“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, andmany others, offer tremendous promise for the personalized treatment of cancer. Thesetechnologies measure the amount of specific proteins, RNA, DNA or other moleculartargets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumorspecimens are heterogeneous; an individual specimen typically contains unknownamounts of multiple tissues types. Thus, the measured molecular concentrations resultfrom an unknown mixture of tissue types, and must be normalized to account for thecomposition of the mixture.For example, a breast tumor biopsy may contain normal, dysplastic and cancerousepithelial cells, as well as stromal components (fatty and connective tissue) and bloodand lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic andcancerous epithelial cells. The remaining tissue components serve to “contaminate”the signal of interest. The proportion of each of the tissue components changes asa function of patient characteristics (e.g., age), and varies spatially across the tumorregion. Because each of the tissue components produces a different molecular signature,and the amount of each tissue type is specimen dependent, we must estimate the tissuecomposition of the specimen, and adjust the molecular signal for this composition.Using the idea of a chemical mass balance, we consider the total measured concentrationsto be a weighted sum of the individual tissue signatures, where weightsare determined by the relative amounts of the different tissue types. We develop acompositional source apportionment model to estimate the relative amounts of tissuecomponents in a tumor specimen. We then use these estimates to infer the tissuespecificconcentrations of key molecular targets for sub-typing individual tumors. Weanticipate these specific measurements will greatly improve our ability to discriminatebetween different classes of tumors, and allow more precise matching of each patient tothe appropriate treatment

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).

Gene Regulation in The Human Immune System. Gene Expression Signatures of Th2 Cell Differentiation, Type 1 Diabetes, and Intrauterine Immune Adaptation

The human immune system is constantly interacting with the surrounding stimuli and microorganisms. However, when directed against self or harmless antigens, these vital defense mechanisms can cause great damage. In addition, the understanding the underlying mechanism of several human diseases caused by aberrant immune cell functions, for instance type 1 diabetes and allergies, remains far from being complete. In this Ph.D. study these questions were addressed using genome-wide transcriptomic analyses. Asthma and allergies are characterized by a hyperactive response of the T helper 2 (Th2) immune cells. In this study, the target genes of the STAT6 transcription factor in naïve human T cells were identified with RNAi for the first time. STAT6 was shown to act as a central activator of the genes expression upon IL-4 signaling, with both direct and indirect effects on Th2 cell transcriptome. The core transcription factor network induced by IL-4 was identified from a kinetic analysis of the transcriptome. Type 1 diabetes is an autoimmune disease influenced by both the genetic susceptibility of an individual and the disease-triggering environmental factors. To improve understanding of the autoimmune processes driving pathogenesis in the prediabetic phase in humans, a unique series of prospective whole-blood RNA samples collected from HLA-susceptible children in the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study was studied. Changes in different timewindows of the pathogenesis process were identified, and especially the type 1 interferon response was activated early and throughout the preclinical T1D. The hygiene hypothesis states that allergic diseases, and lately also autoimmune diseases, could be prevented by infections and other microbial contacts acquired in early childhood, or even prenatally. To study the effects of the standard of hygiene on the development of neonatal immune system, cord blood samples from children born in Finland (high standard of living), Estonia (rapid economic growth) and Russian Karelia (low standard of living) were compared. Children born in Russian Karelia deviated from Finnish and Estonian children in many aspects of the neonatal immune system, which was developmentally more mature in Karelia, resembling that of older infants. The results of this thesis offer significant new information on the regulatory networks associated with immune-mediated diseases in human. The results will facilitate understanding and further research on the role of the identified target genes and mechanisms driving the allergic inflammation and type 1 diabetes, hopefully leading to a new era of drug development.

