Prediction of relapse in paediatric pre-B acute lymphoblastic leukaemia using a three-gene risk index

Genetic Profiling in Children With Acute Lymphoblastic Leukemia Referred for Allogeneic Hematopoietic Stem Cell Transplantation

Introduction

Hematopoietic stem cell transplantation (HSCT) is the essential and often the only curative therapeutic option in high risk and relapsed pediatric acute lymphoblastic leukemia (ALL).

Methods

The objective of the study was to investigate whole-genome expression in children with high risk or relapsed ALL referred for HSCT. Gene expression was assessed in 18 children with ALL referred for HSCT (10 high risk, 8 relapsed; median age of 9.4 years) and in a control group of 38 obese children (median age of 14.1 years). Whole-genome expression was assessed in leukocytes using GeneChip® HumanGene 1.0 ST microarray.

Results

The analysis of genomic profiles revealed a significantly lower expression of 21 genes with a defined function, involved in immunoglobulin production, lymphocyte function, or regulation of DNA processing in ALL patients referred for HSCT compared with the control group.

Conclusion

Genome expression of patients with ALL in remission referred to HSCT revealed deep immunosuppression of both B-cell and T-cell lineages, which may increase the probability of donor cell engraftment.

Insights on the Interplay between Cells Metabolism and Signaling: A Therapeutic Perspective in Pediatric Acute Leukemias

Nowadays, thanks to extensive studies and progress in precision medicine, pediatric leukemia has reached an extremely high overall survival rate. Nonetheless, a fraction of relapses and refractory cases is still present, which are frequently correlated with poor prognosis. Although several molecular features of these diseases are known, still the field of energy metabolism, which is widely studied in adult, has not been frequently explored in childhood leukemias. Metabolic reprogramming is a hallmark of cancer and is deeply connected with other genetic and signaling aberrations generally known to be key features of both acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). This review aims to clear the current knowledge on metabolic rewiring in pediatric ALL and AML, also highlighting the influence of the main signaling pathways and suggesting potential ideas to further exploit this field to discover new prognostic biomarkers and, above all, beneficial therapeutic options.

Ornithine decarboxylase antizyme inhibitor 2 (AZIN2) is a signature of secretory phenotype and independent predictor of adverse prognosis in colorectal cancer

Ornithine decarboxylase (ODC) is the rate-limiting enzyme of polyamine synthesis. The two ODC antizyme inhibitors (AZIN1) and (AZIN2) are regulators of the catalytic activity of ODC. While AZIN1 is a regulator of cell proliferation, AZIN2 is involved in intracellular vesicle transport and secretion. There are no previous reports on the impact of AZIN2 expression in human cancer. We applied immunohistochemistry with antibodies to human AZIN2 on tissue micro- arrays of colorectal cancers (CRC) from 840 patients with a median follow-up of 5.1 years (range 0-25.8). The 5-year disease-specific survival rate was 58.9% (95% Cl 55.0-62.8%). High AZIN2 expression was associated with mucinous histology (p = 0.002) and location in the right hemicolon (p = 0.021). We found no association with age, gender, stage, or histological tumor grade. High tumor expression of AZIN2 predicted an unfavorable prognosis (p<0.0001, log-rank test), compared to low AZIN2 expression. Cox multivariable analysis identified AZIN2 as an independent factor of an unfavorable prognosis in CRC. The strongest AZIN2 expression was seen in invasive tumor cells having morphological features of epithelial-mesenchymal transition (EMT). Induction of EMT in HT-29 CRC cells lead to upregulated expression of endogenous AZIN2. Given that AZIN2 is a regulator of vesicle transport and secretion, we overexpressed human AZIN2 cDNA in T84 CRC cells, and found strongly enhanced accumulation of CD63-positive exosomes in the culture medium. These findings indicate that AZIN2 expression is a signature of EMT-associated secretory phenotype that is linked to an adverse prognosis in CRC.

