Integration Of Clinical Data Research Articles

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

Machine learning-based integration of omics and clinical data reveals an N-glycan biosynthesis signature predictive of the outcome in low-grade glioma: an in silico study

Machine learning-based integration of omics and clinical data reveals an N-glycan biosynthesis signature predictive of the outcome in low-grade glioma: an <i>in silico</i> study

Multimodal fully automated predictive model for therapeutic efficacy of first-line cancer immunotherapy based on clinical information and imaging modalities including brain MRI and chest CT images in advanced non-small cell lung cancer.

1555 Background: The integration of imaging and clinical data through artificial intelligence (AI) holds promise for more accurately predicting the therapeutic efficacy of immune checkpoint inhibitors (ICI) ± platinum-based chemotherapy in individual patients with advanced non-small cell lung cancer (NSCLC). However, tumor segmentation still relies entirely on radiologist evaluation and has not yet achieved fully automated tumor evaluation for image analysis. Furthermore, while central nervous system (CNS) metastasis is a critical prognostic factor in patients with advanced NSCLC, radiomics has primarily focused on chest imaging, with few studies incorporating brain MRI analysis. Methods: This study included patients with advanced NSCLC treated with ICI ± platinum-based chemotherapy as first-line therapy from March 2017 to August 2023. We developed an ensemble model combining a logistic regression model that analyzes clinical information with a deep learning model that extracts image features. Each sub-model was independent, allowing for individual adjustments and the ability to be attached or detached. Both models were trained to predict response, 12-month progression-free survival (PFS), and 12-month overall survival (OS) using cross-entropy loss. The dataset was randomly divided into training, validation, and test sets in a 5:3:2 ratio. This random assignment was repeated four times, and the average area under the curve (AUC) was calculated. The test sets were further classified into high- and low-risk groups based on the predicted results, and log-rank tests were performed on their survival curves. Results: The study included 232 patients, with 89 (38%) receiving ICI alone and 143 (62%) receiving ICI + platinum-based chemotherapy. In the entire population, the overall response rate (ORR) for ICI ± platinum-based chemotherapy was 49.5%, with a median PFS of 7.1 months (95% confidence interval [CI]: 5.8-9.1) and a median OS of 24.5 months (95% CI: 21.2-30.3). The model achieved an AUC of 0.70 (95% CI: 0.65- 0.76) for predicting response, 0.65 (95% CI: 0.59-0.71) for 12-month PFS, and 0.69 (95% CI: 0.48-0.90) for 12-month OS. Test sets were classified into high-risk (N = 55) and low-risk (N = 60) groups. The median PFS was 6.8 months in the high-risk group and 14.7 months in the low-risk group (p &lt; 0.05). The median OS was 21.5 months in the high-risk group and 34.3 months in the low-risk group (p &lt; 0.05). Conclusions: We have demonstrated that a multimodal, fully automated ensemble model has significant predictive accuracy for the efficacy and survival outcomes of ICI ± platinum-based chemotherapy in patients with advanced NSCLC.

Advances in big data and omics: Paving the way for discovery in childhood epilepsies

The insights gained from big data and omics approaches have transformed the field of childhood genetic epilepsy. With an increasing number of individuals receiving genetic testing for seizures, we are provided with an opportunity to identify clinically relevant subgroups and extract meaningful observations from this large-scale clinical data. However, the volume of data from electronic medical records and omics (e.g., genomics, transcriptomics) is so vast that standardized methods, such as the Human Phenotype Ontology, are necessary for reliable and comprehensive characterization. Here, we explore the integration of clinical and omics data, highlighting how these approaches pave the way for discovery in childhood epilepsies.

