Combined TP53 status in tumor-free resection margins and circulating microRNA profiling predicts the risk of locoregional recurrence in head and neck cancer.
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ABSTRACT:
SUBMITTER: Federica G
PROVIDER: S-EPMC10916059 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
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Combined TP53 status in tumor-free resection margins and circulating microRNA profiling predicts the risk of locoregional recurrence in head and neck cancer.
Ganci Federica F Allegretti Matteo M Frascolla Carlotta C Spinella Francesca F Rollo Francesca F Sacconi Andrea A Valentina Pascale De P Palcau Alina Catalina AC Manciocco Valentina V Vescovo Mariavittoria M Cotroneo Ettore E Blandino Francesca F Benevolo Maria M Covello Renato R Muti Paola P Strano Sabrina S Vidiri Antonello A Fontemaggi Giulia G Pellini Raul R Blandino Giovanni G
Biomarker research 20240305 1
Locoregional recurrences represent a frequently unexpected problem in head and neck squamous cell carcinoma (HNSCC). Relapse often (10-30%) occurs in patients with histologically negative resection margins (RMs), probably due to residual tumor cells or hidden pre-cancerous lesions in normal mucosa, both missed by histopathological examination. Therefore, definition of a 'clean' or tumor-negative RM is controversial, demanding for novel approaches to be accurately explored. Here, we evaluated nex ...[more]
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Project description:MicroRNAs (miRNAs) are short single-stranded RNAs, measuring 21 to 23 nucleotides in length and regulate gene expression at the post-transcriptional level through mRNA destabilization or repressing protein synthesis. Dysregulation of miRNAs can lead to tumorigenesis through changes in regulation of key cellular processes such as cell proliferation, cell survival, and apoptosis. miR-125a-5p has been implicated as a tumor suppressor miRNA in malignancies such as non-small cell lung cancer and colon cancer. However, the role of miR-125a-5p has not been fully investigated in head and neck squamous cell carcinoma (HNSCC). We performed microRNA microarray profiling of HNSCC tumor samples obtained from a prospective clinical trial evaluating the role of postoperative radiotherapy in head and neck cancer. We also mined through The Cancer Genome Atlas to evaluate expression and survival data. Biological experiments, including cell proliferation, flow cytometry, cell migration and invasion, clonogenic survival, and fluorescent microscopy, were conducted using HN5 and UM-SCC-22B cell lines. miR-125a-5p downregulation was associated with recurrent disease in a panel of high-risk HNSCC and then confirmed poor survival associated with low expression in HNSCC via the Cancer Genome Atlas, suggesting that miR-125a-5p acts as a tumor suppressor miRNA. We then demonstrated that miR-125a-5p regulates cell proliferation through cell cycle regulation at the G1/S transition. We also show that miR-125a-5p can alter cell migration and modulate sensitivity to ionizing radiation. Finally, we identified putative mRNA targets of miR-125a-5p, including ERBB2, EIF4EBP1, and TXNRD1, which support the tumor suppressive mechanism of miR-125a-5p. Functional validation of ERBB2 suggests that miR-125a-5p affects cell proliferation and sensitivity to ionizing radiation, in part, through ERBB2. Our data suggests that miR-125a-5p acts as a tumor suppressor miRNA, has potential as a diagnostic tool and may be a potential therapeutic target for the management and treatment of squamous cell carcinoma of the head and neck.
