18F-FDG PET/CT in the clinical management of patients with lymphoma

The aim of this work was to review the current recommendations for staging and response assessment of patients with Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) in routine clinical practice after chemotherapy and/or stem cell transplantation. A five-point scale (5-PS) from the First International Workshop on PET in Lymphoma in Deauville, France, in 2009, was recommended as the standard tool to score imaging to assess treatment response in patients with lymphoma using ¹⁸F-Fluorodeoxyglucose (FDG) PET/CT. Following the recommendations of the 11th and 12th International Conferences on Malignant Lymphoma held in Lugano (Switzerland), in 2011 and 2013, respectively, a consensus (the so-called Lugano Classification) was reached regarding the use of PET/CT for staging and response assessment in FDG-avid lymphomas. As a result, ¹⁸F-FDG PET/CT was formally incorporated into standard staging for FDG-avid lymphomas. A bone marrow biopsy is no longer indicated for the routine staging of HL and most diffuse large B-cell lymphomas. PET/CT will be used to assess response in FDG-avid histologies using the 5-point scale. The recent introduction of biological agents with immune mechanisms requires flexibility in interpretations of the Lugano criteria due to tumour flare or a pseudo-progression effect produced by these agents. Provisional criteria have been proposed (Lymphoma Response to Immunomodulatory Therapy Criteria) with the introduction of the term 'Indeterminate Response' in order to identify this phenomenon until confirmed as flare/pseudoprogression or true progression. All these recommendations will improve evaluations of patients with lymphoma, and allow comparison of results from clinical practice and trials.

Abstract S1-01: Whole exome and transcriptome sequencing of resistant ER+ metastatic breast cancer

Background: While great strides have been made in the treatment of estrogen receptor-positive (ER+) metastatic breast cancer (MBC), therapeutic resistance invariably occurs. A better understanding of the underlying resistance mechanisms is critical to enable durable control of this disease.

Methods: We performed whole exome sequencing (WES) and transcriptome sequencing (RNA-seq) on metastatic tumor biopsies from 88 patients with ER+ MBC who had developed resistance to one or more ER-directed therapies. For 27 of these patients, we sequenced the treatment-naïve primary tumors for comparison to the resistant specimens. Tumors were analyzed for point mutations, insertions/deletions, copy number alterations, translocations, and gene expression. Detailed clinicopathologic data was collected for each patient and linked to the genomic information.

Results: WES of all metastatic samples demonstrated several recurrently altered genes whose incidence differed significantly from primary, treatment-naïve ER+ breast cancers sequenced in the TCGA study (TCGA). These include ESR1 mutations (n=17, 19.3%; 32.86 fold enrichment, q.value<7.5e-12), CCND1 amplification (n=52, 59.1%; 2.3 fold enrichment, q.value<0.0073), and MAP2K4 biallelic inactivation (n=14, 15.9%; 3.04 fold enrichment, q.value< 0.054).

Comparing to matched primary samples from the same patient, many alterations were found to be acquired in several cases, including for ESR1, ERBB2, PIK3CA, PTEN, RB1, AKT1, and others. Initial analysis of RNA-seq data from metastatic samples (n=59) allowed classification of individual resistance mechanisms into broader resistance modes based on the observed transcriptional state.

Conclusions: We present a genomic landscape of resistant ER+ MBC using WES and RNA-seq. Multiple genes were recurrently altered in these tumors at significantly higher rates than in ER+ primary breast cancer. When compared with matched primary tumors from the same patient, alterations in these and other genes were often found to be acquired after treatment, suggesting a role in resistance to ER-directed therapies and/or metastasis. Potential resistance mechanisms appear to fall into several categories; integrating RNA-seq data may enhance the ability to identify these categories even when genomic alterations are not identified. Multiple clinically relevant genomic and molecular alterations are identified in metastatic biopsies– with implications for choice of next therapy, clinical trial eligibility, and novel drug targets.

Citation Format: Cohen O, Kim D, Oh C, Waks A, Oliver N, Helvie K, Marini L, Rotem A, Lloyd M, Stover D, Adalsteinsson V, Freeman S, Ha G, Cibulskis C, Anderka K, Tamayo P, Johannessen C, Krop I, Garraway L, Winer E, Lin N, Wagle N. Whole exome and transcriptome sequencing of resistant ER+ metastatic breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr S1-01.

A novel, fully automated, observer-independent program for semiquantifying striatal ¹²³I-FP-CIT uptake

OBJECTIVE

To describe and validate a novel, fully automated program specifically designed for the semiquantification of striatal (123)I-FP-CIT uptake using volumes of interest (VOI) analysis.

