Personalized Precision Medicine Research Articles

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti–vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Luminescence-activated Pt(IV) prodrug for in situ triggerable cancer therapy

Anticancer platinum prodrugs that can be controllably activated are highly desired for personalized precision medicine and patient compliance in cancer therapy. However, the clinical application of platinum(IV) prodrugs (Pt(IV)) is restricted by tissue penetration of external irradiation. Here, we report a novel Pt(IV) activation strategy based on endogenous luminescence of tumor microenvironment responsiveness, which completely circumvents the limitation of external irradiation. The designed Pt(IV)Lu, a mixture of trans, trans, trans-[Pt(N3)2(OH)2(py)2] and luminol (Lu), has controllable activation property: it remains inert in reductant environment and normal tissues, but under tumor microenvironment, Lu will be oxidized to produce blue luminescence, which rapidly reduce Pt(IV) to Pt(II) without the need of any external activator. Pt(IV)Lu shows excellent responsive antitumor ability both in vitro and in vivo. Compared to cisplatin, the median lethal dose in BALB/c mice increased by an order of magnitude. Our results suggest that Pt(IV)Lu exhibits highly controllable activation property, superior antitumor activity, and good biosafety, which may provide a novel strategy for the design of platinum prodrugs.

Extracellular Vesicles from Stromal Vascular Fraction of Human Adipose Tissue in the Development of Non-antibiotic Therapy.

Antibiotic-resistant microorganisms (ARMS) are the leading cause of socio- economic loss in the world, with historical evidence linking them to increased mortality and morbidity. In this systematic review, we highlight a new treatment approach for antibiotic-resistant infections using 'Extracellular vesicle (EVs)-based therapy,' also known as cell- and drug-free therapy. Here, we categorize and summarize studies on EVs derived from various human sources, such as tissues, bodily fluids, or their condition media, emphasizing their anti-infective properties in the treatment of various infections. In addition, we contend that human adipose tissue (HAT) is a superior source of antimicrobial EVs (aEVs) and investigate the distinct antimicrobial properties of aEVs derived from a stromal vascular fraction (SVF) of human adipose tissue. In light of this, we described the limited literature and research gaps that are essential for using SVF-aEVs as personalized precision medicine. The notion behind adipose-derived SVF-EVs is supported by extensive literature searches that demonstrate growing trends in EV-based medical treatments as well as the larger therapeutic potential of HAT because of its extensive history of usage in regenerative medicine. Additionally, the underlying science that explains how the inflammatory process aids in the clearance of infections and the restoration of homeostasis after the host immune system successfully defends against foreign pathogens, as well as the fact that adipose-derived SVF is a noninvasive, cost-effective source of a variety of parent immune cells that produces a good yield of EVs with the same genetic make-up as their parent cells, make this concept worthwhile. This research may thereby increase survival rates and survival quality in cases of resistant infections. Vocabulary: Drug- and cell-free therapy = Nano molecules (extracellular vesicles) used as a therapeutic source without the need for chemical drugs or cell transplantation. Anti-infection EVs (aEVs) = Nature's own anti-infection powered EVs (unmodified).

Development of precision therapies for rare inborn errors of metabolism: Functional investigations in cell culture models.

Due to the low number of patients, rare genetic diseases are a special challenge for the development of therapies, especially for diseases that result from numerous, patient-specific pathogenic variants. Precision medicine makes use of various kinds of molecular information about a specific variant, so that the possibilities for an effective therapy based on the molecular features of the variants can be elucidated. The attention to personalized precision therapies has increased among scientists and clinicians, since the "single drug for all patients" approach does not allow the classification of individuals in subgroups according to the differences in the disease genotype or phenotype. This review article summarizes some approaches of personalized precision medicine that can be used for a cost-effective and fast development of therapies, even for single patients. We have focused on specific examples on inborn errors of metabolism, with special attention on drug repurposing. Furthermore, we provide an overview of cell culture models that are suitable for precision medicine approaches.

