Genome-wide 5-Hydroxymethylcytosine Profiling Analysis Identifies MAP7D1 as A Novel Regulator of Lymph Node Metastasis in Breast Cancer

Although DNA 5-hydroxymethylcytosine(5 hmC) is recognized as an important epigenetic mark in cancer, its precise role in lymph node metastasis remains elusive. In this study, we investigated how 5 hmC associates with lymph node metastasis in breast cancer. Accompanying with high expression of TET1 and TET2 proteins, large numbers of genes in the metastasis-positive primary tumors exhibit higher 5 hmC levels than those in the metastasis-negative primary tumors. In contrast, the TET protein expression and DNA 5 hmC decrease significantly within the metastatic lesions in the lymph nodes compared to those in their matched primary tumors. Through genomewide analysis of 8 sets of primary tumors, we identified 100 high-confidence metastasis-associated5 hmC signatures, and it is found that increased levels of DNA 5 hmC and gene expression of MAP7 D1 associate with high risk of lymph node metastasis. Furthermore, we demonstrate that MAP7 D1, regulated by TET1, promotes tumor growth and metastasis. In conclusion, the dynamic5 hmC profiles during lymph node metastasis suggest a link between DNA 5 hmC and lymph node metastasis. Meanwhile, the role of MAP7 D1 in breast cancer progression suggests that the metastasis-associated 5 hmC signatures are potential biomarkers to predict the risk for lymph node metastasis, which may serve as diagnostic and therapeutic targets for metastatic breast cancer.     

Distinguishing Tumor and Stromal Sources of MicroRNAs Linked to Metastasis in Cutaneous Melanoma

MicroRNA (miRNA) dysregulation in cancer causes changes in gene expression programs regulating tumor progression and metastasis. Candidate metastasis suppressor miRNA are often identified by differential expression in primary tumors compared to metastases. Here, we performed comprehensive analysis of miRNA expression in The Cancer Genome Atlas (TCGA) skin cutaneous melanoma (SKCM) tumors (97 primary, 350 metastatic), and identified candidate metastasis-suppressor miRNAs. Differential expression analysis revealed miRNA significantly downregulated in metastatic tumors, including miR-205, miR-203, miR-200a-c, and miR-141. Furthermore, sequential feature selection and classification analysis identified miR-205 and miR-203 as the miRNA best able to discriminate between primary and metastatic tumors. However, cell-type enrichment analysis revealed that gene expression signatures for epithelial cells, including keratinocytes and sebocytes, were present in primary tumors and significantly correlated with expression of the candidate metastasis-suppressor miRNA. Examination of miRNA expression in cell lines revealed that candidate metastasis-suppressor miRNA identified in the SKCM tumors, were largely absent in melanoma cells or melanocytes, and highly restricted to keratinocytes and other epithelial cell types. Indeed, the differences in stromal cell composition between primary and metastatic tumor tissues is the main basis for identification of differential miRNA that were previously classified as metastasis-suppressor miRNAs. We conclude that future studies must consider tumor-intrinsic and stromal sources of miRNA in their workflow to identify bone fide metastasis-suppressor miRNA in cutaneous melanoma and other cancers.     

Whole-genome sequencing of matched primary and metastatic hepatocellular carcinomas

Background

To gain biological insights into lung metastases from hepatocellular carcinoma (HCC), we compared the whole-genome sequencing profiles of primary HCC and paired lung metastases.

Methods

We used whole-genome sequencing at 33X-43X coverage to profile somatic mutations in primary HCC (HBV+) and metachronous lung metastases (> 2 years interval).

Results

In total, 5,027-13,961 and 5,275-12,624 somatic single-nucleotide variants (SNVs) were detected in primary HCC and lung metastases, respectively. Generally, 38.88-78.49% of SNVs detected in metastases were present in primary tumors. We identified 65–221 structural variations (SVs) in primary tumors and 60–232 SVs in metastases. Comparison of these SVs shows very similar and largely overlapped mutated segments between primary and metastatic tumors. Copy number alterations between primary and metastatic pairs were also found to be closely related. Together, these preservations in genomic profiles from liver primary tumors to metachronous lung metastases indicate that the genomic features during tumorigenesis may be retained during metastasis.

Conclusions

We found very similar genomic alterations between primary and metastatic tumors, with a few mutations found specifically in lung metastases, which may explain the clinical observation that both primary and metastatic tumors are usually sensitive or resistant to the same systemic treatments.     

