Vitali Proutski

Prognostic and Predictive Biomarkers in Resected Colon Cancer: Current Status and Future Perspectives for Integrating Genomics into Biomarker Discovery

In this article, the authors review the current status of biomarker research in the adjuvant treatment of colon cancer, drawing on their experiences and considering future strategies for biomarker discovery in the postgenomic era.

Development and Independent Validation of a Prognostic Assay for Stage II Colon Cancer Using Formalin-Fixed Paraffin-Embedded Tissue

PURPOSE Current prognostic factors are poor at identifying patients at risk of disease recurrence after surgery for stage II colon cancer. Here we describe a DNA microarray-based prognostic assay using clinically relevant formalin-fixed paraffin-embedded (FFPE) samples. PATIENTS AND METHODS A gene signature was developed from a balanced set of 73 patients with recurrent disease (high risk) and 142 patients with no recurrence (low risk) within 5 years of surgery. RESULTS The 634-probe set signature identified high-risk patients with a hazard ratio (HR) of 2.62 (P < .001) during cross validation of the training set. In an independent validation set of 144 samples, the signature identified high-risk patients with an HR of 2.53 (P < .001) for recurrence and an HR of 2.21 (P = .0084) for cancer-related death. Additionally, the signature was shown to perform independently from known prognostic factors (P < .001). CONCLUSION This gene signature represents a novel prognostic biomarker for patients with stage II colon cancer that can be applied to FFPE tumor samples.

Identification and Validation of an Anthracycline/Cyclophosphamide–Based Chemotherapy Response Assay in Breast Cancer

Background There is no method routinely used to predict response to anthracycline and cyclophosphamide–based chemotherapy in the clinic; therefore patients often receive treatment for breast cancer with no benefit. Loss of the Fanconi anemia/BRCA (FA/BRCA) DNA damage response (DDR) pathway occurs in approximately 25% of breast cancer patients through several mechanisms and results in sensitization to DNA-damaging agents. The aim of this study was to develop an assay to detect DDR-deficient tumors associated with loss of the FA/BRCA pathway, for the purpose of treatment selection. Methods DNA microarray data from 21 FA patients and 11 control subjects were analyzed to identify genetic processes associated with a deficiency in DDR. Unsupervised hierarchical clustering was then performed using 60 BRCA1/2 mutant and 47 sporadic tumor samples, and a molecular subgroup was identified that was defined by the molecular processes represented within FA patients. A 44-gene microarray-based assay (the DDR deficiency assay) was developed to prospectively identify this subgroup from formalin-fixed, paraffin-embedded samples. All statistical tests were two-sided. Results In a publicly available independent cohort of 203 patients, the assay predicted complete pathologic response vs residual disease after neoadjuvant DNA-damaging chemotherapy (5-fluorouracil, anthracycline, and cyclophosphamide) with an odds ratio of 3.96 (95% confidence interval [Cl] =1.67 to 9.41; P = .002). In a new independent cohort of 191 breast cancer patients treated with adjuvant 5-fluorouracil, epirubicin, and cyclophosphamide, a positive assay result predicted 5-year relapse-free survival with a hazard ratio of 0.37 (95% Cl = 0.15 to 0.88; P = .03) compared with the assay negative population. Conclusions A formalin-fixed, paraffin-embedded tissue-based assay has been developed and independently validated as a predictor of response and prognosis after anthracycline/cyclophosphamide–based chemotherapy in the neoadjuvant and adjuvant settings. These findings warrant further validation in a prospective clinical study.

Generation of a non-small cell lung cancer transcriptome microarray

BackgroundNon-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples.MethodsA combination of large-scale in-house sequencing, gene expression profiling and public sequence and gene expression data mining were used to characterise the transcriptome of NSCLC and the data used to generate a disease-focused microarray – the Lung Cancer DSA research tool.ResultsBuilt on the Affymetrix GeneChip platform, the Lung Cancer DSA research tool allows for interrogation of ~60,000 transcripts relevant to Lung Cancer, tens of thousands of which are unavailable on leading commercial microarrays.ConclusionWe have developed the first high-density disease specific transcriptome microarray. We present the array design process and the results of experiments carried out to demonstrate the arrays utility. This approach serves as a template for the development of other disease transcriptome microarrays, including non-neoplastic diseases.

Implications for Powering Biomarker Discovery Studies

This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in non-small-cell lung cancer.

