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Pancreatic ductal adenocarcinoma (PDAC) is reported to be amongst the cancers with the lowest survival rate at 5 years. In the present study we aimed to validate a targeted next-generation sequencing (tNGS) panel to use in clinical routine, investigating genes important for PDAC diagnostic, prognostic and potential theragnostic aspect. In this NGS panel we also designed target regions to inquire about loss of heterozygosity (LOH) of chromosome 18 that has been described to be possibly linked to a worse disease progression. Copy number alteration has also been explored for a subset of genes. The last two methods are not commonly used for routine diagnostic with tNGS panels and we investigated their possible contribution to better characterize PDAC. A series of 140 formalin-fixed paraffin-embedded (FFPE) PDAC samples from 140 patients was characterized using this panel. Ninety-two % of patients showed alterations in at least one of the investigated genes (most frequent KRAS, TP53, SMAD4, CDKN2A and RNF43). Regarding LOH evaluation, we were able to detect chr18 LOH starting at 20% cell tumor percentage. The presence of LOH on chr18 is associated with a worse disease- and metastasis-free survival, in uni- and multivariate analyses. The present study validates the use of a tNGS panel for PDAC characterization, also evaluating chr18 LOH status for prognostic stratification.

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This study aimed to explore the clinical significance of genomics features including tumor mutation burden (TMB) and copy number alteration (CNA) for advanced EGFR mutant lung cancer. We retrospectively identified 1378 patients with advanced EGFR mutant lung cancer and next-generation sequencing tests from three cohorts. Multiple co-occurring genomics alternations occurred in a large proportion (97%) of patients with advanced EGFR mutant lung cancers. Both TMB and CNA were predictive biomarkers for these patients. A joint analysis of TMB and CNA found that patients with high TMB and high CNA showed worse responses to EGFR-TKIs and predicted worse outcomes. TMBhighCNAhigh, as a high-risk genomic feature, showed predictive ability in most of the subgroups based on clinical characteristics. These patients had larger numbers of metastatic sites, and higher rates of EGFR copy number amplification, TP53 mutations, and cell-cycle gene alterations, which showed more potential survival gain from combination treatment. Furthermore, a nomogram based on genomic features and clinical features was developed to distinguish prognosis. Genomic features could stratify prognosis and guide clinical treatment for patients with advanced EGFR mutant lung cancer.

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The controversial theory of adaptive amplification states gene amplification mutations are induced by selective environments where they are enriched due to the stress caused by growth restriction on unadapted cells. We tested this theory with three independent assays using an Acinetobacter baylyi model system that exclusively selects for cat gene amplification mutants. Our results demonstrate all cat gene amplification mutant colonies arise through a multistep process. While the late steps occur during selection exposure, these mutants derive from low-level amplification mutant cells that form before growth-inhibiting selection is imposed. During selection, these partial mutants undergo multiple secondary steps generating higher amplification over several days to multiple weeks to eventually form visible high-copy amplification colonies. Based on these findings, amplification in this Acinetobacter system can be explained by a natural selection process that does not require a stress response. These findings have fundamental implications to understanding the role of growth-limiting selective environments on cancer development. We suggest duplication mutations encompassing growth factor genes may serve as new genomic biomarkers to facilitate early cancer detection and treatment, before high-copy amplification is attained.

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Whole exome sequencing (WES) of matched tumor-normal pairs in rare tumors has the potential to identify genome-wide mutations and copy number alterations (CNAs). We evaluated 27 rare cancer patients with tumor-normal matching by WES and tumor-only next generation sequencing (NGS) as a comparator. Our goal was to: 1) identify known and novel variants and CNAs in rare cancers with comparison to common cancers; 2) examine differences between germline and somatic variants and how that functionally impacts rare tumors; 3) detect and characterize alleles in biologically relevant genes-pathways that may be of clinical importance but not represented in classical cancer genes. We identified 3343 germline single nucleotide variants (SNVs) and small indel variants-1670 in oncogenes and 1673 in tumor suppressor genes-generating an average of 124 germline variants/case. The number of somatic SNVs and small indels detected in all cases was 523:306 in oncogenes and 217 in tumor suppressor genes. Of the germline variants, six were identified to be pathogenic or likely pathogenic. In the 27 analyzed rare cancer cases, CNAs are variable depending on tumor type, germline pathogenic variants are more common. Cell fate pathway mutations (e.g., Hippo, Notch, Wnt) dominate pathogenesis and double hit (mutation + CNV) represent ~18% cases.

