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Posts Tagged ‘choice of treatment’


Glioma, Glioblastoma and Neurooncology

Curator: Larry H. Bernstein, MD, FCAP

 

Introduction

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).
http://dx.doi.org:/10.1371/journal.pone.0140528.g004

Discussion

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.

http://www.cancernetwork.com/brain-tumors
http://www.cancernetwork.com/brain-tumors/current-and-emerging-treatments-brain-metastases

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Treatment of Lymphomas [2.4.4C]

Larry H. Bernstein, MD, FCAP, Author, Curator, Editor

http://pharmaceuticalinnovation.com/2015/8/11/larryhbern/Treatment-of-Lymphomas-[2.4.4C]

 

Lymphoma treatment

Overview

http://www.emedicinehealth.com/lymphoma/page8_em.htm#lymphoma_treatment

The most widely used therapies are combinations of chemotherapyand radiation therapy.

  • Biological therapy, which targets key features of the lymphoma cells, is used in many cases nowadays.

The goal of medical therapy in lymphoma is complete remission. This means that all signs of the disease have disappeared after treatment. Remission is not the same as cure. In remission, one may still have lymphoma cells in the body, but they are undetectable and cause no symptoms.

  • When in remission, the lymphoma may come back. This is called recurrence.
  • The duration of remission depends on the type, stage, and grade of the lymphoma. A remission may last a few months, a few years, or may continue throughout one’s life.
  • Remission that lasts a long time is called durable remission, and this is the goal of therapy.
  • The duration of remission is a good indicator of the aggressiveness of the lymphoma and of the prognosis. A longer remission generally indicates a better prognosis.

Remission can also be partial. This means that the tumor shrinks after treatment to less than half its size before treatment.

The following terms are used to describe the lymphoma’s response to treatment:

  • Improvement: The lymphoma shrinks but is still greater than half its original size.
  • Stable disease: The lymphoma stays the same.
  • Progression: The lymphoma worsens during treatment.
  • Refractory disease: The lymphoma is resistant to treatment.

The following terms to refer to therapy:

  • Induction therapy is designed to induce a remission.
  • If this treatment does not induce a complete remission, new or different therapy will be initiated. This is usually referred to as salvage therapy.
  • Once in remission, one may be given yet another treatment to prevent recurrence. This is called maintenance therapy.

Chemotherapy

Many different types of chemotherapy may be used for Hodgkin lymphoma. The most commonly used combination of drugs in the United States is called ABVD. Another combination of drugs, known as BEACOPP, is now widely used in Europe and is being used more often in the United States. There are other combinations that are less commonly used and not listed here. The drugs that make up these two more common combinations of chemotherapy are listed below.

ABVD: Doxorubicin (Adriamycin), bleomycin (Blenoxane), vinblastine (Velban, Velsar), and dacarbazine (DTIC-Dome). ABVD chemotherapy is usually given every two weeks for two to eight months.

BEACOPP: Bleomycin, etoposide (Toposar, VePesid), doxorubicin, cyclophosphamide (Cytoxan, Neosar), vincristine (Vincasar PFS, Oncovin), procarbazine (Matulane), and prednisone (multiple brand names). There are several different treatment schedules, but different drugs are usually given every two weeks.

The type of chemotherapy, number of cycles of chemotherapy, and the additional use of radiation therapy are based on the stage of the Hodgkin lymphoma and the type and number of prognostic factors.

Adult Non-Hodgkin Lymphoma Treatment (PDQ®)

http://www.cancer.gov/cancertopics/pdq/treatment/adult-non-hodgkins/Patient/page1

Key Points for This Section

Adult non-Hodgkin lymphoma is a disease in which malignant (cancer) cells form in the lymph system.

Because lymph tissue is found throughout the body, adult non-Hodgkin lymphoma can begin in almost any part of the body. Cancer can spread to the liver and many other organs and tissues.

Non-Hodgkin lymphoma in pregnant women is the same as the disease in nonpregnant women of childbearing age. However, treatment is different for pregnant women. This summary includes information on the treatment of non-Hodgkin lymphoma during pregnancy

Non-Hodgkin lymphoma can occur in both adults and children. Treatment for children, however, is different than treatment for adults. (See the PDQ summary on Childhood Non-Hodgkin Lymphoma Treatment for more information.)

There are many different types of lymphoma.

Lymphomas are divided into two general types: Hodgkin lymphoma and non-Hodgkin lymphoma. This summary is about the treatment of adult non-Hodgkin lymphoma. For information about other types of lymphoma, see the following PDQ summaries:

Age, gender, and a weakened immune system can affect the risk of adult non-Hodgkin lymphoma.

