Cancer Molecular Profiling Research Articles

PIK3CA and TP53 gene mutations in human breast cancer tumors frequently detected by ion torrent DNA sequencing.

Breast cancer is the most common malignancy and the leading cause of cancer deaths in women worldwide. While specific genetic mutations have been linked to 5–10% of breast cancer cases, other environmental and epigenetic factors influence the development and progression of the cancer. Since unique mutations patterns have been observed in individual cancer samples, identification and characterization of the distinctive breast cancer molecular profile is needed to develop more effective target therapies. Until recently, identifying genetic cancer mutations via personalized DNA sequencing was impractical and expensive. The recent technological advancements in next-generation DNA sequencing, such as the semiconductor-based Ion Torrent sequencing platform, has made DNA sequencing cost and time effective with more reliable results. Using the Ion Torrent Ampliseq Cancer Panel, we sequenced 737 loci from 45 cancer-related genes to identify genetic mutations in 105 human breast cancer samples. The sequencing analysis revealed missense mutations in PIK3CA, and TP53 genes in the breast cancer samples of various histologic types. Thus, this study demonstrates the necessity of sequencing individual human cancers in order to develop personalized drugs or combination therapies to effectively target individual, breast cancer-specific mutations.

Practical Clinical Oncology

It is indeed a privilege and a humbling task to write a review for this masterpiece on cancer. The book carries two names that are legends of our times Dr Praful Desai, the Editor in Chief and Dr Farokh Udwadia (arguably the best medical author of our times). The book provides the reader with a comprehensive information and wisdom about every aspect of cancer. From Edwin Papyrus to the present day biological classification and molecular profiling of cancer, we have come a long way in our understanding about this dreaded disease. Unlike most books that are loaded with information that can often be very confusing, this book comes across as a refreshing and a lucid account on cancer. Some chapters that stand out and are a treat to read include “The Surgical oncology-an overview”, “Changing face of surgical pathology”, “Tumor markers-therapeutic impact”, “Towards the future of minimal access surgery” etc. These chapters while covering up the latest developments in the field of cancer also provide some “Indian solutions to Indian problems”. The book has lively illustrations of surgical procedures and clinical conditions that make reading and understanding of the subject easier and joyful. These chapters are contributed by experts with a vast personal knowledge and clinical experience in the field. The book is a terrific read and is surely a must for every surgeon’s bookshelf. Chintamani MS, FRCS(Ed.), FRCS(Glas.), FRCS (Irel.), FACS, FICS(Surg Oncol), FIMSA Editor in Chief IJS

Fighting a smarter war against cancer

Molecular Profiling in Cancer: Research or Practice, Georgetown University Hotel and Conference Center, Washington, DC, USA, 6–7 December 2013 On the 6th and 7th of December 2013, the Ruesch Center for the Cure of Gastrointestinal Cancers (DC, USA) hosted its fourth annual symposium entitled Molecular Profiling in Cancer: Research or Practice. This topic was selected for the 2013 symposium because of the explosion of molecular profiling assays that are available to us as practicing oncologists, which are believed, by most, to be a significant advancement in cancer care innovation. The Ruesch Center has been hosting topical symposia for the last 4 years. We have focused on: biomarker use in cancer medicine (2010); defining value in cancer care (2011); how to engage the ‘other 97%’ of patients in clinical trial enrollment, when currently we capture only 3% of patients on average (2012); and, of course, molecular profiling in cancer care (2013).

Precision cancer medicine: the future is now, only better.

The promise of precision medicine for cancer is already being realized with the recent introduction of many targeted therapies, some with companion diagnostic tests that identify patients most likely to benefit from treatment. The utility of molecular profiling of cancer to identify actionable aberrations has been suggested by several small clinical trials conducted in patients with advanced cancer and by many anecdotes but is yet to be proven in well-designed, prospective, randomized trials. Several trials that will definitively test this strategy are now underway or soon to be launched. Melanoma, a disease once largely untreatable when metastatic, may be a paradigm for understanding how the molecular drivers of a disease can lead to highly effective targeted therapies, as well as for realizing the enormous therapeutic potential of unleashing the immune system against cancer to produce long-term disease control. Looking to the future, advanced omics technologies and computational techniques will enable assessment of not only genomic variants, as performed today, but also of pathway and network aberrations that will greatly facilitate selection of drug combinations likely to benefit specific patients. As our deepening understanding of tumor biology converges with rapid advances in measurement science and technology and computational analysis, we have an enormous opportunity to create a future for precision medicine in oncology that provides for highly specific, minimally toxic, and dramatically effective treatment for each patient.

Frequent mutations in EGFR, KRAS and TP53 genes in human lung cancer tumors detected by ion torrent DNA sequencing.

