-phase trials and inform dose selection for later-phase studies. However, development and implementation of clinical PD assays are challenging because high sensitivity and specificity are required. Here, we developed a multi-biomarker strategy that combines a proximal biomarker (SLP-76 (Ser376) phosphorylation, a specific substrate of HPK1) and distal biomarkers (cytokine production) to evaluate target engagement (TE) and establish proof of mechanism (POM) of HPK1 inhibitors to determine their comprehensive PD profile. These PD biomarker assays were developed using peripheral blood as a surrogate tissue to assess HPK1 inhibition-induced pSLP-76 level decrease and cytokine levels increase, thus reflecting drug inhibitory effects and T-cell activity recovery. However, the lack of basal levels of these biomarkers presented a challenge for clinical workflow, as ex-vivo T-cell receptor stimulation of the clinical specimen is required after collection. Phosphoflow cytometry was prioritized over several technology platforms evaluated for quantifying blood pSLP-76 levels because of superior assay performance and signal stability under the developed clinical workflow. Blood cytokines upregulated upon HPK1 inhibition were first identified using targeted proteomics. IFNγ, IL2, TNFα, and IL17A were next confirmed and validated using quantitative immunoassays. Both pSLP-76 and whole blood cytokine workflows were thoroughly optimized for every step of sample handling from specimen processing at clinical sites to sample analysis at testing lab to ensure standardized, precise, and robust assay performance for clinical application. In addition, consistent HPK1 inhibition potency values were achieved using blood from cancer patients, demonstrating that the established PD biomarker assays apply to the clinical target population. In conclusion, robust peripheral TE and POM biomarkers were successfully developed to support future phase I/II studies with HPK1 inhibitors. These are expected to enable characterization of the exposure-response relationships and help define the therapeutic window and optimal dose. Citation Format: Lin Jia, Yasmin Marikar-Coplin, Jeff Chen, Kevin Xu, Deanna A. Mele, Nadia Luheshi, Sonia Iyer, Anton I. Rosenbaum, Sonia Terrillon. Development and validation of clinical pharmacodynamic biomarkers to assess treatment response to HPK1 inhibition and guide dosing decisions [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 7177.

Abstract 6244: MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data

Abstract The role of the tumor microenvironment (TME) in cancer evolution and progression is complex and multi-faceted. Multiple cellular phenotypes and patterns of cell-cell communication have been implicated as drivers of drug resistance and metastasis. The spatial architecture of the TME is emerging as particularly important in characterizing the trajectory of tumor growth and response to therapy. Accordingly, there has been a rapid rise in technologies to spatially profile the TME. Each technology focuses on a different modality (e.g. transcriptomics, proteomics, metabolomics) and operates at a different granularity, from coarse-grained tissue spots aggregating dozens of cells to fine-grained subcellular resolution. There is currently no computational method capable of drawing unified inferences across modalities. We introduce MultiTME, a single, holistic generative model for jointly modeling multimodal data across multiple samples in a cohort. MultiTME addresses these challenges through a two-dimensional integration of both samples and multimodal data. MultiTME incorporates a cohort of tissue samples, enabling the joint modeling of different samples within a single analytical framework. This integration allows for a comprehensive analysis of the spatial distribution of cell types across multiple samples, enhancing the understanding of TME variability and the identification of robust biomarkers. MultiTME also integrates multimodal data with varying resolutions, such as Spatial Transcriptomics (ST), single-cell RNA sequencing (scRNA-seq), and Imaging Mass Cytometry (IMC), and provides a multi-layered understanding of the TME. This integration enables the modeling of TME at a finer scale and the discovery of cell types and subtypes that are not distinguishable using single-modal approaches. It also provides concurrent expression profiles across different modalities, leading to the identification of novel biomarkers. We demonstrate MultiTME's efficiency and scalability with a dataset of coupled ST, scRNA-seq, and IMC from a cohort of 8 lung cancer patients. MultiTME's capability to handle both intra-cohort and inter-modality variability offers a panoramic view of TME and helps identify biomarkers and therapeutic targets for cancer screening and treatment. MultiTME is open source. Citation Format: Haoran Zhang, Jeff Quinn, Liron Yoffe, Vivek Mittal, Wesley Tansey. MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data [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 6244.