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Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

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Cervical cancer (CC) is one of the most common cancers in women. However, copy number alteration (CNA)-driven dysregulated genes and their functions in CC are still rarely investigated. In the present study, we conducted integrative analysis of CNA and gene expression data from The Cancer Genome Atlas (TCGA) cervical cancer to identify dysregulated genes triggered by CNAs. The integration of copy number status and RNA expression revealed 763 amplified and 1,391 deleted genes significantly dysregulated by the CNAs (P-value < 1e-8). Among these CNA genes, five driver genes, including PI3KCA, PI3KCB, DVL3, WWTR1, and ERBB2, exhibited a strong association with immune cell infiltration, suggesting that the pathways that they participate in may be involved in regulating immune cell infiltration. Moreover, we also observed that the genes of immunotherapeutic targets were abundantly expressed in the wild-type samples, suggesting that immunotherapy based on these immunotherapeutic targets may be applied to wild-type samples. In addition, the two CNA driver genes, DVL3 and ERBB2, might be sensitive and resistant biomarkers for examining the tumor's response to chemoradiotherapy, respectively. Particularly, the expression of ERBB2 was also observed to be higher in responders of chemotherapy than non-responders. Furthermore, a subset of CNA genes was identified to predict the prognosis of cervical cancer. In summary, our systematic data analysis of these CNA genes not only improved our understanding of the veiled mechanism behind immune cell infiltration, but also provided the potential clinical application of these CNA genes in cervical cancer.

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OBJECTIVES: Most patients with non-small cell lung cancer (NSCLC) are diagnosed at advanced stages where small biopsy specimens obtained through endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) are sometimes the only available samples for diagnosis. We aimed to determine whether EBUS-TBNA specimens are suitable for the evaluation of PD-L1 protein expression and copy number alterations (CNAs). MATERIALS AND METHODS: PD-L1 protein expression and CNAs in 71 EBUS-TBNA specimens of NSCLC were assessed. Sixty-eight corresponding transbronchial biopsy (TBB) specimens from primary sites, thirteen resected primary tumors, and six resected metastases were comparatively analyzed. PD-L1 expression in tumor cells was assessed by immunohistochemistry (E1L3N). Positivity of ≥1% was used as the cutoff. PD-L1 CNAs were assessed with fluorescent in situ hybridization and were classified into three categories: amplification, polysomy, and disomy. Concordance between EBUS-TBNA and other specimens was calculated. RESULTS: The cohort comprised 48 men (67.6%), 15 never-smokers (21.1%), and 39 adenocarcinomas (54.9%). The concordance of PD-L1 positivity between EBUS-TBNA and other specimens was moderate; κ = 0.63 for EBUS-TBNA vs. TBB, κ = 0.68 for EBUS-TBNA vs. resected primary tumors, and κ = 1.0 for EBUS-TBNA vs. resected metastases. The concordance of PD-L1 CNA status was comparable with that of PD-L1 expression: κ = 0.60 for EBUS-TBNA vs. TBB and κ = 0.74 for EBUS-TBNA vs. resected primary tumors. When PD-L1 copy number was assessed as a continuous variable, the correlation of PD-L1 CNAs was superior to that of PD-L1 expression. Intratumorally, PD-L1 copy number was less heterogeneous than protein expression in whole sections of resected tumors. CONCLUSION: EBUS-TBNA specimens can be used to assess PD-L1 CNAs and protein expression. Although spatial heterogeneity should be considered for accurate interpretation, the evaluation of PD-L1 CNAs provides more reproducible results than that of protein expression levels especially with regard to intratumoral heterogeneity.

