Permeability changes and effect of chemotherapy in brain adjacent to tumor in an experimental model of metastatic brain tumor from breast cancer

Abstract Background: Brain tumor vasculature can be significantly compromised and leakier than that of normal brain blood vessels. Little is known if there are vascular permeability alterations in the brain adjacent to tumor (BAT). Changes in BAT permeability may also lead to increased drug permeation in the BAT, which may exert toxicity on cells of the central nervous system. Herein, we studied permeation changes in BAT using quantitative fluorescent microscopy and autoradiography, while the effect of chemotherapy within the BAT region was determined by staining for activated astrocytes. Methods: Human metastatic breast cancer cells (MDA-MB-231Br) were injected into left ventricle of female NuNu mice. Metastases were allowed to grow for 28 days, after which animals were injected fluorescent tracers Texas Red (625 Da) or Texas Red dextran (3 kDa) or a chemotherapeutic agent 14C-paclitaxel. The accumulation of tracers and 14C-paclitaxel in BAT were determined by using quantitative fluorescent microscopy and autoradiography respectively. The effect of chemotherapy in BAT was determined by staining for activated astrocytes. Results: The mean permeability of texas Red (625 Da) within BAT region increased 1.0 to 2.5-fold when compared to normal brain, whereas, Texas Red dextran (3 kDa) demonstrated mean permeability increase ranging from 1.0 to 1.8-fold compared to normal brain. The Kin values in the BAT for both Texas Red (625 Da) and Texas Red dextran (3 kDa) were found to be 4.32 ± 0.2 × 105 mL/s/g and 1.6 ± 1.4 × 105 mL/s/g respectively and found to be significantly higher than the normal brain. We also found that there is significant increase in accumulation of 14C-Paclitaxel in BAT compared to the normal brain. We also observed animals treated with chemotherapy (paclitaxel (10 mg/kg), erubilin (1.5 mg/kg) and docetaxel (10 mg/kg)) showed activated astrocytes in BAT. Conclusions: Our data showed increased permeation of fluorescent tracers and 14C-paclitaxel in the BAT. This increased permeation lead to elevated levels of activated astrocytes in BAT region in the animals treated with chemotherapy

Investigational chemotherapy and novel pharmacokinetic mechanisms for the treatment of breast cancer brain metastases

In women, breast cancer is the most common cancer diagnosis and second most common cause of cancer death. More than half of breast cancer patients will develop metastases to the bone, liver, lung, or brain. Breast cancer brain metastases (BCBM) confers a poor prognosis, as current therapeutic options of surgery, radiation, and chemotherapy rarely significantly extend life and are considered palliative. Within the realm of chemotherapy, the last decade has seen an explosion of novel chemotherapeutics involving targeting agents and unique dosage forms. We provide a historical overview of BCBM chemotherapy, review the mechanisms of new agents such as poly-ADP ribose polymerase inhibitors, cyclin-dependent kinase 4/6 inhibitors, phosphatidyl inositol 3-kinaseinhibitors, estrogen pathway antagonists for hormone-receptor positive BCBM; tyrosine kinase inhibitors, antibodies, and conjugates for HER2+ BCBM; repurposed cytotoxic chemotherapy for triple negative BCBM; and the utilization of these new agents and formulations in ongoing clinical trials. The mechanisms of novel dosage formulations such as nanoparticles, liposomes, pegylation, the concepts of enhanced permeation and retention, and drugs utilizing these concepts involved in clinical trials are also discussed. These new treatments provide a promising outlook in the treatment of BCBM

Permeability changes and effect of chemotherapy in brain adjacent to tumor in an experimental model of metastatic brain tumor from breast cancer

