Immunotherapy Clinical Trials Research Articles

Pooled Rate of Pseudoprogression, Patterns of Response, and Tumor Burden Analysis in Patients Undergoing Immunotherapy Oncologic Trials for Different Malignancies

AimsAssess rates of true pseudoprogression in unconfirmed progressive disease (iUPD) in a pool of immunotherapy clinical trials for different cancers, analyze tumor characteristics that drive iUPD classification, and investigate potentials predictors of pseudoprogression. Materials and methodsRetrospective interpretation of prospectively acquired data. Patients from 18 immunotherapy clinical trials with two arms (RECIST 1.1, iRECIST), of 10 cancer types were selected. Pooled rate of true pseudoprogression among iUPD was estimated using a common effect meta-analysis. Target, Non-target, and new lesions as the trigger of confirmed-vs pseudo-progression were compared using Chi-Square and Fisher exact tests. Conditional logistic regression was used to investigate the association between age, sex, tumor burden at baseline, and number of follow ups and pseudoprogression. Results60/287 (21%) patients (17 women) were classified as iUPD with at least one subsequent confirmatory timepoint. The overall pooled estimate of pseudoprogression was 15% (95%CI: 8%–-26%). Nontarget lesions were significantly more frequent the cause of iUPD than change in Target lesions size (p< 0.001). Most observations of true pseudoprogression occurred in the first follow-up (77%), whereas confirmed progression occurred in later time points during the trial. Pseudoprogression was not significantly associated with age, sex, tumor burden at baseline, or number of timepoints. ConclusionIn a pool of immunotherapy trials, the rate of true pseudoprogression was 15%, most often in the first timepoint after baseline than later in treatment. iUPD categorization was mostly driven by changes in NT lesions rather than objective changes in measurements of target lesions.

Global disparities in immunotherapy clinical trials: A comprehensive analysis of low- and middle-income countries over the past decade.

1606 Background: Immunotherapy (IO) has largely impacted cancer treatment over the last decade, yet its accessibility and representation in clinical trials remain severely limited in low- and middle- income countries. We investigated the involvement of these countries in IO cancer trials, analyzing country-specific rates and influencing factors. Methods: Data was obtained from clinicaltrials.gov. Advanced search focused on cancer interventional studies from 12/31/2013 to 01/01/2024 with specific IO treatments. Only completed trials were included. Studies unrelated to immunotherapy or cancer were excluded. Country trial rates were calculated per 100k population. Statistical analysis used Chi-square test for nominal data. Results: Of 1593 trials, 1282 were included in the final analysis. Of world’s 217 countries and regions (World Bank) 72 (33.2%) participated in IO cancer trials in the last decade. Zero country from low-income group was involved in the trials. Only 2.4% (31) of all trials, included lower-middle-income countries (LMICs). Out of 54 LMICs, only 8 were represented: Ukraine, Philippines, India, Guatemala, Egypt, Vietnam, Morocco, and Eswatini (Swaziland). Ukraine led this group with 16 trials, while the remaining 7 were included in less than 10 trials each. Upper-middle-income countries (UMICs) were included in 21.3% (273) of trials. Out of 54 UMICs, 22 were involved in the trials; China led this group (174), followed by Russia (69), Brazil (52), Mexico and Turkey (41 each). Among the top 30 countries with the highest trial participation, none included LMICs, and only 5 were UMICs. Of 81 high-income countries (HICs) 42 participated in IO trials. USA was involved in over 65% of all trials (840), followed by Spain (222), France (193), and Canada (180). However, population-adjusted rates varied, with European HICs like Latvia (2.16), Belgium (1.09), Norway (0.87) among 25 other countries, showing higher rates than USA (0.25). Majority of trials done solely in one country were also from USA (65%), and China (79%). A significant link was found between the economic status of participating countries and the funding source of trials (p&lt;0.00001). Industry funded 28 of 31 (90%) trials in LMICs, indicating a reliance on industry funding in these regions. Industry favored adult-only over pediatric-inclusive trials (p&lt;0.0001). Out of 80 trials (6.24%) including children, 22 were industry-funded. Exclusively pediatric IO trials were only 4 (0.3%) of all IO trials. LMICs participated in 3.75% (3) of all pediatric-inclusive trials. Conclusions: Countries with low economic status and children with cancer from both low- and high-income areas are largely underrepresented in IO cancer trials globally. More inclusive IO trials across age groups and LMICs are vital to implement trial results in real-world settings and close the care gap.

Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches.

Model-based tumor growth inhibition (TGI) metrics are increasingly used to predict overall survival (OS) data in Phase III immunotherapy clinical trials. However, there is still a lack of understanding regarding the differences between two-stage or joint modeling methods to leverage Phase I/II trial data and help early decision-making. A recent study showed that TGI metrics such as the tumor growth rate constant KG may have good operating characteristics as early endpoints. This previous study used a two-stage approach that is easy to implement and intuitive but prone to bias as it does not account for the relationship between the longitudinal and time-to-event processes. A relevant alternative is to use a joint modeling approach. In the present article, we evaluated the operating characteristics of TGI metrics using joint modeling, assuming an OS model previously developed using historical data. To that end, we used TGI and OS data from IMpower150-a study investigating atezolizumab in over 750 patients suffering from non-small cell lung cancer-to mimic randomized Phase Ib/II trials varying in terms of number of patients included (40 to 15 patients per arm) and follow-up duration (24 to 6 weeks after the last patient included). In this context, joint modeling did not outperform the two-stage approach and provided similar operating characteristics in all the investigated scenarios. Our results suggest that KG geometric mean ratio could be used to support early decision-making provided that 30 or more patients per arm are included and followed for at least 12 weeks.

Advances in Targeted Therapy for Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a rare cancer with high malignancy and aggressiveness on the pleural, caused by the following risk factors including asbestos inhalation, genetic factors, and genetic mutation. The present chemotherapy, antiangiogenic therapy, and immunotherapy methods are ineffective and the survival time of patients is very short. There is an urgent need to find potential therapeutic targets for MPM. At present, it has been found the following types of targets: gene mutation targets such as BRCA associated protein 1 (BAP1) and cyclin-dependent kinase 2A (CDKN2A); epigenetic targets such as lysine (K)-specific demethylase 4A (KDM4A) and lysine-specific demethylase 1 (LSD1), and signal protein targets such as glucose-regulated protein 78 (GRP78) and signal transducer and activator of transcription 3 (STAT3). So far, available clinical trials include phase II clinical trials of histone methyltransferase inhibitor Tazemetostat, poly (ADP-ribose) polymerase (PARP) inhibitor Rucaparib and cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitor Abemaciclib, as well as phase I clinical trials of mesothelin-targeting chimeric antigen receptor T-cell immunotherapy (CAR-T) cell injection in the thoracic cavity and TEA domain family member (TEAD) inhibitor VT3989 and IK-930, and the results of these trials have showed certain clinical efficacy. .

Immunotherapy for Head and Neck Squamous Cell Carcinoma: Present and Future Approaches and Challenges.

Despite significant progress and improving outcomes in the management of head and neck squamous cell carcinoma (HNSCC), there are few effective treatment options for patients with recurrent or metastatic head and neck squamous cell carcinoma. The advent of immune checkpoint inhibitors has changed the treatment algorithm of head and neck squamous cell carcinoma and are approved in the frontline setting for recurrent and metastatic (R/M) head and neck squamous cell carcinomas. Although promising for some patients, most patients with R/M HNSCC do not derive clinical benefit from currently approved checkpoint inhibitors. Many studies are underway to identify the patient population that would benefit the most from immunotherapy as well as postimmunotherapy treatment failures, including novel combinations of immunomodulatory therapies. In this review, we summarize the clinical development of all major clinical trials of immunotherapy in HNSCC.

