However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying IDC progression. The server is publicly available at  The cancer has been categorized into three therapeutic groups: ER - ER+ patients receive endocrine therapy, HER – HER+ group is treated by therapeutic targeting of HER/ERBB2, and TNBC - lacking expression of ER, PR, HER receptors2. It has been categorized into two major histological types- Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC), occurring in 47–79% and 2–15% of invasive cancers amongst women of different worldwide races, respectively4. These two sub-types show similarity in certain features such as tumour site, size, stage and grade, but have different metastatic patterns, characteristic histology and malignant calcifications4. IDC starts from ducts and spreads to the breast fatty tissue, whereas ILC is restricted to milk producing lobules6. These two sub-types are also discriminated at the molecular level with differential expression of gene encoding vimentin, cathepsin D, thrombospondin, E-cadherin, vascular endothelial growth factor, cytokeratin 8, and cyclin A12. The pathological differences between the two sub-types arises as a result of separate gene regulatory networks, which warrants further exploration for the development of appropriate diagnostic and therapeutic treatment strategy6. According to reports, 75% cases of invasive breast carcinoma cases are accounted by IDC, however, advanced treatment of IDC patients still remains a challenge due to lack of molecular targets for IDC treatment14. Also, there is the availability of higher number of datasets for IDC patients in TCGA-BRCA, which is favourable for development of efficient classifiers using machine learning. Hence, we implemented machine-learning and developed a web-server for efficient prediction of the correct IDC stage, which can potentially aid in designing appropriate treatment strategies and precise molecular targeting.Breast cancer ranks second among all the cancer types arranged in the order of increasing death rates, also the most prevalent cancer in women15. Although PET and MR imaging techniques are available for early detection of breast cancer, these techniques are based on morphological features that do not provide any clue for molecular events accompanying cancer progression. In these cases, gene expression based analyses are able to capture early stage markers and also detect molecular events and pathways for driving disease from early to late stage. Early availability of such information can lead to identification of patients who would require targeted or personalized therapy. Further, it may also shed some light on tumors for which no standard treatment is available (https://www.cancer.gov/about-cancer/treatment/types/precision-medicine/tumor-dna-sequencing). Unfortunately, imaging techniques will not be help for such treatments. Further, using ML-based methods, not only can the process be automated , but more information can be derived from a single procedure. Previous studies has been done to identify non coding RNA expression profile associated with early stage of invasive ductal carcinoma16. Also early stage markers of breast cancer has been identified using microarray datasets from peripheral blood cell17. Machine learning has been previously utilized to discriminate early-late stage based on gene expression profile of clear cell renal cell carcinoma patients18. However machine learning based analysis on tissue site based expression profile in invasive ductal carcinoma has not yet been performed.Early detection of breast cancer has led to a significant decrease in mortality rate. Prognostic and predictive factors used for therapy are not sufficient and we need new markers for treatment as individuals differ19. Breast cancer is a heterogeneous disease with varied features, such as morphological appearances, profile, response to therapy, TNM staging, histological grade, etc.20. There is a direct correlation between mortality rate and stages of cancer, and the stage progression could be checked by early detection and appropriate treatment strategies21. Although knowledge about genomic profiling has been identified in terms of varied molecular features associated with subtypes of cancer, its molecular mechanism of progression is poorly understood22. Tumour stage is defined as the anatomic extent of cancer at the time of diagnosis, which is important for an individual patient prognosis, and determination of best treatment strategy23. Pierre Denoix and the Union of International Cancer Control (UICC) has classified tumour staging based on TNM classification23. TNM classification overlaps with breast cancer stages, where T describes the extent of a primary tumour by the size or depth of invasion mainly in stage I or II, N describes the extent of regional lymph node metastasis in mainly stage II or III, and M describes the presence of metastasis mainly in stage IV23. The incorporation of this staging system into molecular or genetic profiles can help in detecting prognostic groups that guide the disease intervention23. There is a sharp decrease in the 5-year survival rate of patients with the stage-wise progression of breast cancer21. Treatment of cancer remains a challenge because of the lack of knowledge about factors for cancer progression and metastasis23. Potential treatment options are available based on clinical and pathological prognostic factors with the histological grade being the most important predictive factor23. High throughput techniques such as Next Generation Sequencing (NGS) that capture expression of thousands of genes in a single assay can act as powerful analytical tools for capturing breast cancer prognostic signature20. We can obtain information about a large number of genes, but their intertwining relationship cannot be captured by traditional techniques like statistical and correlational analyses, hence advanced methods such as machine-learning are important to capture cryptic signatures inherent in these data19. Molecular profiling helps in finding predictive information and identifying prognostic biomarkers that can serve as therapeutic targets20. Most of the cancer research is focussed to determine for finding driver genes, which are related to chimeras or splice junctions, which do not utilize the high resolution features of RNA-seq24. Machine-learning techniques are increasingly being used for modelling the progression and treatment of cancer due to its ability to detect key features from complex datasets25. Personalized treatment strategies could be developed for patients with similar molecular sub-types based on the patterns identified from systematically collected molecular profiles of tumour samples26. In this study, we developed classification methods to analyse the genomic datasets of invasive ductal carcinoma obtained from TCGA, using supervised machine-learning algorithms and feature selection methods. We developed prediction models that could discriminate between early and late stages of IDC using RNA-seq datasets. Different feature selection methods such as RFE, RLASSO, linear modelling, linear regression and random forest were trained and evaluated using Python scikit-learn library which provides individual rankings to gene features. Based on the most comprehensive ranking of gene features by various feature selection methods the top gene features were selected for enriched classifier training that helped us efficiently classify the tumours based on the tumour stage-specific gene expression profiles.The increased incidence of breast cancer and higher mortality rate has attracted significant research efforts to unravel its causes, and development of better treatment options27. The datasets represent 610 IDC patients, the distribution of samples across testing and training set by tumour stage is given in the Table 28. This led to a preliminary reduction of the number of features from 20,505 to 17,373. The training datasets were standardized using z-score normalization. It converts all the features to common scale with mean zero and standard deviation 1 .The normalized datasets were divided into two training sets, the first dataset comprises of complete gene expression datasets which were the original datasets representing expression of 17,373 genes used for feature selection. The second dataset consists of gene expression data corresponding to the driver-gene list in which the training genes were reduced to driver-genes responsible for progression of different cancers. The list of 881 driver genes was obtained from three well curated driver genes lists- Cosmic, IntoGen and Bailey32. Real world datasets have higher composition of ‘normal class’ as compared to ‘abnormal class’, introducing bias in classification model. Combination of over-sampling of minority class along with under-sampling of majority class can aid in increasing the classifier performance33. To check the SMOTE resampling, models were trained on datasets where SMOTE resampling was employed : the complete gene expression-based model, driver gene-based model and the combination of two. We found that as compared to the former two gene sets, more interacting partners are exhibited by STRING analysis of their combination Fig. . Thus, w34. Cell adhesion and ECM pathway interaction are found to be dys-regulated in early tumorigenesis of ER+ cancer35. Whereas in later stages, patients diagnosed at stage IV, develop distant metastasis, which becomes nearly incurable. Although strategies targeting primary tumor has improved, treatment strategies for preventing metastasis is less developed which may be catered using machine learning36. Advanced stage of breast cancer is accompanied by genetic marker associated by cell