Posts Tagged ‘cancer recurrence’

Renal tumor macrophages linked to recurrence are identified using single-cell protein activity analysis

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

When malignancy returns after a period of remission, it is called a cancer recurrence. After the initial or primary cancer has been treated, this can happen weeks, months, or even years later. The possibility of recurrence is determined by the type of primary cancer. Because small patches of cancer cells might stay in the body after treatment, cancer might reoccur. These cells may multiply and develop large enough to cause symptoms or cause cancer over time. The type of cancer determines when and where cancer recurs. Some malignancies have a predictable recurrence pattern.

Even if primary cancer recurs in a different place of the body, recurrent cancer is designated for the area where it first appeared. If breast cancer recurs distantly in the liver, for example, it is still referred to as breast cancer rather than liver cancer. It’s referred to as metastatic breast cancer by doctors. Despite treatment, many people with kidney cancer eventually develop cancer recurrence and incurable metastatic illness.

The most frequent type of kidney cancer is Renal Cell Carcinoma (RCC). RCC is responsible for over 90% of all kidney malignancies. The appearance of cancer cells when viewed under a microscope helps to recognize the various forms of RCC. Knowing the RCC subtype can help the doctor assess if the cancer is caused by an inherited genetic condition and help to choose the best treatment option. The three most prevalent RCC subtypes are as follows:

  • Clear cell RCC
  • Papillary RCC
  • Chromophobe RCC

Clear Cell RCC (ccRCC) is the most prevalent subtype of RCC. The cells are clear or pale in appearance and are referred to as the clear cell or conventional RCC. Around 70% of people with renal cell cancer have ccRCC. The rate of growth of these cells might be sluggish or rapid. According to the American Society of Clinical Oncology (ASCO), clear cell RCC responds favorably to treatments like immunotherapy and treatments that target specific proteins or genes.

Researchers at Columbia University’s Vagelos College of Physicians and Surgeons have developed a novel method for identifying which patients are most likely to have cancer relapse following surgery.

The study

Their findings are detailed in a study published in the journal Cell entitled, “Single-Cell Protein Activity Analysis Identifies Recurrence-Associated Renal Tumor Macrophages.” The researchers show that the presence of a previously unknown type of immune cell in kidney tumors can predict who will have cancer recurrence.

According to co-senior author Charles Drake, MD, PhD, adjunct professor of medicine at Columbia University Vagelos College of Physicians and Surgeons and the Herbert Irving Comprehensive Cancer Center,

the findings imply that the existence of these cells could be used to identify individuals at high risk of disease recurrence following surgery who may be candidates for more aggressive therapy.

As Aleksandar Obradovic, an MD/PhD student at Columbia University Vagelos College of Physicians and Surgeons and the study’s co-first author, put it,

it’s like looking down over Manhattan and seeing that enormous numbers of people from all over travel into the city every morning. We need deeper details to understand how these different commuters engage with Manhattan residents: who are they, what do they enjoy, where do they go, and what are they doing?

To learn more about the immune cells that invade kidney cancers, the researchers employed single-cell RNA sequencing. Obradovic remarked,

In many investigations, single-cell RNA sequencing misses up to 90% of gene activity, a phenomenon known as gene dropout.

The researchers next tackled gene dropout by designing a prediction algorithm that can identify which genes are active based on the expression of other genes in the same family. “Even when a lot of data is absent owing to dropout, we have enough evidence to estimate the activity of the upstream regulator gene,” Obradovic explained. “It’s like when playing ‘Wheel of Fortune,’ because I can generally figure out what’s on the board even if most of the letters are missing.”

The meta-VIPER algorithm is based on the VIPER algorithm, which was developed in Andrea Califano’s group. Califano is the head of Herbert Irving Comprehensive Cancer Center’s JP Sulzberger Columbia Genome Center and the Clyde and Helen Wu professor of chemistry and systems biology. The researchers believe that by including meta-VIPER, they will be able to reliably detect the activity of 70% to 80% of all regulatory genes in each cell, eliminating cell-to-cell dropout.

Using these two methods, the researchers were able to examine 200,000 tumor cells and normal cells in surrounding tissues from eleven patients with ccRCC who underwent surgery at Columbia’s urology department.

The researchers discovered a unique subpopulation of immune cells that can only be found in tumors and is linked to disease relapse after initial treatment. The top genes that control the activity of these immune cells were discovered through the VIPER analysis. This “signature” was validated in the second set of patient data obtained through a collaboration with Vanderbilt University researchers; in this second set of over 150 patients, the signature strongly predicted recurrence.

These findings raise the intriguing possibility that these macrophages are not only markers of more risky disease, but may also be responsible for the disease’s recurrence and progression,” Obradovic said, adding that targeting these cells could improve clinical outcomes

Drake said,

Our research shows that when the two techniques are combined, they are extremely effective at characterizing cells within a tumor and in surrounding tissues, and they should have a wide range of applications, even beyond cancer research.

