Biological convergence of cancer signatures

Gene signatures for cancer research: A 25-year retrospective and future avenues

Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.

A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer

BACKGROUND

Despite evidence indicating the dominance of cell-of-origin signatures in molecular tumor patterns, translating these genome-wide patterns into actionable insights has been challenging. This study introduces breast cancer cell-of-origin signatures that offer significant prognostic value across all breast cancer subtypes and various clinical cohorts, compared to previously developed genomic signatures.

METHODS

We previously reported that triple hormone receptor (THR) co-expression patterns of androgen (AR), estrogen (ER), and vitamin D (VDR) receptors are maintained at the protein level in human breast cancers. Here, we developed corresponding mRNA signatures (THR-50 and THR-70) based on these patterns to categorize breast tumors by their THR expression levels. The THR mRNA signatures were evaluated across 56 breast cancer datasets (5040 patients) using Kaplan-Meier survival analysis, Cox proportional hazard regression, and unsupervised clustering.

RESULTS

The THR signatures effectively predict both overall and progression-free survival across all evaluated datasets, independent of subtype, grade, or treatment status, suggesting improvement over existing prognostic signatures. Furthermore, they delineate three distinct ER-positive breast cancer subtypes with significant survival in differences-expanding on the conventional two subtypes. Additionally, coupling THR-70 with an immune signature identifies a predominantly ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive cases, as well as an ER-negative subgroup with notably poor outcome, characterized by a 15-fold shorter survival.

CONCLUSIONS

The THR cell-of-origin signature introduces a novel dimension to breast cancer biology, potentially serving as a robust foundation for integrating additional prognostic biomarkers. These signatures offer utility as a prognostic index for stratifying existing breast cancer subtypes and for de novo classification of breast cancer cases. Moreover, THR signatures may also hold promise in predicting hormone treatment responses targeting AR and/or VDR.

ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin

Tumor phenotype is shaped both by transforming genomic alterations and the normal cell-of-origin. We identified a cell-of-origin associated prognostic gene expression signature, ET-9, that correlates with remarkably shorter overall and relapse free breast cancer survival, 8.7 and 6.2 years respectively. The genes associated with the ET-9 signature are regulated by histone deacetylase 7 (HDAC7) partly through ZNF92, a previously unexplored transcription factor with a single PubMed citation since its cloning in 1990s. Remarkably, ZNF92 is distinctively over-expressed in breast cancer compared to other tumor types, on a par with the breast cancer specificity of the estrogen receptor. Importantly, ET-9 signature appears to be independent of proliferation, and correlates with outcome in lymph-node positive, HER2+, post-chemotherapy and triple-negative breast cancers. These features distinguish ET-9 from existing breast cancer prognostic signatures that are generally related to proliferation and correlate with outcome in lymph-node negative, ER-positive, HER2-negative breast cancers. Our results suggest that ET-9 could be also utilized as a predictive signature to select patients for HDAC inhibitor treatment.

Strategies to avoid treatment-induced lineage crisis in advanced prostate cancer

The increasing potency of therapies that target the androgen receptor (AR) signalling axis has correlated with a rise in the proportion of patients with prostate cancer harbouring an adaptive phenotype, termed treatment-induced lineage crisis. This phenotype is characterized by features that include soft-tissue metastasis and/or resistance to standard anticancer therapies. Potent anticancer treatments might force cancer cells to evolve and develop alternative cell lineages that are resistant to primary therapies, a mechanism similar to the generation of multidrug- resistant microorganisms after continued antibiotic use. Herein, we assess the hypothesis that treatment-adapted phenotypes harbour reduced AR expression and/or activity, and acquire compensatory strategies for cell survival. We highlight the striking similarities between castration-resistant prostate cancer and triple-negative breast cancer, another poorly differentiated endocrine malignancy. Alternative treatment paradigms are needed to avoid therapy-induced resistance. Herein, we present a new clinical trial strategy designed to evaluate the potential of rapid drug cycling as an approach to delay the onset of resistance and treatment-induced lineage crisis in patients with metastatic castration-resistant prostate cancer.

An Evaluation of Gene Set Analysis for Biomarker Discovery with Applications to Myeloma Research

In this paper, we evaluate 15 methods for gene set analysis in microarray classification problems. We employ four datasets from myeloma research and three types of biological gene sets, encompassing a total of 12 scenarios. Taking a two-step approach, we first identify important genes within gene sets to create summary gene set scores, we then construct predictive models using the gene set scores as predictors. We propose two powerful linear methods in addition to the well-known SuperPC method for calculating scores. By comparing the 15 gene set methods with methods used in individual-gene analysis, we conclude that, overall, the gene set analysis approach provided more accurate predictions than the individual-gene analysis.

