Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Oncofetal IGF2BP3-mediated control of microRNA structural diversity in the malignancy of early-stage lung adenocarcinoma.

The nature of microRNA (miRNA) dysfunction in carcinogenesis remains controversial because of the complex connection between miRNA structural diversity and biological processes. Here, we found that oncofetal IGF2BP3 regulates the selective production of a subset of 3'-isoforms (3'-isomiRs), including miR-21-5p and Let-7 family, which induces significant changes in their cellular seed occupancy and structural components, establishing a cancer-specific gene expression profile. The D-score, reflecting dominant production of a representative miR-21-5p+C (a 3'-isomiR), discriminated between clinical early-stage lung adenocarcinoma (LUAD) cases with low and high recurrence risks, and was associated with molecular features of cell cycle progression, epithelial-mesenchymal transition pressure, and immune evasion. We found that IGF2BP3 controls the production of miR-21-5p+C by directing the nuclear Drosha complex to select the cleavage site. IGF2BP3 was also involved in the production of 3'-isomiRs of miR-425-5p and miR-454-3p. IGF2BP3-regulated these three miRNAs are suggested to be associated with the regulation of p53, TGF-ß, and TNF pathways in LUAD. Knockdown of IGF2BP3 also induced a selective upregulation of Let-7 3'-isomiRs, leading to increased cellular Let-7 seed occupancy and broad repression of its target genes encoding cell cycle regulators. The D-score is an index that reflects this cellular situation. Our results suggest that the aberrant regulation of miRNA structural diversity is a critical component for controlling cellular networks, thus supporting the establishment of a malignant gene expression profile in early stage LUAD.

Molecular characterization and biomarker identification in paediatric B-cell acute lymphoblastic leukaemia.

B-cell acute lymphoblastic leukaemia (B-ALL) is the most prevalent hematologic malignancy in children and a leading cause of mortality. Managing B-ALL remains challenging due to its heterogeneity and relapse risk. This study aimed to delineate the molecular features of paediatric B-ALL and explore the clinical utility of circulating tumour DNA (ctDNA). We analysed 146 patients with paediatric B-ALL who received systemic chemotherapy. The mutational landscape was profiled in bone marrow (BM) and plasma samples using next-generation sequencing. Minimal residual disease (MRD) testing on day 19 of induction therapy evaluated treatment efficacy. RNA sequencing identified gene fusions in 61% of patients, including 37 novel fusions. Specifically, the KMT2A-TRIM29 novel fusion was validated in a boy who responded well to initial therapy but relapsed after 1 year. Elevated mutation counts and maximum variant allele frequency in baseline BM were associated with significantly poorer chemotherapy response (p = 0.0012 and 0.028, respectively). MRD-negative patients exhibited upregulation of immune-related pathways (p < 0.01) and increased CD8+ T cell infiltration (p = 0.047). Baseline plasma ctDNA exhibited high mutational concordance with the paired BM samples and was significantly associated with chemotherapy efficacy. These findings suggest that ctDNA and BM profiling offer promising prognostic insights for paediatric B-ALL management.

rs822336 binding to C/EBPß and NFIC modulates induction of PD-L1 expression and predicts anti-PD-1/PD-L1 therapy in advanced NSCLC.

Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 (PD-1) or its ligand 1 (PD-L1) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1, differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPß and NFIC. As a result, silencing of C/EBPß and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPß and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients.

Early circulating tumor DNA changes predict outcomes in head and neck cancer patients under re-radiotherapy.

Local recurrence after radiotherapy is common in locally advanced head and neck cancer (HNC) patients. Re-irradiation can improve local disease control, but disease progression remains frequent. Hence, predictive biomarkers are needed to adapt treatment intensity to the patient's individual risk. We quantified circulating tumor DNA (ctDNA) in sequential plasma samples and correlated ctDNA levels with disease outcome. Ninety four longitudinal plasma samples from 16 locally advanced HNC patients and 57 healthy donors were collected at re-radiotherapy baseline, after 5 and 10 radiation fractions, at irradiation end, and at routine follow-up visits. Plasma DNA was subjected to low coverage whole genome sequencing for copy number variation (CNV) profiling to quantify ctDNA burden. CNV-based ctDNA burden was detected in 8/16 patients and 25/94 plasma samples. Ten additional ctDNA-positive samples were identified by tracking patient-specific CNVs found in earlier sequential plasma samples. ctDNA-positivity after 5 and 10 radiation fractions (both: log-rank, p = .050) as well as at the end of irradiation correlated with short progression-free survival (log-rank, p = .006). Moreover, a pronounced decrease of ctDNA toward re-radiotherapy termination was associated with worse treatment outcome (log-rank, p = .005). Dynamic ctDNA tracking in serial plasma beyond re-radiotherapy reflected treatment response and imminent disease progression. In five patients, molecular progression was detected prior to tumor progression based on clinical imaging. Our findings emphasize that quantifying ctDNA during re-radiotherapy may contribute to disease monitoring and personalization of adjuvant treatment, follow-up intervals, and dose prescription.