Main Source

Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages


Other Related Articles published in this Open Access Online Scientific Journal include the following:

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc


Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle  and Histone Modulation

Curator: Demet Sag, PhD, CRA, GCP


Artificial Intelligence: Genomics & Cancer


Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond

Curator: Stephen J. Williams, Ph.D.


Deep-learning AI algorithm shines new light on mutations in once obscure areas of the genome

Reporter: Aviva Lev-Ari, PhD, RN


Premalata Pati, PhD, PostDoc in Biological Sciences, Medical Text Analysis with Machine Learning


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Immunopathogenesis Advances in Diabetes and Lymphomas

Larry H Bernstein, MD, FCAP, Curator




 Science team says they’ve taken another step toward a potential cure for diabetes

Wednesday, January 27, 2016 | By John Carroll
Building on years of work on developing new insulin-producing cells that could one day control glucose levels and cure diabetes, a group of investigators led by scientists at MIT and Boston Children’s Hospital say they’ve developed a promising new gel capsule that protected the cells from an immune system assault.

Dr. Jose Oberholzer, a professor of bioengineering at the University of Illinois at Chicago, tested a variety of chemically modified alginate hydrogel spheres to see which ones would be best at protecting the islet cells created from human stem cells.

The team concluded that 1.5-millimeter spheres of triazole-thiomorphine dioxide (TMTD) alginate were best at protecting the cells and allowing insulin to seep out without spurring an errant immune system attack or the development of scar tissue–two key threats to making this work in humans.

They maintained healthy glucose levels in the rodents for 174 days, the equivalent to decades for humans.

“While this is a very promising step towards an eventual cure for diabetes, a lot more testing is needed to ensure that the islet cells don’t de-differentiate back toward their stem-cell states or become cancerous,” said Oberholzer.

Millions of diabetics have effectively controlled the chronic disease with existing therapies, but there’s still a huge unmet medical need to consider. While diabetes companies like Novo ($NVO) like to cite the fact that a third of diabetics have the disease under control, a third are on meds but don’t control it well and a third haven’t been diagnosed. An actual cure for the disease, which has been growing by leaps and bounds all over the world, would be revolutionary.

Their study was published in Nature Medicine.

– here’s the release
– get the journal abstract


Long-term glycemic control using polymer-encapsulated human stem cell–derived beta cells in immune-competent mice

Arturo J Vegas, Omid Veiseh, Mads Gürtler,…, Robert Langer & Daniel G Anderson

Nature Medicine (2016)   http://dx.doi.org:/10.1038/nm.4030

The transplantation of glucose-responsive, insulin-producing cells offers the potential for restoring glycemic control in individuals with diabetes1. Pancreas transplantation and the infusion of cadaveric islets are currently implemented clinically2, but these approaches are limited by the adverse effects of immunosuppressive therapy over the lifetime of the recipient and the limited supply of donor tissue3. The latter concern may be addressed by recently described glucose-responsive mature beta cells that are derived from human embryonic stem cells (referred to as SC-β cells), which may represent an unlimited source of human cells for pancreas replacement therapy4. Strategies to address the immunosuppression concerns include immunoisolation of insulin-producing cells with porous biomaterials that function as an immune barrier56. However, clinical implementation has been challenging because of host immune responses to the implant materials7. Here we report the first long-term glycemic correction of a diabetic, immunocompetent animal model using human SC-β cells. SC-β cells were encapsulated with alginate derivatives capable of mitigating foreign-body responses in vivo and implanted into the intraperitoneal space of C57BL/6J mice treated with streptozotocin, which is an animal model for chemically induced type 1 diabetes. These implants induced glycemic correction without any immunosuppression until their removal at 174 d after implantation. Human C-peptide concentrations and in vivo glucose responsiveness demonstrated therapeutically relevant glycemic control. Implants retrieved after 174 d contained viable insulin-producing cells.

Subject terms: Regenerative medicine  Type 1 diabetes

Figure 1: SC-β cells encapsulated with TMTD alginate sustain normoglycemia in STZ-treated immune-competent C57BL/6J mice.close

(a) Top, schematic representation of the last three stages of differentiation of human embryonic stem cells to SC-β cells. Stage 4 cells (pancreatic progenitors 2) co-express pancreatic and duodenal homeobox 1 (PDX-1) and NK6 homeobox 1…


Potential Cure for Diabetes Discovered  
http://www.rdmag.com/news/2016/01/potential-cure-diabetes-discovered   01/27/2016

Two new scientific papers published on Monday demonstrated tools that could result in potential therapies for patients diagnosed with type 1 diabetes, a condition in which the immune system limits the production of insulin, typically in adolescents.  See —

Bubble Technique Could Create Type 1 Diabetes Therapy


Two new scientific papers published on Monday demonstrated tools that could result in potential therapies for patients diagnosed with type 1 diabetes, a condition in which the immune system limits the production of insulin, typically in adolescents.

Previous treatments for this disease have involved injecting beta cells from dead donors into patients to help their pancreas generate healthy-insulin cells, writes STAT. However, this method has resulted in the immune system targeting these new cells as “foreign” so transplant recipients have had to take immune-suppressing medications for the rest of their lives.

