Connecting blood and intratumoral Treg cell activity in predicting future relapse in breast cancer.

Regulatory T (Treg) cells play a major role in the development of an immunosuppressive tumor microenvironment. The origin of intratumoral Treg cells and their relationship with peripheral blood Treg cells remain unclear. Treg cells consist of at least three functionally distinct subpopulations. Here we show that peripheral blood CD45RA-FOXP3hi Treg cells (Treg II cells) are phenotypically closest to intratumoral Treg cells, including in their expression of CCR8. Analyses of T cell antigen receptor repertoires further support the hypothesis that intratumoral Treg cells may originate primarily from peripheral blood Treg II cells. Moreover, the signaling responsiveness of peripheral blood Treg II cells to immunosuppressive, T helper type 1 (TH1) and T helper type 2 (TH2) cytokines reflects intratumoral immunosuppressive potential, and predicts future relapse in two independent cohorts of patients with breast cancer. Together, our findings give important insights into the relationship between peripheral blood Treg cells and intratumoral Treg cells, and highlight cytokine signaling responsiveness as a key determinant of intratumoral immunosuppressive potential and clinical outcome.

Physics approaches to the spatial distribution of immune cells in tumors.

The goal of immunotherapy is to mobilize the immune system to kill cancer cells. Immunotherapy is more effective and, in general, the prognosis is better, when more immune cells infiltrate the tumor. We explore the question of whether the spatial distribution rather than just the density of immune cells in the tumor is important in forecasting whether cancer recurs. After reviewing previous work on this issue, we introduce a novel application of maximum entropy to quantify the spatial distribution of discrete point-like objects. We apply our approach to B and T cells in images of tumor tissue taken from triple negative breast cancer patients. We find that the immune cells are more spatially dispersed in good clinical outcome (no recurrence of cancer within at least 5 years of diagnosis) compared to poor clinical outcome (recurrence within 3 years of diagnosis). Our results highlight the importance of spatial distribution of immune cells within tumors with regard to clinical outcome, and raise new questions on their role in cancer recurrence.

AISAIC: a software suite for accurate identification of significant aberrations in cancers.

UNLABELLED: Accurate identification of significant aberrations in cancers (AISAIC) is a systematic effort to discover potential cancer-driving genes such as oncogenes and tumor suppressors. Two major confounding factors against this goal are the normal cell contamination and random background aberrations in tumor samples. We describe a Java AISAIC package that provides comprehensive analytic functions and graphic user interface for integrating two statistically principled in silico approaches to address the aforementioned challenges in DNA copy number analyses. In addition, the package provides a command-line interface for users with scripting and programming needs to incorporate or extend AISAIC to their customized analysis pipelines. This open-source multiplatform software offers several attractive features: (i) it implements a user friendly complete pipeline from processing raw data to reporting analytic results; (ii) it detects deletion types directly from copy number signals using a Bayes hypothesis test; (iii) it estimates the fraction of normal contamination for each sample; (iv) it produces unbiased null distribution of random background alterations by iterative aberration-exclusive permutations; and (v) it identifies significant consensus regions and the percentage of homozygous/hemizygous deletions across multiple samples. AISAIC also provides users with a parallel computing option to leverage ubiquitous multicore machines. AVAILABILITY AND IMPLEMENTATION: AISAIC is available as a Java application, with a user's guide and source code, at https://code.google.com/p/aisaic/.

Genome-wide identification of significant aberrations in cancer genome.

BACKGROUND: Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. RESULTS: We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. CONCLUSIONS: Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open-source and platform-independent SAIC software is implemented using C++, together with R scripts for data formatting and Perl scripts for user interfacing, and it is easy to install and efficient to use. The source code and documentation are freely available at http://www.cbil.ece.vt.edu/software.htm.

Prospective nanoparticle treatments for lymphangioleiomyomatosis.