Abstract PO1-07-04: Multiomics analysis reveals the landscape of PD-L1 expression in triple-negative breast cancer

Abstract Background: Tumor-infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) remain controversial in predicting clinical outcomes of triple-negative breast cancer (TNBC), as outcomes do not always correlate with the expression of these biomarkers. Genomic and transcriptomic alterations that might contribute to the expression of these biomarkers remain incompletely uncovered. Methods: We evaluated PD-L1 immunohistochemistry (IHC) scores (SP142 and 28-8) and TILs in our TNBC multiomics dataset and two immunotherapy clinical trial cohorts. Then, we analyzed genomic and transcriptomic alterations correlated with TILs, PD-L1 expression and patient outcomes. Results: Despite TILs serving as a decent predictor for TNBC clinical outcomes, there remained exceptions. Our study revealed that several genomic alterations were correlated with unexpected events. Particularly, PD-L1 expression may cause a paradoxical relationship between TILs and prognosis in certain patients. Consequently, we classified TNBCs into four groups based on PD-L1 and TIL levels. The TIL-PD-L1+ and TIL+PD-L1- groups were not typical “hot” tumors; both were associated with worse prognoses and lower immunotherapy efficacy than TIL+PD-L1+ tumors. Copy number variation of PD-L1 and oncogenic signaling activation were correlated with PD-L1 expression in the TIL-PD-L1+ group, whereas GSK3B-induced degradation might cause undetectable PD-L1 expression in the TIL+PD-L1- group. These factors have the potential to affect the predictive function of both PD-L1 and TILs. Conclusions: Several genomic and transcriptomic alterations may cause paradoxical effects among TILs, PD-L1 expression and prognosis in TNBC. Investigating and targeting these factors will advance precision immunotherapy for TNBC patients. Keywords: Triple-negative breast cancer, PD-L1 expression, Tumor-infiltrating lymphocytes, Multiomics, Immunotherapy Citation Format: Han Wang, Xiao-Hong Ding, Cheng-Lin Liu, Yi Xiao, Ruo-Hong Shui, Yan-Ping Li, Chen Chen, Wen-Tao Yang, Zhi-Ming Shao, Yi-Zhou Jiang. Multiomics analysis reveals the landscape of PD-L1 expression in triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-04.

Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: a global-scale, cross-sectional, and large-sample informatics study.

Neoadjuvant and adjuvant immunotherapies for cancer have evolved through a series of remarkable and critical research advances; however, addressing their similarities and differences is imperative in clinical practice. Therefore, this study aimed to examine their similarities and differences from the perspective of informatics analysis. This cross-sectional study retrospectively analyzed extensive relevant studies published between 2014 and 2023 using stringent search criteria, excluding nonpeer-reviewed and non-English documents. The main outcome variables are publication volume, citation volume, connection strength, occurrence frequency, relevance percentage, and development percentage. Furthermore, an integrated comparative analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression statistics, and Walktrap algorithm analysis. This analysis included 1373 relevant studies. Advancements in neoadjuvant and adjuvant immunotherapies have been promising over the last decade, with an annual growth rate of 25.18 vs. 6.52% and global collaboration (International Co-authorships) of 19.93 vs. 19.84%. Respectively, five dominant research clusters were identified through unsupervised hierarchical clustering based on machine learning, among which Cluster 4 (Balance of neoadjuvant immunotherapy efficacy and safety) and Cluster 2 (Adjuvant immunotherapy clinical trials) [Average Publication Year (APY): 2021.70±0.70 vs. 2017.54±4.59] are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including microsatellite instability (R 2 =0.7500, P =0.0025) and biomarkers (R 2 =0.6505, P =0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R 2 =0.5571, P =0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that 'neoadjuvant immunotherapy, nonsmall cell lung cancer (NSCLC), immune checkpoint inhibitors, melanoma' and 'adjuvant immunotherapy, melanoma, hepatocellular carcinoma, dendritic cells' (Relevance Percentage: 100 vs. 100%, Development Percentage: 37.5 vs. 17.1%) are extremely relevant to this field but remain underdeveloped, highlighting the need for further investigation. This study identified pivotal research signatures and provided substantial predictions for neoadjuvant and adjuvant cancer immunotherapies. In addition, comprehensive quantitative comparisons revealed a notable shift in focus within this field, with neoadjuvant immunotherapy taking precedence over adjuvant immunotherapy after 2020; such a qualitative finding facilitate proper decision-making for subsequent research and mitigate the wastage of healthcare resources.

