Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System.

Patients with multiple myeloma have variable survival and require reliable prognostic and predictive scoring systems. Currently, clinical and biological risk markers are used independently. Here, International Staging System (ISS), fluorescence in situ hybridization (FISH) markers, and gene expression (GEP) classifiers were combined to identify novel risk classifications in a discovery/validation setting. We used the datasets of the Dutch-Belgium Hemato-Oncology Group and German-speaking Myeloma Multicenter Group (HO65/GMMG-HD4), University of Arkansas for Medical Sciences-TT2 (UAMS-TT2), UAMS-TT3, Medical Research Council-IX, Assessment of Proteasome Inhibition for Extending Remissions, and Intergroupe Francophone du Myelome (IFM-G) (total number of patients: 4750). Twenty risk markers were evaluated, including t(4;14) and deletion of 17p (FISH), EMC92, and UAMS70 (GEP classifiers), and ISS. The novel risk classifications demonstrated that ISS is a valuable partner to GEP classifiers and FISH. Ranking all novel and existing risk classifications showed that the EMC92-ISS combination is the strongest predictor for overall survival, resulting in a 4-group risk classification. The median survival was 24 months for the highest risk group, 47 and 61 months for the intermediate risk groups, and the median was not reached after 96 months for the lowest risk group. The EMC92-ISS classification is a novel prognostic tool, based on biological and clinical parameters, which is superior to current markers and offers a robust, clinically relevant 4-group model.

Prognostic gene expression analysis in a retrospective, multinational cohort of 155 multiple myeloma patients treated outside clinical trials.

OBJECTIVES: Typically, prognostic capability of gene expression profiling (GEP) is studied in the context of clinical trials, for which 50%-80% of patients are not eligible, possibly limiting the generalizability of findings to routine practice. Here, we evaluate GEP analysis outside clinical trials, aiming to improve clinical risk assessment of multiple myeloma (MM) patients. METHODS: A total of 155 bone marrow samples from MM patients were collected from which RNA was analyzed by microarray. Sixteen previously developed GEP-based markers were evaluated, combined with survival data, and studied using Cox proportional hazard regression. RESULTS: Gene expression profiling-based markers SKY92 and the PR-cluster were shown to be independent prognostic factors for survival, with hazard ratios and 95% confidence interval of 3.6 [2.0-6.8] (P < .001) and 5.8 [2.7-12.7] (P < .01) for overall survival (OS). A multivariate model proved only SKY92 and the PR-cluster to be independent prognostic factors compared to cytogenetic high-risk patients, the International Staging System (ISS), and revised ISS. A substantial number of high-risk individuals could be further identified when SKY92 was added to the cytogenetic, ISS, or R-ISS. In the cytogenetic standard-risk group, ISS I/II, and R-ISS I/II, 13%, 23%, and 23% of patients with adverse survivals were identified. CONCLUSIONS: For the first time, this study confirmed the prognostic value of GEP markers outside clinical trials. Conventional prognostic models to define high-risk MM are improved by the incorporation of GEP markers.

Analytical Validation of SKY92 for the Identification of High-Risk Multiple Myeloma.

Multiple myeloma (MM) is an incurable plasma cell cancer with a large variability in survival. Patients with MM classified as high risk by the SKY92 gene expression classifier are at high risk of relapse and short survival. Analytical validation of the SKY92 assay was performed with primary bone marrow specimens from 12 patients with MM and 7 reference cell line specimens. The SKY92 results were 100% concordant with the reference and/or their expected result for sensitivity, specificity, microarray stability, and RLT buffer stability. The SKY92 results were 90% concordant for primary specimen stability, 96.4% concordant for intermediate precision, and 80% to 100% concordant for RNA stability. For the cell-line reproducibility, the concordance was at least 92.9%, except for one near-cut point specimen. For the clinical specimen reproducibility, the concordance was 100%, except for two near-cut point specimens. Three independent laboratories were concordant in ≥77.8% and ≥92.9% of experiments for patient specimens and cell lines, respectively. Statistical acceptance thresholds were developed as Δ ≤1.48 (change in SKY92 score) and SD ≤0.45 (SD across SKY92 scores). Using the Clinical and Laboratory Standards Institute method of choice (EP05-A2/A3), restricted maximum likelihood, the observed Δ values (0 to 1.14) and SDs (0.22 to 0.31) passed acceptance criteria. Thus, we successfully present analytical validation for the SKY92 assay as a prognostic molecular test for individual patients with MM.

