Discovery Of Predictive Biomarkers Research Articles

Evaluating Gene Expression and Methylation Profiles of TCF4, MBP, and EGR1 in Peripheral Blood of Drug-Free Patients with Schizophrenia: Correlations with Psychopathology, Intelligence, and Cognitive Impairment.

Discovery and validation of new, reliable diagnostic and predictive biomarkers for schizophrenia (SCZ) are an ongoing effort. Here, we assessed the mRNA expression and DNA methylation of the TCF4, MBP, and EGR1 genes in the blood of patients with SCZ and evaluated their relationships to psychopathology and cognitive impairments. Quantitative real-time PCR and quantitative methylation-specific PCR methods were used to assess the expression level and promoter DNA methylation status of these genes in 70 drug-free SCZ patients and 72 healthy controls. The correlation of molecular changes with psychopathology and cognitive performance of participants was evaluated. We observed downregulation of TCF4 and upregulation of MBP mRNA levels in SCZ cases, relative to controls in our study. DNA methylation status at the promoter region of TCF4 demonstrated an altered pattern in SCZ as well. Additionally, TCF4 mRNA levels were inversely correlated with PANSS and Stroop total errors and positively correlated with WAIS total score and working memory, consistent with previous studies by our group. In contrast, MBP mRNA level was significantly positively correlated with PANSS and Stroop total errors and inversely correlated with WAIS total score and working memory. These epigenetic and expression signatures can help to assemble a peripheral biomarker-based diagnostic panel for SCZ.

Comprehensive overview of biomarkers to predict response to immune checkpoint therapy in lung cancer

ABSTRACT Immune checkpoint (IC) therapy has brought a huge revolution in the field of lung cancer treatment over the past decade. It has also revolutionised treatment paradigm and has tremendously improved patient prognosis. IC inhibitors (ICIs) targeting Programmed Cell Death Protein 1/Programmed cell death Ligand 1 (PD1/PD-L1) have shown remarkable success and are now being used as first-line therapies in metastatic disease, adjuvant therapy following surgical resection and chemotherapy in resectable disease. Despite this remarkable success, only a subset of patients obtains complete benefit and most patients do not respond or develop progressive disease during treatment. ICIs are relatively expensive and some patients suffer from significant immune-related adverse toxicities. Hence, the identification and discovery of new predictive and prognostic immunotherapy biomarkers remains the present crucial need for patient selection, stratification and also for guiding therapeutic decisions. Currently established biomarkers such as PD-L1 determined by immunohistochemistry and tumour mutation burden determined by next-generation sequencing are non-specific and possess limitations. At present, several other biomarkers using peripheral blood, liquid biopsies along with gene expression signatures, and tumour infiltrating lymphocytes are being researched globally which have demonstrated predictive potential to characterise ICIs responders. In this review, we provide a comprehensive overview of the current biomarkers, highlighting the main clinical challenges and possible novel potential biomarkers to better predict responders to ICIs.

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

Genomic and Phenotypic Biomarkers for Precision Medicine Guidance in Advanced Prostate Cancer.

Prostate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen.

Abstract P3021: Circulating Exosomal MicroRNAs As Biomarkers For Pulmonary Embolism

Pulmonary Embolism (PE) is the third leading cause of death worldwide. Current treatment for PE, including thrombolytics and anticoagulants, have serious side effects. Protein and RNA biomarkers presently used to diagnose PE do not have the necessary specificity or sensitivity. Therefore, discovery of predictive biomarkers for therapeutic effects and adverse outcomes is imperative. MicroRNAs (miRNAs) are known to regulate gene expression networks and their expression levels reflect the physiological state of their cell of origin. By investigating these circulating miRNAs, we can gain valuable insight into the state of the damaged cells or tissue. Here, we innovated a novel methodology to isolate exosomes from patient platelet-free plasma that provides better yield of both exosomes and their RNA content. We are the first to stratify the PE patient samples according to clinical severity (low-risk, sub-massive, massive PE) and analyze the correlations of the exosomal miRNA biomarkers with PE severity. We found that exosomes from PE patients are more heterogeneous, lower in concentration and larger in size compared to healthy controls. Further, exosomes from PE patient samples pack more RNA than healthy controls. With the results from ongoing miRNA sequencing, we will be able to identify miRNAs and other short non-coding RNAs as potential diagnostic biomarkers that best correlate with PE severity. Validation and predictive studies using qRT-PCR will push the frontiers on circulating small RNA biomarkers in PE and other cardiovascular diseases. Finally, network analysis on the potential RNA biomarkers will provide information for future studies on disease mechanisms and therapeutics development.

Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.

Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Global Impact of Monoclonal Antibodies (mAbs) in Children: A Focus on Anti-GD2.

Monoclonal antibodies (mAbs), as the name implies, are clonal antibodies that bind to the same antigen. mAbs are broadly used as diagnostic or therapeutic tools for neoplasms, autoimmune diseases, allergic conditions, and infections. Although most mAbs are approved for treating adult cancers, few are applicable to childhood malignancies, limited mostly to hematological cancers. As for solid tumors, only anti-disialoganglioside (GD2) mAbs are approved specifically for neuroblastoma. Inequities of drug access have continued, affecting most therapeutic mAbs globally. To understand these challenges, a deeper dive into the complex transition from basic research to the clinic, or between marketing and regulatory agencies, is timely. This review focuses on current mAbs approved or under investigation in pediatric cancer, with special attention on solid tumors and anti-GD2 mAbs, and the hurdles that limit their broad global access. Beyond understanding the mechanisms of drug resistance, the continual discovery of next generation drugs safer for children and easier to administer, the discovery of predictive biomarkers to avoid futility should ease the acceptance by patient, health care professionals and regulatory agencies, in order to expand clinical utility. With a better integration into the multimodal treatment for each disease, protocols that align with the regional clinical practice should also improve acceptance and cost-effectiveness. Communication and collaboration between academic institutions, pharmaceutical companies, and regulatory agencies should help to ensure accessible, affordable, and sustainable health care for all.

Artificial intelligence algorithm developed to predict immune checkpoint inhibitors efficacy in non–small-cell lung cancer.

9132 Background: Many non-small cell lung cancer (NSCLC) patients with high PD-L1 IHC expression and/or high TMB level do not respond to immune-check point inhibitors (ICIs). Discovery of novel predictive biomarkers for ICI response in NSCLC continues to be a critical unmet medical need, given the expense, potential toxicity, and therapeutic delay due to ICI treatment without benefit. Methods: We developed a supervised deep learning algorithm (Deep-IO) to predict therapeutic response to ICIs in NSCLC patients from standard histology whole slide hematoxylin and eosin (H&E) images, which was trained based on ICI objective response rate. We utilized a convolutional neural network (CNN) implemented in Pytorch. The efficiency of the Deep-IO was evaluated through classification performance metrics and the area under the receiver operating characteristic curve (AUROC). The algorithm was trained and tested on 85218 tiles (size: 512x512 pixels) in 446 advanced stage NSCLC patients from the Dana Farber Cancer Institute (DFCI), all treated with ICI monotherapy. Results: The objective response rate, comprising complete and partial response, was 26% (n = 114) in the overall cohort. The DFCI patient cohort was randomly split into a training set (N=379, 85%) and a test set (N=67, 15%). The classifier’s predicted class (responder vs non-responder) probabilities at the tiles level were averaged for each patient. Quantitative evaluation of whole section test dataset shows that the developed model achieves the accuracy of 0.72, weighted average (WAVG) precision of 0.77, WAVG recall of 0.72 and WAVG F-score of 0.74 on classifying ICI non-responders from responders. Comparing with PD-L1 expression (TPS%) and TMB (mu/Mb), Deep-IO had superior predictive power for ICI response (AUROC=0.75) in the test set. The combined Deep-IO+PD-L1 scores resulted in a significantly greater AUROC value of 0.82 compared to single tests and a 32% improvement in specificity (0.88 vs. 0.56) compared to PD-L1 evaluation alone (Table). Conclusions: This proof-of-concept study shows that an artificial intelligence–powered H&E image classifier can predict ICI effectiveness in NSCLC. Deep-IO outperformed both established predictive biomarkers. We will assess Deep-IO in additional external NSCLC data sets for validation and confirmation. [Table: see text]