Current Immunotherapy Treatments of Primary Breast Cancer Subtypes.

Breast cancer receives the most funding when compared to any other cancer type, according to a global study conducted by The Lancet. Nevertheless, this malignancy remains the most diagnosed cancer among women and relies heavily on a neoadjuvant treatment regimen of chemotherapy and targeted therapy. After standard treatment, 25-30% of breast cancer patients still develop disease recurrence and must undergo cytoreductive debulking surgery followed by intensive chemotherapy. An array of targeted therapies are currently being utilized and developed to alleviate negative side effects, eradicate cancer growth, and diminish disease recurrence. Immunotherapy is a promising cancer therapy that upregulates one's immune system to stimulate a therapeutic effect and is utilized for cancer management among other ailments such as immunodeficiencies, hypersensitivity reactions, autoimmune diseases, inflammatory disorders, tissue and organ transplantation, and infectious diseases. This review highlights the five primary subtypes of breast cancer, provides a brief history of immunotherapy, evaluates the current landscape of treating breast cancer with immunotherapy, analyzes selected ongoing or recently completed immunotherapy clinical trials for hormone receptor-positive, HER2-enriched, and triple-negative breast cancer, and examines future trends for the treatment of breast cancer with immunotherapeutic techniques. This review provides a formal summary categorized by breast cancer subtype rather than types of immunotherapeutic treatment.

Abstract LB403: Suppression of antigen presentation in pediatric rhabdomyosarcoma

Abstract Background: Rhabdomyosarcoma (RMS) is an aggressive soft tissue sarcoma that is diagnosed based on the tumor cell's resemblance to embryonic or poorly differentiated skeletal muscle progenitors. Multiple immunotherapy clinical trials have found no evidence of immune reactivity against RMS, which has been attributed to the relatively low tumor mutation burden in these cancers. However, we made an unexpected observation that many patient RMS tumors had a near absence of MHC-I expression, a key requisite for anti-tumor CD8+ T lymphocytes and many immunotherapies. In this study, we explore the etiology of low antigen presentation in RMS and systematically evaluate therapeutic interventions that sensitize RMS to immunotherapy. Methods: Surface MHC-I expression was evaluated on pediatric tumor cell lines (n=65) from five major pediatric tumor types. RNA-seq, whole exome and genome sequencing data were analyzed from RMS tumors (total of n=247) and iPSC models of skeletal muscle development (PMID: 32011235). Epigentic drugs targeting DNA methylation (decitabine), EZH2 (tazemetostat), Class I/IV HDACs (mocetinostat), and LSD1 (GSK-LSD1) were used at sub-cytotoxic doses on RMS cell lines (n=10) and analyzed for surface MHC-I expression with flow cytometry and gene expression. T cell cytotoxicity assays were performed using engineered human T cells expressing an optimized TCR which recognizes an HLA*A2:01 restricted peptide from PRAME. Results: RMS cell line models exhibited a range of MHC-I expression with most having low or undetectable MHC-I expression, similar to that of a known MHC-I negative tumor type MYCN-amplified neuroblastoma. Exome sequencing of RMS tumors showed no evidence of loss-of-function alterations in essential antigen processing and presentation (APP) pathway genes. Gene expression profiling by RNAseq and western blotting, however, revealed an absence of core APP genes in many RMS tumors and cell lines. We uncovered that during a specific window of embryonic myogenesis, APP is suppressed and that RMS tumors mimicking this state of development show the deepest loss of APP. Multiple classes of epigenetic drugs were evaluated to rescue APP in RMS. Treatment of RMS using a DNA demethylating agent or HDAC inhibitor exhibited the strongest MHC-I restoration across RMS models with no changes observed using LSD1 or EZH2 inhibitors. As a proof-of-concept, we demonstrate that targeting the RMS antigen PRAME using TCR therapy is enhanced by the inclusion of epigenetic therapy. Conclusions and Significance: We revealed that epigenetic suppression of APP is a new hallmark of RMS and normal embryonic skeletal muscle development. Pharmacological reversal of APP pathway downregulation could be achieved using drugs targeting DNA methylation or class I/IV HDACs, and enhanced T cell cytotoxicity against RMS when used in combination. Overall, these findings may explain the historical lack of RMS responsiveness to immunotherapy and unlocks new immunotherapy strategies against RMS that rely on targeting MHC-I antigens. Citation Format: David Milewski, Hsien-Chao Chou, Vineela Gangalapudi, Meijie Tian, Yong Kim, Young Song, Jun Wei, Javed Khan. Suppression of antigen presentation in pediatric rhabdomyosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB403.