Tamoxifen therapy benefit predictive signature coupled with prognostic signature of post-operative recurrent risk for early stage ER+ breast cancer

Two types of prognostic signatures for predicting recurrent risk of ER+ breast cancer patients have been developed: one type for patients accepting surgery only and another type for patients receiving post-operative tamoxifen therapy. However, the first type of signature cannot distinguish high-risk patients who cannot benefit from tamoxifen therapy, while the second type of signature cannot identify patients who will be at low risk of recurrence even if they accept surgery only. In this study, we proposed to develop two coupled signatures to solve these problems based on within-sample relative expression orderings (REOs) of gene pairs. Firstly, we identified a prognostic signature of post-operative recurrent risk using 544 samples of ER+ breast cancer patients accepting surgery only. Then, applying this drug-free signature to 840 samples of patients receiving post-operative tamoxifen therapy, we recognized 553 samples of patients who would have been at high risk of recurrence if they had accepted surgery only and used these samples to develop a tamoxifen therapy benefit predictive signature. The two coupled signatures were validated in independent data. The signatures developed in this study are robust against experimental batch effects and applicable at the individual levels, which can facilitate the clinical decision of tamoxifen therapy.

S100A4 and its role in metastasis – computational integration of data on biological networks

Characterising signal transduction networks is fundamental to our understanding of biology. However, redundancy and different types of feedback mechanisms make it difficult to understand how variations of the network components contribute to a biological process. In silico modelling of signalling interactions therefore becomes increasingly useful for the development of successful therapeutic approaches. Unfortunately, quantitative information cannot be obtained for all of the proteins or complexes that comprise the network, which limits the usability of computational models. We developed a flexible computational framework for the analysis of biological signalling networks. We demonstrate our approach by studying the mechanism of metastasis promotion by the S100A4 protein, and suggest therapeutic strategies. The advantage of the proposed method is that only limited information (interaction type between species) is required to set up a steady-state network model. This permits a straightforward integration of experimental information where the lack of details are compensated by efficient sampling of the parameter space. We investigated regulatory properties of the S100A4 network and the role of different key components. The results show that S100A4 enhances the activity of matrix metalloproteinases (MMPs), causing higher cell dissociation. Moreover, it leads to an increased stability of the pathological state. Thus, avoiding metastasis in S100A4-expressing tumours requires multiple target inhibition. Moreover, the analysis could explain the previous failure of MMP inhibitors in clinical trials. Finally, our method is applicable to a wide range of biological questions that can be represented as directional networks.

Upregulation of adenylate cyclase 3 (ADCY3) increases the tumorigenic potential of cells by activating the CREB pathway

Adenylate cyclase 3 (ADCY3) is a widely expressed membrane-associated protein in human tissues, which catalyzes the formation of cyclic adenosine-3′,5′-monophosphate (cAMP). However, our transcriptome analysis of gastric cancer tissue samples (NCBI GEO GSE30727) revealed that ADCY3 expression was specifically altered in cancer samples. Here we investigated the tumor-promoting effects of ADCY3 overexpression and confirmed a significant correlation between the upregulation of ADCY3 and Lauren's intestinal-type gastric cancers. ADCY3 overexpression increased cell migration, invasion, proliferation, and clonogenicity in HEK293 cells; conversely, silencing ADCY3 expression in SNU-216 cells reduced these phenotypes. Interestingly, ADCY3 overexpression increased both the mRNA level and activity of matrix metalloproteinase 2 (MMP2) and MMP9 by increasing the levels of cAMP and phosphorylated cAMP-responsive element-binding protein (CREB). Consistent with these findings, treatment with a protein kinase A (PKA) inhibitor decreased MMP2 and MMP9 expression levels in ADCY3-overexpressing cells. Knockdown of ADCY3 expression by stable shRNA in human gastric cancer cells suppressed tumor growth in a tumor xenograft model. Thus, ADCY3 overexpression may exert its tumor-promoting effects via the cAMP/PKA/CREB pathway. Additionally, bisulfite sequencing of the ADCY3 promoter region revealed that gene expression was reduced by hypermethylation of CpG sites, and increased by 5-Aza-2′-deoxycytidine (5-Aza-dC)-induced demethylation. Our study is the first to report an association of ADCY3 with gastric cancer as well as its tumorigenic potentials. In addition, we demonstrate that the expression of ADCY3 is regulated through an epigenetic mechanism. Further study on the mechanism of ADCY3 in tumorigenesis will provide the basis as a new molecular target of gastric cancer.