division and proliferation pathway (https://ww5.komen.org/BreastCancer/RecommendedTreatmentsforMetastaticBreastCancer.html). In our analysis we have discovered several genes associated with these pathways suggesting their role in progression from early stage to late stage.Several of these genes have been suggested to play a role in tumor progression from early to late stage. Genomic instability or DNA damage repair is the main driving factor of early stage of cancer development37. Ubiquitin dys-regulation can affect tumour suppressor or oncogene leading to cellular transformation and cancer38. DNAJB1 binds to mitogen-inducible gene MIG6, a tumour suppressor, which positively regulates epidermal growth factor signalling, leading to breast cancer development39.Four proteins encoded by DNAJB1, DNAJA1, CCT5 and FKBP4 are revealed to be in direct interactions, using STRING analysis. These proteins are major components of ubiquitin protein conjugation pathway by interacting with heat shock protein Fig. . This pr40. FKBP4 is found to be over-expressed in ductal carcinoma and under-expressed in lobular carcinoma by expression profiling41.CCT5 belongs to CCT gene family that serves as potential biomarker and display alteration in majority of breast cancer cases42. CTTN encodes cortactin which is a substrate for tyrosine Src nonreceptor tyrosine kinase whose amplification has been reported in primary metastatic breast carcinoma43.Five proteins encoded by CTTN, NCK1, CBL, PLCG1 and ERBB2IP depicts direct interaction in STRING analysis involved in RTK signalling pathway Fig. . Its abe44. CBL over-expression results in inhibition of transforming growth factor tumor suppressor activity and breast cancer prognosis45. PLCG1 is differentially regulated in breast cancer and has role in tumorigenesis of mediating intercellular signalling cascade46. ERBB2IP is a tyrosine kinase that interact with chaperon protein HSP90 and regulates breast tumor progression47.NCK1 is an tyrosine kinase that regulation cell adhesion and has role in breast carcinoma cell invasion and metastasis48. WHSC1 is a methyl transferase that performs histone methylation affecting cell ability to undergo DNA damage repair49.Four proteins encoded by TRAAP, CDKN1A, CHD9 and WHSC1 depict direct interaction in STRING analysis involved DNA replication and DNA damage repair pathway Fig. . TRAP bi50. CHD9 is a chromodomain helicase, found to be under-expressed in tumor with high Nottingham prognostic index (NPI) widely used for breast cancer prognostic in METABARIC cohort51.CDKN1A is a candidate breast cancer biomarker with upregulated expression in breast cancer tissue as compared to adjacent non-tumorous breast tissue52. Dys-regulation of EIF4A1 protein results in preferred translation of gene involved in pro-oncogenic signalling53.Five proteins encoded by EIF6, ITPA, YBX1, UPF3B and EIF4A1 depict direct interaction in STRING analysis involved in protein translational machinery Fig. . Deregul54. ITPA downregulation promotes DNA instability, suppression of cell growth and apoptosis in SKBR3 cell lines55. YBX1 is an oncoprotein that regulates tumorigenesis and malignant progression in breast cancer56. UPF3B is prolactin induced protein that regulates cell cycle progression and found to be upregulated in majority of breast cancer57.EIF6 is potential biomarker for cancer as it downstream modulator of cell division resulting in oncogenesis58. MSI2 directly regulates estrogen receptor by binding to ESR1 resulting in breast cancer cell growth59.Proteins encoded by GAS7, NUP98, MSI2, MLLT10 and PBX1 depict direct interaction in STRING analysis involved dys-regulated DNA binding transcription factor pathway Fig. . DNA bin60. NUP98 overexpression correlates with poor outcome in breast cancer61. GAS7 expression negatively correlates with p53 expression that results in early onset of breast cancer62. PBX1 is found to be up-regulated in metastatic progression ERα-positive breast cancer63.MLLT10 is one of the breast cancer susceptibility loci identified by genome wide association studiesThreshold value is the expression value beyond which the sample will segregate into two groups, in our study- ‘early’ and ‘late’ stages. For example, if Z-score of CDKN1A (over-expressed in early stage) is greater than 0.32 is then it is representative of an early stage sample otherwise if it is less than 0.32 then it is representative of a late stage sample. We calculated threshold for all the genes selected by feature selection methods for the complete gene expression-based model as well as driver gene-based model Table .Table 5T64. Increased expression of glycoprotein-sialytransferase is associated with altered membrane synthesis resulting in invasiveness and neoplastic state65.Clusterprofiler R package was used for gene ontology enrichment analysis of the gene set selected for the complete gene expression-based model and selected gene set for the driver gene-based model. It reveals enrichment in molecular functions such as transferase and hydrolase activity for gene set for the driver gene-based model Fig. . Catheps66. CSE (Cystathion-gamma-lyase) regulates STAT3 signalling which promotes cell proliferation in breast cancer67.The selected training gene set for the complete gene expression-based model was found to be enriched in molecular functions related to oxidoreductase activity, lyase, hydrolase and transferase activity Fig. . Glutath68. Training Gene set for the driver gene expression-based model is enriched in cellular component such as plasma, membrane and organelle membrane  values of the reads for supervised machine-learning analysis. RPKM is a measure of normalization of RNA-seq data with the total read length and number of sequencing reads for a given sample72. The training datasets were standardized using z-score normalization. The normalization converts all the features to common scale with mean zero and standard deviation 1. The normalized data-set was used for training models.As an initial step of pre-processing, which aids in preliminary feature reduction for a feature-rich training dataset, gene features showing near zero variance across the two classes were removed. Near zero variance features are the feature which either have unique value or have few unique value relative to the number of samples. Along similar lines, the features having more than 80% correlation with each other can prove to be problematic for machine-learning. Hence, such feature pairs/groups were also removed in a way where only a single feature of the group remains. These two tasks were performed using the Caret, a R package26. Given the possibly large sets of features, it helps in searching for the subset of features that has relevance in terms of a given predictor variable73. It also helps in improving the accuracy of a classifier by removing irrelevant data74. The main challenge associated with current data mining technologies is the high dimensionality of datasets combined with homogenous nature of data75.Feature selection is an advantageous step before machine-learning which reduces the dimensionality of datasets76. Linear regression assumes that features which are important have highest coefficient in the model, and features which have low importance have lower coefficient in the model. When there are multiple correlated features, small change in data can lead to large change in model. Regression model uses regularization method which adds an additional penalty to a model in order to minimize the sum of squared error of training model using lasso and ridge regression methods. Lasso regression methods performs L1 regularization minimizes absolute sum of the coefficient and producing sparse solution. Ridge regression performs L2 regularization minimizing squared absolute sum of the coefficients. The Least absolute shrinkage and selection operator (LASSO) does regression analysis for parameter estimation and variable selection simultaneously77. Random forest uses decision tree based strategies to rank feature based on attribute “feature importance”. All of the feature selection methods were implemented using the Python 3.6 scikit-learn library.For reducing the dimensionality of the datasets and identifying relevant features for building efficient machine-learning classifiers, we implemented various feature selection algorithms such as RFE, RLASSO, Random forest, linear modelling and linear regression, which provide individual ranking to gene features. Recursive Feature Extraction (RFE) is a method which utilizes recursion for feature extraction where smaller and smaller sets are considered as features until the desired number of features are returned. Randomized lasso is a stability selection method, which is combination of sub-sampling of high dimensional datasets and selection algorithm78.These feature-selection methods were used to rank the gene features of the training datasets. All the methods were implemented using the Python 3.6 scikit-learn library. All of the above-mentioned methods report individual ranking for the features. In order to get consensus ranking, we calculated the overall mean of each feature rank obtained from individual method. Subsequently, the Top 20, 30, 40, 50, 60 and 80 features were used to train and evaluate accuracy of models for binary classification of early versus late IDC, based on 5 machine-learning methods namely - RF, Naive Bayes, SVM, Logistic regression (LR) and Decision tree. Gene features list which gave the highest accuracy for all the machine-learning method were selected for model generation and evaluation. t-SNE technique was used for visualization of our gene expression datasets, returned after feature selection, in order to check if data-sets are segregating into