MATERIAL AND METHODS

The proposed algorithm is based on a template that mimics the striatal (123)I-FP-CIT uptake in a healthy subjects, derived from defined anatomical VOIs available from WFU PickAtlas. Four SPECT studies of the anthropomorphic Alderson phantom filled with variable radioactive concentrations were acquired for the experimental validation. Experimental SPECT images were spatially normalized with respect to the previously created template. The binary VOIs corresponding to left caudate and putamen and right caudate and putamen, which were used to construct the template, were projected onto the experimental images to obtain the counts for these regions. To minimize the partial volume effect, a percentage of the voxels in these regions (threshold), rather than all of them, was used. A binary occipital VOI was used to quantify the non-specific uptake. Experimental binding potentials (BPs) were calculated from the counts in these regions. True BPs were calculated from aliquots taken from the solutions used to fill the phantom.

RESULTS

There were statistically significant differences in the experimental BP values (p<0.002) according to the percentage of voxels used. A highly significant correlation was achieved between true and experimental BP values, regardless of the percentage of voxels included for quantification.

CONCLUSIONS

Our novel, observer-independent program automatically performs the semiquantification of striatal (123)I-FP-CIT uptake in experimental studies.

Predictions for positive nodes in breast cancer using computerized modeling

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Background: The accuracy and consistency of sentinel lymph node (SLN) biopsy for axillary staging in breast cancer has been well established by multiple studies. Although the standard of care has been to perform a completion axillary lymph node dissection (ALND) on patients who have a positive sentinel node, recent data (ACOSG Z0010, Z0011 and NSABP B-32) suggests that an ALND may be avoided in certain subsets of patients. To further study this question, we used a Bayesian model to predict the probability of finding disease in the ALND for breast cancer.

Methods: All SLN procedures performed by a single surgeon, for clinical T1-T2 N0 disease between September 2000 and March 2009 were retrospectively reviewed under an IRB approved protocol. Demographic, disease and surgical procedural variables were collected. Values are reported as mean ± standard deviation. A Bayesian model, a standard statistical model frequently used in medicine, was used to identify variables that could predict a positive ALND.

Results: A total number of 235 SLN procedures were reviewed. The mean patient age was 58.6 ± 11.8 (range 28-88), tumor size was 1.8 ± 1.3 cm (range 0.2-7.4), and BMI was 28 ± 6.1. The number of SLN found was 3.6 ± 1.9 (range 0-10), and in 2 cases no SLN were found, for an overall failure rate of 0.85%. A total of 73 ALND were performed in this group; 27 cases had ALND as national study participants randomized to ALND, 44 cases for positive SLN, and 2 cases in which no SLN could be identified. The mean number of axillary lymph nodes removed was 14.8 ± 7 and the mean number of positive axillary nodes was 2.7 ± 4.1. Tumor size, tumor grade, number of positive SLN by hematoxylin and eosin staining, and a low technetium count of the first SLN were found to be significant predictors of positive axillary nodes during a completion ALND. There was also a trend for progesterone receptor expression as a predictor of axillary disease.

Conclusions: A model to predict a positive ALND for breast cancer is presented. We found tumor characteristics, number of positive SLN and low technetium count for first SLN to be predictors for finding axillary disease. Further studies are needed to validate our model as a means of sparing women an ALND who have a low probability of finding additional breast disease.

NF-κB Activation in Breast Cancer Occurs Via Different Mechanisms in Triple Negative and HER2+ Tumors with Lymphocytic Infiltrate