Predictive biomarkers for immune-related adverse events in cancer patients treated with immune-checkpoint inhibitors.

PURPOSE: The objective of this study was to identify potential predictors of immune-related adverse events (irAEs) in cancer patients receiving immune checkpoint inhibitor therapy among serum indexes, case data, and liquid biopsy results. METHODS: We retrospectively analyzed 418 patients treated with anti-programmed cell death 1(PD-1)/PD-1 ligand (PD-L1) inhibitors from January 2018 to May 2022 in our cancer center. We identified factors that correlated with the occurrence of irAEs and evaluated associations between irAEs and anti-PD-1/PD-L1 inhibitor responses. RESULTS: The incidence of irAEs was 42.1%, and pneumonitis (9.1%), thyroid toxicity (9.1%), cardiotoxicity (8.1%), and dermatologic toxicity (6.9%) were the four most common irAEs. Multivariate logistic analysis identified female sex, antibiotic use, higher post-treatment neutrophil-to-lymphocyte ratio (NLR), and higher baseline circulating tumor cell (CTC) level, as predictive biomarkers for the occurrence of irAEs. A lower baseline prognostic nutritional index (PNI), body mass index (BMI) ≥ 25 kg/m2, and higher post-treatment lactate dehydrogenase (LDH) level were predictive factors for more severe irAEs (higher severity grade). Patients without irAEs had better overall survival than those with irAEs. Specifically, pneumonitis and cardiotoxicity were found to be significant predictors of poor prognosis in the irAE subgroup with different organ-related irAEs. Low-dose steroid (dexamethasone 10 mg) treatment had no significant effect on outcomes. CONCLUSIONS: Gender, antibiotic use, post-treatment NLR, and baseline CTC level are potential predictive biomarkers of irAEs, while baseline PNI, BMI, and post-treatment LDH may predict the severity of irAEs. The predictive effect of irAE occurrence on survival benefit may depend on the type of irAE.

Trastuzumab and first-line taxane chemotherapy in metastatic breast cancer patients with a HER2-negative tumor and HER2-positive circulating tumor cells: a phase II trial.

PURPOSE: HER2 overexpressing circulating tumor cells (CTCs) are observed in up to 25% of HER2-negative metastatic breast cancer patients. Since targeted anti-HER2 therapy has drastically improved clinical outcomes of patients with HER2-positive breast cancer, we hypothesized that patients with HER2 overexpressing CTCs might benefit from the addition of trastuzumab to chemotherapy. METHODS: In this single-arm, phase II trial, patients with HER2-positive CTCs received trastuzumab as addition to first-line treatment with taxane chemotherapy. Patients with detectable CTCs but without HER2 overexpression that received taxane chemotherapy only, were used as control group. The primary outcome measure was progression-free rate at 6 months (PFR6), with a target of 80%. In November 2022, the study was terminated early due to slow patient accrual. RESULTS: 63 patients were screened, of which eight patients had HER2-positive CTCs and were treated with trastuzumab. The median number of CTCs was 15 per 7.5 ml of blood (range 1-131) in patients with HER2-positive CTCs, compared to median 5 (range 1-1047) in the control group. PFR6 was 50% in the trastuzumab group and 54% in the taxane monotherapy group, with no significant difference in median PFS (8 versus 9 months, p = 0.51). CONCLUSION: No clinical benefit of trastuzumab was observed, although this study was performed in a limited number of patients. Additionally, we observed a strong correlation between the number of evaluable CTCs and the presence of HER2-positive CTCs. We argue that randomized studies investigating agents that are proven to be solely effective in the HER2-positive patient group in patients with HER2-positive CTCs and HER2-negative tissue are currently infeasible. Several factors contribute to this impracticality, including the need for more stringent thresholds, and the rapidly evolving landscape of cancer treatments.

Long-term prospective outcome data using EndoPredict as risk stratification and chemotherapy decision biomarker in hormone receptor-positive, HER2-negative early breast cancer.

