Resistance to Combined Anthracycline-Taxane Chemotherapy Is Associated with Altered Metabolism and Inflammation in Breast Carcinomas.

Approximately 30% of early-stage breast cancer (BC) patients experience recurrence after systemic chemotherapy; thus, understanding therapy resistance is crucial in developing more successful treatments. Here, we investigated the mechanisms underlying resistance to combined anthracycline-taxane treatment by comparing gene expression patterns with subsequent therapeutic responses. We established a cohort of 634 anthracycline-taxane-treated patients with pathological complete response (PCR) and a separate cohort of 187 patients with relapse-free survival (RFS) data, each having transcriptome-level expression data of 10,017 unique genes. Patients were categorized as responders and non-responders based on their PCR and RFS status, and the expression for each gene was compared between the two groups using a Mann-Whitney U-test. Statistical significance was set at p < 0.05, with fold change (FC) > 1.44. Altogether, 224 overexpressed genes were identified in the tumor samples derived from the patients without PCR; among these, the gene sets associated with xenobiotic metabolism (e.g., CYP3A4, CYP2A6) exhibited significant enrichment. The genes ORAI3 and BCAM differentiated non-responders from responders with the highest AUC values (AUC > 0.75, p < 0.0001). We identified 51 upregulated genes in the tumor samples derived from the patients with relapse within 60 months, participating primarily in inflammation and innate immune responses (e.g., LYN, LY96, ANXA1). Furthermore, the amino acid transporter SLC7A5, distinguishing non-responders from responders, had significantly higher expression in tumors and metastases than in normal tissues (Kruskal-Wallis p = 8.2 × 10-20). The identified biomarkers underscore the significance of tumor metabolism and microenvironment in treatment resistance and can serve as a foundation for preclinical validation studies.

Association between CYP2B6 genetic variability and cyclophosphamide therapy in pediatric patients with neuroblastoma.

Cyclophosphamide, an oxazaphosphorine prodrug is frequently used in treatment of neuroblastoma, which is one of the most prevalent solid organ malignancies in infants and young children. Cytochrome P450 2B6 (CYP2B6) is the major catalyst and CYP2C19 is the minor enzyme in bioactivation and inactivation pathways of cyclophosphamide. CYP-mediated metabolism may contribute to the variable pharmacokinetics of cyclophosphamide and its toxic byproducts leading to insufficient response to the therapy and development of clinically significant side effects. The aim of the study was to reveal the contribution of pharmacogenetic variability in CYP2B6 and CYP2C19 to the treatment efficacy and cyclophosphamide-induced side effects in pediatric neuroblastoma patients under cyclophosphamide therapy (N = 50). Cyclophosphamide-induced hematologic toxicities were pivotal in all patients, whereas only moderate hepatorenal toxicity was developed. The patients' CYP2B6 metabolizer phenotypes were associated with the occurrence of lymphopenia, thrombocytopenia, and monocytopenia as well as of liver injury, but not with kidney or urinary bladder (hemorrhagic cystitis) toxicities. Furthermore, the patients' age (< 1.5 years, P = 0.03) and female gender (P ≤ 0.02), but not CYP2B6 or CYP2C19 metabolizer phenotypes appeared as significant prognostic factors in treatment outcomes. Our results may contribute to a better understanding of the impact of CYP2B6 variability on cyclophosphamide-induced side effects.

Large-Scale Plasma Proteome Epitome Profiling is an Efficient Tool for the Discovery of Cancer Biomarkers.

Current proteomic technologies focus on the quantification of protein levels, while little effort is dedicated to the development of system approaches to simultaneously monitor proteome variability and abundance. Protein variants may display different immunogenic epitopes detectable by monoclonal antibodies. Epitope variability results from alternative splicing, posttranslational modifications, processing, degradation, and complex formation and possesses dynamically changing availability of interacting surface structures that frequently serve as reachable epitopes and often carry different functions. Thus, it is highly likely that the presence of some of the accessible epitopes correlates with function under physiological and pathological conditions. To enable the exploration of the impact of protein variation on the immunogenic epitome first, here, we present a robust and analytically validated PEP technology for characterizing immunogenic epitopes of the plasma. To this end, we prepared mAb libraries directed against the normalized human plasma proteome as a complex natural immunogen. Antibody producing hybridomas were selected and cloned. Monoclonal antibodies react with single epitopes, thus profiling with the libraries is expected to profile many epitopes which we define by the mimotopes, as we present here. Screening blood plasma samples from control subjects (n = 558) and cancer patients (n = 598) for merely 69 native epitopes displayed by 20 abundant plasma proteins resulted in distinct cancer-specific epitope panels that showed high accuracy (AUC 0.826-0.966) and specificity for lung, breast, and colon cancer. Deeper profiling (≈290 epitopes of approximately 100 proteins) showed unexpected granularity of the epitope-level expression data and detected neutral and lung cancer-associated epitopes of individual proteins. Biomarker epitope panels selected from a pool of 21 epitopes of 12 proteins were validated in independent clinical cohorts. The results demonstrate the value of PEP as a rich and thus far unexplored source of protein biomarkers with diagnostic potential.

