Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review
Abstract
:1. Introduction
- To explore WT’s clinical presentations and biological characteristics.
- To evaluate current and emerging therapeutic strategies.
- To illuminate molecular genetics’ role in refining patient management.
- Publication Date: Articles published within the last five years.
- Language: Articles in English.
- Relevance: Studies that specifically address advancements in personalized medicine for WT, including genetic and molecular diagnostics, novel therapeutic strategies, and prognostic tools.
- Study Type: High-impact research articles, reviews, and clinical trial reports.
- Non-English Publications: Articles not published in English.
- Irrelevant Focus: Studies that do not specifically pertain to personalized medicine in WT.
- Low Impact: Articles published in low-impact or non-peer-reviewed journals.
2. Background
- Ultrasound and MRI as essential tools in the diagnostic pathway.
- Emerging molecular diagnostics for precision prognostication.
3. Diagnosis of Wilms Tumor
4. Histologic Subtypes and Prognostic Implications in Wilms Tumor
4.1. Histologic Subtypes of Wilms Tumor
4.2. Prognostic Implications
5. Prognosis and Risk Stratification
5.1. Tumor Characteristics and Molecular Insights
5.1.1. Predictive Prognostication and Personalized Therapies
5.1.2. Prognostic Markers and Treatment Response
5.1.3. Global Efforts in Advancing Personalized Diagnostics
6. Prediction
7. Management Strategies and Innovations in Wilms Tumor Therapy
- Genetic Profiling: Utilize next-generation sequencing (NGS) and whole-genome sequencing (WGS) to identify critical genetic mutations and alterations associated with Wilms tumor (WT), such as WT1, CTNNB1, and WTX mutations. This genetic information can stratify patients based on risk profiles and guide targeted therapeutic interventions.
- Epigenomic Analysis: Conduct epigenomic profiling to identify DNA methylation patterns and histone modifications that may influence gene expression in WT. This can help understand tumor behavior and potential resistance mechanisms.
- Transcriptomic Data Integration: Use RNA sequencing (RNA-seq) to analyze gene expression profiles. Differentially expressed genes can serve as biomarkers for prognosis and treatment response, aiding in the customization of therapy.
- Radiomic Data Utilization: Incorporate advanced imaging techniques such as diffusion-weighted MRI and radiomic analysis to assess tumor characteristics non-invasively. Radiomic features can be correlated with genetic and transcriptomic data to enhance predictive accuracy.
- Data Integration and Machine Learning: Develop models integrating genetic, epigenomic, transcriptomic, and radiomic data. These models can predict patient outcomes, monitor treatment response, and guide personalized treatment strategies.
- Clinical Implementation: Establish multidisciplinary teams to interpret integrated data and make informed clinical decisions. Regularly update predictive models with new data from clinical trials and patient registries to refine their accuracy and applicability.
8. Discussion
- Precision: Allows for more targeted therapies based on individual genetic profiles, improving treatment efficacy.
- Customization: Enables customization of treatment plans, potentially leading to better patient outcomes.
- Accessibility: Genetic testing may not be widely available in all clinical settings.
- Cost: High costs associated with advanced genetic testing and targeted therapies.
- Risk Stratification: Provides a more precise assessment of patient risk, allowing for tailored treatment intensities.
- Improved Outcomes: Correlating molecular biomarkers with treatment resistance can lead to better management of high-risk patients.
- Validation: Requires further validation in diverse populations to ensure accuracy and applicability.
- Implementation: Challenges in integrating these models into routine clinical practice due to complexity.
- Efficacy: Potentially higher efficacy in treatment-resistant cases.
- Reduced Toxicity: Lower risk of long-term side effects compared to conventional chemotherapy.
- Limited Data: Insufficient long-term data on the safety and effectiveness of these therapies.
- High Costs: Advanced therapies can be expensive and may not be accessible to all patients.
- Non-invasive: Provides a non-invasive method for tracking tumor characteristics and treatment response.
- Precision: Enhances the accuracy of staging and treatment planning.
- Cost: High cost of advanced imaging techniques.
- Validation: Requires further validation and standardization for routine clinical use.
9. Limitations, Knowledge Gaps, and Future Directions
- Population Diversity: Many predictive models are developed using data from specific demographic or geographic populations, which may not represent the broader patient population. This can limit the generalizability of the models.
- Data Quality and Consistency: The accuracy of predictive models depends on the quality and consistency of the data used. Variability in data collection methods and clinical practices can affect model performance and reliability.
- Validation in Diverse Populations: Predictive models must be validated in diverse populations to ensure their applicability across racial and ethnic groups. This requires international collaboration and the establishment of global patient registries.
- Model Complexity and Interpretability: While powerful, complex machine-learning models can be challenging to interpret and implement in clinical practice. To facilitate clinical adoption, efforts should be made to balance model complexity with interpretability.
