Immunotherapy-treated Patients Research Articles

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Clinical Outcomes Among Immunotherapy-Treated Patients With Primary Cardiac Soft Tissue Sarcomas: A Multicenter Retrospective Study

BackgroundPrimary cardiac soft tissue sarcomas (CSTS) affect young adults, with dismal outcomes. ObjectivesThe aim of this study was to investigate the clinical outcomes of patients with CSTS receiving immune checkpoint inhibitors (ICIs). MethodsA retrospective, multi-institutional cohort study was conducted among patients with CSTS between 2015 and 2022. The patients were treated with ICI-based regimens. The Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS). Objective response rates were determined according to Response Evaluation Criteria in Solid Tumors version 1.1. Treatment-related adverse events were graded per the Common Terminology Criteria for Adverse Events version 5.0. ResultsAmong 24 patients with CSTS, 17 (70.8%) were White, and 13 (54.2%) were male. Eight patients (33.3%) had angiosarcoma. At the time of ICI treatment, 18 patients (75.0%) had metastatic CSTS, and 4 (16.7%) had locally advanced disease. ICIs were administered as the first-line therapy in 6 patients (25.0%) and as the second-line therapy or beyond in 18 patients (75.0%). For the 18 patients with available response data, objective response rate was 11.1% (n = 2 of 18). The median PFS and median OS in advanced and metastatic CSTS (n = 22) were 5.7 months (95% CI: 2.8-13.3 months) and 14.9 months (95% CI: 5.7-23.7 months), respectively. The median PFS and OS were significantly shorter in patients with cardiac angiosarcomas than in those with nonangiosarcoma CSTS: median PFS was 1.7 vs 11 months, respectively (P < 0.0001), and median OS was 3.0 vs 24.0 months, respectively (P = 0.008). Any grade treatment-related adverse events occurred exclusively in the 15 patients with nonangiosarcoma CSTS (n = 7 [46.7%]), of which 6 (40.0%) were grade ≥3. ConclusionsAlthough ICIs demonstrate modest activity in CSTS, durable benefit was observed in a subset of patients with nonangiosarcoma, albeit with higher toxicity.

The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study.

Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.

Abstract A015: Prediction of immunotherapy response using mutations to cancer protein assemblies

Abstract While immune checkpoint inhibitors (ICI) have substantially advanced cancer treatment, favorable responses are limited to a subset of patients and are not strongly predicted by histology or overall mutation burden. Here, we factor mutational burden across a landscape of known and putative multimeric protein assemblies, yielding Assembly Mutation Burden (AMB) scores for each patient. Predictive modeling identifies 14 assemblies in which mutation is associated with ICI response in 555 bladder cancer (BLCA) and non-small-cell lung cancer (NSCLC) patients, outperforming predictions based on genome-wide mutation burden alone. The assemblies capture immunogenic hallmarks and accurately predict treatment outcomes in a validation cohort of 68 immunotherapy-treated NSCLC patients. We explore a predictive assembly of 50 immunoregulatory complex proteins, in which we show CRISPR/Cas9 genetic disruptions influence ICI-treatment outcomes in mice. This study provides a roadmap for using knowledge of tumor cell biology to interpret the immunogenome. Citation Format: JungHo Kong, Robin Bachelder, Xiaoyu Zhao, Sungjoon Park, Akshat Singhal, Hannah Carter, Trey Ideker. Prediction of immunotherapy response using mutations to cancer protein assemblies [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A015.

Effects of nutritional indices and inflammatory parameters on patients received immunotherapy for non-small cell lung cancer

