Predictive Biomarkers Of Response Research Articles

Abstract 2799: Intestinal Akkermansia muciniphila complements the therapeutic efficacy of PD1 therapy by reshaping the immunosuppressive microenvironment in nonalcoholic fatty liver disease (NAFLD)-induced hepatocellular carcinoma

Abstract Objectives: Nonalcoholic fatty liver disease (NAFLD)-induced hepatocellular carcinoma (HCC) is an emerging malignancy in the developed world. However, immune checkpoint inhibitors (ICIs) are ineffective for patients with this disease. Advances in research have shown that gut-liver communication contributes to NAFLD progression. Therefore, identifying a key gut microbiota for predicting ICIs response and an effective combinatorial strategy is urgently needed. Design: 16S rRNA sequencing was employed to identify key gut microbiota crucial in developing the mouse NAFLD-induced HCC model. The in vivo roles of Akkermansia muciniphila (Akk) were evaluated by immunofluorescence staining, qPCR, mass spectrometry analysis, ELISA assay, and single-cell RNA sequencing (scRNA-seq) coupled with immune profiling analysis. The clinical and therapeutic value of Akk alone in combination with PD1 treatment was investigated. Results: Akk was decreased from healthy to HCC tissues during HCC tumor development, and daily administration of Akk not only ameliorates liver steatosis and cholesterol biosynthesis but could effectively attenuate the development of NAFLD-induced HCC. Akk repairs the intestinal lining, with a concurrent decrease in the serum concentration of lipopolysaccharide (LPS) and bile acid metabolites. Using scRNA-seq coupled with immune profiling analyses, we found that Akk suppressed the populations of immunosuppressive cells, including monocytic myeloid-derived suppressor cells (m-MDSCs) and M2 macrophages, by suppressing the differentiation of these cells from monocytes, which leads to T cell proliferation and activation. Consistent with this finding, Akk administration, combined with PD1 treatment, exerted a maximal growth-suppressive effect, accompanied by increased infiltration and activation of T cells. Clinically, the fecal level of Akk in HCC patients was decreased in NAFLD-induced HCC patients and serves as a potential biomarker for predicting PD1 response in HCC patients. Conclusions: Akk regulates the immune tumor environment by regulating two key immune suppressive cells via regulation of the LPS flux to develop NALFD-induced HCC. This finding provides a solid therapeutic basis for a rational combination with PD1 for the treatment of this deadly disease. Interestingly, Akk may serve as a predictive biomarker for PD1 response in HCC patients. Citation Format: Xueqian Wu, Fan Ying, Katherine Po Sin Chung, Carmen Oi Ning Leung, Terence Kin Wah Lee. Intestinal Akkermansia muciniphila complements the therapeutic efficacy of PD1 therapy by reshaping the immunosuppressive microenvironment in nonalcoholic fatty liver disease (NAFLD)-induced hepatocellular carcinoma [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 2799.

Abstract 5028: Detection of non-small cell lung and bladder cancer signatures in peripheral blood using a novel antibody-free chromatin capture assay

Abstract Immune checkpoint inhibition (ICI) via anti-PD-1/PD-L1 has improved clinical outcomes for multiple cancers, including bladder and non-small cell lung cancer (NSCLC). Yet, the response rates remain low highlighting a significant knowledge gap in the mechanisms driving response and resistance. While the expression of PD-L1 has been established as a predictive biomarker of ICI response, other features, including molecular tumor burden, tumor microenvironment (TME), and the host’s immune mechanisms may contribute to overall response. The complex interactions between a tumor and the immune system underscore the need for a more comprehensive approach to explore novel biological and immunological insights of cancer progression. Circulating tumor DNA assays, while integral to contemporary decision-making in oncology, fall short in delineating the transcriptional programs that dictate cancer phenotypes and their evolution over the disease trajectory. To bridge this gap, we have developed a plasma-based active chromatin capture method that enables the comprehensive epigenetic and transcriptomic interrogation of both tumor and immune components. In this study, we quantified epigenetic profiles from plasma samples (n=100) of patients with bladder cancer (n=20) and NSCLC (n=24) undergoing ICI therapy, along with samples (n=70) from healthy individuals (n=5). As expected, the total cfDNA yield (ng) of lung (1.7x, p=2.3e-08) and bladder (1.98x, p=1.6e-11) cancer samples were higher than those of healthy individuals. More Interestingly, the longer cfDNA fragments derived from regulatory-active chromatin, also showed a significantly higher abundance in plasma samples from both lung (2.0x, p=2.5e-09) and bladder (3.2x, p=3.2e-14) cancer patients. Furthermore, the patterns of epigenetic regulations across cancer and healthy conditions showed distinct and stable clusters, consistent between gene bodies and transcription start sites, highlighting the epigenetic changes that may be driving cancer progression. We also observed a common enrichment of immune epigenetic signatures in both lung and bladder cancer samples, likely attributed to shared mechanisms associated with the modulation of the tumor microenvironment and immune interactions. Understanding these changes at an epigenetic level could provide insights into novel therapeutic approaches that target the tumor microenvironment and the immune interactions. Citation Format: Kevin Lai, Katharine Dilger, Rachael Cunningham, Timothy Barnes, Khiet Truong, Kathy Lam, Rhea Boquiren, Maggie C. Louie, Diana Abdueva. Detection of non-small cell lung and bladder cancer signatures in peripheral blood using a novel antibody-free chromatin capture assay [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 5028.

