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Glioma, Glioblastoma and Neurooncology

Posted in Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Clinical & Translational, Clinical Diagnostics, Clinical Genomics, Gene Regulation, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genomic Testing: Methodology for Diagnosis, Neuroscience, tagged brain cancer, cancer recurrence, choice of treatment, drug resistant types, gliomas, pharmaceutical choice on October 19, 2015| Leave a Comment »

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 Article | April 15, 2015 | Oncology Journal, Brain Tumors

ONCOLOGY 2015; 29(4)
By Jenny Lin, BA, Rahul Jandial, MD, PhD, Amanda Nesbit, BS, Behnam Badie, MD, and Mike Chen, MD, PhD
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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Brain metastasis in patients with cancer can be indicative of multisystem spread or lead to neurological demise if not locally controlled, and is associated with poor survival and high morbidity. Compared with metastasis to other areas of the body, brain metastasis possesses a unique biology that confers high resistance to systemic therapies. This phenomenon has been historically attributed to the inability of chemotherapeutic agents to pass through the blood-brain barrier. Recent studies challenge this premise, revealing other potentially targetable mechanism(s). Therapies that exploit recent advances in the understanding of brain metastasis are still in early stages of development. Encouragingly, and discovered by happenstance, some molecularly targeted drugs already appear to have efficacy against certain tumors and accompanying cerebral edema. In the meantime, conventional treatment modalities such as surgery and radiation have iteratively reached new levels of refinement. However, these achievements are somewhat muted by the emergence of magnetic resonance (MR)-guided laser interstitial thermal therapy, a minimally invasive neuroablative technique. On the horizon, MR-guided focused ultrasound surgery is similarly intriguing. Even in the absence of further advances, local control is frequently achieved with state-of-the-art therapies. Dramatic improvements will likely require sophisticated approaches that account for the particular effects of the microenvironment of the central nervous system on metastasis.

Introduction
Brain metastases affect 9% to 17% of cancer patients[1]; this frequency is fairly proportionate to the relative cardiac output to the brain. Most cancers spread to distant organs through vascular or lymphatic vessels, but the latter do not exist in the central nervous system. Thus, cancer cells travel through the bloodstream either by colonizing a vascular space or by crossing the blood-brain barrier into the parenchyma.

The cancers that most often metastasize to the brain are those that originate in the lung and breast, which is unsurprising given the high incidence of those two diseases (Figure 1). However, the third most frequent type of brain metastasis is melanoma (Table).[1] This suggests a certain selectivity regarding a given tumor’s propensity to metastasize to the brain, as metastatic melanoma accounts for only a small fraction of cancer diagnoses. This selectivity, perhaps secondary to the brain’s microenvironment, is also evident in the inverse relationship between overall incidence of disease and incidence of brain metastasis with testicular and prostate cancer.[2,3]

Most physicians and patients are unlikely to devote much thought to the nuances of metastasis but are instead focused on the simple matter of survival. Without treatment, median survival for a patient with brain metastasis is estimated to be 3 months for a single lesion, although survival time has likely increased recently due to enhanced screening that detects smaller masses.[4] Fortunately, radiation and surgery are usually sufficient for local control of oligometastasis. The poor life expectancy associated with brain metastases is more often a result of the consequences of widespread visceral metastasis.[5] There are notable exceptions, however, such as with melanoma metastatic to the brain, where the majority of patients will succumb to a neurologic-related death even with treatment.[6] Further, despite satisfactory local control rates with current modalities, the sheer number of total patients with brain metastases results in a substantial number of treatment failures. Thus, improving therapies for metastatic brain tumors is still an unmet need.

Additional advances in the field face the “law of diminishing returns,” since state-of-the-art techniques for the destruction of metastatic brain lesions by surgical or other means are fairly effective. One potential next-generation tool is magnetic resonance (MR)-guided focused ultrasound surgery (MRgFUS). Theoretically, MRgFUS appears to be an appealing and feasible approach due to the noninvasive nature of thermal ablation and its high degree of conformality. However, as with all focal techniques, disease that is diffuse, intermingled with a critical structure, or not clearly visualized with imaging will be challenging to treat. Hence, there remains a great need for effective systemic agents that target mechanisms of chemotherapy resistance unique to metastatic brain lesions.