Predictive biomarkers of immunotherapy response with pharmacological applications in solid tumors.

Immune-checkpoint inhibitors show promising effects in the treatment of multiple tumor types. Biomarkers are biological indicators used to select patients for a systemic anticancer treatment, but there are only a few clinically useful biomarkers such as PD-L1 expression and tumor mutational burden, which can be used to predict immunotherapy response. In this study, we established a database consisting of both gene expression and clinical data to identify biomarkers of response to anti-PD-1, anti-PD-L1, and anti-CTLA-4 immunotherapies. A GEO screening was executed to identify datasets with simultaneously available clinical response and transcriptomic data regardless of cancer type. The screening was restricted to the studies involving administration of anti-PD-1 (nivolumab, pembrolizumab), anti-PD-L1 (atezolizumab, durvalumab) or anti-CTLA-4 (ipilimumab) agents. Receiver operating characteristic (ROC) analysis and Mann-Whitney test were executed across all genes to identify features related to therapy response. The database consisted of 1434 tumor tissue samples from 19 datasets with esophageal, gastric, head and neck, lung, and urothelial cancers, plus melanoma. The strongest druggable gene candidates linked to anti-PD-1 resistance were SPIN1 (AUC = 0.682, P = 9.1E-12), SRC (AUC = 0.667, P = 5.9E-10), SETD7 (AUC = 0.663, P = 1.0E-09), FGFR3 (AUC = 0.657, P = 3.7E-09), YAP1 (AUC = 0.655, P = 6.0E-09), TEAD3 (AUC = 0.649, P = 4.1E-08) and BCL2 (AUC = 0.634, P = 9.7E-08). In the anti-CTLA-4 treatment cohort, BLCAP (AUC = 0.735, P = 2.1E-06) was the most promising gene candidate. No therapeutically relevant target was found to be predictive in the anti-PD-L1 cohort. In the anti-PD-1 group, we were able to confirm the significant correlation with survival for the mismatch-repair genes MLH1 and MSH6. A web platform for further analysis and validation of new biomarker candidates was set up and available at https://www.rocplot.com/immune . In summary, a database and a web platform were established to investigate biomarkers of immunotherapy response in a large cohort of solid tumor samples. Our results could help to identify new patient cohorts eligible for immunotherapy.

New Transcriptomic Biomarkers of 5-Fluorouracil Resistance.

The overall response rate to fluoropyrimidine monotherapy in colorectal cancer (CRC) is limited. Transcriptomic datasets of CRC patients treated with 5-fluorouracil (5FU) could assist in the identification of clinically useful biomarkers. In this research, we aimed to analyze transcriptomic cohorts of 5FU-treated cell lines to uncover new predictive biomarker candidates and to validate the strongest hits in 5FU-treated human colorectal cancer samples with available clinical response data. We utilized an in vitro dataset of cancer cell lines treated with 5FU and used the reported area under the dose-response curve values to determine the therapeutic response to 5FU treatment. Mann-Whitney and ROC analyses were performed to identify significant genes. The strongest genes were combined into a single signature using a random forest classifier. The compound 5-fluorouracil was tested in 592 cell lines (294 nonresponders and 298 responders). The validation cohort consisted of 157 patient samples with 5FU monotherapy from three datasets. The three strongest associations with treatment outcome were observed in SHISA4 (AUC = 0.745, p-value = 5.5 × 10-25), SLC38A6 (AUC = 0.725, p-value = 3.1 × 10-21), and LAPTM4A (AUC = 0.723, p-value = 6.4 × 10-21). A random forest model utilizing the top genes reached an AUC value of 0.74 for predicting therapeutic sensitivity. The model correctly identified 83% of the nonresponder and 73% of the responder patients. The cell line cohort is available and the entire human colorectal cohort have been added to the ROCPlot analysis platform. Here, by using in vitro and in vivo data, we present a framework enabling the ranking of future biomarker candidates of 5FU resistance. A future option is to conduct an independent validation of the established predictors of resistance.