The first paper published in the journal Nature Biotechnology explained how scientists analyzed a seaweed extract called alginate to gauge its effectiveness in supporting the flow of sugar and insulin between cells and the body. An estimated 774 variations were tested in mice and monkeys in which results indicated only a handful could reduce the body’s response to foreign invaders, explains STAT.

The other paper in the journal Nature Medicine detailed a process where scientists developed small capsules infused with alginate and embryonic stem cells. A six-month observation period revealed this “protective bubble” technique “began to produce insulin in response to blood glucose levels” after transplantation in mice subjects with a condition similar to type 1 diabetes, reports Gizmodo.

Essentially, this cured the mice of their diabetes, and the beta cells worked as well as the body’s own cells, according to the researchers. Human trials could still be a few years away, but this experiment could yield a safer alternative to insulin injections.


Combinatorial hydrogel library enables identification of materials that mitigate the foreign body response in primates

Arturo J Vegas, Omid Veiseh, Joshua C Doloff, et al.

Nature Biotechnology (2016)    http://dx.doi.org:/10.1038/nbt.3462

The foreign body response is an immune-mediated reaction that can lead to the failure of implanted medical devices and discomfort for the recipient1, 2, 3, 4, 5, 6. There is a critical need for biomaterials that overcome this key challenge in the development of medical devices. Here we use a combinatorial approach for covalent chemical modification to generate a large library of variants of one of the most widely used hydrogel biomaterials, alginate. We evaluated the materials in vivo and identified three triazole-containing analogs that substantially reduce foreign body reactions in both rodents and, for at least 6 months, in non-human primates. The distribution of the triazole modification creates a unique hydrogel surface that inhibits recognition by macrophages and fibrous deposition. In addition to the utility of the compounds reported here, our approach may enable the discovery of other materials that mitigate the foreign body response.


Video 1: Intravital imaging of 300 μm SLG20 microcapsules.

Video 2: Intravital imaging of 300 μm Z2-Y12 microcapsules.

Video 3: NHP Laparoscopic procedure for the retrieval of Z2-Y12 spheres.


Clinical Focus on Follicular Lymphoma: CAR T-Cells Active in Relapsed Blood Cancers

MedPage Today

CAR T-Cells Active in Relapsed Blood Cancers

Complete responses in half of patients

by Charles Bankhead

Patients with relapsed and refractory B-cell malignancies have responded to treatment with modified T-cells added to conventional chemotherapy, data from an ongoing Swedish study showed.

Six of the first 11 evaluable patients achieved complete responses with increasing doses of chimeric antigen receptor (CAR)-modified T-cells that target the CD19 antigen, although two subsequently relapsed.

Five of the six responding patients received preconditioning chemotherapy the day before CAR T-cell infusion, in addition to chemotherapy administered up to 90 days before T-cell infusion to reduce tumor-cell burden. The remaining five patients received only the earlier chemotherapy, according to a presentation at the inaugural International Cancer Immunotherapy Conference in New York City.

“The complete responses in lymphoma patients despite the fact that they received only low doses of preconditioning compared with other published data surprised us,” Angelica Loskog, PhD, of Uppsala University in Sweden, said in a statement. “The strategy of both providing tumor-reductive chemotherapy for weeks prior to CAR T-cell infusion combined with preconditioning just before CAR T-cell infusion seems to offer promise.

CAR T-cells have demonstrated activity in a variety of studies involving patients with B-cell malignancies. Much of the work has focused on patients with leukemia, including trials in the U.S. B-cell lymphomas have proven more difficult to treat with CAR T-cells because the diseases are associated with higher concentration of immunosuppressive cells that can inhibit CAR T-cell activity, said Loskog. Moreover, blood-vessel abnormalities and accumulation of fibrotic tissue can hinder tumor penetration by therapeutic T-cells.

Each laboratory has its own process for modifying T-cells. Loskog and colleagues in Sweden and at Baylor College of Medicine in Houston have developed third-generation CAR T-cells that contain signaling domains for CD28 and 4-1BB, which act as co-stimulatory molecules. In preclinical models, third-generation CAR T-cells have demonstrated increased activation and proliferation in response to antigen challenge. Additionally, they have chosen to experiment with tumor burden-reducing chemotherapy, a preconditioning chemotherapy to counter the higher immunosuppressive cell count in lymphoma patients.

Loskog reported details of an ongoing phase I/IIa clinical trial involving patients with relapsed or refractory CD19-positive B-cell malignancies. Altogether, investigators have treated 12 patients with increasing doses (2 x 107 to 2 x 108 cells/m2) of CAR T-cells. One patient (with mixed follicular/Burkitt lymphoma) has yet to be evaluated for response. The remaining 11 included three patients with diffuse large B-cell lymphoma (DLBCL), one with follicular lymphoma transformed to DLBCL, two with chronic lymphocytic leukemia, two with mantle cell lymphoma, and three with acute lymphoblastic leukemia.

