Durvalumab with or without tremelimumab plus chemotherapy in HRR non-mutated, platinum-resistant ovarian cancer (KGOG 3045): A phase II umbrella trial.

AIM: We investigated the efficacy and safety of durvalumab (D) with or without tremelimumab (T) in addition to single-agent chemotherapy (CT) in patients with platinum-resistant recurrent ovarian cancer (PROC) lacking homologous recombination repair (HRR) gene mutations. PATIENTS AND METHODS: KGOG 3045 was an open-label, investigator-initiated phase II umbrella trial. Patients with PROC without HRR gene mutations who had received ≥2 prior lines of therapy were enrolled. Patients with high PD-L1 expression (TPS ≥25%) were assigned to arm A (D + CT), whereas those with low PD-L1 expression were assigned to arm B (D + T75 + CT). After completing arm B recruitment, patients were sequentially assigned to arms C (D + T300 + CT) and D (D + CT). RESULTS: Overall, 58 patients were enrolled (5, 18, 17, and 18 patients in arms A, B, C, and D, respectively). The objective response rates were 20.0, 33.3, 29.4, and 22.2%, respectively. Grade 3-4 treatment-related adverse events were observed in 20.0, 66.7, 47.1, and 66.7 of patients, respectively, but were effectively managed. Multivariable analysis demonstrated that adding T to D + CT improved progression-free survival (adjusted HR, 0.435; 95% CI, 0.229-0.824; P = 0.011). Favorable response to chemoimmunotherapy was associated with MUC16 mutation (P = 0.0214), high EPCAM expression (P = 0.020), high matrix remodeling gene signature score (P = 0.017), and low FOXP3 expression (P = 0.047). Patients showing favorable responses to D + T + CT exhibited significantly higher EPCAM expression levels (P = 0.008) and matrix remodeling gene signature scores (P = 0.031) than those receiving D + CT. CONCLUSIONS: Dual immunotherapy with chemotherapy showed acceptable response rates and tolerable safety in HRR non-mutated PROC, warranting continued clinical investigation.

Randomized, two-arm, noncomparative phase 2 study of olaparib plus cediranib or durvalumab in HRR-mutated, platinum-resistant ovarian cancer: A substudy of KGOG 3045.

Choosing an optimal concomitant drug for combination with poly-ADP ribose polymerase (PARP) inhibitor based on patient-specific biomarker status may help increase to improve treatment efficacy in patients with ovarian cancer. However, the efficacy and safety of different PARP inhibitor-based combinations in patients with homologous recombination repair (HRR) mutations have not been evaluated in ovarian cancer. In this sub-study of Korean Gynecologic Oncology Group (KGOG) 3045, we compared the efficacy and safety of two olaparib-based combinations and biomarkers of patients with platinum-resistant ovarian cancer with HRR gene mutations. Patients were randomized to receive either olaparib (200 mg twice a day) + cediranib (30 mg daily) (Arm 1, n = 16) or olaparib (300 mg) + durvalumab (1,500 mg once every 4 weeks) (Arm 2, n = 14). The objective response rates for Arm 1 and Arm 2 were 50.0% and 42.9%, respectively. Most patients (83.3%) had BRCA mutations, which were similarly distributed between arms. Grade 3 or 4 treatment-related adverse events were observed in 37.5% and 35.7% of the patients, respectively, but all were managed properly. A high vascular endothelial growth factor signature was associated with favorable outcomes in Arm 1, whereas immune markers (PD-L1 expression [CPS ≥10], CD8, neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio) were associated with favorable outcomes in Arm 2. The activation of homologous recombination pathway upon disease progression was associated with poor response to subsequent therapy. Based on comprehensive biomarker profiling, including immunohistochemistry, whole-exome and RNA sequencing and whole blood-based analyses, we identified biomarkers that could help inform which of the two combination strategies is appropriate given a patient's biomarker status. Our findings have the potential to improve treatment outcome for patients with ovarian cancer in the PARP inhibitor era.

Analysis of changes in microbiome compositions related to the prognosis of colorectal cancer patients based on tissue-derived 16S rRNA sequences.