Co-Transmission of Alpha-Synuclein and TPPP/p25 Inhibits Their Proteolytic Degradation in Human Cell Models.

The pathological association of alpha-synuclein (SYN) and Tubulin Polymerization Promoting Protein (TPPP/p25) is a key factor in the etiology of synucleinopathies. In normal brains, the intrinsically disordered SYN and TPPP/p25 are not found together but exist separately in neurons and oligodendrocytes, respectively; in pathological states, however, they are found in both cell types due to their cell-to-cell transmission. The autophagy degradation of the accumulated/assembled SYN has been considered as a potential therapeutic target. We have shown that the hetero-association of SYN with TPPP/p25 after their uptake from the medium by human cells (which mimics cell-to-cell transmission) inhibits both their autophagy- and the ubiquitin-proteasome system-derived elimination. These results were obtained by ELISA, Western blot, FACS and immunofluorescence confocal microscopy using human recombinant proteins and living human cells; ANOVA statistical analysis confirmed that TPPP/p25 counteracts SYN degradation by hindering the autophagy maturation at the stage of LC3B-SQSTM1/p62-derived autophagosome formation and its fusion with lysosome. Recently, fragments of TPPP/p25 that bind to the interface between the two hallmark proteins have been shown to inhibit their pathological assembly. In this work, we show that the proteolytic degradation of SYN on its own is more effective than when it is complexed with TPPP/p25. The combined strategy of TPPP/p25 fragments and proteolysis may ensure prevention and/or elimination of pathological SYN assemblies.

Gene expression-based biomarkers designating glioblastomas resistant to multiple treatment strategies.

Despite advances in molecular characterization of glioblastoma multiforme (GBM), only a handful of predictive biomarkers exist with limited clinical relevance. We aimed to identify differentially expressed genes in tumor samples collected at surgery associated with response to subsequent treatment, including temozolomide (TMZ) and nitrosoureas. Gene expression was collected from multiple independent datasets. Patients were categorized as responders/nonresponders based on their survival status at 16 months postsurgery. For each gene, the expression was compared between responders and nonresponders with a Mann-Whitney U-test and receiver operating characteristic. The package 'roc' was used to calculate the area under the curve (AUC). The integrated database comprises 454 GBM patients from 3 independent datasets and 10 103 genes. The highest proportion of responders (68%) were among patients treated with TMZ combined with nitrosoureas, where FCGR2B upregulation provided the strongest predictive value (AUC = 0.72, P < 0.001). Elevated expression of CSTA and MRPS17 was associated with a lack of response to multiple treatment strategies. DLL3 upregulation was present in subsequent responders to any treatment combination containing TMZ. Three genes (PLSCR1, MX1 and MDM2) upregulated both in the younger cohort and in patients expressing low MGMT delineate a subset of patients with worse prognosis within a population generally associated with a favorable outcome. The identified transcriptomic changes provide biomarkers of responsiveness, offer avenues for preclinical studies and may enhance future GBM patient stratifications. The described methodology provides a reliable pipeline for the initial testing of potential biomarker candidates for future validation studies.

miRNA Expression Signatures of Therapy Response in Squamous Cell Carcinomas.

INTRODUCTION: Squamous cell carcinomas (SCC) are a major subgroup of malignant tumors with a platinum-based first-line systematic chemotherapy. miRNAs play a role in various diseases and modulate therapy response as well. The aim of this study was to identify predictive miRNAs in platinum-treated SCCs. METHODS: miRNA expression data of platinum-treated head and neck (HNSC), cervical (CESC) and lung (LUSC) cancer were collected from the TCGA repositories. Treatment response was defined based on presence or absence of disease progression at 18 months. Responder and nonresponder cohorts were compared using Mann-Whitney and Receiver Operating Characteristic tests. Logistic regression was developed to establish a predictive miRNA signature. Significance was set at FDR < 5%. RESULTS: The integrated database includes 266 SCC patient samples with platinum-based therapy and available follow-up. We uncovered 16, 103, and 9 miRNAs correlated to chemotherapy response in the CESC, HNSC, and LUSC cohorts, respectively. Eight miRNAs overlapped between the CESC and HNSC subgroups, and three miRNAs overlapped between the LUSC and HNSC subgroups. We established a logistic regression model in HNSC and CESC which included six miRNAs: hsa-miR-5586 (Exp (B): 2.94, p = 0.001), hsa-miR-632 (Exp (B): 10.75, p = 0.002), hsa-miR-2355 (Exp (B): 0.48, p = 0.004), hsa-miR-642a (Exp (B): 2.22, p = 0.01), hsa-miR-101-2 (Exp (B): 0.39, p = 0.013) and hsa-miR-6728 (Exp (B): 0.21, p = 0.016). The model using these miRNAs was able to predict chemotherapy resistance with an AUC of 0.897. CONCLUSIONS: We performed an analysis of RNA-seq data of squamous cell carcinomas samples and identified significant miRNAs correlated to the response against platinum-based therapy in cervical, head and neck, and lung tumors.

