Conference scene: Immune signatures in the tumor and beyond.

Led by key opinion leaders in the field, the Cancer Immunotherapy Consortium of the Cancer Research Institute 2012 Scientific Colloquium included 179 participants who exchanged cutting-edge information on basic, clinical and translational cancer immunology and immunotherapy. The meeting revealed how rapidly this field is advancing. The keynote talk was given by Wolf H Fridman and it described the microenvironment of primary and metastatic human tumors. Participants interacted through oral presentations and panel discussions on topics that included host reactions in tumors, advances in imaging, monitoring therapeutic immune modulation, the benefit and risk of immunotherapy, and immune monitoring activities. In summary, the annual meeting gathered clinicians and scientists from academia, industry and regulatory agencies from around the globe to interact and exchange important scientific advances related to tumor immunobiology and cancer immunotherapy.

Combined clonotyping and gene signatures of individual tumor-reactive CD8 T-cells define their functional relevance following peptide vaccination in melanoma patients

Novel cancer vaccines are capableto efficiently induce and boost humantumor antigen specific T-cells. However,the properties of these CD8T-cells are only partially characterized.For in depth investigation ofT-cells following Melan-A/MART-1peptide vaccination in melanoma patients,we conducted a detailed prospectivestudy at the single cell level.We first sorted individual human naiveand effector CD8 T-cells from peripheralblood by flow cytometry, andtested a modified RT-PCR protocolincluding a global amplification ofexpressed mRNAs to obtain sufficientcDNAfromsingle cells.We successfullydetected the expression ofseveral specific genes of interest evendown to 106-fold dilution (equivalentto 10-5 cell). We then analyzed tumor-specific effector memory (EM)CD8T-cell subpopulations ex vivo, assingle cells from vaccinated melanomapatients. To elucidate the hallmarksof effective immunity the genesignatures were defined by a panel ofgenes related to effector functions(e.g. IFN-, granzyme B, perforin),and individual clonotypes were identifiedaccording to the expression ofdistinct T-cell receptors (TCR). Usingthis novel single cell analysis approach,we observed that T-cell differentiationis clonotype dependent,with a progressive restriction in TCRBV clonotype diversity from EMCD28pos to EMCD28neg subsets. However,the effector function gene imprintingis clonotype-independent,but dependent on differentiation,since it correlates with the subset oforigin (EMCD28pos or EMCD28neg). We also conducted a detailedcomparative analysis after vaccinationwith natural vs. analog Melan-Apeptide. We found that the peptideused for vaccination determines thefunctional outcome of individualT-cell clonotypes, with native peptideinducing more potent effector functions.Yet, selective clonotypic expansionwith differentiation was preservedregardless of the peptide usedfor vaccination. In summary, the exvivo single cell RT-PCR approach ishighly sensitive and efficient, andrepresents a reliable and powerfultool to refine our current view of molecularprocesses taking place duringT-cell differentiation.

Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R (adj) (2) > 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change.

Statistical evaluation of the influence of writing postures on on-line signatures. Study of the impact of time.

The aim of this study is to investigate the influence of unusual writing positions on a person's signature, in comparison to a standard writing position. Ten writers were asked to sign their signature six times, in each of four different writing positions, including the standard one. In order to take into consideration the effect of the day-to-day variation, this same process was repeated over 12 sessions, giving a total of 288 signatures per subject. The signatures were collected simultaneously in an off-line and on-line acquisition mode, using an interactive tablet and a ballpoint pen. Unidimensional variables (height to width ratio; time with or without in air displacement) and time-dependent variables (pressure; X and Y coordinates; altitude and azimuth angles) were extracted from each signature. For the unidimensional variables, the position effect was assessed through ANOVA and Dunnett contrast tests. Concerning the time-dependent variables, the signatures were compared by using dynamic time warping, and the position effect was evaluated through classification by linear discriminant analysis. Both of these variables provided similar results: no general tendency regarding the position factor could be highlighted. The influence of the position factor varies according to the subject as well as the variable studied. The impact of the session factor was shown to cover the impact that could be ascribed to the writing position factor. Indeed, the day-to-day variation has a greater effect than the position factor on the studied signature variables. The results of this study suggest guidelines for best practice in the area of signature comparisons and demonstrate the importance of a signature collection procedure covering an adequate number of sampling sessions, with a sufficient number of samples per session.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

Individual vocal signatures in barn owl nestlings: does individual recognition have an adaptive role in sibling vocal competition?

