James Hsieh's Recommendations (James is a Cancer Biologist)
- 20170309 Renal cell carcinoma - pdf - General Introduction to all types of Kidney Cancer
- 20161110 Phase II Trial and Correlative Genomic Analysis of Everolimus Plus Bevacizumab in Advanced Non-Clear Cell Renal Cell Carcinoma. - pdf - Dr. Hsieh's current therapeutic approach to pRcc
- 20160315 Multilevel Genomics-Based Taxonomy of Renal Cell Carcinoma. - pdf
- 20180403 The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma - pdf
- 201512 A river model to map convergent cancer evolution and guide therapy in RCC - pdf
Bill Paseman's Recommendations (Bill is a Patient)
- 20160109 “Molecular Genetics for the Practicing Physician” - Gabriel G. Malouf - Relates Kidney Physiology to Gene Expression to Therapeutic Targets - pdf
- 20171103 Papillary RCC - Laurence Albiges - No increase in Overall survival after a decade.
- 201712 - “Recommendations for the Management of Rare Kidney Cancers” - pdf - Rarekidneycancer.org members - includes pRCC clinical trials list.
- 20151105 “Papillary Kidney Carcinoma” - NIH - Web Entry point to pRCC TCGA (The Cancer Genome Atlas) data.
- 20160114 "Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma" - Marston Linehan et. al. - Molecular characterization based on TCGA.
- 20180305 “Papillary Renal Cell Carcinoma Transcriptome Meta-Analysis” - Michael Edwards - Youtube.
- Alex Feltus' Recommendations (Alex is a Bioinformatician)
Dunwoodie LJ1, Poehlman WL1, Ficklin SP2, Feltus FA1. "Discovery and validation of a glioblastoma co-expressed gene module." Oncotarget. 2018 Jan 13;9(13):10995-11008. doi: 10.18632/oncotarget.24228. eCollection 2018 Feb 16. https://www.ncbi.nlm.nih.gov/pubmed/29541392
- ABSTRACT. Tumors exhibit complex patterns of aberrant gene expression. Using a knowledge-independent, noise-reducing gene co-expression network construction software called KINC, we created multiple RNAseq-based gene co-expression networks relevant to brain and glioblastoma biology. In this report, we describe the discovery and validation of a glioblastoma-specific gene module that contains 22 co-expressed genes. The genes are upregulated in glioblastoma relative to normal brain and lower grade glioma samples; they are also hypo-methylated in glioblastoma relative to lower grade glioma tumors. Among the proneural, neural, mesenchymal, and classical glioblastoma subtypes, these genes are most-highly expressed in the mesenchymal subtype. Furthermore, high expression of these genes is associated with decreased survival across each glioblastoma subtype. These genes are of interest to glioblastoma biology and our gene interaction discovery and validation workflow can be used to discover and validate co-expressed gene modules derived from any co-expression network.
Ficklin SP1, Dunwoodie LJ2, Poehlman WL2, Watson C3, Roche KE2, Feltus FA4.Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study. Sci Rep. 2017 Aug 17;7(1):8617. doi: 10.1038/s41598-017-09094-4. https://www.ncbi.nlm.nih.gov/pubmed/28819158
- ABSTRACT. A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.
- Dunwoodie LJ1, Poehlman WL1, Ficklin SP2, Feltus FA1. "Discovery and validation of a glioblastoma co-expressed gene module." Oncotarget. 2018 Jan 13;9(13):10995-11008. doi: 10.18632/oncotarget.24228. eCollection 2018 Feb 16. https://www.ncbi.nlm.nih.gov/pubmed/29541392
- Sean Davis
- Building Genomic Analysis Pipelines in a Hackathon Setting
- Cytogenetic and Molecular Tumor Profiling for Type 1 and Type 2 PRCC
- Papillary renal cell carcinoma Prognostic value of morphological subtypes in a clinicopathologic study of 43 cases
- MET Is a Potential Target across All Papillary Renal Cell Carcinomas, Result from a Large Molecular Study of pRCC with CGH Array and Matching Gene Expression Array
- First-line treatment with sunitinib for type 1 and type 2 locally advanced or metastatic papillary renal cell carcinoma, a phase II study (SUPAP) by the French Genitourinary Group (GETUG)
- Off‐label drug use in oncology a systematic review of literature
- The Cancer Genome Atlas Pan-Cancer analysis project
- Patterns of somatic mutation in human cancer genomes
- A comprehensive catalogue of somatic mutations from a human cancer genome
- Mutational heterogeneity in cancer and the search for new cancer-associated genes
- Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples
- Cancer-Specific High-Throughput Annotation of Somatic Mutations, Computational Prediction of Driver Missense Mutations
- Overcome tumor heterogeneity-imposed therapeutic barriers through convergent genomic biomarker discovery: A braided cancer river model of kidney cancer
- Germline and somatic variant identification using BGISEQ-500 and HiSeq X Ten whole genome sequencing
- Gene set enrichment analysis, A knowledge-based approach for interpreting genome-wide expression profiles
- Neoantigens in cancer immunotherapy
- An Immunogenic Personal Neoantigen Vaccine for Melanoma Patients
- In Silico HLA Typing Using Standard RNA-Seq Sequence Reads