If cancer is found, the following tests may be done to study the cancer cells:

  • Immunohistochemistry : A test that uses antibodies to check for certain antigens in a sample of tissue. The antibody is usually linked to a radioactive substance or a dye that causes the tissue to light up under a microscope. This type of test may be used to tell the difference between different types of cancer.
  • Cytogenetic analysis : A laboratory test in which cells in a sample of tissue are viewed under a microscope to look for certain changes in the chromosomes.
  • Immunophenotyping : A process used to identify cells, based on the types of antigens ormarkers on the surface of the cell. This process is used to diagnose specific types of leukemia and lymphoma by comparing the cancer cells to normal cells of the immune system.

Certain factors affect prognosis (chance of recovery) and treatment options.

The prognosis (chance of recovery) and treatment options depend on the following:

  • The stage of the cancer.
  • The type of non-Hodgkin lymphoma.
  • The amount of lactate dehydrogenase (LDH) in the blood.
  • The amount of beta-2-microglobulin in the blood (for Waldenström macroglobulinemia).
  • The patient’s age and general health.
  • Whether the lymphoma has just been diagnosed or has recurred (come back).

Stages of adult non-Hodgkin lymphoma may include E and S.

Adult non-Hodgkin lymphoma may be described as follows:

E: “E” stands for extranodal and means the cancer is found in an area or organ other than the lymph nodes or has spread to tissues beyond, but near, the major lymphatic areas.

S: “S” stands for spleen and means the cancer is found in the spleen.

Stage I adult non-Hodgkin lymphoma is divided into stage I and stage IE.

  • Stage I: Cancer is found in one lymphatic area (lymph node group, tonsils and nearby tissue, thymus, or spleen).
  • Stage IE: Cancer is found in one organ or area outside the lymph nodes.

Stage II adult non-Hodgkin lymphoma is divided into stage II and stage IIE.

  • Stage II: Cancer is found in two or more lymph node groups either above or below the diaphragm (the thin muscle below the lungs that helps breathing and separates the chest from the abdomen).
  • Stage IIE: Cancer is found in one or more lymph node groups either above or below the diaphragm. Cancer is also found outside the lymph nodes in one organ or area on the same side of the diaphragm as the affected lymph nodes.

Stage III adult non-Hodgkin lymphoma is divided into stage III, stage IIIE, stage IIIS, and stage IIIE+S.

  • Stage III: Cancer is found in lymph node groups above and below the diaphragm (the thin muscle below the lungs that helps breathing and separates the chest from the abdomen).
  • Stage IIIE: Cancer is found in lymph node groups above and below the diaphragm and outside the lymph nodes in a nearby organ or area.
  • Stage IIIS: Cancer is found in lymph node groups above and below the diaphragm, and in the spleen.
  • Stage IIIE+S: Cancer is found in lymph node groups above and below the diaphragm, outside the lymph nodes in a nearby organ or area, and in the spleen.

In stage IV adult non-Hodgkin lymphoma, the cancer:

  • is found throughout one or more organs that are not part of a lymphatic area (lymph node group, tonsils and nearby tissue, thymus, or spleen), and may be in lymph nodes near those organs; or
  • is found in one organ that is not part of a lymphatic area and has spread to organs or lymph nodes far away from that organ; or
  • is found in the liver, bone marrow, cerebrospinal fluid (CSF), or lungs (other than cancer that has spread to the lungs from nearby areas).

Adult non-Hodgkin lymphomas are also described based on how fast they grow and where the affected lymph nodes are in the body.  Indolent & aggressive.

The treatment plan depends mainly on the following:

  • The type of non-Hodgkin’s lymphoma
  • Its stage (where the lymphoma is found)
  • How quickly the cancer is growing
  • The patient’s age
  • Whether the patient has other health problems
  • If there are symptoms present such as fever and night sweats (see above)

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EARLY DETECTION OF PROSTATE CANCER: AUA GUIDELINE

Author-Writer: Dror Nir, PhD

 

 When reviewing the DETECTION OF PROSTATE CANCER section on the AUA website , The first thing that catches one’s attention is the image below; clearly showing two “guys” exploring with interest what could be a CT or MRI image…..

 fig 1

But, if you bother to read the review underneath this image regarding EARLY DETECTION OF PROSTATE CANCER: AUA GUIDELINE produced by an independent group that was commissioned by the AUA to conduct a systematic review and meta-analysis of the published literature on prostate cancer detection and screening; Panel Members: H. Ballentine Carter, Peter C. Albertsen, Michael J. Barry, Ruth Etzioni, Stephen J. Freedland, Kirsten Lynn Greene, Lars Holmberg, Philip Kantoff, Badrinath R. Konety, Mohammad Hassan Murad, David F. Penson and Anthony L. Zietman – You are bound to be left with a strong feeling that something is wrong!

