Personalized Cancer Medicine Research Articles

Patient-Derived Organoids: A Game-Changer in Personalized Cancer Medicine.

Research on cancer therapies has benefited from predictive tools capable of simulating treatment response and other disease characteristics in a personalized manner, in particular three-dimensional cell culture models. Such models include tumor-derived spheroids, multicellular spheroids including organotypic multicellular spheroids, and tumor-derived organoids. Additionally, organoids can be grown from various cancer cell types, such as pluripotent stem cells and induced pluripotent stem cells, progenitor cells, and adult stem cells. Although patient-derived xenografts and genetically engineered mouse models replicate human disease in vivo, organoids are less expensive, less labor intensive, and less time-consuming, all-important aspects in high-throughput settings. Like in vivo models, organoids mimic the three-dimensional structure, cellular heterogeneity, and functions of primary tissues, with the advantage of representing the normal oxygen conditions of patient organs. In this review, we summarize the use of organoids in disease modeling, drug discovery, toxicity testing, and precision oncology. We also summarize the current clinical trials using organoids.

KRAS mutations detection methodology: from RFLP to CRISPR/Cas based methods.

In personalized cancer medicine, the identification of KRAS mutations is essential for making treatment decisions and improving patient outcomes. This work presents a comprehensive review of the current approaches for detection of KRAS mutations in different cancers. We highlight the value of fast and reliable KRAS mutations discovery and the effectiveness of molecular testing for selecting individuals who might benefit from targeted therapy. We provide an overview of various methods and tools available for detecting KRAS mutations, such as digital droplet PCR, next-generation sequencing (NGS), and polymerase chain reaction (PCR). We also address the difficulties and limitations in the identification of KRAS mutations, namely tumor heterogeneity and the emergence of resistance mechanisms. This article aims to guide clinicians in KRAS mutation identification.

Correction: Reproducing the normal and the pathological in personalized cancer medicine clinical trials

Correction: Reproducing the normal and the pathological in personalized cancer medicine clinical trials

Precision Cancer Medicine 2.0-Oncology in the postgenomic era.

Genomic medicine has transformed the lives of patients with cancer byenabling individualised and evidence-based clinical decision-making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single-cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high-resolution personalised clinical decision-making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.

Mitigating the impact of image processing variations on tumour [18F]-FDG-PET radiomic feature robustness

Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.

Reproducing the normal and the pathological in personalized cancer medicine clinical trials

AbstractThe medical practice termed Personalized Medicine ideally uses all the patient’s possible characteristics in predicting disease predisposition and response to therapy, but primarily employs the individual’s unique molecular make-up in the tailoring of treatment. This change in medical practice also entails an epistemic shift towards ‘molecularization’: individuals and disease are now understood and governed through life’s basic building blocks. In this paper we argue that underlying personalized medicine is a continued understanding of the pathological state as a quantitative deviation from a normal state. In this we build on the critique of French philosopher Georges Canguilhem who positioned the quantitative interpretation of the pathological in nineteenth century medical thinking. Personalized cancer medicine takes each patient’s cancer as singular, implying that there is no ‘normal’ baseline for comparing individual pathology. We analyze cases of personalized cancer clinical trials from recent years to show that each displays a quantitative understanding of the pathological reminiscent of past thinking in two main modes: a molecularized interpretation of cancer pathology and a quantitative measuring of targeted therapy efficacy. We situate the analysis in broader discussions of historical medical shifts and in current studies of personalized medicine, to outline implications of this form of continuity.