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Project description:PurposeA reliable locoregional recurrence (LRR) prediction model is important for the personalized management of head and neck cancers (HNC) patients who received radiotherapy. This work aims to develop a delta-radiomics feature-based multi-classifier, multi-objective, and multi-modality (Delta-mCOM) model for post-treatment HNC LRR prediction. Furthermore, we aim to adopt a learning with rejection option (LRO) strategy to boost the reliability of Delta-mCOM model by rejecting prediction for samples with high prediction uncertainties.MethodsIn this retrospective study, we collected PET/CT image and clinical data from 224 HNC patients who received radiotherapy (RT) at our institution. We calculated the differences between radiomics features extracted from PET/CT images acquired before and after radiotherapy and used them in conjunction with pre-treatment radiomics features as the input features. Using clinical parameters, PET radiomics features, and CT radiomics features, we built and optimized three separate single-modality models. We used multiple classifiers for model construction and employed sensitivity and specificity simultaneously as the training objectives for each of them. Then, for testing samples, we fused the output probabilities from all these single-modality models to obtain the final output probabilities of the Delta-mCOM model. In the LRO strategy, we estimated the epistemic and aleatoric uncertainties when predicting with a trained Delta-mCOM model and identified patients associated with prediction of higher reliability (low uncertainty estimates). The epistemic and aleatoric uncertainties were estimated using an AutoEncoder-style anomaly detection model and test-time augmentation (TTA) with predictions made from the Delta-mCOM model, respectively. Predictions with higher epistemic uncertainty or higher aleatoric uncertainty than given thresholds were deemed unreliable, and they were rejected before providing a final prediction. In this study, different thresholds corresponding to different low-reliability prediction rejection ratios were applied. Their values are based on the estimated epistemic and aleatoric uncertainties distribution of the validation data.ResultsThe Delta-mCOM model performed significantly better than the single-modality models, whether trained with pre-, post-treatment radiomics features or concatenated BaseLine and Delta-Radiomics Features (BL-DRFs). It was numerically superior to the PET and CT fused BL-DRF model (nonstatistically significant). Using the LRO strategy for the Delta-mCOM model, most of the evaluation metrics improved as the rejection ratio increased from 0% to around 25%. Utilizing both epistemic and aleatoric uncertainty for rejection yielded nonstatistically significant improved metrics compared to each alone at approximately a 25% rejection ratio. Metrics were significantly better than the no-rejection method when the reject ratio was higher than 50%.ConclusionsThe inclusion of the delta-radiomics feature improved the accuracy of HNC LRR prediction, and the proposed Delta-mCOM model can give more reliable predictions by rejecting predictions for samples of high uncertainty using the LRO strategy.
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Project description:PURPOSE: Detect tumor-related DNA using LigAmp in histologically clear margins and associate results with clinical outcome. EXPERIMENTAL DESIGN: Patients with head and neck cancer were registered for molecular analysis of surgical margins. Adequacy of resection was ensured using histologic margin analysis. Further margins were then harvested and DNA extracted. TP53 mutations in tumor were determined using Affymetrix p53 GeneChip. Margins were analyzed by Ligamp in comparison with standard curves for quantification of mutant DNA. Ligation used two oligonucleotides to isolate DNA targeting the mutation. Ligated DNA was amplified using real-time PCR. The quantity of mutation in the margin was determined as percent of mutant species relative to plasmid and relative to tumor. Cutpoints were identified and defined groups were evaluated for local failure-free, cancer-specific, and overall survival. Study margins were examined for presence of tumor by light microscopy. RESULTS: Tissue from 95 patients with common mutations was analyzed. Fifteen experienced local recurrence. Cutpoints of 0.15% for mutant species relative to plasmid and 0.5% for mutant species relative to tumor were chosen as most selective of recurrent cases. LigAmp had slightly better area under the receiver operator characteristic curve (P = 0.09) than light microscopy correctly predicting 9 of 15 recurrent tumors. There were 6 false negative cases and 26 false positive results. No statistically significant distinctions were observed in cancer-specific or overall survival in this limited cohort. CONCLUSIONS: Ligamp provides quantifiable, sensitive detection of mutant DNA in histologically normal margins. Detection of mutant species in margins may identify patients at risk of local recurrence. (Clin Cancer Res 2009;15(24):7658-65).
