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.

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.

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.

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.

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.

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.

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.

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.

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 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.

The Expression Profiles of lncRNAs Are Associated with Neoadjuvant Chemotherapy Resistance in Locally Advanced, Luminal B-Type Breast Cancer.

lncRNAs are noncoding transcripts with tissue and cancer specificity. Particularly, in breast cancer, lncRNAs exhibit subtype-specific expression; they are particularly upregulated in luminal tumors. However, no gene signature-based laboratory tests have been developed for luminal breast cancer identification or the differential diagnosis of luminal tumors, since no luminal A- or B-specific genes have been identified. Particularly, luminal B patients are of clinical interest, since they have the most variable response to neoadjuvant treatment; thus, it is necessary to develop diagnostic and predictive biomarkers for these patients to optimize treatment decision-making and improve treatment quality. In this study, we analyzed the lncRNA expression profiles of breast cancer cell lines and patient tumor samples from RNA-Seq data to identify an lncRNA signature specific for luminal phenotypes. We identified an lncRNA signature consisting of LINC01016, GATA3-AS1, MAPT-IT1, and DSCAM-AS1 that exhibits luminal subtype-specific expression; among these lncRNAs, GATA3-AS1 is associated with the presence of residual disease (Wilcoxon test, p < 0.05), which is related to neoadjuvant chemotherapy resistance in luminal B breast cancer patients. Furthermore, analysis of GATA3-AS1 expression using RNA in situ hybridization (RNA ISH) demonstrated that this lncRNA is detectable in histological slides. Similar to estrogen receptors and Ki67, both commonly detected biomarkers, GATA3-AS1 proves to be a suitable predictive biomarker for clinical application in breast cancer laboratory tests.

Alterations in Peripheral Lymphocyte Subsets under Immunochemotherapy in Stage IV SCLC Patients: Th17 Cells as Potential Early Predictive Biomarker for Response.

UICC stage IV small-cell lung cancer (SCLC) is a highly aggressive malignancy without curative treatment options. Several randomized trials have demonstrated improved survival rates through the addition of checkpoint inhibitors to first-line platin-based chemotherapy. Consequently, a combination of chemo- and immunotherapy has become standard palliative treatment. However, no reliable predictive biomarkers for treatment response exist. Neither PD-L1 expression nor tumor mutational burden have proven to be effective predictive biomarkers. In this study, we compared the cellular immune statuses of SCLC patients to a healthy control cohort and investigated changes in peripheral blood B, T, and NK lymphocytes, as well as several of their respective subsets, during treatment with immunochemotherapy (ICT) using flow cytometry. Our findings revealed a significant decrease in B cells, while T cells showed a trend to increase throughout ICT. Notably, high levels of exhausted CD4+ and CD8+ cells, alongside NK subsets, increased significantly during treatment. Furthermore, we correlated decreases/increases in subsets after two cycles of ICT with survival. Specifically, a decrease in Th17 cells indicated a better overall survival. Based on these findings, we suggest conducting further investigation into Th17 cells as a potential early predictive biomarkers for response in patients receiving palliative ICT for stage IV SCLC.

An integrated multiomics analysis of rectal cancer patients identified POU2F3 as a putative druggable target and entinostat as a cytotoxic enhancer of 5-fluorouracil.

Rectal cancer (RC) accounts for one-third of colorectal cancers (CRC), and 40% of these are locally advanced rectal cancers (LARC). The use of neoadjuvant chemoradiotherapy (nCRT) significantly reduces the rate of local recurrence compared to adjuvant therapy or surgery alone. However, after nCRT, up to 40%-60% of patients show a poor pathological response, while only about 20% achieve a pathological complete response. In this scenario, the identification of novel predictors of tumor response to nCRT is urgently needed to reduce LARC mortality and to spare poorly responding patients from unnecessary treatments. Therefore, by combining gene and microRNA expression datasets with proteomic data from LARC patients, we developed an integrated network centered on seven hub-genes putatively involved in the response to nCRT. In an independent validation cohort of LARC patients, we confirmed that differential expression of NFKB1, TRAF6 and STAT3 is correlated with the response to nCRT. In addition, the functional enrichment analysis also revealed that these genes are strongly related to hallmarks of cancer and inflammation, whose dysfunction may causatively affect LARC patient's response to nCRT. Furthermore, by constructing the transcription factor-module network, we hypothesized a protective role of POU2F3 gene, which could be used as a new drug target in LARC patients. Finally, we identified and tested in vitro entinostat, a histone deacetylase inhibitor, as a chemical compound that could be combined with a classical therapeutic regimen in order to design more efficient therapeutic strategies in LARC management.

Angioimmunoblastic T-cell lymphoma: Novel recurrent mutations and prognostic biomarkers by cell-free DNA profiling.

Molecular and clinical stratification of patients with angioimmunoblastic T-cell lymphoma (AITL) is unsatisfactory, which hinders the development of personalized therapies. This study aimed to identify molecular biomarkers for AITL based on peripheral cell-free DNA (cfDNA) that could be used to predict prognosis and guide treatment non-invasively. A customized panel containing 46 genes was used to study pretreatment cfDNA and paired tumour tissues in 64 Chinese AITL patients from three clinical centres, and gene mutations in cfDNA and tumour tissue were assessed for concordance (34 paired samples). Then, the association of gene mutations and prognosis was analysed, and a functional enrichment analysis was performed. The sequencing results showed good consistency between cfDNA samples and paired tissue samples. KDM5A, STAT1, FANCM, ERBB4, PIK3R5 and NSD1 were identified as novel recurrent mutations. Mutations in FANCM or combinations of RHOA, KDM5A and FAT1 were associated with poor prognosis. Additionally, functional analysis revealed that RHOAG17 might serve as a predictive biomarker of PD-1 blockade respondence. Our findings confirmed the role of cfDNA as a liquid biopsy in AITL, and revealed novel molecular determinants that can stratify patients and guide treatment options.

BRAF V600E and RNF43 Co-mutations Predict Patient Outcomes With Targeted Therapies in Real-World Cases of Colorectal Cancer.

Anti-BRAF/EGFR therapy is approved for metastatic colorectal cancer (mCRC) with BRAFV600E mutations, although not all patients respond. Novel recent findings indicate the potential of RNF43 mutations to predict outcomes in patients with BRAF-mutated microsatellite stable (MSS) mCRC treated with anti-BRAF/EGFR therapy. This study aimed to independently and rapidly validate BRAFV600E/RNF43 co-mutations as predictive biomarkers of benefit to anti-EGFR/BRAF therapy. Clinical data were derived from electronic health record data from ~280 US cancer clinics between January 2011 and March 2022 from the Flatiron Health-Foundation Medicine real-world clinico-genomic mCRC database. Real-world cases of BRAFV600E-mutated mCRC, with patients receiving anti-BRAF/EGFR therapy (n = 49), were included. Patients who were MSS, with RNF43 mutations, had favorable progression-free survival (hazard ratio [HR] 0.29; 95% CI [CI], 0.13-0.65) and overall survival (HR 0.32, 95% CI, 0.12-0.84) compared with wild type. No difference in outcomes was observed between patient groups with RNF43-mutant versus wild-type receiving standard-of-care chemotherapy. BRAFV600E/RNF43 co-mutations predict mCRC anti-BRAF/EGFR outcomes in diverse clinical settings.