STAT3 Contributes a Favorable Response to Pembrolizumab Through IFN-γ-induced Apoptosis in Urothelial Cancer.

BACKGROUND/AIM: Pembrolizumab, a second-line therapy for platinum-refractory advanced urothelial carcinoma (UC), is needed to improve objective response rate. Hence, it is crucial to identify optimal predictive biomarkers of responses. This study aimed to clarify the predictive value and role of signal transducer and activator of transcription 3 (STAT3) in selecting patients with advanced UC who might benefit clinically from pembrolizumab therapy. PATIENTS AND METHODS: We retrospectively analyzed 31 patients who received pembrolizumab therapy for UC. STAT3, phosphorylated STAT3 (p-STAT3), and PD-L1 expression were determined using tissue microarrays constructed from patient-derived specimens, and the association of these expression levels with overall survival was analyzed. We assessed the functional role of STAT3 in bladder cancer cell lines in response to interferon-gamma (IFN-γ). RESULTS: Patients with high STAT3 or p-STAT3 expression, and high platelet-to-lymphocyte ratio (PLR) (n=6) had a significantly shorter OS; in the other patients (n=25), high STAT3 or p-STAT3 expression was significantly associated with improved prognosis. IFN-γ-induced apoptosis was partially dependent on STAT3 in T24 cells but not in JMSU1 cells. CONCLUSION: In patients with advanced UC, STAT3 plays a key role in mediating the efficacy of pembrolizumab through apoptosis in response to IFN-γ.

Positive regulatory loop of platelet-derived growth factor DD-induced STAT3 activation is associated with poor prognosis in advanced urothelial carcinoma.

Immune checkpoint inhibitor (ICI) therapy has been established for patients with advanced urothelial cancer (UC). The necessity of overcoming resistance to ICIs and identifying a predictive factor for the same has been highlighted, such as the assessment of combination therapy with other targeted drugs and the characterization of molecular signatures in the tumor microenvironment. Recently, we reported that low hemoglobin (Hb) levels and a high platelet-to-lymphocyte ratio (PLR) were significantly associated with overall survival in patients with UC who did not benefit from pembrolizumab treatment. In the present study, we identified a possible link between these unfavorable prognostic indicators and PDGF-DD-induced STAT3 activation in UC. Overlapping patients between the high STAT3- or phosphorylated STAT3-positive score group (as assessed by immunohistochemistry) and low Hb levels or high PLR group (as assessed by blood tests) showed significantly worse outcomes after pembrolizumab treatment. Additionally, using the bladder cancer JMSU1 cell line, we demonstrated a possible positive regulatory loop between autocrine/paracrine PDGF-DD and STAT3 signaling. Therefore, we suggest that STAT3 inhibition and PDGF-DD detection in the tumor microenvironment might represent a potential therapeutic strategy to overcome resistance to pembrolizumab. Moreover, this can help identify patients with UC who could benefit from combination treatment.

Pretreatment Hemoglobin Levels and Platelet-to-Lymphocyte Ratio Predict Survival Benefit from Pembrolizumab in Advanced Urothelial Carcinoma.

BACKGROUND/AIM: Several prognostic risk factors have been recognized when using cisplatin-based conventional chemotherapy for the treatment of advanced urothelial carcinoma (UC); these include performance status (PS), liver metastasis, hemoglobin (Hb) levels, time from prior chemotherapy (TFPC), and other systemic inflammation scores including neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). However, the benefit of these indicators for predicting outcome of immune checkpoint inhibitors is not fully understood. Here, we investigated the predictive value of the indicators in patients who received pembrolizumab for the treatment of advanced UC. PATIENTS AND METHODS: Seventy-five patients who received pembrolizumab treatment for advanced UC were included. The Karnofsky PS, liver metastasis, hemoglobin levels, TFPC, NLR, and PLR were analyzed, and their relationship with overall survival (OS) was determined. RESULTS: All factors were highlighted as significant prognostic indicators for OS in the univariate proportional regression analysis (p<0.05 for each). Multivariate analysis revealed that Karnofsky PS and liver metastasis were independent prognostic indicators for OS (p<0.01) but were applicable only for a small number of patients. Notably, the combined analysis with low Hb levels and high PLR was significantly associated with OS in patients who could gain less benefit from pembrolizumab at a median of 6.6 [95% confidence interval (CI)=4.2-9.0] versus 15.1 (95% CI=12.4-17.8) months (p=0.002). CONCLUSION: The combination of Hb levels and PLR may be a broadly applicable indicator for the outcome of pembrolizumab as second-line chemotherapy in patients with advanced UC.

