Double-negative (CD27IgD) B cells are expanded in NSCLC and inversely correlate with affinity-matured B cell populations.

Background: The presence of B cells in early stage non-small cell lung cancer (NSCLC) is associated with longer survival, however, the role these cells play in the generation and maintenance of anti-tumor immunity is unclear. B cells differentiate into a variety of subsets with differing characteristics and functions. To date, there is limited information on the specific B cell subsets found within NSCLC.

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

Background: Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease.

Alisertib added to rituximab and vincristine is synthetic lethal and potentially curative in mice with aggressive DLBCL co-overexpressing MYC and BCL2.

Pearson correlation coefficient for expression analysis of the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) demonstrated Aurora A and B are highly correlated with MYC in DLBCL and mantle cell lymphoma (MCL), while both Auroras correlate with BCL2 only in DLBCL. Auroras are up-regulated by MYC dysregulation with associated aneuploidy and resistance to microtubule targeted agents such as vincristine. Myc and Bcl2 are differentially expressed in U-2932, TMD-8, OCI-Ly10 and Granta-519, but only U-2932 cells over-express mutated p53.

In Vitro Assessment of the Inflammatory Breast Cancer Cell Line SUM 149: Discovery of 2 Single Nucleotide Polymorphisms in the RNase L Gene.

Background: Inflammatory breast cancer (IBC) is a rare, highly aggressive form of breast cancer. The mechanism of IBC carcinogenesis remains unknown. We sought to evaluate potential genetic risk factors for IBC and whether or not the IBC cell lines SUM149 and SUM190 demonstrated evidence of viral infection.

Using multiple sequence alignment editors and formatters.

Sequence alignment editors enable the user to manually edit a multiple sequence alignment (msa) in order to obtain a more reasonable or expected alignment. Editors allow sequences to be reordered and/or modified using the computer's cut and paste commands. They are designed to accept various msa formats and to provide the output file in a suitable user-designated format.

Using iterative methods for global multiple sequence alignment.

Finding a global optimal alignment of more than two sequences that includes matches, mismatches, and gaps and that takes into account the degree of variation in all of the sequences at the same time is especially difficult. The dynamic programming algorithm used for optimal alignment of pairs of sequences can be extended to global alignment of three sequences, but for more than three sequences, only a small number of relatively short sequences may be analyzed. Thus, approximate methods are used for global alignment.

Using progressive methods for global multiple sequence alignment.

Finding a global optimal alignment of more than two sequences that includes matches, mismatches, and gaps and that takes into account the degree of variation in all of the sequences at the same time is especially difficult. The dynamic programming algorithm used for optimal alignment of pairs of sequences can be extended to global alignment of three sequences, but for more than three sequences, only a small number of relatively short sequences may be analyzed. Thus, approximate methods are used for global sequence alignment.

Using hidden Markov models to align multiple sequences.

A hidden Markov model (HMM) is a probabilistic model of a multiple sequence alignment (msa) of proteins. In the model, each column of symbols in the alignment is represented by a frequency distribution of the symbols (called a "state"), and insertions and deletions are represented by other states. One moves through the model along a particular path from state to state in a Markov chain (i.

Comparing programs and methods to use for global multiple sequence alignment.

It is difficult to find a global optimal alignment of more than two sequences (and, especially, more than three) that includes matches, mismatches, and gaps and that takes into account the degree of variation in all of the sequences at the same time. Thus, approximate methods are used, such as progressive global alignment, iterative global alignment, alignments based on locally conserved patterns found in the same order in the sequences, statistical methods that generate probabilistic models of the sequences, and multiple sequence alignments produced by graph-based methods. When 10 or more sequences are being compared, it is common to begin by determining sequence similarities between all pairs of sequences in the set.

Using gaps and gap penalties to optimize pairwise sequence alignments.

INTRODUCTIONTo obtain the best possible alignment between two sequences, it is necessary to include gaps in sequence alignments and use gap penalties. For aligning DNA sequences, a simple positive score for matches and a negative score for mismatches and gaps are most often used. To score matches and mismatches in alignments of proteins, it is necessary to know how often one amino acid is substituted for another in related proteins.

Using BLOSUM in Sequence Alignments.

INTRODUCTIONThe original Dayhoff percent accepted mutation (PAM) matrices were developed based on a small number of protein sequences and an evolutionary model of protein change. By extrapolating from the observed changes at small evolutionary distances to large ones, it was possible to establish a PAM250 scoring matrix for sequences that were highly divergent. Another approach to finding a scoring matrix for divergent sequences is to start with a more divergent set of sequences and produce a scoring matrix from the substitutions found in those less-related sequences.

Using PAM Matrices in Sequence Alignments.

INTRODUCTIONCertain amino acid substitutions commonly occur in related proteins from different species. Because a protein still functions with these substitutions, the substituted amino acids are compatible with protein structure and function. Knowing the types of changes that are most and least common in a large number of proteins can assist with predicting alignments for any set of protein sequences.