A Systematic Review and Meta-Analysis of Trifluridine/Tipiracil plus Bevacizumab for the Treatment of Metastatic Colorectal Cancer: Evidence from Real-World Series.

Colorectal cancer is the most prevalent gastrointestinal neoplasm. When metastatic, the disease has limited systemic treatment options. Novel targeted therapies have expanded these options for subsets with specific molecular alterations, such as microsatellite instability (MSI)-high cancers, but additional treatments and combinations are in urgent need to improve outcomes and improve survival of this incurable disease. The fluoropyrimidine-derivative trifluridine, in combination with tipiracil, has been introduced as a third-line treatment, and more recently, it was studied in combination with bevacizumab. This meta-analysis reports on studies with this combination in clinical practice outside clinical trials. A literature search in the Medline/PubMed and Embase databases was executed for finding series of trifluridine/tipiracil with bevacizumab in metastatic colorectal cancer. Criteria for inclusion in the meta-analysis were English or French language of the report, inclusion of twenty or more patients with metastatic colorectal cancer treated with trifluridine/tipiracil in combination with bevacizumab outside of a trial and containing information regarding response rates, progression-free survival (PFS), and overall survival (OS). Information on the demographics of the patients and on adverse effects of treatment was also collected. Eight series with a total of 437 patients were eligible for the meta-analysis. The performed meta-analysis discovered a summary response rate (RR) of 2.71% (95% confidence interval (CI): 1.11-4.32%) and a disease control rate (DCR) of 59.63% (95% CI: 52.06-67.21%). Summary PFS was 4.56 months (95% CI: 3.57-5.55 months), and summary OS was 11.17 months (95% CI: 10.15-12.19 months). Common adverse effects identified mirrored the adverse-effect profile of the two components of the combination. The current systematic review and meta-analysis reports the efficacy of trifluridine/tipiracil with bevacizumab in advanced lines of therapy for metastatic colorectal cancer in the setting of clinical practice outside clinical trials. Discovery of predictive biomarkers of response to trifluridine/tipiracil with bevacizumab will promote the tailoring of this treatment to individual patients to maximize clinical benefit.

Combining the amplification refractory mutation system and high-resolution melting analysis for KRAS mutation detection in clinical samples

The success of personalized medicine depends on the discovery of biomarkers that allow oncologists to identify patients that will benefit from a particular targeted drug. Molecular tests are mostly performed using tumor samples, which may not be representative of the tumor’s temporal and spatial heterogeneity. Liquid biopsies, and particularly the analysis of circulating tumor DNA, are emerging as an interesting means for diagnosis, prognosis, and predictive biomarker discovery. In this study, the amplification refractory mutation system (ARMS) coupled with high-resolution melting analysis (HRMA) was developed for detecting two of the most relevant KRAS mutations in codon 12. After optimization with commercial cancer cell lines, KRAS mutation screening was validated in tumor and plasma samples collected from patients with pancreatic ductal adenocarcinoma (PDAC), and the results were compared to those obtained by Sanger sequencing (SS) and droplet digital polymerase chain reaction (ddPCR). The developed ARMS-HRMA methodology stands out for its simplicity and reduced time to result when compared to both SS and ddPCR but showing high sensitivity and specificity for the detection of mutations in tumor and plasma samples. In fact, ARMS-HRMA scored 3 more mutations compared to SS (tumor samples T6, T7, and T12) and one more compared to ddPCR (tumor sample T7) in DNA extracted from tumors. For ctDNA from plasma samples, insufficient genetic material prevented the screening of all samples. Still, ARMS-HRMA allowed for scoring more mutations in comparison to SS and 1 more mutation in comparison to ddPCR (plasma sample P7). We propose that ARMS-HRMA might be used as a sensitive, specific, and simple method for the screening of low-level mutations in liquid biopsies, suitable for improving diagnosis and prognosis schemes.Graphical