Bevacizumab beyond Progression for Newly Diagnosed Glioblastoma (BIOMARK): Phase II Safety, Efficacy and Biomarker Study.

We evaluated the efficacy and safety of bevacizumab beyond progression (BBP) in Japanese patients with newly diagnosed glioblastoma and explored predictors of response to bevacizumab. This phase II study evaluated a protocol-defined primary therapy by radiotherapy with concurrent and adjuvant temozolomide plus bevacizumab, followed by bevacizumab monotherapy, and secondary therapy (BBP: bevacizumab upon progression). Ninety patients received the protocol-defined primary therapy (BBP group, n = 25). Median overall survival (mOS) and median progression-free survival (mPFS) were 25.0 and 14.9 months, respectively. In the BBP group, in which O6-methylguanine-DNA methyltransferase (MGMT)-unmethylated tumors predominated, mOS and mPFS were 5.8 and 1.9 months from BBP initiation and 16.8 and 11.4 months from the initial diagnosis, respectively. The primary endpoint, the 2-year survival rate of the BBP group, was 27.0% and was unmet. No unexpected adverse events occurred. Expression profiling using RNA sequencing identified that Cluster 2, which was enriched with the genes involved in macrophage or microglia activation, was associated with longer OS and PFS independent of the MGMT methylation status. Cluster 2 was identified as a significantly favorable independent predictor for PFS, along with younger age and methylated MGMT. The novel expression classifier may predict the prognosis of glioblastoma patients treated with bevacizumab.

Prognostic biomarker study in patients with clinical stage I esophageal squamous cell carcinoma: JCOG0502-A1.

We undertook genomic analyses of Japanese patients with stage I esophageal squamous cell carcinoma (ESCC) to investigate the frequency of genomic alterations and the association with survival outcomes. Biomarker analysis was carried out for patients with clinical stage T1bN0M0 ESCC enrolled in JCOG0502 (UMIN000000551). Whole-exome sequencing (WES) was performed using DNA extracted from formalin-fixed, paraffin-embedded tissue of ESCC and normal tissue or blood sample. Single nucleotide variants (SNVs), insertions/deletions (indels), and copy number alterations (CNAs) were identified. We then evaluated the associations between each gene alteration with a frequency of 10% or more and progression-free survival (PFS) using a Cox regression model. We controlled for family-wise errors at 0.05 using the Bonferroni method. Among the 379 patients who were enrolled in JCOG0502, 127 patients were successfully analyzed using WES. The median patient age was 63 years (interquartile range, 57-67 years), and 78.0% of the patients ultimately underwent surgery. The 3-year PFS probability was 76.3%. We detected 20 genes with SNVs, indels, or amplifications with a frequency of 10% or more. Genomic alterations in FGF19 showed the strongest association with PFS with a borderline level of statistical significance of P = .00252 (Bonferroni-adjusted significance level is .0025). Genomic alterations in FGF4, MYEOV, CTTN, and ORAOV1 showed a marginal association with PFS (P < .05). These genomic alterations were all CNAs at chromosome 11q13.3. We have identified new genomic alterations associated with the poor efficacy of ESCC (T1bN0M0). These findings open avenues for the development of new potential treatments for patients with ESCC.

Foreign DNA detection by high-throughput sequencing to regulate genome-edited agricultural products.

Although the advent of several new breeding techniques (NBTs) is revolutionizing agricultural production processes, technical information necessary for their regulation is yet to be provided. Here, we show that high-throughput DNA sequencing is effective for the detection of unintended remaining foreign DNA segments in genome-edited rice. A simple k-mer detection method is presented and validated through a series of computer simulations and real data analyses. The data show that a short foreign DNA segment of 20 nucleotides can be detected and the probability that the segment is overlooked is 10-3 or less if the average sequencing depth is 30 or more, while the number of false hits is less than 1 on average. This method was applied to real sequencing data, and the presence and absence of an external DNA segment were successfully proven. Additionally, our in-depth analyses also identified some weaknesses in current DNA sequencing technologies. Hence, for a rigorous safety assessment, the combination of k-mer detection and another method, such as Southern blot assay, is recommended. The results presented in this study will lay the foundation for the regulation of NBT products, where foreign DNA is utilized during their generation.

