Stem cell architecture drives myelodysplastic syndrome progression and predicts response to venetoclax-based therapy.

Myelodysplastic syndromes (MDS) are heterogeneous neoplastic disorders of hematopoietic stem cells (HSCs). The current standard of care for patients with MDS is hypomethylating agent (HMA)-based therapy; however, almost 50% of MDS patients fail HMA therapy and progress to acute myeloid leukemia, facing a dismal prognosis due to lack of approved second-line treatment options. As cancer stem cells are the seeds of disease progression, we investigated the biological properties of the MDS HSCs that drive disease evolution, seeking to uncover vulnerabilities that could be therapeutically exploited. Through integrative molecular profiling of HSCs and progenitor cells in large patient cohorts, we found that MDS HSCs in two distinct differentiation states are maintained throughout the clinical course of the disease, and expand at progression, depending on recurrent activation of the anti-apoptotic regulator BCL-2 or nuclear factor-kappa B-mediated survival pathways. Pharmacologically inhibiting these pathways depleted MDS HSCs and reduced tumor burden in experimental systems. Further, patients with MDS who progressed after failure to frontline HMA therapy and whose HSCs upregulated BCL-2 achieved improved clinical responses to venetoclax-based therapy in the clinical setting. Overall, our study uncovers that HSC architectures in MDS are potential predictive biomarkers to guide second-line treatments after HMA failure. These findings warrant further investigation of HSC-specific survival pathways to identify new therapeutic targets of clinical potential in MDS.

HPV transcript expression affects cervical cancer response to chemoradiation.

Persistent HPV infection is causative for the majority of cervical cancer cases; however, current guidelines do not require HPV testing for newly diagnosed cervical cancer. Using an institutional cohort of 88 patients with cervical cancer treated uniformly with standard-of-care chemoradiation treatment (CRT) with prospectively collected clinical outcome data, we observed that patients with cervical tumors containing HPV genotypes other than HPV 16 have worse survival outcomes after CRT compared with patients with HPV 16+ tumors, consistent with previously published studies. Using RNA sequencing analysis, we quantified viral transcription efficiency and found higher levels of E6 and the alternative transcript E6*I in cervical tumors with HPV genotypes other than HPV 16. These findings were validated using whole transcriptome data from The Cancer Genome Atlas (n = 304). For the first time to our knowledge, transcript expression level of HPV E6*I was identified as a predictive biomarker of CRT outcome in our complete institutional data set (n = 88) and within the HPV 16+ subset (n = 36). In vitro characterization of HPV E6*I and E6 overexpression revealed that both induce CRT resistance through distinct mechanisms dependent upon p53-p21. Our findings suggest that high expression of E6*I and E6 may represent novel biomarkers of CRT efficacy, and these patients may benefit from alternative treatment strategies.

Optimized polyepitope neoantigen DNA vaccines elicit neoantigen-specific immune responses in preclinical models and in clinical translation.

BACKGROUND: Preclinical studies and early clinical trials have shown that targeting cancer neoantigens is a promising approach towards the development of personalized cancer immunotherapies. DNA vaccines can be rapidly and efficiently manufactured and can integrate multiple neoantigens simultaneously. We therefore sought to optimize the design of polyepitope DNA vaccines and test optimized polyepitope neoantigen DNA vaccines in preclinical models and in clinical translation. METHODS: We developed and optimized a DNA vaccine platform to target multiple neoantigens. The polyepitope DNA vaccine platform was first optimized using model antigens in vitro and in vivo. We then identified neoantigens in preclinical breast cancer models through genome sequencing and in silico neoantigen prediction pipelines. Optimized polyepitope neoantigen DNA vaccines specific for the murine breast tumor E0771 and 4T1 were designed and their immunogenicity was tested in vivo. We also tested an optimized polyepitope neoantigen DNA vaccine in a patient with metastatic pancreatic neuroendocrine tumor. RESULTS: Our data support an optimized polyepitope neoantigen DNA vaccine design encoding long (≥20-mer) epitopes with a mutant form of ubiquitin (Ubmut) fused to the N-terminus for antigen processing and presentation. Optimized polyepitope neoantigen DNA vaccines were immunogenic and generated robust neoantigen-specific immune responses in mice. The magnitude of immune responses generated by optimized polyepitope neoantigen DNA vaccines was similar to that of synthetic long peptide vaccines specific for the same neoantigens. When combined with immune checkpoint blockade therapy, optimized polyepitope neoantigen DNA vaccines were capable of inducing antitumor immunity in preclinical models. Immune monitoring data suggest that optimized polyepitope neoantigen DNA vaccines are capable of inducing neoantigen-specific T cell responses in a patient with metastatic pancreatic neuroendocrine tumor. CONCLUSIONS: We have developed and optimized a novel polyepitope neoantigen DNA vaccine platform that can target multiple neoantigens and induce antitumor immune responses in preclinical models and neoantigen-specific responses in clinical translation.

HPV-EM: an accurate HPV detection and genotyping EM algorithm.

Accurate HPV genotyping is crucial in facilitating epidemiology studies, vaccine trials, and HPV-related cancer research. Contemporary HPV genotyping assays only detect < 25% of all known HPV genotypes and are not accurate for low-risk or mixed HPV genotypes. Current genomic HPV genotyping algorithms use a simple read-alignment and filtering strategy that has difficulty handling repeats and homology sequences. Therefore, we have developed an optimized expectation-maximization algorithm, designated HPV-EM, to address the ambiguities caused by repetitive sequencing reads. HPV-EM achieved 97-100% accuracy when benchmarked using cell line data and TCGA cervical cancer data. We also validated HPV-EM using DNA tiling data on an institutional cervical cancer cohort (96.5% accuracy). Using HPV-EM, we demonstrated HPV genotypic differences in recurrence and patient outcomes in cervical and head and neck cancers.

