Готові списки джерел за темами / Pancancer

Добірка наукової літератури з теми "Pancancer"

Автор: Grafiati
Опубліковано: 7 липня 2024
Оновлено: 7 липня 2024

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Зміст

  1. Статті в журналах
  2. Дисертації
  3. Частини книг
  4. Тези доповідей конференцій

Статті в журналах з теми "Pancancer":

1

Wong, Chi Chun, and Jun Yu. "Mapping the pancancer metastasis tumor microbiome." Cell 187, no. 9 (April 2024): 2126–28. http://dx.doi.org/10.1016/j.cell.2024.03.040.

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2

Kappmeier, Claudia, Sherina Edward, Corinna Hochstein, Ellen Inga Bruske, Francesca Di Pasquale, and Ronny Kellner. "Abstract 1470: Advancing cancer research: A novel PanCancer digital PCR tool for simultaneous detection of multiple hallmark mutations in BRAF and EGFR." Cancer Research 84, no. 6_Supplement (March 22, 2024): 1470. http://dx.doi.org/10.1158/1538-7445.am2024-1470.

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Abstract The paradigm shift towards precision medicine in oncology emphasizes the importance of identifying specific genetic mutations driving cancer. Some hallmark mutations in cancer-associated genes, such as BRAF and EGFR, are important in analyzing many cancer types. With the emergence of digital PCR (dPCR) technology, high-sensitivity and quantitative mutation analysis can be performed, lending valuable support to cancer research. In this work, we introduce a new and innovative dPCR method, the dPCR PanCancer assays (RUO), designed for concurrently detecting multiple hallmark mutations in BRAF and EGFR. This work includes two novel PanCancer assays for BRAF and EGFR, each tailored to target a spectrum of mutations associated with these genes, facilitating a comprehensive mutation analysis. The dPCR assay for BRAF targets multiple V600 hallmark mutations, and the dPCR assay for EGFR targets various important deletions on exon 19. This also includes a reference gene as a control for PCR efficiency in the duplex reaction. Here, we present our initial data from various sample types, including blood, plasma and FFPE samples. Through a meticulously optimized dPCR setup, we achieved exceptional sensitivity and specificity, enabling the detection of multiple mutations in a single channel at allelic frequencies below 1%. Both dPCR PanCancer Kits (RUO) have potential use in research for pre-screening samples (e.g., prior to next-generation sequencing) or monitoring cancer cells. They simultaneously assess the mutations, reducing time and costs and saving sample material. Additionally, our technology is adaptable to other cancer-associated genes, where similar assays can potentially be developed. Overall, we have demonstrated that our dPCR PanCancer Kits (RUO) provide a robust, fast and efficient technology to identify critical mutations, ultimately enhancing our understanding of BRAF and EGFR-driven cancers. The dPCR PanCancer Kit is for research use only. Not for the diagnosis, prevention, or treatment of a disease. Citation Format: Claudia Kappmeier, Sherina Edward, Corinna Hochstein, Ellen Inga Bruske, Francesca Di Pasquale, Ronny Kellner. Advancing cancer research: A novel PanCancer digital PCR tool for simultaneous detection of multiple hallmark mutations in BRAF and EGFR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1470.
3

Li, Fei, Jing Chen, Xiaoyan Zhang, Dongxiao Yang, Liang Xia, Dazhong Wang, Kai Jin, et al. "Characterization of MET exon 14 skipping in pancancer." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e20530-e20530. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e20530.