In vitro throughput screening of anticancer drugs using patient-derived cell lines cultured on vascularized three-dimensional stromal tissues

The development of high-throughput anticancer drug screening methods using patient-derived cancer cell (PDC) lines that maintain their original characteristics in an in vitro three-dimensional (3D) culture system poses a significant challenge to achieving personalized cancer medicine. Because stromal tissue plays a critical role in the composition and maintenance of the cancer microenvironment, in vitro 3D-culture using reconstructed stromal tissues has attracted considerable attention. Here, a simple and unique in vitro 3D-culture method using heparin and collagen together with fibroblasts and endothelial cells to fabricate vascularized 3D-stromal tissues for in vitro culture of PDCs is reported. Whereas co-treatment with bevacizumab, a monoclonal antibody against vascular endothelial growth factor, and 5-fluorouracil significantly reduced the survival rate of 3D-cultured PDCs to 30%, separate addition of each drug did not induce comparable strong cytotoxicity, suggesting the possibility of evaluating the combined effect of anticancer drugs and angiogenesis inhibitors. Surprisingly, drug evaluation using eight PDC lines with the 3D-culture method resulted in a drug efficacy concordance rate of 75% with clinical outcomes. The model is expected to be applicable to in vitro throughput drug screening for the development of personalized cancer medicine. Statement of significanceTo replicate the cancer microenvironment, we constructed a cancer-stromal tissue model in which cancer cells are placed above and inside stromal tissue with vascular network structures derived from vascular endothelial cells in fibroblast tissue using CAViTs method. Using this method, we were able to reproduce the invasion and metastasis processes of cancer cells observed in vivo. Using patient-derived cancer cells, we assessed the possibility of evaluating the combined effect with an angiogenesis inhibitor.Further, primary cancer cells also grew on the stromal tissues with the normal medium. These data suggest that the model may be useful for new in vitro drug screening and personalized cancer medicine.

Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine.

The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

PCM4EU and PRIME-ROSE: Collaboration for implementation of precision cancer medicine in Europe.

In the two European Union (EU)-funded projects, PCM4EU (Personalized Cancer Medicine for all EU citizens) and PRIME-ROSE (Precision Cancer Medicine Repurposing System Using Pragmatic Clinical Trials), we aim to facilitate implementation of precision cancer medicine (PCM) in Europe by leveraging the experience from ongoing national initiatives that have already been particularly successful. PCM4EU and PRIME-ROSE gather 17 and 24 partners, respectively, from 19 European countries. The projects are based on a network of Drug Rediscovery Protocol (DRUP)-like clinical trials that are currently ongoing or soon to start in 11 different countries, and with more trials expected to be established soon. The main aims of both the projects are to improve implementation pathways from molecular diagnostics to treatment, and reimbursement of diagnostics and tumour-tailored therapies to provide examples of best practices for PCM in Europe. PCM4EU and PRIME-ROSE were launched in January and July 2023, respectively. Educational materials, including a podcast series, are already available from the PCM4EU website (http://www.pcm4eu.eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024. PCM4EU and PRIME-ROSE were launched in January and July 2023, respectively. Educational materials, including a podcast series, are already available from the PCM4EU website (http://www.pcm4eu.eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024. European collaboration can facilitate the implementation of PCM and thereby provide affordable and equitable access to precision diagnostics and matched therapies for more patients.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond.

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

Overview of Molecular Diagnostics in Irish Clinical Oncology

Background Molecular diagnostics are critical for informing cancer patient care. In Ireland, the National Cancer Control Programme (NCCP) develops cancer therapy regimens, which include relevant information on molecular indications. Here, we present a collated overview of the current molecular indications of all NCCP systemic anti-cancer therapy regimens and the funding statuses of their associated drugs. Furthermore, we also provide estimates for the scale of required molecular testing in cancer therapy and for the clinical genetic sequencing capacity of Ireland, and provide a summary of current cancer clinical trials in Ireland which have molecular components. Methods Through a combination of web scraping, keyword search, and manual review, we performed a full review of all 757 indications included in the 476 therapy regimens published to date by the NCCP to identify therapy indications with explicit molecular criteria. For all cancer types identified in these indications, we obtained incidence rates in Ireland from National Cancer Registry Ireland to predict the number of patients yearly who stand to benefit from a molecular test. We then applied molecular subtype rates from published literature to estimate the number of patients who would then qualify for a relevant molecularly guided therapy. Results We identified 210 indications for 148 NCCP therapy regimens that include molecular criteria. These 210 molecular indications encompassed 85 genetic criteria, 137 cellular biomarker criteria, 57 molecularly informed drugs, and over 20 cancer types. We estimated that up to approximately 50% of cancer patients in Ireland could qualify for a molecular test and that the majority of tested patients would qualify for a treatment informed by a molecular test. Conclusions As personalised cancer medicine continues to develop in Ireland, this study will provide a baseline understanding of current practices. We anticipate that work such as this will help to inform planning in the healthcare system.