defined class based on selected features for visualization of high dimensional data-point. t-SNE uses random walk on neighbourhood graph that allows implicit structure of data point to influence the way groups of data is present79. Imbalance results in poor predictive accuracy of minority class and difficulty in assessment of performance of classifier as most new sample are classified into minority class80. Class imbalanced datasets shows biasness which can be attenuated using SMOTE resulting in class balanced datasets80. Feature space similarity between minority classes are used to generate artificial data in SMOTE resampling81. It has been proven that over sampling of minority class with under sampling of majority class results in improvement of accuracy79. This method has been used to increase predicative accuracy of model for multiclass microarray datasets82. To check the usage of SMOTE resampling, models were trained on datasets where SMOTE resampling was employed.Real world datasets are imbalanced, predominately composed of “normal” example and a small percentage of “abnormal” examples83. Random forest uses ensemble of decision tree by random selection of features to split node84. SVM implements Sequential Optimization Algorithm for decision function85.After feature selection and data processing, we trained different algorithms to generate efficient classifiers for early and late tumour stage. We used five different algorithms – Random Forest, Naive Bayes, LinSVM (Support Vector Machines with linear kernels), Logistic regression and Decision tree. Naive Bayes is based on Bayesian theorem that calculates the probability of attribute to fall in particular instance with the assumption that every attribute is independent from other attributesThe five supervised machine-learning algorithms  were trained on subset of features obtained from feature selection and validated by 10-fold cross validation. The training models were compared by their accuracy, auROC, precision -recall and F-measure value.We further re-evaluated the best-trained model on an independent dataset which was not used in the classifier training at all.We performed differential expression analysis for the selected gene features by the two models, for early-late datasets to find out the differential expression of gene features selected by our model. Each gene feature selected by our model had range of expression across all the samples. We executed machine-learning and model evaluation for every single feature selected by our classifiers with threshold set across its expression range. The value that was giving highest ROC was considered as threshold value of expression value that could discriminate between early-late stages. Threshold value is the expression value beyond which the sample will segregate into two groups, in our case ‘early’ and ‘late’ stage.29. IntoGen identifies somatic mutation, gene, pathways that are involved in tumorigenesis by analysis of 13 cancers30. Bailey list identifies 299 molecular cancer genes by pan-cancer and pan-software analysis of 9,423 tumour exome datasets using 26 computational tools31. We reduced the data-set to these gene features, which was then used for feature selection and model building, repeating the above-mentioned steps to generate driver gene expression-based model for the web server.The available driver gene list for the cancer were also used for building model to discriminate early-late stages of breast cancer. We compiled a list of driver genes using Cosmic, intoGen and baile, which are expert curated lists of driver genes in human cancers. Cosmic stands for catalogue of somatic mutations in cancer which is an expert curated list of driver genes in human cancers, which is widely used in medical research86. We also performed STRING protein-protein interaction analysis to discover major pathways targeted by selected gene features.GO was performed on the list of genes returned by the feature selection methods to determine which gene families play role in the progression of breast cancer. We performed enrichment analysis using clusterprofiler R package. The package makes use of the datasets from the post genomic era high throughput technologies such as RNA-seq, micro-array, etc. to examine cellular molecules at systems level87. Biomart was used to annotate the probe IDs of microarray datasets with gene symbols88. If a particular probe is sequenced multiple times, WGCNA R package collapserow function was used to select one single representative row of each probe ID89. Subsequently, RMA normalization was performed using GCRMA R package converting the expression in log 2 scale to make its distribution comparable to RNA-seq datasets91. This independent-testing dataset were segregated into driver-testing datasets and complete gene expression datasets for performance evaluation of the generated models.To further check the performance of our model, we obtained independent datasets form GEO with accession ID GSE61304 containing 60 samples of IDC with clinical stage information obtained using microarray profiling. GEOquery package helps the user to access the information stored in GEO directly using Bioconductor without any formatting or parsing problemWe have successfully applied supervised machine-learning classification on gene expression profiles to develop classification models for discriminating early and late stage of invasive ductal carcinoma. The RNA-seq data obtained from TCGA had various additional information related to the samples ranging from age, survivability, TNM staging, histological subtype and pathological stage in the form of metadata or clinical data.The data yielded 20,505 gene expression values used as training features to be considered for classification model trainings. This voluminous dimensionality was reduced using various data pre-processing and feature selection methods. After this, the classifier models were generated by applying various machine-learning algorithms. Based on best trained classifiers, we developed a web-server Duct-BRCA-CSP that could predict whether the sample represents early or late stage using patient’s gene expression profile. The expression-based gene features returned by the feature selection methods can be used to differentiate samples between early and late stage with high accuracy, also providing candidate biomarkers for improved diagnosis and treatment, subsequent to adequate experimental validations. Thus, the combined power of machine-learning and next generation sequencing can provide important insights into the progression IDC from early to late stage. Our study proves that accurate prediction models captured features of the relevant genes, that could be candidates for further experimental evaluation for therapeutic and prognostic potential in the cancer treatment.We developed a web-server Duct-BRCA-CSP for invasive ductal carcinoma which predicts tumour stage of a sample on the basis of RNA-seq expression profile of selected genes, rather than its tumour size, gene expression profile of all the genes, imaging or survivability. Our study is preliminary in nature, however, in the future, the availability of datasets from higher number of patients, especially those representing late stage may help in building more efficient stage specific classifiers which may be suitable for personalized treatment strategy in clinics. In addition, further inclusion of more datasets such as mutation profile, methylation data and protein isoform data may improve accuracy of classifiers. Inclusion of paired datasets can also further aid in gaining valuable insights into the progression of breast cancer. To the best of our knowledge, the webserver Duct-BRCA-CSP is a server which is first of its kind for prediction of IDC tumour stages based on gene expression profiles of selected genes.Supplementary information.


Idiopathic infertile men suffer from unexplained male infertility; they are infertile despite having a normal semen analysis, a normal history, and physical examination, and when female infertility factor has been ruled out.The present study aimed to develop a metabolic fingerprinting methodology using Raman spectroscopy combined with Chemometrics to detect idiopathic infertile men vs. fertile ones by seminal plasma. In this experimental study, the seminal plasma of 26 men including 13 fertile and 13 with unexplained infertility who reffered to, Avicenna Infertility Clinic, 2018, Tehran, Iran, have been investigated. The seminal metabolomic fingerprinting was evaluated using Raman spectrometer from 100 to 4250 cm-1. The principal component analysis and discriminate analysis methods were used.The total of 26 samples were divided into 20 training and 6 test sets. The Principal component analysis score plot of the training set showed that the data were perfectly divided into two sides of the plot, which statistically approves the direct effect of semen metabolome changes on the Raman spectra. A classification model was constructed by linear discriminant analysis using the training set and evaluated by the test group which resulted in completely correct classification. While three of the six test samples appeared in the fertile group, the rest appeared in the infertile as expected.Metabolic fingerprinting of seminal plasma using Raman spectroscopy combined with chemometric classification methods accurately discriminated between the idiopathic infertile men and the fertile ones and predicted their fertility type. Unexplained male infertility (UMI) introduces as infertile men despite having normal semen analysis, normal history and physical examination, and when female factor infertility has been ruled out (1). A routine semen analysis remains the backbone of the evaluation of