Key Biologic Concepts of Metastatic Brain Tumors
Metastatic cascade

The metastatic cascade serves as a model for invasion of a secondary site by primary tumor cells. The first step involves invasion through the extracellular matrix and stromal cell layers at the primary site,[7] followed by invasion into the blood vessel lumen, with support for transport through the vasculature. Once the metastatic cells have been transported, they will come to rest in the brain by adhering to the intimal surface. The mechanisms of initial establishment are still obscure, however. The cells either start proliferating in the vascular space, followed by penetration through the blood-brain barrier, or the reverse occurs. Regardless of the mechanism, at some point there is permeability of the blood-brain barrier that is putatively mediated by vascular endothelial growth factor (VEGF).[8]

Blood-brain/metastasis barrier

The resistance of metastatic brain tumors to systemic therapy has historically been attributed to the blood-brain barrier.[9,10] It was previously assumed that the blood-brain barrier was intact in the vessels of the metastatic lesion, thus preventing drug distribution. Recent studies suggest otherwise; however, there has always been some suspicion that the barrier was not intact because almost all nonminiscule metastatic lesions enhance intensely with gadolinium.

On a morphologic level, the neovasculature of renal cell brain metastasis was reported to be similar to its counterpart at the primary extracranial site.[11] Additionally, the recent study by Nduom et al of three metastatic brain lesions (melanoma, adenocarcinoma, and cervical cancer) revealed thin and dysmorphic CD31-positive endothelial cells within the tumor mass, further accentuated by the absence of glial fibrillary acidic protein (GFAP) and aquaporin 4 (AQP4) immunoreactivity adjacent to CD31-positive vessels, findings that are indicative of an absence of the blood-brain barrier.[12] The investigators also noted that upon passing through the tumor/brain interface into normal brain, endothelial morphology and associated immunostaining characteristics immediately normalized. There are also functional differences in the endothelium of metastatic brain tumors compared with normal brain. P-glycoprotein (P-gp), the product of the multidrug resistance 1 (MDR1) gene, is highly expressed in the vascular endothelial cells of the normal brain and performs as a drug efflux pump. However, endothelial cells of metastatic brain tumors demonstrate absent or variable P-gp staining reflective of the vasculature from tumor of origin.[13,14] It is important to note that submillimeter nests of cancer cells may cross the blood-brain barrier without violating its integrity. Therefore, development of oncologic therapies for brain metastasis that can penetrate the blood-brain barrier is still pertinent, although it may be a lower priority than creating treatments for brain metastasis that are visualized on MRI.

Brain microenvironment

The data collectively cast significant doubt on the idea of an intact blood-brain barrier within metastatic brain tumors. So what underlies the resistance of brain metastases to systemic agents? Some investigators have speculated that this phenomenon is a result of the interaction between the tumors’ cells and the brain’s microenvironment. In essence, this concept is Paget’s “seed and soil hypothesis,” in which cancer stem cells—that is, the “seeds”—metastasize to locations—the “soil”—that are favorable for implantation and growth.[15] To facilitate integration with neural tissues during reimplantation, the circulating tumor cells that have previously undergone epithelial to mesenchymal transition (EMT) may reacquire some of the original epithelial traits by reversing the EMT process.[16]

There are a number of mechanisms by which the brain’s microenvironment potentially nurtures the development of treatment-resistant metastasis. One hypothesis is that cells intrinsic to the central nervous system secrete macromolecules that activate pro-survival pathways. For example, astrocytes can promote survival and growth of metastatic tumor cells by secretion of cytokines, growth factors, and neurotrophins.[17,18] Neman et al recently demonstrated that the neurotransmitter γ-aminobutyric acid (GABA) binds to GABA receptors present on breast cancer cells. The GABA is then catabolized into succinate that is used in the GABA shunt, conferring a metabolic advantage.[19] Interestingly, GABAergic pathways have also been shown to be present in testicular and airway epithelial cells, perhaps suggesting a reason for the propensity of cancers derived from those cells to metastasize to the brain.[20,21]