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 68: Imaging Endothelial PD-L1 expression with ultrasound molecular imaging to stratify immune checkpoint inhibitor responders

Abstract Immune checkpoint blockade therapies (ICTs) have revolutionized oncology through substantial and lasting treatment outcomes, but only in 15-20% of patients. Non-invasive diagnostic tools to select patients that are most likely to respond to ICT and guide treatment regimens, and thus minimize toxicities to non-responder patients, are in high demand. Program death-1 ligand (PD-L1) marker evaluation in the tumor vasculature and tumor microenvironment is an essential criterion for identifying a suitable patient for PD-L1 based ICT, and it also serves as a prognostic marker in tumor response evaluation. Ultrasound molecular imaging (USMI) is an up-and-coming non-invasive imaging modality that enables longitudinal characterization of endothelial-specific markers. We are developing USMI methods to image immune markers expressed specifically on endothelial cells (EC) of tumors as a decision-support tool for guiding ICT strategies. In this work, we demonstrate for the first time the potential of imaging EC-specific immunosuppressive markers such as PD-L1 and investigate whether it can act as a marker for predicting ICT response. Target-ready microbubble (MB) was used (Trust Bio-sonics, Taiwan) to generate PD-L1 antibody (Ab) linked MBs (TMBs). DSPE-PEG was functionalized to anti-PD-L1 Ab or isotype-Ab (control) to generate stable TMBs (~5 × 107 MBs/mL) to target PD-L1 expressing ECs in vivo. We studied the treatment effect of anti-PD-L1-Ab and isotype-Ab using a syngeneic CT-26 tumor in mice and demonstrated the targeting potency of TMB or iso-Ab-MB (NMB). US imaging was performed using Vevo 2100 high-frequency US system with M250 transducer to evaluate and quantify the differentially targeted enhancement parameter (dTE). We observed that TMBs bound PD-L1 expressing ECs in vivo, with high and low enhancement for TMB and NMB, respectively, in tumors validated by dTE quantification through characterization of the slower washout kinetics. Furthermore, TMBs exhibited increased tumor retention and prolonged binding with average differential targeted enhancement values (dTE) (+/- SD) of 5.2 ± 7.4 for TMBs compared to 2.4 ± 3.9 for NMBs (p=0.14). Preliminary results of USMI (dTE values of TMBs) indicate that PD-L1-treated mice showed a higher response (4.5 ± 5.2; n=9) to the treatment group than control mice that did not receive Ab (1.4 ± 0.8; n=4). The tumor growth rate for the treated mice group was 5.4 -fold retarded compared to the control group which correlated with USMI dTE values. The USMI signals correlated with IHC performed on tumor tissue ex vivo from the respective mouse used for US imaging. Overall, our pre-clinical study suggests that USMI using TMBs may contribute to non-invasive assessment of IC expression on tumor ECs, and that it may serve as a predictive biomarker of ICT response. This novel TMB agent could be a game changer for CE USMI-based cancer prognosis in ICB therapy. Citation Format: Arutselvan Natarajan, Farbod Tabesh, Mahsa Bataghva, Ramasamy Paulmurugan, Ahmed Nagy El Kaffas. Imaging Endothelial PD-L1 expression with ultrasound molecular imaging to stratify immune checkpoint inhibitor responders [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 68.

Abstract 3799: Spatial biomarker prediction in papillary renal cell carcinoma using artificial intelligence-based immunofluorescent inference

Abstract Papillary renal cell carcinoma (pRCC) accounts for ~15-20% of the global burden for kidney cancers but still lacks effective mechanisms to predict patient responses to therapeutic intervention. Existing therapies targeting kidney cancers (anti-angiogenesis and immunotherapy) intimately involve the tumor microenvironment in their mechanism of action. However, the role of the physical organization of the tumor in determining therapeutic efficacy remains unknown. Previous efforts have utilized transcriptional profiling to stratify patients into treatment categories but here we propose to leverage artificial neural networks (“AI”) to infer the expression of marker genes for particular cell types of interest based on ubiquitously expressed immunofluorescence channels, unstained brightfield images, and hematoxylin and eosin (H&E) stained images. We have developed AI models that are capable of the prediction of a range of relevant cell types based on the expression profile of DAPI, type IV collagen, and integrin beta-1, including endothelial cells, T cells, macrophages and other cell types. This work applies an AI model class known as generative adversarial networks (GANs) to produce photo-realistic simulated immunofluorescence images based on the input channels. This approach may allow us to accurately predict complex multiplex immunofluorescence (mIF) images from simpler, more routine histological methods, greatly reducing the cost burden required to profile histopathology images for cell types of interest. Our previous work identified a number of interesting cell-cell association clusters correlated with patient prognosis and this extension of that previous work aims to allow for the prediction of these interactions while obviating the need to perform the original, costly multiplex immunofluorescence assay or even a more reduced immunofluorescence staining panel which may still be out of reach for many clinical pathology labs and the interpretation of which may be challenging. By simplifying and increasing the availability of predictive AI models for digital pathology, we hope to further develop and apply predictive biomarkers for patient prognosis and treatment response in papillary renal cell carcinoma. Citation Format: Jared Brewer, Joseph Vento, Scott Haake, Anupama Reddy. Spatial biomarker prediction in papillary renal cell carcinoma using artificial intelligence-based immunofluorescent inference [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 3799.