the male factor infertility (2).Human semen characteristics are evaluated by the World Health Organization (WHO) using semen volume, sperm concentration, sperm motility, sperm morphology (3). Related to the conventional semen analysis, it is difficult to determine a threshold to distinguish the fertile from infertile based on ejaculate results (4).Metabolites are generally considered low-molecular-weight components .In addition, recently, gas chromatography-mass spectrometry has demonstrated significant changes at the metabolomic level in patients' sperm cells (11).Not being sensitive to water and the intensity of spectral features in solution is directly proportional to the concentration of the particular specie are two important fetures of Raman spectroscopy which make it more beneficial other than optical spetctorcopies (OS). Also, human semen plasma sample preparation and analysis are quite easy, safe, and fast. The Raman frequency shifts are conventionally measured in wavelength (cm-1), depending on the atomic mass or molecular bonds of specimens (12).In the present study, Raman spectroscopy combined with multivariate pattern recognition techniques was used to analyze human seminal plasma. The principal component analysis (PCA) as a data projection and dimension reduction technique has shown that the microscopic structure of the metabolome of seminal plasma from idiopathic infertile and fertile men are totally different and could be used to construct a valid classification model. Linear discriminate analysis (LDA) was applied as a classification technique to construct a model to predict the fertility. Classification results reveal that the analysis of seminal plasma using Raman spectroscopy combined with chemometric techniques accurately predicted the type of fertility of all test samples and can be used as a complementary fast diagnostic test for men's fertility (12).13 men with a proven normal spermogram test as assessed by the WHO Manual and 13 idiopathic infertile men who were done the infertility treatment in the Avicenna Infertility Clinic  were selected. The main criteria for classification of idiopathic infertile men was low sperm motility , and after that supernatant was collected and stored at -20°C for further analysis. 500 µL of cold methanol/water (9:1) was added to 400 µL of the semen plasma to extract the polar metabolite. The mixture was kept in 4°C for 20 min and than centrifuged for 8 min at 6,000 rpm. The supernatant was used for Raman spectroscopy (13).Almega thermo nicolet dispersive was used for Raman analysis. The following parameters and specifications of Raman spectrometr were applied: the spectral range: 100-4250 cm-1; Laser: second harmonic at 532 nm of a ND: YLF laser; Resolution: 4 cm-1; and Laser power: 30 mW in order to keep the sample safe (13). Each sample was analyzed thrice by Raman spectroscopy.This project is a prospective study and has been approved by the ethics committee of the Reproductive Immunology Research Center, Avicenna Research Institute, ACECER, Tehran, Iran and the letter as the supplementary material is available.Principale component analysis (PCA) is a method to build multivariate models by transformation and visualization of complex data sets into a new perspective in which the most relevant information is made more obvious . PCA is a dimension-reducing technique, which projects objects and variables to low-dimensional spaces to exploratory purposes (16).In supervised pattern recognition technique or calssifcation method, objects fall into groups when we have prior knowledge of the groups to be expected. Linear discriminant analysis (LDA) is a classifaction method which is based on the Mahalanobis distance involving the determination of a linear equation-like regression that will predict which group the case belongs to (17). Data were analyzed by PCA and LDA classification models were developed using the MATLAB version 9.0.0.341360 (R2016a).In this experimental study, Raman spectroscopy combined with the multivariate pattern recognition techniques of Chemometrics was used to find the metabolome changes in the metabolome of the human semen plasma of fertile men and those with unexplained infertility. In this respect, the spectra of the metabolome of the semen plasma of a total of 26 men -13 with unexplained infertility and 13 fertile - have been investigated. The collected data include the intensity of Raman signals between 100 and 4250 cm-1, which is shown in Figure 1a. All of the Raman spectra were autoscaled prior to multivariate data analysis. Autoscaling is the most commonly used scaling method. In autoscaling, each spectrum is divided by its standard deviation as the scaling factor. Figure 1b shows the Raman spectra after autoscaling. Comparing Figure 1b with 1a demonstrates the positive effect of the autoscaling to differentiate between the Raman spectra of fertile and infertile men.The autoscaled data sets were randomly divided into two groups of training and test sets for further analysis. Accordingly, 20 samples (10 samples of fertile and 10 samples of unexplained infertile men) were included in the training set and 6 samples (3 samples of fertile and 3 samples of unexplained infertile men) in the test set. The PCA was used as a dimension reduction of data and visualization purposes. In the score plot, one PC against those of another was plotted. The most common score plot involves the scores of PC2 versus PC1. Each sample group is represented by a different colour. Figures 2a and 2b show the PCA scores plot, the PC1 versus PC2, before and after autoscaling, respectively.The LDA method was used for supervised classification of Raman spectra data of fertile versus unexplained infertile men. The performances of classification model are defined by the classification performance (CP) equation which is as follows: In this study, the LDA classification method predicted all the test set samples correctly and the CP% for classification model was 100%.Combination of analytical, biochemical, and spectral analysis make it possible the identification and quantification of biomarkers which establishing the signatures of the metabolites for healthy control and illnesses gropus. Pattern recognition methods combined with OS is one of the easiest and cheapest techniques for clinical diagnosis. Here, Raman spectroscopy combined with LDA was used to model the metabolome of the human semen plasma of fertile and unexplained infertile men.et al. (12). The peak ratios between 2955 and 2841 cm-1 are distinguishing parts of the Raman spectra of seminal plasma from normal and abnormal semen samples. As can be seen in Figures 2a and 2b, about more than 99.5% of the total variance of data was explained with two first principal components (PC1% + PC2%). In Figure 2b, the scores of healthy and unhealthy samples were completely divided into two sides of plot which comes from autoscaling effect, however, the scores of samples in Figure 2a are spread randomly in the plot. The result of preprocessed PCA score plots, Figure 2b, statistically approve that the data of Raman spectroscopy combined with chemometrics can be applied in the metabolomics studies.Figures 1a and 1b, respectively, show the Raman spectra of samples before and after autoscaling. Figure 1b confirms the possibility of the existence of two different types of samples. As can be seen around 2800-3000 cm-1, there are two dominant peaks in unexplained infertile men which does not exist in Raman spectra fertile group. This result is completely in accordance with the result of Liu DA from Statistic Toolbox of MATLAB was implemented on the data set as the method of classification. The method with the “linear” function, LDA, was used to model the training data set. In the training set, fertile and infertile spectra were coded by 1 and 0, respectively. Next, the constructed model was used to predict the fertility type of the test set. A variable selection method was employed to get better model prediction, to improve model statistics, and to achieve more interpretable model (18). The orthogonal projection approach (OPA) was implemented for identifying selective wavelengths in Raman spectra which are responsible for fertility and idiopathic infertility. The extensive description of OPA theory can be found in the literature (19). Figure 3 shows the PCA scores plot, the PC1 versus PC2, including the test set samples. From the six test samples, three appeared in the fertile group and three in the idiopathic infertile group. The test samples are presented in circles.As can be shown in Figure 3, the test set samples appeared correctly in its group, samples 11-13 were the fertile test set and appeared in the lower part of Figure 3 in the fertile group, and accordingly samples 24-26 were the infertile test set and appeared in the upper part of Figure 3 in the infertile group. In Raman spectroscopy the sample preparation is not necessary, it does not destroy specimens, and is less time-consuming .PCA score plot of the preprocessed data after autoscaling statistically approved the direct effect of metabolome changes on the Raman spectra. LDA as a classification method was used to classify the Raman spectra of fertile and infertile men. The model was implemented on the test samples to validate the model. The result of classification model shows that the group type prediction of all test samples was determined correctly. Accordingly, the metabolic fingerprinting of semen using chemometric classification methods combined with Raman spectroscopy paves the way for developing a simple, safe, fast, cheap, and non-invasive technique to discriminate idiopathic infertile men versus fertile.The authors declare that they have not no conflict of interest.


Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient’s state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms. In this scenario, data mining and machine learning techniques have widely contribute to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, existing public gene-expression databases are characterized by the unfavorable imbalance between the huge number of genes (in the order of tenths of thousands) and the small number of samples (in the order of a few hundreds) available. Despite diverse feature selection and extraction strategies have been traditionally applied to surpass derived over-fitting issues, the efficacy of standard machine learning pipelines is far from being satisfactory for the prediction of relevant clinical outcomes like follow-up end-points or patient’s survival. Using the public Pan-Cancer dataset, in this study we pre-train convolutional neural network architectures for survival prediction on a subset composed of thousands of gene-expression samples from thirty-one tumor types. The resulting architectures are subsequently fine-tuned to predict lung cancer progression-free interval. The application of convolutional networks to gene-expression data has many limitations, derived from the unstructured nature of these data. In this work we propose a methodology to rearrange RNA-seq data by transforming RNA-seq samples into gene-expression images, from which convolutional networks can extract high-level features. As an additional objective, we investigate whether leveraging the information extracted from other tumor-type samples contributes to the extraction of high-level features that improve lung cancer progression prediction, compared to other machine learning approaches. In recent years, the analysis of gene-expression data is gaining a growing interest in the area of precision medicine, which establishes a framework for the advance and research in a series of clinical procedures aimed at making medicine more participatory, personalized, preventive and predictive . In this context, the emergence of Next Generation Sequencing (NGS) techniques has allowed the transformation of areas such as precision medicine, biology or biochemistry. An unprecedented amount of data has been generated from which disciplines such as genomics, proteomics, transcriptomics, epigenetics or metabolomics are favored . In partM) of available gene-expression samples (i.e. the size of available cohorts) is usually much smaller (300-1K) than the number (N) of genes (10K-60K), which is known as the “curse of dimensionality” Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This is a very interesting work revealing the use of RNA-Seq data for cancer survival prediction in terms of deep learning and conventional ML techniques. I have no doubts about the work done and the methodology that is proposed in terms of transfer learning and survival prediction. Some concerns that need to be addressed before publication by the authors:The class imbalance problem should be handled in different ways for obtained unbiased results. Under sampling, over sampling and ensemble techniques do exist for performing this task.The figures need to be updated in terms of resolution, they are not in the preferred format I think.A comparison table with similar studies-methodologies should be presented in order to clearly highlight the impact of the current methodology and its usage in another studies.Reviewer #2: The article seems to be sufficiently novel and interesting. The main goal is to propose and apply the DL-with-TL methodology to rearrange RNA sequence data by transforming them into gene-expression images. Based on these images CNN can extract high-level features. Additionally, authors investigate whether extracted knowledge contributes to the extraction of features that improve lung cancer prediction. The results are compared with other selected ML approaches. This approach imply promising results about successful prediction of investigated problem. I think that this paper adhere to the journal standards.Presented paper is well organised. Abstract of the article is relevant to its content. It is formulated correctly and relates directly to the substantive content of the article. The article is divided into appropriate sections like introduction with some theoretical background, some historical steps, presentation of investigated approach, materials and methods, gathered research results and summary. It also contains a detailed and thematically relevant bibliography.The technical content of the article is generally understandable. Authors can add information about the result of the selection of features using applied FS methods. How many original features were considered significant. Maybe also describing the technical aspects of the algorithm implementation might be of interest to readers. Methods must be described in sufficient detail for another researcher to reproduce the experiments described. Authors should provide adequate justification that the methods presented in the paper are significantly different from previously presented.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  27 Feb 2020As indicated by the academic editor, a rebuttal letter responding to each point raised by the academic editor and reviewers has been uploaded as separate file and labeled 'Response to Reviewers'. The content of that file will be copied and pasted here:PONE-D-19-33176Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression dataPLOS ONEWe would like to thank the editor and the reviewers for their fair comments and suggestions and for giving us the opportunity to improve and to re-submit our paper. In this revised version of our paper we have addressed all the comments pointed out by the reviewers and emphasised the novel contributions beyond our previous related work.COMMENTS FOR REVIEWER #1:We would like to thank this reviewer for her/his comments and suggestions.The class imbalance problem should be handled in different ways for obtained unbiased results. Under sampling, over sampling and ensemble techniques do exist for performing this task.As it has been fairly suggested by this reviewer, we conducted additional experiments in order to handle the severe class imbalance of the gene-expression datasets. Different techniques―random under-sampling, SMOTE and random over-sampling―were employed to re-sample the data before feeding it to the predictive models. We analyzed the performance of the models using each technique, and finally selected the one that maximized the mean CV AUC metric. In this way, random over-sampling procedure applied both during pre-training and fine-tuning phases of the TL approach gave the best results both using CNN and MLNN models. When applied in combination with the traditional ML approach, SMOTE gave the highest AUC values. The revised version of the paper has been updated with the new results obtained using random-oversampling and SMOTE in combination with DL-with-TL approach and the traditional ML approach, respectively. Figure 8 has also been updated.The figures need to be updated in terms of resolution, they are not in the preferred format I think.Following the submission guidelines, all figures were uploaded as separate files. Besides, we used PACE software, as suggested in Figures guidelines, to ensure all figures met PLOS requirements.A comparison table with similar studies-methodologies should be presented in order to clearly highlight the impact of the current methodology and its usage in another studies.To the best of our knowledge, we are the first authors to explore a TL approach using CNN models on gene-expression data to tackle a cancer-survival predictive task. For this reason, the results obtained in this paper are not directly comparable with the results achieved in previous works, as different methodologies are applied as well as completely distinct cancer prediction tasks are tackled. However, we totally agree with this reviewer on the necessity of performing a thorough comparison between our proposed methodology and similar-studies methodologies to lucidly emphasize the impact of our contribution. In this way, the final paragraphs of the Results and Discussion section in the revised version of the manuscript addresses this concern.COMMENTS FOR REVIEWER #2:We would like to thank this reviewer for her/his comments and suggestions.Authors can add information about the result of the selection of features using applied FS methods. How many original features were considered significant.We considered the number of input features to be selected by the FS method as a hyper-parameter of the traditional ML approach. The range of possible values of this hyper-parameter, as well as the possible numbers of features to be extracted by the feature extraction methods, are described in detail in S3 Table.Maybe also describing the technical aspects of the algorithm implementation might be of interest to readers. Methods must be described in sufficient detail for another researcher to reproduce the experiments described.https://github.com/guilopgar/GeneExpImgTLAs stated in the revised version of the paper, for reproducing the results obtained in this work, all the data, code and technical descriptions of the algorithm implementation have been made publicly available at In addition, the Supporting Information file contains a detailed description of the hyper-parameter optimization procedure employed to perform automatic tuning of the hyper-parameters of all the models analyzed in this work.Authors should provide adequate justification that the methods presented in the paper are significantly different from previously presented.As it has been fairly suggested by this reviewer, the Results and Discussion section from the revised version of the manuscript contains a detailed description of the novelties and methodological contributions of the current study, as well as a thorough comparison between our proposed methodology and other similar-studies methodologies.Attachmentlopez_garcia_et_al_IWANN_Response_Reviewers.pdfSubmitted filename: Click here for additional data file. 3 Mar 2020Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression dataPONE-D-19-33176R1Dear Dr. Lopez-Garcia,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Ruxandra StoeanAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 6 Mar 2020PONE-D-19-33176R1 Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data Dear Dr. Lopez-Garcia:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. For any other questions or concerns, please email Thank you for submitting your work to PLOS ONE.With kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. Ruxandra Stoean Academic EditorPLOS ONE