Tubers from patients with tuberous sclerosis complex are characterized by changes in microtubule biology through ROCK2 signalling

Most patients with tuberous sclerosis complex (TSC) develop cortical tubers that cause severe neurological disabilities. It has been suggested that defects in neuronal differentiation and/or migration underlie the appearance of tubers. However, the precise molecular alterations remain largely unknown. Here, by combining cytological and immunohistochemical analyses of tubers from nine TSC patients (four of them diagnosed with TSC2 germline mutations), we show that alteration of microtubule biology through ROCK2 signalling contributes to TSC neuropathology. All tubers showed a larger number of binucleated neurons than expected relative to control cortex. An excess of normal and altered cytokinetic figures was also commonly observed. Analysis of centrosomal markers suggested increased microtubule nucleation capacity, which was supported by the analysis of an expression dataset from cortical tubers and control cortex, and subsequently linked to under-expression of Rho-associated coiled-coil containing kinase 2 (ROCK2). Thus, augmented microtubule nucleation capacity was observed in mouse embryonic fibroblasts and human fibroblasts deficient in the Tsc2/TSC2 gene product, tuberin. Consistent with ROCK2 under-expression, microtubule acetylation was found to be increased with tuberin deficiency; this alteration was abrogated by rapamycin treatment and mimicked by HDAC6 inhibition. Together, the results of this study support the hypothesis that loss of TSC2 expression can alter microtubule organization and dynamics, which, in turn, deregulate cell division and potentially impair neuronal differentiation.

Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis

BACKGROUND

Gene expression signatures indicative of tumor proliferative capacity and tumor-immune cell interactions have emerged as principal biology-driven predictors of breast cancer outcomes. How these signatures relate to one another in biological and prognostic contexts remains to be clarified.

RESULTS

To investigate the relationship between proliferation and immune gene signatures, we analyzed an integrated dataset of 1,954 clinically annotated breast tumor expression profiles randomized into training and test sets to allow two-way discovery and validation of gene-survival associations. Hierarchical clustering revealed a large cluster of distant metastasis-free survival-associated genes with known immunological functions that further partitioned into three distinct immune metagenes likely reflecting B cells and/or plasma cells; T cells and natural killer cells; and monocytes and/or dendritic cells. A proliferation metagene allowed stratification of cases into proliferation tertiles. The prognostic strength of these metagenes was largely restricted to tumors within the highest proliferation tertile, though intrinsic subtype-specific differences were observed in the intermediate and low proliferation tertiles. In highly proliferative tumors, high tertile immune metagene expression equated with markedly reduced risk of metastasis whereas tumors with low tertile expression of any one of the three immune metagenes were associated with poor outcome despite higher expression of the other two metagenes.

CONCLUSIONS

These findings suggest that a productive interplay among multiple immune cell types at the tumor site promotes long-term anti-metastatic immunity in a proliferation-dependent manner. The emergence of a subset of effective immune responders among highly proliferative tumors has novel prognostic ramifications.

A combined oncogenic pathway signature of BRAF, KRAS and PI3KCA mutation improves colorectal cancer classification and cetuximab treatment prediction

OBJECTIVE

To develop gene expression profiles that characterise KRAS-, BRAF- or PIK3CA-activated- tumours, and to explore whether these profiles might be helpful in predicting the response to the epidermal growth factor receptor (EGFR) pathway inhibitors better than mutation status alone.

DESIGN

Fresh frozen tumour samples from 381 colorectal cancer (CRC) patients were collected and mutations in KRAS, BRAF and PIK3CA were assessed. Using microarray data, three individual oncogenic and a combined model were developed and validated in an independent set of 80 CRC patients, and in a dataset from metastatic CRC patients treated with cetuximab.

RESULTS

175 tumours (45.9%) harboured oncogenic mutations in KRAS (30.2%), BRAF (11.0%) and PIK3CA (11.5%). Activating mutation signatures for KRAS (75 genes), for BRAF (58 genes,) and for PIK3CA (49 genes) were developed. The development of a combined oncogenic pathway signature-classified tumours as 'activated oncogenic', or as 'wildtype-like' with a sensitivity of 90.3% and a specificity of 61.7%. The identified signature revealed other mechanisms that can activate ERK/MAPK pathway in KRAS, BRAF and PIK3CA wildtype patients. The combined signature is associated with response to cetuximab treatment in patients with metastatic CRC (HR 2.51, p<0.0009).