Metabolic reprogramming of acute lymphoblastic leukemia cells in response to glucocorticoid treatment

Glucocorticoids (GCs) are metabolic hormones with immunosuppressive effects that have proven effective drugs against childhood acute lymphoblastic leukemia (ALL). Yet, the role of metabolic reprogramming in GC-induced ALL cell death is poorly understood. GCs efficiently block glucose uptake and metabolism in ALL cells, but this does not fully explain the observed induction of autophagy and cell death. Here, we have performed parallel time-course proteomics, metabolomics, and isotope-tracing studies to examine in detail the metabolic effects of GCs on ALL cells. We observed metabolic events associated with growth arrest, autophagy, and catabolism prior to onset of apoptosis: nucleotide de novo synthesis was reduced, while certain nucleobases accumulated; polyamine synthesis was inhibited; and phosphatidylcholine synthesis was induced. GCs suppressed not only glycolysis but also entry of both glucose and glutamine into the TCA cycle. In contrast, expression of glutamine-ammonia ligase (GLUL) and cellular glutamine content was robustly increased by GC treatment, suggesting induction of glutamine synthesis, similar to nutrient-starved muscle. Modulating medium glutamine and dimethyl-α-ketoglutarate (dm-αkg) to favor glutamine synthesis reduced autophagosome content of ALL cells, and dm-αkg also rescued cell viability. These data suggest that glutamine synthesis affects autophagy and possibly onset of cell death in response to GCs, which should be further explored to understand mechanism of action and possible sources of resistance.

Identification of early-stage lung adenocarcinoma prognostic signatures based on statistical modeling

BACKGROUND

Current staging methods are lack of precision in predicting prognosis of early-stage lung adenocarcinomas.

OBJECTIVE

We aimed to develop a gene expression signature to identify high- and low-risk groups of patients.

METHODS

We used the Bayesian Model Averaging algorithm to analyze the DNA microarray data from 442 lung adenocarcinoma patients from three independent cohorts, one of which was used for training.

RESULTS

The patients were assigned to either high- or low-risk groups based on the calculated risk scores based on the identified 25-gene signature. The prognostic power was evaluated using Kaplan-Meier analysis and the log-rank test. The testing sets were divided into two distinct groups with log-rank test p-values of 0.00601 and 0.0274 respectively.

CONCLUSIONS

Our results show that the prognostic models could successfully predict patients' outcome and serve as biomarkers for early-stage lung adenocarcinoma overall survival analysis.

Rewired metabolism in drug-resistant leukemia cells: a metabolic switch hallmarked by reduced dependence on exogenous glutamine

Cancer cells that escape induction therapy are a major cause of relapse. Understanding metabolic alterations associated with drug resistance opens up unexplored opportunities for the development of new therapeutic strategies. Here, we applied a broad spectrum of technologies including RNA sequencing, global untargeted metabolomics, and stable isotope labeling mass spectrometry to identify metabolic changes in P-glycoprotein overexpressing T-cell acute lymphoblastic leukemia (ALL) cells, which escaped a therapeutically relevant daunorubicin treatment. We show that compared with sensitive ALL cells, resistant leukemia cells possess a fundamentally rewired central metabolism characterized by reduced dependence on glutamine despite a lack of expression of glutamate-ammonia ligase (GLUL), a higher demand for glucose and an altered rate of fatty acid β-oxidation, accompanied by a decreased pantothenic acid uptake capacity. We experimentally validate our findings by selectively targeting components of this metabolic switch, using approved drugs and starvation approaches followed by cell viability analyses in both the ALL cells and in an acute myeloid leukemia (AML) sensitive/resistant cell line pair. We demonstrate how comparative metabolomics and RNA expression profiling of drug-sensitive and -resistant cells expose targetable metabolic changes and potential resistance markers. Our results show that drug resistance is associated with significant metabolic costs in cancer cells, which could be exploited using new therapeutic strategies.