Lymph node metastasis in oral squamous cell carcinoma: Where we are and where we are going

AbstractBackgroundOral cancer is a common malignant tumour in the head and neck region, with over 90% of cases being oral squamous cell carcinoma (OSCC). Lymph node metastasis (LNM) is a significant adverse prognostic factor in OSCC; however, the mechanism of LNM in OSCC remains unclear, while the strategies for managing cervical lymph nodes (CLNs) in OSCC continue to evolve. At present, neck dissection is necessary for the vast majority of OSCC patients, but complications of surgery can significantly impair patients’ postoperative quality of life. A recent clinical trial discovery indicates that immunotherapy can activate anti‐tumour T cells in nearby lymph nodes, leading to vibrant discussions among clinicians about the importance of preserving lymph nodes during surgical treatment.AimThis review aims to provide an overview of where we are: the current knowledge of OSCC LNM patterns and management strategies, and where we are going: prospects for clinical and basic research on the issue of OSCC LNM in the era of cancer immunotherapy.ResultWe summarize the general patterns and management strategies of OSCC LNM. The process of LNM in OSCC encompasses four stages: Preparation, Unleash, Migration, and Planting (‘PUMP’ principle). We propose adopting the ‘PRECISE’ model, an overall workflow that includes seven elements—prevention, radiology, preoperative evaluation, chemotherapy, immunotherapy, surgery, and postoperative evaluation—for neck management in OSCC, promoting personalized precision medicine augmented by neoadjuvant therapy. We further discussed recent advances in clinical and basic research on OSCC LNM and provided an outlook on future research directions.ConclusionOver the past two centuries, our understanding and management strategies of LNM in OSCC have evolved, seeking more precise and personalized approaches to reduce patient burden and enhance survival and quality of life. Further research should explore lymph nodes’ role in cancer immunotherapy and OSCC interactions, leading to better, less invasive treatments and improved outcomes.

Research progress in mRNA drug modification and its delivery system.

Messenger RNA (mRNA) has shown tremendous potential in disease prevention and therapy. The clinical application requires mRNA with enhanced stability and high translation efficiency, ensuring it not to be degraded by nuclease and to target to specific tissues and cells. The mRNA immunogenicity can be reduced by nucleotide modification, and its translation efficiency can be enhanced by codon optimization. The 5´ capping structure and 3´ poly A increase mRNA stability, and the addition of 5' and 3' non-translational region regulates mRNA translation initiation and protein production. A nanoparticle delivery system protects mRNA from degradation by ubiquitous nuclease, enhances its circulation concentration and assists it entrancing cytoplasm for the purpose of treatment and prevention. In this article, we review the recent advances of mRNA technology, discuss the methods and principles to enhance mRNA stability and translation efficiency; summarize the requirements involved in designing mRNA delivery systems with the potential of industrial translation and biomedical application. Furthermore, we provide insights into future directions of mRNA therapeutics to meet the need for personalized precision medicine.

Glioblastoma-on-a-chip construction and therapeutic applications.

Glioblastoma (GBM) is the most malignant type of primary intracranial tumor with a median overall survival of only 14 months, a very poor prognosis and a recurrence rate of 90%. It is difficult to reflect the complex structure and function of the GBM microenvironment in vivo using traditional in vitro models. GBM-on-a-chip platforms can integrate biological or chemical functional units of a tumor into a chip, mimicking in vivo functions of GBM cells. This technology has shown great potential for applications in personalized precision medicine and GBM immunotherapy. In recent years, there have been efforts to construct GBM-on-a-chip models based on microfluidics and bioprinting. A number of research teams have begun to use GBM-on-a-chip models for the investigation of GBM progression mechanisms, drug candidates, and therapeutic approaches. This review first briefly discusses the use of microfluidics and bioprinting technologies for GBM-on-a-chip construction. Second, we classify non-surgical treatments for GBM in pre-clinical research into three categories (chemotherapy, immunotherapy and other therapies) and focus on the use of GBM-on-a-chip in research for each category. Last, we demonstrate that organ-on-a-chip technology in therapeutic field is still in its initial stage and provide future perspectives for research directions in the field.

From the Histological Model to the Mutational Model: The Study of Heavy Metals and Other Substances in New Antineoplastic Therapies

The study of genetic mutations in tumours changes the therapeutic approach; in fact, we move into a different advanced and strategic phase by entering personalized precision medicine and shifting the focus from the tissue study of the tumour to the modification and proliferation of neoplastic cells. The therapeutic programs, especially in oncology, are changing. We are in a phase of transition from the histological model to the model of genetic mutation (mutation). When a person's immune defences do not respond to a disease it is because the body cannot produce or activate specific lymphocytes and antibodies against that disease. This causes of a lack of therapeutic response or recurrence of the disease. A fundamental role is played by genomic tests, which are carried out directly on the neoplastic tissue and other modifies tissues, and the dosage of toxic substances (heavy metals, dioxins, furans, PCBs, etc.) analysed 'primarily' in the tumour and subsequently in the various biological matrices (tissues, modified, blood, urine, hair, nails, breast milk, saliva, etc.) as well as genetic tests. A significant role is a search for toxic substances and therapeutic integration of trace elements beneficial for cellular metabolism, especially for defence mechanisms, absent or deficient in these patients, such as Copper, Selenium, Zinc, Cobalt, Iron, and Manganese to improve or modify a lack of therapeutic response. Another parameter to consider is hyperglycaemia in diabetic subjects, which according to various scientific research, is considered a personal risk factor not so much in the formation phase of the neoplastic disease, but in the healing response phase.