ObjectiveThis research explored the relationship between a patient's nutritional state and inflammatory markers and the prognosis of their non-small cell lung cancer (NSCLC) treatment while receiving a combination of chemotherapy and immunotherapy. MethodThis retrospective and single-center analysis included NSCLC patients who received a combination of chemotherapy and immunotherapy at the Department of Oncology at Shanghai Lung Hospital. Patients were categorized based on malnutrition, sarcopenia, sarcopenic obesity, and advanced-lung-cancer-inflammation-index (ALI) scores after collecting nutritional and inflammatory indices. Kaplan-Meier and the Cox models were utilized to analyze survival. ResultsThere was a significant correlation between malnutrition, sarcopenia, sarcopenic obesity, and low ALI scores with lower overall survival (OS) and progression-free survival (PFS) (p < 0.05). Low ALI score and malnutrition were independent factors influencing patient survival in terms of both OS and PFS (p < 0.01). ConclusionThe nutritional and inflammatory indices of immunotherapy-treated NSCLC patients substantially affect their prognosis. Assessing these variables could aid in optimizing treatment strategies and improving patient outcomes. Additional research is required to comprehend the intricate relationship between nutrition, inflammation, and cancer progression and to develop individualized therapies.

Cemiplimab as First Line Therapy in Advanced Penile Squamous Cell Carcinoma: A Real-World Experience.

In the treatment of cancer, immune checkpoint inhibitors (ICIs) have demonstrated significantly greater effectiveness compared to conventional cytotoxic or platinum-based chemotherapies. To assess the efficacy of ICI's in penile squamous cell carcinoma (pSCC) we performed a retrospective observational study. We reviewed electronic medical records of patients with penile squamous cell carcinoma (SCC), diagnosed between January 2020 and February 2023. Nine patients were screened, of whom three were ineligible for chemotherapy and received immunotherapy, cemiplimab, in a first-line setting. Each of the three immunotherapy-treated patients achieved almost a complete response (CR) after only a few cycles of therapy. The first patient had cerebral arteritis during treatment and received a high-dose steroid treatment with resolution of the symptoms of arteritis. After tapering down the steroids dose, the patient continued cemiplimab without further toxicity. The other two patients did not have any toxic side effects of the treatment. To the best of our knowledge, this is the first real world report of near CR with cemiplimab as a first-line treatment in penile SCC.

Antibody-Negative Autoimmune Encephalitis: A Single-Center Retrospective Analysis.

Autoimmune encephalitis (AE) refers to a heterogenous group of inflammatory CNS diseases. Subgroups with specified neural autoantibodies are more homogeneous in presentation, trigger factors, outcome, and response to therapy. However, a considerable fraction of patients has AE features but does not harbor detectable autoantibodies and is referred to as antibody-negative AE. Our aim was to describe clinical features, trigger factors, treatments, and outcome of a cohort of comprehensively tested antibody-negative AE patients. This retrospective monocentric study recruited adult patients whose serum and/or CSF was sent to our tertiary center for neural antibody testing between 2011 and 2020, who entered the diagnostic algorithm as possible antibody-negative AE and had the following: (1) probable antibody-negative AE, definite antibody-negative acute disseminated encephalomyelitis (ADEM), or definite autoimmune limbic encephalitis (LE) according to diagnostic criteria; (2) available data on MRI of the brain, CSF, and EEG; and (3) stored serum and/or CSF samples. These samples were reanalyzed using a comprehensive combination of cell-based and tissue-based assays. Of 2,250 patients tested, 33 (1.5%) were classified as possible antibody-negative AE. Of these, 5 were found to have antibodies by comprehensive testing, 5 fulfilled the criteria of probable AE (3F:2M, median age 67, range 42-67), 4 of definite autoimmune LE (2F:2M, median age 45.5, range 27-60 years), one of definite antibody-negative ADEM, 2 of Hashimoto encephalopathy, one had no samples available for additional testing, and 15 had no further categorization. Of 10 probable/definite AE/LE/ADEM, one had a malignancy and none of them received an alternative diagnosis until the end of follow-up (median 18 months). In total, 80% (8/10) of patients received immunotherapy including corticosteroids, and 6/10 (60%) patients received rituximab, azathioprine, cyclophosphamide, plasma exchange, or IV immunoglobulins. Five (50%) patients improved, one (10%) stabilized, one (10%) worsened, and 3 (30%) died. All deaths were considered to be related to encephalitis. We did not observe differences of immunotherapy-treated patients in likelihood of improvement with or without nonsteroidal immunotherapy (with 2/6, without 1/2). Antibody-negative AE should be diagnosed only after comprehensive testing. Diagnostic effort is important because many patients benefit from immunotherapy and some have malignancies.