PURPOSE: To report the prospective long-term outcome data of patients whose chemotherapy decision was guided by the EndoPredict test. METHODS: Patients with hormone receptor-positive HER2-negative early breast cancer with 0-3 positive lymph nodes were enrolled. The EndoPredict test was carried out on all tumor samples. Treatment compliance, local recurrence, distant metastases, and survival were evaluated. Associations of EPclin risk stratification with 5-year disease-free survival and distant metastasis-free survival were evaluated by time-to-event analysis. RESULTS: 368 consecutive patients were included in the analysis. Median follow-up was 8.2 years. EndoPredict allocated 238 (65%) in the low-risk and 130 (35%) patients in the high-risk group. Risk for disease recurrence or death in EPclin high-risk patients was twofold higher than in EPclin low-risk patients (hazard ratio [HR] 2.08; 95% CI 1.26-3.44; p = 0.004). EPclin low-risk patients had a 5-year disease-free survival of 95.3% (95% CI 92.6-98.0%). EPclin high-risk patients were at higher risk of developing distant metastases or death (HR 2.21; 95% CI 1.27-3.88; p = 0.005). EPclin high-risk patients who underwent chemotherapy had a 5-year DFS of 89.1% (95% CI 82.7-96.1%) in contrast to high-risk patients without chemotherapy (68.9%; 95% CI 56.2-84.5%; HR 0.46; 95% CI 0.23-0.95; p = 0.036). EPclin high-risk patients were at higher risk of experiencing distant metastases or death than EPclin low-risk patients regardless of menopausal status (premenopausal: HR 3.55; 95% CI 1.17-12.32; p = 0.025; postmenopausal: HR 1.92; 95% CI 0.99-3.7; p = 0.054). CONCLUSION: EndoPredict can guide decisions on adjuvant chemotherapy in early luminal breast cancer. EndoPredict risk stratification is also applicable in premenopausal women.

Predicting gestational diabetes mellitus risk at 11-13 weeks' gestation: the role of extrachromosomal circular DNA.

BACKGROUND: Gestational diabetes mellitus (GDM) significantly impacts maternal and infant health both immediately and over the long term, yet effective early diagnostic biomarkers are currently lacking. Thus, it is essential to identify early diagnostic biomarkers for GDM risk screening. Extrachromosomal circular DNA (eccDNA), being more stable than linear DNA and involved in disease pathologies, is a viable biomarker candidate for diverse conditions. In this study, eccDNA biomarkers identified for early diagnosis and assessment of GDM risk were explored. METHODS: Using Circle-seq, we identified plasma eccDNA profiles in five pregnant women who later developed GDM and five matched healthy controls at 11-13 weeks of gestation. These profiles were subsequently analyzed through bioinformatics and validated through outward PCR combined with Sanger sequencing. Furthermore, candidate eccDNA was validated by quantitative PCR (qPCR) in a larger cohort of 70 women who developed GDM and 70 normal glucose-tolerant (NGT) subjects. A ROC curve assessed the eccDNA's diagnostic potential for GDM. RESULTS: 2217 eccDNAs were differentially detected between future GDM patients and controls, with 1289 increased and 928 decreased in abundance. KEGG analysis linked eccDNA genes mainly to GDM-related pathways such as Rap1, MAPK, and PI3K-Akt, and Insulin resistance, among others. Validation confirmed a significant decrease in eccDNA PRDM16circle in the plasma of 70 women who developed GDM compared to 70 NGT women, consistent with the eccDNA-seq results. PRDM16circle showed significant diagnostic value in 11-13 weeks of gestation (AUC = 0.941, p < 0.001). CONCLUSIONS: Our study first demonstrats that eccDNAs are aberrantly produced in women who develop GDM, including PRDM16circle, which can predict GDM at an early stage of pregnancy, indicating its potential as a biomarker. TRIAL REGISTRATION: ChiCTR2300075971, http://www.chictr.org.cn . Registered 20 September 2023.

Blood-based molecular and cellular biomarkers of early response to neoadjuvant PD-1 blockade in patients with non-small cell lung cancer.

BACKGROUND: Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. METHODS: Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. RESULTS: The gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines. CONCLUSION: This study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients.

Drug-resistant profiles of extracellular vesicles predict therapeutic response in TNBC patients receiving neoadjuvant chemotherapy.