“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, and many others, offer tremendous promise for the personalized treatment of cancer. These technologies measure the amount of specific proteins, RNA, DNA or other molecular targets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumor specimens are heterogeneous; an individual specimen typically contains unknown amounts of multiple tissues types. Thus, the measured molecular concentrations result from an unknown mixture of tissue types, and must be normalized to account for the composition of the mixture. For example, a breast tumor biopsy may contain normal, dysplastic and cancerous epithelial cells, as well as stromal components (fatty and connective tissue) and blood and lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic and cancerous epithelial cells. The remaining tissue components serve to “contaminate” the signal of interest. The proportion of each of the tissue components changes as a function of patient characteristics (e.g., age), and varies spatially across the tumor region. Because each of the tissue components produces a different molecular signature, and the amount of each tissue type is specimen dependent, we must estimate the tissue composition of the specimen, and adjust the molecular signal for this composition. Using the idea of a chemical mass balance, we consider the total measured concentrations to be a weighted sum of the individual tissue signatures, where weights are determined by the relative amounts of the different tissue types. We develop a compositional source apportionment model to estimate the relative amounts of tissue components in a tumor specimen. We then use these estimates to infer the tissuespecific concentrations of key molecular targets for sub-typing individual tumors. We anticipate these specific measurements will greatly improve our ability to discriminate between different classes of tumors, and allow more precise matching of each patient to the appropriate treatment

Identification of novel cholesteatoma-related gene expression signatures using full-genome microarrays

This study demonstrates that the expression profile of cholesteatoma is similar to a metastatic tumour and chronically inflamed tissue. Based on the investigated profiles we present novel protein-protein interaction and signal transduction networks, which include cholesteatoma-regulated transcripts and may be of great value for drug targeting and therapy development.

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.

A novel gene expression signature in peripheral blood mononuclear cells for early detection of colorectal cancer.

BACKGROUND: Early detection and treatment of colorectal adenomatous polyps (AP) and colorectal cancer (CRC) is associated with decreased mortality for CRC. However, accurate, non-invasive and compliant tests to screen for AP and early stages of CRC are not yet available. A blood-based screening test is highly attractive due to limited invasiveness and high acceptance rate among patients. AIM: To demonstrate whether gene expression signatures in the peripheral blood mononuclear cells (PBMC) were able to detect the presence of AP and early stages CRC. METHODS: A total of 85 PBMC samples derived from colonoscopy-verified subjects without lesion (controls) (n = 41), with AP (n = 21) or with CRC (n = 23) were used as training sets. A 42-gene panel for CRC and AP discrimination, including genes identified by Digital Gene Expression-tag profiling of PBMC, and genes previously characterised and reported in the literature, was validated on the training set by qPCR. Logistic regression analysis followed by bootstrap validation determined CRC- and AP-specific classifiers, which discriminate patients with CRC and AP from controls. RESULTS: The CRC and AP classifiers were able to detect CRC with a sensitivity of 78% and AP with a sensitivity of 46% respectively. Both classifiers had a specificity of 92% with very low false-positive detection when applied on subjects with inflammatory bowel disease (n = 23) or tumours other than CRC (n = 14). CONCLUSION: This pilot study demonstrates the potential of developing a minimally invasive, accurate test to screen patients at average risk for colorectal cancer, based on gene expression analysis of peripheral blood mononuclear cells obtained from a simple blood sample.

Gene expression profiling of human glioblastomas for the identification of predictive factors and characterisation of molecular mechanism implicated in response to therapy and outcome