Abstract 619: Comprehensive cell line profiling of monomer- and dimer-selective small molecule RAF inhibitors uncovers determinants of cellular responses

Abstract Dysregulation of the Mitogen-Activated Protein Kinase (MAPK) pathway is a frequent event in many cancers. The RAF family of kinases, which includes ARAF, BRAF, and CRAF (RAF1), plays a key role in MAPK signaling. Of the three isoforms, only BRAF is currently targeted by approved RAF inhibitors. These inhibitors were designed to target BRAF-mutant variants that act as oncogenic monomers. However, BRAF-monomer inhibitors can induce activation of wild-type RAF by the formation of dimers, resulting in hyperproliferative lesions of BRAF wild-type cells. Additionally, BRAF-monomer inhibitors do not target RAF-dimers, although this is a potential vulnerability of many MAPK-driven cancers. Novel inhibitors are in development to circumvent these limitations. Further insights into the precise targeting and mechanism of action of RAF inhibitors can help in determining the most promising avenues for drug development. We performed extensive cellular profiling experiments with three approved and five clinical stage inhibitors of RAF to gain more insight into the differences and cellular targeting of these agents. The inhibitors were tested in cell viability assays on a panel of 130 human cancer cell lines from diverse tissue origins. Cell lines were exposed to dose ranges of each inhibitor spanning four logarithmic orders, and viability was determined by measurement of intracellular ATP. Effects of compound on cell viability were related to genomic, transcriptomic, and proteomic status using bioinformatics, with the aim to identify predictive biomarkers for drug response, and to determine selective targeting of MAPK-driven cell lines. Additionally, the primary mechanism of cellular inhibitor activity was determined by relating cell line responses to gene dependency data from large CRISPR knockout screens. Our results showed striking similarity in the overall inhibitory profile of BRAF-monomer inhibitors and the vemurafenib-derivative and paradox breaker plixorafenib, which all selectively targeted BRAF-mutant cell lines. However, whereas vemurafenib increased cell viability in RAS-mutant cell lines at high drug concentrations, this effect was greatly reduced for plixorafenib. Western blot analysis of phosphorylated MEK and ERK levels in RAS-mutant cell lines treated with BRAF-monomer inhibitors confirmed MAPK pathway activation as cause of the increased cell viability. In addition, RAF-dimer inhibitors potently targeted BRAF-mutant cell lines, as well as several KRAS- and NRAS-mutant cell lines. Lastly, correlation analyses of cell line profiling data with CRISPR knockout data revealed the preferential targeting of RAF1-dependent cell lines by the dual RAF/MEK inhibitor avutometinib. The results of our analyses shed light on the intricacies of cellular targeting by RAF inhibitors and provide insights to guide the development of new RAF inhibitors. Citation Format: Jeffrey J. Kooijman, Awan Al Koerdi, Sjoerd B. van der Leeuw, Daphne J. Kluitmans, Esmee van den Bossche, Jelle Dylus, Jeroen A. de Roos, Janneke J. Melis, Nicole Willemsen-Seegers, Guido J. Zaman. Comprehensive cell line profiling of monomer- and dimer-selective small molecule RAF inhibitors uncovers determinants of cellular responses [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 619.

Abstract 3514: A deep learning model for detection and characterization of tertiary lymphoid structures in H&E-stained images from pancreatic ductal adenocarcinoma

Abstract Tertiary lymphoid structures (TLSs) are ectopic aggregates of a number of immune cells in nonlymphoid tissues under chronically inflamed environments and cancer. Emergence evidence suggested that TLSs were found in diverse cancers and TLSs have been referred as an independently predictive biomarker for immunotherapy response, especially immune checkpoint inhibitors. Moreover, the density and mature status of TLSs often positively associated with the favorable outcomes in cancers. Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality with low survival rate in the whole world. Here, we developed a multi-resolution machine learning model for detection of TLSs from hema-toxylin and eosin (H&E) stained digital pathology slides as a potential clinical biomarker in PDAC. We selected 120 H&E slides (patients) with a total of 1,896 TLSs manually annotated as immature or mature TLSs by two pathologists. We also used immunohistochemistry (IHC) for CD20 and CD23 markers as a reference standard for H&E-based manual annotations. Our model mainly included detection and segmentation of individual TLSs. First, we used a transformer convolutional neural network to build a classification task to detect the presence of immature or mature TLSs. Then the instance segmentation model was applied to quantify the number of lymphocytes per unit square to validate the presence and maturation of TLSs. F1-score, calculated by the harmonic mean of precision (positive predictive value) and recall (sensitivity), was finally used to quantitatively evaluate the performance of model for each H&E slide. In validation dataset with 250 H&E slides (patients), we selected 50 slides to calculate F1-score, suggesting that our machine learning model achieved high F1-scores for detection and classification of immature TLSs (mean = 0.87, range from 0.79 to 0.95), and mature TLSs (mean = 0.92, range from 0.86 to 0.97). We further stratified 370 PDAC patients into three groups with mature TLSs (23%), immature TLSs (16%), and no TLSs (61%), respectively. Combined the clinical information, survival analysis revealed that patients with mature TLS had better survival outcomes than those without TLS (HR = 0.63; 95% CI, 0.44 - 0.90; P = 0.010). We developed a machine learning model that can accurately detect and classify TLSs in PDAC, and also demonstrated the prognosis value of predictive TLSs in H&E slides by automatedly machine learning model. These data highlight the promise of machine learning model for automated identification and quantification of the TLS on H&E slides in PDAC pathology. Citation Format: Chaoxian Zhao, Jidong Jia, Mingxi Lv, Jiayi Feng, Yijun Wang, Yingbin Liu. A deep learning model for detection and characterization of tertiary lymphoid structures in H&E-stained images from pancreatic ductal adenocarcinoma [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 3514.