All of the patients with lymphoma received standard tumor cell-reducing chemotherapy, beginning 3 to 90 days before administration of CAR T-cells. Beginning with the sixth patient in the cohort, patients also received preconditioning chemotherapy (cyclophosphamide/fludarabine) 1 to 2 days before T-cell infusion to reduce the number and activity of immunosuppressive cells.

Cytokine release syndrome is a common effect of CAR T-cell therapy and occurred in several patients treated. In general, the syndrome has been manageable and has not interfered with treatment or response to the modified T-cells.

On the basis of the data produced thus far, the investigators have proceeded with patient evaluation and enrollment. They have already begun cell production for the next patient that will be treated with autologous CAR T-cells.

Although laboratories have their own cell production techniques, the treatment strategy has broad applicability to the treatment of B-cell malignancies, said Loskog.

“The results using different CARs and different techniques for manufacturing them is very similar in the clinic, in terms of initial complete response,” she told MedPage Today. “By using 4-1BB as a co-stimulator in the CAR intracellular region, it seems possible to achieve long-term complete responses in some patients. However, preconditioning of the patients with chemotherapy to reduce the regulatory immune cells seems crucial for effect.”

In an effort to manage the effect of patients’ immunosuppressive cells, the investigators have begun studying each the immune profile before and after treatment. Preliminary results suggest that the population of immunosuppressive cells increases over time, which has the potential to interfere with CAR T-cell responses.

“Especially for lymphoma, it may be crucial to deplete such cells prior to CAR infusion,” said Loskog. “It may even be necessary with supportive treatment for some time after CAR T-cell infusion. A supportive treatment needs to specifically regulate the suppressive cells while sparing the effect of CARs.”

The immunotherapy conference is jointly sponsored by the American Association for Cancer Research, the Cancer Research Institute, the Association for Cancer Immunotherapy, and the European Academy of Tumor Immunology.


PET-CT Best for FL Response Assessment

PET-CT associated with better progression-free and overall survival rates in follicular lymphoma.

Kay Jackson

PET-CT (PET) rather than contrast-enhanced CT scanning should be considered the new gold standard for response assessment after first-line rituximab therapy for high-tumor burden follicular lymphoma (FL), a pooled analysis of a central review in three multicenter studies indicated.

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Glioma, Glioblastoma and Neurooncology

Curator: Larry H. Bernstein, MD, FCAP



A Korean and American team profiles gene expression patterns in glioblastoma tumors in a PLOS One paper. The researchers scrutinized gene expression patterns in 43 tumor samples obtained from 28 individuals with glioblastoma — a set that included more than a dozen paired primary and recurrent tumors. They saw two transcriptional clusters in the glioblastoma tumors: a G1 sub-type containing tumors with marked expression of proliferation-related genes and a G2 sub-type with gene expression patterns resembling those in neurons. And by folding in information on expression characteristics of the recurrent tumors, the group gained clues to the types of drug resistance typically displayed by each sub-type.

Recurrent Glioblastomas Reveal Molecular Subtypes Associated with Mechanistic Implications of Drug-Resistance

So Mee Kwon, Shin-Hyuk Kang, Chul-Kee Park, Shin Jung, Eun Sung Park, Ju Seog Lee, Se-Hyuk Kim, Hyun Goo Woo
PLoS ONE  2015; 10(10):e0140528   http://dx.doi.org:/10.1371/journal.pone.0140528

Previously, transcriptomic profiling studies have shown distinct molecular subtypes of glioblastomas. It has also been suggested that the recurrence of glioblastomas could be achieved by transcriptomic reprograming of tumors, however, their characteristics are not yet fully understood. Here,to gain the mechanistic insights on the molecular phenotypes of recurrent glioblastomas, gene expression profiling was performed on the 43 cases of glioblastomas including 15 paired primary and recurrent cases. Unsupervised clustering analyses revealed two subtypesof G1 and G2, which were characterized by proliferation and neuron-like gene expression traits, respectively. While the primary tumors were classified as G1 subtype, the recurrent glioblastomas showed two distinct expression types. Compared to paired primary tumors, the recurrent tumors in G1 subtype did not show expression alteration. By contrast, the recurrent tumors in G2 subtype showed expression changes from proliferation type to neuron-like one. We also observed the expression of stemness related genes in G1 recurrent tumors and the altered expression of DNA-repair genes(i.e., AURK, HOX, MGMT, and MSH6) in the G2 recurrent tumors, which might be responsible for the acquisition of drug resistance mechanism during tumor recurrence in a subtype-specific manner. We suggest that recurrent glioblastomas may choose two different strategies for transcriptome reprogramming to escape the chemotherapeutic treatment during tumor recurrence. Our results might be helpful to determine personalized therapeutic strategy against heterogeneous glioma recurrence.