CONCLUSION

A combined oncogenic pathway signature allows the identification of patients with an active EGFR-signalling pathway that could benefit from downstream pathway inhibition.

Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review

Introduction

The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.

Methods

A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.

Results

Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.

Conclusions

The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.

Cancer develops, progresses and responds to therapies through restricted perturbation of the protein-protein interaction network

The products of genes mutated or differentially expressed in cancer tend to occupy central positions within the network of protein-protein interactions, or the interactome network. Integration of different types of gene and protein relationships has considerably increased the understanding of the mechanisms of carcinogenesis, while also enhancing the applicability of expression signatures. In this scenario, however, it remains unknown how cancer develops, progresses and responds to therapies in a potentially controlled manner at the systems level. Here, by applying the concepts of load transfer and cascading failures in power grids, we examine the impact and transmission of cancer-related gene expression changes in the interactome network. Relative to random perturbations, this study reveals topological robustness associated with all cancer conditions. In addition, experimental perturbation of a central cancer node, which consists of over-expression of the α-synuclein (SNCA) protein in MCF7 breast cancer cells, also reveals robustness. Conversely, a search for proteins with an opposite topological impact identifies the autophagy pathway. Mechanistically, the existence of smaller shortest paths among cancer-related proteins appears to be a topological feature that partially contributes to the restricted perturbation of the network. Together, the results of this study suggest that cancer develops, progresses and responds to therapies following controlled, restricted perturbation of the interactome network.

Improving biomarker list stability by integration of biological knowledge in the learning process

BACKGROUND

The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes.

RESULTS

Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy.

CONCLUSIONS

The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html.

Effect of size and heterogeneity of samples on biomarker discovery: synthetic and real data assessment

MOTIVATION

The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods.

METHODS

We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state.

RESULTS

The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results.

Signaling pathways in breast cancer metastasis - novel insights from functional genomics

The advent of genomic profiling technology has brought about revolutionary changes in our understanding of breast cancer metastasis. Gene expression analyses of primary tumors have been used to predict metastatic propensity with high accuracy. Animal models of metastasis additionally offer a platform to experimentally dissect components of the metastasis genetic program. Recent integrated studies have synergized clinical bioinformatic analyses with advanced experimental methodology and begun to uncover the identities and dynamics of signaling programs driving breast cancer metastasis. Such functional genomics studies hold great promise for understanding the genetic basis of metastasis and improving therapeutics for advanced diseases.

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Translating tumor antigens into cancer vaccines

Vaccines represent a strategic successful tool used to prevent or contain diseases with high morbidity and/or mortality. However, while vaccines have proven to be effective in combating pathogenic microorganisms, based on the immune recognition of these foreign antigens, vaccines aimed at inducing effective antitumor activity are still unsatisfactory. Nevertheless, the effectiveness of the two licensed cancer-preventive vaccines targeting tumor-associated viral agents (anti-HBV [hepatitis B virus], to prevent HBV-associated hepatocellular carcinoma, and anti-HPV [human papillomavirus], to prevent HPV-associated cervical carcinoma), along with the recent FDA approval of sipuleucel-T (for the therapeutic treatment of prostate cancer), represents a significant advancement in the field of cancer vaccines and a boost for new studies in the field. Specific active immunotherapies based on anticancer vaccines represent, indeed, a field in continuous evolution and expansion. Significant improvements may result from the selection of the appropriate tumor-specific target antigen (to overcome the peripheral immune tolerance) and/or the development of immunization strategies effective at inducing a protective immune response. This review aims to describe the vast spectrum of tumor antigens and strategies to develop cancer vaccines.