Background: Brain tumor vasculature can be significantly compromised and leakier than that of normal brain blood vessels. Little is known if there are vascular permeability alterations in the brain adjacent to tumor (BAT). Changes in BAT permeability may also lead to increased drug permeation in the BAT, which may exert toxicity on cells of the central nervous system. Herein, we studied permeation changes in BAT using quantitative fluorescent microscopy and autoradiography, while the effect of chemotherapy within the BAT region was determined by staining for activated astrocytes. Methods: Human metastatic breast cancer cells (MDA-MB-231Br) were injected into left ventricle of female NuNu mice. Metastases were allowed to grow for 28 days, after which animals were injected fluorescent tracers Texas Red (625 Da) or Texas Red dextran (3 kDa) or a chemotherapeutic agent 14C-paclitaxel. The accumulation of tracers and 14C-paclitaxel in BAT were determined by using quantitative fluorescent microscopy and autoradiography respectively. The effect of chemotherapy in BAT was determined by staining for activated astrocytes. Results: The mean permeability of texas Red (625 Da) within BAT region increased 1.0 to 2.5-fold when compared to normal brain, whereas, Texas Red dextran (3 kDa) demonstrated mean permeability increase ranging from 1.0 to 1.8-fold compared to normal brain. The Kin values in the BAT for both Texas Red (625 Da) and Texas Red dextran (3 kDa) were found to be 4.32 ± 0.2 × 105 mL/s/g and 1.6 ± 1.4 × 105 mL/s/g respectively and found to be significantly higher than the normal brain. We also found that there is significant increase in accumulation of 14C-Paclitaxel in BAT compared to the normal brain. We also observed animals treated with chemotherapy (paclitaxel (10 mg/kg), erubilin (1.5 mg/kg) and docetaxel (10 mg/kg)) showed activated astrocytes in BAT. Conclusions: Our data showed increased permeation of fluorescent tracers and 14C-paclitaxel in the BAT. This increased permeation lead to elevated levels of activated astrocytes in BAT region in the animals treated with chemotherapy

Identification of human short introns

<div><p>Canonical pre-mRNA splicing requires snRNPs and associated splicing factors to excise conserved intronic sequences, with a minimum intron length required for efficient splicing. Non-canonical splicing–intron excision without the spliceosome–has been documented; most notably, some tRNAs and the <i>XBP1</i> mRNA contain short introns that are not removed by the spliceosome. There have been some efforts to identify additional short introns, but little is known about how many short introns are processed from mRNAs. Here, we report an approach to identify RNA short introns from RNA-Seq data, discriminating against small genomic deletions. We identify hundreds of short introns conserved among multiple human cell lines. These short introns are often alternatively spliced and are found in a variety of RNAs–both mRNAs and lncRNAs. Short intron splicing efficiency is increased by secondary structure, and we detect both canonical and non-canonical short introns. In many cases, splicing of these short introns from mRNAs is predicted to alter the reading frame and change protein output. Our findings imply that standard gene prediction models which often assume a lower limit for intron size fail to predict short introns effectively. We conclude that short introns are abundant in the human transcriptome, and short intron splicing represents an added layer to mRNA regulation.</p></div

A pool of potential introns was extensively screened for high-quality short introns.

<p>(A) For each cell line, average intron length was calculated, ranging between 5 kb and 6 kb, consistent with the work of others [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175393#pone.0175393.ref030" target="_blank">30</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175393#pone.0175393.ref031" target="_blank">31</a>]. (B) For each cell line, introns were binned and counted according to length (10 nt to 1010 nt are shown). The data are consistent between cell lines, and in each case, there is a peak at ~86 nt. (C) Canonical introns (GU-AG, GC-AG, and AU-AC for major or minor class spliceosomes) were separated from non-canonical introns. We calculated the percentage of introns that were either canonical or non-canonical over a range of sizes (from 10 nt to 90 nt) for each cell line (shown as multiple lines on the graph). At 70 nt (*), there is a sharp decrease in the percentage of non-canonical introns. (D) A non-parametric Irreproducible Discovery Rate (npIDR) was calculated for short introns between each pair of biological replicates. We repeated the calculation with increasing numbers of minimum sequencing read support for each cell line. To be included in subsequent analysis, predicted intron pools had to cross an npIDR = 0.90 threshold, meaning that introns in the final pool were consistent 90% of the time between biological replicates. (E) Remaining introns were then binned according to length as in (B) and counted over the range from 10 nt to 70 nt. The remaining predicted intron pool was fairly evenly distributed by size, but with a peak at 10 nt. (F) After removing likely genomic deletions, we compared our remaining short intron pool to both annotated and unannotated longer introns. Annotated longer introns were present in the largest number of samples, whereas unannotated longer introns were present in considerably fewer samples. We then checked for short intron conservation across samples as a function of increasing read support (≥ n U.R. Short; where n is the number of unique reads). With more read support, short introns were found in a larger number of biological samples. Note: inner window is a zoomed in view of the region between 8 and 12 on the x-axis.</p

Summary of RNA-Seq analysis for the <i>XBP1s</i> intron.