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Project description:BackgroundSecuring negative surgical margins is a critical goal for head and neck surgery. Local recurrence develops even in some patients who have histologically negative surgical margins. Minimal residual tumor cells may lead to locoregional recurrence despite clear histologic margins reported at the time of resection of head and neck squamous cell carcinoma (HNSCC). To identify subclinical residual disease, the authors analyzed deep margin imprint samples collected on 1-layer nitrocellulose sheets.MethodsBisulfite-treated DNA samples from 73 eligible patients were amplified by quantitative methylation-specific polymerase chain reaction (QMSP) targeting 6 genes (deleted in colorectal cancer [DCC], endothelin receptor type B [EDNRB], homeobox protein A9 [HOXA9], kinesin family member 1A [KIF1A], nidogen-2 [NID2], and N-methyl D-aspartate receptor subtype 2B [NR2B]). QMSP values were dichotomized as positive or negative. Associations between the QMSP status of deep margin samples and clinical outcomes were evaluated.ResultsTwo-gene methylation combinations among the genes DCC, EDNRB, and HOXA9 were associated with decreased locoregional recurrence-free survival, recurrence-free survival, and overall survival. The methylated gene combination of EDNRB and HOXA9 in margin imprints was the most powerful predictor of poor locoregional recurrence-free survival (hazard ratio [HR], 3.31; 95% confidence interval [CI], 1.30-8.46; P = .012) independent of standard histologic factors. In addition, methylation of both EDNRB and HOXA9 indicated a trend toward reduced recurrence-free survival (HR, 2.74; 95% CI, 0.90-8.33; P = .075) and reduced OS (HR, 5.78; 95% CI, 0.75-44.7; P = .093) in multivariable analysis.ConclusionsA panel of gene methylation targets in deep surgical margin imprints provides a potential predictive marker of postoperative locoregional recurrence. Intraoperative use of molecular margin imprint analysis may assist surgeons in obtaining rigorously negative surgical margins and improve the outcome of head and neck surgery.
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Project description:Radiotherapy plays an integral role in the treatment of head and neck squamous cell carcinoma (HNSCC). Although proteins involved in DNA repair may predict HNSCC response to radiotherapy, none has been validated in this context. We examined whether differential expression of double-strand DNA break (DSB) repair proteins in HNSCC, the chief mediators of DNA repair following irradiation, predict for treatment outcomes.Archival HNSCC tumor specimens (n = 89) were assembled onto a tissue microarray and stained with antibodies raised against 38 biomarkers. The biomarker set was enriched for proteins involved in DSB repair, in addition to established mechanistic markers of radioresistance. Staining was correlated with treatment response and survival alongside established clinical and pathologic covariates. Results were validated in an independent intramural cohort (n = 34).Ku80, a key mediator of DSB repair, correlated most closely with clinical outcomes. Ku80 was overexpressed in half of all tumors, and its expression was independent of all other covariates examined. Ku80 overexpression was an independent predictor for both locoregional failure and mortality following radiotherapy (P < 0.01). The predictive power of Ku80 overexpression was confined largely to HPV-negative HNSCC, where it conferred a nine-fold greater risk of death at two years.Ku80 overexpression is a common feature of HNSCC, and is a candidate DNA repair-related biomarker for radiation treatment failure and death, particularly in patients with high-risk HPV-negative disease. It is a promising, mechanistically rational biomarker to select individual HPV-negative HNSCC patients for strategies to intensify treatment.