Prognostic and predictive performance of R-ISS with SKY92 in older patients with multiple myeloma: the HOVON-87/NMSG-18 trial.

The standard prognostic marker for multiple myeloma (MM) patients is the revised International Staging System (R-ISS). However, there is room for improvement in guiding treatment. This applies particularly to older patients, in whom the benefit/risk ratio is reduced because of comorbidities and subsequent side effects. We hypothesized that adding gene-expression data to R-ISS would generate a stronger marker. This was tested by combining R-ISS with the SKY92 classifier (SKY-RISS). The HOVON-87/NMSG-18 trial (EudraCT: 2007-004007-34) compared melphalan-prednisone-thalidomide followed by thalidomide maintenance (MPT-T) with melphalan-prednisone-lenalidomide followed by lenalidomide maintenance (MPR-R). From this trial, 168 patients with available R-ISS status and gene-expression profiles were analyzed. R-ISS stages I, II, and III were assigned to 8%, 75%, and 7% of patients, respectively (3-year overall survival [OS] rates: 80%, 65%, 33%, P = 8 × 10-3). Using the SKY92 classifier, 13% of patients were high risk (HR) (3-year OS rates: standard risk [SR], 70%; HR, 28%; P < .001). Combining SKY92 with R-ISS resulted in 3 risk groups: SKY-RISS I (SKY-SR + R-ISS-I; 15%), SKY-RISS III (SKY-HR + R-ISS-II/III; 11%), and SKY-RISS II (all other patients; 74%). The 3-year OS rates for SKY-RISS I, II, and III are 88%, 66%, and 26%, respectively (P = 6 × 10-7). The SKY-RISS model was validated in older patients from the CoMMpass dataset. Moreover, SKY-RISS demonstrated predictive potential: HR patients appeared to benefit from MPR-R over MPT-T (median OS, 55 and 14 months, respectively). Combined, SKY92 and R-ISS classify patients more accurately. Additionally, benefit was observed for MPR-R over MPT-T in SKY92-RISS HR patients only.

Prediction of BRCA1-association in hereditary non-BRCA1/2 breast carcinomas with array-CGH.

BACKGROUND: While new defects in BRCA1 are still being found, it is unclear whether current breast cancer diagnostics misses many BRCA1-associated cases. A reliable test that is able to indicate the involvement of BRCA1 deficiency in cancer genesis could support decision making in genetic counselling and clinical management. To find BRCA1-specific markers and explore the effectiveness of the current diagnostic strategy, we designed a classification method, validated it and examined whether we could find BRCA1-like breast tumours in a group of patients initially diagnosed as non-BRCA1/2 mutation carriers. METHODS: A classifier was built based on array-CGH profiles of 18 BRCA1-related and 32 control breast tumours, and validated on independent sets of 16 BRCA1-related and 16 control breast carcinomas. Subsequently, we applied the classifier to 48 breast tumours of patients from Hereditary Breast and Ovarian Cancer (HBOC) families in whom no germ line BRCA1/BRCA2 mutations were identified. RESULTS: The classifier showed an accuracy of 91% when applied to the validation sets. In 48 non-BRCA1/2 patients, only two breast tumours presented a BRCA1-like CGH profile. Additional evidence for BRCA1 dysfunction was found in one of these tumours. CONCLUSION: We here describe the specific chromosomal aberrations in BRCA1-related breast carcinomas. We developed a predictive genetic test for BRCA1-association and show that BRCA1-related tumours can still be identified in HBOC families after routine DNA diagnostics.

Genome-wide linkage scan in Dutch hereditary non-BRCA1/2 breast cancer families identifies 9q21-22 as a putative breast cancer susceptibility locus.