High levels of PD-L1 on platelets of NSCLC patients contributes to the pharmacological activity of Atezolizumab

Several studies have associated platelets (PLTs) to NSCLC prognosis. To understand the role of PLTs in immunotherapy-treated patients, we used blood samples of NSCLC patients at different TNM stage. We found that PLTs count and the expression of PD-L1 (pPD-L1) were significantly higher in NSCLC patients at Stage IV than Stage I-III and healthy subjects. The presence of high pPD-L1 was associated to upregulated genes for the extracellular matrix organization and tumor immunosuppression. When patients’ survival was correlated to the levels of pPD-L1, longer survival rate was observed, but not when progression disease occurred. The in vitro stimulation of pPD-L1 with Atezolizumab induced CXCL4 release, accompanied by higher levels of TGFβ at the time of drug resistance when the levels of CD16, CD32 and CD64 significantly increased. Leiden-clustering method defined the phenotype of PLTs which showed that the ezrin-radixin-moesin (ERM) family proteins, underlying the PD-L1 signalosome, were involved in high pPD-L1 and higher survival rate. These data imply that Stage IV NSCLC patients characterized by high pPD-L1 are associated with longer progression-free survival rate because the blockade of pPD-L1 by Atezolizumab avoids the exacerbation of a T cell-mediated immune-suppressive environment. pPD-L1 could be an easy-to-use clinical approach to predict ICI responsiveness.

The prognostic Value of Thyroid Hormone Levels in Immunotherapy-Treated Patients With Metastatic Urothelial Carcinoma.

A low fT3/fT4 ratio has been associated with a poorer prognosis in patients treated for different solid malignancies. However, the prognostic role of baseline thyroid function in patients with metastatic urothelial carcinoma (mUC) has not yet been established. We analyzed 72 consecutive immunotherapy-treated patients with mUC from a single institution. We recorded clinical data, baseline blood test results, and oncological outcomes. We stratified patients into three groups according to the fT3/fT4 ratio value and analyzed differences in progression-free survival (PFS), overall survival (OS), and radiological response in the three groups. We also conducted univariate and multivariate analyses to identify prognostic factors for PFS and OS. The median PFS in the low, intermediate, and high fT3/fT4 ratio groups was 2.2, 4.1, and 8.2 months, respectively (P < 0.01). The median OS in the low, intermediate, and high fT3/fT4 groups was 3.6, 10.3, and 19.1 months, respectively (P < .01). The low fT3/fT4 ratio maintained its prognostic role independently of other prognostic factors. Patients with a high fT3/fT4 ratio had an increased radiological response. Thyroid hormone impairment, as measured by the fT3/fT4 ratio, is a strong prognostic factor in patients treated with immunotherapy for urothelial carcinoma.

Effectiveness of first-line anticancer treatment may predict treatment response in further lines in stage III/IV patients with non-small cell lung cancer

PurposeThe aim of our study was to evaluate if therapeutic success in the first-line of anticancer treatments in patients with NSCLC may predict treatment success in the following lines.MethodsWe analyzed the data of patients with NSCLC stage III/IV from the TULUNG registry separately for chemotherapy, TKIs, ALK inhibitors, and immunotherapy in the first line during the years 2011–2019. „Succesful treatment “ was defined as PFS ≥ 6 months, a „good responder “ was a patient with ˃50% of „successful treatment “ lines. Treatment responses were analyzed separately for each drug group. Descriptive statistics, Fisher exact test, Pearson Chi-Squared test, log-rank test, and univariate/multivariate logistic regression models were used.ResultsThe first-line TKI therapy was successful in 66.2%, while good responders accounted for 50.7% of the cohort and their rates were similar for all types of TKIs. First-line platinum-based chemotherapy was successful in 43.1% and 48.6% for combinations with pemetrexed and bevacizumab, respectively. Good responders accounted for 29.5% and 25.9%, respectively. In the group of ALK inhibitors, we observed treatment success in 52.3% of cases, while alectinib showed the highest effectiveness (up to 70%). Good responders constituted 50% of the group. In the first-line immunotherapy group, survival benefit was observed in 52.3%, and good responders constituted 52.3% of the cohort.ConclusionWe concluded that the treatment success in first-line therapies in patients with NSCLC may predict survival benefits in the subsequent lines, particularly in EGFR- or ALK-positive disease and immunotherapy-treated patients.