Abstract 2521: New predictive markers for dasatinib responsiveness in triple-negative breast cancer: A biomarker discovery study

Abstract Triple negative breast cancer is the most heterogeneous and aggressive type of breast tumor type and is characterized by the lack of expression of clinical biomarkers (ER, PR, and HER2) and targeted therapies. In this regard, different clinical trials have failed, unable to stratify these patients to find an effective response to specific therapy. This study aimed to identify biomarkers exclusively expressed in the basal mammary compartment that can help us to subclassify the triple-negative breast cancer subtype. Based on computational analysis from single-cell RNA sequencing data, we have identified a list of basal identity-associated genes (BC-markers). Subsequent histological validation confirmed the expression pattern of these markers in a cohort of 50 samples, which has allowed us to stratify triple-negative breast cancer patients into BC-TNBC and luminal-like subtypes. Importantly, the expression of these markers correlated with poorer prognosis in TNBC patients. Functional analysis revealed that TAGLN played a significant role in cell proliferation and migration, potentially impacting tumor aggressiveness. Moreover, the expression of these BC-markers is associated with a mesenchymal phenotype which corresponds to the previously described Lehmannn’s mesenchymal-like signature. In this study, TAGLN appeared to influence dasatinib sensitivity, suggesting its potential as a predictive biomarker for dasatinib response in TNBC patients. Citation Format: Daniel Ortega-Álvarez, David Tebar, Marta Casado, Elena Castillo, Cristina Guardia, David Olivares-Osuna, Eva Musulén, Elisabetta Mereu, Ginés Luengo, Eva M. Galán-Moya, Veronica Rodilla. New predictive markers for dasatinib responsiveness in triple-negative breast cancer: A biomarker discovery study [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 2521.

Abstract 5162: Tumor transcriptional identity as a key predictor of clinical outcome in EGFR-Mutant non-small cell lung cancer

Abstract Background: EGFR tyrosine-kinase inhibitors (TKIs) produce significant clinical benefit in patients (pts) harboring EGFR-activating mutations, however, patient outcomes remain heterogeneous, highlighting the need to identify novel biomarkers for treatment response prediction. We aim to investigate the association of tumor transcriptional identity with clinical outcomes in EGFR-mutant NSCLC. Methods: We used consensus classification to derive unsupervised transcriptionally distinct clusters in EGFR-mutant NSCLC from the TCGA (N= 44). Then, we built a predictive model using extreme-gradient boosting to (1) classify an additional 88 surgically resected tumors from the LCCS study and analyzed for overall survival (OS) prediction; (2) classify metastatic EGFR-mutant NSCLC from the RELAY trial with available RNAseq data (N = 106) and analyzed for progression-free survival (PFS) with erlotinib treatment. Results: Consensus clustering revealed two distinct clusters, one associated with more lineage-preserving features and one with more lineage-independent features that could transcriptionally distinguish EGFR-mutant lung cancer. No differences in primary EGFR mutations were detected across the two clusters. Ingenuity Pathway Analysis revealed TGFB1 as the most significantly enriched pathway in the lineage-independent cluster which was also significantly more mesenchymal (p = 0.0028). For surgically resectioned EGFR-mutant NSCLC, OS was consistently shorter in the lineage-independent cluster for TCGA (23.6 vs 58.3 mo; p = 0.049; HR = 0.4) and the LCCS cohort (55.7 vs not-reached; p = 0.0073; HR = 0.27). In the metastatic setting with erlotinib treatment, there was a trend for PFS to be shorter in the lineage-independent cluster (15.2 vs 19.4 mo; p = 0.12; HR = 0.61). Conclusions: Transcriptional identity in EGFR-mutant lung cancer are prognostic of clinical outcome. The lineage-independent tumors have inferior benefit to EGFR TKI as well as inferior OS, indicating that transcriptional biomarkers can be independent of oncogene biomarkers. These transcriptional biomarkers may help identify pts with poor outcome for future intensification of therapies. Citation Format: Simon Heeke, Monique Nilsson, Don Gibbons, Jianjun Zhang, Ignatio I. Wistuba, Keunchil Park, John V. Heymach, Xiuning Le. Tumor transcriptional identity as a key predictor of clinical outcome in EGFR-Mutant non-small cell lung 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 5162.

Abstract 81: High-resolution monitoring of B cell isotype switching for biomarker analysis of adenosine pathway inhibition immunotherapy

Abstract Background B cell isotype switching gives rise to IgG-, IgA-, and IgE-expressing B cells and is an essential step in generating effective humoral responses to antigen challenge. Within the tumor microenvironment, extracellular adenosine may contribute to cancer immune evasion by inhibiting isotype switching through interaction with A2aR, a possibility that has driven investigation of adenosine pathway inhibition as an immunotherapeutic modality. In this context, methods to quantify isotype switching may provide insight into the pharmacodynamics of candidate agent activity and serve as a source of predictive biomarkers of response. We recently clinically validated the Oncomine IGH-LR assay for the measurement of CLL somatic hypermutation (SHM). Given that this assay also detects isotype switching events, we retrospectively analyzed our validation dataset to determine the suitability of the assay as a potential tool for biomarker analysis of adenosine pathway inhibition immunotherapy. Methods The Oncomine IGH-LR assay determines B cell SHM and isotype through targeted next-generation sequencing of IGH chains from RNA. The assay software organizes detected IGH chains into B cell clonal lineages, of which a subset may comprise multi-isotype lineages indicative of isotype switching events. The software further reports secondary repertoire features such as clonality/evenness. Total RNA was extracted from peripheral blood mononuclear cells or bone marrow aspirates from patients identified as having >10% monoclonal B-cell population by flow cytometry. Intra-assay precision was evaluated by processing RNA from 7 samples in triplicate and sequencing in the same run. Inter-assay precision was evaluated by having different operators process 10 samples on different days with different sequencing barcodes. Dominant clone isotype and clonal lineage assignment, total isotype abundance, and repertoire clonality were evaluated across replicates. Results Overall, 100% concordance was achieved for dominant clone isotype and clonal lineage calls for both intra- and inter-assay studies. Intra-assay and inter-assay median coefficient of variation (CV) for reported isotype frequencies ranged from 0.10-0.16 and 0.15-0.28, respectively. Intra-assay and inter-assay median CV for clonality (1 - Normalized Shannon Entropy) was 0.06 and 0.04, respectively. Conclusions We observe high reproducibility and repeatability of the IGH-LR assay for B cell isotyping, clonal lineage analysis, and clonality assessment. Multi-isotype clonal lineages are automatically reported by the software and provide a means to directly monitor isotype switching events in the context of antigen challenge or immunomodulation. Taken together, we anticipate this assay to be a useful tool for biomarker analysis of adenosine pathway inhibition immunotherapy. Citation Format: Ramadevi Prathapam, Kristen Champion, Anne Burkhardt, Chelsey L. Smith, Rajesh Singh, Timothy J. Looney. High-resolution monitoring of B cell isotype switching for biomarker analysis of adenosine pathway inhibition immunotherapy [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 81.