To compete over limited parental resources, young animals communicate with their parents and siblings by producing honest vocal signals of need. Components of begging calls that are sensitive to food deprivation may honestly signal need, whereas other components may be associated with individual-specific attributes that do not change with time such as identity, sex, absolute age and hierarchy. In a sib-sib communication system where barn owl (Tyto alba) nestlings vocally negotiate priority access to food resources, we show that calls have individual signatures that are used by nestlings to recognize which siblings are motivated to compete, even if most vocalization features vary with hunger level. Nestlings were more identifiable when food-deprived than food-satiated, suggesting that vocal identity is emphasized when the benefit of winning a vocal contest is higher. In broods where siblings interact iteratively, we speculate that individual-specific signature permits siblings to verify that the most vocal individual in the absence of parents is the one that indeed perceived the food brought by parents. Individual recognition may also allow nestlings to associate identity with individual-specific characteristics such as position in the within-brood dominance hierarchy. Calls indeed revealed age hierarchy and to a lower extent sex and absolute age. Using a cross-fostering experimental design, we show that most acoustic features were related to the nest of origin (but not the nest of rearing), suggesting a genetic or an early developmental effect on the ontogeny of vocal signatures. To conclude, our study suggests that sibling competition has promoted the evolution of vocal behaviours that signal not only hunger level but also intrinsic individual characteristics such as identity, family, sex and age.

Gene expression signatures delineate biological and prognostic subgroups in peripheral T-cell lymphoma

Peripheral T-cell lymphoma (PTCL) encompasses a heterogeneous group of neoplasms with generally poor clinical outcome. Currently 50% of PTCL cases are not classifiable: PTCL-not otherwise specified (NOS). Gene-expression profiles on 372 PTCL cases were analyzed and robust molecular classifiers and oncogenic pathways that reflect the pathobiology of tumor cells and their microenvironment were identified for major PTCL-entities, including 114 angioimmunoblastic T-cell lymphoma (AITL), 31 anaplastic lymphoma kinase (ALK)-positive and 48 ALK-negative anaplastic large cell lymphoma, 14 adult T-cell leukemia/lymphoma and 44 extranodal NK/T-cell lymphoma that were further separated into NK-cell and gdT-cell lymphomas. Thirty-seven percent of morphologically diagnosed PTCL-NOS cases were reclassified into other specific subtypes by molecular signatures. Reexamination, immunohistochemistry, and IDH2 mutation analysis in reclassified cases supported the validity of the reclassification. Two major molecular subgroups can be identified in the remaining PTCL-NOS cases characterized by high expression of either GATA3 (33%; 40/121) or TBX21 (49%; 59/121). The GATA3 subgroup was significantly associated with poor overall survival (P = .01). High expression of cytotoxic gene-signature within the TBX21 subgroup also showed poor clinical outcome (P = .05). In AITL, high expression of several signatures associated with the tumor microenvironment was significantly associated with outcome. A combined prognostic score was predictive of survival in an independent cohort (P = .004).

Key electrophysiological, molecular, and metabolic signatures of sleep and wakefulness revealed in primary cortical cultures.