Abstract 5410: Predictive biomarker discovery method to bridge the gap between preclinical disease model dose-response and clinical trials

Abstract The translational gap of drug and biomarker discovery remains one of the biggest challenges in the pharmaceutical industry. On one hand high throughput screening and omics methods facilitate the generation of in-vitro and in-vivo data of dose-response for a particular drug or combinations. On the other hand, clinical omics techniques allow for the creation of large scale treatment-naive clinical datasets, with the TCGA and CPTAC as prominent examples. However, the question of which disease model best represents the response/resistance mechanisms of an oncology patient remains challenging. In this study we present a machine learning (ML) based computational technique for integrating omics from preclinical dose-response studies with clinical treatment-naive samples to create putative predictive biomarkers, and exemplify its application 2 case studies - PARP or AKT inhibitors. We utilize the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) as a resource for in-vitro dose response data for PARPi and AKTi, coupled with multi-omic molecular data. We utilize the treatment naive molecular characterization from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data of Breast cancer patients. Multi-omic analysis of these datasets derived 2 putative predictive biomarkers for PARPi and AKTi. The multi-omic analysis resembles the data availability at the critical drug development stage of transition from preclinical response models to a clinical trial. These biomarkers are tested on a validation dataset obtained from the iSPY adaptive clinical trial and achieve superior results compared to the original enrolment biomarkers and even the retrospectively derived biomarkers. This study presents a novel computational approach to bridge the gap between preclinical dose-response and clinical datasets and suggests an efficient way to discover predictive biomarkers based on data accessible in a preclinical setting. Citation Format: David Futorian, Oren Fischman, Gali Arad, Nitzan Simchi, Omri Erez, Eran Seger, Rozanne Groen, Kirill Pevzner. Predictive biomarker discovery method to bridge the gap between preclinical disease model dose-response and clinical trials. [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 5410.

Abstract 5426: Multi-omic characterization and predictive features of advanced ovarian cancer patients in a large phase III cohort

Abstract Discovery and validation of biomarkers derived from multi-dimensional clinico-genomic datasets have become critical in precision medicine and oncology drug development. The integration of multi-dimensional genomic and imaging datasets from patients in late-stage oncology clinical trials can be difficult, in part due to limited patient enrollment and sample collection, especially tumor tissue biopsies. The objective of this project was to conduct predictive biomarker discovery on integrated clinico-genomics data spanning tumor genomics, germline genetics, tumor imaging, and circulating blood-based biomarkers for a cohort of 800+ advanced ovarian cancer patients enrolled in the phase III trial of the PARP-1 inhibitor Veliparib (VELIA). Genomic (DNA & RNA) and imaging datasets were generated from 800+ patient-matched tumor biopsies, liquid biopsies and whole blood to enable various biomarker analyses for this study, notably in BRCA-deficient and HRD+ subgroups. Pairwise analysis of individual features with clinical outcomes shows that increased tumor-mutation burden (TMB), RNA-based estimates of immune activity (ICR and MIRACLE scores), CA-125 elimination constant (KELIM score), and image-based estimates tumor-infiltrating lymphocytes (TILs) were each significantly associated with longer PFS and less progressive disease (PD). Similarly, homologous recombination deficiency (HRD) and BRCA alteration were associated with better clinical outcomes, while high CA-125 was associated with worse outcomes. To understand the concerted impact of these features on clinical outcome, we developed multivariate classifiers of PD and regressors (Cox Proportional Hazards) of PFS using XGBoost; using a train-test split of 75/25, we trained the models with 500 rounds of 10-fold cross validation hyperparameter tuning, which resulted in fit models. The PD classifier achieved a validation accuracy of 0.71 and F1-score of 0.78, with high immune activation, high TMB, high KELIM, and HRD and BRCA alteration predicting better outcomes. The PFS survival model achieved a validation C-index of 0.61, with a rank importance of features similar to that of PD classification models; for both models, patients receiving Veliparib had clear benefit relative to that of control arms. Collectively, this study illustrates the value of integrating multi-dimensional datasets with predictive machine learning to identify clinically-relevant biomarkers. CR, DM, PA, BR, JD, TB, PN, JS, XH, and JFW are employees of AbbVie. RLC an employee at The University of Texas MD Anderson Cancer Center and has no funding to disclose. The design, study conduct, and financial support for this research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication. Citation Format: Cyril Ramathal, David Masica, Peter Ansell, Bridget Riley-Gillis, Jacob Degner, Thanh Bui, Priya Narayanan, Josue Samayoa, Xin Huang, Robert F. Coleman, Jeffrey F. Waring. Multi-omic characterization and predictive features of advanced ovarian cancer patients in a large phase III cohort. [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 5426.