506 Deep Learning-Based Image-to-Image Translation to Identify Macrophage Infiltration in High-Grade Glioma Using Label-Free Stimulated Raman Histology

INTRODUCTION: 5-Aminolevulinic acid (5-ALA) is the most widely used fluorophore in image-guided glioma surgery, and previous work at our institution demonstrated that 5-ALA highlights tumor-associated macrophages (TAMs) in two-photon microscopy images of brain tumor tissue. Using a unique, paired dataset of stimulated Raman histology (SRH) and two-photon images that share one-to-one spatial resolution, we propose a deep-learning approach to identify macrophages from intraoperative SRH images without requiring fluorescent labels. METHODS: We compiled a dataset of 9,554 non-overlapping, 300-by-300 pixel fields of view from paired SRH and two-photon images representing 40 cases of high-grade glioma. A deep generative adversarial network (pix2pix), consisting of a U-Net generator and PatchGAN discriminator, was trained to generate synthetic two-photon images from each SRH patch. The model was trained for 200 epochs, and similarity between the synthetic and real distributions was assessed qualitatively while training and quantitatively using FrÉchet inception distance (FID). RESULTS: Our model was successfully trained to generate synthetic two-photon images nearly indistinguishable from ground truth examples. The FID between a held-out test set of real and synthetic images was 8.58, compared to a mean FID of 9.26 ± 0.24 between randomly sampled sets of real images. Our model consistently inferred the location of brightly fluorescing TAMs in held-out, previously unseen test images. CONCLUSIONS: We used deep learning to visualize TAMs in label-free SRH images. Analysis of TAM infiltration has the potential to identify patients most likely to benefit from immunotherapy clinical trials. Ongoing work will leverage additional deep-learning approaches to automatically identify and quantify TAM infiltration with SRH, enabling rapid patient-specific analysis of the glioma immune microenvironment.

CD155-TIGIT Axis as a Therapeutic Target for Cancer Immunotherapy.

Immune checkpoint inhibitors (ICIs) have shown unprecedented efficacy in treating many advanced cancers. Although FDA-approved ICIs have shown promising efficacy in treating many advanced cancers, their application is greatly limited by the low response rate, immune-related adverse events (irAE), and drug resistance. Developing novel ICIs holds great promise to improve the survival and prognosis of advanced cancer patients. T-Cell immunoglobulin and ITIM domain (TIGIT) is an inhibitory receptor expressed on T cells, natural killer (NK) cells, and T regulatory cells. Increasing reports have shown that the disrupting CD155-TIGIT axis could activate the immune system and restore antitumor immune response. This review briefly summarized the role of TIGIT in tumor immune escape and targeting CD155-TIGIT axis drugs in preclinical and clinical trials for cancer immunotherapy.

The opportunities and challenges of the disease-modifying immunotherapy for type 1 diabetes: A systematic review and meta-analysis