Glioblastoma is the most aggressive and frequent primary brain tumor with dismal prognosis [1,2].The incurable outcomeofthe glioblastoma is largely due to high recurrence rate even after total resection of glioblastoma mass [2,3]. Also, highly infiltrative characteristics of the glioblastoma make it impossible to dissect tumor tissues completely and the majority of glioblastomas are destined to recur less than 6 months after surgical resection [4,5].Therefore, new diagnostic and therapeutic strategies for tumor recurrence might be required to improve clinical outcomes of patients. Previously, numerous genomic profiling studies have addressed the marked heterogeneity of glioblastomas [6–9]. Particularly, The Cancer Genome Atlas(TCGA) project recognized four distinct molecular subtypes of proneural, neural, classical, and mesenchymal, which are different inresponseto aggressive therapies [10,11]. In addition, an earlier study has shown that about one third (8 out of 26) of the recurrent glioblastomas shifted their subtypes toward mesenchymal subtype [12]. However,there is a conflicting observation that the molecular subtypes are not altered by recurrence [11],remaining the mechanisms for tumor recurrence still unveiled. With this concern, in the present study, we re-evaluated the alteration of the molecular phenotypes of recurrent glioblastomas bycomparing geneexpression profiles ofpairedprimary and recurrent glioblastomas. We could identify two different modes of transcriptome reprogramming during recurrence of glioblastomas, and which implied subtype-specific mechanisms for the acquisition of drug-resistance by tumor recurrence.Our analysis may provide new mechanistic and clinical insights on the recurrent glioblastoma management.

 Gene ExpressionProfiling Total RNA was extracted from frozen tumor section (10 to 15 mg: mirVanaTM miRNA isolation Kit, Ambion, AM1560) based on the manufacturer’s guideline. The quantification of RNA was performed using the Nanodrop ND-1000 spectrophotometer (Thermo-Fisher) and the quality of total RNA was evaluated using the RNA 6000 nano kit (Agilent Technologies, 5067–1513) and the Agilent 2100 Bioanalyzer (Agilent Technologies). Cut off value of the integrity of RNAs used in RNA amplification is over 7.0 in the RIN level. For microarray experiments, five hundred (500) ng of total RNA per sample was used for complement RNA (cRNA) production by the Illumina TotalPrep RNA amplification kit (Ambion, IL1791) according to the provided protocol. A total of 750 ng cRNA was used for hybridization toa human HT12-v4 Illumina Beadchip gene expression array (Illumina) according to the manufacturer’s protocol. The arrays were scanned and fluorescence signals obtained using Illumina bead Array Reader confocal scanner, and obtained the intensity datawith Genome Studio software. Raw data were normalized by applying log 2 transformation, quantile normalization, and gene and array centering. All of the data processing was performed using the R/Bioconductorpackages. For validation analysis, two independent gene expression data of REMBRANDT [14] and TCGA[11] were obtained fromtheir websites, respectively. To integrate different dataset, preprocessing ofeach data setwas applied including log2 transformation, quantile normalization, and gene and array centering.

Classification of subtypes For subtype prediction, three independent methods of unsupervised hierarchical clustering, consensus clustering[15], and nearest template prediction (NTP) [16] were applied. For consensus clustering, hierarchical clustering with the distance metric by Pearson(1—Pearson correlation) was used. For K ranging from 2 to 6, hierarchical clustering was run over 10,000 iterations with a sub-sampling ratio of 0.8 for estimating the consensus matrix. For the purpose of visualization and cluster identification, hierarchical clustering with the Pearson (1— Pearson correlate) distance metric and the average linkage option was applied to the estimated consensus matrix. NTP analysis was performed using Gene Pattern software (http:// www.genepattern.org). The classifiers for the four class subtypes in TCGA dataset [11] were applied and annotated with the numeric code representing the unique subtype that each gene represents (1, 2, 3, 4, 5 for proneural, neural, classical, mesenchymal, and unclassified subtypes) with statistical significance of Bonferroni p value < 0.05 with 1,000 resampling bootstrap test.

Gene expression profiling reveals two subtypes of recurrent glioblastoma. A total of 28 glioblastoma patients were enrolled for this study. The patients were treated with temozolomide (TMZ) after surgical resection. Overall, the progression free survival time (PFS) of the patients was ranged from 5 to 62.4 months, and the median PFS and median overall survival time were 10.75 and 20.50 months, respectively. Detailed clinical information of the patients were summarized inTable 1. To characterize the gene expression patterns of the primary and recurrent glioblastomas, we performed gene expression profiling of the 43 tumor tissues which included the 15 cases of paired primary and recurrent glioblastomas and 13 unpaired tumor tissues. First, to demonstrate the overall gene expression patterns, unsupervised clustering analysis was performed using most variable 4,650 genes with standard deviation(S.D.) greater than 0.5.This revealed two distinct clusters of G1(n=32) and G2(n=11) subtypes (Fig 1A, top). The consistency of the cluster was validated by applying consensus clustering algorithm implemented in Gene pattern software, which could confirm the robustness of the two clusters showing the same two clusters (Fig 1B).