The integrative analysis of copy number alteration (CNA) and gene expression (GE) is an essential part of cancer research considering the impact of CNAs on cancer progression and prognosis. In this research, an integrative analysis was performed with generalized differentially coexpressed gene sets (gdCoxS), which is a modification of dCoxS. In gdCoxS, set-wise interaction is measured using the correlation of sample-wise distances with Renyi’s relative entropy, which requires an estimation of sample density based on omics profiles. To capture correlations between the variables, multivariate density estimation with covariance was applied. In the simulation study, the power of gdCoxS outperformed dCoxS that did not use the correlations in the density estimation explicitly. In the analysis of the lower-grade glioma of the cancer genome atlas program (TCGA-LGG) data, the gdCoxS identified 577 pathway CNAs and GEs pairs that showed significant changes of interaction between the survival and non-survival group, while other benchmark methods detected lower numbers of such pathways. The biological implications of the significant pathways were well consistent with previous reports of the TCGA-LGG. Taken together, the gdCoxS is a useful method for an integrative analysis of CNAs and GEs. Copy number alteration (CNA) is a cytogenetic hallmark of cancer pathophysiology . Due to The array comparative genomic hybridization (aCGH) facilitated the discovery of the CNAs in cancer . The parAlthough the microarray platforms enable the efficient screening of the CNAs, they give no information about gene expression (GE). For the identification of their impact on GE, they should be validated at the transcription level because the GEs of CNA loci can show no significant change . To thisThe integrative analysis of the CNV and GE datasets has been focused on single gene-wise correlations or regression-based approaches that found significant relationships between CNA and GE, which are focused on identifying the coordinated variation between CNA and GE. To capture the variation, several computational methods were applied ,20. LathIn addition to the single gene-wise method, gene set approaches were also applied to the integrative analysis of CNA and GE. Menezes et al. used the global test to identify the relationship between single copy number alteration and corresponding gene set expression profiles . By mappIn this research, the integrative analysis of CNA and gene expression is performed in terms of the gene set approach. The rationale for the set-wise analysis was to identify biological findings that were not detected by the single gene-wise analysis. Moreover, conditional changes in the similarity between CNAs and gene expressions are explicitly tested to identify whether a pair of CNAs and GEs is associated with the condition, which indicates that the CNAs and GEs are likely to be involved in the biology of the condition. For this purpose, the dCoxS method is modified to capture the variation between heterogenous omics data, especially for CNAs. The dCoxS was originally designed to detect interaction between a pair of GEs . The intThe overview of analysis is illustrated in n and p represent the number of samples and variables. The µ is a mean vector of CNA or GE profiles, and n was the number of samples and d was the number of CNAs or GEs in a pathway. The corpcor R package was used for the shrinkage estimation of the covariance matrix and its inverse form [n < p). For the estimation of sample-wise distances, Renyi’s relative entropy is estimated by the ratio of densities from two different samples. The densities were computed using the multivariate productive kernel that multiplies the single density values and bandwidth parameters obtained from standard deviations of the variables. The dCoxS performs well in the estimation of differential interaction between a set of gene expressions. However, when the method is applied to CNAs and gene expressions, the productive kernel may not represent the dynamics of CNA changes because it integrates no explicit correlation structure into the density estimation. The CNA status includes only three possible values, which are loss, neutral and gain, and these are frequently coded as −1, 0 and 1, respectively. Since the CNAs occur in a small portion of samples, it is likely that the density of the CNA matrix had small variations because combinations of the CNA status are not considered explicitly in the dCoxS. Thus, in this analysis, a multivariate normal density estimation that uses a covariance matrix representing combinations of the CNA status is adopted. The multivariate density function is:rse form  to handlFor each corresponding copy number and expression matrix, sample-wise distances were measured with Renyi’s quadratic divergence.D2(P||Q) represents Renyi’s quadratic diversity [iS and jS indicate different samples. The iS and jS. Therefore, the higher divergence implies that two samples are more distant from each other. In Equation (2), iversity . The Si Using the Renyi’s diversity, set-wise CNA and expression matrices were transformed to sample-wise distance matrices. The upper trigonal members of the sample-wise distance matrices were used for the computation of the IAS. The IAS was obtained through the correlation coefficient between the upper trigonal members of the sample-wise distance matrices.CRE and GRE are the sample-wise distance (relative entropy) matrices of the CNAs and GEs, respectively. After the IASs were determined in each condition, the significance of the IAS and the differences in the IAS between conditions were tested non-parametrically (In Equation (3), trically . Since the IAS is used for determining the similarity between set-wise CNA and gene expression matrices, unlike the original application, a simulation study tests whether the IAS reflects the similarity between CNAs and GEs.First, a CNA matrix was generated using binomial distribution. In general, CNA occurs in a small proportion of samples. Neutral status was therefore set to the predefined proportion of total samples. Then, gain (+1) or loss (−1) status was assigned to the rest of the samples using binomial distribution with number of trials = 1 and probability = 0.5. The rbinom R function generates a 0 or 1 status according to the predefined probability, and 0 is assigned to the −1. The proportion of samples having CNAs in the total sample was selected among the predefined values  for each simulated CNA.After the generation of CNAs, the GEs matrix with similarity with the CNA matrix was simulated. The random values from the normal distribution with different standard deviation (SD) values were added to a simulated CNA for the generation of GEs having various similarities according to the SD values. To simulate a GE matrix having less similarity with the CNA matrix, a greater SD value was applied in the generation of random numbers.Power analysis was also performed with the simulation data. First, two random CNAs–GEs pairs were generated. The CNA matrices were generated by the same method used in similarity analysis. Then, a random expression matrix was generated and the same matrix was used as an expression matrix in both conditions. The random expression matrix was generated by random numbers from standard normal distribution. Since the CNA matrices were different and the expression matrices were the same between conditions, this generated the true differential interaction of CNAs and GEs between conditions. Simulation data were generated with different parameters, including the number of samples and genes.https://portal.gdc.cancer.gov/), and clinical information was also obtained from the portal. For the detection of CNAs and GEs, Affymetrix 6.0 SNP microarray and Illumina Hiseq 2500 sequencing platform were used, respectively.In addition to the simulation study, to test whether the current approach identifies valid biological phenomena, TCGA-LGG data were analyzed. The data were downloaded from the genomic data commons (GDC) portal (www.biocarta.com), KEGG (www.genome.jp/kegg) and the Reactome (www.reactome.org) websites, was downloaded from MSigDB of the Broad Institute (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp).For the set-wise CNA expression interaction analysis of the TCGA-LGG data, biological pathway information was used. The current analysis framework can be applied straightforwardly to gene sets that are constructed with the other biological knowledge, such as gene ontology. The pathway information, which is mainly compiled from the Bio-Carta  × 0.1 to the (i − 1)-th GE matrix. This generated GE matrices that were less similar to the simulated CNA matrix compared with the previously generated matrix. For each simulated CNA matrix, five GE matrices were generated in total, and this process was iterated 1000 times. When the number of variables in a GE matrix was 100, the same CNA vectors were repeatedly sampled and used for the generation of the GE matrix. p < 2.2 × 10−16). These indicate that the IAS represents similarity between CNAs and GEs. Since the CNA and GE matrices are different types of data, the simulated matrices should have different distributions. While it was obvious that the simulated CNA matrices have binomial distributions, it was not clear that the simulated GE matrices have multivariate normal distributions that are frequently used in the simulation of a gene expression matrix, because they were generated by adding numbers from binomial and normal distributions. Therefore, normality tests were applied to the GE matrices and the result showed that the matrices had multivariate normal distributions with Bonferroni’s multiple testing correction (data not shown).To generate simulation data for testing whether IAS represents similarity between CNAs and