There are multiple disease-modifying immunotherapies showing the potential of preventing or delaying the progression of type 1 diabetes (T1D). We designed and performed this systematic review and meta-analysis to gain an overview of what a role immunotherapy plays in the treatment of T1D. We searched PubMed, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) from inception to December 2023. We included clinical trials of immunotherapy conducted in patients with T1D that reported the incidence of hypoglycemia or changes from baseline in at least one of following outcomes: 2 h and 4 h mixed-meal-stimulated C-peptide area under the curve (AUC), fasting C-peptide, daily insulin dosage, glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG). The results were computed as the weighted mean differences (WMDs) or odds ratios (ORs) and 95% confidence intervals (CIs) in random-effect model. In all, 34 clinical trials were included. When compared with control groups, 2 h C-peptide AUC was marginally higher in patient treated with nonantigen-based immunotherapies (WMD, 0.04nmol/L, 95% CI, 0.00–0.09 nmol/L, P=0.05), which was mainly driven by the effects of T cell-targeted therapy. A greater preservation in 4 h C-peptide AUC was observed in patients with nonantigen-based immunotherapies (WMD, 0.10nmol/L, 95% CI, 0.04–0.16 nmol/L, P=0.0007), which was mainly driven by the effects of tumor necrosis factor α (TNF-α) inhibitor and T cell-targeted therapy. After excluding small-sample trials, less daily insulin dosage was observed in patient treated with nonantigen-based immunotherapies when compared with control groups (WMD, −0.07units/kg/day, 95% CI, −0.11 to −0.03units/kg/day, P=0.0004). The use of antigen-based immunotherapies was also associated with a lower daily insulin dosage versus control groups (WMD, −0.11units/kg/day, 95% CI, −0.23 to −0.00units/kg/day, P=0.05). However, changes of HbA1c or FPG were comparable between nonantigen-based immunotherapies or antigen-based immunotherapies and control groups. The risk of hypoglycemia was not increased in patients treated with nonantigen-based immunotherapies or patients treated with antigen-based immunotherapies when compared with control groups. In conclusion, nonantigen-based immunotherapies were associated with a preservation of 2 h and 4 h C-peptide AUC in patients with T1D when compared with the controls, which was mainly driven by the effects of TNF-a inhibitor and T cell-targeted therapy. Both nonantigen-based immunotherapies and antigen-based immunotherapies tended to reduce the daily insulin dosage in patients with T1D when compared with the controls. However, they did not contribute to a substantial improvement in HbA1c or FPG. Both nonantigen-based immunotherapies and antigen-based immunotherapies were well tolerated with not increased risk of hypoglycemia in patients with T1D.

Abstract 3755: Impact of antigen retrieval and other parameters on detection of FGFR2b in various solid tumors on a BenchMark ULTRA automated staining platform: Development of a new IHC assay for FGFR2b

Abstract Background: Fibroblast growth factor receptor 2b (FGFR2b) is part of the FGFR transmembrane tyrosine kinase receptor family. This protein has gained more attention in recent years as a tumor progression biomarker in numerous solid tumors. The detection of FGFR2b by immunohistochemistry (IHC) has been used recently in immunotherapy clinical trials with multiple tumor types. During IHC assay development on formalin-fixed paraffin embedded tissue, a thorough study of antigen retrieval through cell conditioning or enzyme incubation, primary antibody incubation, and detection parameters is critical to evaluate staining performance. Design: A study of an investigational anti-FGFR2b (FPR2-D) assay developed on a BenchMark ULTRA automated staining platform explored the effect of antigen retrieval and detection parameters on staining performance. VENTANA System Software was used to modify steps in the IHC staining protocol resulting in 42 permutations. Ten different solid tumors were stained with these modifications to the assay and subsequently evaluated for FGFR2b expression. Result:s The results demonstrate that the staining clarity, intensity, and dynamic range within tissue cases of FGFR2b staining can vary widely between different solid tumor types. While most permutations of the initial assay parameters had minimal impact on the staining performance and readability by a pathologist, the order of the enzyme treatment and the cell conditioning steps (in addition to increased incubation time of primary antibody) had a significant impact when comparing across different solid tumors. Detection of FGFR2b seemed to have a greater susceptibility by tumor type to the antigen retrieval process than has traditionally been observed in other biomarkers. Conclusion: The overall results of this investigation show the importance of an extensive parameter screening approach when evaluating new antibody targets and indication combinations in IHC to ensure optimal staining performance. This is critical to downstream testing and the potential use of new assays in the context of in vitro diagnostics. Citation Format: Autumn Sky Watson, Catherine Le, Anne M. Wertheimer, Spencer Escobedo, Lisa So, Bhavani Bagevalu Siddegowda, John D. Palting, Susan Marion, Steven P. Stratton, Birte Aggeler. Impact of antigen retrieval and other parameters on detection of FGFR2b in various solid tumors on a BenchMark ULTRA automated staining platform: Development of a new IHC assay for FGFR2b [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 3755.