Lung cancer is the most common malignancy and the leading cause of cancer deaths worldwide. While smoking is by far the leading cause of lung cancer, other environmental and genetic factors influence the development and progression of the cancer. Since unique mutations patterns have been observed in individual cancer samples, identification and characterization of the distinctive lung cancer molecular profile is essential for developing more effective, tailored therapies. Until recently, personalized DNA sequencing to identify genetic mutations in cancer was impractical and expensive. The recent technological advancements in next-generation DNA sequencing, such as the semiconductor-based Ion Torrent sequencing platform, has made DNA sequencing cost and time effective with more reliable results. Using the Ion Torrent Ampliseq Cancer Panel, we sequenced 737 loci from 45 cancer-related genes to identify genetic mutations in 76 human lung cancer samples. The sequencing analysis revealed missense mutations in KRAS, EGFR, and TP53 genes in the breast cancer samples of various histologic types. Thus, this study demonstrates the necessity of sequencing individual human cancers in order to develop personalized drugs or combination therapies to effectively target individual, breast cancer-specific mutations.

Tumor heterogeneity in the clinic: is it a real problem?

Tumor heterogeneity is one of the major problems limiting the efficacy of targeted therapies and compromising treatment outcomes. A better understanding of tumor biology has advanced our knowledge of the molecular landscape of cancer to an unprecedented level. However, most patients with advanced cancers treated with appropriately selected targeted therapies become resistant to the therapy, ultimately developing disease progression and succumbing to metastatic disease. Multiple factors account for therapeutic failures, which include cancer cells accumulating new molecular aberrations as a consequence of tumor progression and selection pressure of cancer therapies. Therefore, single agent targeted therapies, often administered in advanced stages, are unlikely to have a sufficiently lethal effect in most cancers. Finally, the molecular profile of cancer can change over time, which we are not able to monitor with existing strategies using tumor tissue biopsies as the gold standard for molecular diagnostics. Novel technologies focusing on testing low-risk, easily obtainable material, such as molecular cell-free DNA from plasma, can fill that gap and allow personalized therapy to be delivered in real time.

Abstract P3-06-15: Breast cancer molecular profile according to BMI and menopausal status

Abstract Introduction: High body mass index (BMI) is considered a risk factor for breast cancer onset and is correlated with bad prognosis in breast cancer patients. However, very few studies have associated clinical and molecular tumor characteristics with the patients’ BMI at the time of breast cancer diagnosis. The present study analyses how BMI influences tumor biology and correlates with different molecular subtypes according to menopausal status in a large cohort of patients with new diagnoses of early breast cancer (EBC). Methods: Overall 967 patients with a new diagnosis of EBC from 2007 till 2012 were recruited for this study. Clinical and tumor characteristics such as age, menopausal status, weight, height, tumor size (T), grading, estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2) status, were prospectively collected. BMI was categorized into two groups (low: ≤25; high: >25). Associations between BMI and clinicopathological variables were performed by χ2 test. Results: A total of 594 (61%) post-menopause and 373 (39%) pre-menopause women were enrolled. Regardless of menopausal status, patients with high BMI were older (p<0.0001) and had larger tumors (p<0.0001). However, among postmenopausal patients, high BMI correlates with increased ER expression (p = 0.015), lower HER2 expression (p = 0.05) and less aggressive tumor subtypes as luminal A (p = 0.043). In the same patient subset, women with low BMI were more likely to develop a Luminal B, HER2 like or TN breast cancer. No correlation was observed between tumor molecular phenotypes and BMI among premenopausal women. Conclusion: Among postmenopausal patient, high BMI is associated with less aggressive and more endocrine sensitive EBC. This is consistent with the hypotheses that higher estrogen exposure of breast tissue in women with higher BMI may drive growth of these cancers. If further confirmed, our data suggests that weight control in this subset of women may help to prevent cancer development. Table 1Clinical and Molecular CharacteristicsBMI ≤25BMI>25 N%N%totalp valueER .015Positive15376.132884.3481 Negative4823.96115.7109 PgR .2Positive14471.629676.1440 Negative5728.49323.9150 HER2 .05Positive3819.55013.388 Negative15780.532786.7484 MOLECULAR SUBTYPES .043Luminal A11560.225771.2372 Luminal B3015.74813.378 HER2147.3143.928 TN3216.84211.674 Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P3-06-15.

Interplay between p53 and VEGF: how to prevent the guardian from becoming a villain

Interplay between p53 and VEGF: how to prevent the guardian from becoming a villain

Abstract 5145: Integrative analysis of multidimensional colorectal cancer molecular profiles reveals an aggressive subpopulation characterized by specific gene methylation/expression patterns.