A novel [89Zr]-anti-PD-1-PET-CT to assess response to PD-1/PD-L1 blockade in lung cancer.

Harnessing the anti-tumor immune system response by targeting the program cell death protein (PD-1) and program cell death ligand protein (PD-L1) axis has been a major breakthrough in non-small cell lung cancer (NSCLC) therapy. Nonetheless, conventional imaging tools cannot accurately assess response in immunotherapy-treated patients. Using a lung cancer syngeneic mouse model responder to immunotherapy, we aimed to demonstrate that [89Zr]-anti-PD-1 immuno-PET is a safe and feasible imaging modality to assess the response to PD-1/PD-L1 blockade in NSCLC. A syngeneic mouse model responder to anti-PD-1 therapy was used. Tumor growth and response to PD-1 blockade were monitored by conventional 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG) PET scans. Additionally, tumor lymphocyte infiltration was analyzed by the use of an [89Zr]-labeled anti-PD-1 antibody and measured as 89Zr tumor uptake. Conventional [18F]-FDG-PET scans failed to detect the antitumor activity exerted by anti-PD-1 therapy. However, [89Zr]-anti-PD-1 uptake was substantially higher in mice that responded to PD-1 blockade. The analysis of tumor-infiltrating immune cell populations and interleukins demonstrated an increased anti-tumor effect elicited by activation of effector immune cells in PD-1-responder mice. Interestingly, a positive correlation between [89Zr]-anti-PD-1 uptake and the proportion of tumor-infiltrating lymphocytes (TILs) was found (Cor = 0.8; p = 0.001). Our data may support the clinical implementation of immuno-PET as a promising novel imaging tool to predict and assess the response of PD-1/PD-L1 inhibitors in patients with NSCLC.

Relationship between tumor microenvironment (TME)-based histomic TGFβ signature (TGFBs), stromal fibroblast recruitment, and exclusion of immune cells as immunotherapy resistance mechanisms.

2585 Background: TGF-beta activates fibroblasts within the TME and may be associated with poor response to immunotherapy.However, objective and reliable assessment of the TGFBs by gene expression profiling (GEP) is challenging. Using H&amp;E whole-slide images (WSI) from The Cancer Genome Atlas (TCGA), we developed an artificial intelligence (AI) model to predict a normalized TGFBs, applying it to real-world datasets (RWDs) of advanced solid tumor patients treated with immunotherapy. Methods: The TGFBs was defined using the mean GEP values of the 'Hallmark TGF-beta signaling' gene set. H&amp;E WSIs of 23 carcinoma types from TCGA (n=6,945) were used to discover the association of the TGFBs with various cell types, including fibroblasts and tumor-infiltrating lymphocytes (TILs) detected by an AI model, Lunit SCOPE IO (AI-1). Another custom AI model (AI-2) to predict TGFBs was developed by integrating self-supervised and supervised features and other semantic content extracted by AI-1 with multilayered perceptron classifiers. TGFBs prediction scores from the AI-2 were applied to 5 different RWDs containing 1,792 immunotherapy-treated patients (&gt; 16 primary tumor types). The immune-excluded phenotype (IEP) was defined by high stromal TIL and low intratumoral TIL density. Results: In the TCGA dataset, TGFBs and fibroblast density detected by AI-1 were closely correlated (R=0.19, p&lt;0.001) compared to other cell types, with both highest in pancreatic adenocarcinoma (PAC)(median [interquartile range, IQR] 0.73 [0.66-0.80], 1306 [985-1588]/mm2, respectively) and CMS4 colorectal cancer (0.61 [0.54-0.67], 868 [582-1128]/mm2) compared to other cancers (0.57 [0.47-0.66], 577 [295-890]/mm2). The AI-2 predicted TGFBs with area-under-the-curve of 0.781 on cross-validation within the TCGA dataset. The TGFBs of immunotherapy-treated RWDs were inferred from their corresponding H&amp;E WSI, with scores comparable to those from the TCGA (0.42 [0.16-0.60] vs 0.58 [0.48-0.67]), with PAC having the highest TGFBs (0.65 [0.45-0.77]) in the RWD as well. Interestingly, TGFBs inferred scores and fibroblast density were significantly correlated (R=0.7, p&lt;0.001) in the RWD and also significantly correlated with the IEP (0.52 [0.30-0.66]) compared to other immune phenotypes (0.42 [0.16-0.60], p&lt;0.001). In the RWD, patients with inferred TGFBs in the upper 75% quartile had a poor clinical response to immunotherapy (objective response rate: 15.1% vs 20.7%, median progression-free survival 3.0 m vs 4.0 m, hazard ratio 1.21, 95% confidence interval 1.07-1.36, p=0.002). Conclusions: TGFBs is closely correlated with fibroblast density within the TME and may confer resistance to immunotherapy. Unique morphologic features of the TGFBs can be captured by AI, enabling scalable application to various datasets for clinical and translational research.