One unexplained aspect of these theories, which propose that the brain parenchyma supplies some type of nutrient or growth factor that vitalizes the metastatic tumor, is that most of the tumor mass is distant from the adjacent brain. Brain metastasis almost invariably grows as a mass with distinct borders composed of reactive astrocytes and microglia.[22] Pro-survival factors would therefore have to diffuse considerable distances, particularly with larger tumors. That effective concentration gradients would be maintained across centimeters is counterintuitive. There is some speculation that the neuronal/astrocytic population may be admixed with metastatic cells; however, this has not been clearly demonstrated.[23] Thus, alternate mechanisms must always be considered, including the possibility that the brain passively selects for hardier metastatic tumor cells.

Current Therapeutic Strategies
Surgical treatment

Surgery remains a highly successful treatment approach for accessible brain tumors (Figure 2). Its judicious application necessitates an understanding of the limitations and efficacy of various surgical strategies and technologies. The most significant technical advances in surgery over the last decade have arguably occurred in the field of preoperative brain mapping. These advances include functional MRIs with fiber tract mapping and transcranial magnetic stimulation coupled with three-dimensional MRI renderings.[24] More importantly, the advances in systemic therapy and radiation therapy have had the greatest influence on surgical indications. Better systemic therapies have increased the pool of patients eligible for surgery, while enhanced screening and improved radiosurgery techniques are often used to manage smaller lesions, obviating the need for a craniotomy.

Surgery for a single lesion originating from a solid cancer in an accessible location, and in a patient who has very limited or no systemic cancer and an absence of leptomeningeal infiltration, classically results in significant improvements in neurologic function and improved survival.[25,26] Indications are less defined in patients with moderate systemic tumor burden or multiple brain metastases.[27,28] For these patients, especially if radiation has already been attempted, a craniotomy may be the only remedy that can provide rapid relief of symptoms linked to mass effect, such as intracranial hypertension, seizures, obstructive hydrocephalus, or peritumoral edema.[29] Deviation from classical indications for surgery is often considered when the patient has significant extracranial disease but is eligible for other lines of effective systemic therapies. In addition, the role of surgery for multiple metastases is currently being redefined. Because multiple lesions often require separate craniotomies, there is a higher cumulative probability of complications developing as a result of the multiple surgeries.[30] Nevertheless, this risk seems justified, as median patient survival time is 9 to 14 months, which is 3 to 6 months longer than the survival of those who are treated solely with radiation.[30-32] In fact, surgical removal of all lesions in patients with up to three brain metastases can lead to survival comparable to that of patients with a single metastasis.[30]

Surgical technique has been shown to influence local recurrence rates. Tumors resected in a piecemeal fashion, which violates the tumor capsule, have a recurrence rate 1.7 times higher than that of tumors removed with circumferential resection (en bloc).[33] En bloc resection has been shown to be particularly important for lesions of the posterior fossa and those in contact with cerebrospinal fluid pathways.[34] Moreover, piecemeal resections are not always safe because of the size of the lesion or the proximity of eloquent cortex. To reduce the incidence of local recurrence, Yoo et al suggested a microscopic total resection approach in which a 5-mm margin of normal-appearing tissue is removed by ultrasonic aspiration.[35] Compared with standard piecemeal gross total resection without adjuvant therapy, microscopic total resection reduced 1-year local recurrence rates from 59% to 29%.[35] Since most resection cavities are now treated with postoperative stereotactic radiosurgery (SRS), this finding may be most relevant to the resection of metastatic tumors progressing despite radiation or initial surgery for lesions known to have high radioresistance.