Predictive biomarkers of platinum and taxane resistance using the transcriptomic data of 1816 ovarian cancer patients.

OBJECTIVE: The first-line chemotherapy for ovarian cancer is based on a combination of platinum and taxane. To date, no reliable predictive biomarker has been recognized that is capable of identifying patients with pre-existing resistance to these agents. Here, we have established an integrated database and identified the most significant biomarker candidates for chemotherapy resistance in serous ovarian cancer. METHODS: Gene arrays were collected from the GEO and TCGA repositories. Treatment response was defined based on pathological response or duration of relapse-free survival. The responder and nonresponder cohorts were compared using the Mann-Whitney and receiver operating characteristic tests. An independent validation set was established to investigate the correlation between chemotherapy response for the top 8 genes. Statistical significance was set at p < 0.05. RESULTS: The entire database included 1816 tumor samples from 12 independent datasets. From analyzing all the genes for platinum + taxane response, we identified the eight strongest genes correlated to chemotherapy resistance: AKIP1 (p = 1.60E-08, AUC = 0.728), MARVELD1 (p = 2.70E-07, AUC = 0.712), AKIRIN2 (p = 2.60E-07, AUC = 0.704), CFL1 (p = 8.10E-08, AUC = 0.694), SERBP1 (p = 8.10E-07, AUC = 0.684), PDXK (p = 1.30E-04, AUC = 0.634), TFE3 (p = 7.90E-05, AUC = 0.631) and NCOR2 (p = 1.90E-03, AUC = 0.611). Of these, the independent validation confirmed TFE3 (p = 0.012, AUC = 0.718), NCOR2 (p = 0.048, AUC = 0.671), PDXK (p = 0.019, AUC = 0.702), AKIP1 (p = 0.002, AUC = 0.773), MARVELD1 (p = 0.044, AUC = 0.675) and AKIRIN2 (p = 0.042, AUC = 0.676). An online interface was set up to enable future validation and ranking of new biomarker candidates in an automated manner (www.rocplot.org/ovar). CONCLUSIONS: We compiled a large integrated database with available treatment and response information and used this to uncover new biomarkers of chemotherapy response in serous ovarian cancer.

Gene Expression Indicates Altered Immune Modulation and Signaling Pathway Activation in Ovarian Cancer Patients Resistant to Topotecan.

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological malignancies. Topotecan remains an essential tool in second-line therapy; even so, most patients develop resistance within a short period of time. We aimed to identify biomarkers of topotecan resistance by using gene expression signatures derived from patient specimens at surgery and available subsequent responses to therapy. Gene expression was collected for 1436 patients and 10,103 genes. Based on disease progression, patients were categorized as responders/nonresponders depending on their progression free survival (PFS) state at 9, 12, 15 and 18 months after surgery. For each gene, the median expression was compared between responders and nonresponders for two treatment regimens (chemotherapy including/excluding topotecan) with Mann-Whitney U test at each of the four different PFS cutoffs. Statistical significance was accepted in the case of p < 0.05 with a fold change (FC) ≥ 1.44. Four genes (EPB41L2, HLA-DQB1, LTF and SFRP1) were consistently overexpressed across multiple PFS cutoff times in initial tumor samples of patients with disease progression following topotecan treatment. A common theme linked to topotecan resistance was altered immune modulation. Genes associated with disease progression after systemic chemotherapy emphasize the role of the initial organization of the tumor microenvironment in therapy resistance. Our results uncover biomarkers with potential utility for patient stratification.