Abstract 2312: Integrating spatial multi-omics data with spatial quantitative pharmacology (spQSP) model to simulate human neoadjuvant immunotherapy clinical trial of hepatocellular carcinoma

Abstract Human clinical trials provided tremendous insights to advance novel systemic therapies and to improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have demonstrated that new combination of immunotherapeutic regimens provide augmented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapies entirely in silico. To facilitate designing dosing regimens and selecting potential predictive biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at cellular and molecular scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. The model is first calibrated using clinical outcomes and measured immune cell densities based on recent neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib (NCT03299946). Then, we validate the results from the spQSP model by leveraging real-world spatial multi-omics data from the same clinical trial. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and Arginase 1 (Arg1) positive macrophages among non-responders while the reverse trend was observed for responders. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular and cellular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial. Citation Format: Shuming Zhang, Atul Deshpande, Babita K. Verma, Hanwen Wang, Haoyang Mi, Long Yuan, Won Jin Ho, Elizabeth M. Jaffee, Qingfeng Zhu, Robert A. Anders, Mark Yarchoan, Luciane T. Kagohara, Elana J. Fertig, Aleksander S. Popel. Integrating spatial multi-omics data with spatial quantitative pharmacology (spQSP) model to simulate human neoadjuvant immunotherapy clinical trial of 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 2312.

Abstract 3638: Pathologic endpoints as surrogates for survival endpoints in neoadjuvant immunotherapy clinical trials for non-small cell lung cancer

Abstract Background: The use of neoadjuvant immunotherapy alone or in combination has rapidly evolved in the last 5 years. There is significant debate as to whether pathologic complete response (pCR) or major pathologic response (MPR) can be considered a surrogate endpoint for survival. We therefore performed a systematic review and meta-analysis to evaluate the surrogacy of pCR and MPR for event free survival (EFS) in neoadjuvant clinical trials for early-stage NSCLC. Methods: We performed a systematic literature search on PubMed and reviewed abstracts of the most relevant international conferences until June 2023. Relevant information relative to response rates, Odds Ratios (ORs) of response, 2-years EFS rates and Hazard ratios (HRs) were retrieved from all eligible clinical trials and the association were analyzed. To evaluate patients level surrogacy the association between response rate and 2-year EFS was evaluated. In trials presenting EFS rates by achievement of pCR and MPR a forest plot with random effect model was also produced. For trial level surrogacy, ORs for pCR and MPR and HRs for EFS were retrieved and analyzed using a linear regression model weighted by sample size. R2 and linear regression slope were used respectively to estimate the proportion of variation in EFS effect explained by pCR effect and the magnitude of change in EFS effect as a function of the magnitude of change in pCR effect. The R2 with 95% CI were calculated by bootstrapping approach. Results: Seven RCTs were identified for a total of 2940 patients. At patient level, the R2 of pCR and MPR with 2-years EFS were 0.82 (0.66-0.94) and 0.81 (0.63-0.93), respectively, with slopes of 0.96 (0.68-1.19) and 0.64 (0.45-0.85), respectively. At trial level, R2 for association of OR for response and HR for EFS was 0.58 (0.00-0.97) and 0.61 (0.00-0.97) for pCR and MPR, respectively. Conclusions: While we were able to show a strong correlation between pCR and EFS, trial level surrogacy was not proven. The lack of surrogacy can be related to the high heterogeneity among clinical trials and the still scarce number of data available. More research to reduce the heterogeneity (e.g., pathological evaluation, surgery, and staging harmonization) must be done to introduce pCR as a primary measure to predict EFS. Citation Format: Robert B. Cameron, Jacobi B. Hines, Alessandra Esposito, Christine Bestvina, Marina C. Garassino, Valter Torri, Rajat Thawani. Pathologic endpoints as surrogates for survival endpoints in neoadjuvant immunotherapy clinical trials for 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 3638.