Abstract 2250: Immuno-oncology study to profile the tumor microenvironment in multiple human cancers using high-plex imaging mass cytometry

Abstract Immune profiling of tumor tissues has become a key tool in understanding the complexity of the tumor microenvironment (TME), for predictive biomarker discovery, and in cancer treatment. The presence of tumor-infiltrating lymphocytes has been associated with benefit from therapy. Furthermore, the TME also contains immunosuppressive elements that can impede immune response. Imaging Mass Cytometry™ (IMC™) enables detailed assessment of cell phenotype and function using 40-plus markers simultaneously at subcellular resolution on a single slide without spectral overlap or background autofluorescence. High-plex IMC has enabled us to evaluate the TME in different cancer histologies including in highly autofluorescent tissue types like lung, hepatocellular carcinoma, and skin melanoma. The Maxpar® Human Immuno-Oncology IMC Panel Kit (201508) was customized using antibodies from the Standard BioTools™ catalog to create panels for tissue-based immuno-oncology research. Data acquisition was performed using a Hyperion™ Imaging System. To facilitate cell segmentation, an IMC Cell Segmentation Kit (TIS-00001) was applied to enhance cell membrane boundaries. We applied a pixel classification approach and CellProfiler™ for single-cell segmentation. We used histoCAT™ for single-cell analysis to visualize protein expression in various cancer types via PhenoGraph clustering and t-SNE maps. Our panels were applied to normal and cancer human tissue microarrays (TMAs) to phenotype and analyze cell populations in these tissues. We provide detailed analysis of the TME by classifying activation state of immune cell populations, epithelial-to-mesenchymal transition (EMT) progression, and composition of the extracellular matrix. In-depth single-cell analysis quantitatively evaluated the cellular makeup and immune cell component in the TME of cancer tissues and identified major tumor, immune, and stromal cell phenotypes. This work demonstrates the capability of IMC for quantitative and spatial identification of multiple immune parameters in the TME on a single slide of cancer patient samples (e.g. tumor microarray). For Research Use Only. Not for use in diagnostic procedures. Citation Format: Thomas D. Pfister, Liang Lim, Shaida Ouladan, Nick Zabinyakov, Qanber Raza, Christina Loh. Immuno-oncology study to profile the tumor microenvironment in multiple human cancers using high-plex imaging mass cytometry [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 2250.

Systemic treatment for metastatic colorectal cancer.