Whole-brain radiotherapy

Whole-brain radiotherapy (WBRT) is often utilized for multiple and disseminated metastases and for salvaging stereotactic radiation therapy failures. Studies have shown that 64% to 83% of patients with multiple metastases who undergo WBRT experience significant symptomatic improvement and a mean increase in survival of 2 to 6 months.[36] Combined with surgery, WBRT drastically lowers recurrence rates to 10% to 18%, compared with 46% to 70% with surgery alone.[37] However, the toxicity of WBRT should be considered. Although WBRT was initially found to be minimally toxic, recent studies have shown potential adverse short- and long-term effects, especially in the elderly population.[38,39]

Stereotactic radiosurgery

SRS focuses beams of radiation on the tumor(s). The convergence enhances tumoricidal effect and results in a rapid dose drop-off in normal tissues, allowing for treatment of lesions adjacent to critical structures.[40] The efficacy of SRS for brain lesions is generally encouraging, with local control rates ranging from approximately 64% to 94%.[41-49] Lesions less likely to respond are generally larger than 2 cm3, receive less than 18 Gy of radiation, or have a radiation-resistant histology (eg, melanoma or renal cell carcinoma).

There are overlapping considerations when deciding whether to use SRS alone, as an adjunct to surgery or WBRT, or not at all. One widely used determinant has been lesion number. For single brain metastasis, SRS alone is potentially as effective as surgery with SRS of the resection cavity when the lesion is small and radiosensitive.[50-52] SRS alone is usually a better option than surgery if the lesion is surgically inaccessible, or if the patient has uncontrolled systemic metastasis or is a poor operative candidate due to other medical conditions. In practice, surgery is often performed to resect or cytoreduce a bulky metastatic lesion, allowing SRS to be used adjunctively. With combined treatment, 1-year local control rates have ranged from 80% to 93%, with salvage WBRT (typically for distant metastasis) required in 33% to 46% of patients.[53-55] The substantial rate of salvage WBRT then raises the issue of whether the traditional approach of surgery with postoperative WBRT is a more rational option. A recent retrospective study by Patel et al provides some insight.[56] The investigators found that in a series of 132 patients, overall survival and local control were similar between patients treated with surgery plus SRS and those treated with surgery plus WBRT. WBRT was associated with a higher rate of distant brain control (70% vs 48% at 1 year; P = .03) and greater freedom from leptomeningeal disease (87% vs 69% at 18 months; P = .045) compared with SRS; however, radiographic leukoencephalopathy was remarkably higher with WBRT (47% vs 7% at 12 months; P = .001). Therefore, if these results hold true in further studies, the choice of surgery followed by SRS or WBRT will be determined by balancing certain tradeoffs in the light of each patient’s circumstances.

SRS is also commonly used to treat multiple (ie, two to four) brain metastases. In a number of studies, SRS was associated with significantly improved local control compared with WBRT, although overall survival was equivalent.[57,58] When used in conjunction, SRS plus WBRT increases distant brain control without affecting survival.[59,60] However, treatment times are longer and there is a higher probability of neurocognitive side effects.[56,61,62] Thus, the limited benefit of this approach is unlikely to justify the additional effort and morbidity.

There has been a reluctance to use SRS for more than four brain metastases because of restricted inclusion criteria for several randomized studies.[49] Experience with aggressive SRS treatment has blurred the distinction between oligo and disseminated metastases, the latter once solely within the purview of WBRT. Multiple large retrospective studies have demonstrated that patients with up to 15 lesions treated with SRS had a similar clinical course to those with 1 to 4.[63-66] It has been suggested that total tumor volume is more important than the absolute number of lesions[67]; however, the point at which this notion ceases to be true requires further investigation.