Abstract 5186: Spatial single-cell deconvolution of the bone marrow microenvironment to define predictive biomarker for response to immunotherapy in patients treated with a check point inhibitor in smoldering multiple myeloma, phase II trial of nivolumab in combination with lenalidomide and low dose dexamethasone

Abstract Background:The immune system is critical for response and resistance in all cancer types and therapies. Patients with Smoldering Multiple Myeloma (SMM) have altered immune cell composition in the bone marrow (BM). However, little is known about the role of BM spatial immune profiling in patients treated with checkpoint inhibitors in MM. Methods: We performed spatial profiling with imaging mass cytometry of trephine biopsies of the BM (n=15; pre-treatment n=7 and post-treatment n=8) from patients with high-risk SMM enrolled in a phase II trial of nivolumab in combination with lenalidomide and low dose dexamethasone (NCT02903381). Patients received 6 cycles of induction therapy of nivolumab (240mg) at days 1 and 15, in combination with lenalidomide (25mg) at days 1-21 and dexamethasone at days 1, 8, and 15. The induction phase was followed by nivolumab (240mg) and lenalidomide (25mg) maintenance for another 6 cycles. A treatment cycle was defined as 28 consecutive days for a total of 12 months period. Event monitoring was performed up to 3 years. The primary efficacy outcome was progression-free survival 2 years after treatment. Results: Eight patients were enrolled on this study from January 2017 to June 2017. The study was put on hold due to the toxicity of checkpoint inhibitors observed in other trials of MM. The median age for all patients at enrolment was 60 years (range 49 to 81), with 5 males (62%) and 3 females (38%). All patients met criteria for high risk SMM disease. Median follow-up for all 8 patients was 38.7 months. Median overall survival was not reached; median progression-free survival was 33.8 months. Seven total patients had a response of Minimal Response (MR) or better, and 4 patients who initially responded eventually progressed. The total number of patients who were followed for at least two years and remained progression-free was 4 of 8 evaluable patients (50%; 90% CI: 19 - 81%).Spatial profiling with a panel of 37 markers on the pre-treatment samples allowed us to assess the states and abundances of multiple immune subpopulations, including rare regulatory NK cells. To assess the accuracy of our cell type annotation, we correlated the proportion of malignant plasma cells identified per patient with the value determined by pathologists for clinical diagnosis. These measures were significantly positively correlated (R=0.92, p<0.01). Spatial neighborhood analysis revealed statistically significant interactions enriched in the responders, even with our small sample size (t-test; p < 0.05). Conclusions and future directions: This pilot study suggests that BM spatial immune profiling before receiving immune check point inhibitors can be a biomarker for response in patients with SMM. Citation Format: Yoshinobu Konishi, Giuseppe Tarantino, Yiwen He, Romanos Sklavenitis-Pistofidis, Sujal I. Shah, Katherine Towle, Christian J. Cea-Curry, Oksana Zavidij, Ruben D. Carrasco, Scott J. Rodig, Jon C. Aster, David Liu, Gad Getz, Irene M. Ghobrial. Spatial single-cell deconvolution of the bone marrow microenvironment to define predictive biomarker for response to immunotherapy in patients treated with a check point inhibitor in smoldering multiple myeloma, phase II trial of nivolumab in combination with lenalidomide and low dose dexamethasone [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 5186.

Abstract 1995: DUSP4 loss enhances resistance to encorafenib plus cetuximab in BRAF mutant colorectal cancer

Abstract The 5-year survival rate for metastatic colorectal cancer (CRC) is less than 14%. Hyperactivation of the ERK/MAPK pathway occurs at a high frequency in CRC, due to mutations (RAS, BRAF) or overexpression/amplification (EGFR) of its signalling components. Aberrant activation of this pathway drives tumour growth and survival and several inhibitors of this pathway are now clinically utilized. The BRAF V600E activating mutation occurs in ~10% of CRC patients and is correlated with an extremely poor prognosis. Use of the small molecule BRAF inhibitor encorafenib in combination with the EGFR inhibitor cetuximab is now approved for BRAF V600E mutant patients, however, the objective response rate is only ~20% and the treatment is rarely curative. Therefore, there is an urgent need to identify predictive biomarkers of response to encorafenib+cetuximab treatment. Dual-specificity phosphatase 4 (DUSP4) is rapidly induced upon activation of ERK/MAPK signaling upon which it acts as a critical negative regulator of signaling output by directly de-phosphorylating ERK. We have observed that DUSP4 is highly expressed in BRAF-mutant CRC cell lines and primary tumours, although there is a range of expression. Whether DUSP4 expression status impacts on response to encorafenib+cetuximab therapy is unknown. The aim of this study therefore was to determine the role of DUSP4 on sensitivity of BRAF-mutant CRC cells to encorafenib+cetuximab treatment in vitro. To examine this, we determined the impact of DUSP4 knock-down, knock-out and re-expression on sensitivity of BRAF-mutant CRC cancer cell lines to encorafenib+cetuximab treatment. Screening a panel of BRAF mutant CRC cell lines for sensitivity to encorafenib+cetuximab revealed a subset of resistant cell lines had either low or undetectable DUSP4 protein expression, while all sensitive cell lines had high DUSP4 expression. siRNA-mediated knockdown of DUSP4 in DUSP4 expressing BRAF-mutant CRC cell lines increased pERK and pFRA1, suggesting activation of ERK/MAPK signalling. DUSP4 knockdown also reduced sensitivity of BRAF mutant CRC cell lines to encorafenib+cetuximab compared to control. These findings were further validated using DUSP4 knock-out with CRISPR-Cas9. Conversely, DUSP4 re-expression in BRAF-mutant CRC cell lines lacking DUSP4 expression, decreased pERK and increased sensitivity to encorafenib+cetuximab treatment. These results demonstrate that DUSP4 regulates ERK/MAPK signalling in BRAF mutant CRC cell lines and that loss of DUSP4 promotes resistance to encorafenib plus cetuximab treatment. These findings warrant investigation of DUSP4 expression status as a predictive biomarker of encorafenib+cetuximab response in patient samples. Citation Format: Kristen Needham, Fiona Chionh, Stan Kaczmarczyk, Connor Kearney, Laura Jenkins, Ian Luk, John M. Mariadason. DUSP4 loss enhances resistance to encorafenib plus cetuximab in BRAF mutant colorectal 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 1995.