Breast cancer accounts for over 20% of all female cancers. A positive family history remains one of the most important risk factors for the disease, with first-degree relatives of patients having a twofold elevated risk. Known breast cancer susceptibility genes such as BRCA1 and BRCA2 explain only 20-25% of this risk, suggesting the existence of other breast cancer susceptibility genes. Here, we report the results of a genome-wide linkage scan in 55 high-risk Dutch breast cancer families with no mutations in BRCA1 and BRCA2. Twenty-two of these families were also part of a previous linkage study by the Breast Cancer Linkage Consortium. In addition, we performed CGH analyses in 61 tumors of these families and 31 sporadic tumors. Three regions were identified with parametric HLOD scores >1, and three with nonparametric LOD scores >1.5. Upon further marker genotyping for the candidate loci, and the addition of another 30 families to the analysis, only the locus on chromosome 9 (9q21-22, marker D9S167) remained significant, with a nonparametric multipoint LOD score of 3.96 (parametric HLOD 0.56, alpha = 0.18). With CGH analyses we observed preferential copy number loss at BAC RP11-276H19, containing D9S167 in familial tumors as compared to sporadic tumors (P < 0.001). Five candidate genes were selected from the region around D9S167 and their coding regions subjected to direct sequence analysis in 16 probands. No clear pathogenic mutations were found in any of these genes.

Pathogenicity of the BRCA1 missense variant M1775K is determined by the disruption of the BRCT phosphopeptide-binding pocket: a multi-modal approach.

A number of germ-line mutations in the BRCA1 gene confer susceptibility to breast and ovarian cancer. However, it remains difficult to determine whether many single amino-acid (missense) changes in the BRCA1 protein that are frequently detected in the clinical setting are pathologic or not. Here, we used a combination of functional, crystallographic, biophysical, molecular and evolutionary techniques, and classical genetic segregation analysis to demonstrate that the BRCA1 missense variant M1775K is pathogenic. Functional assays in yeast and mammalian cells showed that the BRCA1 BRCT domains carrying the amino-acid change M1775K displayed markedly reduced transcriptional activity, indicating that this variant represents a deleterious mutation. Importantly, the M1775K mutation disrupted the phosphopeptide-binding pocket of the BRCA1 BRCT domains, thereby inhibiting the BRCA1 interaction with the proteins BRIP1 and CtIP, which are involved in DNA damage-induced checkpoint control. These results indicate that the integrity of the BRCT phosphopeptide-binding pocket is critical for the tumor suppression function of BRCA1. Moreover, this study demonstrates that multiple lines of evidence obtained from a combination of functional, structural, molecular and evolutionary techniques, and classical genetic segregation analysis are required to confirm the pathogenicity of rare variants of disease-susceptibility genes and obtain important insights into the underlying pathogenetic mechanisms.

Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects.

Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient's treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that STL can predict treatment benefit for both; a twofold progression free survival (PFS) benefit is observed for bortezomib for 19.8% and a threefold PFS benefit for lenalidomide for 31.1% of the patients. This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.

Potential therapeutic and economic value of risk-stratified treatment as initial treatment of multiple myeloma in Europe.

Biomarkers associated with prognosis in multiple myeloma (MM) can be used to stratify patients into risk categories. An attractive alternative to uniform treatment (UT), risk-stratified treatment (RST) is proposed where high-risk patients receive bortezomib-based regimens while standard-risk patients receive alternative less costly regimens. An early Markov-type decision analytic model evaluated the potential therapeutic and economic value of different RST strategies compared with UT in MM patients in key European countries. Results suggest RST strategies were both cheaper and more effective than UT across all countries, with the molecular marker-only strategy RST-SKY92 producing maximum health gains (0.031-0.039 QALYs). The conclusions remained consistent in the univariate sensitivity analyses. These findings should encourage stakeholders to support the adoption of RST approaches in MM.

Automated array-CGH optimized for archival formalin-fixed, paraffin-embedded tumor material.

BACKGROUND: Array Comparative Genomic Hybridization (aCGH) is a rapidly evolving technology that still lacks complete standardization. Yet, it is of great importance to obtain robust and reproducible data to enable meaningful multiple hybridization comparisons. Special difficulties arise when aCGH is performed on archival formalin-fixed, paraffin-embedded (FFPE) tissue due to its variable DNA quality. Recently, we have developed an effective DNA quality test that predicts suitability of archival samples for BAC aCGH. METHODS: In this report, we first used DNA from a cancer cell-line (SKBR3) to optimize the aCGH protocol for automated hybridization, and subsequently optimized and validated the procedure for FFPE breast cancer samples. We aimed for highest throughput, accuracy, and reproducibility applicable to FFPE samples, which can also be important in future diagnostic use. RESULTS: Our protocol of automated array-CGH on archival FFPE ULS-labeled DNA showed very similar results compared with published data and our previous manual hybridization method. CONCLUSION: This report combines automated aCGH on unamplified archival FFPE DNA using non-enzymatic ULS labeling, and describes an optimized protocol for this combination resulting in improved quality and reproducibility.