Melanoma immunotherapy longitudinal database: A comprehensive approach to capture oncological and toxicity data.

e23314 Background: Evaluating immunotherapy approaches is consistently performed in melanoma, a forefront cancer in immunotherapy responsiveness. Although immune checkpoint inhibitors (ICIs) showed the highest significant survival benefits in melanoma, 50% of patients experience recurrence/refractory disease, and ~59% develop grade 3/4 immune-related adverse events (irAEs). Thus, understanding the disease's natural history, exploring biomarkers of response/toxicity, and identifying the efficacy/safety of immunotherapy using real-world data is warranted. Methods: We established a centralized pro-/retrospective database on Research Electronic Data Capture (REDCap), encompassing patients' demographics/comorbidities, disease risk factors, clinicopathological parameters, genomic/immune profiling, treatment modalities, tumor outcomes, irAEs, and patients’ reported outcomes (PROs). Additionally, to streamline data collection and minimize the required time, effort, and cost for manual retrieval, we developed an automated pathway for data capture using MD Anderson Cancer Center (MDACC) electronic data capture (EDC) tools which pull clinical data from EPIC. Data quality will be validated by comparing it to manually retrieved data from an ongoing study of melanoma patients treated with ICIs in the adjuvant setting (NCT04990726). Results: A total of 4074 immunotherapy-treated melanoma patients were identified. We automatically captured demographics and laboratory values at predefined time points from REDCap, along with PROs using validated electronic self-reported questionnaires already built in REDCap. We also automatically captured family history, comorbidities, pathology data, and cancer treatments from the Data Management Initiative (DMI) validated software that automatically captures data from 12 various institutional sources that complement each other, including Radiation Oncology Database, the Biospecimen Information Management System,...etc. Moreover, genomic data were automatically pulled from the Molecular and Clinical Data Integration Platform for 1850 patients. Automated capture of tumor response and irAEs according to the Response Evaluation Criteria in Solid Tumors 1.1. and Common Terminology Criteria for Adverse Events version-5, respectively are currently ongoing and will be presented. Conclusions: Integrating EDC tools demonstrated accurate real-time data capture and minimized the manual retrieval burden. Our immunotherapy-treated melanoma patients’ cohort provides a comprehensive and a valuable resource for exploring the safety signals and efficacy of various immunotherapeutic approaches beyond clinical trials.

A Multimodal Ensemble Deep Learning Model for Functional Outcome Prognosis of Stroke Patients.

The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS. We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3-6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3-6. Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3-6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004). The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.

Regional differences in predictive biomarker testing rates for patients with metastatic NSCLC in the Netherlands

BackgroundPredictive biomarker testing has a key role in the treatment decision-making for patients with non-small cell lung cancer (NSCLC) and is mandated by (inter)national guidelines. The aim of this study was to establish guideline-adherent biomarker testing rates in the Netherlands in 2019 and to examine associations of demographical, clinical, and environmental factors with guideline-adherent testing. MethodsThis study involved the integration of clinical data of the Netherlands Cancer Registry with pathology reports of the Dutch Nationwide Pathology Databank. Data extracted from these reports included sample type, diagnosis, and molecular testing status of predictive biomarkers. The study population comprised all patients diagnosed with metastatic non-squamous NSCLC in the Netherlands in 2019. ResultsIn the cohort of 3877 patients with metastatic non-squamous NSCLC under investigation, overall molecular testing rates for non-fusion predictive biomarkers (EGFR, KRAS, BRAF, ERBB2, MET) ranged from 73.9 to 89.0 %, while molecular testing for fusion-drivers (ALK, ROS1, RET, NTRK) ranged from 12.6 % to 63.9 %. Guideline-adherent testing of EGFR, KRAS, and ALK was performed in 85.2 % of patients, with regional rates spanning from 76.0 % to 90.8 %. Demographical and clinical factors associated with guideline-adherent biomarker testing included lower age (OR = 1.05 per one year decrease; p < 0.001), female sex (OR = 1.36; p = 0.002), diagnosis of adenocarcinoma (OR = 2.48; p < 0.001), availability of histological tumor material (OR = 2.46; p < 0.001), and clinical stage of metastatic disease (p = 0.002). Other factors associated with guideline-adherent biomarker testing included diagnosis at academic center (OR = 1.87; p = 0.002) and patient’s region of residence (p < 0∙001). ConclusionOptimization of the chain-of-care of predictive biomarker testing in patients with NSCLC in the Netherlands is needed to provide adequate care for these patients.

