Kosuke Yoshihara

Inferring tumour purity and stromal and immune cell admixture from expression data

Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe ‘Estimation of STromal and Immune cells in MAlignant Tumours using Expression data’ (ESTIMATE)—a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/.

A pan-cancer proteomic perspective on The Cancer Genome Atlas

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumors. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyze 3,467 patient samples from 11 TCGA “Pan-Cancer” diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data is integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumor lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumor lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

The landscape and therapeutic relevance of cancer-associated transcript fusions.

Transcript fusions as a result of chromosomal rearrangements have been a focus of attention in cancer as they provide attractive therapeutic targets. To identify novel fusion transcripts with the potential to be exploited therapeutically, we analyzed RNA sequencing, DNA copy number and gene mutation data from 4366 primary tumor samples. To avoid false positives, we implemented stringent quality criteria that included filtering of fusions detected in RNAseq data from 364 normal tissue samples. Our analysis identified 7887 high confidence fusion transcripts across 13 tumor types. Our fusion prediction was validated by evidence of a genomic rearrangement for 78 of 79 fusions in 48 glioma samples where whole-genome sequencing data were available. Cancers with higher levels of genomic instability showed a corresponding increase in fusion transcript frequency, whereas tumor samples harboring fusions contained statistically significantly fewer driver gene mutations, suggesting an important role for tumorigenesis. We identified at least one in-frame protein kinase fusion in 324 of 4366 samples (7.4%). Potentially druggable kinase fusions involving ALK, ROS, RET, NTRK and FGFR gene families were detected in bladder carcinoma (3.3%), glioblastoma (4.4%), head and neck cancer (1.0%), low-grade glioma (1.5%), lung adenocarcinoma (1.6%), lung squamous cell carcinoma (2.3%) and thyroid carcinoma (8.7%), suggesting a potential for application of kinase inhibitors across tumor types. In-frame fusion transcripts involving histone methyltransferase or histone demethylase genes were detected in 111 samples (2.5%) and may additionally be considered as therapeutic targets. In summary, we described the landscape of transcript fusions detected across a large number of tumor samples and revealed fusion events with clinical relevance that have not been previously recognized. Our results support the concept of basket clinical trials where patients are matched with experimental therapies based on their genomic profile rather than the tissue where the tumor originated.

Predicting time to ovarian carcinoma recurrence using protein markers

Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression-based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.

Comparison of gene expression patterns across 12 tumor types identifies a cancer supercluster characterized by TP53 mutations and cell cycle defects

Transcriptional profile-based subtypes of cancer are often viewed as identifying different diseases from the same tissue origin. Understanding the mechanisms driving the subtypes may be key in development of novel therapeutics but is challenged by lineage-specific expression signals. Using a t-test statistics approach, we compared gene expression subtypes across 12 tumor types, which identified eight transcriptional superclusters characterized by commonly activated disease pathways and similarities in gene expression. One of the largest superclusters was determined by the upregulation of a proliferation signature, significant enrichment in TP53 mutations, genomic loss of CDKN2A (p16ARF), evidence of increased numbers of DNA double strand breaks and high expression of cyclin B1 protein. These correlations suggested that abrogation of the P53-mediated apoptosis response to DNA damage results in activation of cell cycle pathways and represents a common theme in cancer. A second consistent pattern, observed in 9 of 11 solid tumor types, was a subtype related to an activated tumor-associated stroma. The similarity in transcriptional footprints across cancers suggested that tumor subtypes are commonly unified by a limited number of molecular themes.

Corrigendum: A pan-cancer proteomic perspective on The Cancer Genome Atlas

Nature Communications 5: Article number: 3887 (2014); Published 29 May 2014; Updated 28 Jan 2015 This Article contains an error in the Author contributions section that has resulted in incorrect credit for supervision of the network analysis. The correct Author contributions section is as follows: R.A.

Abstract 4795: A novel gene fusion in glioblastoma and a radiation response methylation signature identified by genomic characterization of glioma sphere-forming cells

Purpose: High fidelity models of the lethal primary brain tumor glioblastoma (GBM) are essential to develop new therapies. Glioma sphere-forming cells (GSCs) are derived from surgical specimens and are thought to play important roles in tumor maintenance and treatment resistance. We performed genomic characterization of the largest reported panel of GSCs. We hypothesized that GSCs would recapitulate the genomic alterations of their GBMs of origin while identifying novel changes identifiable only in a pure tumor cell population. Methods: All GSCs were obtained at the time of surgical resection and all analyses were conducted at early passage. We performed exome and transcriptome sequencing, DNA methylation profiling (Illumina Infinium 450K Bead Arrays) and DNA copy number determination (Affymetrix OncoScan). Radiation (RT) sensitivity was determined by clonogenic survival and in vivo survival by orthotopic xenograft. Results: We analyzed 43 GSCs, 40 of which had tissue available from their tumors of origin. Somatically mutated genes previously described in GBM, such as TP53, EGFR, PTEN, NF1, PIK3CA and RB1, were found at similar mutation frequencies. Likewise, DNA copy number variations were similar to their matched tumor and those reported by the TCGA, with novel or more pronounced alterations, such as MYC application and QKI deletion, identified in the GSCs. GSCs were classified into TCGA GBM subtypes by expression signatures, identifying a subset of GSCs with a subtype differing from their matched tumors that correlated to decreased stromal enrichment. GSCs exhibited upregulation of self-renewal pathways, such as MYC, WNT, and NOTCH, and of stem-cell factors, such as MSI1, NESTIN, OLIG2, and SOX2, consistent with the stem-like phenotype attributed to GSCs. Transcript analyses identified the previously reported FGFR3-TACC3 and EGFR-SEPT14 gene fusions as well as a novel KIF1B-KMT2A (MLL) fusion, which was found to have been retained in the matching recurrent GBM as well as the GSC derived from the recurrence. A signature derived by the differential methylation pattern of RT sensitive vs. resistant GSCs was applied to the subset of TCGA cases that received upfront RT. Survival by methylation class in this subset was significantly different (median survival 84 vs. 61 weeks; HR 1.64 adjusting for patient age, p-value Conclusions: Based on genomic analyses, GSCs are robust models of GBM which can be used for therapeutic development. We have identified a novel gene fusion involving MLL with a predicted driving role suggesting a new mode of gliomagenesis. A methylation signature predictive of RT response may have potential for personalizing RT treatment of GBM patients and provides insights into RT sensitivity phenotypes. Citation Format: Qianghu Wang, Ravesanker Ezhilarasan, Lindsey D. Goodman, Joy Gumin, Siyuan Zheng, Kosuke Yoshihara, Peng Sun, Jie Yang, Tim Heffernan, Giulio Draetta, Kenneth D. Aldape, Frederick F. Lang, Roel G.W. Verhaak, Erik P. Sulman. A novel gene fusion in glioblastoma and a radiation response methylation signature identified by genomic characterization of glioma sphere-forming cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4795. doi:10.1158/1538-7445.AM2015-4795