Comparative genomic hybridization profiles in human BRCA1 and BRCA2 breast tumors highlight differential sets of genomic aberrations.

BRCA1 or BRCA2 germline mutations cause approximately 30% of breast cancers within high-risk families. This represents 5% of total breast cancer incidence. Although BRCA1 and BRCA2 are both implicated in DNA repair and genome stability, it is unknown whether BRCA1 and BRCA2 are associated with similar or distinct diseases. In a previous study we reported that BRCA1-related breast carcinomas show a distinct genomic profile as determined by comparative genomic hybridization (CGH). We now hypothesize that, if functionally equivalent, mutations in BRCA1 and BRCA2 would result in similar genomic profiles in tumors. Here we report the chromosomal gains and losses as measured by CGH in 25 BRCA2-associated breast tumors and compared them with our existing 36 BRCA1 and 30 control profiles. We compared all chromosomal regions and determined the regions of differential gain or loss between tumor classes and controls. BRCA2 and control tumors have very similar genomic profiles. As a consequence, and in contrast to BRCA1-associated tumors, CGH profiles from BRCA2-associated tumors could not be distinguished from control tumors using the classification methodology as we have developed before. The largest number of significant differences existed between BRCA1 and controls, followed by BRCA1 compared with BRCA2, suggesting different tumor development pathways for BRCA1 and BRCA2.

Prognostic Validation of SKY92 and Its Combination With ISS in an Independent Cohort of Patients With Multiple Myeloma.

BACKGROUND: High risk and low risk multiple myeloma patients follow a very different clinical course as reflected in their PFS and OS. To be clinically useful, methodologies used to identify high and low risk disease must be validated in representative independent clinical data and available so that patients can be managed appropriately. A recent analysis has indicated that SKY92 combined with the International Staging System (ISS) identifies patients with different risk disease with high sensitivity. PATIENTS AND METHODS: Here we computed the performance of eight gene expression based classifiers SKY92, UAMS70, UAMS80, IFM15, Proliferation Index, Centrosome Index, Cancer Testis Antigen and HM19 as well as the combination of SKY92/ISS in an independent cohort of 91 newly diagnosed MM patients. RESULTS: The classifiers identified between 9%-21% of patients as high risk, with hazard ratios (HRs) between 1.9 and 8.2. CONCLUSION: Among the eight signatures, SKY92 identified the largest proportion of patients (21%) also with the highest HR (8.2). Our analysis also validated the combination SKY92/ISS for identification of three classes; low risk (42%), intermediate risk (37%) and high risk (21%). Between low risk and high risk classes the HR is >10.

Array-CGH and breast cancer.

The introduction of comparative genomic hybridization (CGH) in 1992 opened new avenues in genomic investigation; in particular, it advanced analysis of solid tumours, including breast cancer, because it obviated the need to culture cells before their chromosomes could be analyzed. The current generation of CGH analysis uses ordered arrays of genomic DNA sequences and is therefore referred to as array-CGH or matrix-CGH. It was introduced in 1998, and further increased the potential of CGH to provide insight into the fundamental processes of chromosomal instability and cancer. This review provides a critical evaluation of the data published on array-CGH and breast cancer, and discusses some of its expected future value and developments.

A standardized microarray assay for the independent gene expression markers in AML: EVI1 and BAALC.

High levels of BAALC, ERG, EVI1 and MN1 expression have been associated with shorter overall survival in AML but standardized and clinically validated assays are lacking. We have therefore developed and optimized an assay for standardized detection of these prognostic genes for patients with intermediate cytogenetic risk AML. In a training set of 147 intermediate cytogenetic risk cases we performed cross validations at 5 percentile steps of expression level and observed a bimodal significance profile for BAALC expression level and unimodal significance profiles for ERG and MN1 levels with no statistically significant cutoff points near the median expression level of BAALC, ERG or MN1. Of the possible cutoff points for expression levels of BAALC, ERG and MN1, just the 30th and 75th percentile of BAALC expression level and the 30th percentile of MN1 expression level cutoff points showed clinical significance. Of these only the 30th percentile of BAALC expression level reproduced in an independent verification (extended training) data set of 242 cytogenetically normal AML cases and successfully validated in an external cohort of 215 intermediate cytogenetic risk AML cases. Finally, we show independent prognostic value for high EVI1 and low BAALC in multivariate analysis with other clinically relevant molecular AML markers. We have developed a highly standardized molecular assay for the independent gene expression markers EVI1 and BAALC.