Significant progress has been achieved in the treatment of metastatic colorectal cancer (mCRC) patients during the last 20 years. There are currently numerous treatments available for the first-line treatment of mCRC. Sophisticated molecular technologies have been developed to reveal novel prognostic and predictive biomarkers for CRC. The development of next-generation sequencing and whole-exome sequencing, which are strong new tools for the discovery of predictive molecular biomarkers to facilitate the delivery of customized treatment, has resulted in tremendous breakthroughs in DNA sequencing technology in recent years. The appropriate adjuvant treatments for mCRC patients are determined by the tumor stage, presence of high-risk pathologic characteristics, microsatellite instability status, patient age, and performance status. Chemotherapy, targeted therapy, and immunotherapy are the main systemic treatments for patients with mCRC. Despite the fact that these novel treatment choices have increased overall survival for mCRC, survival remains optimal for individuals with non-metastatic disease. The molecular technologies currently being used to support our ability to practice personalized medicine; the practical aspects of applying molecular biomarkers to regular clinical practice; and the evolution of chemotherapy, targeted therapy, and immunotherapy strategies for the treatment of mCRC in the front-line setting are all reviewed here.

Abstract PD2-01: Local and lymphoid immune surveillance mechanisms in “exceptional survivors” of stage 4 cancers following standard of care chemo- and targeted therapies

Abstract Breast cancer patients with advanced metastatic disease can exhibit rapid disease progression, disease stabilisation or partial responses of varying duration. However, for reasons that are not fully elucidated, a small fraction of patients will elicit an exceptional durable response to standard anticancer treatments or survive significantly longer than patients with clinically comparable tumours. Here, we investigate the drivers of immune surveillance mechanisms across breast cancer subtypes in stage IV exceptional survivors (n=13) with matched control cohorts of stage IV typical responders (n=6), early breast cancer patients (n=5) and healthy volunteers (n=17). Peripheral blood mononuclear cells (PBMCs) from patients were stained with 8 panels providing 241 non-redundant immune parameters for flow cytometry analysis. Principle Component Analysis (PCA) showed distinct segregation of the exceptional survivors from the other control groups with an immune signature in exceptional survivors constituting of activated NK, CD8 T cells and gamma delta (gd) T cells pointing towards higher innate immunogenicity in these individuals. Specifically, although these metastatic exceptional responder patients possessed comparable NK cell frequencies, the proportion of NKG2D+CD56dimCD16+ NK cells were significantly enriched compared to the typical responders. Additionally, proportions of CD8+ central memory (CD45RA- CD27+) and effector memory (CD45RA- CD27-) gd T cells, were also seen to be significantly increased. Functional in vitro validation of these findings along with scRNA sequencing of lymph node and tumour tissue is currently underway. To our knowledge, this work is the first to explore in depth the immune signatures in the peripheral blood of exceptional survivors with metastatic breast cancer. Elucidating the immunological reasons for favourable atypical responses alongside functional tumour microenvironment analysis offers unique insights for predictive biomarker identification and discovery of axes that could be exploited therapeutically to benefit those with less favourable responses. Written informed consent was obtained from all individuals in accordance with the Declaration of Helsinki under the following research ethics committee; London-Chelsea approved study (REC ID 13/LO/1248). Citation Format: Helen Kakkassery, Thanussuyah Alaguthurai, Rosalind Graham, Esme Carpenter, Farhana Hossain, Sean Keane, Sheeba Irshad. Local and lymphoid immune surveillance mechanisms in “exceptional survivors” of stage 4 cancers following standard of care chemo- and targeted therapies [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD2-01.

Therapeutic implications of transcriptomics in head and neck cancer patient-derived xenografts.

