Cancer genomics of lung cancer including malignant mesothelioma: A brief overview of current status and future prospects
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Jhanwar, Suresh C.↗
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Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers
Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient’s specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox’s proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.
59 BASIC BIOLOGICAL SCIENCES↗
The topography of mutational processes in breast cancer genomes
Somatic mutations in human cancers show unevenness in genomic distribution that correlate with aspects of genome structure and function. These mutations are, however, generated by multiple mutational processes operating through the cellular lineage between the fertilized egg and the cancer cell, each composed of specific DNA damage and repair components and leaving its own characteristic mutational signature on the genome. Using somatic mutation catalogues from 560 breast cancer whole-genome sequences, here we show that each of 12 base substitution, 2 insertion/deletion (indel) and 6 rearrangement mutational signatures present in breast tissue, exhibit distinct relationships with genomic features relating to transcription, DNA replication and chromatin organization. This signature-based approach permits visualization of the genomic distribution of mutational processes associated with APOBEC enzymes, mismatch repair deficiency and homologous recombinational repair deficiency, as well as mutational processes of unknown aetiology. Lastly, it highlights mechanistic insights including a putative replication-dependent mechanism of APOBEC-related mutagenesis.
59 BASIC BIOLOGICAL SCIENCES↗
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas
Abstract not provided
Knijnenburg, Theo A.↗
Experimental and pan-cancer genome analyses reveal widespread contribution of acrylamide exposure to carcinogenesis in humans
Humans are frequently exposed to acrylamide, a probable human carcinogen found in commonplace sources such as most heated starchy foods or tobacco smoke. Prior evidence has shown that acrylamide causes cancer in rodents, yet epidemiological studies conducted to date are limited and, thus far, have yielded inconclusive data on association of human cancers with acrylamide exposure. In this study, we experimentally identify a novel and unique mutational signature imprinted by acrylamide through the effects of its reactive metabolite glycidamide. We next show that the glycidamide mutational signature is found in a full one-third of approximately 1600 tumor genomes corresponding to 19 human tumor types from 14 organs. The highest enrichment of the glycidamide signature was observed in the cancers of the lung (88% of the interrogated tumors), liver (73%), kidney (>70%), bile duct (57%), cervix (50%), and, to a lesser extent, additional cancer types. Overall, our study reveals an unexpectedly extensive contribution of acrylamide-associated mutagenesis to human cancers.
60 APPLIED LIFE SCIENCES↗
The Antiviral and Cancer Genomic DNA Deaminase APOBEC3H Is Regulated by an RNA-Mediated Dimerization Mechanism
Human APOBEC3H and homologous single-stranded DNA cytosine deaminases are unique to mammals. These DNA-editing enzymes function in innate immunity by restricting the replication of viruses and transposons. APOBEC3H also contributes to cancer mutagenesis. Here, in this paper, we address the fundamental nature of RNA in regulating human APOBEC3H activities. APOBEC3H co-purifies with RNA as an inactive protein, and RNase A treatment enables strong DNA deaminase activity. RNA-binding-defective mutants demonstrate clear separation of function by becoming DNA hypermutators. Biochemical and crystallographic data demonstrate a mechanism in which double-stranded RNA mediates enzyme dimerization. Additionally, APOBEC3H separation-of-function mutants show that RNA binding is required for cytoplasmic localization, packaging into HIV-1 particles, and antiviral activity. Overall, these results support a model in which structured RNA negatively regulates the potentially harmful DNA deamination activity of APOBEC3H while, at the same time, positively regulating its antiviral activity.