Fig1. Gene expression profiling of primary and recurrent glioblastomas. (A)Unsupervised clustering analysis showed two distinct clusters of G1 and G2 tumors(top). The primary and recurrent glioblastomas were marked with dark blue and dark orange color, respectively (bottom). The 15 paired primary and recurrent glioblastomas were marked based on the defined two clusters, G1 and G2. (B )Heatmap shows the consistency of the consensus clustering analysis with k=2.  http://dx.doi.org:/10.1371/journal.pone.0140528.g001

When we examined the distribution of primary and recurrent glioblastomas from the cluster result, most of the primary glioblastomas were classified into the G1 cluster. However, the recurrent glioblastomas were found in both G1 (n=10) and G2 (n=8) clusters. Recurrent glioblastomas were more frequent in G2 cluster with statistical significance (P =0.031,odd ratio =5.60, Fisher’s exact test), implying the enriched expression of recurrence-related genes in the G2 tumors. To address the functional characteristics of the clusters, we identified differentially expressed genes between G1 and G2 tumors as subtype classifiers (i.e.,94 up-regulated and 318 down-regulated genes, respectively) byapplying permutationt-test (P < 0.001) and fold differences greater than two (S1 Table).The gnes expressed in the G1 cluster were significantly enriched with cell cycle-related gene functions such as M phase, chromosome segregation, cell cycle regulation, and DNA metabolic process, while the genes expressed in the G2 cluster were enriched with neuron development-related genes such as neuron projection morphogenesis, regulation of cell projection organization, ion homeostasis(Fig 2). Comparing to the previous TCGA subtypes [10,11],  this result suggests that theG1 tumors are similar to proliferation type and the G2 tumors are similar to neuronal type, respectively. The expressionof neuronal differentiation-related genes might be a key feature of the transcriptomic switch from primary G1 tumors to the paired recurrent G2 tumors. Next,we compared the gene expression changes between the 15 paired primary and recurrent glioblastomas. Remarkably, we found two distinct behaviors of gene expressions in the recurrent glioblastomas compared to those in the paired primary tumors (Fig 1A, bottom). A totalof 7 outof 15 recurrent glioblastomas showed the cluster migration from G1 to G2 subtype. The other 6 recurrent tumors resided in the same cluster with the paired primary tumors. Exceptionally, only one case of recurrent tumor showed opposite migration from G2 to G1 cluster, and one caseof G2 recurrent tumor did not migrate to other cluster. These results suggest that the recurrent glioblastomas might have at least two distinct patterns of molecular changes after being recurred. The G1 type recurrent tumors (G1R,  n=6) showed no subtype migration, while the G2 type recurrent tumors (G2R, n =7) showed subtype migration from G1 to G2 subtype (see S2 Table).

Table 1.  http://dx.doi.org:/10.1371/journal.pone.0140528.t001

Validation of the subtype classifiers of glioblastoma using independent datasets

Fig 2. Functional characteristics of G1 and G1 subtypes. (A-B) The enriched GO terms of the subtype classifiers are indicated. The significance of the enrichment is plotted as value of—log10 (p-value). (C-D) Unsupervised hierarchical clustering analysis showed the conserved expression patterns of the classifiers in independent dataset, REMBRANDT (C) and TCGA (D). (E) Gene expression similarity with the four subtypes of TCGA are evaluated by applying three different methods of consensus clustering, unsupervised clustering, and nearest template prediction(NTP) as described in the Materials and Methods. The primary and recurrent tumors are indicated with different colors. The predicted four classes of proneuronal, mesenchymal, classical, neural type are indicated. Unclassified tumors are indicated as rest.  http://dx.doi.org:/10.1371/journal.pone.0140528.g002

As shown above, the G1 and G2 classification is clearly associated with the expression migration during tumor recurrence. To further validate the robustness and the significance of our classification, we examined the expression pattern ofour subtype classifiers in the independent two datasets of REMBRANT [14] and TCGA [10]. We could observe that the expressions of the subtype classifiers were well conserved in both data sets stratifying G1-like and G2-like subtypes, respectively (Fig 2C and 2D). This result indicated that our subtype classifiers were well conserved independent of patient cohorts and/or data platforms, and might be useful in predicting the subtypes of tumor recurrence. However, when we evaluated the clinical outcomes of the G1-like and G2-like subtypes by Kaplan-Meir plot analysis, there was no significant difference of overall survival between the groups (S1 Fig). This may indicate that our classification does not represent a prognostic sub-classification, but a classification for different mode of mechanistic pathways for tumor recurrence. Confirming the conserved expression of the classifiers in the independent datasets, we next evaluated the relationship between our subtypes and the TCGA subtypes of mesenchymal, proneural, classical,and neural type [11]. Prediction of the subtypes was performed on the integrated data set of TCGA and ours using the overlapped genes with variable expressions (n=4,378, S.D. > 0.5). By applying three different classification methods of unsupervised hierarchical clustering, consensus clustering, and nearest template prediction (NTP) on the integrated data set (for details  see the Materials and Methods), we could successfully re-identify the four subtypes, respectively (S2 Fig and S3 Table). Unsupervised clustering analysis with the integrated data set could reveal four classes which were compatible with the previous TCGA subtypes (S2A Fig). Consensus clustering analysis also showed four distinct expression subtypes (S2B and S2C Fig). When we compared these classification results with our subtypes of G1 and G2,we could observe that the G2 tumors had similar expression pattern to that of neural subtype,while the G1 tumor was similar to those of other three groups of mesenchymal, proneuronal, and classical subtypes (Fig 2E). This result was consistent with the resul tof GO analysis (seeFig 2B). Taken together, we could suggest that the recurrent glioblastomas have at least two different patterns of G1 and G2 subtype. The G2 subtype is similar to neural subtype, while the G1 subtype is likely to be mixed with the other types.