Biological reprogramming in acquired resistance to endocrine therapy of breast cancer

Endocrine therapies targeting the proliferative effect of 17β-estradiol through estrogen receptor α (ERα) are the most effective systemic treatment of ERα-positive breast cancer. However, most breast tumors initially responsive to these therapies develop resistance through molecular mechanisms that are not yet fully understood. The long-term estrogen-deprived (LTED) MCF7 cell model has been proposed to recapitulate acquired resistance to aromatase inhibitors in postmenopausal women. To elucidate this resistance, genomic, transcriptomic and molecular data were integrated into the time course of MCF7-LTED adaptation. Dynamic and widespread genomic changes were observed, including amplification of the ESR1 locus consequently linked to an increase in ERα. Dynamic transcriptomic profiles were also observed that correlated significantly with genomic changes and were predicted to be influenced by transcription factors known to be involved in acquired resistance or cell proliferation (for example, interferon regulatory transcription factor 1 and E2F1, respectively) but, notably, not by canonical ERα transcriptional function. Consistently, at the molecular level, activation of growth factor signaling pathways by EGFR/ERBB/AKT and a switch from phospho-Ser118 (pS118)- to pS167-ERα were observed during MCF7-LTED adaptation. Evaluation of relevant clinical settings identified significant associations between MCF7-LTED and breast tumor transcriptome profiles that characterize ERα-negative status, early response to letrozole and tamoxifen, and recurrence after tamoxifen treatment. In accordance with these profiles, MCF7-LTED cells showed increased sensitivity to inhibition of FGFR-mediated signaling with PD173074. This study provides mechanistic insight into acquired resistance to endocrine therapies of breast cancer and highlights a potential therapeutic strategy.

Piwil2 expressed in various stages of cervical neoplasia is a potential complementary marker for p16

Generally, cancers may undergo the developmental stages of benign proliferation, precancer and invasive cancer. Identification of biomarkers that are expressed throughout the developmental stages will facilitate detection, prevention and therapy of cancer. Piwil2, a member of AGO/PIWI family of proteins, has been suggested to be associated with tumor development. Here we reported that piwil2 can be detected by immunohistochemistry (IHC) in various stages of human cervical squamous cell carcinomas and adenocarcinomas. Interestingly, piwil2 was also detected in some metaplastic epithelial cells as well as histologically "normal" appearing tissues adjacent to malignant lesions. While all the premalignant and malignant lesions expressed varying levels of piwil2, p16(INK4a) (p16), a surrogate indicator of high-risk human papillomavirus (HR-HPV) infection, was detected in only 84.62% of the specimens. In Papanicolaou (Pap) test, piwil2 was also detected in atypical glandular cells (AGC), low-grade (LSIL) and high-grade squamous intraepithelial lesions (HSIL), whereas p16 was not always concomitantly detected in the same specimens. The results suggest that piwil2 might play important roles throughout the process of cervical cancer development and have the potential to be used as a complementary marker for p16(INK4a). It is worth further study to improve the sensitivity and specificity of current screening methods for cervical cancers.

Identification of a panel of ten cell surface protein antigens associated with immunotargeting of leukemias and lymphomas by peripheral blood gammadelta T cells

BACKGROUND

Vgamma9Vdelta2 T lymphocytes are regarded as promising mediators of cancer immunotherapy due to their capacity to eliminate multiple experimental tumors, particularly within those of hematopoietic origin. However, Vgamma9Vdelta2 T-cell based lymphoma clinical trials have suffered from the lack of biomarkers that can be used as prognostic of therapeutic success.

DESIGN AND METHODS

We have conducted a comprehensive study of gene expression in acute lymphoblastic leukemias and non-Hodgkin's lymphomas, aimed at identifying markers of susceptibility versus resistance to Vgamma9Vdelta2 T cell-mediated cytotoxicity. We employed cDNA microarrays and quantitative real-time PCR to screen 20 leukemia and lymphoma cell lines, and 23 primary hematopoietic tumor samples. These data were analyzed using state-of-the-art bioinformatics, and gene expression patterns were correlated with susceptibility to Vgamma9Vdelta2 T cell mediated cytolysis in vitro.

RESULTS

We identified a panel of 10 genes encoding cell surface proteins that were statistically differentially expressed between "gammadelta-susceptible" and "gammadelta-resistant" hematopoietic tumors. Within this panel, 3 genes (ULBP1, TFR2 and IFITM1) were associated with increased susceptibility to Vgamma9Vdelta2 T-cell cytotoxicity, whereas the other 7 (CLEC2D, NRP2, SELL, PKD2, KCNK12, ITGA6 and SLAMF1) were enriched in resistant tumors. Furthermore, some of these candidates displayed a striking variance of expression among primary follicular lymphomas and T-cell acute lymphoblastic leukemias.

CONCLUSIONS

Our results suggest that hematopoietic tumors display a highly variable repertoire of surface proteins that can impact on Vgamma9Vdelta2 cell-mediated immunotargeting. The prognostic value of the proposed markers can now be evaluated in upcoming Vgamma9Vdelta2 T cell-based lymphoma/leukemia clinical trials.