BACKGROUND: Predicting tumor responses to neoadjuvant chemotherapy (NAC) is critical for evaluating prognosis and designing treatment strategies for patients with breast cancer; however, there are no reliable biomarkers that can effectively assess tumor responses. Therefore, we aimed to evaluate the clinical feasibility of using extracellular vesicles (EVs) to predict tumor response after NAC. METHODS: Drug-resistant triple-negative breast cancer (TNBC) cell lines were successfully established, which developed specific morphologies and rapidly growing features. To detect resistance to chemotherapeutic drugs, EVs were isolated from cultured cells and plasma samples collected post-NAC from 36 patients with breast cancer. RESULTS: Among the differentially expressed gene profiles between parental and drug-resistant cell lines, drug efflux transporters such as MDR1, MRP1, and BCRP were highly expressed in resistant cell lines. Drug efflux transporters have been identified not only in cell lines but also in EVs released from parental cells using immunoaffinity-based EV isolation. The expression of drug resistance markers in EVs was relatively high in patients with residual disease compared to those with a pathological complete response. CONCLUSIONS: The optimal combination of drug-resistant EV markers was significantly efficient in predicting resistance to NAC with 81.82% sensitivity and 92.86% specificity.

PPIH acts as a potential predictive biomarker for patients with common solid tumors.

BACKGROUND: Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker. METHODS: We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers. RESULTS: Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis. CONCLUSION: Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers.

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

Finding the best subgroup with differential treatment effect with multiple outcomes.

Precision medicine aims to identify specific patient subgroups that may benefit the most from a particular treatment than the whole population. Existing definitions for the best subgroup in subgroup analysis are based on a single outcome and do not consider multiple outcomes; specifically, outcomes of different types. In this article, we introduce a definition for the best subgroup under a multiple-outcome setting with continuous, binary, and censored time-to-event outcomes. Our definition provides a trade-off between the subgroup size and the conditional average treatment effects (CATE) in the subgroup with respect to each of the outcomes while taking the relative contribution of the outcomes into account. We conduct simulations to illustrate the proposed definition. By examining the outcomes of urinary tract infection and renal scarring in the RIVUR clinical trial, we identify a subgroup of children that would benefit the most from long-term antimicrobial prophylaxis.

Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation.

BACKGROUND: It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in machine learning (ML) models. METHODS: This was a nested case-control study including 100 pairs of GDM and euglycemic (control) pregnancies in the Early Life Plan cohort in Shanghai, China. High sensitivity C reactive protein, sex hormone binding globulin, insulin-like growth factor I, IGF binding protein 2 (IGFBP-2), total and high molecular weight adiponectin and glycosylated fibronectin concentrations were measured in serum samples at 11-14 weeks of gestation. Routine first-trimester blood test biomarkers included fasting plasma glucose (FPG), serum lipids and thyroid hormones. Five ML models [stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, support vector machine and k-nearest neighbor] were employed to predict GDM. The study subjects were randomly split into two sets for model development (training set, n = 70 GDM/control pairs) and validation (testing set: n = 30 GDM/control pairs). Model performance was evaluated by the area under the curve (AUC) in receiver operating characteristics. RESULTS: FPG and IGFBP-2 were consistently selected as predictors of GDM in all ML models. The random forest model including FPG and IGFBP-2 performed the best (AUC 0.80, accuracy 0.72, sensitivity 0.87, specificity 0.57). Adding more predictors did not improve the discriminant power. CONCLUSION: The combination of FPG and IGFBP-2 at early gestation (11-14 weeks) could predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts.

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models.

BACKGROUND: Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. METHODS: Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. RESULTS: The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. CONCLUSION: Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Assessing clinical response in early oncology development with a predictive biomarker.

In early oncology clinical trials there is often limited data for biomarkers and their association with response to treatment. Thus, it is challenging to decide whether a biomarker should be used for patient selection and enrollment. Most evidence about any potential predictive biomarker comes from preclinical research and, sometimes, clinical observations. How to translate the preclinical predictive biomarker data to clinical study remains an active field of research. Here, we propose a method to incorporate existing knowledge about a predictive biomarker - its prevalence, association with response and the performance of the assay used to measure the biomarker - to estimate the response rate in a clinical study designed with or without using the predictive biomarker. Importantly, we quantify the uncertainty associated with the biomarker and its predictability in a probabilistic model. This model estimates the distribution of the clinical response when a predictive biomarker is used to select patients and compares it to unselected cohort. We applied this method to two real world cases of approved biomarker-guided therapies to demonstrate its utility and potential value. This approach helps to make a data-driven decision whether to select patients with a predictive biomarker in early oncology clinical development.

Serum cholesterol level as a predictive biomarker for prognosis of Neuroblastoma.