INTRODUCTION: Lymphangioleiomyomatosis (LAM) is a rare lung disease that is characterized by smooth muscle-like cell growth in the lungs. The current available oral treatment rapamycin slows down the disease progression but does not result in a cure. Rapamycin is also limited by its low bioavailability and dose-related adverse side effects. New treatments are, therefore, underway to investigate alternative targets and combination therapies for LAM. In recent years, much focus has been on the development of therapies based on inhaled nanotechnology using carriers to deliver drugs, as it is shown to improve drug solubility, local targeted treatment, and bioavailability. AREAS COVERED: This review, therefore, focuses on future prospective treatments for LAM using nanoparticles and lipid-based nanocarriers, including liposomes, solid lipid nanoparticles, micelles, and polymeric nanoparticles. It also investigates how nanoparticles' physicochemical factors such as size and charge can affect the treatment of both pulmonary and extrapulmonary LAM. EXPERT OPINION: Advanced clinical research is still needed to demonstrate the full potential and drive future commercialization of LAM treatments delivered via inhaled lipid nanobased formulations. If successful, the resultant effects will be seen in the improvement in the life expectancy and life quality of LAM patients.

Prolonged effects of short-term anti-CD20 B cell depletion therapy in murine systemic lupus erythematosus.

OBJECTIVE: Although B cells are implicated in the pathogenesis of systemic lupus erythematosus, the role of B cell depletion (BCD) as a treatment is controversial, given the variable benefit in human disease. This study was undertaken to test the effects of BCD therapy in a murine lupus model to better understand the mechanisms, heterogeneity, and effects on disease outcomes. METHODS: (NZB x NZW)F(1) female mice with varying degrees of disease severity were treated with an anti-mouse CD20 (anti-mCD20) antibody (IgG2a), BR3-Fc fusion protein (for BAFF blockade), or control anti-human CD20 monoclonal antibody (approximately 10 mg/kg each). Tissue samples were harvested and analyzed by flow cytometry. The development and extent of nephritis were assessed by monitoring proteinuria (using a urine dipstick) and by immunohistochemical analysis of the kidneys. Serum immunoglobulin levels were measured by enzyme-linked immunosorbent assay. RESULTS: After a single injection of anti-mCD20, BCD was more efficient in the peripheral blood, lymph nodes, and spleen compared with the bone marrow and peritoneum of normal mice as well as younger mice with lupus. Since depletion of the marginal zone and peritoneal B cells was incomplete and variable, particularly in older mice with established nephritis, a strategy of sequential weekly dosing was subsequently used, which improved the extent of depletion. BAFF blockade further enhanced depletion in the spleen and lymph nodes. Early BCD therapy delayed disease onset, whereas BCD therapy in mice with advanced disease reduced the progression of nephritis. These effects were long-lasting, even after B cell reconstitution occurred, and were associated with a reduction in T cell activation but no significant change in autoantibody production. CONCLUSION: The lasting benefit of a short course of BCD therapy in lupus-prone mice with an intact immune system and established disease highlights the validity of this treatment approach.

Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome.

While tumor infiltration by CD8+ T cells is now widely accepted to predict outcomes, the clinical significance of intratumoral B cells is less clear. We hypothesized that spatial distribution rather than density of B cells within tumors may provide prognostic significance. We developed statistical techniques (fractal dimension differences and a box-counting method 'occupancy') to analyze the spatial distribution of tumor-infiltrating lymphocytes (TILs) in human triple-negative breast cancer (TNBC). Our results indicate that B cells in good outcome tumors (no recurrence within 5 years) are spatially dispersed, while B cells in poor outcome tumors (recurrence within 3 years) are more confined. While most TILs are located within the stroma, increased numbers of spatially dispersed lymphocytes within cancer cell islands are associated with a good prognosis. B cells and T cells often form lymphocyte clusters (LCs) identified via density-based clustering. LCs consist either of T cells only or heterotypic mixtures of B and T cells. Pure B cell LCs were negligible in number. Compared to tertiary lymphoid structures (TLS), LCs have fewer lymphocytes at lower densities. Both types of LCs are more abundant and more spatially dispersed in good outcomes compared to poor outcome tumors. Heterotypic LCs in good outcome tumors are smaller and more numerous compared to poor outcome. Heterotypic LCs are also closer to cancer islands in a good outcome, with LC size decreasing as they get closer to cancer cell islands. These results illuminate the significance of the spatial distribution of B cells and LCs within tumors.