Abstract 4644: Single strand DNA GAP accumulation as a functional biomarker for USP1 inhibitor sensitivity in ovarian cancer patient-derived organoid models

Abstract Recent studies suggest that PARP inhibitors and POLQ inhibitors confer synthetic lethality in BRCA1-deficient tumors by accumulation of single-stranded DNA gaps (ssDNA gaps) at replication forks. Loss of USP1, a deubiquitinating enzyme, is also synthetic lethal with BRCA1 deficiency, and USP1 inhibitors are now undergoing clinical development for these cancers. Here, we show that USP1 inhibitors also promote the accumulation of ssDNA gaps during replication in BRCA1-deficient cells. USP1 inhibition increased monoubiquitinated PCNA at replication forks and resulted in accumulation of ssDNA gaps in BRCA1-deficient cancer cells. Knockdown of RAD18, the E3 ubiquitin ligase known to ubiquitinate PCNA, caused USP1 inhibitor resistance and suppression of ssDNA gaps. ssDNA gap accumulation induced by USP1 inhibition correlates with drug sensitivity of BRCA1-mutated cancer cells and overcomes PARP inhibitor resistance in cell line and xenograft models. Furthermore, USP1 inhibition was synergistic with PARP and POLQ inhibition in BRCA1-mutant cells, with enhanced ssDNA gap accumulation. A set of patient-derived ovarian cancer organoids (PDOs) were used to confirm the sensitivity of BRCA1-deficient cells to USP1 inhibition. Five PDOs were derived from patients with high grade-serous ovarian cancer (HGSOC). Among the five PDOs, two of them were from tumors harboring germline BRCA1 pathogenic mutations. BRCA1-mutant PDOs, but not BRCA1-WT PDOs, were sensitive to a USP1 inhibitor and to PARP inhibitor monotherapies. The accumulation of ssDNA gaps after treatment with a USP1 inhibitor or a PARP inhibitor correlated with the sensitivity to these drugs in all the models tested. Moreover, the combination of a PARP inhibitor and a USP1 inhibitor showed synergy in a BRCA1-WT model, which was resistant to both monotherapies. Interestingly, ssDNA gaps accumulated with the combination treatment and not with monotherapy in this model. Ovarian cancer PDOs therefore provide a powerful tool for rapid in vitro sensitivity testing. The detection of ssDNA gap accumulation may be a useful predictive biomarker for response to USP1 inhibition as monotherapy or in combination in ongoing clinical trials. Citation Format: Alexandre Andre B. A. Da Costa, Ozge Somuncu, Ramya Ravindranathan, Sirisha Mukkavalli, David Martigneti, Huy Nguyen, Yuqing Jiao, Benjamin Lamarre, Golbahar Sadatrezai, Lisa Moreau, Joyce Liu, Divya Iyer, Jean-Bernard Lazaro, Geoffrey Shapiro, Kalindi Parmar, Alan D. D'Andrea. Single strand DNA GAP accumulation as a functional biomarker for USP1 inhibitor sensitivity in ovarian cancer patient-derived organoid models [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 4644.

Abstract 6181: Digital pathology workflow: Building a bridge for translational biomarker development in humanized mice

Abstract The introduction of immune checkpoint inhibitors (ICIs) directed against programmed death protein 1 (PD-1), as well as cytotoxic T lymphocyte antigen 4 (CTLA-4), has revolutionized the treatment of non-small cell lung cancer (NSCLC) in the last decade. These therapies have shown significant success in a subset of NSCLC patients, leading to durable response and improved overall survival. However, clear predictive biomarkers for response are still yet to be defined. Tumor-infiltrating lymphocytes (TILs) as one important part of the tumor microenvironment (TME) have proven to be predictive biomarkers in other tumor types such as melanoma, whereas not fully understood in NSCLC. The assessment of TILs typically involves techniques such as flow cytometry (FC) or immunohistochemistry (IHC). While the former has the advantage of being quantitative and relatively easy to standardize it lacks translational value as most clinical samples will be examined using IHC. The current study aims to compare both methods at terminal end point and for treatment outcome, using a panel of 14 NSCLC PDX models, implanted in humanized NSG mice and treated with anti-CTLA4, anti-PD1 and the combination thereof. Based on FC analyses of the untreated tumor tissue the PDX models were stratified into seven hot (>5% TILs) and seven cold (<5% TILs) tumors. The IHC samples were analyzed by digital pathology for TIL infiltration by making use of automated segmentation of the three main tissue classes: tumor, stroma and necrosis. Small number of IHC slides were manually annotated to form a training set, which were then used to train a deep learning model using Visiopharm, a commercial AI platform for image analysis. By quantification of TILs in a spatial context of the tumor tissue, across all models, tumor was the most prominent tissue class. The tissue class distribution as well as the TIL rate (FC or IHC measure) is a model immanent feature. Under treatment, the ratio was stable in case of the cold tumors. In the hot tumors the stroma compartment increased while tumor tissue decreased. Treatment sensitive models displayed an increase of TILs confirmed by FC and IHC. The latter revealed that the TILs specifically increased in the tumor tissue whereas decreased or was stable in the stroma. This observation was significantly more pronounced in cold tumors compared to hot tumors (Oneway ANOVA, p < 0.001). Furthermore, a positive correlation between FC and IHC results on CD45 positive cells in the tumors (p< 0.002) could be confirmed. In this study, we proofed the feasibility of software-based image analysis as translational relevant read-out in preclinical animal experiments. The possibility to reproducibly quantify tissue classes, immune cell markers and including spatial information paves the way towards discovery of novel features predicting response in translational immuno-oncology research. Citation Format: Matti Hakkarainen, Timmo Bragge, Jussi Rytkönen, Eva Oswald, Julia B. Schueler. Digital pathology workflow: Building a bridge for translational biomarker development in humanized mice [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 6181.