BACKGROUND: Neuroblastoma (NB), a type of solid tumor in children, has a poor prognosis. Few blood biomarkers can accurately predict the prognosis, including recurrence and survival, in children with NB. In this study, we found that the serum total cholesterol (Tchol) level was associated with the prognosis of patients through a retrospective study. METHODS: Multivariate Cox regression model was used to identify the independent risk factors in the children with NB. Kaplan-Meier method was used to analyze the correlation between the common biomarkers, including the serum Tchol level, and the prognosis of the patients. ROC curves were used to predict the accuracy of the International Neuroblastoma Staging System (INSS) stage and Children's Oncology Group (COG) risk stratification after adding the serum Tchol level. RESULTS: Compared with the other patients, serum Tchol level was significantly increased in the relapsed and died patients (P < 0.05). Subsequently, serum Tchol level was found as an independent risk factor to affect the outcome of patients (P < 0.05). Finally, we added serum Tchol level into traditional stage and risk classification system to form the new INSS stage and COG risk classification system. It was found that the areas under the ROC curve (AUC) of recurrence-free survival in the new INSS stage and COG risk classification system were increased to 0.691 (95%CI: 0.535-0.847) and 0.748 (95%CI: 0.622-0.874), respectively. Moreover, the AUC areas of overall survival in the new INSS stage and COG risk classification system were increased to 0.722 (95%CI: 0.561-0.883) and 0.668 (95%CI: 0.496-0.819), respectively. CONCLUSION: We found that serum Tchol level, a clinical biomarker, is a risk factor for recurrence and death among the children with NB. The serum Tchol level could significantly increase the accuracy of the prediction for NB prognosis.

Sunitinib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers.

Currently, renal cell carcinoma (RCC) is the most prevalent type of kidney cancer. Targeted therapy has replaced radiation therapy and chemotherapy as the main treatment option for RCC due to the lack of significant efficacy with these conventional therapeutic regimens. Sunitinib, a drug used to treat gastrointestinal tumors and renal cell carcinoma, inhibits the tyrosine kinase activity of a number of receptor tyrosine kinases, including vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), c-Kit, rearranged during transfection (RET) and fms-related receptor tyrosine kinase 3 (Flt3). Although sunitinib has been shown to be efficacious in the treatment of patients with advanced RCC, a significant number of patients have primary resistance to sunitinib or acquired drug resistance within the 6-15 months of therapy. Thus, in order to develop more efficacious and long-lasting treatment strategies for patients with advanced RCC, it will be crucial to ascertain how to overcome sunitinib resistance that is produced by various drug resistance mechanisms. In this review, we discuss: 1) molecular mechanisms of sunitinib resistance; 2) strategies to overcome sunitinib resistance and 3) potential predictive biomarkers of sunitinib resistance.

MGMT methylation and its prognostic significance in inoperable IDH-wildtype glioblastoma: the MGMT-GBM study.

INTRODUCTION: The methylation of the O6-Methylguanine-DNA Methyltransferase (MGMT) promoter is a valid biomarker for predicting response to therapy with alkylating agents and, independently, prognosis in IDH-wildtype(IDH-w) glioblastoma. We aim to study the impact of its methylation in overall survival of the unresectable IDH-w glioblastoma undergoing biopsy and systemic treatment. METHODS: We collected six-year retrospective (2017-2023) data at a quaternary neurosurgery center for patients undergoing biopsy as the only surgical procedure for an unresectable IDH wildtype glioblastoma. Data was collected from patient records including neuro-oncology multidisciplinary team meeting (MDT) documentation. Patients were grouped into categories according to different types of treatment received after biopsy (no treatment, chemotherapy (CT), radiotherapy (RT), chemoradiotherapy (CRT), chemoradiotherapy with adjuvant temozolomide (CRT with adjuvant TMZ), EORTC-NCIC protocol followed by second line treatment) and according to methylation status (unmethylated (< 5%), borderline methylated (5-15%) and strongly methylated (> 15%)). Survival analysis was performed. RESULTS: 166 glioblastoma IDH wildtype patients were included in the study with mean age of 62.5 years (M: F = 1.5: 1). 70 (49.3%) patients had unmethylated MGMT status (< 5%), 29 (20.4%) patients had borderline methylated MGMT status (5-15%) and 43 (30.2%) patients had methylated MGMT status (> 15%). 36 (25.3%) patients did not receive any treatment post biopsy, 13 (9.1%) received CT only, 27 (19%) RT only, 12 (8.4%) CRT, 33 (23.2%) CRT with adjuvant TMZ, whereas 21 (14.7%) received EORTC-NCIC protocol along with second line treatment. In biopsy only group, there was no notable difference in survival outcomes among the different methylation statuses. For biopsy and any-other-form-of-treatment methylated groups showed a distinct trend of better survival compared to the borderline or unmethylated groups. Overall, methylated patients had better survival as compared to unmethylated or borderline groups. CONCLUSION: Methylated MGMT status are predictors for better overall survival in unresectable IDH wildtype glioblastoma patients undergoing biopsy and treatment regardless of the treatment modality.