The incidence of HPV-related oropharyngeal squamous cell carcinoma (OPSCC) has increased more tha... more The incidence of HPV-related oropharyngeal squamous cell carcinoma (OPSCC) has increased more than 200% in the past 20 years. Recent genetic sequencing efforts have elucidated relevant genes in head and neck cancer, but HPV-related tumors have consistently shown few DNA mutations. In this study, we sought to analyze alternative splicing events (ASE) that could alter gene function independent of mutations. To identify ASE unique to HPV-related tumors, RNA sequencing was performed on 46 HPV-positive OPSCC and 25 normal tissue samples. A novel algorithm using outlier statistics on RNA-sequencing junction expression identified 109 splicing events, which were confirmed in a validation set from The Cancer Genome Atlas. Because the most common type of splicing event identified was an alternative start site (39%), MBD-seq genome-wide CpG methylation data were analyzed for methylation alterations at promoter regions. ASE in six genes showed significant negative correlation between promoter m...
Supplementary Table from Differential Variation Analysis Enables Detection of Tumor Heterogeneity... more Supplementary Table from Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data
One 1 of the goals of a literary analysis of the tragedies is to identify the relationships among... more One 1 of the goals of a literary analysis of the tragedies is to identify the relationships among the heroes. We aim to provide computational tools which can help in this task. Ideally, this analysis would be based on the raw text. Instead, we rely on tables extracted by researchers from the text which summarize objectively the qualities of each hero from three famous classic stories in the available sources: Trojan War from the Homer’s Iliad, Julius Caesar from the Shakespeare’s play, and knights in the Ferdowsi’s Shahnameh, a Persian epic story. We develop a statistical analysis for identifying clusters (groups) among a group of characters in a story starting from such tables with ordinal data. We define the Ordinal-Contextual (OC) similarity (or equivalently the OC dissimilarity) as a linguistically meaningful and context-based similarity (or dissimilarity) for ordinal data. Using classical multidimensional scaling, we represent the heroes in 2-D space and cluster the heroes usin...
2008 Seventh International Conference on Machine Learning and Applications, 2008
We describe our contribution to the ICMLA2008 "Automated Micro-Array Classification Challenge". T... more We describe our contribution to the ICMLA2008 "Automated Micro-Array Classification Challenge". The design of our classifier is motivated by the special scenario encountered in molecular cancer classification based on the mRNA concentrations provided by gene microarray data. Our classifier is rank-based; it only depends on expression comparisons among selected pairs of genes. Such comparisons are invariant to most of the transformations involved in preprocessing and normalization. Every pair of genes determines a binary classifier-choose the class for which the observed ordering is most likely. Pairs are scored by maximizing accuracy. In our k-TSP (k-disjoint Top Scoring Pairs) classifier, k disjoint pairs of genes are learned from training data; the discriminant function is simply the difference in the number of votes for the two classes. This rule involves exactly 2k genes, is readily interpretable, and provides some state-of-the-art results in cancer diagnosis and prognosis for small values of k, even k=1.
Background A small number of prognostic and predictive tests based on gene expression are current... more Background A small number of prognostic and predictive tests based on gene expression are currently offered as reference laboratory tests. In contrast to such success stories, a number of flaws and errors have recently been identified in other genomic-based predictors and the success rate for developing clinically useful genomic signatures is low. These errors have led to widespread concerns about the protocols for conducting and reporting of computational research. As a result, a need has emerged for a template for reproducible development of genomic signatures that incorporates full transparency, data sharing and statistical robustness. Results Here we present the first fully reproducible analysis of the data used to train and test MammaPrint, an FDA-cleared prognostic test for breast cancer based on a 70-gene expression signature. We provide all the software and documentation necessary for researchers to build and evaluate genomic classifiers based on these data. As an example of...