Although sleep is defined as a behavioral state, at the cortical level sleep has local and use-dependent features suggesting that it is a property of neuronal assemblies requiring sleep in function of the activation experienced during prior wakefulness. Here we show that mature cortical cultured neurons display a default state characterized by synchronized burst-pause firing activity reminiscent of sleep. This default sleep-like state can be changed to transient tonic firing reminiscent of wakefulness when cultures are stimulated with a mixture of waking neurotransmitters and spontaneously returns to sleep-like state. In addition to electrophysiological similarities, the transcriptome of stimulated cultures strikingly resembles the cortical transcriptome of sleep-deprived mice, and plastic changes as reflected by AMPA receptors phosphorylation are also similar. We used our in vitro model and sleep-deprived animals to map the metabolic pathways activated by waking. Only a few metabolic pathways were identified, including glycolysis, aminoacid, and lipids. Unexpectedly large increases in lysolipids were found both in vivo after sleep deprivation and in vitro after stimulation, strongly suggesting that sleep might play a major role in reestablishing the neuronal membrane homeostasis. With our in vitro model, the cellular and molecular consequences of sleep and wakefulness can now be investigated in a dish.

Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures.

INTRODUCTION: Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance. METHOD: To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures. RESULTS: Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes. CONCLUSION: This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.

Two Signatures For A New Blood-Based Test For Colorectal Cancer Screening

Background: Detection rates for adenoma and early colorectal cancer (CRC) are unsatisfactory due to low compliance towards invasive screening procedures such as colonoscopy. There is a large unmet screening need calling for an accurate, non-invasive and cost-effective test to screen for early neoplastic and pre-neoplastic lesions. Our goal is to identify effective biomarker combinations to develop a screening test aimed at detecting precancerous lesions and early CRC stages, based on a multigene assay performed on peripheral blood mononuclear cells (PBMC).Methods: A pilot study was conducted on 92 subjects. Colonoscopy revealed 21 CRC, 30 adenomas larger than 1 cm and 41 healthy controls. A panel of 103 biomarkers was selected by two approaches: a candidate gene approach based on literature review and whole transcriptome analysis of a subset of this cohort by Illumina TAG profiling. Blood samples were taken from each patient and PBMC purified. Total RNA was extracted and the 103 biomarkers were tested by multiplex RT-qPCR on the cohort. Different univariate and multivariate statistical methods were applied on the PCR data and 60 biomarkers, with significant p-value (< 0.01) for most of the methods, were selected.Results: The 60 biomarkers are involved in several different biological functions, such as cell adhesion, cell motility, cell signaling, cell proliferation, development and cancer. Two distinct molecular signatures derived from the biomarker combinations were established based on penalized logistic regression to separate patients without lesion from those with CRC or adenoma. These signatures were validated using bootstrapping method, leading to a separation of patients without lesion from those with CRC (Se 67%, Sp 93%, AUC 0.87) and from those with adenoma larger than 1cm (Se 63%, Sp 83%, AUC 0.77). In addition, the organ and disease specificity of these signatures was confirmed by means of patients with other cancer types and inflammatory bowel diseases.Conclusions: The two defined biomarker combinations effectively detect the presence of CRC and adenomas larger than 1 cm with high sensitivity and specificity. A prospective, multicentric, pivotal study is underway in order to validate these results in a larger cohort.

Tumor-host interactions Part 1: Stromal signatures of breast and prostate cancer Part 2: GLG1 and the control of the secretory pathway in tumor cells