Expression of stemness and drug-resistance-related genes might be involved in the subtypes of recurrence glioblastomas

To further gain an insight on the differential molecular determinants in the G1 and G2clusters, a network analysis was applied by using GeneMANIA software (version 3.2)[17]. This revealed CDK1 (cyclin-dependent kinase 1), AURKA (aurorakinase A), and AURKB (aurorakinase B) as key hub regulators for G1 tumors(Fig3 A). Indeed, AURKA is well known to play an important function in tumor development, progression,and patient survival [18–21]. Moreover, AURKA is strongly correlated with survival of glioma stem cells[22]. AURKB has also been associated with TMZ susceptibility [23]and aggressive outcomes of glioblastomas [24]. CDK1 isalso known to play regulatory roles in the self-renewal of mouse embryonic stemcells [25] as well as for cell survival of glioblastoma [26].These findings may support that the selective targeting of these genes for G1 recurrent tumors might be beneficial in the clinic. In addition, when we performed geneset enrichment analysis, the G1 tumors showed significant enrichment of stemness-related genes, ES1 (ES=0.526, P-value < 0.001, False Discovery Rate(FDR) < 0.001) which has been identified previously elsewhere [27]. Among the ES1 genes, HMMR (Hyaluronan-mediated motility receptor) was top ranked (Fig 3B), suggesting its pivotal role in the stem cell-like characteristics of G1 tumors. HMMR has recently been reported to express in the gliomas and to play a crucial role in self-renewal and tumorigenic potential of glioblastoma stem cells[28]. Supporting this, we also observed that HOX genes were enriched and differentially expressed (ES =0.704, P-value < 0.001,FDR < 0.001) in the G1 tumors (Fig 3C), which have been notified as “self- renewal”-associated genes in gliomas [29,30]. Of these, HOXA10 showed marked over-expression in G1 tumors (Fig 3D). HOXA10 has been known to involve in homologous recombinant DNA repair pathway [31], playing a key role inTMZ resistance in glioblastomas [29]. Congruent with these findings, the G1 tumors showed significant enrichment of the DNA_REPAIR genes (ES=0.686, P value < 0.001, FDR < 0.001, S3A Fig). Therefore, we could suggest that resistance tothe chemotherapeutic agent may be attributed by the inherited stem-cell-like characteristics of the G1 tumors. The self-renewal properties and the activated DNA repair system (e.g.,HOXA10) might be responsible for the relapseof the recurrent G1 glioblastomas after resection and adjuvant treatment.

 Fig 3. Expression of stemness-like traits in G1 recurrent tumors. (A) Network analysis using G1 signature genes reveals the CDK and AURK as the key hub genes (top). Pathway(light blue) and physical interactions (light pink) are indicated with different colors. The heatmap of the expression of the keyhub genes (CDK1, AURKA, AURKB, HMMR, RAD45L) are plotted (bottom). (B) The GSEA result show the enrichment of the ES1 signature (top) and the expression of the top 20 differentially expressed genes are shown (bottom). (C) The plots showed the enrichment scores (ES) for the HOX_GENE signature (top) and their expression heatmap is shown(bottom).(D) The expression of HOX10a in G1 and G2 tumors are plotted. Statistical significance is calculated using Welch TwoSampleT-test. http://dx.doi.org:/10.1371/journal.pone.0140528.g003

Differential expression of MGMT and MSH6 genes in the subtypes of recurrentglioblastomas