Retraction Note: Integration of clinical and spatial data to explore lipid metabolism-related genes for predicting prognosis and immune microenvironment in gliomas.

Retraction Note: Integration of clinical and spatial data to explore lipid metabolism-related genes for predicting prognosis and immune microenvironment in gliomas.

Developing predictive models for HIV Drug resistance: A genomic and AI approach

This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With the increasing prevalence of drug-resistant strains of HIV, there is a critical need for innovative strategies to predict and manage resistance mutations, thereby optimizing treatment outcomes and prolonging the efficacy of antiretroviral therapy (ART). Drawing on advances in genomics and AI, this study outlines a conceptual framework for the development of predictive models that can identify potential drug-resistance mutations in HIV genomes and inform clinical decision-making. The proposed framework integrates genomic data from HIV-infected individuals with AI algorithms capable of learning complex patterns within the data. By analyzing genomic sequences obtained from HIV-positive patients, the models aim to identify genetic variations associated with drug resistance, predict the likelihood of resistance development, and guide the selection of appropriate treatment regimens. This approach holds promise for personalized medicine in HIV care, enabling clinicians to tailor therapy based on an individual's genetic profile and risk of resistance. Key components of the conceptual framework include data preprocessing to extract relevant genomic features, model training using machine learning techniques such as deep learning and ensemble methods, and validation of predictive performance through cross-validation and independent testing. Furthermore, the integration of clinical data, such as treatment history and viral load measurements, enhances the predictive accuracy of the models and provides valuable insights into treatment response dynamics.The development of predictive models for HIV drug resistance represents a paradigm shift in HIV care, offering a proactive approach to treatment management and surveillance. By leveraging genomic and AI technologies, healthcare providers can anticipate and address emerging resistance mutations before they compromise treatment efficacy. Ultimately, the implementation of predictive models holds the potential to improve patient outcomes, reduce the transmission of drug-resistant HIV strains, and advance the global fight against HIV/AIDS. Keywords: Developing, Predictive Models, HIV Drug Resistance, Genomic, AI Approach.

VDR mRNA Expression And Serum Vitamin D Levels in Post-Covid Vaccinated Patients

The emergence of COVID-19 vaccines has reshaped the trajectory of the ongoing pandemic, offering hope for widespread immunity. Beyond conferring protection against SARS-CoV-2, these vaccines have exhibited intriguing immunomodulatory effects. This research explores the dynamic interplay among VDR mRNA expression levels, calcium (ionized and total), and intact parathyroid hormone (iPTH) concentrations in individuals post-COVID-19 vaccination. The Vitamin D Receptor (VDR) plays a pivotal role in immune regulation and is closely intertwined with calcium homeostasis. This study investigates the hypothesis that COVID-19 vaccination may induce alterations in VDR mRNA expression, subsequently influencing calcium metabolism and iPTH secretion. Our findings reveal dynamic shifts in VDR mRNA expression following COVID-19 vaccination, with distinct patterns observed across individuals. Concurrently, we observe ionized and total calcium levels alterations, hinting at potential links between VDR activity and calcium metabolism post-vaccination. Furthermore, iPTH levels exhibit intriguing fluctuations, suggesting a regulatory role of VDR in parathyroid hormone secretion. The integration of clinical outcomes and vaccine response data sheds light on the significance of these molecular and biochemical alterations. This research underscores the multifaceted impact of COVID-19 vaccination on VDR mRNA expression, calcium homeostasis, and iPTH regulation. Beyond the scope of vaccination, our findings may bear implications for immunomodulation in various disease contexts, particularly in individuals with pre-existing calcium-related disorders. In conclusion, our study unveils the intricate relationships among VDR mRNA expression, calcium levels, and iPTH concentrations in the context of post-COVID-19 vaccination. These discoveries extend our understanding of vaccine-induced immunomodulation and may pave the way for personalized vaccination strategies, while also opening new avenues for investigating the role of VDR in immune responses and calcium regulation beyond the pandemic.

Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery.

This review article focuses on the applications of deep learning with neural networks and multimodal neural networks in the orthopaedic domain. By providing practical examples of how artificial intelligence (AI) is being applied successfully in orthopaedic surgery, particularly in the realm of imaging data sets and the integration of clinical data, this study aims to provide orthopaedic surgeons with the necessary tools to not only evaluate existing literature but also to consider AI's potential in their own clinical or research pursuits. We first review standard deep neural networks which can analyze numerical clinical variables, then describe convolutional neural networks which can analyze image data, and then introduce multimodal AI models which analyze various types of different data. Then, we contrast these deep learning techniques with related but more limited techniques such as radiomics, describe how to interpret deep learning studies, and how to initiate such studies at your institution. Ultimately, by empowering orthopaedic surgeons with the knowledge and know-how of deep learning, this review aspires to facilitate the translation of research into clinical practice, thereby enhancing the efficacy and precision of real-world orthopaedic care for patients.

Non-invasive biomarkers for sperm retrieval in non-obstructive patients: a comprehensive review.

Recent advancements in reproductive medicine have guided novel strategies for addressing male infertility, particularly in cases of non-obstructive azoospermia (NOA). Two prominent invasive interventions, namely testicular sperm extraction (TESE) and microdissection TESE (micro-TESE), have emerged as key techniques to retrieve gametes for assisted reproduction technologies (ART). Both heterogeneity and complexity of NOA pose a multifaceted challenge to clinicians, as the invasiveness of these procedures and their unpredictable success underscore the need for more precise guidance. Seminal plasma can be aptly regarded as a liquid biopsy of the male reproductive tract, encompassing secretions from the testes, epididymides, seminal vesicles, bulbourethral glands, and prostate. This fluid harbors a variety of cell-free nucleic acids, microvesicles, proteins, and metabolites intricately linked to gonadal activity. However, despite numerous investigations exploring potential biomarkers from seminal fluid, their widespread inclusion into the clinical practice remains limited. This could be partially due to the complex interplay of diverse clinical and genetic factors inherent to NOA that likely contributes to the absence of definitive biomarkers for residual spermatogenesis. It is conceivable that the integration of clinical data with biomarkers could increase the potential in predicting surgical procedure outcomes and their choice in NOA cases. This comprehensive review addresses the challenge of sperm retrieval in NOA through non-invasive biomarkers. Moreover, we delve into promising perspectives, elucidating innovative approaches grounded in multi-omics methodologies, including genomics, transcriptomics and proteomics. These cutting-edge techniques, combined with the clinical and genetics features of patients, could improve the use of biomarkers in personalized medical approaches, patient counseling, and the decision-making continuum. Finally, Artificial intelligence (AI) holds significant potential in the realm of combining biomarkers and clinical data, also in the context of identifying non-invasive biomarkers for sperm retrieval.

Deep Learning Model for Colon Cancer Classification using InceptionV3

Colon cancer is a significant global public health concern, necessitating an accurate and timely diagnosis for effective treatment. Leveraging advancements in deep learning, this study proposes a novel approach to colon cancer classification using InceptionV3 convolutional neural network architecture. A dataset comprising 1600 colonoscopy images divided into colon_aca (adenocarcinoma) and colon_n (normal) classes was utilized. The model demonstrated promising performance, achieving a training accuracy of 98.86% and a validation accuracy of 99.74% after 100 epochs. This success was accomplished by employing a Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.0001 and momentum of 0.9, along with categorical cross-entropy loss. Our findings underscore the importance of deep learning models, specifically InceptionV3, in facilitating the precise classification of colon cancer, thus offering a valuable tool for assisting clinicians in early detection and treatment decision-making. Future research may explore the integration of additional clinical data and the evaluation of alternative deep learning architectures to further enhance diagnostic accuracy.

Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case

Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups.

Abstract LB286: Extrachromosomal DNA in lung cancer from never smokers

Abstract Extrachromosomal DNA (ecDNA), circular DNA that resides outside of the linear chromosome, is a potent form of oncogene amplification in cancer. The structure and properties of ecDNA are markedly different from those of linear DNA, characterized by features like non-Mendelian inheritance and an altered epigenetic landscape. A prior pan-cancer analysis ​​indicated patients with cancers harboring ecDNA have statistically worse overall survival when compared to cancer patients with other types of focal amplification. Moreover, its unique characteristics also make it a promising target for therapy. Recently, efforts have intensified to profile ecDNA across many cancer types, yet comprehensive studies of ecDNA in lung cancer in never smokers (LCINS) is lacking. Given that LCINS is partially driven by somatic copy number alterations, we hypothesized that ecDNA-driven focal amplifications significantly influence LCINS tumor evolution. As part of the Sherlock-Lung project (current data freeze, 1217 samples), we comprehensively profiled 871 treatment-naive samples using a robust computational pipeline that refined copy number segmentation by accounting for tumor purity and ploidy. ecDNA was present in 151 (17.3%) of tumor samples, with a third of ecDNA-positive samples harboring more than one distinct ecDNA amplicon. Consistent with prior reports, no ecDNA was found in bulk sequenced blood or matched normal lung tissues. These ecDNA often included genes (84%), frequently overlapping with known oncogenes like MDM2, TERT, and MYC, which showed higher expression levels compared to non-amplified or other amplified genes. Notably, most MDM2 amplifications (36/45) were on ecDNA. ecDNA was significantly associated with whole genome doubling, tumor stage, and several endogenous mutational signatures, including APOBEC (SBS2 and SBS13), and clock-like signature (SBS1). Intriguingly, ecDNA-positive tumors were associated with significantly worse survival compared to tumors without any focal amplifications, though tumors with other amplification types (e.g. breakage-fusion-bridge, linear amplification, or complex events) had similar survival impacts. The unprecedented size of our cohort and the integration of clinical, epidemiologic, and multi-omics data allowed us to perform the first in-depth characterization of ecDNA in LCINS. We reconstructed the genomic regions involved in ecDNA and revealed the full spectrum of simple and complex ecDNAs across cancer histologies as well as the clinical and functional impact of ecDNA on LCINS. Citation Format: Azhar Khandekar, Tongwu Zhang, Marcos Diaz-Gay, Jens Luebeck, Ludmil Alexandrov, Maria Landi. Extrachromosomal DNA in lung cancer from never smokers [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 LB286.

Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study

BackgroundDiagnosing mediastinal tumours, including incidental lesions, using low-dose CT (LDCT) performed for lung cancer screening, is challenging. It often requires additional invasive and costly tests for proper characterisation and surgical...

Development and validation of novel retina biomarkers and artificial intelligence models for Behçet disease uveitis prediction

ObjectiveThe aim of this study was to develop and validate quantitative diagnostic biomarkers and artificial intelligence (AI) models for Behçet disease uveitis (BU). MethodsWe conducted a single-center retrospective study using AI-based algorithms to evaluate OCT angiography (OCTA) fundus scans from 62 BU patients and 61 healthy individuals. ResultsFrom the 837 radiomic features derived from the OCTA scans, the top 20 features were utilized for AI modeling. The neural network (NN) model displayed the highest area under the receiver operating characteristic curve (AUC) of 0.900 in the test set. The integration of clinical data further enhanced the model's performance, leading to an AUC of 0.908 in the temporal validation cohort. ConclusionOur research demonstrates that the radiomic score and the clinical integrated score can independently serve as diagnostic biomarkers for BU for future potential clinical translational research. SignificanceThe AI models we developed show great promise for clinical and translational research, marking progress towards fulfilling the demand for quantitative imaging biomarkers in BU diagnosis. However, further validation and evaluation are crucial to confirm their real-world applicability in clinical settings.