Whole-proteome phylogeny of large dsDNA viruses and parvoviruses through a composition vector method related to dynamical language model.

BACKGROUND: The vast sequence divergence among different virus groups has presented a great challenge to alignment-based analysis of virus phylogeny. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignment could not be directly applied to the whole-genome comparison and phylogenomic studies of viruses. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among the alignment-free methods, a dynamical language (DL) method proposed by our group has successfully been applied to the phylogenetic analysis of bacteria and chloroplast genomes. RESULTS: In this paper, the DL method is used to analyze the whole-proteome phylogeny of 124 large dsDNA viruses and 30 parvoviruses, two data sets with large difference in genome size. The trees from our analyses are in good agreement to the latest classification of large dsDNA viruses and parvoviruses by the International Committee on Taxonomy of Viruses (ICTV). CONCLUSIONS: The present method provides a new way for recovering the phylogeny of large dsDNA viruses and parvoviruses, and also some insights on the affiliation of a number of unclassified viruses. In comparison, some alignment-free methods such as the CV Tree method can be used for recovering the phylogeny of large dsDNA viruses, but they are not suitable for resolving the phylogeny of parvoviruses with a much smaller genome size.

Proper distance metrics for phylogenetic analysis using complete genomes without sequence alignment.

A shortcoming of most correlation distance methods based on the composition vectors without alignment developed for phylogenetic analysis using complete genomes is that the "distances" are not proper distance metrics in the strict mathematical sense. In this paper we propose two new correlation-related distance metrics to replace the old one in our dynamical language approach. Four genome datasets are employed to evaluate the effects of this replacement from a biological point of view. We find that the two proper distance metrics yield trees with the same or similar topologies as/to those using the old "distance" and agree with the tree of life based on 16S rRNA in a majority of the basic branches. Hence the two proper correlation-related distance metrics proposed here improve our dynamical language approach for phylogenetic analysis.

Multi-panel immunofluorescence analysis of tumor infiltrating lymphocytes in triple negative breast cancer: Evolution of tumor immune profiles and patient prognosis.

The evolutionary changes in immune profiles of triple negative breast cancer (TNBC) are not well understood, although it is known that immune checkpoint inhibitors have diminished activity in heavily pre-treated TNBC patients. This study was designed to characterize immune profile changes of longitudinal tumor specimens by studying immune subsets of tumor infiltrating lymphocytes (TILs) in paired primary and metastatic TNBC in a cohort of "poor outcome" (relapsed within 5 years) patients. Immune profiles of TNBCs in a cohort of "good outcome" (no relapse within 5 years) patients were also analyzed. Immune subsets were characterized for CD4, CD8, FOXP3, CD20, CD33, and PD1 using immuno-fluorescence staining in stroma, tumor, and combined stroma and tumor tissue. TIL subsets in "good outcome" versus "poor outcome" patients were also analyzed. Compared with primary, metastatic TNBCs had significantly lower TILs by hematoxylin and eosin (H&E) staining. Stromal TILs (sTILs), but not tumoral TILs (tTILs) had significantly reduced cytotoxic CD8+ T cells (CTLs), PD1+ CTLs, and total PD1+ TILs in metastatic compared with matched primary TNBCs. Higher PD1+ CTLs, PD1+CD4+ helper T cells (PD1+TCONV) and all PD1+ T cells in sTILs, tTILs and total stromal and tumor TILS (s+tTIL) were all associated with better prognosis. In summary, TIL subsets decrease significantly in metastatic TNBCs compared with matched primary. Higher PD1+ TILs are associated with better prognosis in early stage TNBCs. This finding supports the application of immune checkpoint inhibitors early in the treatment of TNBCs.