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e20530 Background: MET exon 14 skipping ( METex14) is characterized oncogenic that possesses susceptibility to targeted inhibitors. We retrospectively investigated a Chinese Pan-cancer cohort to characterize METex14 alterations and co-mutation. Methods: We screened tumor tissues or body fluids of a pan-cancer patients by DNA-based next-generation sequencing assay (Genetron Health) from 2017 to 2020. And distribution of co-mutation with METex14 was analyzed. Results: 233 samples from 187 patients (190 tissues and 43 circulating tumor DNA) were identified with METex14. Average age of these METex14 patients was 67.84 ± 12.19 years old, indicating they were characterized by advanced age. The cancer distribution involved METex14 was 95.72% of lung cancer (179 cases), 3.21% of brain tumors (4 glioma blastoma, 1 anaplastic astrocytoma and 1 ependymoma), 0.535% of gastrointestinal stromal tumor (1 case) and 0.535% of sarcoma (1 case). Referring to different transcripts (NM_001127500.1/2 or NM_000245.2), a total of 68 distinct sequence variants of METex14 were identified spanning functional regions: 31 forms in splice donor site (SD, 148 cases, including 1 case of D1010H), 5 in splice acceptor site (SA, 5 cases), 13 in poly-pyrimidine tract (PPT, 15 cases), 19 cover PPT and SA (19 cases). In non-lung cancers, 5 of 8 cases were detected of METex14 variation in SD site. Among the driver coexisting cases of lung cancer, METex14 samples mainly coexisted with MET amplification, EGFR driver mutations and functional fusions. Co-mutation cases with MET amplification (13) and SEMA3D-MET fusion (1) may benefit from MET inhibitors, which may be the same as the samples of METex14 alone. 13 of METex14 cases occurred with MDM2 and 9 of CDK4 amplification, respectively. There were 2 of METex14 cases coexisted with EGFR 19del or L858R, which may benefit from combination or sequential therapies of MET inhibitor and EGFR TKIs. In addition, 2 of METex14 cases were detected with functional fusions (1 ALK-EML4, 1 KIFI5B-RET), resulting in potential benefit from combination of corresponding targeted agents. Among non-lung cancer cases, 2 of 8 case had coexistence of METex14 and MET amplification, 6 of 8 patients had coexistence of METex14 and TP53, similar phenomenon was also observed in lung cancer samples. Conclusions: Here we reported diverse METex14 alterations in a Chinese pan-cancer cohort that contribute to oncogenic transformation. METex14 mutations of pan-cancer mainly occurred in SD site. TP53 was its main co-mutant gene among pan-cancers, and MET amplification was also observed.
4

Cheerla, Anika, and Olivier Gevaert. "Deep learning with multimodal representation for pancancer prognosis prediction." Bioinformatics 35, no. 14 (July 2019): i446—i454. http://dx.doi.org/10.1093/bioinformatics/btz342.

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Abstract Motivation Estimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type—using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs. Results We used pancancer data to train these feature encodings and predict single cancer and pancancer overall survival, achieving a C-index of 0.78 overall. This work shows that it is possible to build a pancancer model for prognosis that also predicts prognosis in single cancer sites. Furthermore, our model handles multiple data modalities, efficiently analyzes WSIs and represents patient multimodal data flexibly into an unsupervised, informative representation. We thus present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients. Availability and implementation https://github.com/gevaertlab/MultimodalPrognosis
5

Liu, Kui, Jing Ma, Jiao Ao, Lili Mu, Yixian Wang, Yue Qian, Jin Xue, and Wei Zhang. "The Oncogenic Role and Immune Infiltration for CARM1 Identified by Pancancer Analysis." Journal of Oncology 2021 (October 27, 2021): 1–15. http://dx.doi.org/10.1155/2021/2986444.

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Chromatin-modifying enzymes, especially protein arginine methyltransferases (PRMTs), have been identified as candidate targets for cancer. Cellular or animal-based evidence has suggested an association between coactivator-linked arginine methyltransferase 1 (CARM1) and cancer progression. However, the relationship between CARM1 and patient prognosis and immune infiltration in pancancer patients is unknown. On the basis of the GEO and TCGA databases, we first investigated the possible oncogenic functions of CARM1 in thirty-three tumor types. CARM1 expression was elevated in many types of tumors. In addition, there was a significant association between CARM1 expression and the survival rate of tumor patients. Uterine corpus endometrial carcinoma (UCES) samples had the highest CARM1 mutation frequency of all cancer types. In head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC), CARM1 expression was associated with the level of CD8+ T cell infiltration, and cancer-associated fibroblast infiltration was also observed in other tumors including kidney renal papillary cell carcinoma (KIRC) and prostate adenocarcinoma (PRAD). CARM1 was involved in immune modulation and played an important role in the tumor microenvironment (TME). Furthermore, activities associated with RNA transport and its metabolism were included in the possible mechanisms of CARM1. Herein, our first pancancer research explores the oncogenic role of CARM1 in various tumors. CARM1 is associated with immune infiltrates and can be employed as a predictive biomarker in pancancer.
6

Jiang, Aimin, Ye Zhou, Wenliang Gong, Xin Pan, Xinxin Gan, Zhenjie Wu, Bing Liu, Le Qu, and Linhui Wang. "CCNA2 as an Immunological Biomarker Encompassing Tumor Microenvironment and Therapeutic Response in Multiple Cancer Types." Oxidative Medicine and Cellular Longevity 2022 (March 31, 2022): 1–35. http://dx.doi.org/10.1155/2022/5910575.