BACKGROUND: Comparing the microbiome compositions obtained under different physiological conditions has frequently been attempted in recent years to understand the functional influence of microbiomes in the occurrence of various human diseases. METHODS: In the present work, we analyzed 102 microbiome datasets containing tumor- and normal tissue-derived microbiomes obtained from a total of 51 Korean colorectal cancer (CRC) patients using 16S rRNA amplicon sequencing. Two types of comparisons were used: 'normal versus (vs.) tumor' comparison and 'recurrent vs. nonrecurrent' comparison, for which the prognosis of patients was retrospectively determined. RESULTS: As a result, we observed that in the 'normal vs. tumor' comparison, three phyla, Firmicutes, Actinobacteria, and Bacteroidetes, were more abundant in normal tissues, whereas some pathogenic bacteria, including Fusobacterium nucleatum and Bacteroides fragilis, were more abundant in tumor tissues. We also found that bacteria with metabolic pathways related to the production of bacterial motility proteins or bile acid secretion were more enriched in tumor tissues. In addition, the amount of these two pathogenic bacteria was positively correlated with the expression levels of host genes involved in the cell cycle and cell proliferation, confirming the association of microbiomes with tumorigenic pathway genes in the host. Surprisingly, in the 'recurrent vs. nonrecurrent' comparison, we observed that these two pathogenic bacteria were more abundant in the patients without recurrence than in the patients with recurrence. The same conclusion was drawn in the analysis of both normal and tumor-derived microbiomes. CONCLUSIONS: Taken together, it seems that understanding the composition of tissue microbiomes is useful for predicting the prognosis of CRC patients.

Fast Healthcare Interoperability Resources (FHIR)-Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study.

BACKGROUND: Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process. OBJECTIVE: The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)-based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests. METHODS: We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server. RESULTS: Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution. CONCLUSIONS: This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data.

Performance evaluation of commercial library construction kits for PCR-based targeted sequencing using a unique molecular identifier.

BACKGROUND: Target enrichment is a critical component of targeted deep next-generation sequencing for the cost-effective and sensitive detection of mutations, which is predominantly performed by either hybrid selection or PCR. Despite the advantages of efficient enrichment, PCR-based methods preclude the identification of PCR duplicates and their subsequent removal. Recently, this limitation was overcome by assigning a unique molecular identifier(UMI) to each template molecule. Currently, several commercial library construction kits based on PCR enrichment are available for UMIs, but there have been no systematic studies to compare their performances. In this study, we evaluated and compared the performances of five commercial library kits from four vendors: the Archer® Reveal ctDNA™ 28 Kit, NEBNext Direct® Cancer HotSpot Panel, Nugen Ovation® Custom Target Enrichment System, Qiagen Human Comprehensive Cancer Panel(HCCP), and Qiagen Human Actionable Solid Tumor Panel(HASTP). RESULTS: We evaluated and compared the performances of the five kits using 50 ng of genomic DNA for the library construction in terms of the library complexity, coverage uniformity, and errors in the UMIs. While the duplicate rates for all kits were dramatically decreased by identifying unique molecules with UMIs, the Qiagen HASTP achieved the highest library complexity based on the depth of unique coverage indicating superb library construction efficiency. Regarding the coverage uniformity, the kits from Nugen and NEB performed the best followed by the kits from Qiagen. We also analyzed the UMIs, including errors, which allowed us to adjust the depth of unique coverage and the length required for sufficient complexity. Based on these comparisons, we selected the Qiagen HASTP for further performance evaluations. The targeted deep sequencing method based on PCR target enrichment combined with UMI tagging sensitively detected mutations present at a frequency as low as 1% using 6.25 ng of human genomic DNA as the starting material. CONCLUSION: This study is the first systematic evaluation of commercial library construction kits for PCR-based targeted deep sequencing utilizing UMIs. Because the kits displayed significant variability in different quality metrics, our study offers a practical guideline for researchers to choose appropriate options for PCR-based targeted sequencing and useful benchmark data for evaluating new kits.

Phylogenetic analyses of melanoma reveal complex patterns of metastatic dissemination.