Abstract 225: Integration of patient-derived tumor organoids and patient clinical multimodal data to investigate the role of organoids in predicting treatment response

Abstract Introduction: Patient-derived organoids (PDOs) enable the ex vivo study of tumor heterogeneity and its effect on response to treatment. Our previous work demonstrated that our robust pan-cancer organoid platform shows genetic and transcriptomic concordance with clinical tumor samples and can be deployed for high-throughput drug response screening. Here, we ran a standard of care (SOC) panel on PDOs with paired de-identified clinically curated data to enable a comparison of patient response to the same drugs ex vivo and exploration of potentially more effective treatment alternatives. Methods: Patient tumor samples were cultivated into tumor PDOs in extracellular matrix + chemically defined media. PDOs underwent pathological review to ensure development of malignant tissue. PDOs were identified by clusters of Hoechst-positive cells and live and dead cell counts for each PDO were determined using fluorescent stains. The median number of cells per PDO in the well was calculated, excluding PDOs with &amp;lt;3 cells and the top 1% of PDOs by area. Drug screens were run with 6 doses of the compound and the inverse area under the curve of ToPro3 live cell measurements was calculated to quantify response. Tempus xT DNA-seq and xR whole-transcriptome assays were used to perform NGS on organoids and patient samples where available. Data was processed through our standard pipeline to identify targetable mutations, neoantigens, copy number variants (CNVs) and fusions. Results: We analyzed 38 PDOs with paired patient data across 9 different cancer types comprising mostly lung (n=14) and colorectal (n=12); 17 PDOs had responses associated with treatment given close to the biopsy collection date. Across the full PDO cohort, we observed a range of responses to SOC compounds, providing a platform to better understand biomarkers and mechanisms of response. Treatment response in PDOs correlated with patient response in many cases. In the 3 patients with progression-free survival &amp;gt;1 year and outcomes of either complete response or stable disease, all showed strong responses to the corresponding drugs in the SOC panel. For most patients with progressive disease recorded after treatment the corresponding PDO cluster showed limited response to treatment. The SOC panel includes several targeted therapies that provide insight into how patients respond to treatment beyond chemotherapy. For two patients with limited response to treatment in both patient and PDO, we identified targeted therapies relevant to their clinico-genomic landscape. One patient showed improved clinical outcomes to a later line of therapy with a similar mechanism of action to the drug that showed response in the organoid. Conclusion: These results suggest that PDOs may serve as a powerful tool for predicting patient response to treatment and aid the development of new therapies. Citation Format: Kathleen A. Burke, Brian Larsen, Yi-Hung Carol Tan, Jessica Barbeau, Curtis Brinkman, Elle Moore, Nick Callamaras, Jonathan R. Dry, Iker Huerga, Kate Sasser, Jeffrey A. Borgia. Integration of patient-derived tumor organoids and patient clinical multimodal data to investigate the role of organoids in predicting treatment response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 225.

Abstract 1746: KRAS mutant lung adenocarcinoma is associated with distinct mutational signature profiles