A subset of neutrophils as a predictive biomarker for immunotherapy response in patients with non–small-cell lung cancer and melanoma.

2557 Background: Despite the remarkable success of immunotherapy, only ~20-40% of cancer patients display an extended durable response. Pre-existing biomarkers for immunotherapy outcome are significantly limited in their predictive power, e.g., PDL-1 expression by tumor cells (AUC ~ 0.6-0.75). This requires additional efforts to search for new approaches to discover new biomarkers in immune-oncology. While mice are the most widely used and cost-effective model to study human disease, translating preclinical biomarkers into clinical practice faces significant obstacles – in part due to the lack of diverse or appropriate models. In this study we have taken a holistic approach to search for new biomarkers for immunotherapy outcomes, with the aim to improve translatability of the findings and their validity in cancer patients. Methods: Several pre-clinical models were used, each capturing one possible mechanistic aspect of immunotherapy response, such as tumor- and host-dependency, in order to reflect the variability seen in human cancers. These models have been analyzed for myeloid cell composition within tumors and peripheral blood using high throughput techniques such as single-cell RNA sequencing and mass cytometry, followed by rigorous bioinformatic tools. Peripheral blood mononuclear cells (PBMCs) were obtained from non-small cell lung cancer (NSCLC, n = 34) and melanoma (n = 38) patients prior to immune-checkpoint inhibitor backbone therapy as monotherapy or in combination with chemotherapy. These results were validated on additional human bulk mRNA datasets (n = 417). Results: Using the preclinical models, we identified interferon-stimulated, Ly6Ehi neutrophils as a pre-treatment, blood-borne biomarker for anti-PD1 response. These results were validated in PBMCs of advanced metastatic NSCLC and malignant melanoma patients predominately treated with immune checkpoint inhibitor-based therapy, with a high degree of accuracy (AUC ~ 0.9). These results were also validated in independent cohorts of immunotherapy-treated patients with other cancer types obtained from bulk mRNA datasets including renal cell carcinoma (n = 109), urothelial carcinoma (n = 228) and additional melanoma patients (n = 80). Functional studies revealed that Ly6Ehi neutrophils sensitize otherwise resistant tumors to anti-PD1 therapy, in part by directly activating cytotoxic T cells and contributing to tumor cell killing, while operate upstream of T cells in the immunotherapy response. Conclusions: Our study demonstrates a pragmatic approach to search for new, clinically relevant and functionally active cellular biomarkers for immunotherapy outcomes in humans. This study paves the way for a conceptual framework to advance the field of immuno-oncology by utilizing this approach for successful clinical trials.