59 BASIC BIOLOGICAL SCIENCES↗
Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics
Abstract not provided
Ding, Li↗
Mathematical modelling the pathway of genomic instability in lung cancer
Genomic instability plays a significant role in lung cancer. Although substantial research has been conducted using both clinical and theoretical studies, it is still a hotly debated issue to whether genomic instability is necessary or whether genomic instability precedes oncogenes activation and tumor suppressor genes inactivation for lung cancer. In response to this issue, we come up with a mathematical model incorporating effects of genomic instability to investigate the genomic instability pathway of human lung cancer. The presented model are applied to match the incidence rate data of lung cancer from the Life Span Study cohort of the atomic bomb survivors in Nagasaki and Hiroshima and the Surveillance Epidemiology and End Results registry in the United States. Model results suggest that genomic instability is necessary in the tumorigenesis of lung cancer, and genomic instability has no significant impact on the net proliferation rate of cells by statistical criteria. By comparing the results of the LSS data to those of the SEER data, we conclude that the genomic instability pathway exhibits a sensitivity to radiation exposure, more intensive in male patients.
60 APPLIED LIFE SCIENCES↗
Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data
Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.
Zhu, Yitan↗
Genomic Prostate Cancer Classifier Predicts Biochemical Failure and Metastases in Patients After Postoperative Radiation Therapy
Purpose: To test the hypothesis that a genomic classifier (GC) would predict biochemical failure (BF) and distant metastasis (DM) in men receiving radiation therapy (RT) after radical prostatectomy (RP). Methods and Materials: Among patients who underwent post-RP RT, 139 were identified for pT3 or positive margin, who did not receive neoadjuvant hormones and had paraffin-embedded specimens. Ribonucleic acid was extracted from the highest Gleason grade focus and applied to a high-density-oligonucleotide microarray. Receiver operating characteristic, calibration, cumulative incidence, and Cox regression analyses were performed to assess GC performance for predicting BF and DM after post-RP RT in comparison with clinical nomograms. Results: The area under the receiver operating characteristic curve of the Stephenson model was 0.70 for both BF and DM, with addition of GC significantly improving area under the receiver operating characteristic curve to 0.78 and 0.80, respectively. Stratified by GC risk groups, 8-year cumulative incidence was 21%, 48%, and 81% for BF (P<.0001) and for DM was 0, 12%, and 17% (P=.032) for low, intermediate, and high GC, respectively. In multivariable analysis, patients with high GC had a hazard ratio of 8.1 and 14.3 for BF and DM. In patients with intermediate or high GC, those irradiated with undetectable prostate-specific antigen (PSA ≤0.2 ng/mL) had median BF survival of >8 years, compared with <4 years for patients with detectable PSA (>0.2 ng/mL) before initiation of RT. At 8 years, the DM cumulative incidence for patients with high GC and RT with undetectable PSA was 3%, compared with 23% with detectable PSA (P=.03). No outcome differences were observed for low GC between the treatment groups. Conclusion: The GC predicted BF and metastasis after post-RP irradiation. Patients with lower GC risk may benefit from delayed RT, as opposed to those with higher GC; however, this needs prospective validation. Genomic-based models may be useful for improved decision-making for treatment of high-risk prostate cancer.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer
Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.
60 APPLIED LIFE SCIENCES↗
Identification of important invasion and proliferation related genes in adrenocortical carcinoma
Adrenocortical carcinoma (ACC) is an end‐stage hormonal syndrome. Although profound attempts have been made to illuminate the pathogenesis, the molecular mechanisms of ACC remain to be clarified. To identify the important genes in the progression of ACC, microarray datasets GSE19775 was downloaded from the gene expression omnibus database. The differentially-expressed genes (DEGs) were identified, and pathway and GO enrichment analyses were performed. The protein–protein interaction (PPI) network was constructed and the module analysis was performed using the protein interaction network analysis and Cytoscape. Also constructed target genes–miRNA regulatory network and target genes–TF regulatory network. Correlation of the hub genes were analyzed in The Cancer Genome Atlas. The prognostic values of hub genes were further validated by online tool UALCAN. Mutation analysis was done by online tool CBio Cancer Genomics Portal. A total of 884 DEGs were identified, with 441 in up regulation and 443 in down regulation. Pathways in catecholamine biosynthesis, aldosterone synthesis and secretion, pyrimidine deoxyribonucleosides salvage and systemic lupus erythematosus were the most significantly enriched for DEGs (up and down regulated). Blood vessel morphogenesis and cell cycle phase transition were the most significantly enriched term in biological processes, while extracellular matrix and chromosome, centromeric region were in cellular component and heparin binding and protein dimerization activity were in molecular function. Among the PPI networks and its module, target genes–miRNA regulatory network and target genes–TF regulatory network, hub genes were YWHAZ, FN1, GRK5, VCAM1, GATA6, TXNIP, HSPA1A, and F11R. Hub genes such as YWHAZ, STAT1, ICAM1, SH3BP5, CD83, FN1, TK1, HIST1H1C, CABLES1, and MCM3 were associated with poor overall survival, while hub genes such as STAT1, ICAM1, CD83, FN1, TK1, HIST1H1C, and MCM3 were highly expressed in stage 4. In conclusion, DEGs and hub genes diagnosed in this study may deepen our understanding of molecular mechanisms underlying the progression of ACC, and provide important targets for diagnosis and treatment of ACC.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Chemoproteogenomic stratification of the missense variant cysteinome
Abstract Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics—a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues—with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.
