Kuan-lin Huang

Patterns and functional implications of rare germline variants across 12 cancer types

Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine.

Systematic discovery of complex insertions and deletions in human cancers

Complex insertions and deletions (indels) are formed by simultaneously deleting and inserting DNA fragments of different sizes at a common genomic location. Here we present a systematic analysis of somatic complex indels in the coding sequences of samples from over 8,000 cancer cases using Pindel-C. We discovered 285 complex indels in cancer-associated genes (such as PIK3R1, TP53, ARID1A, GATA3 and KMT2D) in approximately 3.5% of cases analyzed; nearly all instances of complex indels were overlooked (81.1%) or misannotated (17.6%) in previous reports of 2,199 samples. In-frame complex indels are enriched in PIK3R1 and EGFR, whereas frameshifts are prevalent in VHL, GATA3, TP53, ARID1A, PTEN and ATRX. Furthermore, complex indels display strong tissue specificity (such as VHL in kidney cancer samples and GATA3 in breast cancer samples). Finally, structural analyses support findings of previously missed, but potentially druggable, mutations in the EGFR, MET and KIT oncogenes. This study indicates the critical importance of improving complex indel discovery and interpretation in medical research.

Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.

Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.

Breast tumors educate the proteome of stromal tissue in an individualized but coordinated manner

Proteomic analysis of the tumor-associated stroma reveals extensive and coordinated regulation by breast cancers. Profiling the tumor stroma proteome Communication between a tumor and cells in the surrounding stroma contributes to tumor growth, progression, and drug resistance. Thus, targeting this communication, in the primary tumor and especially in metastatic niches, may be an effective way to treat cancer. Wang et al. grew patient breast tumors subcutaneously in mice and obtained species-distinguished proteomic profiles of the tumors (human) and tumor-associated stroma (mouse). The authors found that all breast tumors consistently altered clustered subsets of the stromal proteome, particularly proteins involved in immune signaling, but that these varied in a subtype- and stage-specific manner. These findings may have future implications for treatment stratification and provide a platform from which to understand this experimental model and tumor-stroma interactions on a large-scale protein level. Cancer forms specialized microenvironmental niches that promote local invasion and colonization. Engrafted patient-derived xenografts (PDXs) locally invade and colonize naïve stroma in mice while enabling unambiguous molecular discrimination of human proteins in the tumor from mouse proteins in the microenvironment. To characterize how patient breast tumors form a niche and educate naïve stroma, subcutaneous breast cancer PDXs were globally profiled by species-specific quantitative proteomics. Regulation of PDX stromal proteins by breast tumors was extensive, with 35% of the stromal proteome altered by tumors consistently across different animals and passages. Differentially regulated proteins in the stroma clustered into six signatures, which included both known and previously unappreciated contributors to tumor invasion and colonization. Stromal proteomes were coordinately regulated; however, the sets of proteins altered by each tumor were highly distinct. Integrated analysis of tumor and stromal proteins, a comparison made possible in these xenograft models, indicated that the known hallmarks of cancer contribute pleiotropically to establishing and maintaining the microenvironmental niche of the tumor. Education of the stroma by the tumor is therefore an intrinsic property of breast tumors that is highly individualized, yet proceeds by consistent, nonrandom, and defined tumor-promoting molecular alterations.

Mass spectrometry-based proteomics reveals potential roles of NEK9 and MAP2K4 in resistance to PI3K inhibitors in triple negative breast cancers

Activation of PI3K signaling is frequently observed in triple-negative breast cancer (TNBC), yet PI3K inhibitors have shown limited clinical activity. To investigate intrinsic and adaptive mechanisms of resistance, we analyzed a panel of patient-derived xenograft models of TNBC with varying responsiveness to buparlisib, a pan-PI3K inhibitor. In a subset of patient-derived xenografts, resistance was associated with incomplete inhibition of PI3K signaling and upregulated MAPK/MEK signaling in response to buparlisib. Outlier phosphoproteome and kinome analyses identified novel candidates functionally important to buparlisib resistance, including NEK9 and MAP2K4. Knockdown of NEK9 or MAP2K4 reduced both baseline and feedback MAPK/MEK signaling and showed synthetic lethality with buparlisib in vitro A complex in/del frameshift in PIK3CA decreased sensitivity to buparlisib via NEK9/MAP2K4-dependent mechanisms. In summary, our study supports a role for NEK9 and MAP2K4 in mediating buparlisib resistance and demonstrates the value of unbiased omic analyses in uncovering resistance mechanisms to targeted therapy.Significance: Integrative phosphoproteogenomic analysis is used to determine intrinsic resistance mechanisms of triple-negative breast tumors to PI3K inhibition. Cancer Res; 78(10); 2732-46. ©2018 AACR.