An empirical assessment of validation practices for molecular classifiers

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and proteomic data. Cross-validation methods are often used to obtain estimates of classification accuracy, but both simulations and case studies suggest that, when inappropriate methods are used, bias may ensue. Bias can be bypassed and generalizability can be tested by external (independent) validation. We evaluated 35 studies that have reported on external validation of a molecular classifier. We extracted information on study design and methodological features, and compared the performance of molecular classifiers in internal cross-validation versus external validation for 28 studies where both had been performed. We demonstrate that the majority of studies pursued cross-validation practices that are likely to overestimate classifier performance. Most studies were markedly underpowered to detect a 20% decrease in sensitivity or specificity between internal cross-validation and external validation [median power was 36% (IQR, 21-61%) and 29% (IQR, 15-65%), respectively]. The median reported classification performance for sensitivity and specificity was 94% and 98%, respectively, in cross-validation and 88% and 81% for independent validation. The relative diagnostic odds ratio was 3.26 (95% CI 2.04-5.21) for cross-validation versus independent validation. Finally, we reviewed all studies (n = 758) which cited those in our study sample, and identified only one instance of additional subsequent independent validation of these classifiers. In conclusion, these results document that many cross-validation practices employed in the literature are potentially biased and genuine progress in this field will require adoption of routine external validation of molecular classifiers, preferably in much larger studies than in current practice.

Extended exposure to dietary melatonin reduces tumor number and size in aged male mice

Several sets of male mice were given dietary melatonin over a series of experiments performed during a nine year period. Overall, melatonin-supplemented mice aged ≥26 months at sacrifice had significantly fewer tumors with lower severity than similarly aged control animals. The studies were originally designed to explore the potential of this agent for reducing the rate of onset of some genetic indices of brain aging. When these animals were sacrificed they were routinely examined for overt evidence of tumors and when these were found, a note was made of their occurrence, and of their size. Tumors are commonly found during senescence of several strains of mice. Since tumorigenesis was not the original intent of the study, these observations were recorded but not pursued in greater detail. In this report, these data have now been collated and summarized. This analysis has the disadvantage that tumor origin and morphology were not recorded. However, the study also has the advantage of being conducted over an extended period of time with many groups of animals. In consequence, many extraneous factors, which could be potential confounders, such as seasonal or dietary variations, are unlikely to have interfered with the analysis. The use of more than one mouse strain strengthens the possibility that the findings may have general relevance. Both aged and young animals were included in the original experiments but the tumor incidence in animals younger than 25 months was very low.

Gene-based outcome prediction in multiple cohorts of pediatric T-cell acute lymphoblastic leukemia: a Children's Oncology Group study

Background

Continuous complete clinical remission in T-cell acute lymphoblastic leukemia (T-ALL) is now approaching 80% due to the implementation of aggressive chemotherapy protocols but patients that relapse continue to have a poor prognosis. Such patients could benefit from augmented therapy if their clinical outcome could be more accurately predicted at the time of diagnosis. Gene expression profiling offers the potential to identify additional prognostic markers but has had limited success in generating robust signatures that predict outcome across multiple patient cohorts. This study aimed to identify robust gene classifiers that could be used for the accurate prediction of relapse in independent cohorts and across different experimental platforms.

Results

Using HG-U133Plus2 microarrays we modeled a five-gene classifier (5-GC) that accurately predicted clinical outcome in a cohort of 50 T-ALL patients. The 5-GC was further tested against three independent cohorts of T-ALL patients, using either qRT-PCR or microarray gene expression, and could predict patients with significantly adverse clinical outcome in each. The 5-GC featured the interleukin-7 receptor (IL-7R), low-expression of which was independently predictive of relapse in T-ALL patients. In T-ALL cell lines, low IL-7R expression was correlated with diminished growth response to IL-7 and enhanced glucocorticoid resistance. Analysis of biological pathways identified the NF-κB and Wnt pathways, and the cell adhesion receptor family (particularly integrins) as being predictive of relapse. Outcome modeling using genes from these pathways identified patients with significantly worse relapse-free survival in each T-ALL cohort.

Conclusions

We have used two different approaches to identify, for the first time, robust gene signatures that can successfully discriminate relapse and CCR patients at the time of diagnosis across multiple patient cohorts and platforms. Such genes and pathways represent markers for improved patient risk stratification and potential targets for novel T-ALL therapies.