Genomic Profiling of Oral Squamous Cell Carcinoma by Array-Based Comparative Genomic Hybridization

We designed a study to investigate genetic relationships between primary tumors of oral squamous cell carcinoma (OSCC) and their lymph node metastases, and to identify genomic copy number aberrations (CNAs) related to lymph node metastasis. For this purpose, we collected a total of 42 tumor samples from 25 patients and analyzed their genomic profiles by array-based comparative genomic hybridization. We then compared the genetic profiles of metastatic primary tumors (MPTs) with their paired lymph node metastases (LNMs), and also those of LNMs with non-metastatic primary tumors (NMPTs). Firstly, we found that although there were some distinctive differences in the patterns of genomic profiles between MPTs and their paired LNMs, the paired samples shared similar genomic aberration patterns in each case. Unsupervised hierarchical clustering analysis grouped together 12 of the 15 MPT-LNM pairs. Furthermore, similarity scores between paired samples were significantly higher than those between non-paired samples. These results suggested that MPTs and their paired LNMs are composed predominantly of genetically clonal tumor cells, while minor populations with different CNAs may also exist in metastatic OSCCs. Secondly, to identify CNAs related to lymph node metastasis, we compared CNAs between grouped samples of MPTs and LNMs, but were unable to find any CNAs that were more common in LNMs. Finally, we hypothesized that subpopulations carrying metastasis-related CNAs might be present in both the MPT and LNM. Accordingly, we compared CNAs between NMPTs and LNMs, and found that gains of 7p, 8q and 17q were more common in the latter than in the former, suggesting that these CNAs may be involved in lymph node metastasis of OSCC. In conclusion, our data suggest that in OSCCs showing metastasis, the primary and metastatic tumors share similar genomic profiles, and that cells in the primary tumor may tend to metastasize after acquiring metastasis-associated CNAs.     

Lack of Genomic Heterogeneity at High-Resolution aCGH between Primary Breast Cancers and Their Paired Lymph Node Metastases

Lymph-node metastasis (LNM) predict high recurrence rates in breast cancer patients. Systemic treatment aims to eliminate (micro)metastatic cells. However decisions regarding systemic treatment depend largely on clinical and molecular characteristics of primary tumours. It remains, however, unclear to what extent metastases resemble the cognate primary breast tumours, especially on a genomic level, and as such will be eradicated by the systemic therapy chosen. In this study we used high-resolution aCGH to investigate DNA copy number differences between primary breast cancers and their paired LNMs. To date, no recurrent LNM-specific genomic aberrations have been identified using array comparative genomic hybridization (aCGH) analysis. In our study we employ a high-resolution platform and we stratify on different breast cancer subtypes, both aspects that might have underpowered previously performed studies.To test the possibility that genomic instability in triple-negative breast cancers (TNBCs) might cause increased random and potentially also recurrent copy number aberrations (CNAs) in their LNMs, we studied 10 primary TNBC–LNM pairs and 10 ER-positive (ER+) pairs and verified our findings adding additionally 5 TNBC-LNM and 22 ER+-LNM pairs. We found that all LNMs clustered nearest to their matched tumour except for two cases, of which one was due to the presence of two distinct histological components in one tumour. We found no significantly altered CNAs between tumour and their LNMs in the entire group or in the subgroups. Within the TNBC subgroup, no absolute increase in CNAs was found in the LNMs compared to their primary tumours, suggesting that increased genomic instability does not lead to more CNAs in LNMs. Our findings suggest a high clonal relationship between primary breast tumours and its LNMs, at least prior to treatment, and support the use of primary tumour characteristics to guide adjuvant systemic chemotherapy in breast cancer patients.     

Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.     

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

BackgroundOsteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients.MethodWe studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.ResultsAge, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups.ConclusionWe established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.     

Dissection of a metastatic gene expression signature into distinct components

Background

Metastasis, the process whereby cancer cells spread, is in part caused by an incompletely understood interplay between cancer cells and the surrounding stroma. Gene expression studies typically analyze samples containing tumor cells and stroma. Samples with less than 50% tumor cells are generally excluded, thereby reducing the number of patients that can benefit from clinically relevant signatures.

Results

For a head-neck squamous cell carcinoma (HNSCC) primary tumor expression signature that predicts the presence of lymph node metastasis, we first show that reduced proportions of tumor cells results in decreased predictive accuracy. To determine the influence of stroma on the predictive signature and to investigate the interaction between tumor cells and the surrounding microenvironment, we used laser capture microdissection to divide the metastatic signature into six distinct components based on tumor versus stroma expression and on association with the metastatic phenotype. A strikingly skewed distribution of metastasis associated genes is revealed.

Conclusion

Dissection of predictive signatures into different components has implications for design of expression signatures and for our understanding of the metastatic process. Compared to primary tumors that have not formed metastases, primary HNSCC tumors that have metastasized are characterized by predominant down-regulation of tumor cell specific genes and exclusive up-regulation of stromal cell specific genes. The skewed distribution agrees with poor signature performance on samples that contain less than 50% tumor cells. Methods for reducing tumor composition bias that lead to greater predictive accuracy and an increase in the types of samples that can be included are presented.     

Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data

Tumor formation is in part driven by DNA copy number alterations (CNAs), which can be measured using microarray-based Comparative Genomic Hybridization (aCGH). Multiexperiment analysis of aCGH data from tumors allows discovery of recurrent CNAs that are potentially causal to cancer development. Until now, multiexperiment aCGH data analysis has been dependent on discretization of measurement data to a gain, loss or no-change state. Valuable biological information is lost when a heterogeneous system such as a solid tumor is reduced to these states. We have developed a new approach which inputs nondiscretized aCGH data to identify regions that are significantly aberrant across an entire tumor set. Our method is based on kernel regression and accounts for the strength of a probe's signal, its local genomic environment and the signal distribution across multiple tumors. In an analysis of 89 human breast tumors, our method showed enrichment for known cancer genes in the detected regions and identified aberrations that are strongly associated with breast cancer subtypes and clinical parameters. Furthermore, we identified 18 recurrent aberrant regions in a new dataset of 19 p53-deficient mouse mammary tumors. These regions, combined with gene expression microarray data, point to known cancer genes and novel candidate cancer genes.     

Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

Purpose

Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.

Methods

A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.

Results

Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.

Conclusion

A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.     

Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning

Hepatocellular carcinoma (HCC) is one of the most common and aggressive human malignancies. Its high mortality rate is mainly a result of intra-hepatic metastases. We analyzed the expression profiles of HCC samples without or with intra-hepatic metastases. Using a supervised machine-learning algorithm, we generated for the first time a molecular signature that can classify metastatic HCC patients and identified genes that were relevant to metastasis and patient survival. We found that the gene expression signature of primary HCCs with accompanying metastasis was very similar to that of their corresponding metastases, implying that genes favoring metastasis progression were initiated in the primary tumors. Osteopontin, which was identified as a lead gene in the signature, was over-expressed in metastatic HCC; an osteopontin-specific antibody effectively blocked HCC cell invasion in vitro and inhibited pulmonary metastasis of HCC cells in nude mice. Thus, osteopontin acts as both a diagnostic marker and a potential therapeutic target for metastatic HCC.     

Chromosomal imbalances in primary and metastatic melanomas revealed by comparative genomic hybridization

Characteristic genetic changes underlying the metastatic progression of malignant melanoma is incompletely understood. The goal of our study was to explore specific chromosomal alterations associated with the aggressive behavior of this neoplasm. Comparative genomic hybridization was performed to screen and compare genomic imbalances present in primary and metastatic melanomas. Sixteen primary and 12 metastatic specimens were analyzed. We found that the pattern of chromosomal aberrations is similar in the two subgroups; however, alterations present only in primary and/or metastatic tumors were also discovered. The mean number of genetic changes was 6.3 (range 1-14) in primary and 7.8 (range 1-16) in metastatic lesions. Frequent losses involved 9p and 10q, whereas gains most often occurred at 1q, 6p, 7q, and 8q. Distinct, high-level amplifications were mapped to 1p12-p21 and 1p22-p31 in both tumor types. Amplification of 4q12-q13.1, 7q21.3-qter and 8q23-qter were detected only in primary tumors. The 20q13-qter amplicon was present in a metastatic tumor. The number of genetic alterations were significantly higher in primary tumors which developed metastases within one year after the surgery compared to tumors without metastasis during this time period. Fluorescence in situ hybridization with centromeric and locus-specific probes was applied to validate CGH results on a subset of tumors. Comparison of FISH and CGH data gave good correlation. The aggressive behavior of melanoma is associated with accumulation of multiple genetic alterations. Chromosome regions, which differ in the primary and metastatic lesions, may represent potential targets to identify metastases-related chromosomal alterations.     

Toward predicting metastatic progression of melanoma based on gene expression data

Primary and metastatic melanoma tumors share the same cell origin, making it challenging to identify genomic biomarkers that can differentiate them. Primary tumors themselves can be heterogeneous, reflecting ongoing genomic changes as they progress toward metastasizing. We developed a computational method to explore this heterogeneity and to predict metastatic progression of the primary tumors. We applied our method separately to gene expression and to microRNA (miRNA) expression data from ~450 primary and metastatic skin cutaneous melanoma (SKCM) samples from the Cancer Genome Atlas (TCGA). Metastatic progression scores from RNA‐seq data were significantly associated with clinical staging of patients’ lymph nodes, whereas scores from miRNA‐seq data were significantly associated with Clark's level. The loss of expression of many characteristic epithelial lineage genes in primary SKCM tumor samples was highly correlated with predicted progression scores. We suggest that those genes/miRNAs might serve as putative biomarkers for SKCM metastatic progression.     