Comprehensive pan-cancer analysis reveals SIRT5 is a predictive biomarker for prognosis and immunotherapy response.

Sirtuin 5 (SIRT5) is a promising therapeutic target involved in regulating multiple metabolic pathways in cells and organisms. The role of SIRT5 in cancer is currently unclear, and a comprehensive systematic pan-cancer analysis is required to explore its value in diagnosis, prognosis, and immune function. We investigated the role of SIRT5 in tumorigenesis, diagnosis, prognosis, metabolic pathways, the immune microenvironment, and pan-cancer therapeutic response. Moreover, we explored chemicals affecting the expression of SIRT5 and computed the relationship between SIRT5 and drug sensitivity. Finally, the role of SIRT5 in melanoma was analyzed using a series of experiments in vitro and in vivo. We found that SIRT5 is differentially expressed and shows early diagnostic value in various tumors and that somatic cell copy number alterations and DNA methylation contribute to its aberrant expression. SIRT5 expression correlates with clinical features. Besides, it is negatively (positively) correlated with several metabolic pathways and positively (negatively) correlated with several important metastasis-related and immune-related pathways. High SIRT5 expression predicts poor (or good) prognosis in various tumors and can affect drug sensitivity. We also demonstrated that SIRT5 expression significantly correlates with immunomodulator-associated molecules, lymphocyte subpopulation infiltration, and immunotherapeutic response biomarkers. In addition, we showed that SIRT5 is differentially expressed in immunotherapy cohorts. In addition, we explored various chemicals that may affect SIRT5 expression. In conclusion, we demonstrated that SIRT5 is a key pathogenic gene that promotes melanoma progression. Our study provides a systematic analysis of SIRT5 and its regulatory genes. SIRT5 has excellent diagnostic and prognostic capabilities for many cancers. This may remodel the tumor microenvironment. The potential of SIRT5-based cancer therapies is emphasized and helps predict the response to immunotherapy.

Multi-omics analysis of TLCD1 as a promising biomarker in pan-cancer.

Background: The TLC Domain Containing 1 (TLCD1) protein, a key regulator of phosphatidylethanolamine (PE) composition, is distributed across several cellular membranes, including mitochondrial plasma membranes. Existing research has revealed the impact of TLCD1 on the development of non-alcoholic fatty liver disease. However, there remains a gap in comprehensive pan-cancer analyses of TLCD1, and the precise role of TLCD1 in cancer patient prognosis and immunological responses remains elusive. This study aims to provide a comprehensive visualization of the prognostic landscape associated with TLCD1 across a spectrum of cancers, while shedding light on the potential links between TLCD1 expression within the tumor microenvironment and immune infiltration characteristics. Methods: TLCD1 expression data were obtained from GTEx, TCGA, and HPA data repositories. Multiple databases including TIMER, HPA, TISIDB, cBioPortal, GEPIA2, STRING, KEGG, GO, and CancerSEA were used to investigate the expression pattern, diagnostic and prognostic significance, mutation status, functional analysis, and functional status of TLCD1. In addition, we evaluated the relationship between TLCD1 expression and immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and immune-related genes in pan-cancer. Furthermore, the association of TLCD1 with drug sensitivity was analyzed using the CellMiner database. Results: We found that TLCD1 is generally highly expressed in pan-cancers and is significantly associated with the staging and prognosis of various cancers. Furthermore, our results also showed that TLCD1 was significantly associated with immune cell infiltration and immune regulatory factor expression. Using CellMiner database analysis, we then found a strong correlation between TLCD1 expression and sensitivity to anticancer drugs, indicating its potential as a therapeutic target. The most exciting finding is that high TLCD1 expression is associated with worse survival and prognosis in GBM and SKCM patients receiving anti-PD1 therapy. These findings highlight the potential of TLCD1 as a predictive biomarker for response to immunotherapy. Conclusion: TLCD1 plays a role in the regulation of immune infiltration and affects the prognosis of patients with various cancers. It serves as both a prognostic and immunologic biomarker in human cancer.

Potential association of HSPD1 with dysregulations in ribosome biogenesis and immune cell infiltration in lung adenocarcinoma: An integrated bioinformatic approach.