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Project description:OBJECTIVE: Hepatocellular carcinoma (HCC) is difficult to manage due to the high frequency of post-surgical recurrence. Early detection of the HCC recurrence after liver resection is important in making further therapeutic options, such as salvage liver transplantation. In this study, we utilized microRNA expression profiling to assess the risk of HCC recurrence after liver resection. METHODS: We examined microRNA expression profiling in paired tumor and non-tumor liver tissues from 73 HCC patients who satisfied the Milan Criteria. We constructed prediction models of recurrence-free survival using the Cox proportional hazard model and principal component analysis. The prediction efficiency was assessed by the leave-one-out cross-validation method, and the time-averaged area under the ROC curve (ta-AUROC). RESULTS: The univariate Cox analysis identified 13 and 56 recurrence-related microRNAs in the tumor and non-tumor tissues, such as miR-96. The number of recurrence-related microRNAs was significantly larger in the non-tumor-derived microRNAs (N-miRs) than in the tumor-derived microRNAs (T-miRs, P<0.0001). The best ta-AUROC using the whole dataset, T-miRs, N-miRs, and clinicopathological dataset were 0.8281, 0.7530, 0.7152, and 0.6835, respectively. The recurrence-free survival curve of the low-risk group stratified by the best model was significantly better than that of the high-risk group (Log-rank: P?=?0.00029). The T-miRs tend to predict early recurrence better than late recurrence, whereas N-miRs tend to predict late recurrence better (P<0.0001). This finding supports the concept of early recurrence by the dissemination of primary tumor cells and multicentric late recurrence by the 'field effect'. CONCLUSION: MicroRNA profiling can predict HCC recurrence in Milan criteria cases.
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Project description:BackgroundThe extent of hepatic resection In HCC depends on the remnant liver reserve or the proximity of the tumor to major vessels. In this study, we evaluated the effects of very close resection margins on postoperative recurrence.MethodsConsecutive LR for HCC between 2003 and 2009 were studied. Patients were divided into groups with very narrow (≤1 mm) or wider (>1 mm) resection margins. Propensity score matching (PSM) was used to balance demographic, surgical, and pathological factors.Results983 patients were included in the study. After PSM, 173 patients were analyzed in each group. 5-year tumor recurrence and survival rates were comparable. Most recurrences were multiple intrahepatic. Section margin recurrences were similar in both groups. By multivariate analysis, tumor size >5 cm was associated with a very narrow resection margin, whereas low platelet count and tumor macrovascular invasion were significant factors related to tumor recurrence.ConclusionsPatients with very narrow surgical margins showed outcomes comparable to those with wider surgical margins. Most recurrences were multiple intrahepatic and associated with the degree of portal hypertension and adverse tumor biology. Although wide surgical margins should be aimed whenever possible, a narrow tumor-free margin resection still represents an effective therapeutic strategy.
| S-EPMC9330627 | biostudies-literature
Project description:BackgroundTP53 mutation is associated with decreased survival rate in head and neck squamous cell carcinoma (HNSCC) patients. We set out to identify microRNAs (miRNAs) whose expression associates with TP53 mutation and survival in HNSCC.Patients and methodsWe analyzed TP53 status by direct sequencing of exons 2 through 11 of a prospective series of 121 HNSCC samples and assessed its association with outcome in 109 followed-up patients. We carried out miRNA expression profiling on 121 HNSCC samples and 66 normal counterparts. miRNA associations with TP53 mutations and outcome were evaluated.ResultsA TP53 mutation was present in 58% of the tumors and TP53 mutations were significantly associated with a shorter recurrence-free survival. This association was stronger in the clinical subgroup of patients subjected to adjuvant therapy after surgery. The expression of 49 miRNAs was significantly associated with TP53 status. Among these 49, we identified a group of 12 miRNAs whose expression correlates with recurrence-free survival and a group of 4 miRNAs that correlates with cancer-specific survival. The two groups share three miRNAs. Importantly, miRNAs that correlate with survival are independent prognostic factors either when considered individually or as signatures.ConclusionsmiRNAs expression associates with TP53 status and with reduced survival after surgical treatment of squamous cell carcinoma of the head and neck.