There are currently no clinical strategies utilizing tumor gene expression to inform therapeutic selection for patients with head and neck squamous cell carcinoma (HNSCC). One of the challenges in developing predictive biomarkers is the limited characterization of preclinical HNSCC models. Patient-derived xenografts (PDXs) are increasingly recognized as translationally relevant preclinical avatars for human tumors; however, the overall transcriptomic concordance of HNSCC PDXs with primary human HNSCC is understudied, especially in human papillomavirus-associated (HPV+) disease. Here, we characterized 64 HNSCC PDXs (16 HPV+ and 48 HPV-) at the transcriptomic level using RNA-sequencing. The range of human-specific reads per PDX varied from 64.6%-96.5%, with a comparison of the most differentially expressed genes before and after removal of mouse transcripts revealing no significant benefit to filtering out mouse mRNA reads in this cohort. We demonstrate that four previously established HNSCC molecular subtypes found in The Cancer Genome Atlas (TCGA) are also clearly recapitulated in HNSCC PDXs. Unsupervised hierarchical clustering yielded a striking natural division of HNSCC PDXs by HPV status, with C19orf57 (BRME1), a gene previously correlated with positive response to cisplatin in cervical cancer, among the most significantly differentially expressed genes between HPV+ and HPV- PDXs. In vivo experiments demonstrated a possible relationship between increased C19orf57 expression and superior anti-tumor responses of PDXs to cisplatin, which should be investigated further. These findings highlight the value of PDXs as models for HPV+ and HPV- HNSCC, providing a resource for future discovery of predictive biomarkers to guide treatment selection in HNSCC.

Artificial intelligence (AI)-powered immune phenotyping to predict outcomes of immuno-oncology (IO)-based regimens in hepatocellular carcinoma (HCC).

601 Background: Anti-PD-(L)1 monotherapy or combination with bevacizumab or ipilimumab are approved treatment options in HCC. Nevertheless, not all patients benefit from IO treatment, and discovery of predictive biomarkers for IO outcome has not been fully investigated. Immune phenotype analysis by AI-powered spatial tumor-infiltrating lymphocyte (TIL) analyzer in H&E whole-slide images (WSI), Lunit SCOPE IO, has shown predictive effect across multiple cancer types. Here, we investigated the correlation between immune phenotype (IP) and the real-world outcomes in HCC. Methods: Real-world dataset of 177 patients with HCC were retrospectively collected from CHA Bundang Medical Center. Inflamed score was defined by the proportion of area with high intra-tumoral TIL density per 0.5 mm2-sized grid. IP status was sub-analyzed by PD-L1 combined positive score (CPS) and prognostic factors including Child-Pugh class and Barcelona Clinic Liver Cancer (BCLC) stage. We evaluated the correlation between IP and IO outcome by regimen (atezolizumab+bevacizumab [Atez+Bev], n=100; nivolumab+/-ipilimumab [Niv+/-Ipi], n=77). Results: The majority of patients (75.3%) were Child-Pugh class A (75.3%) and BCLC stage C (86.5%), respectively. Atez+Bev was mostly received as first-line therapy (92.0%), whereas Niv+/-Ipi as second-line or beyond (98.7%). The inflamed score was significantly higher in patients who had lymph node metastasis ( p=0.008), but it was not correlated with other metastatic sites, or any other prognostic factors. Of 177 patients, 105 (59.3%) patients were classified as having a high inflamed score (≥10%) and 72 (40.7%) patients as having lower than 10%. PD-L1 CPS was significantly higher in the sample with high score compared to those with low inflamed score ( p=0.01387). PFS of Niv+/-Ipi were significantly favorable in the patients with high inflamed score (3-m PFS rate; 44.0% vs 3.61%, 12-m PFS rate; 17.8% vs 0%, Hazard ratio [HR] 0.37, 95% confidence interval [CI] 0.22-0.63, p<0.001), while PFS of Atez+Bev were not different according to inflamed score (3-m PFS rate; 67.5% vs 57.3%, 12-m PFS rate; 36.6% vs 31.0%, HR 0.80, 95% CI 0.48-1.32, p=0.400). Conclusions: Inflamed score, the proportion of area with high intra-tumoral TIL infiltration, can be a clinically significant predictor of PFS of Niv+/-Ipi, but it was not relevant for Atez+Bev treatment outcomes.

Chromatin-Accessible miRNA Regulons Driving Thyroid Tumorigenesis and Progression.