Lung adenocarcinoma (LUAD) is a major histological subtype of lung cancer with a high mortality rate worldwide. Heat shock protein family D member 1 (HSPD1, also known as HSP60) is reported to be increased in tumor tissues of lung cancer patients compared with healthy control tissues. We aimed to investigate the roles of HSPD1 in prognosis, carcinogenesis, and immune infiltration in LUAD using an integrative bioinformatic analysis. HSPD1 expression in LUAD was investigated in several transcriptome-based and protein databases. Survival analysis was performed using the KM plotter and OSluca databases, while prognostic significance was independently confirmed through univariate and multivariate analyses. Integrative gene interaction network and enrichment analyses of HSPD1-correlated genes were performed to investigate the roles of HSPD1 in LUAD carcinogenesis. TIMER and TISIDB were used to analyze correlation between HSPD1 expression and immune cell infiltration. The mRNA and protein expressions of HSPD1 were higher in LUAD compared with normal tissues. High HSPD1 expression was associated with male gender and LUAD with advanced stages. High HSPD1 expression was an independent prognostic factor associated with poor survival in LUAD patients. HSPD1-correlated genes with prognostic impact were mainly involved in aberrant ribosome biogenesis, while LUAD patients with high HSPD1 expression had low tumor infiltrations of activated and immature B cells and CD4+ T cells. HSPD1 may play a role in the regulation of ribosome biogenesis and B cell-mediated immunity in LUAD. It could serve as a predictive biomarker for prognosis and immunotherapy response in LUAD.

Repolarization of Immunosuppressive Macrophages by Targeting SLAMF7-Regulated CCL2 Signaling Sensitizes Hepatocellular Carcinoma to Immunotherapy.

CCL2 upregulation caused by SLAMF7 deficiency in hepatocellular carcinoma cells induces immunosuppressive macrophage polarization and confers resistance to immune checkpoint blockade, providing potential biomarkers and targets to improve immunotherapy response in patients.

Association of PD-L1 expression with driver gene mutations and clinicopathological characteristics in non-small cell lung cancer: A real-world study of 10 441 patients.

Programmed death ligand-1 (PD-L1) expression is a well-known predictive biomarker of response to immune checkpoint blockade in non-small cell lung cancer (NSCLC). However, there is limited evidence of the relationship between PD-L1 expression, clinicopathological features, and their association with major driver mutations in NSCLC patients in Latin America. This retrospective study included patients from Argentina with advanced NSCLC, and centralized evaluation of PD-L1 expression concurrently with genomic alterations in the driver genes EGFR, ALK, ROS1, BRAF, and/or KRAS G12C in FFPE tissue samples. A total of 10 441 patients with advanced NSCLC were analyzed. Adenocarcinoma was the most frequent histological subtype (71.1%). PD-L1 expression was categorized as PD-L1 negative (45.1%), PD-L1 positive low-expression 1%-49% (32.3%), and PD-L1 positive high-expression ≥50% (22.6%). Notably, current smokers and males were more likely to have tumors with PD-L1 tumor proportion score (TPS) ≥50% and ≥ 80% expression, respectively (p < 0.001 and p = 0.013). Tumors with non-adenocarcinoma histology had a significantly higher median PD-L1 expression (p < 0.001). Additionally, PD-L1 in distant nodes was more likely ≥50% (OR 1.60 [95% CI: 1.14-2.25, p < 0.01]). In the multivariate analysis, EGFR-positive tumors were more commonly associated with PD-L1 low expression (OR 0.62 [95% CI: 0.51-0.75], p < 0.01), while ALK-positive tumors had a significant risk of being PD-L1 positive (OR 1.81 [95% CI: 1.30-2.52], p < 0.01). PD-L1 expression was associated with well-defined clinicopathological and genomic features. These findings provide a comprehensive view of the expression of PD-L1 in patients with advanced NSCLC in a large Latin American cohort.

NLR Outperforms Low Hemoglobin and High Platelet Count as Predictive and Prognostic Biomarker in Metastatic Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors

BackgroundReliable biomarkers in renal cell carcinoma (RCC) remain elusive. While several markers have been shown to be associated with prognosis, and may aid in risk assessment, predictive biomarkers of response to immune checkpoint inhibitors (ICIs) have not been established. Previous studies have shown that a high pretreatment neutrophil-lymphocyte ratio (NLR) is a negative prognostic factor in RCC. However, a clinically useful cut-off for the predictive and prognostic value of NLR has not been well defined. MethodsWe conducted a retrospective analysis of 132 patients with previously untreated metastatic clear cell RCC (ccRCC) who received first line ICI-based therapy. ICI-based therapy included anti-PD-1/PD-L1 alone or in combination with anti-CTLA-4 or VEGF-TKI. Platelet, haemoglobin, neutrophil and lymphocyte counts were collected prior to treatment and at 12-weeks after treatment initiation. Radiologic response at 12-weeks and overall survival (OS) data was also collected. ResultsLow haemoglobin, high platelet count, and NLR ≥3 were statistically significant negative predictive biomarkers when assessed at 12-weeks, but not at baseline. Median OS was shorter in patients with low haemoglobin (20.3 months vs. 51.6 months, P = .009), high platelet count (14.3 months vs. 43.8 months, P = .003), and NLR ≥ 3 (17.5 months vs. 40.3 months, P < .001) at 12-weeks. In an IMDC-risk adjusted analysis, only NLR ≥3 at 12-weeks remained statistically significant (OR of 2.11, P = .003) A dynamic change towards lower absolute NLR overtime was associated with longer OS. In patients who had baseline NLR ≥ 3, those who achieved NLR < 3 at 12-weeks demonstrated significant longer median OS compared to those whose NLR remained persistently ≥ 3 (40.3 months vs. 14.7 months, P = .004). ConclusionNLR ≥3, low haemoglobin and elevated platelet count after 12-weeks of ICI-based first line therapy were negatively prognostic and predictive in patients with metastatic RCC. Normalization of NLR in patients with baseline elevation was associated with longer median OS and response to therapy. These results suggest that monitoring of routine haematologic biomarkers during therapy may provide important predictive and prognostic information, beyond what is available with baseline risk assessment scoring systems.