A Review on Patient-Derived 3D Micro Cancer Approach for Drug Screen in Personalized Cancer Medicine.

Precision medicine in oncology aims to identify an individualized treatment plan based on genomic alterations in a patient's tumor. It helps to select the most beneficial therapy for an individual patient. As it is now known that no patient's cancer is the same, and therefore, different patients may respond differently to conventional treatments, precision medicine, which replaces the one-size-fits-all approach, supports the development of tailored treatments for specific cancers of different patients. Patient-specific organoid or spheroid models as 3D cell culture models are very promising for predicting resistance to anti-cancer drugs and for identifying the most effective cancer therapy for high-throughput drug screening combined with genomic analysis in personalized medicine. Because tumor spheroids incorporate many features of solid tumors and reflect resistance to drugs and radiation, as in human cancers, they are widely used in drug screening studies. Testing patient-derived 3D cancer spheroids with some anticancer drugs based on information from molecular profiling can reveal the sensitivity of tumor cells to drugs and provide the right compounds to be effective against resistant cells. Given that many patients do not respond to standard treatments, patient-specific treatments will be more effective, less toxic. They will affect survival better compared to the standard approach used for all patients.

Cancer metabolic reprogramming and precision medicine-current perspective.

Despite the advanced technologies and global attention on cancer treatment strategies, cancer continues to claim lives and adversely affects socio-economic development. Although combination therapies were anticipated to eradicate this disease, the resilient and restorative nature of cancers allows them to proliferate at the expense of host immune cells energetically. This proliferation is driven by metabolic profiles specific to the cancer type and the patient. An emerging field is exploring the metabolic reprogramming (MR) of cancers to predict effective treatments. This mini-review discusses the recent advancements in cancer MR that have contributed to predictive, preventive, and precision medicine. Current perspectives on the mechanisms of various cancer types and prospects for MR and personalized cancer medicine are essential for optimizing metabolic outputs necessary for personalized treatments.

Applications of Innovation Technologies for Personalized Cancer Medicine: Stem Cells and Gene-Editing Tools.

Personalized medicine is a new approach toward safer and even cheaper treatments with minimal side effects and toxicity. Planning a therapy based on individual properties causes an effective result in a patient's treatment, especially in a complex disease such as cancer. The benefits of personalized medicine include not only early diagnosis with high accuracy but also a more appropriate and effective therapeutic approach based on the unique clinical, genetic, and epigenetic features and biomarker profiles of a specific patient's disease. In order to achieve personalized cancer therapy, understanding cancer biology plays an important role. One of the crucial applications of personalized medicine that has gained consideration more recently due to its capability in developing disease therapy is related to the field of stem cells. We review various applications of pluripotent, somatic, and cancer stem cells in personalized medicine, including targeted cancer therapy, cancer modeling, diagnostics, and drug screening. CRISPR-Cas gene-editing technology is then discussed as a state-of-the-art biotechnological advance with substantial impacts on medical and therapeutic applications. As part of this section, the role of CRISPR-Cas genome editing in recent cancer studies is reviewed as a further example of personalized medicine application.

Effects of Chemotherapy on the Immune System: Implications for Cancer Treatment and Patient Outcomes.