Melanoma is difficult to treat once it becomes metastatic. However, the precise ancestral relationship between primary tumors and their metastases is not well understood. We performed whole-exome sequencing of primary melanomas and multiple matched metastases from eight patients to elucidate their phylogenetic relationships. In six of eight patients, we found that genetically distinct cell populations in the primary tumor metastasized in parallel to different anatomic sites, rather than sequentially from one site to the next. In five of these six patients, the metastasizing cells had themselves arisen from a common parental subpopulation in the primary, indicating that the ability to establish metastases is a late-evolving trait. Interestingly, we discovered that individual metastases were sometimes founded by multiple cell populations of the primary that were genetically distinct. Such establishment of metastases by multiple tumor subpopulations could help explain why identical resistance variants are identified in different sites after initial response to systemic therapy. One primary tumor harbored two subclones with different oncogenic mutations in CTNNB1, which were both propagated to the same metastasis, raising the possibility that activation of wingless-type mouse mammary tumor virus integration site (WNT) signaling may be involved, as has been suggested by experimental models.

Practical approach to determine sample size for building logistic prediction models using high-throughput data.

An empirical method of sample size determination for building prediction models was proposed recently. Permutation method which is used in this procedure is a commonly used method to address the problem of overfitting during cross-validation while evaluating the performance of prediction models constructed from microarray data. But major drawback of such methods which include bootstrapping and full permutations is prohibitively high cost of computation required for calculating the sample size. In this paper, we propose that a single representative null distribution can be used instead of a full permutation by using both simulated and real data sets. During simulation, we have used a dataset with zero effect size and confirmed that the empirical type I error approaches to 0.05. Hence this method can be confidently applied to reduce overfitting problem during cross-validation. We have observed that pilot data set generated by random sampling from real data could be successfully used for sample size determination. We present our results using an experiment that was repeated for 300 times while producing results comparable to that of full permutation method. Since we eliminate full permutation, sample size estimation time is not a function of pilot data size. In our experiment we have observed that this process takes around 30min. With the increasing number of clinical studies, developing efficient sample size determination methods for building prediction models is critical. But empirical methods using bootstrap and permutation usually involve high computing costs. In this study, we propose a method that can reduce required computing time drastically by using representative null distribution of permutations. We use data from pilot experiments to apply this method for designing clinical studies efficiently for high throughput data.

Single-cell transcriptomics reveal cellular diversity of aortic valve and the immunomodulation by PPARγ during hyperlipidemia.

Valvular inflammation triggered by hyperlipidemia has been considered as an important initial process of aortic valve disease; however, cellular and molecular evidence remains unclear. Here, we assess the relationship between plasma lipids and valvular inflammation, and identify association of low-density lipoprotein with increased valvular lipid and macrophage accumulation. Single-cell RNA sequencing analysis reveals the cellular heterogeneity of leukocytes, valvular interstitial cells, and valvular endothelial cells, and their phenotypic changes during hyperlipidemia leading to recruitment of monocyte-derived MHC-IIhi macrophages. Interestingly, we find activated PPARγ pathway in Cd36+ valvular endothelial cells increased in hyperlipidemic mice, and the conservation of PPARγ activation in non-calcified human aortic valves. While the PPARγ inhibition promotes inflammation, PPARγ activation using pioglitazone reduces valvular inflammation in hyperlipidemic mice. These results show that low-density lipoprotein is the main lipoprotein accumulated in the aortic valve during hyperlipidemia, leading to early-stage aortic valve disease, and PPARγ activation protects the aortic valve against inflammation.

Clinical advantage of targeted sequencing for unbiased tumor mutational burden estimation in samples with low tumor purity.