Desai, Heta (ORCID:0000000343621707)↗
Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor
Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.
59 BASIC BIOLOGICAL SCIENCES↗
Connecting Genomic Alterations to Cancer Biology with Proteomics: The NCI Clinical Proteomic Tumor Analysis Consortium
The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium is applying the latest generation of proteomic technologies to genomically annotated tumors from The Cancer Genome Atlas (TCGA) program, a joint initiative of the NCI and the National Human Genome Research Institute. By providing a fully integrated accounting of DNA, RNA, and protein abnormalities in individual tumors, these datasets will illuminate the complex relationship between genomic abnormalities and cancer phenotypes, thus producing biologic insights as well as a wave of novel candidate biomarkers and therapeutic targets amenable to verifi cation using targeted mass spectrometry methods.
Ellis, Matthew↗
A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks
Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.
59 BASIC BIOLOGICAL SCIENCES↗
A mutational signature in gastric cancer suggests therapeutic strategies
Targeting defects in the DNA repair machinery of neoplastic cells, for example, those due to inactivating BRCA1 and/or BRCA2 mutations, has been used for developing new therapies in certain types of breast, ovarian and pancreatic cancers. Recently, a mutational signature was associated with failure of double-strand DNA break repair by homologous recombination based on its high mutational burden in samples harbouring BRCA1 or BRCA2 mutations. In pancreatic cancer, all responders to platinum therapy exhibit this mutational signature including a sample that lacked any defects in BRCA1 or BRCA2. Here, we examine 10,250 cancer genomes across 36 types of cancer and demonstrate that, in addition to breast, ovarian and pancreatic cancers, gastric cancer is another cancer type that exhibits this mutational signature. Furthermore, our results suggest that 7–12% of gastric cancers have defective double-strand DNA break repair by homologous recombination and may benefit from either platinum therapy or PARP inhibitors.
59 BASIC BIOLOGICAL SCIENCES↗
Clinical potential of mass spectrometry-based proteogenomics
Cancer genomics research aims to advance personalized oncology by finding and targeting genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients based on the genomic sequence of their tumor. This personalized oncology approach has prolonged survival for subsets of cancer patients. However, many patients do not respond to the predicted therapies based on genomic profiles of their tumors. Recent studies pairing genomic and proteomic analyses in the same tumors have shown that the proteome encodes novel information that is not discerned through genomic analysis alone. This has led to the concept of “proteogenomics,” in which both types of data are leveraged for a more complete view of tumor biology that may more effectively match cancer patients to efficacious treatments. Technological advances in methods for quantifying proteins, including targeted mass spectrometry, have opened a path for the clinical translation of proteogenomic findings into clinical research. We discuss the added value of a combined proteogenomics approach over the current genome-centric approach in characterizing human cancers, and summarize current efforts to incorporate targeted proteomic measurements based on multiple reaction monitoring mass spectrometry (MRM) into the clinical laboratory to facilitate clinical proteogenomics.
Zhang, Bing↗