The Colorectal cancer disease-specific transcriptome may facilitate the discovery of more biologically and clinically relevant information

BackgroundTo date, there are no clinically reliable predictive markers of response to the current treatment regimens for advanced colorectal cancer. The aim of the current study was to compare and assess the power of transcriptional profiling using a generic microarray and a disease-specific transcriptome-based microarray. We also examined the biological and clinical relevance of the disease-specific transcriptome.MethodsDNA microarray profiling was carried out on isogenic sensitive and 5-FU-resistant HCT116 colorectal cancer cell lines using the Affymetrix HG-U133 Plus2.0 array and the Almac Diagnostics Colorectal cancer disease specific Research tool. In addition, DNA microarray profiling was also carried out on pre-treatment metastatic colorectal cancer biopsies using the colorectal cancer disease specific Research tool. The two microarray platforms were compared based on detection of probesets and biological information.ResultsThe results demonstrated that the disease-specific transcriptome-based microarray was able to out-perform the generic genomic-based microarray on a number of levels including detection of transcripts and pathway analysis. In addition, the disease-specific microarray contains a high percentage of antisense transcripts and further analysis demonstrated that a number of these exist in sense:antisense pairs. Comparison between cell line models and metastatic CRC patient biopsies further demonstrated that a number of the identified sense:antisense pairs were also detected in CRC patient biopsies, suggesting potential clinical relevance.ConclusionsAnalysis from our in vitro and clinical experiments has demonstrated that many transcripts exist in sense:antisense pairs including IGF2BP2, which may have a direct regulatory function in the context of colorectal cancer. While the functional relevance of the antisense transcripts has been established by many studies, their functional role is currently unclear; however, the numbers that have been detected by the disease-specific microarray would suggest that they may be important regulatory transcripts. This study has demonstrated the power of a disease-specific transcriptome-based approach and highlighted the potential novel biologically and clinically relevant information that is gained when using such a methodology.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Abstract Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

Identification of a novel breast cancer molecular subgroup associated with a deficiency in DNA-damage response

10511 Background: Loss of a functional DNA-damage response (DDR) sensitizes tumors to DNA-damaging as well as targeted therapeutics such as PARP-1 inhibitors. However, there is no assay to detect DDR proficiency and guide therapeutic choice. Although abrogation of the DDR can result from multiple mechanisms, including loss of components of the BRCA/Fanconi anemia pathway, the resultant DNA-damage may activate common molecular pathways. We hypothesized that these pathways could define a molecular subgroup and form the basis of a diagnostic test for sensitivity to DNA-damaging and targeted therapies. METHODS Using DNA-microarray technology, we profiled a cohort of BRCA mutant enriched and thus DDR-deficient (DDRD) primary breast tumor samples. Hierarchical agglomerative clustering analysis was performed and identified a molecular subtype in breast cancer characterized by activation of pathways known to respond to DNA damage. Computational classification was performed resulting in the generation of a gene signature that could identify this DDRD molecular subgroup. RESULTS A subset of tumors was identified as displaying biology associated with DDRD. Computational classification was performed based upon expression of this DDRD-related biology resulting in the generation of a 44-gene signature. Retrospective validation in independent breast cancer datasets indicated that the DDRD signature was predictive of response to anthracycline-based chemotherapy with an odds ratio of 15.02 (CI 3.51 - 63.49). In addition, the signature could accurately identify non-responding patients with a negative predictive value of 0.96 (CI 0.88-0.99). CONCLUSIONS We report the identification of a novel molecular subgroup associated with a deficiency in DDR that can be identified in both ER-positive and ER-negative breast cancer using a 44-gene signature. This subgroup is enriched for BRCA1/2 mutant tumors and demonstrates sensitivity to DNA-damaging agents. We propose that the DDRD signature could be used as a patient stratification tool for existing chemotherapy or as a clinical trial enrichment tool for DNA-damaging or repair targeted drugs in development.

Abstract 326: Investigation of molecular subtypes within FFPE breast cancer tumors using the Breast Cancer DSA®

Microarray gene expression profiling has facilitated identification of molecular subtypes which can be associated with disease outcome and treatment response. However current platforms lack disease focus, potentially missing vital information contained in patient tissue samples. Moreover, a wide range of FFPE sample cohorts are available but their use is limited by the mRNA degradation inherent to the sample fixation process. By combining the Breast Cancer DSA® with powerful data analysis methodology, we reproduced the five major molecular subtypes in breast cancer based on a cohort of 107 FFPE samples. Furthermore, we showed that the established subtypes do not extend to BRCA1 and BRCA2 mutant tumours, necessitating expansion of the original breast cancer subtypes definition. Following RMA pre-processing of the 107 raw profiles, the data was transformed to overcome the association between profile quality and expression variability. If left untreated, this technical variability, often observed with FFPE tissues, can impair the study of inherent biological variations. Using the intrinsic list of 277 genes previously identified as differentiating breast cancer subtypes in fresh frozen samples [Sorlie et al., 2003], initially the sporadic samples were examined using hierarchical agglomerative clustering. All five original subtypes were identified, verifying the ability to detect subtypes in FFPE samples using the breast cancer DSA®. When the intrinsic list was applied to all 107 samples including BRCA1 and BRCA2 mutant samples, the subtypes were still distinguishable, although 31% of the mutant tumors were unclassified suggesting that specific biological processes defining BRCA1/BRCA2 mutant tumours are not captured by the original breast cancer molecular classification. We have successfully overcome the limitation imposed by FFPE sample microarray profiling by using the Breast Cancer DSA® and advanced data manipulation and exploration to reproduce molecular subtypes previously identified using fresh frozen samples. Further investigation to extend the previously identifed subtypes will progress the investigation of the heterogeneous biology underlying breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 326. doi:10.1158/1538-7445.AM2011-326