| S-EPMC3841017 | biostudies-literature
Project description:BackgroundRecurrent cancers of the head and neck are associated with poor survival outcome. Yet, their burden in Africa is not reliably known. We therefore aimed to estimate the prevalence of recurrence and the 5-year overall survival among patients treated for head and neck cancers (HNC) in Africa.MethodIn this systematic review and meta-analysis, we searched four electronic databases (Pubmed, CINAHL, MEDLINE, and Web of Science) and the grey literature for studies reporting the prevalence of HNC recurrence and 5-year overall survival post treatment, published between January 1, 2002, and December 31, 2022. We contacted corresponding authors of relevant studies. Searches were extended to reference lists of review articles and other relevant sources for potentially eligible studies. Each record was assessed for inclusion or exclusion by two independent reviewers. Records with individual-level data on recurrence and survival conducted in Africa were included while exclusion was based on the study design and availability of relevant data. Data were independently extracted by three reviewers from eligible studies, and summary estimates were sought. Our primary outcomes were recurrence and 5-year overall survival of patients who have been treated for HNC, and our secondary outcomes included risk factors, tumor site, squamous cell histology, clinical stage of tumor, and treatment options received. Only records selected for primary outcomes were assessed for secondary outcome data extraction. Random-effects meta-analysis was conducted for each outcome. Meta-regression models were used in addressing sample heterogeneity among the studies. Protocol for this study was registered with PROSPERO, CRD42022372307.FindingsThis systematic review and meta-analysis returned 3998 records, yielding 28 included studies after exclusion. Eighteen studies reported on the prevalence of HNC recurrence while 24 articles reported on the 5-year overall survival. Of the pooled total study population, 7199 (70.5%) of 10,218 patients were males while 2603 (25.5%) were females. We found that the prevalence of HNC recurrence was 15.4% (I2 = 96.2%; 95% CI: 9.5-22.3; n = 3214; k = 18), and the 5-year overall survival was 54.4% (I2 = 99.5%; 95% CI: 40.1-68.4; n = 9798; k = 24). We also found that the prevalence of smoking and alcohol consumption as risk factors for HNC were 42.6% (I2 = 98.8%; 95% CI: 25.2-61.0; n = 4374; k = 15) and 35.8% (I2 = 98.9%; 95% CI: 21.7-51.4; n = 4110; k = 11) respectively. The pooled current prevalence for advanced HNC (clinical stages III-IV) was 80.0% (I2 = 99.2%; 95% CI: 68.6-89.5; n = 7624; k = 18) compared to 12.2% (I2 = 96.4%; 95% CI: 6.2-19.8; n = 7624; k = 18) in early disease (clinical stages I-II).InterpretationThe results showed significantly high prevalence of cancer recurrence, poor 5-year overall survival and very high prevalence of advanced cancers at time of diagnosis. This study provides robust evidence for strategies towards prompt diagnosis and appropriate management of HNC to improve patients' outcome in the African continent.FundingThis study was not supported by any funding.
| S-EPMC10139903 | biostudies-literature
Project description:ObjectiveTo develop and validate a machine learning model to identify patients at high risk of 30-day mortality and hospital readmission using routinely collected health care data.Study designPrognostic predictive modeling and retrospective cohort study. The study was conducted in 2024 using data from 2006 to 2018, with at least a 30-day follow-up.SettingThe 2006 to 2018 National Cancer Database (NCDB).MethodsThe study used deidentified NCDB data on 103,891 head and neck squamous cell carcinoma (HNSCC) patients who underwent surgical resection. Machine learning models were trained on 80% of the data, tested on the remaining 20%, and evaluated using the area under the curve (AUC) and SHapley Additive exPlanations (SHAP) analysis to identify key predictors for 30-day mortality and readmission.ResultsAmong 103,891 patients, 5838 (5.6%) were readmitted, and 829 (0.8%) died within 30 days. The median age was 62, 69% male, and 89% white. Predictors included demographic and clinical data from the NCDB. Five machine learning models were combined and achieved an AUC of 0.80 (95% CI: 0.77-0.83) for mortality prediction and 0.67 (95% CI: 0.65-0.68) for readmission prediction. SHAP analysis identified sex and urban-rural index as key predictors of mortality and readmission, respectively.ConclusionMachine learning models can accurately predict mortality and readmission risks, offering insights into the most influential factors. With further validation, these models may enhance clinical decision-making in postsurgical care for HNSCC patients.
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