Background A major challenge in computational biology is to extract knowledge about the genetic n... more Background A major challenge in computational biology is to extract knowledge about the genetic nature of disease from high-throughput data. However, an important obstacle to both biological understanding and clinical applications is the "black box" nature of the decision rules provided by most machine learning approaches, which usually involve many genes combined in a highly complex fashion. Achieving biologically relevant results argues for a different strategy. A promising alternative is to base prediction entirely upon the relative expression ordering of a small number of genes. Results We present a three-gene version of "relative expression analysis" (RXA), a rigorous and systematic comparison with earlier approaches in a variety of cancer studies, a clinically relevant application to predicting germline BRCA1 mutations in breast cancer and a cross-study validation for predicting ER status. In the BRCA1 study, RXA yields high accuracy with a simple decision ...
Adenoid cystic carcinoma (ACC) is the second most common malignancy of the salivary gland. Althou... more Adenoid cystic carcinoma (ACC) is the second most common malignancy of the salivary gland. Although characterized as an indolent tumor, ACC often leads to incurable metastatic disease. Patients with ACC respond poorly to currently available therapeutic drugs and factors contributing to the limited response remain unknown. Determining the role of molecular alterations frequently occurring in ACC may clarify ACC tumorigenesis and advance the development of effective treatment strategies. Applying Splice Expression Variant Analysis and outlier statistics on RNA sequencing of primary ACC tumors and matched normal salivary gland tissues, we identified multiple alternative splicing events (ASE) of genes specific to ACC. In ACC cells and patient-derived xenografts, FGFR1 was a uniquely expressed ASE. Detailed PCR analysis identified three novel, truncated, intracellular domain-lacking FGFR1 variants (FGFR1v). Cloning and expression analysis suggest that the three FGFR1v are cell surface pr...
The GSReg package allows to analyze pathways based on the variability of the expression of sets o... more The GSReg package allows to analyze pathways based on the variability of the expression of sets of genes that are targets of those pathways. Basing this set statistic on variability enables inference of dysregulated pathways in diseases, including notably cancers. The first set statistic for gene variability was in the work of Eddy and his colleagues (see [1]) which used a ranked based methodology called DIRAC. DIRAC calculates a measure of variability of the ordering of the expression of genes in a pathway for specific phenotype. The basic idea behind DIRAC is to generate a template for the pair-wise comparisons of gene expressions
A Molecular Features Set (MFS), is a result of vast diversity of bioinformatics pipelines. In cas... more A Molecular Features Set (MFS), is a result of vast diversity of bioinformatics pipelines. In case when MFS is used for further analysis to distinguish between phenotypes, it is often referred to as a signature. Lack of the “gold standard” for most experimental data modalities makes it hard to provide valid estimation for a particular MFS’s quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrix (DM) and their power to distinguish between phenotypes.The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS’s quality.Here we propose Hobotnica, an approach to estimate MFS’s quality by their ability to stratify data, and assign them significance scores, that allows for collating various signatures and comparing their quality for contrasting groups.
Determining the etiologic basis of the mutations that are responsible for cancer is one of the fu... more Determining the etiologic basis of the mutations that are responsible for cancer is one of the fundamental challenges in modern cancer research. Different mutational processes induce different types of DNA mutations, providing ‘mutational signatures’ that have led to key insights into cancer etiology. The most widely used signatures for assessing genomic data are based on unsupervised patterns that are then retrospectively correlated with certain features of cancer. We show here that supervised machine-learning techniques can identify signatures, called SuperSigs, that are more predictive than those currently available. Surprisingly, we found that aging yields different SuperSigs in different tissues, and the same is true for environmental exposures. We were able to discover SuperSigs associated with obesity, the most important lifestyle factor contributing to cancer in Western populations.
The neighbors principle implicit in any machine learning algorithm says that samples with similar... more The neighbors principle implicit in any machine learning algorithm says that samples with similar labels should be close to one another in feature space as well. For example, while tumors are heterogeneous, tumors that have similar genomics profiles can also be expected to have similar responses to a specific therapy. Simple correlation coefficients provide an effective way to determine whether this principle holds when features and labels are both scalar, but not when either is multivariate. A new class of generalized correlation coefficients based on inter-point distances addresses this need and is called “distance correlation”. There is only one rank-based distance correlation test available to date, and it is asymmetric in the samples, requiring that one sample be distinguished as a fixed point of reference. Therefore, we introduce a novel, nonparametric statistic, REVA, inspired by the Kendall rank correlation coefficient. We use U-statistic theory to derive the asymptotic dist...
Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancer... more Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in...
Current non-invasive approaches for bladder cancer (BC) detection are suboptimal. We report the d... more Current non-invasive approaches for bladder cancer (BC) detection are suboptimal. We report the development of non-invasive molecular test for BC using DNA recovered from cells shed into urine. This “UroSEEK” test incorporates assays for mutations in 11 genes and copy number changes on 39 chromosome arms. We first evaluated 570 urine samples from patients at risk for BC (microscopic hematuria or dysuria). UroSEEK was positive in 83% of patients that developed BC, but in only 7% of patients who did not develop BC. Combined with cytology, 95% of patients that developed BC were positive. We then evaluated 322 urine samples from patients soon after their BCs had been surgically resected. UroSEEK detected abnormalities in 66% of the urine samples from these patients, sometimes up to 4 years prior to clinical evidence of residual neoplasia, while cytology was positive in only 25% of such urine samples. The advantages of UroSEEK over cytology were particularly evident in low-grade tumors, ...
Upper tract urothelial carcinomas (UTUC) of the renal pelvis or ureter can be difficult to detect... more Upper tract urothelial carcinomas (UTUC) of the renal pelvis or ureter can be difficult to detect and challenging to diagnose. Here, we report the development and application of a non-invasive test for UTUC based on molecular analyses of DNA recovered from cells shed into the urine. The test, called UroSEEK, incorporates assays for mutations in eleven genes frequently mutated in urologic malignancies and for allelic imbalances on 39 chromosome arms. At least one genetic abnormality was detected in 75% of urinary cell samples from 56 UTUC patients but in only 0.5% of 188 samples from healthy individuals. The assay was considerably more sensitive than urine cytology, the current standard-of-care. UroSEEK therefore has the potential to be used for screening or to aid in diagnosis in patients at increased risk for UTUC, such as those exposed to herbal remedies containing the carcinogen aristolochic acid.
The incidence of HPV-related oropharyngeal squamous cell carcinoma (OPSCC) has increased more tha... more The incidence of HPV-related oropharyngeal squamous cell carcinoma (OPSCC) has increased more than 200% in the past 20 years. Recent genetic sequencing efforts have elucidated relevant genes in head and neck cancer, but HPV-related tumors have consistently shown few DNA mutations. In this study, we sought to analyze alternative splicing events (ASE) that could alter gene function independent of mutations. To identify ASE unique to HPV-related tumors, RNA sequencing was performed on 46 HPV-positive OPSCC and 25 normal tissue samples. A novel algorithm using outlier statistics on RNA-sequencing junction expression identified 109 splicing events, which were confirmed in a validation set from The Cancer Genome Atlas. Because the most common type of splicing event identified was an alternative start site (39%), MBD-seq genome-wide CpG methylation data were analyzed for methylation alterations at promoter regions. ASE in six genes showed significant negative correlation between promoter m...
Supplementary Table from Differential Variation Analysis Enables Detection of Tumor Heterogeneity... more Supplementary Table from Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data
One 1 of the goals of a literary analysis of the tragedies is to identify the relationships among... more One 1 of the goals of a literary analysis of the tragedies is to identify the relationships among the heroes. We aim to provide computational tools which can help in this task. Ideally, this analysis would be based on the raw text. Instead, we rely on tables extracted by researchers from the text which summarize objectively the qualities of each hero from three famous classic stories in the available sources: Trojan War from the Homer’s Iliad, Julius Caesar from the Shakespeare’s play, and knights in the Ferdowsi’s Shahnameh, a Persian epic story. We develop a statistical analysis for identifying clusters (groups) among a group of characters in a story starting from such tables with ordinal data. We define the Ordinal-Contextual (OC) similarity (or equivalently the OC dissimilarity) as a linguistically meaningful and context-based similarity (or dissimilarity) for ordinal data. Using classical multidimensional scaling, we represent the heroes in 2-D space and cluster the heroes usin...