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Background. Cancer is a major threat to human health worldwide. Although recent innovations and advances in early detection and effective therapies such as targeted drugs and immune checkpoint inhibitors have saved more lives of cancer patients and improved their quality of life, our knowledge about cancer remains largely unknown. CCNA2 belongs to the cell cyclin family and has been demonstrated to be a tumorigenic gene in multiple solid tumor types. The aim of the present study was to make a comprehensive analysis on the role of CCNA2 at a pancancer level. Methods. Multidatabases were collected to evaluate the different expression, prognostic value, DNA methylation, tumor mutation burden, microsatellite instability, mismatch repair, tumor immune microenvironment, and drug sensitivity of CCNA2 across pancancer. IHC was utilized to validate the expression and prognostic value of CCNA2 in ccRCC patients from SMMU cohort. Results. CCNA2 was differentially expressed in most cancer types vs. normal tissues. CCNA2 may significantly influence the prognosis of multiple cancer types, especially clear cell renal cell carcinoma (ccRCC). CCNA2 was also frequently mutated in most cancer types. Notably, CCNA2 was significantly correlated with immune cell infiltration and immune checkpoint inhibitory genes. In addition, CCNA2 was also strongly related to drug resistance. Conclusion. CCNA2 may prove to be a new biomarker for prognostic prediction, tumor immunity assessment, and drug susceptibility evaluation in pancancer level, especially in ccRCC.
7

Karpova, Alla, Nadezhda V. Terekhanova, Siqi Chen, Reyka G. Jayasinghe, Andrew Houston, Wagma Caravan, Ryan C. Fields, and Li Ding. "Abstract 2627: PanCancer epigenetic regulators of lymphocyte activation states." Cancer Research 84, no. 6_Supplement (March 22, 2024): 2627. http://dx.doi.org/10.1158/1538-7445.am2024-2627.

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Abstract Several immune cell types have been successfully targeted for immunomodulatory therapy; however, the efficacies of such therapies are hindered by incompatible immune cell activation states. CD8+ T-cell exhaustion is one of the most studied T-cell states in the context of cancer and was shown to limit checkpoint blockade efficacy. Although CD8+ T-cell exhaustion studies in mice showed this state is tightly regulated by chromatin accessibility of cis-regulatory elements (CREs) and transcription factor activation, the exploration of these mechanisms, their prevalence, and their specificity in human cancers remains limited. In addition, chromatin accessibility signatures of other lymphocyte (CD4+ and NK) states frequently found in solid tumors have not been carefully characterized in a pan-cancer setting. Here we introduce the first single-nuclei atlas of pan-cancer and disease-specific cis- and trans-regulatory elements in tumor infiltrating lymphocytes. We profiled ~140K T-cell and NK cell nuclei with snATAC-seq and snRNA-seq from 227 tumor and normal samples from eleven cancer types. We distinguished two major subpopulations among exhausted CD8+ T-cells: GZMK expressing (CD8+ GZMK+ Tex) and ITGAE+ tissue-resident (CD8+ Trm ex). CD8+ GZMK+ Tex was enriched in clear cell renal cell carcinoma, while CD8+ Trm ex was found in all other cancers. Both exhausted groups featured high motif accessibility and expression of NR4A1, NFATC2, and NFKB2 transcription factors (previously reported to regulate exhaustion), but differed in EOMES motif accessibility and gene expression. We have identified other lymphocyte subpopulations showing exhaustion signatures, including CD4+ follicular helpers (CD4+ Tfh), CD4+ regulatory cells (CD4+ Tregs), and weakly cytotoxic tissue-resident NK cells (trNK weak). All these subpopulations showed increased expression and motif accessibility of the NR4A1 transcription factor, highlighting its role in regulating dysfunctional states not only in CD8+ T-cells, but also in CD4+ and NK cells. Additionally, CD4+ Tfh and Tregs showed enhanced activity of NFKB1, NFKB2, and POU2F2, and trNK weak cells had increased activity of NR4A2 and NR2F2. To put our results into clinical perspective, we have collected cohorts of renal cancer, melanoma and triple-negative breast tumors treated with immunotherapy in clinical trial settings, and showed elevated GZMK expression in exhausted T-cells in responders compared to non-responders. Citation Format: Alla Karpova, Nadezhda V. Terekhanova, Siqi Chen, Reyka G. Jayasinghe, Andrew Houston, Wagma Caravan, Ryan C. Fields, Li Ding. PanCancer epigenetic regulators of lymphocyte activation states [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2627.
8

Ji, Haizhou, Mi Ren, Tongyu Liu, and Yang Sun. "Prognostic and Immunological Significance of CXCR2 in Ovarian Cancer: A Promising Target for Survival Outcome and Immunotherapeutic Response Assessment." Disease Markers 2021 (November 19, 2021): 1–21. http://dx.doi.org/10.1155/2021/5350232.