- Genetic Mutations: Mutations in genes such as TP53 and MYCN have been associated with resistance to chemotherapy and poor prognosis in WT patients. These mutations can lead to cell cycle regulation and apoptosis alterations, making tumor cells less responsive to treatment.
- Epigenetic Modifications: Epigenetic changes, such as DNA methylation and histone modifications, can influence gene expression and contribute to treatment resistance. For example, hypermethylation of the RASSF1A gene has been linked to resistance to chemotherapeutic agents in WT.
- Tumor Microenvironment: The tumor microenvironment, including immune cells, stromal cells, and extracellular matrix components, can impact treatment response. For instance, tumor-associated macrophages (TAMs) have been correlated with resistance to chemotherapy and radiotherapy.
- Drug Efflux Mechanisms: Overexpression of drug efflux transporters, such as P-glycoprotein, can decrease intracellular concentrations of chemotherapeutic agents, resulting in treatment resistance. Targeting these transporters may enhance the efficacy of chemotherapy.
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
WT | Wilms tumor |
SIOP | The Société Internationale d’Oncologie Pédiatrique (International Society of Pediatric Oncology) |
COG | Children’s Oncology Group |
NWTS | The National Wilms Tumor Study Group |
US | Ultrasonography |
CT | Computed Tomography |
MRI | Magnetic Resonance Imaging |
ddPCR | Digital Droplet Polymerase Chain Reaction |
ROC | Receiver Operating Characteristic |
SEER database | Surveillance, Epidemiology, and End Results Program |
OS | Overall Survival |
CSS | Cancer-Specific Survival |
AUCs | Area Under the Curve |
EFS | Event-Free Survival |
HCT | Hematopoietic Cell Transplantation |
GWA | Genome-Wide Analysis |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
TAMs | Tumor-Associated Macrophages |
ELISA | Enzyme-Linked Immunosorbent Assay |
LND | Lymph Node Density |
PHB | Prohibitin |
GEO | The Gene Expression Omnibus |
TARGET | Therapeutically Applicable Research to Generate Effective Treatments |
lncRNAs | Long Non-Coding RNAs |
IMRT | Intensity-Modulated Radiation |
CAR T-Cell treatment | Chimeric Antigen Receptor T-Cell treatment |
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Study | Objective | Methods | Sample | Key Findings | Implications |
---|---|---|---|---|---|
Hu et al. [29] | Explore WT1 gene mutations in bilateral WT | High-res melting, sequencing, Western blot | 8 bilateral WT patients | High rate of WT1 mutations in exon 8; early onset of bilateral WT | Personalized genetic screening could enable earlier and more targeted interventions for patients with a familial predisposition to bilateral WT. |
Song et al. [30] | Evaluate the SIX2 gene in WT diagnosis/prognosis | MSP, qRT-PCR, ROC analysis | Tissues from 38 WT patients | AUCs indicate the high diagnostic utility of SIX2 expression in blood | Utilizing blood-based biomarkers such as SIX2 expression can enhance non-invasive screening and early diagnosis, tailoring individual patient management. |
Waberski et al. [25] | Assess PIK3CA mutations in PROS and WT | ddPCR on plasma, urine cfDNA | Samples from PROS patients | The high sensitivity of ddPCR for PIK3CA variants; potential urine cfDNA as WT marker | Detecting PIK3CA mutations in urine cfDNA offers a less invasive method for identifying WT, facilitating a personalized approach to diagnosis. |
Brillantino et al. [31] | Detect somatic mutations in WT at diagnosis | WES, Ion Reporter variant detection | WT tissues from surgery | Somatic mutations were identified in all cases; urine/plasma was the less invasive monitoring tool | Identifying somatic mutations through less invasive samples like urine or blood enables ongoing, non-invasive monitoring of disease status, which can be personalized to the mutation profile of a patient’s tumor. |
Rickard et al. [32] | Semi-automatic volumetric assessment of WT and renal parenchyma | MATLAB-3D volumetric imaging, segmentation | 98 CT scans of WT patients | Effective capture of treatment responses; preservation of parenchyma post-surgery | Volumetric imaging allows for personalized and precise assessment of tumor response and kidney preservation, informing tailored surgical strategies. |
Chaussy et al. [33] | Utilize 3D reconstruction for WT surgical planning | 3D Slicer software, inter-individual Dice index assessment | 14 scans from 12 patients | High Dice index for WT and healthy kidney; feasibility of 3D reconstruction for NSS | 3D reconstruction for surgical planning can lead to more personalized surgeries that minimize the impact on healthy tissue and improve postoperative outcomes. |
Trink et al. [34] | Characterize WT heterogeneity using gene expression | Microarray data, PCA, CPM algorithm | 53 CEL files from the GEO database | Identification of distinct WT types and unique cell populations | Characterizing WT heterogeneity through gene expression allows for more precise tumor classification, which can guide tailored treatment decisions. |
Study | Objective | Methods | Sample | Key Findings | Implications |
---|---|---|---|---|---|