BACKGROUND: Tumor mutational burden (TMB) measurement is limited by low tumor purity of samples, which can influence prediction of the immunotherapy response, particularly when using whole-exome sequencing-based TMB (wTMB). This issue could be overcome by targeted panel sequencing-based TMB (pTMB) with higher depth of coverage, which remains unexplored. METHODS: We comprehensively reanalyzed four public datasets of immune checkpoint inhibitor (ICI)-treated cohorts (adopting pTMB or wTMB) to test each biomarker's predictive ability for low purity samples (cut-off: 30%). For validation, paired genomic profiling with the same tumor specimens was performed to directly compare wTMB and pTMB in patients with breast cancer (paired-BRCA, n=165) and ICI-treated patients with advanced non-small-cell lung cancer (paired-NSCLC, n=156). RESULTS: Low tumor purity was common (range 30%-45%) in real-world samples from ICI-treated patients. In the survival analyzes of public cohorts, wTMB could not predict the clinical benefit of immunotherapy when tumor purity was low (log-rank p=0.874), whereas pTMB could effectively stratify the survival outcome (log-rank p=0.020). In the paired-BRCA and paired-NSCLC cohorts, pTMB was less affected by tumor purity, with significantly more somatic variants identified at low allele frequency (p<0.001). We found that wTMB was significantly underestimated in low purity samples with a large proportion of clonal variants undetected by whole-exome sequencing. Interestingly, pTMB more accurately predicted progression-free survival (PFS) after immunotherapy than wTMB owing to its superior performance in the low tumor purity subgroup (p=0.054 vs p=0.358). Multivariate analysis revealed pTMB (p=0.016), but not wTMB (p=0.32), as an independent predictor of PFS even in low-purity samples. The net reclassification index using pTMB was 21.7% in the low-purity subgroup (p=0.016). CONCLUSIONS: Our data suggest that TMB characterization with targeted deep sequencing might have potential strength in predicting ICI responsiveness due to its enhanced sensitivity for hard-to-detect variants at low-allele fraction. Therefore, pTMB could act as an invaluable biomarker in the setting of both clinical trials and practice outside of trials based on its reliable performance in mitigating the purity-related bias.

Clinical Targeted Next-Generation sequencing Panels for Detection of Somatic Variants in Gliomas.

PURPOSE: Targeted next-generation sequencing (NGS) panels for solid tumors have been useful in clinical framework for accurate tumor diagnosis and identifying essential molecular aberrations. However, most cancer panels have been designed to address a wide spectrum of pan-cancer models, lacking integral prognostic markers that are highly specific to gliomas. MATERIALS AND METHODS: To address such challenges, we have developed a glioma-specific NGS panel, termed "GliomaSCAN," that is capable of capturing single nucleotide variations and insertion/deletion, copy number variation, and selected promoter mutations and structural variations that cover a subset of intron regions in 232 essential glioma-associated genes. We confirmed clinical concordance rate using pairwise comparison of the identified variants from whole exome sequencing (WES), immunohistochemical analysis, and fluorescence in situ hybridization. RESULTS: Our panel demonstrated high sensitivity in detecting potential genomic variants that were present in the standard materials. To ensure the accuracy of our targeted sequencing panel, we compared our targeted panel to WES. The comparison results demonstrated a high correlation. Furthermore, we evaluated clinical utility of our panel in 46 glioma patients to assess the detection capacity of potential actionable mutations. Thirty-two patients harbored at least one recurrent somatic mutation in clinically actionable gene. CONCLUSION: We have established a glioma-specific cancer panel. GliomaSCAN highly excelled in capturing somatic variations in terms of both sensitivity and specificity and provided potential clinical implication in facilitating genome-based clinical trials. Our results could provide conceptual advance towards improving the response of genomically guided molecularly targeted therapy in glioma patients.

Performance evaluation method for read mapping tool in clinical panel sequencing.

In addition to the rapid advancement in Next-Generation Sequencing (NGS) technology, clinical panel sequencing is being used increasingly in clinical studies and tests. However, tools that are used in NGS data analysis have not been comparatively evaluated in performance for panel sequencing. This study aimed to evaluate the tools used in the alignment process, the first procedure in bioinformatics analysis, by comparing tools that have been widely used with ones that have been introduced recently. With the accumulated panel sequencing data, detected variant lists were cataloged and inserted into simulated reads produced from the reference genome (h19). The amount of unmapped reads and misaligned reads, mapping quality distribution, and runtime were measured as standards for comparison. As the most widely used tools, Bowtie2 and BWA-MEM each showed explicit performance with AUC of 0.9984 and 0.9970 respectively. Kart, maintaining superior runtime and less number of misaligned read, also similarly possessed high level of AUC (0.9723). Such selection and optimization method of tools appropriate for panel sequencing can be utilized for fields requiring error minimization, such as clinical application and liquid biopsy studies.