ABSTRACT Poor outcome for glioblastoma patients is largely due to resistance to chemoradiation therapy. While epigenetic inactivation of MGMT mediated DNA repair is highly predictive for benefit from the alkylating agent therapy Temozolomide, additional mechanisms for resistance associated with molecular alterations exist. Furthermore, new concepts in cancer suggest that resistance to treatment may be linked to cancer stem cells that escape therapy and act as source for tumour recurrence. We determined gene expression signatures associated with outcome in glioblastoma patients enrolled in a phase II and phase III clinical trial establishing the new combination therapy of radiation plus concomitant and adjuvant Temozolomide. Correlating stable gene clusters emerging from unsupervised analysis with survival of 42 treated patients identified a number of biological processes associated with outcome. Most prominent, a gene cluster dominated by HOX genes and comprising PROM1, was associated with resistance. PROM1 encodes CD133, a marker for a subpopulation of tumour cells enriched for glioblastoma stem- like cells. The core of this correlated HOX cluster was comprised in the top genes of a "self-renewal signature" defined in a mouse model for MLL-AF9 initiated leukaemia. The association of the HOX gene cluster with tumour resistance was confirmed in two external data sets of 146 malignant glioma As additional resistance factors we identified over-expression of the epidermal growth factor receptor gene, EGFR, while increased gene expression related to biological features of tumour host interaction, including markers for tumour vascular and cell adhesion, and innate immune response, were associated with better outcome. The "self-renewal" signature associated with resistance to the new combination chemoradiation therapy provides first clinical evidence that glioma stem like cells may implicated in resistance in a uniformly treated cohort of glioblastoma patients. This study underlines the need to target the tumour stem cell compartment, and provides some testable hypothesis for biological mechanisms relevant for malignant behaviour of glioblastoma that may be targeted in new treatment approaches. Résumé Le glioblastome, tumeur cérébrale primaire maligne la plus fréquente, est connue pour son mauvais pronostique. Des avancées chimiothérapeutiques récentes avec des agents alkylants comme le témozolomide (TMZ), ont permis une amélioration notable dans la survie de certains patients. Les bénéficiaires ont la caractéristique commune de présenter une particularité génétique, la methylation du MGMT (methylguanine methyltransferase). Néanmoins, d'autres mécanismes de résistance en fonction des aberrations moléculaires existent. Nous avons établi les profils d'expressions génétiques des patients traités par irradiation et TMZ dans des études cliniques de phase II et III. En combinant des méthodes non-supervisées et supervisées, de l'étude de la cohorte des patients traités nous avons découvert des groupes de gènes associés à la survie. Un ensemble de gènes contenant les gènes Hox semble lié au mécanisme de résistance au traitement. Récemment, les gènes Hox ont été décrits comme faisant partie d"une signature d'autorenouvellement (self-renewal) des cellules souches cancéreuses de la leucémie. L'autorenouvellement est un processus grâce auquel les cellules souches se maintiennent tout au long de la vie. Cette association à la résistance est confirmée dans deux autres études indépendantes. Un autre facteur de résistance au traitement est la surexpression du gène EGFR. D'autre part, deux groupes de gènes associés à la relation entre hôte-tumeur tels que les marqueurs des vaisseaux tumoraux et de la réponse immunitaire innée s'avèrent avoir un effet positif sur la survie des patients traités. La découverte de la signature d'autorenouvellement comme facteur de résistance à la nouvelle chimio-radiothérapie offre une preuve clinique que les cellules souches cancéreuses sont impliquées dans la résistance au traitement. If est donc logique de penser que le traitement ciblé contre des cellules souches cancéreuses va dans l'avenir permettre des thérapies anticancéreuses plus performantes.

Pheochromocytoma in rats with multiple endocrine neoplasia (MENX) shares gene expression patterns with human pheochromocytoma

Pheochromocytomas are rare neoplasias of neural crest origin arising from chromaffin cells of the adrenal medulla and sympathetic ganglia (extra-adrenal pheochromocytoma). Pheochromocytoma that develop in rats homozygous for a loss-of-function mutation in p27Kip1 (MENX syndrome) show a clear progression from hyperplasia to tumor, offering the possibility to gain insight into tumor pathobiology. We compared the gene-expression signatures of both adrenomedullary hyperplasia and pheochromocytoma with normal rat adrenal medulla. Hyperplasia and tumor show very similar transcriptome profiles, indicating early determination of the tumorigenic signature. Overrepresentation of developmentally regulated neural genes was a feature of the rat lesions. Quantitative RT-PCR validated the up-regulation of 11 genes, including some involved in neural development: Cdkn2a, Cdkn2c, Neurod1, Gal, Bmp7, and Phox2a. Overexpression of these genes precedes histological changes in affected adrenal glands. Their presence at early stages of tumorigenesis indicates they are not acquired during progression and may be a result of the lack of functional p27Kip1. Adrenal and extra-adrenal pheochromocytoma development clearly follows diverged molecular pathways in MENX rats. To correlate these findings to human pheochromocytoma, we studied nine genes overexpressed in the rat lesions in 46 sporadic and familial human pheochromocytomas. The expression of GAL, DGKH, BMP7, PHOX2A, L1CAM, TCTE1, EBF3, SOX4, and HASH1 was up-regulated, although with different frequencies. Immunohistochemical staining detected high L1CAM expression selectively in 27 human pheochromocytomas but not in 140 nonchromaffin neuroendocrine tumors. These studies reveal clues to the molecular pathways involved in rat and human pheochromocytoma and identify previously unexplored biomarkers for clinical use.