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BACKGROUND: Targeted therapy for lung cancers is based on prognostic genes and needs more investigation. No study has yet evaluated the effects of Sideroflexin1 (SFXN1) on lung cancer. METHODS: Data were analyzed from large patient cohorts in the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). RESULTS: The mRNA and protein expression of SFXN1 is apparently upregulated in lung adenocarcinoma (LUAD) tissues. Among LUAD patients, upregulated SFXN1 mRNA expression can independently predict unfavorable overall survival (OS, P=0.006) and recurrence-free survival (RFS; P=0.003). In addition, detection data from DNA copy number alterations (CNAs) showed that 164 (32.03%) of the 512 cases had copy number loss (-2 and -1), while 121 (23.63%) of the cases had copy number amplification (+1 and +2). We also found a weak negative correlation between the methylation status of one CpG site (chr5: 174, 944, 791-174, 944, 793) and SFXN1 expression (P<0.0001). CONCLUSIONS: Upregulated SFXN1 mRNA expression can be a prognostic biomarker of unfavorable OS and RFS in patients with LUAD.

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CpG island methylator phenotype of breast cancer is associated with widespread aberrant methylation at specified CpG islands and distinct patient outcomes. However, the influence of copy number contributing to the prognosis of tumors with different CpG island methylator phenotypes is still unclear. We analyzed both genetic (copy number) and epigenetic alterations in 765 breast cancers from The Cancer Genome Atlas data portal and got a panel of 15 biomarkers for copy number and methylation status evaluation. The gene panel identified two groups corresponding to distinct copy number profiles. In status of mere-loss copy number, patients were faced with a greater risk if they presented a higher CpG islands methylation pattern in biomarker panels. But for samples presenting merely-gained copy number, higher methylation level of CpG islands was associated with improved viability. In all, the integration of copy number alteration and methylation information enhanced the classification power on prognosis. Moreover, we found the molecular subtypes of breast cancer presented different distributions in two CpG island methylation phenotypes. Generated by the same set of human methylation 450K data, additional copy number information could provide insights into survival prediction of cancers with less heterogeneity and might help to determine the biomarkers for diagnosis and treatment for breast cancer patients in a more personalized approach.

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The genome-wide discoveries such as detection of copy number alterations (CNA) from high-throughput whole-genome sequencing data enabled new developments in personalized medicine. The CNAs have been reported to be associated with various diseases and cancers including acute myeloid leukemia. However, there are multiple challenges to the use of current CNA detection tools that lead to high false-positive rates and thus impede widespread use of such tools in cancer research. In this paper, we discuss these issues and propose possible solutions. First, since the entire genome cannot be mapped due to some regions lacking sequence uniqueness, current methods cannot be appropriately adjusted to handle these regions in the analyses. Thus, detection of medium-sized CNAs is also being directly affected by these mappability problems. The requirement for matching control samples is also an important limitation because acquiring matching controls might not be possible or might not be cost efficient. Here we present an approach that addresses these issues and detects medium-sized CNAs in cancer genomes by (1) masking unmappable regions during the initial CNA detection phase, (2) using pool of a few normal samples as control, and (3) employing median filtering to adjust CNA ratios to its surrounding coverage and eliminate false positives.

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The robust identification and comprehensive profiling of copy number alterations (CNAs) is highly challenging. The amount of data obtained from high-throughput technologies such as array-based comparative genomic hybridization is often too large and it is required to develop a comprehensive and versatile tool for the detection and visualization of CNAs in a genome-wide scale. With this respective, we introduce a software framework, CGHscape that was originally developed to explore the CNAs for the study of copy number variation (CNV) or tumor biology. As a standalone program, CGHscape can be easily installed and run in Microsoft Windows platform. With a user-friendly interface, CGHscape provides a method for data smoothing to cope with the intrinsic noise of array data and CNA detection based on SW-ARRAY algorithm. The analysis results can be demonstrated as log2 plots for individual chromosomes or genomic distribution of identified CNAs. With extended applicability, CGHscape can be used for the initial screening and visualization of CNAs facilitating the cataloguing and characterizing chromosomal alterations of a cohort of samples.

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