Chemotherapy is a cornerstone of cancer treatment, but it can also induce immune suppression, which can have significant implications for patient outcomes. This review paper aims to give a general overview of how chemotherapy affects the immune system and how it affects cancer treatment. Chemotherapy can directly affect immune cells, leading to cytotoxic effects, cell differentiation and function alterations, and cell communication and signaling pathways disruptions. Such immune suppression can weaken the anti-tumor immune response and increase the risk of immune-related toxicities. Understanding the mechanisms of chemotherapy-induced immune suppression is crucial for optimizing treatment strategies. Strategies to mitigate immune suppression include immunomodulatory agents as adjuvants to chemotherapy, combination therapies to enhance immune function, and supportive care measures of the immune system. Additionally, identifying potential biomarkers to predict immune suppression and guide treatment decisions holds promise for personalized cancer medicine. Future directions in this field involve further elucidating underlying mechanisms, exploring novel combination therapies, and developing targeted interventions to minimize immune suppression. By understanding and addressing chemotherapy-induced immune suppression, we can optimize cancer treatment strategies, enhance the anti-tumor immune response, and improve patient outcomes.

The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer

The traditional prognostic model may induce the possibility of incorrect assessment of mortality risk under the assumption of linearity. It is urgent to develop a non-linearity precise prognostic model for achieving personalized medicine in lung cancer. In our study, we develop and validate a prognostic model "Modified-DeepSurv" for patients with lung carcinoma based on deep learning and evaluate its value for prognosis, while Cox proportional hazard regression was used to develop another model "CPH." The C-index of the Modified-DeepSurv and CPH was 0.956 (95% confidence interval [CI]: 0.946-0.974) and 0.836 (95% CI: 0.774-0.896), respectively, in the training cohort, while the C-index of the Modified-DeepSurv and CPH was 0.932 (95%CI: 0.908-0.964) and 0.777 (95%CI: 0.633-0.919), respectively, in the test dataset. The Modified-DeepSurv model visualization was realized by a user-friendly graphic interface. Modified-DeepSurv can effectively predict the survival of lung cancer patients and is superior to the conventional CPH model.

The Incidence Trend and Management of Thyroid Cancer-What Has Changed in the Past Years: Own Experience and Literature Review.

Because of ambiguous and widely debated observations concerning the incidence, trend, and management of TC, we performed this analysis. We drew attention to some events, such as "cancer screening activity", introduction of noninvasive follicular neoplasm with papillary-like nuclear features (NIFTP) to TC types, possibility of papillary thyroid microcarcinoma (PTMC) active surveillance (AS), occurrence of personalized medicine in TC management, and, finally, COVID-19 pandemic time. Because of the opinion that all changes have been made mostly by PTC, we compared it to the remaining types of TC in terms of incidence, clinical and pathological characteristics, and treatment. We analyzed patients treated in a single surgical center in eastern Europe (Poland). The prevalence of TC significantly increased from 5.15% in 2008 to 13.84% in 2015, and then significantly decreased to 1.33% in 2022 when the COVID-19 pandemic lasted (p < 0.0001). A similar trend was observed for PTC, when the incidence significantly increased to 13.99% in 2015 and then decreased to 1.38% in 2022 (p < 0.0001). At that time, the NIFTP category was introduced, and observation of PTMC began. The prevalence of FTC and MTC also increased until 2015 and then decreased. Significant differences in age, types of surgery, necessity of reoperation, and pTNM between PTCs and other types of TCs were observed. The average age was significantly lower in PTC patients than in patients with the remaining types of TC (p < 0.0001). Four milestones, including NIFTP introduction, the possibility of PTMC AS, personalized cancer medicine, and the COVID-19 pandemic, may have influenced the general statistics of TC.