Although papillary thyroid cancer can remain indolent, associated lymph node metastases and recurrence rates are approximately 50% and 20%, respectively. Omics-based medicine has led to the discovery of predictive biomarkers that can be used to predict tumor progression and clinical outcomes. We aimed to develop a noninvasive omics-driven blood test to allow accurate risk stratification and help tailor individual patient treatment plans. RNA sequencing (seq) and microRNA analysis of The Cancer Genome Atlas and Gene Expression Omnibus datasets were employed to identify an epigenetic prognostic panel. Integrated bulk assay for transposase-accessible chromatin-seq and RNA-seq experiments confirmed the results. Sixty-two paired tumor and adjacent control thyroid tissues and 67 blood samples (62 papillary thyroid cancer and 5 controls) were analyzed for validation using sequencing and real-time polymerase chain reaction and correlated to clinical outcomes. A liposome-exosome fusion clustered regularly interspaced short palindromic repeats (CRISPR)-fluorescent detection system miRNA assay was developed. A predictive risk nomogram was generated and tested for performance. Our miRNA panel (miR-146b-5p and miR-221-3p) from tissue and blood was associated with aggressive features and was located within accessible chromatin regions. The miRNA risk score and prognostic nomogram showed higher accuracy in predicting lymph node metastases (miR-146b: area under the curve [AUC] 0.816, sensitivity 76.9%; miR-221: AUC 0.740, sensitivity 79.5%) and recurrence (miR-146b: AUC 0.921, sensitivity 75.0%; miR-221: AUC 0.756, sensitivity 70.0%; p < 0.001) than staging and American Thyroid Association risk stratification. CRISPR-based miRNA assays showed upregulation in the blood of cancer cohorts. CRISPR-based detection of miR-146b and miR-221 in the blood of thyroid cancer patients is a reliable and noninvasive tool for real-time assessment and prognostication that has great potential to provide a direct impact on the care of these patients.

Generation and Culturing of High-Grade Serous Ovarian Cancer Patient-Derived Organoids.

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the discovery of novel therapeutics and predictive biomarkers for ovarian cancer. These models recapitulate clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Additionally, they have been shown to match the primary tumor morphologically, cytologically, immunohistochemically, and genetically. Thus, organoids facilitate research on tumor cells and the tumor microenvironment and are superior to cell lines. The present protocol describes distinct methods to generate patient-derived ovarian cancer organoids from patient tumors, ascites, and pleural fluid samples with a higher than 97% success rate. The patient samples are separated into cellular suspensions by both mechanical and enzymatic digestion. The cells are then plated utilizing a basement membrane extract (BME) and are supported with optimized growth media containing supplements specific to the culturing of high-grade serous ovarian cancer (HGSOC). After forming initial organoids, the PDOs can sustain long-term culture, including passaging for expansion for subsequent experiments.

Proteomic‐Based Discovery of Predictive Biomarkers for Drug Therapy Response and Personalized Medicine in Chronic Immune Thrombocytopenia

Purpose. ITP is the most prevalent autoimmune blood disorder. The lack of predictive biomarkers for therapeutic response is a major challenge for physicians caring of chronic ITP patients. This study is aimed at identifying predictive biomarkers for drug therapy responses. Methods. 2D gel electrophoresis (2‐DE) was performed to find differentially expressed proteins. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometer (MALDI‐TOF MS) analysis was performed to identify protein spots. The Cytoscape software was employed to visualize and analyze the protein‐protein interaction (PPI) network. Then, enzyme‐linked immunosorbent assays (ELISA) were used to confirm the results of the proteins detected in the blood. The DAVID online software was used to explore the Gene Ontology and pathways involved in the disease. Results. Three proteins, including APOA1, GC, and TF, were identified as hub‐bottlenecks and confirmed by ELISA. Enrichment analysis results showed the importance of several biological processes and pathway, such as the PPAR signaling pathway, complement and coagulation cascades, platelet activation, vitamin digestion and absorption, fat digestion and absorption, cell adhesion molecule binding, and receptor binding. Conclusion and Clinical Relevance. Our results indicate that plasma proteins (APOA1, GC, and TF) can be suitable biomarkers for the prognosis of the response to drug therapy in ITP patients.