Validation of a mouse xenograft model system for gene expression analysis of human acute lymphoblastic leukaemia

BACKGROUND

Pre-clinical models that effectively recapitulate human disease are critical for expanding our knowledge of cancer biology and drug resistance mechanisms. For haematological malignancies, the non-obese diabetic/severe combined immunodeficient (NOD/SCID) mouse is one of the most successful models to study paediatric acute lymphoblastic leukaemia (ALL). However, for this model to be effective for studying engraftment and therapy responses at the whole genome level, careful molecular characterisation is essential.

RESULTS

Here, we sought to validate species-specific gene expression profiling in the high engraftment continuous ALL NOD/SCID xenograft. Using the human Affymetrix whole transcript platform we analysed transcriptional profiles from engrafted tissues without prior cell separation of mouse cells and found it to return highly reproducible profiles in xenografts from individual mice. The model was further tested with experimental mixtures of human and mouse cells, demonstrating that the presence of mouse cells does not significantly skew expression profiles when xenografts contain 90% or more human cells. In addition, we present a novel in silico and experimental masking approach to identify probes and transcript clusters susceptible to cross-species hybridisation.

CONCLUSIONS

We demonstrate species-specific transcriptional profiles can be obtained from xenografts when high levels of engraftment are achieved or with the application of transcript cluster masks. Importantly, this masking approach can be applied and adapted to other xenograft models where human tissue infiltration is lower. This model provides a powerful platform for identifying genes and pathways associated with ALL disease progression and response to therapy in vivo.

Genome-wide expression analysis of paired diagnosis-relapse samples in ALL indicates involvement of pathways related to DNA replication, cell cycle and DNA repair, independent of immune phenotype

Almost a quarter of pediatric patients with acute lymphoblastic leukemia (ALL) suffer from relapses. The biological mechanisms underlying therapy response and development of relapses have remained unclear. In an attempt to better understand this phenomenon, we have analyzed 41 matched diagnosis-relapse pairs of ALL patients using genome-wide expression arrays (82 arrays) on purified leukemic cells. In roughly half of the patients, very few differences between diagnosis and relapse samples were found ('stable group'), suggesting that mostly extra-leukemic factors (for example, drug distribution, drug metabolism, compliance) contributed to the relapse. Therefore, we focused our further analysis on 20 sample pairs with clear differences in gene expression ('skewed group'), reasoning that these would allow us to better study the biological mechanisms underlying relapsed ALL. After finding the differences between diagnosis and relapse pairs in this group, we identified four major gene clusters corresponding to several pathways associated with changes in cell cycle, DNA replication, recombination and repair, as well as B-cell developmental genes. We also identified cancer genes commonly associated with colon carcinomas and ubiquitination to be upregulated in relapsed ALL. Thus, about half of the relapses are due to the selection or emergence of a clone with deregulated expression of genes involved in pathways that regulate B-cell signaling, development, cell cycle, cellular division and replication.

Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia

To determine whether gene expression profiling could improve outcome prediction in children with acute lymphoblastic leukemia (ALL) at high risk for relapse, we profiled pretreatment leukemic cells in 207 uniformly treated children with high-risk B-precursor ALL. A 38-gene expression classifier predictive of relapse-free survival (RFS) could distinguish 2 groups with differing relapse risks: low (4-year RFS, 81%, n = 109) versus high (4-year RFS, 50%, n = 98; P < .001). In multivariate analysis, the gene expression classifier (P = .001) and flow cytometric measures of minimal residual disease (MRD; P = .001) each provided independent prognostic information. Together, they could be used to classify children with high-risk ALL into low- (87% RFS), intermediate- (62% RFS), or high- (29% RFS) risk groups (P < .001). A 21-gene expression classifier predictive of end-induction MRD effectively substituted for flow MRD, yielding a combined classifier that could distinguish these 3 risk groups at diagnosis (P < .001). These classifiers were further validated on an independent high-risk ALL cohort (P = .006) and retainedindependent prognostic significance (P < .001) in the presence of other recently described poor prognostic factors (IKAROS/IKZF1 deletions, JAK mutations, and kinase expression signatures). Thus, gene expression classifiers improve ALL risk classification and allow prospective identification of children who respond or fail current treatment regimens. These trials were registered at http://clinicaltrials.gov under NCT00005603.