Utility of Genomic Analysis in Differentiating Synchronous and Metachronous Lung Adenocarcinomas from Primary Adenocarcinomas with Intrapulmonary Metastasis

Distinguishing synchronous and metachronous primary lung adenocarcinomas from adenocarcinomas with intrapulmonary metastasis is essential for optimal patient management. In this study, multiple lung adenocarcinomas occurring in the same patient were evaluated using comprehensive histopathologic evaluation supplemented with molecular analysis. The cohort included 18 patients with a total of 52 lung adenocarcinomas. Eleven patients had a new diagnosis of multiple adenocarcinomas in the same lobe (n = 5) or different lobe (n = 6). Seven patients had a history of lung cancer and developed multiple new tumors. The final diagnosis was made in resection specimens (n = 49), fine needle aspiration (n = 2), and biopsy (n = 1). Adenocarcinomas were non‐mucinous, and histopathologic comparison of tumors was performed. All tumors save for one were subjected to ALK gene rearrangement testing and targeted Next Generation Sequencing (NGS). Using clinical, radiologic, and morphologic features, a confident conclusion favoring synchronous/metachronous or metastatic disease was made in 65% of patients. Cases that proved challenging included ones with more than three tumors showing overlapping growth patterns and lacking a predominant lepidic component. Genomic signatures unique to each tumor were helpful in determining the relationship of multiple carcinomas in 72% of patients. Collectively, morphologic and genomic data proved to be of greater value and achieved a conclusive diagnosis in 94% of patients. Assessment of the genomic profiles of multiple lung adenocarcinomas complements the histological findings, enabling a more comprehensive assessment of synchronous, metachronous, and metastatic lesions in most patients, thereby improving staging accuracy. Targeted NGS can identify genetic alterations with therapeutic implications.     

iTRAQ Quantitative Proteomic Comparison of Metastatic and Non-Metastatic Uveal Melanoma Tumors

Background

Uveal melanoma is the most common malignancy of the adult eye. The overall mortality rate is high because this aggressive cancer often metastasizes before ophthalmic diagnosis. Quantitative proteomic analysis of primary metastasizing and non-metastasizing tumors was pursued for insights into mechanisms and biomarkers of uveal melanoma metastasis.

Methods

Eight metastatic and 7 non-metastatic human primary uveal melanoma tumors were analyzed by LC MS/MS iTRAQ technology with Bruch’s membrane/choroid complex from normal postmortem eyes as control tissue. Tryptic peptides from tumor and control proteins were labeled with iTRAQ tags, fractionated by cation exchange chromatography, and analyzed by LC MS/MS. Protein identification utilized the Mascot search engine and the human Uni-Prot/Swiss-Protein database with false discovery ≤ 1%; protein quantitation utilized the Mascot weighted average method. Proteins designated differentially expressed exhibited quantitative differences (p ≤ 0.05, t-test) in a training set of five metastatic and five non-metastatic tumors. Logistic regression models developed from the training set were used to classify the metastatic status of five independent tumors.

Results

Of 1644 proteins identified and quantified in 5 metastatic and 5 non-metastatic tumors, 12 proteins were found uniquely in ≥ 3 metastatic tumors, 28 were found significantly elevated and 30 significantly decreased only in metastatic tumors, and 31 were designated differentially expressed between metastatic and non-metastatic tumors. Logistic regression modeling of differentially expressed collagen alpha-3(VI) and heat shock protein beta-1 allowed correct prediction of metastasis status for each of five independent tumor specimens.

Conclusions

The present data provide new clues to molecular differences in metastatic and non-metastatic uveal melanoma tumors. While sample size is limited and validation required, the results support collagen alpha-3(VI) and heat shock protein beta-1 as candidate biomarkers of uveal melanoma metastasis and establish a quantitative proteomic database for uveal melanoma primary tumors.     

A New Mouse Model for the Study of Human Breast Cancer Metastasis

Breast cancer is the most common cancer in women, and this prevalence has a major impact on health worldwide. Localized breast cancer has an excellent prognosis, with a 5-year relative survival rate of 85%. However, the survival rate drops to only 23% for women with distant metastases. To date, the study of breast cancer metastasis has been hampered by a lack of reliable metastatic models. Here we describe a novel in vivo model using human breast cancer xenografts in NOD scid gamma (NSG) mice; in this model human breast cancer cells reliably metastasize to distant organs from primary tumors grown within the mammary fat pad. This model enables the study of the entire metastatic process from the proper anatomical site, providing an important new approach to examine the mechanisms underlying breast cancer metastasis. We used this model to identify gene expression changes that occur at metastatic sites relative to the primary mammary fat pad tumor. By comparing multiple metastatic sites and independent cell lines, we have identified several gene expression changes that may be important for tumor growth at distant sites.     

Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

BackgroundGastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.MethodsFrom January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.ResultsThere was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.ConclusionBoth nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.