2008 Seventh International Conference on Machine Learning and Applications, 2008
We describe our contribution to the ICMLA2008 "Automated Micro-Array Classification Challenge". T... more We describe our contribution to the ICMLA2008 "Automated Micro-Array Classification Challenge". The design of our classifier is motivated by the special scenario encountered in molecular cancer classification based on the mRNA concentrations provided by gene microarray data. Our classifier is rank-based; it only depends on expression comparisons among selected pairs of genes. Such comparisons are invariant to most of the transformations involved in preprocessing and normalization. Every pair of genes determines a binary classifier-choose the class for which the observed ordering is most likely. Pairs are scored by maximizing accuracy. In our k-TSP (k-disjoint Top Scoring Pairs) classifier, k disjoint pairs of genes are learned from training data; the discriminant function is simply the difference in the number of votes for the two classes. This rule involves exactly 2k genes, is readily interpretable, and provides some state-of-the-art results in cancer diagnosis and prognosis for small values of k, even k=1.
Background A small number of prognostic and predictive tests based on gene expression are current... more Background A small number of prognostic and predictive tests based on gene expression are currently offered as reference laboratory tests. In contrast to such success stories, a number of flaws and errors have recently been identified in other genomic-based predictors and the success rate for developing clinically useful genomic signatures is low. These errors have led to widespread concerns about the protocols for conducting and reporting of computational research. As a result, a need has emerged for a template for reproducible development of genomic signatures that incorporates full transparency, data sharing and statistical robustness. Results Here we present the first fully reproducible analysis of the data used to train and test MammaPrint, an FDA-cleared prognostic test for breast cancer based on a 70-gene expression signature. We provide all the software and documentation necessary for researchers to build and evaluate genomic classifiers based on these data. As an example of...
Background A major challenge in computational biology is to extract knowledge about the genetic n... more Background A major challenge in computational biology is to extract knowledge about the genetic nature of disease from high-throughput data. However, an important obstacle to both biological understanding and clinical applications is the "black box" nature of the decision rules provided by most machine learning approaches, which usually involve many genes combined in a highly complex fashion. Achieving biologically relevant results argues for a different strategy. A promising alternative is to base prediction entirely upon the relative expression ordering of a small number of genes. Results We present a three-gene version of "relative expression analysis" (RXA), a rigorous and systematic comparison with earlier approaches in a variety of cancer studies, a clinically relevant application to predicting germline BRCA1 mutations in breast cancer and a cross-study validation for predicting ER status. In the BRCA1 study, RXA yields high accuracy with a simple decision ...
Adenoid cystic carcinoma (ACC) is the second most common malignancy of the salivary gland. Althou... more Adenoid cystic carcinoma (ACC) is the second most common malignancy of the salivary gland. Although characterized as an indolent tumor, ACC often leads to incurable metastatic disease. Patients with ACC respond poorly to currently available therapeutic drugs and factors contributing to the limited response remain unknown. Determining the role of molecular alterations frequently occurring in ACC may clarify ACC tumorigenesis and advance the development of effective treatment strategies. Applying Splice Expression Variant Analysis and outlier statistics on RNA sequencing of primary ACC tumors and matched normal salivary gland tissues, we identified multiple alternative splicing events (ASE) of genes specific to ACC. In ACC cells and patient-derived xenografts, FGFR1 was a uniquely expressed ASE. Detailed PCR analysis identified three novel, truncated, intracellular domain-lacking FGFR1 variants (FGFR1v). Cloning and expression analysis suggest that the three FGFR1v are cell surface pr...