Pathogenic Germline Variants in 10,389 Adult Cancers.

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines.

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects.

Clinical outcomes and differential effects of PI3K pathway mutation in obese versus non-obese patients with cervical cancer.

The purpose of this study was to evaluate the effect of obesity and obesity-associated factors on the outcomes of patients with cervical cancer. Outcomes were evaluated in 591 patients with FIGO Ib to IV cervical cancer treated uniformly with definitive radiation. Patients were stratified into 3 groups based upon pretreatment Body Mass Index (BMI): A ≤ 18.5; B 18.6 - 34.9; and C ≥ 35. The 5-year freedom from failure rates were 58, 59, and 73% for BMI groups A, B, and C (p = 0.01). Overall survival rates were 50, 59, and 68%, respectively (p = 0.02). High expression of phosphorylated AKT (pAKT) was associated with poor outcomes only in non-obese patients. Obese patients with PI3K pathway mutant tumors had a trend toward favorable outcomes, while a similar effect was not observed in non-obese patients. Compared to similar tumors from non-obese hosts, PIK3CA and PTEN mutant tumors from obese patients failed to express high levels of phosphorylated AKT and its downstream targets. These results show that patients with obesity at the time of diagnosis of cervical cancer exhibit improved outcomes after radiation. PI3K/AKT pathway mutations are common in obese patients, but are not associated with activation of AKT signaling.

BreakPoint Surveyor: a pipeline for structural variant visualization.

SUMMARY: BreakPoint Surveyor (BPS) is a computational pipeline for the discovery, characterization, and visualization of complex genomic rearrangements, such as viral genome integration, in paired-end sequence data. BPS facilitates interpretation of structural variants by merging structural variant breakpoint predictions, gene exon structure, read depth, and RNA-sequencing expression into a single comprehensive figure. AVAILABILITY AND IMPLEMENTATION: Source code and sample data freely available for download at https://github.com/ding-lab/BreakPointSurveyor, distributed under the GNU GPLv3 license, implemented in R, Python and BASH scripts, and supported on Unix/Linux/OS X operating systems. CONTACT: lding@wustl.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Gain-of-function mutations in GATA6 lead to atrial fibrillation.

BACKGROUND: The genetic basis of atrial fibrillation (AF) and congenital heart disease remains incompletely understood. OBJECTIVE: We sought to determine the causative mutation in a family with AF, atrial septal defects, and ventricular septal defects. METHODS: We evaluated a pedigree with 16 family members, 1 with an atrial septal defect, 1 with a ventricular septal defect, and 3 with AF; we performed whole exome sequencing in 3 affected family members. Given that early-onset AF was prominent in the family, we then screened individuals with early-onset AF, defined as an age of onset <66 years, for mutations in GATA6. Variants were functionally characterized using reporter assays in a mammalian cell line. RESULTS: Exome sequencing in 3 affected individuals identified a conserved mutation, R585L, in the transcription factor gene GATA6. In the Massachusetts General Hospital Atrial Fibrillation (MGH AF) Study, the mean age of AF onset was 47.1 ± 10.9 years; 79% of the participants were men; and there was no evidence of structural heart disease. We identified 3 GATA6 variants (P91S, A177T, and A543G). Using wild-type and mutant GATA6 constructs driving atrial natriuretic peptide promoter reporter, we found that 3 of the 4 variants had a marked upregulation of luciferase activity (R585L: 4.1-fold, P < .0001; P91S: 2.5-fold, P = .0002; A177T; 1.7-fold, P = .03). In addition, when co-overexpressed with GATA4 and MEF2C, GATA6 variants exhibited upregulation of the alpha myosin heavy chain and atrial natriuretic peptide reporter activity. CONCLUSION: Overall, we found gain-of-function mutations in GATA6 in both a family with early-onset AF and atrioventricular septal defects as well as in a family with sporadic, early-onset AF.

Mutation of a common amino acid in NKX2.5 results in dilated cardiomyopathy in two large families.

BACKGROUND: The genetic basis for dilated cardiomyopathy (DCM) can be difficult to determine, particularly in familial cases with complex phenotypes. Next generation sequencing may be useful in the management of such cases. METHODS: We report two large families with pleiotropic inherited cardiomyopathy. In addition to DCM, the phenotypes included atrial and ventricular septal defects, cardiac arrhythmia and sudden death. Probands underwent whole exome sequencing to identify potentially causative variants. RESULTS: Each whole exome sequence yielded over 18,000 variants. We identified distinct mutations affecting a common amino acid in NKX2.5. Segregation analysis of the families support the pathogenic role of these variants. CONCLUSION: Our study emphasizes the utility of next generation sequencing in identifying causative mutations in complex inherited cardiac disease. We also report a novel pathogenic NKX2.5 mutation.

Protein-structure-guided discovery of functional mutations across 19 cancer types.

Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation-drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Proteogenomics connects somatic mutations to signalling in breast cancer.

Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. Here we describe quantitative mass-spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers, of which 77 provided high-quality data. Integrated analyses provided insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. Interrogation of the 5q trans-effects against the Library of Integrated Network-based Cellular Signatures, connected loss of CETN3 and SKP1 to elevated expression of epidermal growth factor receptor (EGFR), and SKP1 loss also to increased SRC tyrosine kinase. Global proteomic data confirmed a stromal-enriched group of proteins in addition to basal and luminal clusters, and pathway analysis of the phosphoproteome identified a G-protein-coupled receptor cluster that was not readily identified at the mRNA level. In addition to ERBB2, other amplicon-associated highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We demonstrate that proteogenomic analysis of breast cancer elucidates the functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets.

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer.

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis.

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.