Resident memory CD8+ T cells within cancer islands mediate survival in breast cancer patients.

CD8+ tumor-infiltrating lymphocytes (TILs) correlate with relapse-free survival (RFS) in most cancer types, including breast cancer. However, subset composition, functional status, and spatial location of CD8+ TILs in relation to RFS in human breast tumors remain unclear. Spatial tissue analysis via quantitative immunofluorescence showed that infiltration of CD8+ T cells into cancer islands was more significantly associated with RFS than CD8+ T cell infiltration into either tumor stroma or total tumor. Localization into cancer islands within tumors is mediated by expression of the integrin CD103, which is a marker for tissue-resident memory T cells (TRMs). Analysis of fresh tumor samples revealed that CD8+ TRMs are functionally similar to other CD8+ TILs, suggesting that the basis of their protective effect is their spatial distribution rather than functional differences. Indeed, CD103+ TRMs, as compared with CD103-CD8+ TILs, are enriched within cancer islands, and CD8+ TRM proximity to cancer cells drives the association of CD8+ TIL densities with RFS. Together, these findings reveal the importance of cancer island-localized CD8+ TRMs in surveillance of the breast tumor microenvironment and as a critical determinant of RFS in patients with breast cancer.

Composition vector method based on maximum entropy principle for sequence comparison.

The composition vector (CV) method is an alignment-free method for sequence comparison. Because of its simplicity when compared with multiple sequence alignment methods, the method has been widely discussed lately; and some formulas based on probabilistic models, like Hao's and Yu's formulas, have been proposed. In this paper, we improve these formulas by using the entropy principle which can quantify the nonrandomness occurrence of patterns in the sequences. More precisely, existing formulas are used to generate a set of possible formulas from which we choose the one that maximizes the entropy. We give the closed-form solution to the resulting optimization problem. Hence, from any given CV formula, we can find the corresponding one that maximizes the entropy. In particular, we show that Hao's formula is itself maximizing the entropy and we derive a new entropy-maximizing formula from Yu's formula. We illustrate the accuracy of our new formula by using both simulated and experimental data sets. For the simulated data sets, our new formula gives the best consensus and significant values for three different kinds of evolution models. For the data set of tetrapod 18S rRNA sequences, our new formula groups the clades of bird and reptile together correctly, where Hao's and Yu's formulas failed. Using real data sets with different sizes, we show that our formula is more accurate than Hao's and Yu's formulas even for small data sets.

Minocycline-induced drug hypersensitivity syndrome followed by multiple autoimmune sequelae.

BACKGROUND: Drug hypersensitivity syndrome (DHS) is a severe, multisystem adverse drug reaction that may occur following the use of numerous medications, including anticonvulsants, sulfonamides, and minocycline hydrochloride. Long-term autoimmune sequelae of DHS have been reported, including hypothyroidism. OBSERVATIONS: A 15-year-old female adolescent developed DHS 4 weeks after starting minocycline therapy for acne vulgaris. Seven weeks later she developed autoimmune hyperthyroidism (Graves disease), and 7 months after discontinuing minocycline therapy she developed autoimmune type 1 diabetes mellitus. In addition, she developed elevated titers of several markers of systemic autoimmune disease, including antinuclear, anti-Sjögren syndrome A, and anti-Smith antibodies. CONCLUSIONS: Minocycline-associated DHS may be associated with multiple autoimmune sequelae, including thyroid disease, type 1 diabetes mellitus, and elevated markers of systemic autoimmunity. Long-term follow-up is needed in patients with DHS to determine the natural history of DHS-associated sequelae.