Gene-expression profiling reveals distinct expression patterns for classic versus variant Merkel cell phenotypes and new classifier genes to distinguish Merkel cell from small-cell lung carcinoma

Merkel cell carcinoma (MCC) is a rare aggressive skin tumor which shares histopathological and genetic features with small-cell lung carcinoma (SCLC), both are of neuroendocrine origin. Comparable to SCLC, MCC cell lines are classified into two different biochemical subgroups designated as 'Classic' and 'Variant'. With the aim to identify typical gene-expression signatures associated with these phenotypically different MCC cell lines subgroups and to search for differentially expressed genes between MCC and SCLC, we used cDNA arrays to pro. le 10 MCC cell lines and four SCLC cell lines. Using significance analysis of microarrays, we defined a set of 76 differentially expressed genes that allowed unequivocal identification of Classic and Variant MCC subgroups. We assume that the differential expression levels of some of these genes reflect, analogous to SCLC, the different biological and clinical properties of Classic and Variant MCC phenotypes. Therefore, they may serve as useful prognostic markers and potential targets for the development of new therapeutic interventions specific for each subgroup. Moreover, our analysis identified 17 powerful classifier genes capable of discriminating MCC from SCLC. Real-time quantitative RT-PCR analysis of these genes on 26 additional MCC and SCLC samples confirmed their diagnostic classification potential, opening opportunities for new investigations into these aggressive cancers.

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes

Background: Prostate cancer cells in primary tumors have been typed CD10(-)/CD13(-)/CD24(hi)/CD26(+)/CD38(lo)/CD44(-)/CD104(-). This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure. Methods: CD26(+) cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells. Results: The G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines. Conclusions: Genes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.

Mutant huntingtin's effects on striatal gene expression in mice recapitulate changes observed in human Huntington's disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage.

To test the hypotheses that mutant huntingtin protein length and wild-type huntingtin dosage have important effects on disease-related transcriptional dysfunction, we compared the changes in mRNA in seven genetic mouse models of Huntington's disease (HD) and postmortem human HD caudate. Transgenic models expressing short N-terminal fragments of mutant huntingtin (R6/1 and R6/2 mice) exhibited the most rapid effects on gene expression, consistent with previous studies. Although changes in the brains of knock-in and full-length transgenic models of HD took longer to appear, 15- and 22-month CHL2(Q150/Q150), 18-month Hdh(Q92/Q92) and 2-year-old YAC128 animals also exhibited significant HD-like mRNA signatures. Whereas it was expected that the expression of full-length huntingtin transprotein might result in unique gene expression changes compared with those caused by the expression of an N-terminal huntingtin fragment, no discernable differences between full-length and fragment models were detected. In addition, very high correlations between the signatures of mice expressing normal levels of wild-type huntingtin and mice in which the wild-type protein is absent suggest a limited effect of the wild-type protein to change basal gene expression or to influence the qualitative disease-related effect of mutant huntingtin. The combined analysis of mouse and human HD transcriptomes provides important temporal and mechanistic insights into the process by which mutant huntingtin kills striatal neurons. In addition, the discovery that several available lines of HD mice faithfully recapitulate the gene expression signature of the human disorder provides a novel aspect of validation with respect to their use in preclinical therapeutic trials.

Integrated Analysis of High-Resolution Gene Expression and Copy Number Profiling Identified Biallelic Deletion of CDKN2A/2B Tumor Suppressor Locus As the Most Frequent and Unique Genomic Abnormality in Diffuse Large B-Cell Lymphoma (DLBCL) with Strong Prognostic Value in Both GCB and ABC Subtypes and Not Overcome by a Dose-Intensive Immunochemotherapy Regimen Plus Rituximab. Results of a Prospective GELA Clinical Trial Program