Gene features of tumor-specific T cells relevant to immunotherapy, targeted therapy and chemotherapy in lung cancer

(1) BackgroundIn lung cancer, the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices. (2) MethodsWe defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups. (3) ResultsWe identified six clusters of T cells as TSTs in lung cancer, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in lung cancer provides the theoretical basis for combined anti-angiogenic and immunotherapy. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy. (4) ConclusionThe proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in lung cancer patients for helping clinical treatment choices.

Abstract 2552: Real-world analysis of biomarker testing and use of targeted therapies in metastatic non-small cell lung cancer (mNSCLC) in the United States (US)

Abstract Background: Between 1998 and 2022, over 40% of new oncology drugs approved by the US FDA were precision therapies. In nonsquamous mNSCLC, detecting biomarkers where targeted therapies exist (EGFR, ALK, ROS1, BRAF V600E variant, MET exon 14 skipping variant, RET and PD-L1) has become essential for adequate clinical care. This study investigated real-world use of these biomarker tests, their characteristics, and associated use of targeted therapies in mNSCLC patients in the US. Methods: Adults ≥18 years old with stage IV mNSCLC from the nationwide Flatiron Health electronic health record-derived de identified database (01/2011 - 04/2023). The proportion of patients receiving biomarker tests, timing, characteristics, test type (single, multiple or next-generation sequencing), and proportion of patients receiving guideline recommended associated targeted therapies (i.e. actionable treatment) was assessed. Actionable treatment was defined in study years where at least one actionable therapy was FDA approved (biomarker, therapy, date: ALK, crizotinib, Aug 2011; BRAF V600E, dabrafenib+trametinib, Jul 2015; EGFR, afatinib, Jul 2013; MET exon 14 skipping, capmatinib, May 2020; PD-L1, pembrolizumab, Oct 2015; RET, pralsetinib, Sep 2020; ROS-1, crizotinib, Mar 2016). Results: In 42,037 patients with mNSCLC, the proportion of patients receiving each test were: ALK: 71.6%, BRAF: 48.8%, EGFR: 74.9%, MET: 41.4%, PD-L1: 49.3%, RET: 42.3%, and ROS1: 54.2%. Median (Q1, Q3) time from mNSCLC diagnosis to first biomarker result was 21 (12, 37) days. Tissue was the most common sample type for biomarker testing, although sampling by blood has increased in recent years. Multiple testing methods were more common in ALK and PD-L1 (48.1% and 50.3%, respectively) and next generation sequencing was more common in the other biomarkers (BRAF: 77.7%, EGFR: 57.1%, MET: 74.1%, RET: 70.7%, ROS1: 54.0%). Testing rates increased from 2011 through 2023 for each biomarker, with the highest rates in 2023: ALK (87.7%), BRAF (85.2%), EGFR (88.7%), MET (85.7%), PDL1 (78.7%), RET (84.8%), and ROS1 (86.9%). Testing rates were similar when timing was restricted to a 1-year window from mNSCLC diagnosis. Receipt of actionable treatment varied by biomarker, in order of increasing frequency: RET+ (23.0%), MET+ (34.0%), BRAF+ (42.7%), PDL1+ (56.3%), ROS1+ (59.8%), EGFR+ (70.6%), and ALK+ (89.0%). Generally, across all biomarkers, receipt of actionable therapy increased in more recent years. Conclusion: Current real-world biomarker testing rates and use of actionable treatments may reflect increased adoption of clinical guideline recommendations. Despite increased testing, use of actionable therapy remains low for certain biomarkers. Further improvements in the implementation of robust biomarker testing to use appropriate therapies are needed, which can lead to better patient outcomes. Citation Format: Michael J. Dennis, Devin Abrahami, Maria Cecilia Vieira, Darrin Benjumea, Marley Boyd, Anran Shao, Kirsten Duncan, John Kelton, Sandip P. Patel. Real-world analysis of biomarker testing and use of targeted therapies in metastatic non-small cell lung cancer (mNSCLC) in the United States (US) [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 2552.