Revealing phenotype-associated functional differences by genome-wide scan of ancient haplotype blocks.

Genome-wide scans for positive selection have become important for genomic medicine, and many studies aim to find genomic regions affected by positive selection that are associated with risk allele variations among populations. Most such studies are designed to detect recent positive selection. However, we hypothesize that ancient positive selection is also important for adaptation to pathogens, and has affected current immune-mediated common diseases. Based on this hypothesis, we developed a novel linkage disequilibrium-based pipeline, which aims to detect regions associated with ancient positive selection across populations from single nucleotide polymorphism (SNP) data. By applying this pipeline to the genotypes in the International HapMap project database, we show that genes in the detected regions are enriched in pathways related to the immune system and infectious diseases. The detected regions also contain SNPs reported to be associated with cancers and metabolic diseases, obesity-related traits, type 2 diabetes, and allergic sensitization. These SNPs were further mapped to biological pathways to determine the associations between phenotypes and molecular functions. Assessments of candidate regions to identify functions associated with variations in incidence rates of these diseases are needed in the future.

Next-generation survey sequencing and the molecular organization of wheat chromosome 6B.

Common wheat (Triticum aestivum L.) is one of the most important cereals in the world. To improve wheat quality and productivity, the genomic sequence of wheat must be determined. The large genome size (∼17 Gb/1 C) and the hexaploid status of wheat have hampered the genome sequencing of wheat. However, flow sorting of individual chromosomes has allowed us to purify and separately shotgun-sequence a pair of telocentric chromosomes. Here, we describe a result from the survey sequencing of wheat chromosome 6B (914 Mb/1 C) using massively parallel 454 pyrosequencing. From the 4.94 and 5.51 Gb shotgun sequence data from the two chromosome arms of 6BS and 6BL, 235 and 273 Mb sequences were assembled to cover ∼55.6 and 54.9% of the total genomic regions, respectively. Repetitive sequences composed 77 and 86% of the assembled sequences on 6BS and 6BL, respectively. Within the assembled sequences, we predicted a total of 4798 non-repetitive gene loci with the evidence of expression from the wheat transcriptome data. The numbers and chromosomal distribution patterns of the genes for tRNAs and microRNAs in wheat 6B were investigated, and the results suggested a significant involvement of DNA transposon diffusion in the evolution of these non-protein-coding RNA genes. A comparative analysis of the genomic sequences of wheat 6B and monocot plants clearly indicated the evolutionary conservation of gene contents.

Population model-based inter-diplotype similarity measure for accurate diplotype clustering.

Classification of the individuals' genotype data is important in various kinds of biomedical research. There are many sophisticated clustering algorithms, but most of them require some appropriate similarity measure between objects to be clustered. Hence, accurate inter-diplotype similarity measures are always required for classification of diplotypes. In this article, we propose a new accurate inter-diplotype similarity measure that we call the population model-based distance (PMD), so that we can cluster individuals with diplotype SNPs data (i.e., unphased-diplotypes) with higher accuracies. For unphased-diplotypes, the allele sharing distance (ASD) has been the standard to measure the genetic distance between the diplotypes of individuals. To achieve higher clustering accuracies, our new measure PMD makes good use of a given appropriate population model which has never been utilized in the ASD. As the population model, we propose to use an hidden Markov model (HMM)-based model. We call the PMD based on the model the HHD (HIT HMM-based Distance). We demonstrate the impact of the HHD on the diplotype classification through comprehensive large-scale experiments over the genome-wide 8930 data sets derived from the HapMap SNPs database. The experiments revealed that the HHD enables significantly more accurate clustering than the ASD.

New kernel methods for phenotype prediction from genotype data.

Phenotype prediction from genotype data is one of the most important issues in computational genetics. In this work, we propose a new kernel (i.e., an SVM: Support Vector Machine) method for phenotype prediction from genotype data. In our method, we first infer multiple suboptimal haplotype candidates from each genotype by using the HMM (Hidden Markov Model), and the kernel matrix is computed based on the predicted haplotype candidates and their emission probabilities from the HMM. We validated the performance of our method through experiments on several datasets: One is an artificially constructed dataset via a program GeneArtisan, others are a real dataset of the NAT2 gene from the international HapMap project, and a real dataset of genotypes of diseased individuals. The experiments show that our method is superior to ordinary naive kernel methods (i.e., not based on haplotype prediction), especially in cases of strong LD (linkage disequilibrium).