Applied models and molecular characteristics of small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care

Predictors of Chemosensitivity in Triple Negative Breast Cancer: An Integrated Genomic Analysis

Background: Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive disease, and although no effective targeted therapies are available to date, about one-third of patients with TNBC achieve pathologic complete response (pCR) from standard-of-care anthracycline/taxane (ACT) chemotherapy. The heterogeneity of these tumors, however, has hindered the discovery of effective biomarkers to identify such patients. Methods and Findings: We performed whole exome sequencing on 29 TNBC cases from the MD Anderson Cancer Center (MDACC) selected because they had either pCR (n = 18) or extensive residual disease (n = 11) after neoadjuvant chemotherapy, with cases from The Cancer Genome Atlas (TCGA; n = 144) and METABRIC (n = 278) cohorts serving as validation cohorts. Our analysis revealed that mutations in the AR- and FOXA1-regulated networks, in which BRCA1 plays a key role, are associated with significantly higher sensitivity to ACT chemotherapy in the MDACC cohort (pCR rate of 94.1% compared to 16.6% in tumors without mutations in AR/FOXA1 pathway, adjusted p = 0.02) and significantly better survival outcome in the TCGA TNBC cohort (log-rank test, p = 0.05). Combined analysis of DNA sequencing, DNA methylation, and RNA sequencing identified tumors of a distinct BRCA-deficient (BRCA-D) TNBC subtype characterized by low levels of wild-type BRCA1/2 expression. Patients with functionally BRCA-D tumors had significantly better survival with standard-of-care chemotherapy than patients whose tumors were not BRCA-D (log-rank test, p = 0.021), and they had significantly higher mutation burden (p < 0.001) and presented clonal neoantigens that were associated with increased immune cell activity. A transcriptional signature of BRCA-D TNBC tumors was independently validated to be significantly associated with improved survival in the METABRIC dataset (log-rank test, p = 0.009). As a retrospective study, limitations include the small size and potential selection bias in the discovery cohort. Conclusions: The comprehensive molecular analysis presented in this study directly links BRCA deficiency with increased clonal mutation burden and significantly enhanced chemosensitivity in TNBC and suggests that functional RNA-based BRCA deficiency needs to be further examined in TNBC. © 2016 Jiang et al

Detection of species in a metagenomic datasets.

1<p>The data was a mock community dataset provided by the Human Microbiome Project and consisted of 22 strains.</p>2<p>Only hits (read-taxon assignments) were considered where the worst alignment score was at least 0.9. Positive taxa predicted by Taxoner are those that received at least 1000 hits (dataset G).</p>3<p>True positives.</p>4<p>False negatives.</p>5<p>False positives.</p>6<p>Not available.</p>7<p>Hits (read-taxon assignments) were only considered where the worst alignment score was at least 0.9. Positive taxa predicted by Taxoner are those that received at least 100 hits (dataset H).</p

Typical running times for the alignment.

1<p>Read dataset: Dataset A, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103441#pone-0103441-t001" target="_blank">Table 1</a>. Processor: Intel(R) Xeon(R) CPU E5-2640;</p>2<p>The built-in dataset is 366,988,039 nucleotides (367 MB) and contains only bacterial sequences;</p>3<p>15,400,949,699 nucleotides (15 GB), downloaded on 11/07/2013;</p>4<p>52,380,339,934 nucleotides (54 GB), downloaded on 11/07/2013;</p>5<p>Times include taxon assignment;</p>6<p>time of taxon assignment by MEGAN is not included.</p

Logical structure of Index file (example).

<p>In this example the sub-word found in position 6 is also found in at least one other location of the reference sequence (see example in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054294#pone-0054294-g005" target="_blank">Figure 5</a>).</p

<em>HeurAA</em>: Accurate and Fast Detection of Genetic Variations with a Novel Heuristic Amplicon Aligner Program for Next Generation Sequencing

<div><p>Next generation sequencing (NGS) of PCR amplicons is a standard approach to detect genetic variations in personalized medicine such as cancer diagnostics. Computer programs used in the NGS community often miss insertions and deletions (indels) that constitute a large part of known human mutations. We have developed <em>HeurAA,</em> an open source, <u>heur</u>istic <u>a</u>mplicon <u>a</u>ligner program. We tested the program on simulated datasets as well as experimental data from multiplex sequencing of 40 amplicons in 12 oncogenes collected on a 454 Genome Sequencer from lung cancer cell lines. We found that <em>HeurAA</em> can accurately detect all indels, and is more than an order of magnitude faster than previous programs. <em>HeurAA</em> can compare reads and reference sequences up to several thousand base pairs in length, and it can evaluate data from complex mixtures containing reads of different gene-segments from different samples. <em>HeurAA</em> is written in C and Perl for Linux operating systems, the code and the documentation are available for research applications at <a href="http://sourceforge.net/projects/heuraa/">http://sourceforge.net/projects/heuraa/</a></p> </div

Fast and Sensitive Alignment of Microbial Whole Genome Sequencing Reads to Large Sequence Datasets on a Desktop PC: Application to Metagenomic Datasets and Pathogen Identification

<div><p>Next generation sequencing (NGS) of metagenomic samples is becoming a standard approach to detect individual species or pathogenic strains of microorganisms. Computer programs used in the NGS community have to balance between speed and sensitivity and as a result, species or strain level identification is often inaccurate and low abundance pathogens can sometimes be missed. We have developed Taxoner, an open source, taxon assignment pipeline that includes a fast aligner (e.g. Bowtie2) and a comprehensive DNA sequence database. We tested the program on simulated datasets as well as experimental data from Illumina, IonTorrent, and Roche 454 sequencing platforms. We found that Taxoner performs as well as, and often better than BLAST, but requires two orders of magnitude less running time meaning that it can be run on desktop or laptop computers. Taxoner is slower than the approaches that use small marker databases but is more sensitive due the comprehensive reference database. In addition, it can be easily tuned to specific applications using small tailored databases. When applied to metagenomic datasets, Taxoner can provide a functional summary of the genes mapped and can provide strain level identification. Taxoner is written in C for Linux operating systems. The code and documentation are available for research applications at <a href="http://code.google.com/p/taxoner" target="_blank">http://code.google.com/p/taxoner</a>.</p></div

Analysis of the polymorphic region (schematic flowchart).

<p>Each polymorphic region within a read is analyzed according to this general procedure. Dynamic programming refers to the use of the Needleman Wunsch or the Smith Waterman algorithm Mutation calling refers to converting the results of the previous steps into standard mutation description format (<a href="http://www.hgvs.org/mutnomen/recs-DNA.html" target="_blank">http://www.hgvs.org/mutnomen/recs-DNA.html</a>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054294#pone.0054294-denDunnen1" target="_blank">[20]</a>.</p

Read assignment for <i>Staphylococcus aureus</i> genome sequencing data.

1<p>100,000 random selected reads from experimental data, details in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103441#s4" target="_blank">Methods</a></b> section 4.1.</p>2<p>False Negative Rate.</p>3<p>Not available.</p

<i>HeurAA</i> output.

<p>Sample: Name of the sample; Reference: name of the reference sequence; Mutation: Mutation found (mutations with >5 nucleotides are replaced with the length of the mutation); Percent: Percentage of mutation in all sequences identified with specified sample an reference; Forw: Number of mutations found in forward sequences; Rev: Mutations found in reverse complement sequences; Mut no.: Total number of mutations found; Reads: Total reads found for specified sample/reference pair; Annotation: For mutations longer than 5 nucleotides), <i>HeurAA</i> prints out the entire mutation here.</p