Cross-platform comparison of immune signatures in immunotherapy-treated patients with advanced melanoma using a rank-based scoring approach

BackgroundGene expression profiling is increasingly being utilised as a diagnostic, prognostic and predictive tool for managing cancer patients. Single-sample scoring approach has been developed to alleviate instability of signature scores due to variations from sample composition. However, it is a challenge to achieve comparable signature scores across different expressional platforms.MethodsThe pre-treatment biopsies from a total of 158 patients, who have received single-agent anti-PD-1 (n = 84) or anti-PD-1 + anti-CTLA-4 therapy (n = 74), were performed using NanoString PanCancer IO360 Panel. Multiple immune-related signature scores were measured from a single-sample rank-based scoring approach, singscore. We assessed the reproducibility and the performance in reporting immune profile of singscore based on NanoString assay in advance melanoma. To conduct cross-platform analyses, singscores between the immune profiles of NanoString assay and the previous orthogonal whole transcriptome sequencing (WTS) data were compared through linear regression and cross-platform prediction.Resultssingscore-derived signature scores reported significantly high scores in responders in multiple PD-1, MHC-1-, CD8 T-cell-, antigen presentation-, cytokine- and chemokine-related signatures. We found that singscore provided stable and reproducible signature scores among the repeats in different batches and cross-sample normalisations. The cross-platform comparisons confirmed that singscores derived via NanoString and WTS were comparable. When singscore of WTS generated by the overlapping genes to the NanoString gene set, the signatures generated highly correlated cross-platform scores (Spearman correlation interquartile range (IQR) [0.88, 0.92] and r2 IQR [0.77, 0.81]) and better prediction on cross-platform response (AUC = 86.3%). The model suggested that Tumour Inflammation Signature (TIS) and Personalised Immunotherapy Platform (PIP) PD-1 are informative signatures for predicting immunotherapy-response outcomes in advanced melanoma patients treated with anti-PD-1-based therapies.ConclusionsOverall, the outcome of this study confirms that singscore based on NanoString data is a feasible approach to produce reliable signature scores for determining patients’ immune profiles and the potential clinical utility in biomarker implementation, as well as to conduct cross-platform comparisons, such as WTS.

Abstract 2037: Tracking the emergence of immunotherapy resistance in lung cancer metastases

Abstract Metastatic non-small cell lung cancer (NSCLC) results from a complex evolutionary process in which cancer cells migrate from a primary tumour to a new anatomical site. Immunotherapy is frequently used to treat NSCLC, but drug resistance is frequent (&amp;gt;50%) and metastasis remains the most common cause of death. Recent studies of primary and metastatic tumours suggest that anti-cancer treatments can impact cancer evolutionary dynamics by applying selective pressures that promote the development of treatment resistance in genetically distinct subpopulations of cancer cells, or tumour clones. However, the relationship between metastatic progression and the development of resistance to immunotherapy has not been fully explored. In particular, whether metastases become resistant to immunotherapy as a result of the migration of resistant cells or whether distinct metastatic sites converge in parallel toward an immunotherapy resistance phenotype irrespective of cell migration, is not known. In this work, we investigate the role of metastatic cell migration in the development of immunotherapy resistance using DNA sequencing data from primary and metastatic tumour samples from twelve immunotherapy-treated patients with NSCLC co-recruited to the national TRACERx and PEACE studies. Using computational approaches, we identified distinct tumour clones in primary and metastatic tumours and used these to reconstruct tumour phylogenies for each patient. By evaluating tumour clonal dynamics in relation to treatment, we classified tumour clones as either sensitive or resistant to immunotherapy. We reconstructed the metastatic migration histories of these clones using existing computational methods based on somatic single nucleotide variants. Our results suggest that heterogeneous mechanisms of immune evasion develop in parallel at distinct metastatic sites. The ongoing analysis of the genetic changes that distinguish resistant from sensitive clones, and migrating from non-migrating clones, will provide further detail regarding the impact of metastatic migrations on immunotherapy resistance. Using this approach to map the evolutionary history of metastatic NSCLC, we provide insight into the mechanism by which immunotherapy resistance develops at distinct metastatic sites. Citation Format: Sonya Hessey, Ariana Huebner, Oriol Pich, Abi Bunkum, Wing Liu, David A. Moore, Cristina Naceur-Lombardelli, Selvaraju Veeriah, Sophie Ward, Roberto Salgado, Nicholas McGranahan, Simone Zaccaria, Charles Swanton, Mariam Jamal-Hanjani. Tracking the emergence of immunotherapy resistance in lung cancer metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2037.