Detection of CEBPA double mutants in acute myeloid leukemia using a custom gene expression array.

Double (bi-allelic) mutations in the gene encoding the CCAAT/enhancer-binding protein-alpha (CEBPA) transcription factor have a favorable prognostic impact in acute myeloid leukemia (AML). Double mutations in CEBPA can be detected using various techniques, but it is a notoriously difficult gene to sequence due to its high GC-content. Here we developed a two-step gene expression classifier for accurate and standardized detection of CEBPA double mutations. The key feature of the two-step classifier is that it explicitly removes cases with low CEBPA expression, thereby excluding CEBPA hypermethylated cases that have similar gene expression profiles as a CEBPA double mutant, which would result in false-positive predictions. In the second step, we have developed a 55 gene signature to identity the true CEBPA double-mutation cases. This two-step classifier was tested on a cohort of 505 unselected AML cases, including 26 CEBPA double mutants, 12 CEBPA single mutants, and seven CEBPA promoter hypermethylated cases, on which its performance was estimated by a double-loop cross-validation protocol. The two-step classifier achieves a sensitivity of 96.2% (95% confidence interval [CI] 81.1 to 99.3) and specificity of 100.0% (95% CI 99.2 to 100.0). There are no false-positive detections. This two-step CEBPA double-mutation classifier has been incorporated on a microarray platform that can simultaneously detect other relevant molecular biomarkers, which allows for a standardized comprehensive diagnostic assay. In conclusion, gene expression profiling provides a reliable method for CEBPA double-mutation detection in patients with AML for clinical use.

Detection of mutant NPM1 mRNA in acute myeloid leukemia using custom gene expression arrays.

Mutations in the gene encoding nucleophosmin (NPM1) carry a prognostic value for patients with acute myeloid leukemia (AML). Various techniques are currently being used to detect these mutations in routine molecular diagnostics. Incorporation of accurate NPM1 mutation detection on a gene expression platform would enable simultaneous detection with various other expression biomarkers. Here we present an array-based mutation detection using custom probes for NPM1 WT mRNA and NPM1 type A, B, and D mutant mRNA. This method was 100% accurate on a training cohort of 505 newly diagnosed unselected AML cases. Validation on an independent cohort of 143 normal-karyotype AML cases revealed no false-negative results, and one false positive (sensitivity 100.0% and specificity 98.7%). Based on this, we conclude that this method provides a reliable method for NPM1 mutation detection. The method can be applied to other genes/mutations as long as the mutant alleles are sufficiently highly expressed.

Analysis of PALB2/FANCN-associated breast cancer families.

No more than approximately 30% of hereditary breast cancer has been accounted for by mutations in known genes. Most of these genes, such as BRCA1, BRCA2, TP53, CHEK2, ATM, and FANCJ/BRIP1, function in DNA repair, raising the possibility that germ line mutations in other genes that contribute to this process also predispose to breast cancer. Given its close relationship with BRCA2, PALB2 was sequenced in affected probands from 68 BRCA1/BRCA2-negative breast cancer families of Ashkenazi Jewish, French Canadian, or mixed ethnic descent. The average BRCAPRO score was 0.58. A truncating mutation (229delT) was identified in one family with a strong history of breast cancer (seven breast cancers in three female mutation carriers). This mutation and its associated breast cancers were characterized with another recently reported but unstudied mutation (2521delA) that is also associated with a strong family history of breast cancer. There was no loss of heterozygosity in tumors with either mutation. Moreover, comparative genomic hybridization analysis showed major similarities to that of BRCA2 tumors but with some notable differences, especially loss of 18q, a change that was previously unknown in BRCA2 tumors and less common in sporadic breast cancer. This study supports recent observations that PALB2 mutations are present, albeit not frequently, in breast cancer families. The apparently high penetrance noted in this study suggests that at least some PALB2 mutations are associated with a substantially increased risk for the disease.