Topology of cell-free DNA from tumour cells influences its immunogenicity in macrophages and dendritic cells in vitro

Abstract Activation of DNA-sensing pathways can induce an anti-tumour immune response, but the mechanisms by which tumour-derived cell-free DNA (cfDNA) activates pro-inflammatory signaling is not well-understood. In particular, the topological features of cfDNA that contribute to DNA sensor activation in phagocytes are unknown. As such, we investigated how tumour-derived cfDNA topology influences DNA sensor activation in macrophages and dendritic cells. We treated a murine colon adenocarcinoma cell line (MC38) with chemotherapy (topotecan or staurosporine) for varying durations to induce cfDNA release. We then harvested cfDNA, separated the protected (membrane-bound) and accessible (non-membranous) fractions, and quantified nuclear and mitochondrial cfDNA abundance. The cfDNA fractions were then added to murine RAW 264.7 macrophages or bone marrow-derived dendritic cells (BMDCs), and a panel of inflammatory markers was measured by RT-qPCR. We found that accessible cfDNA from MC38 cells treated with chemotherapy for 48h increased Il6expression in RAW 264.7, but other inflammatory markers were not elevated. Interestingly, both nuclear and mitochondrial cfDNA abundance directly correlated with Il6expression. Surprisingly, cfDNA did not induce pro-inflammatory signalling in BMDCs. These results demonstrate that tumour-derived cfDNA immunogenicity depends on its mechanistic origins, abundance, and the immune cell type to which it is exposed. We are currently examining the impact of other cfDNA biophysical properties on macrophage activation, as well as elucidating the DNA sensing pathways responsible for inducing Il6expression. Our findings will help uncover the role of cfDNA in mediating anti-tumour immunity. E.Z. Malkin is supported by the Frederick Banting and Charles Best Canada Graduate Scholarship (FRN: FBD-175873) through CIHR. S.V. Bratman is supported by the Gattuso-Slaight Personalized Cancer Medicine Fund at the Princess Margaret Cancer Centre and the Dr. Mariano Elia Chair in Head and Neck Cancer Research at University Health Network and the University of Toronto.

Abstract 3166: CanSig: De novo discovery of shared transcriptional states in cancer single cells

Abstract Tumor heterogeneity is regarded as a significant obstacle to successful personalized cancer medicine. Specifically, multiple cancer types have been shown to exhibit heterogeneity in the transcriptional states of malignant cells even within the same tumor. Some of these specific transcriptional states of malignant cells have been linked to cancer relapse and resistance to treatment. However, today there is no universal and easy-to-use computational method to extract information about shared transcriptional states from single-cell RNA sequencing measurements (scRNA-seq). To reliably identify shared transcriptional states of cancer cells, we propose a novel computational tool, CanSig. CanSig automatically preprocesses, integrates, and analyzes cancer scRNA-seq data from multiple patients to provide novel signatures of shared transcriptional states; it also associates these states to known and potentially targetable biological pathways. CanSig jointly analyzes cells from multiple cancer patients while correcting for batch effects and differences in gene expressions caused by genetic heterogeneity. For this, CanSig automatically infers copy number variations in malignant cells and trains a deep learning architecture based on conditional variational autoencoders integrating scRNA-seq measurements. In our benchmarks, CanSig shows state-of-the-art performance in data integration and reliably re-discovers known transcriptional signatures on four previously published cancer scRNA-seq datasets. For instance, CanSig re-discovers four main cellular states of glioblastoma cells previously reported by Neftel et al., Cell, 2019. We further illustrate CanSig’s investigative potential by uncovering signatures of novel transcriptional states in several cancer types; some of the novel signatures are linked to such cancer hallmarks as cell migration and proliferation and are enriched in more advanced tumors. We also uncover signatures associated with specific genomic aberrations such as gene copy number gains. In conclusion, CanSig detects shared transcriptional states in tumors using as input scRNA-seq data for cells with different genetic backgrounds and possibly exhibiting strong batch effects. It can significantly facilitate the analysis of scRNA-seq cancer data and efficiently identify transcriptional signatures linked to known biological pathways. The CanSig method implemented in Python is available at https://github.com/BoevaLab/CanSig Citation Format: Josephine Yates, Florian Barkmann, Pawel Piotr Czyz, Agnieszka Kraft, Niko Beerenwinkel, Valentina Boeva. CanSig: De novo discovery of shared transcriptional states in cancer single cells [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 3166.