Background and aim of the study: Genomic gains and losses play a crucial role in the development and progression of DLBCL and are closely related to gene expression profiles (GEP), including the germinal center B-cell like (GCB) and activated B-cell like (ABC) cell of origin (COO) molecular signatures. To identify new oncogenes or tumor suppressor genes (TSG) involved in DLBCL pathogenesis and to determine their prognostic values, an integrated analysis of high-resolution gene expression and copy number profiling was performed. Patients and methods: Two hundred and eight adult patients with de novo CD20+ DLBCL enrolled in the prospective multicentric randomized LNH-03 GELA trials (LNH03-1B, -2B, -3B, 39B, -5B, -6B, -7B) with available frozen tumour samples, centralized reviewing and adequate DNA/RNA quality were selected. 116 patients were treated by Rituximab(R)-CHOP/R-miniCHOP and 92 patients were treated by the high dose (R)-ACVBP regimen dedicated to patients younger than 60 years (y) in frontline. Tumour samples were simultaneously analysed by high resolution comparative genomic hybridization (CGH, Agilent, 144K) and gene expression arrays (Affymetrix, U133+2). Minimal common regions (MCR), as defined by segments that affect the same chromosomal region in different cases, were delineated. Gene expression and MCR data sets were merged using Gene expression and dosage integrator algorithm (GEDI, Lenz et al. PNAS 2008) to identify new potential driver genes. Results: A total of 1363 recurrent (defined by a penetrance > 5%) MCRs within the DLBCL data set, ranging in size from 386 bp, affecting a single gene, to more than 24 Mb were identified by CGH. Of these MCRs, 756 (55%) showed a significant association with gene expression: 396 (59%) gains, 354 (52%) single-copy deletions, and 6 (67%) homozygous deletions. By this integrated approach, in addition to previously reported genes (CDKN2A/2B, PTEN, DLEU2, TNFAIP3, B2M, CD58, TNFRSF14, FOXP1, REL...), several genes targeted by gene copy abnormalities with a dosage effect and potential physiopathological impact were identified, including genes with TSG activity involved in cell cycle (HACE1, CDKN2C) immune response (CD68, CD177, CD70, TNFSF9, IRAK2), DNA integrity (XRCC2, BRCA1, NCOR1, NF1, FHIT) or oncogenic functions (CD79b, PTPRT, MALT1, AUTS2, MCL1, PTTG1...) with distinct distribution according to COO signature. The CDKN2A/2B tumor suppressor locus (9p21) was deleted homozygously in 27% of cases and hemizygously in 9% of cases. Biallelic loss was observed in 49% of ABC DLBCL and in 10% of GCB DLBCL. This deletion was strongly correlated to age and associated to a limited number of additional genetic abnormalities including trisomy 3, 18 and short gains/losses of Chr. 1, 2, 19 regions (FDR < 0.01), allowing to identify genes that may have synergistic effects with CDKN2A/2B inactivation. With a median follow-up of 42.9 months, only CDKN2A/2B biallelic deletion strongly correlates (FDR p.value < 0.01) to a poor outcome in the entire cohort (4y PFS = 44% [32-61] respectively vs. 74% [66-82] for patients in germline configuration; 4y OS = 53% [39-72] vs 83% [76-90]). In a Cox proportional hazard prediction of the PFS, CDKN2A/2B deletion remains predictive (HR = 1.9 [1.1-3.2], p = 0.02) when combined with IPI (HR = 2.4 [1.4-4.1], p = 0.001) and GCB status (HR = 1.3 [0.8-2.3], p = 0.31). This difference remains predictive in the subgroup of patients treated by R-CHOP (4y PFS = 43% [29-63] vs. 66% [55-78], p=0.02), in patients treated by R-ACVBP (4y PFS = 49% [28-84] vs. 83% [74-92], p=0.003), and in GCB (4y PFS = 50% [27-93] vs. 81% [73-90], p=0.02), or ABC/unclassified (5y PFS = 42% [28-61] vs. 67% [55-82] p = 0.009) molecular subtypes (Figure 1). Conclusion: We report for the first time an integrated genetic analysis of a large cohort of DLBCL patients included in a prospective multicentric clinical trial program allowing identifying new potential driver genes with pathogenic impact. However CDKN2A/2B deletion constitutes the strongest and unique prognostic factor of chemoresistance to R-CHOP, regardless the COO signature, which is not overcome by a more intensified immunochemotherapy. Patients displaying this frequent genomic abnormality warrant new and dedicated therapeutic approaches.

Minimization of biosynthetic costs in adaptive gene expression responses of yeast to environmental changes

Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism. This fine-tuning is achieved through regulation of both gene expression and protein activity, and it is shaped by various physiological requirements. Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile, generating a suitable adaptive response to each environmental change. Although some of the requirements are stress specific, it is likely that others are common to various situations. We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses. To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression. Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress. These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis. In one group, significant trends towards downregulation of large proteins and upregulation of small ones are observed. In the other group we find no such trends. These results are consistent with resource limitation being important in the evolution of the first group of stress responses.