Glucocorticoid resistance in T-lineage acute lymphoblastic leukaemia is associated with a proliferative metabolism

Glucocorticoids (GCs) are among the most important drugs for acute lymphoblastic leukaemia (ALL), yet despite their clinical importance, the exact mechanisms involved in GC cytotoxicity and the development of resistance remain uncertain. We examined the baseline profile of a panel of T-ALL cell lines to determine factors that contribute to GC resistance without prior drug selection. Transcriptional profiling indicated GC resistance in T-ALL is associated with a proliferative phenotype involving upregulation of glycolysis, oxidative phosphorylation, cholesterol biosynthesis and glutamate metabolism, increased growth rates and activation of PI3K/AKT/mTOR and MYC signalling pathways. Importantly, the presence of these transcriptional signatures in primary ALL specimens significantly predicted patient outcome. We conclude that in lymphocytes the activation of bioenergetic pathways required for proliferation may suppress the apoptotic potential and offset the metabolic crisis initiated by GC signalling. It is likely that the link between GC resistance and proliferation in T-ALL has not been fully appreciated to date because such effects would be masked in the context of current multiagent therapies. The data also provide the first evidence that altered expression of wild-type MLL may contribute to GC-resistant phenotypes. Our findings warrant the continued development of selective metabolic inhibitors for the treatment of ALL.

Perspectives of gene expression profiling for diagnosis and therapy in haematological malignancies

Considering the heterogeneity of leukaemias and the widening spectrum of therapeutic strategies, novel diagnostic methods are urgently needed for haematological malignancies. For a decade, gene expression profiling (GEP) has been applied in leukaemia research. Thus, various studies demonstrated worldwide that the majority of genetically defined leukaemia subtypes are accurately predictable by GEP, for example, with respect to reciprocal rearrangements in acute myeloid leukaemia (AML). Moreover, novel prognostically relevant gene classifiers were developed as, for example, in normal karyotype AML. Considering the lymphatic malignancies, GEP studies defined novel clinically relevant subtypes in diffuse large B cell lymphoma (DLBCL), and improved the discrimination of Burkitt lymphoma and DLBCL cases, overcoming considerable overlaps of these entities that exist from morphological and genetic perspectives. Treatment-specific sensitivity assays are being developed for targeted drugs such as farnesyl transferase inhibitors in AML or imatinib in BCR-ABL1 positive acute lymphoblastic leukaemia (ALL). Irrespectively of these proceedings, an introduction of the microarray technology in haematological practice requires diagnostic algorithms and strategies for interaction with currently established diagnostic techniques. Large multicentre studies such as the MILE Study (Microarray Innovations in LEukemia) aim at translating this methodology into clinical routine workflows and to catalyze this process.

Current status of gene expression profiling in the diagnosis and management of acute leukaemia

Gene expression profiling (GEP) enables the simultaneous investigation of the expression of tens of thousands of genes and was successfully introduced in leukaemia research a decade ago. Aiming to better understand the diversity of genetic aberrations in acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL), pioneer studies investigated and confirmed the predictability of many cytogenetic and molecular subclasses in AML and ALL. In addition, GEP can define new prognostic subclasses within distinct leukaemia subgroups, as illustrated in AML with normal karyotype. Another approach is the development of treatment-specific sensitivity assays, which might contribute to targeted therapy studies. Finally, GEP might enable the detection of new molecular targets for therapy in patients with acute leukaemia. Meanwhile, large multicentre studies, e.g. the Microarray Innovations in LEukaemia (MILE) study, prepare for a standardised introduction of GEP in leukaemia diagnostic algorithms, aiming to translate this novel methodology into clinical routine for the benefit of patients with the complex disorders of AML and ALL.