The GSReg package allows to analyze pathways based on the variability of the expression of sets o... more The GSReg package allows to analyze pathways based on the variability of the expression of sets of genes that are targets of those pathways. Basing this set statistic on variability enables inference of dysregulated pathways in diseases, including notably cancers. The first set statistic for gene variability was in the work of Eddy and his colleagues (see [1]) which used a ranked based methodology called DIRAC. DIRAC calculates a measure of variability of the ordering of the expression of genes in a pathway for specific phenotype. The basic idea behind DIRAC is to generate a template for the pair-wise comparisons of gene expressions
A Molecular Features Set (MFS), is a result of vast diversity of bioinformatics pipelines. In cas... more A Molecular Features Set (MFS), is a result of vast diversity of bioinformatics pipelines. In case when MFS is used for further analysis to distinguish between phenotypes, it is often referred to as a signature. Lack of the “gold standard” for most experimental data modalities makes it hard to provide valid estimation for a particular MFS’s quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrix (DM) and their power to distinguish between phenotypes.The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS’s quality.Here we propose Hobotnica, an approach to estimate MFS’s quality by their ability to stratify data, and assign them significance scores, that allows for collating various signatures and comparing their quality for contrasting groups.
Determining the etiologic basis of the mutations that are responsible for cancer is one of the fu... more Determining the etiologic basis of the mutations that are responsible for cancer is one of the fundamental challenges in modern cancer research. Different mutational processes induce different types of DNA mutations, providing ‘mutational signatures’ that have led to key insights into cancer etiology. The most widely used signatures for assessing genomic data are based on unsupervised patterns that are then retrospectively correlated with certain features of cancer. We show here that supervised machine-learning techniques can identify signatures, called SuperSigs, that are more predictive than those currently available. Surprisingly, we found that aging yields different SuperSigs in different tissues, and the same is true for environmental exposures. We were able to discover SuperSigs associated with obesity, the most important lifestyle factor contributing to cancer in Western populations.
The neighbors principle implicit in any machine learning algorithm says that samples with similar... more The neighbors principle implicit in any machine learning algorithm says that samples with similar labels should be close to one another in feature space as well. For example, while tumors are heterogeneous, tumors that have similar genomics profiles can also be expected to have similar responses to a specific therapy. Simple correlation coefficients provide an effective way to determine whether this principle holds when features and labels are both scalar, but not when either is multivariate. A new class of generalized correlation coefficients based on inter-point distances addresses this need and is called “distance correlation”. There is only one rank-based distance correlation test available to date, and it is asymmetric in the samples, requiring that one sample be distinguished as a fixed point of reference. Therefore, we introduce a novel, nonparametric statistic, REVA, inspired by the Kendall rank correlation coefficient. We use U-statistic theory to derive the asymptotic dist...
Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancer... more Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in...
Current non-invasive approaches for bladder cancer (BC) detection are suboptimal. We report the d... more Current non-invasive approaches for bladder cancer (BC) detection are suboptimal. We report the development of non-invasive molecular test for BC using DNA recovered from cells shed into urine. This “UroSEEK” test incorporates assays for mutations in 11 genes and copy number changes on 39 chromosome arms. We first evaluated 570 urine samples from patients at risk for BC (microscopic hematuria or dysuria). UroSEEK was positive in 83% of patients that developed BC, but in only 7% of patients who did not develop BC. Combined with cytology, 95% of patients that developed BC were positive. We then evaluated 322 urine samples from patients soon after their BCs had been surgically resected. UroSEEK detected abnormalities in 66% of the urine samples from these patients, sometimes up to 4 years prior to clinical evidence of residual neoplasia, while cytology was positive in only 25% of such urine samples. The advantages of UroSEEK over cytology were particularly evident in low-grade tumors, ...
Upper tract urothelial carcinomas (UTUC) of the renal pelvis or ureter can be difficult to detect... more Upper tract urothelial carcinomas (UTUC) of the renal pelvis or ureter can be difficult to detect and challenging to diagnose. Here, we report the development and application of a non-invasive test for UTUC based on molecular analyses of DNA recovered from cells shed into the urine. The test, called UroSEEK, incorporates assays for mutations in eleven genes frequently mutated in urologic malignancies and for allelic imbalances on 39 chromosome arms. At least one genetic abnormality was detected in 75% of urinary cell samples from 56 UTUC patients but in only 0.5% of 188 samples from healthy individuals. The assay was considerably more sensitive than urine cytology, the current standard-of-care. UroSEEK therefore has the potential to be used for screening or to aid in diagnosis in patients at increased risk for UTUC, such as those exposed to herbal remedies containing the carcinogen aristolochic acid.
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Papers by Bahman Afsari