Provenzi et al. [42] | Examine tumor volume changes post-chemotherapy | Statistical analyses: Chi-square, Kaplan–Meier, Cox regression | Patients from 1989 to 2009 | The significant relationship between post-chemotherapy tumor volume >500 mL and survival | Large tumor volume post-chemotherapy reveals a need for potentially more aggressive or alternative treatment paths in personalized care planning. |
Tang et al. [43] | Develop nomograms for survival prediction | Cox regression, ROC curves | 1613 WT patients (SEER database) | Nomograms predicted 3- and 5-year OS and CSS with AUCs 0.65–0.74 | Nomograms aid in creating personalized survival predictions, directly influencing patient-specific treatment decisions. |
Malogolowkin et al. [46] | Investigate post-transplant survival | Kaplan–Meier and Cox models | 253 relapsed WT patients’ post-chemotherapy and HCT | 5-year EFS and OS were 36% and 45%, respectively | Highlighting post-transplant survival challenges informs tailored follow-up protocols and personalized intervention strategies. |
Cresswell et al. [47] | Explore genetic heterogeneity within WT | Genome-wide analysis (GWA), MEDICC algorithm | 20 WT cases | Multiple samples needed to identify genetic heterogeneity and evolution | Understanding intra-tumor genetic heterogeneity aids in developing personalized risk assessment and treatment modifications. |
Cone et al. [48] | Review biomarkers for prognostic significance | PRISMA guidelines, systematic reviews, and meta-analyses | 32 biomarkers in 7381 WT patients | 11p15 loss of heterozygosity strongly predicts recurrence | Biomarkers help to tailor prognostic evaluations and personalize therapeutic approaches based on individual risk profiles. |
Diets et al. [49] | Assess TRIM28 as a prognostic marker | Exome sequencing, tumor DNA analysis | 31 WT cases | Identified TRIM28 mutations in germline and somatic samples | TRIM28’s role accentuates the need for personalized molecular diagnostics and tailored therapeutic interventions. |
Liu et al. [50] | Analyze the role of miR-144-3p in WT | qRT-PCR, bioinformatics, functional assays | 80 WT tissues and matched standard samples | Downregulation of miR-144-3p linked to increased tumor aggressiveness | The miR-144-3p levels are potential personalized markers for monitoring treatment efficacy and adjusting therapeutic strategies. |
Fernandez et al. [51] | Assess the association of biomarkers with WT recurrence | Analysis of AREN0532 study cases, biomarker evaluation | 116 WT patients | 11p15 methylation status correlated with relapse risk | Methylation status could aid in shaping individualized therapeutic strategies by predicting relapse risk. |
You et al. [52] | Evaluate lymph node density’s impact on survival | Kaplan–Meier, Cox regression models | 1924 WT patients (SEER database) | Lower LND associated with better survival rates | Lymph node density findings inform personalized surgical and therapeutic approaches to boost survival outcomes. |
Ortiz et al. [53] | Identify prognostic markers in urine for WT | Mass spectrometry, ELISA | 49 WT patients and controls | Urine PHB levels correlated with tumor recurrence and survival | Urine biomarkers provide a non-invasive approach for personalizing prognosis and subsequent treatment pathways. |
Taskinen et al. [45] | Examine the histological response to preoperative chemotherapy | Statistical analysis, Wilcoxon signed-rank test | 52 WT cases with pre- and post-chemotherapy imaging | Preoperative chemotherapy significantly reduced total and viable tumor volumes | The response to preoperative chemotherapy highlights the need for personalized treatment planning to maximize tumor shrinkage before surgery. |
Liu et al. [54] | Identify prognostic biomarkers through data analysis | Differentially Expressed Gene Analysis, Random Survival Forest | RNA-seq and clinical data from the TARGET and GEO databases | Identified gene signatures predictive of WT prognosis | Identified gene signatures offer personalized prognostic information that can guide treatment and monitoring strategies. |
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Karam, S.; Gebreil, A.; Alksas, A.; Balaha, H.M.; Khalil, A.; Ghazal, M.; Contractor, S.; El-Baz, A. Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review. Biomedicines 2024, 12, 1455. https://doi.org/10.3390/biomedicines12071455
Karam S, Gebreil A, Alksas A, Balaha HM, Khalil A, Ghazal M, Contractor S, El-Baz A. Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review. Biomedicines. 2024; 12(7):1455. https://doi.org/10.3390/biomedicines12071455
Chicago/Turabian StyleKaram, Salma, Ahmad Gebreil, Ahmed Alksas, Hossam Magdy Balaha, Ashraf Khalil, Mohammed Ghazal, Sohail Contractor, and Ayman El-Baz. 2024. "Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review" Biomedicines 12, no. 7: 1455. https://doi.org/10.3390/biomedicines12071455
APA StyleKaram, S., Gebreil, A., Alksas, A., Balaha, H. M., Khalil, A., Ghazal, M., Contractor, S., & El-Baz, A. (2024). Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review. Biomedicines, 12(7), 1455. https://doi.org/10.3390/biomedicines12071455