Chemotherapy and targeted agents

With few exceptions, most notably germ cell tumors and small-cell lung cancer, metastatic brain tumors do not respond to systemic chemotherapy.[26,68] However, molecularly targeted therapies have shown some promise against brain metastases, especially those that are “oncogene addicted.” One such example is seen in non–small-cell lung cancer (NSCLC) that possesses activating epidermal growth factor receptor (EGFR) mutations.[69] Complete and partial response rates to tyrosine kinase inhibitors have been recorded in clinical studies: Rates of response with gefitinib ranged from 10% to 38%, with a median duration of 9 to 13.5 months, and similar findings were documented with erlotinib.[70,71] These treatments also improved overall survival rates.[72]

Also, a remarkable number of patients with human epidermal growth factor receptor 2 (HER2)-positive breast carcinoma have had a favorable response to lapatinib, a tyrosine kinase inhibitor that targets the C-terminus domain of HER2 and EGFR receptors. In a multicenter phase II trial, which included 242 patients who had received prior trastuzumab and cranial radiation treatment, 6% of patients had an objective response and 21% had at least a 20% volumetric reduction with lapatinib alone.[73] In a subgroup of 50 patients who entered an extension of this study, 20% experienced an objective response and 40% experienced at least a 20% volumetric reduction in their brain metastasis with lapatinib plus capecitabine. Although the current efficacy of targeted therapy is modest, these results inspire great hopes for future systemic treatments.

Use of bevacizumab for treatment of malignant brain edema associated with brain metastasis

A treatment also worth noting is a targeted antiangiogenic approach to managing severe cerebral edema following SRS.[74]. In a small clinical trial, eight patients were treated with CyberKnife and bevacizumab (Figure 3).[75] The rationale was to use bevacizumab to block VEGF in leakage-prone capillaries to decrease the amount of cerebral edema. Seven of the eight patients had drastically improved neurologic functioning, while only one patient suffered bevacizumab-related hypertension. This novel approach warrants further investigation and should be considered for this frequently encountered clinical scenario.

Emerging Ablative Technologies
Laser interstitial thermal therapy

Laser interstitial thermal therapy (LITT) is a new ablative tool that promises to be useful for the treatment of central nervous system metastasis. LITT is a minimally invasive cytoreductive treatment strategy that utilizes a low-voltage laser inserted through a burr hole to induce hyperthermia to kill tumor cells.[76] LITT differentiates itself from previous ablative technologies by using MR thermography to assess tissue heating in near-real time. Conformality is sufficient, but procedural times are often comparable to that of an actual craniotomy. The use of LITT for treatment of metastatic disease is limited, but in trials that have looked at its use in recurrent glioblastoma multiforme, results show increases in radiographic responses and progression-free survival.[76-79] With its unique approach and encouraging results, LITT potentially fulfills a need for a treatment that can be applied to metastatic brain lesions that have not responded to radiotherapy when surgery is undesirable or unfeasible.

MRgFUS

MRgFUS is another fascinating technology that can be used for precise, incisionless, thermal ablation in the brain. A hemispherical phased-array transducer combined with patient-specific treatment planning are implemented based on acoustic models, with feedback control based on MR thermography used to deliver ultrasound energy to a specific focal point.[80,81] MRgFUS has been successfully used as a minimally invasive treatment method to target deep brain structures in a small series of patients with essential tumor.[82] MRgFUS has also been used to treat three inoperable thalamic glioblastomas[81,83]; however, in one of the three inoperable glioblastomas, a serious complication occurred, in which treatment caused an interruption of blood flow. Currently, MRgFUS can only target deep brain structures, although further iterations of the device are expected in the near future that will facilitate its application to brain metastasis.

Conclusion
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, and an enhanced understanding of the biology of metastatic brain tumors and the advent of molecularly targeted therapies have spurred the development of effective systemic therapies. Recently developed ablative technologies—as well as those currently in development—which can be visualized in real-time, promise to further strengthen the neuro-oncologic armamentarium.

Financial Disclosure: The authors have no significant financial interest in or other relationship with the manufacturers of any products or providers of any service mentioned in this article.