<p>(A) Summarized are whether sequencing reads predicted the <i>XBP1s</i> intron in the cell lines listed. Sequence support was obtained in no (-), one (+), or two (++) samples as indicated. (B) For the Mcf7 and K562 cell lines, the sequencing read depth is plotted for the <i>XBP1</i> locus. The region where the <i>XBP1</i> mRNA short intron is found is indicated (XBP1s). Note the dip in sequencing read depth in this region only in the Mcf7 cell line, indicating some <i>XBP1s</i> splicing.</p

Summary of short intron identification strategy.

<p>Ten different cell lines, representing different tissue types, were analyzed. RNA-Seq data for two biological replicates for each cell line were aligned to the human genome using the STAR sequence aligner [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175393#pone.0175393.ref029" target="_blank">29</a>]. Predicted introns were compared between replicates and included if they were identified with more unique sequencing reads than an experimentally-determined threshold (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175393#pone.0175393.g002" target="_blank">Fig 2D</a>). Short introns were then selected based on size, and repetitive sequences were removed from the predicted intron pool. Predicted short introns were then compared to unannotated, longer introns on the basis of conservation between cell lines to finalize an intron pool.</p

Canonical short introns may be produced by unconventional splicing.

<p>(A) Of the 600 short introns with the greatest sequencing read support, canonical introns were examined for their positions within mRNAs. The majority were known introns, with annotated 5ˈ and 3ˈ splice sites (Annotated), but a smaller number were unannotated, using a known splice site and an unannotated 3ˈ (AS 3ˈ end) or 5ˈ (AS 5ˈ end) splice site for intron excision. For short introns in ORFs, 52% of these are predicted to change the mRNA reading frame. (B) A small subset of short introns is examined more closely. Shown are the <i>SAMD11</i>, <i>CSNK1G2</i>, <i>FBXW5</i>, and <i>ZNF598</i> loci. The <i>SAMD11</i> and <i>CSNK1G2</i> loci both encode pre-mRNAs harboring annotated short introns. Note that both loci encode pre-mRNAs with clusters of short introns (with the shortest intron indicated by a red arrow). The <i>FBXW5</i> and <i>ZNF598</i> loci both encode pre-mRNAs that are likely alternatively spliced to yield a short intron. In both cases, the flanking exons and intron are shown. For the <i>FBXW5</i> mRNA, it is predicted that short intron splicing would change the reading frame of the protein product resulting in a premature termination codon (PTC). (C) For <i>SAMD11</i>, <i>CSNK1G2</i>, <i>FBXW5</i>, and <i>ZNF598</i> pre-mRNAs, the short intron with ten nucleotides from each flanking exon was folded computationally. Note that each intron folds into extensive secondary structure with intron ends positioned in proximity. Intron ends are indicated with arrowheads, and for <i>FBXW5</i> and <i>ZNF598</i> pre-mRNAs, both the annotated splice sites (green) and alternate splice sites (red) are indicated. (D) We used RT-PCR to confirm short intron removal in the <i>SAMD11</i>, <i>CSNK1G2</i>, <i>FBXW5</i>, and <i>ZNF598</i> pre-mRNAs. For the <i>FBXW5</i> and <i>ZNF598</i> pre-mRNAs, primers were designed to specifically detect short intron splicing (one primer sequence within the alternatively spliced region); for the other pre-mRNAs, primers were located outside the short intron to detect both pre-mRNA and spliced mRNA. As predicted from our splicing efficiency analysis, every short intron was alternatively spliced. Samples that omitted reverse transcriptase (from the RT step) serve as a control for contaminating DNA.</p

Short introns are both alternatively spliced and structured.

<p>(A) Short intron splicing efficiency was calculated for 3,027 predicted short introns. The introns were binned in groups of 20 according to sequencing read support, and splicing efficiency was plotted. There is a positive correlation between sequencing read support and splicing efficiency (R = 0.64, p < 0.01). (B) Short introns were separated according to whether they had canonical or non-canonical sequences at the ends of the introns. Both average and maximal splicing efficiency (across all cell lines) were calculated for every intron. Plotted are the box-and-whisker plots with the highest, lowest, 25^th, 50^th, and 75^th percentiles indicated. There was no significant (n.s.) difference between canonical and non-canonical intron splicing efficiency. (C) The free energy of folding (ΔG_folding) was calculated for every short intron. The introns were ranked by folding energy and binned in groups of 20; average splicing efficiency per group was then calculated. The data are plotted showing a strong negative correlation between folding free energy and splicing efficiency (R = -0.79, p < 0.01).</p