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Objective. Uncovering genetic and immunologic tumor features is critical to gain insights into the mechanisms of immunotherapeutic response. Herein, this study observed the functions of CXCR2 in prognosis and immunology of ovarian cancer. Methods. Expression, prognostic significance, and genetic mutations of CXCR2 were analyzed in diverse cancer types based on TCGA and GTEx datasets. Associations of CXCR2 expression with immune checkpoints, neoantigens, tumor mutational burden (TMB), and microsatellite instability (MSI) were evaluated across pancancer. CXCR2-relevant genes were identified, and their biological functions were investigated in ovarian cancer. Through three algorithms (TIMER, quanTIseq, and xCell), we assessed the relationships of CXCR2 with immune cell infiltration in ovarian cancer. GSEA was adopted for inferring KEGG and hallmark pathways involved in CXCR2. Results. CXCR2 presented abnormal expression in tumors than paired normal tissues across pancancer. Higher expression of CXCR2 was found in ovarian cancer. Moreover, its expression was in relation to overall survival and progression including ovarian cancer. Prominent associations of CXCR2 with immune checkpoints, neoantigens, TMB, and MSI were observed in human cancers. Somatic mutations of CXCR2 frequently occurred across pancancer. Amplification was the main mutational type of CXCR2 in ovarian cancer. CXCR2-relevant genes were markedly enriched in immunity activation and carcinogenic pathways in ovarian cancer. Moreover, it participated in modulating immune cell infiltration in the tumor microenvironment of ovarian cancer such as macrophage and immune response was prominently modulated by CXCR2. Conclusion. Collectively, CXCR2 acts as a promising prognostic and immunological biomarker as well as a novel immunotherapeutic target of ovarian cancer.
9

Cooper, Lee AD, Elizabeth G. Demicco, Joel H. Saltz, Reid T. Powell, Arvind Rao, and Alexander J. Lazar. "PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective." Journal of Pathology 244, no. 5 (February 22, 2018): 512–24. http://dx.doi.org/10.1002/path.5028.

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10

Norton, John T., Callie B. Pollock, Chen Wang, Julian C. Schink, J. Julie Kim, and Sui Huang. "Perinucleolar compartment prevalence is a phenotypic pancancer marker of malignancy." Cancer 113, no. 4 (August 15, 2008): 861–69. http://dx.doi.org/10.1002/cncr.23632.

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Статті в журналах

Дисертації з теми "Pancancer":

1

Pradat, Yoann. "Analyses of genomic and transcriptomic profiles of metastatic tumors from precision medicine clinical trials". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL010.