The above mentioned literature review was done using rigorous approach.

“The AUA commissioned an independent group to conduct a systematic review and meta-analysis of the published literature on prostate cancer detection and screening. The protocol of the systematic review was developed a priori by the expert panel. The search strategy was developed and executed

by reference librarians and methodologists and spanned across multiple databases including Ovid Medline In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Ovid Cochrane Database of Systematic Reviews, Ovid Cochrane Central Register of Controlled Trials and Scopus. Controlled vocabulary supplemented with keywords was used to search for the relevant concepts of prostate cancer, screening and detection. The search focused on DRE, serum biomarkers (PSA, PSA Isoforms, PSA kinetics, free PSA, complexed PSA, proPSA, prostate health index, PSA velocity, PSA

doubling time), urine biomarkers (PCA3, TMPRSS2:ERG fusion), imaging (TRUS, MRI, MRS, MR-TRUS fusion), genetics (SNPs), shared-decision making and prostate biopsy. The expert panel manually identified additional references that met the same search criteria”

While reading through the document, I was looking for the findings related to the roll of imaging in prostate cancer screening; see highlighted above. The only thing I found: “With the exception of prostate-specific antigen (PSA)-based prostate cancer screening, there was minimal evidence to assess the outcomes of interest for other tests.

This must mean that: Notwithstanding hundreds of men-years and tens of millions of dollars which were invested in studies aiming to assess the contribution of imaging to prostate cancer management, no convincing evidence to include imaging in the screening progress was found by a group of top-experts in a thorough and rigorously managed literature survey! And it actually  lead the AUA to declare that “Nothing new in the last 20 years”…..

My interpretation of this: It says-it-all on the quality of the clinical studies that were conducted during these years, aiming to develop an improved prostate cancer workflow based on imaging. I hope that whoever reads this post will agree that this is a point worth considering!

For those who do not want to bother reading the whole AUA guidelines document here is a peer reviewed summary:

Early Detection of Prostate Cancer: AUA Guideline; Carter HB, Albertsen PC, Barry MJ, Etzioni R, Freedland SJ, Greene KL, Holmberg L, Kantoff P, Konety BR, Murad MH, Penson DF, Zietman AL; Journal of Urology (May 2013)”

It says:

“A systematic review was conducted and summarized evidence derived from over 300 studies that addressed the predefined outcomes of interest (prostate cancer incidence/mortality, quality of life, diagnostic accuracy and harms of testing). In addition to the quality of evidence, the panel considered values and preferences expressed in a clinical setting (patient-physician dyad) rather than having a public health perspective. Guideline statements were organized by age group in years (age<40; 40 to 54; 55 to 69; ≥70).

RESULTS: With the exception of prostate-specific antigen (PSA)-based prostate cancer screening, there was minimal evidence to assess the outcomes of interest for other tests. The quality of evidence for the benefits of screening was moderate, and evidence for harm was high for men age 55 to 69 years. For men outside this age range, evidence was lacking for benefit, but the harms of screening, including over diagnosis and over treatment, remained. Modeled data suggested that a screening interval of two years or more may be preferred to reduce the harms of screening.

CONCLUSIONS: The Panel recommended shared decision-making for men age 55 to 69 years considering PSA-based screening, a target age group for whom benefits may outweigh harms. Outside this age range, PSA-based screening as a routine could not be recommended based on the available evidence. The entire guideline is available at www.AUAnet.org/education/guidelines/prostate-cancer-detection.cfm.”

Other research papers related to the management of Prostate cancer were published on this Scientific Web site:

From AUA2013: “Histoscanning”- aided template biopsies for patients with previous negative TRUS biopsies

Imaging-biomarkers is Imaging-based tissue characterization

On the road to improve prostate biopsy

State of the art in oncologic imaging of Prostate

Imaging agent to detect Prostate cancer-now a reality

Scientists use natural agents for prostate cancer bone metastasis treatment

Today’s fundamental challenge in Prostate cancer screening

ROLE OF VIRAL INFECTION IN PROSTATE CANCER

Men With Prostate Cancer More Likely to Die from Other Causes

New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests

New clinical results supports Imaging-guidance for targeted prostate biopsy

Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease

Prostate Cancer and Nanotecnology

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition

Imaging agent to detect Prostate cancer-now a reality

Scientists use natural agents for prostate cancer bone metastasis treatment

ROLE OF VIRAL INFECTION IN PROSTATE CANCER

Prostate Cancers Plunged After USPSTF Guidance, Will It Happen Again?

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