Advanced lung cancer inflammation index predicts survival outcomes of hepatocellular carcinoma patients receiving immunotherapy.

We evaluate the predictive significance of the Advanced Lung Cancer Inflammation Index (ALI) in patients with advanced hepatocellular carcinoma (HCC) following therapy with immune checkpoint drugs. In 2018-2020, 98 patients with advanced hepatocellular carcinoma who were treated with immune checkpoint inhibitors at our hospital were compiled. Using the receiver operating characteristic (ROC) curve, the appropriate cut-off point for ALI was determined. Kaplan-Meier analysis, the Cox proportional hazards model, and Nomogram plots highlighted the relationship between ALI and overall survival (OS). The model was validated using calibration plots, receiver operating characteristic curves (ROC), and decision curve analysis (DCA), which was performed on 52 patient sets by external validation. The AUC for ALI was 0.663. The best cutoff value was 36.5, with a median overall survival (OS) of 473 days for patients with ALI≤ 36.5 and 611 days for those with ALI > 36.5. Univariate analysis revealed that the presence or absence of local treatment, alpha-fetoprotein (AFP), and ALI were prognostic factors; LASSO regression analysis identified four candidate variables. Multifactorial COX analysis revealed that high ALI was an independent prognostic factor for overall survival in both groups (HR = 0.411; 95% CI: 0.244-0.651; P<0.001). In addition, the Nomogram model that included ALI was able to predict the success of immunotherapy in patients with advanced liver cancer more accurately. ALI is a novel prognostic marker in immunotherapy-treated patients with advanced hepatocellular cancer.

The influence of metastatic patterns and tumor load on therapeutic efficacy of immunotherapy in patients with metastatic melanoma as determined by quantitative PET-parameters using [ 18 F]-fluorodeoxyglucose PET/computed tomography.

The introduction of immunotherapy was a revolution in the treatment of metastatic melanoma. Nevertheless, there are only few clinical parameters to predict response to immunotherapy. The purpose of this study was to identify metastatic patterns that can predict response by using noninvasive 18 F-FDG PET/CT imaging. In 93 immunotherapy-treated patients, total metabolic tumor volume (MTV) was measured before and after treatment. The differences were compared to quantify therapy response. Patients were divided into seven subgroups regarding the affected organ systems. The results as well as clinical factors were evaluated in multivariate analyses. No subgroup of metastatic patterns had a significant difference in response rates, but with a trend towards poorer response regarding osseous and hepatic metastases. Osseous metastases presented with significant lower disease-specific survival (DSS) ( P = 0.001). Sole lymph node metastases were the only subgroup with MTV reduction and with significant higher DSS (57.6 months; P = 0.033). Patients, who ever developed brain metastases, showed a high progression of MTV of 201 ml ( P = 0.583) and poor DSS of 49.7 months ( P = 0.077). Lower numbers of affected organs indicated significantly higher DSS (hazard ratio, 1.346; P = 0.006). Osseous metastases represented a negative predictive factor for response to immunotherapy and survival. Cerebral metastases, especially when nonresponsive to immunotherapy, predicted poor survival and high increase of MTV. A high number of affected organ systems was identified as a negative factor for response and survival. Patients with only lymph node metastases showed a better response and survival.