Abstract PD12-09: PD12-09 Serum Advanced Glycation End-Products are Associated with Breast Cancer Prognosis

Abstract Background: Tumor characteristics such as grade, stage and receptor status are associated with breast cancer (BC) prognosis. Less is known about modifiable factors and BC prognosis. Advanced glycation end-products (AGEs) are reactive metabolites produced as a by-product of sugar metabolism, generated in conditions of increased oxidative stress. AGEs irreversibly accumulate in our tissues over time with pathogenic effects on genetic fidelity, protein function, cell signaling pathways, and chronic inflammatory diseases. The total body AGE pool is composed of endogenous AGEs and exogenous AGEs (consumed mainly through processed foods and those cooked at high temperatures). Serum AGE (sAGE) levels, a reflection of total body AGE, are higher in people with poor diet quality, lower levels of physical activity, and in women with BC compared to healthy controls. AGEs promote growth, migration and invasion in BC cell lines and activate prognostic inflammatory mediators such as interleukin-6 and C-reactive protein. Dietary AGEs have been associated with increased BC risk and increased mortality after BC diagnosis whereas lifestyle interventions can lower dietary and sAGE levels. The impact of sAGE levels, a better estimate of total body AGE than dietary AGE, on BC prognosis has not been previously evaluated. Methods: The Women’s Healthy Eating and Living (WHEL) study randomized 3088 BC patients stage I-III who completed their primary therapy to a high-vegetable, low-fat diet or control and followed for a median of 7.3 years. Main outcomes were invasive BC events (recurrence or new primary N=518), death due to BC (N=262) and deaths from any cause (N=315). sAGE was measured as the AGE metabolite carboxymethyllysine (ug/ml) from WHEL fasting blood specimens at study entry. sAGE was logged and corrected for plate batch effect via linear regression and analyzed in continuous scale and in quintiles. The Kaplan-Meier method and Cox regression model were performed for risk impact of sAGE on overall survival (OS), recurrence free survival (RFS), breast cancer specific survival (BCSS) and distant metastasis free survival (DMFS). We additionally adjusted in Cox models for potential confounding variables (age, race, BMI, smoking, alcohol use, physical activity, tumor characteristics). Results: 2564 participants had sAGE available. After excluding samples for excessive variabilities, 2315 samples were analyzed. Raw corrected sAGE ranged from 0.0-48.15 ug/ml (median 7.39); logged and corrected range -5.04-1.67. sAGE was positively associated with BMI (p&amp;lt;.0001, rs 0.10) and negatively associated with physical activity (p&amp;lt; .001, rs.-0.06). sAGE was not significantly associated with tumor stage, grade, receptor status, or race or menopausal status. Comparing the highest quintile (logged and corrected range=0.33~1.67) to the lowest quintile (range=-5.04~-0.30), sAGE was significantly associated with all survival outcomes (Table 1). As a continuous variable, AGE was associated with worse OS (HR 1.38, P=.031, β .32) and RFS (HR 1.29, P=.028, β .25) with a trend towards worse DMFS (HR 1.28, P=.067, β .25) and BCSS (HR 1.36, P=.06, β .31). Conclusions: Higher sAGEs are associated with worse survival outcomes in BC and may represent a novel, lifestyle-linked, modifiable prognostic biomarker in BC. Interventions aimed at lowering sAGE levels should be tested for their impact on known prognostic biomarkers as well as clinical outcomes. Individualized cancer-specific lifestyle recommendations are a crucial but currently lacking component of personalized cancer medicine. Citation Format: Lindsay L. Peterson, Yu Tao, Jingqin Luo, Graham A. Colditz, Yikyung Park, Jennifer A. Ligibel, David Turner. PD12-09 Serum Advanced Glycation End-Products are Associated with Breast Cancer Prognosis [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 PD12-09.