Abstract Background: Lung cancer is the 3rd most common cancer in the UK and leading cause of cancer mortality worldwide. Somatic RAS mutations are the most common oncogenes in human cancer, and KRAS mutations are the largest molecular subset of lung adenocarcinoma (LUAD). Each cancer genome accumulates a unique combination of somatic mutations, and these mutational signatures provide a genetic imprint of mutational processes that have occurred during tumorigenesis. Mutational signatures provide information on tumor aetiology/maintenance and could highlight potential therapeutic targets. This is the largest study to date to profile KRAS mutant LUAD mutational signatures using whole genome sequencing (WGS) data. Methods: Paired tumor and germline WGS data were obtained through the 100 000 Genomes Project (Genomics England). KRAS mutations were profiled using Ensembl Variant Effect Predictor (VEP) v109.0. Single base substitution (SBS), doublet base substitution (DBS), small insertion and deletions (ID) and copy number (CN) mutational signature extraction was performed using SigProfiler (COSMIC version 3.3). Statistical analysis and plotting were performed in R; a two-tailed p-value &amp;lt;0.05 was considered statistically significant. Results: 680 LUAD patients were included; 301 KRAS mutant (44%), 379 KRAS wild type (63%). Common KRAS variants were present, including G12C (38.9%), G12V (15.9%) and G12D (14.0%). 17 SBS, 9 DBS, 10 ID and 9 CN signatures were extracted and present in both KRAS mutant and wild type cohorts. KRAS mutant samples were enriched in smoking related signatures (SBS4, DBS2, ID3; odds (OR) ratio 5.54, 3.80, 4.97 respectively, p &amp;lt;0.001) and diploid CN signature (CN1, OR 1.41, p = 0.03). Conversely, KRAS mutant samples were less likely to have clocklike (ID5, OR 0.61, p = 0.012; ID8, OR 0.44, p &amp;lt;0.001), APOBEC (SBS2, OR 0.49, p &amp;lt;0.001; SBS13; 0.50, p &amp;lt;0.001), chromosomal instability (CN9, OR 0.63, p = 0.019), loss of heterozygosity (CN14, OR 0.47, p = 0.015), or homologous recombination deficiency (CN17, OR 0.58, p &amp;lt;0.001) signatures. Discussion: These results show distinct mutational signature profiles between KRAS mutant and wild type LUAD. As previously described, KRAS mutant LUAD is dominated by smoking related signatures; however, the majority of remaining signatures were more common in the KRAS wild type cohort where there was a statistically significant difference. This data suggests different mutational processes are observed in KRAS mutant compared to wild type LUAD. KRAS mutations are commonly clonal and thought to initiate tumorigenesis: timing signatures relative to KRAS mutations is necessary to further our understanding of KRAS cancer evolution and the subject of future work. Combined with clinical data integration, mutational signatures could provide prognostic information and inform predictive models. Citation Format: Laura Woodhouse, Aliah Hawari, Andreas Gruber, Colin Lindsay, David Wedge. KRAS mutant lung adenocarcinoma is associated with distinct mutational signature profiles [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 1746.

Abstract 6241: A large scale proteogenomic atlas for precision oncology research

Abstract Recent advances in the field of mass spectrometry proteomics have led to growth in proteomics research resulting in a massive and rapid increase in the volume of high-quality publicly available proteomics datasets. This growth creates an underutilized resource with potential for new discoveries. Effectively utilizing this vast resource of preclinical and clinical samples is a major challenge. We utilize the growing availability of proteomics and post-translational modifications (PTMs) data for drug and biomarker discovery. This innovative computational approach enables the integration of preclinical and clinical data, both public and proprietary, with a specific emphasis on extracting valuable biological insights that are only observable at the protein level or through PTMs. We developed a platform that combines multi-omic data curated from the public domain, together with internally generated high quality proteomic and phosphoproteomic data. The platform incorporates diverse clinical annotations, tumor and normal samples, drug sensitivity, patient outcomes and relevant bioinformatic knowledge bases. The platform can be leveraged to generate insights addressing diverse clinical needs. For example, we demonstrate how our platform can be utilized to estimate the clinical prevalence of a certain protein or protein signature, in order to identify novel response biomarkers with the highest likelihood of success by confirming its presence and variance in clinical datasets. In addition, these insights enable novel understanding of response and resistance mechanisms, thus establishing a framework that expedites the generation of hypotheses for subsequent investigation and experimental validation. Citation Format: Nitzan Simchi, Ofer Givton, Dina Daitchman, Eran Seger, Kirill Pevzner. A large scale proteogenomic atlas for precision oncology research [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 6241.