As the glioblastoma subtypes were associated with drug-resistance, we hypothesized that different tactics to escape the chemotherapeutics might be involved in recurrent glioblastomasof each subtype. TMZ has been currently emerged as a new standard regimen in glioblastoma. Previous studies have demonstrated that the therapeutic effects of TMZ might be restricted to  the patients whose MGMT (O-6-methylguanine–DNA methyltransferase) promoters were methylated [32,33], which might be due to the MGMT repairing DNA-alkylated adducts could diminish the TMZ cytotoxicity induced by O6-methylguanine-DNA adducts [34]. In addition, it has been suggested that MGMT-independent DNA repair pathway could affect TMZ effectiveness [35–37].Indeed, it has been demonstrated that the activation of DNA mismatch repair (MMR) system could promote TMZ resistance [35–38].With respect to this, we examined the expression of both MGMT and MMR genes (i.e., MLH1,  MSH2, and MSH6). MGMT was significantly up-regulated in the G2 subtype than theG1 subtype  (P=1 .145 x 10−5,Fig  4A). By contrast, the MSH6 expression was significantly down-regulated inG2 subtype implying their decreased activity of MMR pathway (P=4 .45 x10−3). When we compared the paired primary and recurrent tumors, marked change of MGMT expression could be observed in recurrent G2 (G2R) but not in recurrent G1 (G1R) tumors (P<0.005, Fig 4B, left). Vice versa, MSH6 showed significant lower expression in the G2R tumors compared to the G1R tumors (P=0 .0098). Taken together, our results strongly suggest that the G2 but not G1 tumors may acquire TMZ tolerance via altered expression of MGMT and MMR pathway genes. As the G2 subtype showed similar expression pattern with neural subtype (see Fig 2),we next compared the expression of MGMT and MSH6 among the subtypes of TCGA data. As expected, the neural subtype showed significant overexpression of MGMT
(P = 1 .18x 10−3, Fig 4C,  left) and down-expression of MSH6 (P=1 .34x 10−2, Fig 4C, left) compared to the other subtypes, respectively. When we compared the four subtypes of TCGA, the neural subtype showed the highest expression ofMGMT and the lowest expression of MSH6 compared to other subtypes (S4A and S4B Fig). These resulst may support our result showing the subtype specific mechanism of TMZ resistance

Fig 4. Differential expressionof MGMTand MSH6 genes between G1 and G2tumors. (A) The expressions of MGMT (left) and MSH6 (right) were evaluated in G1 and G2 tumors. (B) Paired comparison of MGMT (left) and MSH6 (right) expressions between primary (P) and paired recurrent(R) tumors. Traced lines indicate the expression changes between primary and paired recurrent tumors. (C) The comparison of MGMT (left) and MSH6 (right) expressions between the neural subtype (N) and the other subtypes. The statistical significance is evaluated using Welch Two Sample t-test (*significantatP<0.05,**significantat P<0.005).


In this study, by performing integrative gene expression profile analyses, we have demonstrated that there are two distinct subtypes of transcriptomic reprogramming during recurrence of glioblastomas. From the results,we could suggest that the distinct two different mechanisms might be involved in for the TMZ resistance in each subtype.The G1 recurrent tumors had similar expression with the paired primary tumors, which express stemness and DNA-repair related genes. By contrast, the G2 recurrent tumors showed gene expression migration acquiring neuron-like traits. This may reflect the two different mechanisms might be involved in the acquisition of the recurrence phenotypes. Further interrogation has revealed the differential expression of MGMT and MSH6 between the subtypes (Fig 4B), which suggested the involvement of distinct mechanisms for TMZ resistance during recurrence of glioblastomas. The G1 tumors expressed the stem cell-related “self-renewal” signature including HOX_genes, stemness genes (ES1), CDK, and AURKA/B genes in both the paired primary and recurrent tumors. The G1 recurrent tumors didn’t show subtype migration by recurrence, indicating that the initial gene expression profiles were remained without change even after treatment and disease progression. Thus,the expression of stemness genes might be a possible explanation for the TMZ resistance in G1 recurrent tumors. On the other hand, the G2 tumors showed significant differential expression of MGMT and MSH6 genes compared to the primary tumors. As an  underlying mechanism for the TMZ resistance, it has been addressed that MGMT protein removes the methyl orchloroethyl damage at the O6 position of guanine [40]. In addition,the mismatch repair system (MMR) is also considered to be involved in theTMZ resistance, amending the DNA damage and base mismatches [41]. MMR recognizes unrepaired O6-methylated guanine adduct and induces cytotoxicity. Thus, inactivation of MMR may induce TMZ tolerance [34, 38]. In this regards, the G2 tumors showed the acquired expressions of MGMT and inactivation of MMR system genes (MSH6), which might be responsible for the acquisition of TMZ resistance. It is interesting to find that the G2 recurrent tumors acquire neuron-like features. Indeed, we have previously demonstrated the xenografted tumors in the brain acquire neuron-like expression traits,mimicking neurogenesis during development [42]. The results showed the connection of tumors with brain microenvironment such as neighbor astrocytes can give rise to chemo-resistant nature of brain metastatic tumors. Congruently, our data strongly support that brain environment may contribute to the neuron-like transcriptional reprogramming in G2 recurrent tumors. In addition, we have shown in theprevious study the high concordance between promoter methylation and gene expression profiles, suggesting the contribution of epigenetic events to transcriptome reprogramming [42]. This raises a possibility that the acquisitionof neuron-like trait in the G2 subtype might be related with the methylation reprogramming. However,we could not observe from TCGA data the associations between methylation status and the tumor recurrence subtypes. To address the roles of epigenetic reprogramming to the transcriptomic reprogramming during glioma recurrence accurately, further large scale studies with detailed methylation profiling might be needed.

Current and Emerging Treatments for Brain Metastases

Review ArticleApril 15, 2015Oncology Journal, Brain Tumors

ONCOLOGY  2015; 29(4)
By , , , , and
Conventional methods for treating brain metastasis, such as surgery, WBRT, and SRS, each compete with and complement one another. A plethora of recent studies have helped define and expand the utility of these tools.


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