Integrative omics analyses broaden treatment targets in human cancer

BackgroundAlthough large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.MethodsTo overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.ResultsWithin the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.ConclusionsOur results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.

Pan-cancer analysis of somatic mutations across 21 neuroendocrine tumor types

Dear Editor, Neuroendocrine tumors (NETs) comprise a heterogeneous spectrum of neoplasms originating from neuroendocrine cells in various organs — most commonly in the endocrine glands and the gastrointestinal tract. The molecular and etiological features of NETs arising from different organs are still far from clarified. Therefore, systematic analysis of genomic alternations and their contribution to core pathways in NETs is urgently needed for the development of novel diagnostic, therapeutic strategies and personalized management of patients. Here, we investigated somatic mutations across 21 NET types through pan-cancer analysis and identified 86 candidate driver genes. Further analysis of druggability and panel sequencing of these genes provide potential diagnostic and therapeutic targets for NETs. To investigate the landscape of common and specific somatic mutations in NETs, we collected mutation data of 1,103 tumors (1,034 published and 69 new whole-exome sequencing data) from 38 research projects (Supplementary information, Table S1). We performed whole-exome sequencing on tumor-normal pairs of 38 insulinoma (INS), 20 Cushing’s disease (CD) induced by corticotroph pituitary adenoma and 11 pheochromocytoma (PCC) (Supplementary information, Tables S2–5, Figure S1). Using these data, we compiled somatic mutation data across 21 NET types. The data set consisted of five types of adrenocortical tumors: aldosterone-producing adenomas (APA), cortisol-producing adrenocortical adenomas (ACA), ACTH-independent macronodular adrenocortical hyperplasia (AIMAH), adrenocortical carcinomas (ACC) and adrenocortical oncocytoma; seven types of pituitary tumors: growth hormone-secreting pituitary adenomas, gonadotropins including follicle-stimulating hormone and luteinizing hormone pituitary adenomas, prolactin pituitary adenomas, thyrotropin-stimulating hormone pituitary adenomas, CD induced by corticotroph pituitary adenoma, plurihormonal pituitary adenoma and nonfunctioning pituitary adenomas; two types of pancreatic tumors: non-functional NETs (PNETs) and INS; medullary thyroid cancer (MTC), parathyroid adenomas and parathyroid carcinomas (PTC); pulmonary carcinoids (PC); PCC and paraganglioma (PCC/PGL); small intestine NETs (SINET) and neuroblastoma (NB) (Supplementary information, Table S6). The data from 21 NET types were re-analyzed and annotated to obtain a uniform set of somatic mutations (Supplementary information, Tables S7 and 8). Malignant NETs have a larger number of non-silent mutations and a higher mutation frequency than benign NETs (P= 7.33 × 10; Supplementary information, Figures S2 and 3). Mutation spectrum across 21 NET types reveals that the C−>T transversion is the predominant substitution, consistent with findings in other cancer types (Supplementary information, Figure S4 and Table S9). To comprehensively identify the significantly mutated genes (SMGs) with a statistically higher mutation rate in NETs, we performed systematic and stepwise analysis using the MuSiC suite. The results of SMG analysis are associated with the background mutation rates (BMRs) of tumor types and BMRs between benign and malignant tumors are significantly different (Supplementary information, Figure S5). Therefore, MuSiC analyses of 21 NETs were separately conducted in the combined benign set, combined malignant set, combined organ set (adrenal, gastrointestinal, pituitary and thyroid) and individual tumor types. The resulting SMGs were further filtered by gene mutation frequency, deleterious mutation rate and gene expression (Supplementary information, Figure S6). We reliably identified a total of 86 candidate driver genes in NETs, including 80 SMGs and 6 known cancer genes (Supplementary information, Figure S7 and Table S10). Of the 86 candidate driver genes, 34 are novel SMGs in NETs and 52 have been reported in previous studies of specific NET types. The mutations of 86 genes showed common and specific distribution in NETs. Novel type-specific SMGs were identified in less frequently mutated genes, such as DNMT3A in benign NETs and AHNAK, COL1A1, SF3B1 and ZNF292 in malignant NETs (Fig. 1a and Supplementary information, Figure S8). Our data reveal that MEN1 is the most common SMGs in NETs (8 out of 21 types). Notably, mutations of three novel SMGs and six known candidate driver genes are identified in as least five NET types, indicating that more common driver genes emerge across both benign and malignant NETs (Supplementary information, Figure S9). To comprehensively understand the mechanistic classification and further illustrate the cellular processes involved in NETs, we performed gene ontology and hierarchical clustering analysis. The 86 genes were classified into 20 categories of cellular processes (Supplementary information, Figure S10). Clustering analysis showed that in addition to MEN1, RET and GNAS, mutations in transcription factor genes (YY1, CTNNB1, NF1) and mutations in genome integrity genes (ATM, ATRX, TP53) are critical for clustering of multiple NET types, suggesting the importance of these SMGs in molecular classification of NETs (Supplementary information, Figure S11). Clustering of the 21 types of NET and 13 other cancers with the 86 candidate driver genes and previously described cancer genes showed that the majority of NETs, except ACC, are distinctive from common malignant tumors (Supplementary information, Figure S12). Notably, chromatin modification and remodeling genes (22 genes in the categories of histone modifiers, genome integrity and DNA methylation) are the most significant set in NETs. In silico prediction analysis showed that all the 22 genes have pathogenic or truncating mutations (131/171, 76.6% in total), supporting the functional roles of the chromatin modification and remodeling genes in NETs (Supplementary information, Figures S13 and 14, Tables S11 and 12). MEN1 is the representative mutated driver gene in diverse inherited and sporadic NET types. The variant