GEs, CNA matrices that have 20, 50, and 100 variables, and 100 samples, were generated. For each simulated CNA, the proportion of the CNA in the total samples was randomly selected from among the predefined frequencies as described in the methods. When a CNA matrix was generated, random values from the normal distribution with SD = 0.01 were added, which resulted in high IAS between the CNA and GE matrices. The second GE matrix was generated by adding random values from normal distribution with SD = 0.1 to the previously generated GE matrix. Likewise, the n = 10). Since the gdCoxS used a covariance matrix for estimating the relationship between variables, gdCoxS captured the difference in CNA matrices more efficiently than the dCoxS, which adopted the productive kernel in estimating density without the use of such a covariance matrix, which was more evident in the higher number of elements in the gene set. Considering the high-dimensional characteristics of functional genomics data, the gdCoxS is a more efficient and robust method, which can detect the dynamics between matrices from two different sets of genomic data.Power analysis was performed with changes in the number of samples and number of elements in the simulation data. The number of samples included {100, 200, 400}, and the number of variables in the set were set within {10, 20, 30}. The number of permutations was set to 100. https://www.genenames.org/download/cus-tom/). The CNA data had 533 samples and the RNA sequencing data had 530 samples. Of the samples, 507 samples with CNA, gene expression and survival information were used in the analysis. In the MSigDB, there were 1335 canonical pathways from the open databases including the KEGG, BioCarta and Reactome. The class was labeled into two groups according to the survival status . In the analysis of the TCGA-LGG dataset, the GDC provided CNA information that had been computed using the Genomic Identification of Significant Targets in Cancer (GISITC) algorithm [In the TCGA-LGG, genes of CNA and expression data were mapped to the ensemble identifier system. Since the pathway information used gene symbols, the mapping table of the HUGO Gene Nomenclature Committee (HGNC) for gene symbols and ensemble identifiers was used for mapping gene symbols to ensemble identifier . With the threshold, 577 pathways were found to exhibit significantly different interactions of CNAs and expressions of the pathways between the survival and death groups of TCGA-LGG patients  increased from 0.023 in the survival group to 0.407 in the non-survival group, which was the greatest absolute diffIAS among the results . The ‘GLp < 1.29 × 10−9).For the benchmark analysis of gdCoxS, differential co-expression analysis and Mantel statistics were applied. The differential coexpression analysis includes an estimation of the correlation coefficient between a CNA and GE in each condition, and a test of the significance of the difference in correlations between conditions See . In the n = 171). In the benchmark analysis, the Mantel statistics were also applied to compare the performance of gdCoxS when different similarity measures other than Renyi’s relative entropy were applied . DiffereIn this research, the gdCoxS performs an integrative analysis of CNAs and GEs. In the simulation analysis, the gdCoxS shows an improvement in the performance in terms of power, especially with larger numbers of gene set elements. In the real data analysis, the gdCoxS detected 577 significant results, while the single gene-wise differential coexpression analysis gave no significant result, and the set-wise analysis with Mantel statistics identified fewer significant pathways than gdCoxS. These results seem to indicate that the gdCoxS outperforms the other benchmark methods.P values. Therefore, the comparison between the gdCoxS and the modified CCA was not possible.When the single gene differential coexpression analysis was applied, no significant results could be found in the result of the single gene-wise analysis. However, gene set methods including gdCoxS and Mantel tests identified a lot of significant pathway CNA–GE set pairs. These findings clearly indicate the benefit of gene set-wise analysis, which has more power to detect significant interactions between CNAs and GEs. In the benchmark study using Mantel statistics, the results with Mahalanobis distance showed a far better performance than the other measures. This seems to result from the fact that the Mahalanobis distance uses a covariance matrix that can capture the relationship between elements of gene sets. This finding supports the validity of the concept in gdCoxS, which is an application of the multivariate density function with covariance information to capture the relationship between CNAs explicitly. The dCoxS method was not compared in the real data analysis because variations in sample-wise distances in CNA matrices tended to be zero, which made the computation of IAS intractable. Among the pathways, more than a thousand of pathway CNA matrices showed such variations. This finding strongly indicates that the productive kernel of the dCoxS was not suitable for detecting combinatorial variations in CNAs. In the benchmark analysis, the set-wise methods, such as modified canonical correlation analysis (CCA), that were presented in the introduction could be applied. However, the methods can estimate the similarity between CNA and GE matrices only, and the differences in the similarities between conditions were not considered. Moreover, the methods provided no statistical testing for the estimation of In the result, many pathways were related to the glioma pathophysiology in previous studies. For example, 10 pathways were related to p53, which has impacts on the glioma pathophysiology . The mutThe EGF pathway also indicated the validity of the analysis result . The EGFIn conclusion, the set-wise identification of the interaction between CNAs and GEs revealed pathways that are consistent with the molecular pathophysiology of lower-grade glioma, which was not found in single-variable analysis. This gene set method for performing the integrative analysis of multi-omics data will promote the discovery of hidden biologic mechanisms.


Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients.We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity.Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naïve patient cohort.EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk. Metastasis is one of the key hallmarks of cancer  and accoAlthough implicated in cancer progression and metastasis, the clinical significance of processes like epithelial-to-mesenchymal transition (EMT) and mesenchymal-to-amoeboid transition (MAT) remains to be fully appreciated. EMT, a cellular transformation process that plays a key role in embryonic development, is widely considered to be one such factor influencing metastasis. Cancer cells derepress the normally silenced EMT molecular program, acquiring malignant properties that enable them to invade tissues surrounding their site of origin thereby effectively spreading and colonizing distant sites , 6. LikeSince the EMT process is exploited by cancer cells progressing to metastasis, there have been several attempts to subtype patient tumors based on an EMT signature, but these have not been successful in demonstrating a discernible difference in associated breast cancer prognosis –11. AddiWe constructed an “EMAT” gene signature comprising a list of 253 previously reported EMT-related genes  and 138 http://oasis-genomics.org/, [http://www.ncbi.nlm.nih.gov/geo/ GEO accession number GSE11121, http://ccb.nki.nl/data/ [Gene expression and clinical data for 562 LNN breast cancer patient samples from the METABRIC study was utilized as a training dataset  and twonl/data/ ), as sumnl/data/  were alsn = 3, 4, and 5 clusters and found that n = 4 yields the best grouping of samples based on average silhouette scores [Hierarchical clustering of samples was performed using the python module SciPy, using Ward’s variance minimization algorithm. To obtain EMAT clusters, we partitioned METABRIC LNN samples into e scores , when cowww.knoweng.org) [https://CRAN.R-project.org/package=survival). Variables included in the multivariate analysis were age, tumor size, tumor grade, subtype status based on receptor profile, PAM50 centroid or EMAT centroid, adjuvant chemotherapy, hormone therapy or radiotherapy. All tests were two-sided, and p < 0.05 was considered statistically significant.Survival analysis (Kaplan-Meier and Cox regression) was performed using KnowEnG analytical platform (eng.org) , lifelinn = 4 clusters using the EMAT signature. A Cox regression model was trained on these samples, using clinical variables as well as EMAT cluster status. In parallel, Cox regression models were trained on the same samples using the other types of predictors. Each trained model was then used to estimate the expected survival of the remaining samples . In this step, EMAT cluster status was assigned to test samples using a centroid-based classifier trained on the training samples. Then, the estimated survival times were compared to the observed survival times using the concordance index (C-index). To ensure the specific choice of training and test set does not influence the results, this process was repeated 200 times, each time using a distinct random partition of data. To determine the EMAT cluster assignment of the test samples, we first used hierarchical clustering to cluster samples in the training set into four clusters. Then we trained a centroid-based classifier on the training set and predicted the cluster labels for the test set. The p-values were calculated using a one-sided Wilcoxon signed rank test and represent the significance of the improvement obtained using EMAT cluster status and clinical parameters as compared to other predictors. The measure PIF shows the percent of times in which EMAT + clinical parameters provided a more accurate prediction compared to the baseline.To compare the predictive ability of different classes of features, we used a cross-validation framework, in which half of the samples (patients) were randomly selected as the training set and the other half were used as a test set. The