scholarly journals Evolving Technologies of RNA Sequencing and Their Applications in Precision Oncology

2021 ◽  
pp. 100007
Author(s):  
Xinmin Li ◽  
Cun-Yu Wang
Keyword(s):  
Rna Sequencing ◽  
Precision Oncology

Precision oncology for the treatment of salivary gland tumors.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17577-e17577
Author(s):  
Damian Tobias Rieke ◽  
Mario Lamping ◽  
Serge Leyvraz ◽  
Theo Daniel Kim ◽  
Lutz Brinkmann ◽  
...  
Keyword(s):  
Salivary Gland ◽  
Rna Sequencing ◽  
Salivary Gland Tumors ◽  
Tumor Board ◽  
Molecular Testing ◽  
Precision Oncology ◽  
Rna Seq ◽  
Mixed Response ◽  
Antiandrogen Therapy ◽  
Panel Sequencing

e17577 Background: Salivary gland tumors (SGT) represent a rare and heterogeneous group of malignancies. No standard treatment exists in the advanced situation and the prognosis is poor. We here report characteristics and clinical outcomes of patients with SGT discussed at the Charité molecular tumor board (MTB). Methods: Patients with advanced cancer and no curative treatment option were discussed at the Charité MTB. Eligible patients underwent fresh tissue sampling and subsequent whole exome (WES) and RNA sequencing (RNA-seq) and immunohistochemical analyses (EGFR, HER2, AR as well as validation tests) or panel sequencing. Results from molecular testing were discussed at the MTB and patients were followed-up after recommendations were made. Results: 24 patients (median age 56 years, 13 male, 11 female) with advanced SGT were presented at the MTB between 2016 and 2019 (9 adenoidcystic carcinomas, 5 adenocarcinomas, 3 mucoepidermoid, 2 carcinosarcoma, 5 miscellaneous). WES/RNA sequencing was performed on tumor tissue from 16 patients. 2 patients were not included in the sequencing program and WES/RNA-Seq was ongoing for another 4 patients at the time of analysis. For another 2 patients, panel sequencing and IHC analysis, respectively was done. Results from analyses were discussed and a median of 2 recommendations, ranked by priority according to prespecified evidence levels, were made for 17 patients, each. Most commonly proposed treatment options by the MTB were FGFR inhibitors in 6 patients, mTOR or PARP inhibitors in 5 each, EGFR, HDAC inhibitors or antiandrogen therapy in 4, each. Treatments following MTB recommendations were initiated in 8 patients, 1 of which received a second recommended therapy after progression (antiandrogen therapy in 4, EGFR inhibitor in 2, a PDGFR, mTOR and PARP inhibitor in 1, each). A clinical benefit (CR = 1; Mixed Response = 1, SD = 3) was achieved in 5 patients, including a complete response in a patient with a metastatic adenocarcinoma of the parotid gland, treated with antiandrogen therapy. Conclusions: Precision oncology represents a feasible treatment strategy in patients with advanced SGT and shows early evidence of activity in a subset of patients. These results suggest further exploration of personalized therapy in these hard-to-treat tumors.

scholarly journals Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach

2018 ◽  
pp. 1-17 ◽  
Author(s):  
Alessandro Laganà ◽  
Itai Beno ◽  
David Melnekoff ◽  
Violetta Leshchenko ◽  
Deepu Madduri ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Precision Medicine ◽  
Rna Sequencing ◽  
Plasma Cells ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Precision Oncology ◽  
Dna Mutation ◽  
Dna And Rna ◽  
Disease Marker

Purpose Multiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed. Methods We have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM. Results We generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days. Conclusion Our results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.

scholarly journals Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

2021 ◽  
Vol 22 (9) ◽  
pp. 4563
Author(s):  
Marco Del Giudice ◽  
Serena Peirone ◽  
Sarah Perrone ◽  
Francesca Priante ◽  
Fabiola Varese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cancer Genomics ◽  
Computational Algorithms ◽  
Precision Oncology ◽  
Rare Cancer ◽  
Sequencing Data ◽  
Cancer Subtypes ◽  
Single Cell Rna Sequencing

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

scholarly journals From bulk, single-cell to spatial RNA sequencing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xinmin Li ◽  
Cun-Yu Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Global Gene Expression ◽  
Precision Oncology ◽  
Gene Fusions ◽  
Rna Biology ◽  
Splicing Variants ◽  
Differential Gene ◽  
Characteristic Features

AbstractRNA sequencing (RNAseq) can reveal gene fusions, splicing variants, mutations/indels in addition to differential gene expression, thus providing a more complete genetic picture than DNA sequencing. This most widely used technology in genomics tool box has evolved from classic bulk RNA sequencing (RNAseq), popular single cell RNA sequencing (scRNAseq) to newly emerged spatial RNA sequencing (spRNAseq). Bulk RNAseq studies average global gene expression, scRNAseq investigates single cell RNA biology up to 20,000 individual cells simultaneously, while spRNAseq has ability to dissect RNA activities spatially, representing next generation of RNA sequencing. This article highlights these technologies, characteristic features and suitable applications in precision oncology.