Tumor-host interaction is a key determinant during cancer progression, from primary tumor growth to metastatic dissemination. At each step, tumor cells have to adapt to and subvert different types of microenvironment, leading to major phenotypic and genotypic alterations that affect both tumor and surrounding stromal compartments. Understanding the molecular mechanisms that govern tumor-host interplay may be essential for better comprehension of tumorigenesis in an effort to improve current anti-cancer therapies. The present work is composed of two projects that address tumor-host interactions from two different perspectives, the first focusing on the characterization of tumor-associated stroma and the second on membrane trafficking in tumor cells. Part 1. To selectively address stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to analyze the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Comparison showed that invasive breast and prostate cancer elicit distinct, tumor-specific stromal responses, with a limited panel of shared induced and/or repressed genes. Both breast and prostate tumor-specific deregulated stromal gene sets displayed statistically significant survival-predictive ability for their respective tumor type. By contrast, a stromal gene signature common to both tumor types did not display prognostic value, although expression of two individual genes within this common signature was found to be associated with patient survival. Part 2. GLG1 is known as an E-selectin ligand and an intracellular FGF receptor, depending on cell type and context. Immunohistochemical and immunofluorescence analyses showed that GLG1 is primarily localized in the Golgi of human tumor cells, a central location in the biosynthetic/secretory pathways. GLG1 has been shown to interact with and to recruit the ARF GEF BIGI to the Golgi membrane. Depletion of GLG1 or BIGI markedly reduced ARF3 membrane localization and activation, and altered the Golgi structure. Interestingly, these perturbations did not impair constitutive secretion in general, but rather seemed to impair secretion of a specific subset of proteins that includes MMP-9. Thus, GLG1 coordinates ARF3 activation by recruiting BIGI to the Golgi membrane, thereby affecting secretion of specific molecules. - Les interactions tumeur-hôte constituent un élément essentiel à la progression tumorale, de la croissance de la tumeur primaire à la dissémination des métastases. A chaque étape, les cellules tumorales doivent s'adapter à différents types de microenvironnement et les détourner à leur propre avantage, donnant lieu à des altérations phénotypiques et génotypiques majeures qui affectent aussi bien la tumeur elle-même que le compartiment stromal environnant. L'étude des mécanismes moléculaires qui régissent les interactions tumeur-hôte constitue une étape essentielle pour une meilleure compréhension du processus de tumorigenèse dans le but d'améliorer les thérapies anti cancer existantes. Le travail présenté ici est composé de deux projets qui abordent la problématique des interactions tumeur-hôte selon différentes perspectives, le premier se concentrant sur la caractérisation du stroma tumoral et le second sur le trafic intracellulaire des cellules tumorales. Partie 1. Pour examiner les changements d'expression des gènes dans le stroma en réponse à la progression du cancer, des puces à ADN Affymetrix ont été utilisées afin d'analyser les transcriptomes des cellules stromales issues de carcinomes invasifs du sein et de la prostate et collectées par microdissection au laser. L'analyse comparative a montré que les cancers invasifs du sein et de la prostate provoquent des réponses stromales spécifiques à chaque type de tumeur, et présentent peu de gènes induits ou réprimés de façon similaire. L'ensemble des gènes dérégulés dans le stroma associé au cancer du sein, ou à celui de la prostate, présente une valeur pronostique pour les patients atteints d'un cancer du sein, respectivement de la prostate. En revanche, la signature stromale commune aux deux types de cancer n'a aucune valeur prédictive, malgré le fait que l'expression de deux gènes présents dans cette liste soit liée à la survie des patients. Partie 2. GLG1 est connu comme un ligand des sélectines E ainsi que comme récepteur intracellulaire pour des facteurs de croissances FGFs selon le type de cellule dans lequel il est exprimé. Des analyses immunohistochimiques et d'immunofluorescence ont montré que dans les cellules tumorales, GLG1 est principalement localisé au niveau de l'appareil de Golgi, une place centrale dans la voie biosynthétique et sécrétoire. Nous avons montré que GLG1 interagit avec la protéine BIGI et participe à son recrutement à la membrane du Golgi. L'absence de GLG1 ou de BIGI réduit drastiquement le pool d'ARF3 associé aux membranes ainsi que la quantité d'ARF3 activés, et modifie la structure de l'appareil de Golgi. Il est particulièrement intéressant de constater que ces perturbations n'ont pas d'effet sur la sécrétion constitutive en général, mais semblent plutôt affecter la sécrétion spécifique d'un sous-groupe défini de protéines comprenant MMP-9. GLG1 coordonne donc l'activation de ARF3 en recrutant BIGI à la membrane du Golgi, agissant par ce moyen sur la sécrétion de molécules spécifiques.