Whole-Exome Sequencing and cfDNA Analysis Uncover Genetic Determinants of Melanoma Therapy Response in a Real-World Setting

Although several studies have explored the molecular landscape of metastatic melanoma, the genetic determinants of therapy resistance are still largely unknown. Here, we aimed to determine the contribution of whole-exome sequencing and circulating free DNA (cfDNA) analysis in predicting response to therapy in a consecutive real-world cohort of 36 patients, undergoing fresh tissue biopsy and followed during treatment. Although the underpowered sample size limited statistical analysis, samples from non-responders had higher copy number variations and mutations in melanoma driver genes compared to responders in the BRAF V600+ subset. In the BRAF V600- subset, Tumor Mutational Burden (TMB) was twice that in responders vs. non-responders. Genomic layout revealed commonly known and novel potential intrinsic/acquired resistance driver gene variants. Among these, RAC1, FBXW7, GNAQ mutations, and BRAF/PTEN amplification/deletion were present in 42% and 67% of patients, respectively. Both Loss of Heterozygosity (LOH) load and tumor ploidy were inversely associated with TMB. In immunotherapy-treated patients, samples from responders showed higher TMB and lower LOH and were more frequently diploid compared to non-responders. Secondary germline testing and cfDNA analysis proved their efficacy in finding germline predisposing variants carriers (8.3%) and following dynamic changes during treatment as a surrogate of tissue biopsy, respectively.

Gut microbial signature in lung cancer patients highlights specific taxa as predictors for durable clinical benefit

We aimed to determine microbial signature linked with lung cancer (LC) diagnosis and to define taxa linked with durable clinical benefit (DCB) of advanced LC patients. Stool samples for microbial 16S amplicon sequencing and clinical data were collected from 75 LC patients (50 of which were treated with checkpoint inhibitors) and 31 matched healthy volunteers. We compared LC to healthy controls and patients with DCB to those without. LC patients had lower α-diversity and higher between-subject diversity. Random Forests model to differentiate LC cases from controls ROC-AUC was 0.74. Clostridiales, Lachnospiraceae, and Faecalibacterium prausnitzii taxa abundance was decreased in LC compared to controls. High Akkermansia muciniphila correlated with DCB (HR 4.26, 95% CI 1.98–9.16), not only for the immunotherapy-treated patients. In addition, high Alistipes onderdonkii (HR 3.08, 95% CI 1.34–7.06) and high Ruminococcus (HR 7.76, 95% CI 3.23–18.65) correlated with DCB.Our results support the importance of gut microbiome in LC. We have validated the apparent predictive value of Akkermansia muciniphila, and highlighted Alistipes onderdonkii and Ruminococcus taxa correlation with DCB. Upon additional validations those can be used as biomarkers or as targets for future therapeutic interventions.

The alterations in peripheral lymphocyte subsets predict the efficacy and prognosis of immune checkpoint inhibitors in hepatocellular carcinoma.

Background: Immune checkpoint inhibitor (ICI) treatments are promising therapies for hepatocellular carcinoma (HCC) patients. However, not all HCC patients benefit from immunotherapy. Therefore, it is urgent to explore markers for the clinical efficacy and prognosis of immunotherapy for liver cancer. This study aimed to investigate changes in peripheral blood lymphocyte subsets after immunotherapy and to assess their predictive and prognostic value. Methods: Sixty-one patients with advanced HCC were enrolled. Peripheral blood samples were collected from HCC patients before and after ICI treatment, and lymphocytes were detected by flow cytometry. The rank sum test, chi-square test, Kaplan‒Meier curve, and Cox regression model were used to determine the relationship between the changes in the percentages of peripheral blood lymphocyte subsets and clinicopathological characteristics, clinical efficacy, progression-free survival (PFS) and overall survival (OS). Results: After ICI treatment, the percentage of CD3+CD8+ T cells increased, and the percentage of B cells decreased. The changes in memory T cells percentages varied according to different immune efficacy groups. Age, history of hepatitis B infection, first-line therapy, and distant metastasis influenced the proportion of peripheral blood lymphocyte subsets in patients with advanced HCC. Furthermore, univariate analysis demonstrated that high percentage changes in the natural killer (NK) cells percentage change predicted longer PFS and OS. Conclusions: ICI treatment alters the percentage of peripheral blood lymphocyte subsets in immunotherapy-treated HCC patients. Changes in the proportion of lymphocyte subsets are influenced by variances in immunological response and clinicopathological features. A high degree of NK cells percentage change in HCC patients treated with ICI represents an independent prognostic predictor.