training set was grouped into We implemented a KNN . Cluster EMAT3 had the lowest average expression of VIM (p = 3.7E−79) and the highest average expression of CDH1 (p = 1.3E−3) and JUP (p = 8.3E−6), suggesting that early onset of epithelial collective cell migration may be manifested in this group of patients. EMAT2 had the highest average expression of RHOA (p = 3.8E−3), suggesting A-like characteristics. Finally, EMAT1 had high expressions of VIM and RHOA but low expression of CDH1 and JUP, with the average expression of JUP being smallest (p = 3.9E−15) in this cluster. These results indicate existence of clusters having hybrid characteristics rather than discrete E-, M- and A- subtypes and emphasize the advantage of using the EMAT signature over using only E, M, or A biomarkers to distinguish groups of patient tumors associated with distinct prognosis.Hierarchical clustering of patient samples from the METABRIC study using the EMAT gene signature yielded four separate clusters Fig.  with stat-test p < 0.01) included both EMT and MAT genes for all clusters (Supplementary Table Sp < 0.01) included six EMT and seven MAT genes  and mesenchymal  marker expressions, failed to show significantly different DSS  and amoeboid marker  expressions, failed to demonstrate significantly different DSS  prominently track proliferation biology, and it is believed that the group of proliferation-associated genes has the biggest impact on the measurement of prognosis . We therNext, we examined whether the EMAT clusters are associated with clinical parameters or previously described breast cancer subtypes Fig. b. Of notp-value  and PR-positive (p = 2E−06) samples, while EMAT4 was enriched in ER-negative (p = 2.64E−28), PR-negative (p = 4.18E−18), and HER2-negative (p = 4.8E−3) samples. EMAT subtypes displayed enrichments for PAM50 intrinsic subtypes . Despite these enrichments, however, 68% of “normal-like” samples are in clusters other than EMAT1, 43% of luminal A patients are in clusters other than EMAT2, 47% of luminal B patients are in clusters other than EMAT3, and 46% of basal patients are in clusters other than EMAT4 . However, because EMAT cluster status was defined based on samples used in the CRMs, while the other subtypes were defined a priori, this result while promising was not conclusive.To ascertain whether EMAT clustering provides prognostic information beyond what is captured by the intrinsic subtypes, we trained a Cox regression model (CRM) on the LNN samples from the METABRIC dataset using four types of predictors: (1) clinical parameters , (2) CP and receptor status, (3) CP and PAM50 subtype status, and (4) CP and EMAT cluster status. The Cox regression model trained on CP and EMAT cluster status had the smallest To rigorously compare the predictive ability of the various groups while removing the effect of the varying number of predictive features, and also to test the generalizability of these models on unseen data, we next used a cross-validation framework see d. In addp = 2.56E−03, log rank test, 151 LNN cohort in NKI 295, and p = 1.39E−2, log rank test, GSE11121, Fig. We next evaluated the prognostic power of the EMAT clusters on independent, lymph node negative datasets (151 LNN patients in the NKI295 dataset and GSE11121), one of which was a treatment-naive dataset  GEO acc” sectiont-test (corrected for multiple hypothesis testing) to identify TFs that are differentially expressed in one cluster compared to others, in LNN samples. We identified PPP1R13L and MNDA (EMAT1), ETV7 and TSHZ3 (EMAT2), SLUG and AFF3 (EMAT3), and FOXA1 and FOXC1 (EMAT4), which were under-expressed and over-expressed in each cluster, respectively. Hierarchical clustering of LNN samples from the METABRIC dataset using the eight identified TFs had a very high concordance with clusters obtained using the full EMAT gene signature. In addition, Kaplan-Meier analysis using the clusters defined using these TFs demonstrated a significant and similar separation of survival curves  present in the METABRIC dataset  are not recommended to be treated with adjuvant chemotherapy. We therefore used lymph node negativity as a criterion to select clinical samples for analysis to ensure that the samples obtained were from early clinical stages of the metastasis cascade. Furthermore, this would help fulfill the greater unmet clinical need of identifying atypical LNN breast cancer patients who possibly harbor elevated metastasis risk, as indicated by an EMAT3 or EMAT4 cluster designation, and may potentially benefit from life-extending adjuvant chemotherapy. Similarly, patients diagnosed with LNN HER2+ breast cancer or LNN TN breast cancer are known to harbor greater metastatic recurrence risk than patients diagnosed with LNN ER+ breast cancer, for which reason many such patients are typically recommended to be treated with adjuvant chemotherapy. In this situation identifying atypical LNN HER2+ or TN breast cancer patients who possibly harbor lower metastatic recurrence risk, as indicated by an EMAT1 cluster designation, would also be important as such patients may not require treatment with adjuvant chemotherapy and thereby avoid unnecessary chemotherapeutic side effects.Limitations of the present study include our inability to consider the potential impact of the host immune response, tumor stromal factors, and/or that of non-cancer cells of the tumor microenvironment on influencing metastatic proclivity and prognostic prediction thereof. Because of the retrospective design of the study, reported results while suggestive are not conclusive regarding the potential clinical impact of the EMAT signature. As the dataset patients were treated at different time-points, conclusions cannot be generalized because the distribution of clinical characteristics may be different in patients from other areas . The current study therefore suffers from biases inherent to such a study design. Our results do however suggest that they merit further investigation. To this end, the centroid-based EMAT classifier developed in the present study can be used as a single sample predictor to determine the EMAT cluster designation status of new patient samples, even if they were not included in the present study.Our study revealed the existence of breast cancer progression and metastasis subtypes that display a dynamic overlap of EMT and MAT rather than discrete E-, M-, and A-like clusters, emphasizing the advantage of using the EMAT signature over using only E, M, or A biomarkers, to distinguish patient groups with distinct prognosis. What is important to note is that the metastatic propensity of breast cancer cannot be accurately captured or predicted by consideration of clinical stage, receptor status, PAM50 molecular subtype status, or treatment variables alone and that the additional consideration of metastasis biology-based predictors is warranted.Additional file 1: Figure S1. Kaplan-Meier survival analysis corresponding to clusters of LNN METABRIC samples based on EMT and MAT signatures. (A) Kaplan-Meier survival analysis for clusters obtained based on EMT gene signature using hierarchical clustering. (B) Kaplan-Meier survival analysis for clusters obtained based on MAT gene signature using hierarchical clustering. Figure S2. Kaplan-Meier survival analysis of EMAT clusters within each PAM50 subtypes of LNN METABRIC samples. Figure S3. Kaplan-Meier survival analysis of EMAT clusters within HER2-positive and triple negative (TN) subtypes of LNN METABRIC samples. Figure S4. Kaplan-Meier survival analysis of EMAT clusters within treatment-naïve and treated patients of LNN METABRIC samples. Figure S5. Kaplan-Meier survival analysis of treatment-naïve versus treated patients of LNN METABRIC samples within each EMAT cluster. Figure S6. Cross-dataset analysis. The Kaplan-Meier survival plots correspond to EMAT subtypes of LNN breast cancer samples from the GSE11121 dataset. A 5-NN classifier trained on LNN METABRIC samples is used to assign EMAT subtype labels to each sample. In the figure, C1 = EMAT1, C2 = EMAT2, C3 = EMAT3 and C4 = EMAT4.Additional file 2: Table S1. List of genes in the EMAT, EMT and MAT signatures. The table content is provided as a separate xlsx file.Additional file 3: Table S2. EMAT cluster labels of samples in the METABRIC, GSE11121 and NKI295 datasets. The table content is provided as a separate xlsx file. The labels are obtained using hierarchical clustering with 4 clusters, as described in the manuscript.Additional file 4: Table S3. Percent of EMT and MAT genes present among differentially expressed genes (DEGs) for each cluster. The table content is provided as a separate xlsx file. DEGS for each EMAT cluster were defined as differentially expressed in that cluster compared to other clusters .Additional file 5: Table S4. The association of EMAT genes with survival outcome. The p-values are obtained using a univariable Cox regression analysis.Additional file 6: Table S5. A summary of the characteristics of the EMAT clusters obtained using lymph node-negative breast cancer patients from the METABRIC study. In this table, P stands for positive and N for negative. EMAT1 has the least similarity to hESC and is enriched in normal-like PAM50 subtype of breast cancer and has a good prognosis. EMAT2, the cluster with a relatively good prognosis, has little similarity to hESC, is enriched in Luminal A subtype and in ER-positive and PR-positive samples. EMAT3, the cluster with a relatively moderate prognosis, has a high degree of similarity to hESC, is enriched in Luminal B subtype and in ER-positive, PR-positive and HER2-negative samples. EMAT4, the cluster with the worst prognosis, shows the highest degree of similarity to hESC, is enriched in the basal-like subtype of breast cancer as well as ER-negative, PR-negative and HER2-negative samples.Additional file 7: Table S6. Univariable and multivariable Cox regression analysis for validation dataset samples from GSE11121 and NKI295. The table content is provided as a separate xlsx file.Additional file 8: Table S7 Differential expression analysis of TFs for each EMAT cluster. The table content is provided as a separate xlsx file. The p-values were obtained using a two-sided t-test and were corrected for multiple hypothesis testing.