Loss of Rnf43 to accelerate KRAS-mediated neoplasia in a clinically relevant genetically engineered mouse model of pancreatic adenocarcinoma.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 733-733
Author(s):  
Abdel Nasser Hosein ◽  
Gita Dangol ◽  
Takashi Okumura ◽  
Lotfi Abou-Elkacem ◽  
Anirban Maitra
Keyword(s):  
Mouse Model ◽  
Tumor Progression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Primary Cultures ◽  
Genetically Engineered ◽  
Negative Regulator ◽  
Cystic Tumor ◽  
Precision Oncology ◽  
Genetically Engineered Mouse Model

733 Background: The genetic heterogeneity of pancreatic ductal adenocarcinoma (PDAC) demands a personalized molecular-targeted treatment approach. While activating KRAS mutations are a near ubiquitous event in PDAC pathogenesis, 5-10% of cases display deleterious driver mutations in the Wnt-signaling negative regulator, ring finger 43 (RNF43). Despite this characterization there are no personalized treatment options for this subset of patients. Methods: We have developed a genetically engineered mouse model (GEMM) of PDAC, driven by an activating mutation in Kras and deletion of Rnf43 under control of a pancreas specific promoter (KRC). Mice were followed for survival outcomes and histological changes which were compared to a Kras driven PDAC GEMM (KC). Mice underwent serial magnetic resonance imaging (MRI), with and without dynamic contrast enhanced (DCE) imaging, to evaluate cystic tumor morphology and contrast enhancement during tumor progression. Single cell RNA sequencing (scRNAseq) was also performed to assess changes in single cell populations during tumor progression. Lastly, we established ex vivo cultures from KRC and KC tumors and performed bulk RNA-sequencing (RNAseq) and in vitro pharmacology studies. Results: KRC mice displayed a decrease in overall survival and higher incidence of both high grade pre-neoplastic lesions and invasive PDAC compared to KC mice. Serial MRI revealed increased cystic morphology of KRC mice during tumor progression with increasing DCE intensity. scRNAseq of KRC tumors from moribund mice displayed two distinct populations of both macrophages and fibroblasts, similar to our previous report of Kras/Tp53 and Kras/Ink4a driven GEMMs. Lastly, primary cultures from KRC tumors demonstrated increased expression of Wnt-related genes by RNAseq and increased sensitivity to small molecule porcupine inhibition relative to KC cell lines, demonstrating functional Wnt dependence of the KRC system. Conclusions: Rnf43 is a bona fide tumor suppressor gene in PDAC. This GEMM is a novel platform for drug discovery in RNF43-mutated PDAC with the eventual goal of implementing a precision oncology approach in these patients.

scholarly journals Single‐Cell RNA Sequencing for Precision Oncology: Current State-of-Art

2020 ◽  
Vol 100 (3) ◽  
pp. 579-588 ◽  
Author(s):  
Justine Jia Wen Seow ◽  
Regina Men Men Wong ◽  
Rhea Pai ◽  
Ankur Sharma
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Precision Oncology ◽  
Current State ◽  
Single Cell Rna Sequencing ◽  
State Of Art

scholarly journals Elucidation of tumor-stromal heterogeneity and the ligand-receptor interactome by single cell transcriptomics in real-world pancreatic cancer biopsies

2020 ◽  
Author(s):  
Jaewon J. Lee ◽  
Vincent Bernard ◽  
Alexander Semaan ◽  
Maria E. Monberg ◽  
Jonathan Huang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Clinical Decision Making ◽  
De Novo ◽  
Ex Vivo ◽  
Ductal Adenocarcinoma ◽  
Precision Oncology ◽  
Single Cell Rna Sequencing

AbstractPrecision medicine approaches in pancreatic ductal adenocarcinoma (PDAC) are imperative for improving disease outcomes. However, the long-term fidelity of recently deployed ex vivo preclinical platforms, such as patient-derived organoids (PDOs) remains unknown. Through single-cell RNA sequencing (scRNA-seq), we identify substantial transcriptomic evolution of PDOs propagated from the parental tumor, which may alter predicted drug sensitivity. In contrast, scRNA-seq is readily applicable to limited biopsies from human primary and metastatic PDAC and identifies most cancers as being an admixture of previously described epithelial transcriptomic subtypes. Integrative analyses of our data provide an in-depth characterization of the heterogeneity within the tumor microenvironment, including cancer-associated fibroblast (CAF) subclasses, and predicts for a multitude of ligand-receptor interactions, revealing potential targets for immunotherapy approaches. While PDOs continue to enable prospective therapeutic prediction, our analysis also demonstrates the complementarity of using orthogonal de novo biopsies from PDAC patients paired with scRNA-seq to inform clinical decision-making.Statement of SignificanceThe application of single-cell RNA sequencing to diagnostic pancreatic cancer biopsies provides in-depth transcriptomic characterization of the tumor epithelium and microenvironment, while minimizing potential artifacts introduced by an intervening ex vivo passaging step. Thus, this approach can complement the use of patient-derived organoids in implementing precision oncology.

Research round-up: Precision oncology

Nature ◽  
2020 ◽  
Vol 585 (7826) ◽  
pp. S2-S3
Author(s):  
Laura Vargas-Parada
Keyword(s):  
Precision Oncology
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