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REFERENCES

1. Nayak L, Lee EQ, Wen PY. Epidemiology of brain metastases. Curr Oncol Rep. 2012;14:48-54.

2. Gandaglia G, Abdollah F, Schiffmann J, et al. Distribution of metastatic sites in patients with prostate cancer: a population-based analysis. Prostate. 2014;74:210-6.

3. Bredael JJ, Vugrin D, Whitmore WF, Jr. Autopsy findings in 154 patients with germ cell tumors of the testis. Cancer. 1982;50:548-51.

4. Nussbaum ES, Djalilian HR, Cho KH, Hall WA. Brain metastases. Histology, multiplicity, surgery, and survival. Cancer. 1996;78:1781-8.

5. Johnson JD, Young B. Demographics of brain metastasis. Neurosurg Clin N Am. 1996;7:337-44.

6. Carella RJ, Gelber R, Hendrickson F, et al. Value of radiation therapy in the management of patients with cerebral metastases from malignant melanoma: Radiation Therapy Oncology Group Brain Metastases Study I and II. Cancer. 1980;45:679-83.

7. Pantel K, Brakenhoff RH. Dissecting the metastatic cascade. Nat Rev Cancer. 2004;4:448-56.

8. Lee TH, Avraham HK, Jiang S, Avraham S. Vascular endothelial growth factor modulates the transendothelial migration of MDA-MB-231 breast cancer cells through regulation of brain microvascular endothelial cell permeability. J Biol Chem. 2003;278:5277-84.

9. Alonso A. Ultrasound-Induced blood-brain barrier opening for drug delivery. Front Neurol Neurosci. 2015;36:106-15.

10. Barani IJ, Larson DA, Berger MS. Future directions in treatment of brain metastases. Surg Neurol Int. 2013;4:S220-30.

11. Hirano A, Zimmerman HM. Fenestrated blood vessels in a metastatic renal carcinoma in the brain. Lab Invest. 1972;26:465-8.

12. Nduom EK, Yang C, Merrill MJ, et al. Characterization of the blood-brain barrier of metastatic and primary malignant neoplasms. J Neurosurg. 2013;119:427-33.

13. Henson JW, Cordon-Cardo C, Posner JB. P-glycoprotein expression in brain tumors. J Neurooncol. 1992;14:37-43.

14. Gerstner ER, Fine RL. Increased permeability of the blood-brain barrier to chemotherapy in metastatic brain tumors: establishing a treatment paradigm. J Clin Oncol. 2007;25:2306-12.

15. Ramakrishna R, Rostomily R. Seed, soil, and beyond: The basic biology of brain metastasis. Surg Neurol Int. 2013;4:S256-64.

more….

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Treatment of Lymphomas [2.4.4C]

Posted in Academic Publishing, Automated Cell Processing, Best evidence, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Cancer Screening, Cell Biology, Circulating Progenitor Cells, Clinical & Translational, Clinical Diagnostics, Clinical Genomics, Curation, Discovery process, Disease Biology, Evolutionary cognition, Experimental validation, Explanatory, Gene Regulation, Genetics & Pharmaceutical, Genome Biology, Genomic Expression, Hematology, Hematopoiesis, Historical relevance, Lymphoma, Pharmaceutical Analytics, Pharmaceutical Drug Discovery, Pharmacogenomics, Technology Advance Assessment of, Translational Science, tagged choice of treatment, hematological cancers, lymphomas, treatment of lymphomas on August 11, 2015| Leave a Comment »

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: American Urological Association (AUA) Guideline

Posted in Biomarkers & Medical Diagnostics, Cancer Prevention: Research & Programs, Imaging-based Cancer Patient Management, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, tagged American Urological Association, AUA, cancer management, choice of treatment, CT, Medical guideline, Medical imaging, MRI, Prostate, Prostate biopsy, Prostate cancer, prostate cancer imaging, Prostate cancer screening, Prostate-specific antigen, psa screening, urology on May 21, 2013| 9 Comments »

Early Detection of Prostate Cancer: American Urological Association (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…..

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.”