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À l’ère de l’analyse des données, les connaissances sur l’apparition et la progression du cancer se sont approfondies grâce à l’analyse moléculaire de nombreuses tumeurs dans le monde. Le séquençage de nouvelle génération, apparu dans les années 2000, a transformé la recherche sur les cellules cancéreuses en permettant le profilage complet de l’exome, du transcriptome et même du génome entier. Bien que le séquençage à haut débit ne soit pas systématique dans la pratique clinique, il est couramment utilisé dans les essais thérapeutiques. Le vaste réservoir de données ainsi généré alimente de nombreuses recherches qui contribuent aux progrès de l’oncologie de précision. Cette thèse explore l’analyse de cohortes de patients atteints de cancer et les outils modernes d’oncologie. Le premier chapitre couvre les principes essentiels de la biologie du cancer, en mettant l'accent sur le rôle évolutif du profilage moléculaire dans le traitement et la recherche. Le deuxième chapitre passe en revue les outils informatiques et les bases de données employés pour l’analyse des données de séquençage. Ces chapitres donnent les clés pour le troisième chapitre, axé sur la cohorte META- PRISM, comprenant 1 031 patients issus d’essais de médecine de précision à Gustave Roussy. Il met en évidence les spécificités génétiques des patients réfractaires et les possibilités de modélisation prédictive sur les données du séquençage haut débit. Le quatrième chapitre examine les marqueurs de résistance aux traitements connus et émergents dans la cohorte META-PRISM et dans deux études cliniques récentes, révélant des altérations de cibles et des activations de voies alternatives comme facteurs de résistance clés
In the era of extensive data analysis, insights into cancer onset and progression have deepened through molecular analysis of numerous tumors globally. Next-generation sequencing, emerging in the 2000s, transformed cancer cell investigation by enabling exome, transcriptome, and now whole genome profiling. While high-throughput sequencing has not yet entered clinical pratice for all, it is commonly used in trials. The vast data pool thus generated fuels many research areas which contribute to precision oncology advancements. This thesis explores cancer patient cohort analysis and modern oncology tools. The first chapter covers cancer biology fundamentals, emphasizing molecular profiling's evolving role in treatment and research. The second chapter reviews computing tools and databases for sequencing data analysis. These chapters set the stage for the third chapter, focusing on the META-PRISM cohort, comprising 1,031 patients from precision medicine trials at Gustave Roussy. It highlights the molecular specificities of refractory and the promises of predictive modeling based on high-throughput sequencing data. The fourth chapter delves into known and emerging treatment resistance markers in the META-PRISM cohort and two recent clinical studies, revealing target alterations and alternative pathway activations as key resistance factors

Частини книг з теми "Pancancer":

1

Mallona, Izaskun, Alberto Sierco, and Miguel A. Peinado. "The Pancancer DNA Methylation Trackhub: A Window to The Cancer Genome Atlas Epigenomics Data." In Methods in Molecular Biology, 123–35. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7768-0_7.

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Тези доповідей конференцій з теми "Pancancer":

1

Lindgren, Caleb M., Chelsie Minor, Lindsey K. Olsen, Brittany Henderson, CPTAC Investigators, and Samuel H. Payne. "Abstract 251: Data distribution for easy pancancer analysis." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-251.

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2

Niavarani, Ahmadreza, Asieh Shahrabi Farahani, Maryam Sharafkhah, Ludmil B. Alexandrov, and Reza Malekzadeh. "Abstract 1319: Distinct pancancer mutational signatures are determined byAPOBEC/ADARaberrations." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-1319.

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3

Niavarani, Ahmadreza, Milad Bagheri, Joseph CF Ng, Franca Fraternali, and Reza Malekzadeh. "Abstract 1318:APOBEC/ADARaberrations are potentially implicated in certain pancancer hypermutation patterns." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-1318.

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4

Elkhanany, A., K. Takabe, T. Khoury, A. Omilian, D. Cheng, E. Katsuta, W. Davis, et al. "Abstract P4-06-05: PanCancer profiling reveals population difference in breast cancer immune microenvironment." In Abstracts: 2018 San Antonio Breast Cancer Symposium; December 4-8, 2018; San Antonio, Texas. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-p4-06-05.

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5

Zare, Fatima, Javad Noorbakhsh, Tianyu Wang, Jeffrey H. Chuang, and Sheida Nabavi. "Integrative Deep Learning for PanCancer Molecular Subtype Classification Using Histopathological Images and RNAseq Data." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3412414.

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6

Elmas, Abdulkadir, Pedro Molina-Sanchez, Serena Tharakan, Suraj Jaladanki, Tao Liu, Amaia Lujambio, and Kuan-lin Huang. "Abstract LB-329: Pancancer proteomic investigation identifies overexpressed kinases as novel cancer dependent targets." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-lb-329.

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7

Mongan, Ann, Warren Tom, Janice Au-Young, Aleksandr Pankov, Gauri Ganpule, and Fiona Hyland. "Abstract 5363: Measuring gene expression at the tumor microenvironment: A comparison between nCounter PanCancer Immune Profiling Panel and Oncomine Immune Response Research Assay." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-5363.

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8

Dennis, Lucas, Patrick Danaher, Maribeth Eagan, Andrew White, Nathan Elliot, Namratha Ram, Gayathri Balasundaram, et al. "Abstract A49: Building a comprehensive view of tumor biology in breast cancer by combining NanoString's Prosigna assay with the Pancancer Pathways, Immune Profiling, and Progression Panels." In Abstracts: AACR Special Conference: Advances in Breast Cancer; October 17-20, 2015; Bellevue, WA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1557-3125.advbc15-a49.

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