Studies of varying alignment algorithm, amino Acid scoring matrix, and gap penalties.

INTRODUCTIONComparing different amino acid scoring matrix-gap penalty combinations poses several problems. For example, the analysis often overlooks the purposes of different matrices; e.g.

Comparison of the PAM and BLOSUM Amino Acid Substitution Matrices.

INTRODUCTIONThe choice of a scoring system including scores for matches, mismatches, substitutions, insertions, and deletions influences the alignment of both DNA and protein sequences. To score matches and mismatches in alignments of proteins, it is necessary to know how often one amino acid is substituted for another in related proteins. Percent accepted mutation (PAM) matrices list the likelihood of change from one amino acid to another in homologous protein sequences during evolution and thus are focused on tracking the evolutionary origins of proteins.

A test of the markov model of evolution in proteins.

INTRODUCTIONThe percent accepted mutation (PAM) scoring matrix is based on the Dayhoff model of protein evolution, which is a Markov process. In the Markov model of amino acid change, the probability of mutation at each site is independent of the previous history of mutations. Use of this model makes it possible to extrapolate amino acid substitutions observed over a relatively short period of evolutionary time to longer periods of evolutionary time.

The maximum likelihood approach for phylogenetic prediction.

INTRODUCTIONMaximum likelihood (ML) methods are especially useful for phylogenetic prediction when there is considerable variation among the sequences in the multiple sequence alignment (msa) to be analyzed. ML methods start with a simple model, in this case a model of rates of evolutionary change in nucleic acid or protein sequences and tree models that represent a pattern of evolutionary change, and then adjust the model until there is a best fit to the observed data. For phylogenetic analysis, the observed data are the observed sequence variations found within the columns of an msa.

Distance methods for phylogenetic prediction.

INTRODUCTIONPhylogenetic analysis of a multiple sequence alignment (msa) can be performed using distance methods, which are based on genetic distances between sequence pairs in an msa. The genetic distance between two sequences is the fraction of aligned positions in which the sequence has been changed. In contrast, sequence identity is the fraction of the aligned positions that are identical.

Maximum parsimony method for phylogenetic prediction.

INTRODUCTIONMaximum parsimony predicts the evolutionary tree or trees that minimize the number of steps required to generate the observed variation in the sequences from common ancestral sequences. For this reason, the method is also sometimes referred to as the minimum evolution method. A multiple sequence alignment (msa) is required to predict which sequence positions are likely to correspond.

Choosing a method for phylogenetic prediction.

INTRODUCTIONThree methods--maximum parsimony, distance, and maximum likelihood--are generally used to find the evolutionary tree or trees that best account for the observed variation in a group of sequences. Each of these methods uses a different type of analysis. Programs based on distance methods are commonly used in the molecular biology laboratory because they are straightforward and can be used with a large number of sequences.

Using the Basic Local Alignment Search Tool (BLAST).

INTRODUCTIONThe BLAST algorithm was developed as a way to perform DNA and protein sequence similarity searches by an algorithm that is faster than FASTA but considered to be equally as sensitive. Both of these methods follow a heuristic (tried-and-true) method that almost always works to find related sequences in a database search, but does not have the underlying guarantee of an optimal solution like the dynamic programming algorithm. FASTA finds short common patterns in query and database sequences and joins these into an alignment.

Using a FASTA Sequence Database Similarity Search.

INTRODUCTIONFASTA is a program for rapid alignment of pairs of protein and DNA sequences. Rather than comparing individual residues in the two sequences, FASTA searches for matching sequence patterns or words, called k-tuples. These patterns comprise k consecutive matches of letters in both sequences.

Strategies for sequence similarity database searches.

INTRODUCTIONDatabase similarity search programs tend to produce large volumes of output. It can become difficult to screen this volume of material and to assess whether the more remotely related sequences are really related to the query sequence. Thus, it is important to limit the sequence output; there are some relatively simple procedures that may be followed for each program, as described in this article.

Steps Used by the BLAST Algorithm.

INTRODUCTIONThe BLAST algorithm performs DNA and protein sequence similarity searches by an algorithm that is faster than FASTA but considered to be equally as sensitive. BLAST is very popular due to availability of the program on the World Wide Web through a large server at the National Center for Biotechnology Information (NCBI) and at many other sites. The BLAST algorithm has evolved to provide a set of very powerful search tools for the molecular biologist that are freely available to run on many computer platforms.

Recommended Steps for a FASTA Search.

INTRODUCTIONThe following strategy is recommended for searches with FASTA for finding the most homologous sequences in a database search while avoiding false-negative matches.

Dot matrix pairwise sequence comparison.

INTRODUCTIONA dot matrix analysis is primarily a method for comparing two sequences to look for possible alignment of characters between the sequences. The method is also used for finding direct or inverted repeats in protein and DNA sequences, and for predicting regions in RNA that are self-complementary and that, therefore, have the potential of forming secondary structure through base-pairing.