A Method to Evaluate the Quality of Clinical Gene-Panel Sequencing Data for Single-Nucleotide Variant Detection.

Customized gene-panel tests, based on next-generation sequencing, have demonstrated their usefulness in a plethora of clinical settings. As with other clinical diagnostic techniques, gene-panel sequencing for clinical purposes requires precise quality control (QC) measures to ensure its reliability. Only detected variants are currently recorded in clinical reports; however, identifying whether a nondetected variant is a true or false negative is regarded essential in a clinical setting and, thus, a comprehensive QC measure is in demand. Conventional QC metrics, such as mean coverage and uniformity, are considered inadequate for such an evaluation. As such, a more specific measure focused on clinically important variants is herein proposed. In this study, we suggest a new scoring method for assessing the quality of clinical gene-panel sequencing data, specifically for the detection of a set of single-nucleotide variants. The performance of the method was analyzed using 2295 clinical samples (1012 formalin-fixed, paraffin-embedded and 1283 fresh-frozen tissues), and was shown to provide additional information that conventional methods do not show, such as mean depth and uniformity. Customized sequencing protocols, which include QC criteria, have been optimized by each genomic laboratory. The pass rate scoring method proposed in this study provides an appropriate QC response variable for the customized panel, which strengthens the reliability of calls on clinically relevant variants implicated in clinical reports.

The minimal amount of starting DNA for Agilent's hybrid capture-based targeted massively parallel sequencing.

Targeted capture massively parallel sequencing is increasingly being used in clinical settings, and as costs continue to decline, use of this technology may become routine in health care. However, a limited amount of tissue has often been a challenge in meeting quality requirements. To offer a practical guideline for the minimum amount of input DNA for targeted sequencing, we optimized and evaluated the performance of targeted sequencing depending on the input DNA amount. First, using various amounts of input DNA, we compared commercially available library construction kits and selected Agilent's SureSelect-XT and KAPA Biosystems' Hyper Prep kits as the kits most compatible with targeted deep sequencing using Agilent's SureSelect custom capture. Then, we optimized the adapter ligation conditions of the Hyper Prep kit to improve library construction efficiency and adapted multiplexed hybrid selection to reduce the cost of sequencing. In this study, we systematically evaluated the performance of the optimized protocol depending on the amount of input DNA, ranging from 6.25 to 200 ng, suggesting the minimal input DNA amounts based on coverage depths required for specific applications.

Exome sequencing of desmoplastic melanoma identifies recurrent NFKBIE promoter mutations and diverse activating mutations in the MAPK pathway.

Desmoplastic melanoma is an uncommon variant of melanoma with sarcomatous histology, distinct clinical behavior and unknown pathogenesis. We performed low-coverage genome and high-coverage exome sequencing of 20 desmoplastic melanomas, followed by targeted sequencing of 293 genes in a validation cohort of 42 cases. A high mutation burden (median of 62 mutations/Mb) ranked desmoplastic melanoma among the most highly mutated cancers. Mutation patterns strongly implicate ultraviolet radiation as the dominant mutagen, indicating a superficially located cell of origin. Newly identified alterations included recurrent promoter mutations of NFKBIE, encoding NF-κB inhibitor ɛ (IκBɛ), in 14.5% of samples. Common oncogenic mutations in melanomas, in particular in BRAF (encoding p.Val600Glu) and NRAS (encoding p.Gln61Lys or p.Gln61Arg), were absent. Instead, other genetic alterations known to activate the MAPK and PI3K signaling cascades were identified in 73% of samples, affecting NF1, CBL, ERBB2, MAP2K1, MAP3K1, BRAF, EGFR, PTPN11, MET, RAC1, SOS2, NRAS and PIK3CA, some of which are candidates for targeted therapies.

Erratum to: Modeling precision treatment of breast cancer.

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes.

Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC(-/-) background. XPC(-/-) cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.

Modeling precision treatment of breast cancer.

BACKGROUND: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. RESULTS: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. CONCLUSIONS: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.