Physical function and severe side effects matter most to patients with RA (< 5 years): a discrete choice experiment assessing preferences for personalized RA treatment

AimEarly assessment of patient preferences has the potential to support shared decisions in personalized precision medicine for patients with rheumatoid arthritis (RA). The aim of this study was to assess treatment preferences of patients with RA (< 5 years) with previous experience of inadequate response to first-line monotherapy.MethodPatients were recruited (March–June 2021) via four clinics in Sweden. Potential respondents (N = 933) received an invitation to answer a digital survey. The survey included an introductory part, a discrete choice experiment (DCE) and demographic questions. Each respondent answered 11 hypothetical choice questions as part of the DCE. Patient preferences and preference heterogeneity were estimated using random parameter logit models and latent class analysis models.ResultsPatients (n = 182) assessed the most important treatment attributes out of physical functional capacity, psychosocial functional capacity, frequency of mild side effects and likelihood of severe side effects. In general, patients preferred a greater increase in functional capacity and decreased side effects. However, a substantial preference heterogeneity was identified with two underlying preference patterns. The most important attribute in the first pattern was the ‘likelihood of getting a severe side effect’. Physical functional capacity was the most important attribute in the second pattern.ConclusionRespondents focused their decision-making mainly on increasing their physical functional capacity or decreasing the likelihood of getting a severe side effect. These results are highly relevant from a clinical perspective to strengthen communication in shared decision making by assessing patients’ individual preferences for benefits and risks in treatment discussions.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma.

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

Personalizing Personalized Medicine: The Confluence of Pharmacogenomics, a Person's Medication Experience and Ethics.

Truly personalized precision medicine combines pharmacogenomics (PGx), a person's lived medication experiences and ethics; person-centeredness lies at the confluence of these considerations. A person-centered perspective can help inform PGx-related treatment guidelines, shared decision-making for PGx-related therapeutics and PGx-related healthcare policy. This article examines the interplay between these components of person-centered PGx-related care. Ethics concepts addressed include privacy, confidentiality, autonomy, informed consent, fiduciary responsibility, respect, the burden of pharmacogenomics knowledge for both the patient and healthcare provider and the pharmacist's ethical role in PGx-testing. Incorporating the patient's lived medication experience and ethics principles into PGx-based discussions of treatment can optimize the ethical, person-centered application of PGx testing to patient care.

A Review on the Full Chain Application of 3D Printing Technology in Precision Medicine

Personalized precision medicine is a new direction for medical development, and advanced manufacturing technology can provide effective support for the development of personalized precision medicine. Based on the layered accumulation manufacturing principle, 3D printing technology has unique advantages in personalized rapid manufacturing, and can form complex geometric shape parts at low cost and high efficiency. This article introduces the application progress of 3D printing technology in medical models, surgical navigation templates, invisible aligners, and human implants, analyzes their advantages and limitations, and provides an outlook for the development trend of 3D printing technology in precision medicine.

Abstract A033: Exploring synthetic lethality of targeting miR346-unfolded protein response dependent DNA damage response mechanisms in treatment-resistant prostate cancer

Abstract Background: The Unfolded Protein Response (UPR) is a key homeostatic mechanism that is activated during endoplasmic reticulum (ER) stress caused by both intrinsic (genomic instability, metabolic demand) and extrinsic (chemo/radiotherapy, nutrient deprivation, hypoxia) factors. IRE1 is the main UPR transducer, signalling the transcription factor XBP1, and IRE1 Regulated Dependent Decay (RIDD) to resolve ER stress by controlling the expression of mRNA and miRNAs. IRE1 also regulates aspects of the DNA damage response (DDR) and IRE1-binding drugs have clinical effects as monotherapies or as adjuvants to chemotherapeutics. One miRNA involved in both the DDR and IRE1 biology is miR-346, which we have shown to cause widespread DNA damage in prostate cancer (PCa), sensitising PCa to DNA damaging drugs and associating with improved PCa outcomes. Rationale: In RNAseq data datasets we have shown that miR-346 causes a widespread transcriptional downregulation of DDR markers whilst we observed the opposite in IRE1-CRISPR-KO PCa cells. We have also developed novel IRE1 modulators that sensitise cancers to chemotherapy in vivo. These findings offer a strong rationale to explore co-targeting of miR-346 and IRE1 in the presence of DNA damaging agents to treat aggressive forms of PCa. Consequently, we want to elucidate the exact biology underpinning the miR346-IRE1/XBP1/RIDD-DDR axis to prognosticate treatment groups with gene signatures as proxies of PARPi/chemotherapeutic susceptibility. Impact: If miR-346 is a downstream effector of IRE1 activity, then an IRE1 prognostic signature can act as proxy for stratification and selection of patients for miR-346 treatment as sensitiser to DNA damaging agents whilst if miR-346 and IRE1 work in parallel, we could achieve clinically significant results by exploiting synthetic lethality. By stratifying large cohorts through IRE1/miR-346/DDR signatures (personalised precision medicine) we can increase the pool of patients for whom DNA damaging agents are effective by inducing a BRCAness phenotype, improving treatment responses and quality of life. Citation Format: Dimitrios Doultsinos, Ian Mills, Claire Fletcher. Exploring synthetic lethality of targeting miR346-unfolded protein response dependent DNA damage response mechanisms in treatment-resistant prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A033.