NF-κB transcription factors regulate expression of genes involved in immune response, inflammation, apoptosis and cancer. NF-κB is known to be regulated via two main mechanisms: (i) through the IκB kinase complex (via the canonical pathway), and (ii) through the IKKα and NIK kinases (via the alternative pathway). Recently, NF-κB has also been shown to be activated by the IKBKE kinase. In breast cancer the level of NF-κB was found elevated only in triple negative (basal) and in HER2+ tumors. Inhibition of NF-κB has been shown to block cell proliferation and tumor formation, and therefore, genes that activate NF-κB might be potential drug targets in the treatment of cancer. Our study aims to identify the mechanisms of activation of NF-κB in various breast cancer subtypes.We analyzed several breast cancer gene expression microarray datasets from public repositories (GSE 2034, 4922, 7390) and used our molecular classification of breast cancer proposed in (Can Res 2007 67: 10669-76) to identify the subtypes of breast cancer in which the NF-κB pathway is altered. The NF-κB activity was evaluated based on upstream and downstream events for the canonical and the alternative pathways, and quantified through a consensus of geneset enrichment scores. We found that the NF-κB downstream genes are upregulated only in triple negative (subtype basal BA) and Her2+ tumors having an immune signature provided by the presence of a lymphocytic infiltrate (subtype Her2I).In order to identify the pathways involved in NF-κB activation, we analyzed the expression of the upstream genes in the canonical, alternative, IKBKE and PI3K/AKT pathways. We found that AKT1 -- an AKT isoform that activates IKKα -- is overexpressed in Her2I, while other AKT isoforms, AKT2 and AKT3, are overexpressed in BA. PI3K is overexpressed in BA, but not in Her2I. We also found that PDK1, which phosphorylates and activates AKT, is overexpressed in Her2I but not in BA. TNFα -- an activator of the NF-κB canonical pathway -- was found overexpressed in BA. However, in BA the components of the IKK complex have low expression apart from IKKγ. IKBKE was found upregulated in both BA and Her2I subtypes.In Her2I, the absence of high expression of TRAF2/5, which recruits the IKK complex, and also the low expression of IKKβ, indicates that NF-κB is not activated by the canonical pathway. Combined with the overexpression of the NIK gene in Her2I, this leads to the hypothesis that in Her2I, NF-κB is being activated by IKBKE and the alternative pathway.In BA, the overexpression of TRAF3 -- an inhibitor of the alternative pathway, suggests that the activation of NF-κB may also occur via the canonical pathway.In summary, we hypothesize that the activation of NF-κB in Her2I tumors is initiated by the IKBKE oncogene and the alternative pathway, while the activation of NF-κB in BA tumors is initiated by IKBKE and the canonical pathway. Boehm et al (Cell, 2007) shown that NF-κB can be inhibited by simultaneously targeting AKT and IKBKE. Combined with our findings, this raises the interesting possibility of developing a targeted therapy for the BA and Her2I breast tumors.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 1164.

Clozapine may partially compensate for task-related brain perfusion abnormalities in risperidone-resistant schizophrenia patients

BACKGROUND

Previous reports show different cerebral activity patterns during treatment with clozapine and typical neuroleptics. However, to date no study has directly compared the brain activity patterns while subjects are undergoing treatment with clozapine and other atypical antipsychotics. This comparison is of interest, given the probably different mechanism of action of clozapine in comparison with other atypicals.

OBJECTIVE

To assess the effect of clozapine on perfusion deviations still evident during treatment with risperidone.

METHODS

Here we used hexamethylene-propylenaminoxime single photon emission computed tomography to compare the perfusion patterns observed during the performance of a Stroop test in 10 patients sequentially treated with risperidone and clozapine, owing to a lack of response to the former, and in 10 healthy controls.

RESULTS

Patients on risperidone showed decreased perfusion as compared to controls in the medial prefrontal, middle cingulate and insular regions, as well as increased activities in brain stem and the posterior hippocampus. After receiving clozapine, the same patients showed an even wider prefrontal perfusion deficit and the brain stem was still hyperactive, but the abnormalities in the cingulate cortex, insula and hippocampus had disappeared. Clinical improvement was directly related to an increase in thalamic perfusion.

CONCLUSION

Clozapine may alleviate hyperactivity in the limbic system in schizophrenia and may facilitate activation of the regions involved in cognitive tasks to a greater degree than risperidone, as well as eliciting greater inhibition of the PF region.

GeneCluster 2.0: an advanced toolset for bioarray analysis

SUMMARY

GeneCluster 2.0 is a software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding classification, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting and k-nearest neighbor algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.

AVAILABILITY

http://www.broad.mit.edu/cancer/software

Molecular classification of multiple tumor types

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.

Multiclass cancer diagnosis using tumor gene expression signatures

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

Chemosensitivity prediction by transcriptional profiling

In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells' tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P < 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible.

Cascades of transcriptional induction during human lymphocyte activation

Lymphocyte activation is known to be associated with the induction of genes implicated in cytokine signaling and cellular proliferation. High-density microarrays offer the means to monitor global cellular expression profiles, temporal relationships between classes of transcripts, and alterations associated with human disease or immunosuppression. We sought to determine whether microarray analysis would accurately reflect the normal pattern of gene expression following human T cell activation, and whether the complex expression patterns identified could be analyzed to produce a functional profile of lymphocyte activation. We examined a time course of sequential expression profiles for 6,800 cellular transcripts in human lymphocytes activated with concanavalin A. Expression patterns were grouped using clustering analysis and validated using Northern blotting. Genes known to be induced following T cell activation were accurately identified, and the qualitative patterns of gene expression were well correlated between Northern and microarray analyses. Quantitative differences in gene expression levels were less well correlated between these two techniques. Expression profile analysis revealed the sequential induction of groups of functionally similar genes, whose temporal coregulation underscores known cellular events during T cell activation. This functional "fingerprint" of lymphocyte activation may prove useful for comparisons of lymphocyte responses under experimental conditions and in disease states.

Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks

In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.

Expression analysis with oligonucleotide microarrays reveals that MYC regulates genes involved in growth, cell cycle, signaling, and adhesion

MYC affects normal and neoplastic cell proliferation by altering gene expression, but the precise pathways remain unclear. We used oligonucleotide microarray analysis of 6,416 genes and expressed sequence tags to determine changes in gene expression caused by activation of c-MYC in primary human fibroblasts. In these experiments, 27 genes were consistently induced, and 9 genes were repressed. The identity of the genes revealed that MYC may affect many aspects of cell physiology altered in transformed cells: cell growth, cell cycle, adhesion, and cytoskeletal organization. Identified targets possibly linked to MYC's effects on cell growth include the nucleolar proteins nucleolin and fibrillarin, as well as the eukaryotic initiation factor 5A. Among the cell cycle genes identified as targets, the G1 cyclin D2 and the cyclin-dependent kinase binding protein CksHs2 were induced whereas the cyclin-dependent kinase inhibitor p21(Cip1) was repressed. A role for MYC in regulating cell adhesion and structure is suggested by repression of genes encoding the extracellular matrix proteins fibronectin and collagen, and the cytoskeletal protein tropomyosin. A possible mechanism for MYC-mediated apoptosis was revealed by identification of the tumor necrosis factor receptor associated protein TRAP1 as a MYC target. Finally, two immunophilins, peptidyl-prolyl cis-trans isomerase F and FKBP52, the latter of which plays a role in cell division in Arabidopsis, were up-regulated by MYC. We also explored pattern-matching methods as an alternative approach for identifying MYC target genes. The genes that displayed an expression profile most similar to endogenous Myc in microarray-based expression profiling of myeloid differentiation models were highly enriched for MYC target genes.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation-for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.

Effects of perinatal administration of haloperidol on GH pituitary contents and serum levels during the first postnatal month

Perinatal dopaminergic blockade with haloperidol caused PRL increases in rat pituitary gland and serum which persisted during the first postnatal month. However the effects of dopamine on the synthesis and secretion of GH at these early ages are unknown. With the aim of investigating the effects of this blockade on postnatal GH secretion, haloperidol (1 mg/kg i.p.) was injected daily to pregnant rats from gestational day 16 until delivery and to pups from untreated mothers between postnatal days 2-6. GH pituitary contents and serum levels were measured weekly by RIA during the first postnatal month. The results showed that haloperidol induced a long-term increase in GH pituitary contents as well as a transient increase in serum levels. The results in serum are similar to those from human neonates indicating that dopamine plays a more important role as controller of the GH secretion in newborns than in adults.

Alterations in prolactin secretion during the 1st postnatal month following perinatal dopaminergic blockade with haloperidol

This research was intended to study the effects of perinatal haloperidol administration on the postnatal secretion of prolactin (PRL) with the aim of investigating the existence of a 'critical period' during which the lack of dopamine influence could cause long-term alterations in the secretion of this hormone. A first group of animals, composed of pregnant rats, was injected daily with haloperidol (1 mg/kg) from day 16 of gestation to delivery. A second group of newborn rats received the same dose from days 2 to 6 after birth. Pituitary and serum PRL were measured weekly by radioimmunoassay during the 1st postnatal month in pups from the injected mothers, in postnatally injected rats, and in controls. The results showed a significant increase in the pituitary amounts of PRL that was more intense after the prenatal treatment, especially in the females. In serum, the prenatal treatment induced PRL levels higher than in the controls, whereas the postnatally injected group exhibited a V-shaped response which has been described as characteristic of neuroleptic withdrawal. These data confirm the existence of a 'critical period' during which perinatal administration of haloperidol alters the postnatal PRL production and secretion patterns. The persistence of high PRL contents in pituitary may reflect an alteration in the hormone synthesis and/or an increase in the rate of somatomammotrophes that differentiate into lactotrophes after suppression of dopamine influence. The high PRL levels in serum indicate a failure in the control of PRL release, perhaps after damaging the tuberoinfundibular neurons as a consequence of the high prolactinemia induced by the treatment.