CharGer: clinical Characterization of Germline variants

Summary: CharGer (Characterization of Germline variants) is a software tool for interpreting and predicting clinical pathogenicity of germline variants. CharGer gathers evidence from databases and annotations, provided by local tools and files or via ReST APIs, and classifies variants according to ACMG guidelines for assessing variant pathogenicity. User‐designed pathogenicity criteria can be incorporated into CharGers flexible framework, thereby allowing users to create a customized classification protocol. Availability and implementation: Source code is freely available at https://github.com/ding‐lab/CharGer and is distributed under the GNU GPL‐v3.0 license. Software is also distributed through the Python Package Index (PyPI) repository. CharGer is implemented in Python 2.7 and is supported on Unix‐based operating systems. Supplementary information: Supplementary data are available at Bioinformatics online.

Corrigendum: Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.

Nature Communications 8: Article number: 14864 (2017)); Published: 28 March 2017; Updated: 25 April 2017 The original version of this Article contained a typographical error in the spelling of the author Beifang Niu, which was incorrectly given as Beifung Niu. This has now been corrected in both thePDF and HTML versions of the Article.

Abstract IA29: Proteogenomic and phosphoproteomic analysis of breast cancer

The genetic landscape of human breast cancer has been well defined in The Cancer Genome Atlas (TCGA) project. Mass spectrometry (MS)-based global proteome and phosphoproteome analyses provide a complementary, orthogonal approach to genomic studies to further improve the molecular taxonomy and biological understanding of breast cancer. We analyzed human breast cancer samples that had previously undergone comprehensive genomic and reversed phase protein array (RPPA) characterization by TCGA. Tumor samples were analyzed by global shotgun proteomics and phosphoproteomics at an unprecedented coverage of >11,000 quantified proteins and >27,000 phosphorylation sites for each tumor. We verified the translation of hundreds of genomically characterized single nucleotide and splice junction variants at the protein level. The correlation of mRNA to protein abundance was significant for 6,135 out of 9,302 protein/mRNA pairs, but differed amongst protein classes. Genes that did not correlate on the protein/mRNA level included components of basic cellular machineries such as the ribosome, RNA polymerase and spliceosome, as well as those involved in processes regulated by proteolysis. Hierarchical clustering yielded three major clusters in both the proteome and the phosphoproteome data: basal-enriched, luminal-enriched and stroma-enriched groups, the last also enriched for what have been previously designated “reactive-type” tumors by RPPA. Our deep proteome analysis promoted new insights including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. The 5q trans effects were interrogated using the Library of Integrated Network-based Cellular Signatures. Theses analyses connected the 5q genes CETN3 and SKP1 to elevated expression of EGFR, and SKP1 also to SRC. Differential phosphopeptide analyses, integrated with activity maps derived from knock-in mutated cell lines, identified multiple novel downstream effects of PIK3CA and TP53 mutation. Besides ERBB2, other amplicon-associated, highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. These and other examples demonstrate that proteogenomic analysis of breast cancer elucidates functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies potential therapeutic targets. Citation Format: Philipp Mertins, DR Mani, Kelly Ruggles, Michael Gillette, Karl Clauser, Pei Wang, Xianlong Wang, Jana Qiao, Song Cao, Francesca Petralia, Filip Mundt, Zhidong Tu, Jonathan Lei, Michael Gatza, Matthew Wilkerson, Charles Perou, Venkata Yellapantula, Kuan-lin Huang, Chenwei Lin, Michael McLellan, Ping Yan, Sherri Davies, Reid Townsend, Steven Skates, Jing Wang, Bing Zhang, Christopher Kinsinger, Mehdi Mesri, Henry Rodriguez, Li Ding, Amanda Paulovich, David Fenyo, Matthew Ellis, Steven Carr, NCI CPTAC. Proteogenomic and phosphoproteomic analysis of breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr IA29.