Spatially Resolved Metabolomics Method for Mapping the Global Molecular Landscape of Whole-Body Zebrafish (Danio rerio) Using Ambient Mass Spectrometry Imaging.

Zebrafish (Danio rerio) represent an effective model biological material for human disease research, even for personalized precision medicine. Thus, it is necessary to fully characterize their molecular information in order to obtain a global metabolic profile. Here, a spatially resolved metabolomics method for whole-body zebrafish analysis was established based on an air-flow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) system. Using the optimized experimental conditions, the method provided high-quality visual distribution information for >1000 functional metabolites, thereby organ-specific metabolites characterizing nine regions were obtained comprehensively, including the eyes, brain, gill, heart, liver, kidney, intestine, muscle, and spinal cord. Then, combined with metabolic pathway analysis, a global metabolic network with in situ information on zebrafish was mapped for the first time. We also tried to use the recently published MSI database to annotate the metabolites in this study; however, the annotation rate was only 33.7 and 10.4% in positive and negative modes, respectively. This further demonstrated the necessity of establishing a suitable AFADESI-MSI method for zebrafish samples. These results offer comprehensive and in-depth molecular information about zebrafish at the metabolic level, which facilitates the use of zebrafish models to understand metabolic reprogramming in human diseases and the development of zebrafish disease models.

Patient-derived organoid culture of gastric cancer for disease modeling and drug sensitivity testing

BackgroundGastric cancer treatment is complicated by the molecular heterogeneity of human tumor cells, which limits the efficacy of standard therapy and necessitates the need for personalized treatment development. Patient-derived organoids (PDOs) are promising preclinical cancer models, exhibiting high clinical efficacy in predicting drug sensitivity, thus providing a new means for personalized precision medicine. MethodsPDOs were established from surgically resected gastric cancer tumor tissues. Molecular characterization of the tumor tissues and PDOs was performed using whole-exome sequencing analysis. Drug sensitivity tests were performed by treating the PDO cultures with 21 standard-of-care drugs corresponding to patient treatment. We evaluated whether the PDO drug phenotype reflects the corresponding patient's treatment response by comparing the drug sensitivity test results with clinical data. ResultsTwelve PDOs that satisfied the drug sensitivity test criteria were successfully constructed. PDOs closely recapitulated the pathophysiology and genetic changes in the corresponding tumors, and exhibited different sensitivities to the tested drugs. In one clinical case study, the PDO accurately predicted the patient's sensitivity to capecitabine and oxaliplatin, and in a second case study the PDO successfully predicted the patient's insensitivity to S-1 chemotherapy. In summary, six of the eight cases exhibited consistency between PDO drug susceptibility test results and the clinical response of the matched patient. ConclusionsPDO drug sensitivity tests can predict the clinical response of patients with gastric cancer to drugs, and PDOs can therefore be used as a preclinical platform to guide the development of personalized cancer treatment.

Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features

To investigate the prognostic value of the radiomic-based prediction model in predicting the interval growth rate of persistent subsolid nodules (SSNs) with an initial size of ≤ 3 cm manifesting as lung adenocarcinomas. A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent a series of CT examinations at our hospital between 2012 and 2022 were included in this study. Forty-one radiomic features were extracted from each volumetric region of interest. Radiomic features combined with conventional clinical and semantic parameters were then selected for radiomic-based model building. To investigate the model performance in terms of substantial SSN growth and stage shift growth, the model performance was compared by the area under the curve (AUC) obtained by receiver operating characteristic analysis. The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based model (Model 2) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.869 (95% CI: 0.799-0.922). In comparison with Model 1 (clinical characteristics and CT semantic features), Model 2 performed better than Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p=0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic features was built for substantial SSN growth prediction. For the stage shift growth, a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815-0.933). Compared with Model 3 (clinical characteristics and CT semantic features), Model 4 performed better than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883, p=0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification, follow-up period, and three radiomic features was built to predict the stage shift growth. Radiomic-based models have superior utility in estimating the prognostic interval growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic models in terms of substantial interval growth and stage shift growth, potentially guiding clinical decision-making with follow-up strategies of SSNs in personalized precision medicine.

Personalized precision medicine of strategic health action in Niemann-Pick type A/B disease: Case report

Niemann-Pick Diseases (NPD) is lysosomal storage diseases caused by Acid Sphingo Myelinase (ASM) deficiency, which catalyzes the hydrolysis of Sphingo Myelin

Abstract 4670: True bench-to-bedside science: An international pilot study modeling hard-to-cure pediatric cancers to prioritize therapeutic intervention

Abstract There has been dramatic improvement in treatment outcomes for many pediatric cancers over the last three decades. However, for the 20% of young people with relapsed or refractory cancer, the prognosis remains grim. Canada’s PRecision Oncology For Young peopLE (PROFYLE) and Australia’s Zero Childhood Cancer (ZERO) programs, leverage nation-wide scientific and clinical oncology expertise to provide personalized precision medicine to children, adolescent and young adult (CAYA) cancer patients who lack treatment options. Beyond improving the lives of young cancer patients nationally, PROFYLE and ZERO have partnered to create an international pipeline for knowledge sharing and sample acquisition. A key component of both programs is the use of mice for patient-derived xenografts (PDXs). However, long lead times for mouse PDX generation make the timely return of preclinical drug response data challenging. PROFYLE has uniquely incorporated zebrafish larval xenografts, which have the potential to provide comparable information in a clinically actionable timeframe. In a pilot study, we compared retrospective matched ZERO patient and mouse PDX therapeutic response data with prospective zebrafish larval PDX data as a proof-of-principle that drug efficacy signals were maintained across model systems. Three ZERO avatars: high-risk neuroblastoma, Ewing’s sarcoma, and anaplastic large cell lymphoma were shipped from Australia to Canada and transplanted into 48h casper zebrafish. Following dose optimization, zebrafish PDXs were treated with targeted single and combination drug treatments by immersion therapy. Strikingly, in as little time as a week, cell proliferation rates and drug responses to single agents and combinatorial therapy in zebrafish PDXs recapitulated mouse and patient data. To expand on this pilot project, we tested additional patient samples, including multiple subtypes of sarcomas, T-cell acute lymphoblastic leukemia and an embryonal tumor with multilayered rosettes. Results further validated the practical utility of the zebrafish larval PDX model and in fact provided drug response data when mouse PDX data were unavailable. This study demonstrates the robustness and feasibility of the zebrafish larval PDX model as a preclinical tool for personalized precision therapeutic decision-making and highlights the value of international collaboration in improving outcomes of rare childhood cancers. Citation Format: Nadine Azzam, Nicole Melong, Lissandra Tuzi, Lisa Pinto, Jamie I. Fletcher, Alvin Kamili, Biljana Dumevska, Loretta Lau, Jennifer A. Chan, Donna L. Senger, Stephanie A. Grover, Michelle Haber, David Malkin, Jason N. Berman. True bench-to-bedside science: An international pilot study modeling hard-to-cure pediatric cancers to prioritize therapeutic intervention. [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 4670.

Patient-derived models: Promising tools for accelerating the clinical translation of breast cancer research findings

Breast cancer has become the highest incidence of cancer in women. It was extensively and deeply studied by biologists and medical workers worldwide. However, the meaningful results in lab researches cannot be realized in clinical, and a part of new drugs in clinical experiments do not obtain as good results as the preclinical researches. It is urgently that promote a kind of breast cancer research models that can get study results closer to the physiological condition of the human body. Patient-derived models (PDMs) originating from clinical tumor, contain primary elements of tumor and maintain key clinical features of tumor. So they are promising research models to facilitate laboratory researches translate to clinical application, and predict the treatment outcome of patients. In this review, we summarize the establishment of PDMs of breast cancer, reviewed the application of PDMs in clinical translational researches and personalized precision medicine with breast cancer as an example, to improve the understanding of PDMs among researchers and clinician, facilitate them to use PDMs on a large scale of breast cancer researches and promote the clinical translation of laboratory research and new drug development.

MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review.

Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.