Abstract IA005: Regional combating of immunosuppressive forces in the ovarian tumor microenvironment

Abstract IA005: Regional combating of immunosuppressive forces in the ovarian tumor microenvironment

Perioperative immunotherapy: the main clinical treatment for resectable non-small cell lung cancer.

Phase 3 clinical trials of perioperative immunotherapy for resectable non-small cell lung cancer (NSCLC): In recent years, immunotherapy for NSCLC is not only limited to advanced disease, but also has shown gratifying efficacy for early resectable NSCLC. With the publication of the results of several phase 3 clinical trials, perioperative immunotherapy will become one of the main treatment modalities for resectable NSCLC.

Tumor beta2-microglobulin and HLA-A expression is increased by immunotherapy and can predict response to CIT in association with other biomarkers.

Downregulation of MHC class I expression and/or defects in the antigen presentation pathways are commonly reported in human cancers. Numerous studies previously have explored extensively the molecular mechanisms that underlie HLA-class I and Beta2-Microglobulin (B2M) downregulation. However, the techniques presently available to detect expression of MHC class I proteins lack the robustness, specificity and sensitivity needed for systematic integration and analysis in clinical trials. Furthermore, the dynamics of HLA-class I and B2M expression have not been comprehensively studied as a potential biomarker for immunotherapy. Using novel, validated, immunohistochemistry (IHC)-based methods for quantifying B2M and HLA-A in tumor samples from diverse cancer types, we have determined loss of B2M and HLA-A proteins in 336 archived, primary specimens and 329 biopsies from metastatic patients collected during Roche-sponsored Phase 1 clinical trials investigating novel immunotherapy candidates as monotherapy or in combination with CPI. Up to 56% of cases with B2M or HLA-A loss were noted in the investigated tumor types. The frequency of loss was dependent on indication and stage of disease and revealed heterogeneous expression patterns across patients. B2M and HLA-A loss was increased in metastatic lesions compared to primary tumors, indicating selection of MHC class I low clones in metastatic and refractory tumor cells. High on-treatment B2M expression correlated with successful clinical outcome (RECIST), while high baseline B2M did not. A treatment-induced increase of B2M expression was noted in most of the patients with low B2M levels at baseline. The triple biomarker combination of B2M, CD8 and PDL1 strongly improved response prediction to cancer immunotherapy. Our results indicate that B2M and HLA-A loss occurs frequently in tumors and is reversed in most instances following immunotherapy which supports the conclusion that MHC class I loss is not the dominant resistance mechanism to CPI treatment. This investigation reveals a highly dynamic expression of HLA-A and B2M in tumors affected by indication, metastatic status, immunophenotype and immunotherapy treatment. Baseline expression levels of B2M on tumors may be of utility as a constituent of a biomarker panel used for selecting patients for immunotherapy clinical trials.

Long-Term Follow-up after IMPACT Peanut Oral Immunotherapy Clinical Trial

Long-Term Follow-up after IMPACT Peanut Oral Immunotherapy Clinical Trial

Miscellaneous Care: Bridging the In-between of Translational Medicine

Based on fieldwork in China on oncology clinical trials for immunotherapy which involve foreign patients as trial subjects, in this paper, I illustrate how the source material needed for the trial (the patient’s T cells) is a scarce biological substance that needs to be generated and crafted though the efforts of patients, doctors, nurses, or lab technicians, as well as other staff: chauffeurs, translators, and foreign patient coordinators. Among the varied tasks done by these support staff, I focus on the intersubjective practices they perform to respond to the needs at the bench and the bedside, what I call “miscellaneous care.” I argue that although invisible and neglected, these practices are constitutive of the techno-scientific assemblage of translational medicine. Miscellaneous care suggests that the capitalization of life itself does not only refer to the capitalization of biological substances and biological processes, it in fact includes capitalization of intersubjective processes, such as responding to the presence of others.