Abstract Colorectal cancer (CRC) is clinically and molecularly heterogenous. With the advent of approaches based on multidimensional genomic profiling, such as the one carried out by The Cancer Genome Atlas (TCGA) consortium, new analytical tools have to be developed and employed to fully integrate and analyze multiple layers of molecular and clinical annotation, and so highlight previously unknown features of the disease. To tackle this issue, we reasoned that highly efficient and integrative visualization of molecular and clinical data may enable intuitive identification of recurrent correlation patterns within complex datasets. We therefore developed a 3D-graphical, real-time interactive software, the “GenomeCruzer," that greatly accelerates global data visualization and analysis, facilitating the rapid screening of a high number of working hypotheses and allowing swift prioritization of the most promising ones. We initially focused on a publicly available dataset of expression and methylation profiles provided by the TCGA consortium on a panel of 145 colon cancer specimens, and selected a subset of 3600 genes expressed with high variability across the dataset. Unsupervised clustering of genes based on their methylation profiles across samples identified three main subsets, displaying, respectively, high, low and intermediate overall methylation status. Data visualization and analysis with GenomeCruzer revealed that the three gene methylation clusters were clearly associated with three patient subgroups: patients with a CpG island methylator phenotype (CIMP) were mostly captured, as expected, by the high methylation gene subgroup, while the 1471-gene cluster with an alternative methylation pattern defined a subgroup of 75 patients, that we named “alternative methylator phenotype” (AMP), most notably enriched for Stage IV cases (o/e = 1.7, p.val < 1.10-4) and depleted of MSI-H patients (o/e =0.21, 1*10-5). Integrative analysis of gene expression and methylation for the AMP patient group revealed transcriptional modules associated with interferon-alfa signalling (p < 10-7, downregulated in AMPs) and with protein translation (p< 10-6, upregulated in AMPs). Expression profiles obtained from the Cancer Cell Encyclopaedia were subsequently analysed to verify that expression of such modules occurs in cancer cells and is not derived from infiltrating cells. These data show that integrative analysis of multidimensional clinical and molecular datasets such as those provided by the TCGA can be greatly facilitated by ad-hoc developed software like the GenomeCruzer, providing unprecedented insight into the biology of cancer and defining new cancer subtypes of potential clinical relevance. Citation Format: Claudio Isella. Integrative analysis of multidimensional colorectal cancer molecular profiles reveals an aggressive subpopulation characterized by specific gene methylation/expression patterns. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5145. doi:10.1158/1538-7445.AM2013-5145

A genomic approach to active surveillance: a step toward precision medicine

In the last 25 years, Prostate Specific Antigen (PSA) screening has resulted in a large gap between the likelihood of being diagnosed with and of dying of prostate cancer, leading to the clinical problems of overdiagnosis and overtreatment. Despite the favorable outcomes reported for active surveillance, its clinical use is limited, with .90% of menintheUSdiagnosedwithpotentiallyindolent disease undergoing immediate treatment with radiation or surgery. We have designed a novelstrategy of molecularprofiling of prostate cancers,allowinganassessmentoftumoraggres

Molecular Classification and Drug Response Prediction in Cancer

Molecular profiling of cancers can potentially yield novel gene markers of therapeutic prediction, which would aid our ability to tailor targeted therapy regimens specific to each patient. Public data from gene expression profiling may yield clues as to what oncogenic signaling pathways are deregulated in cancers, and what drugs may effectively counteract the aberrant gene regulation patterns observed. Data are also available on panels of cancer cell lines, which have been both profiled at the gene expression level and extensively characterized for drug responses, allowing us to identify gene-to-drug correlations. Profiling tumors from patients undergoing adjuvant or neoadjuvant drug treatment can also yield markers of therapeutic response. In this review, we will examine recent studies aimed at our eventually being able to use the molecular profile of a tumor to predict drug response. The profiling data from these studies is publicly available, and can be re-examined by researchers with different questions in mind, offering us a large number of biomarker candidates that could potentially be tested in the clinical setting.

Differential frequency of breast cancer molecular profiling by socioeconomic status in San Diego, California.

e21061 Background: Previous reports demonstrate inferior outcomes for cancer patients of low socioeconomic status (SES). The development of breast tumor molecular profiling has optimized anticancer therapy but its effect on outcome for those with barriers to care is unknown. Methods: Breast cancer patients appropriate for OncotypeDX testing (Genomic Health, Inc) were identified from The Scripps Health Tumor Registry. High SES was defined as patients residing in zip codes with a median household income in the top quartile for the county according to the San Diego Association of Governments. Low SES was defined as patients residing in zip codes with median income in the bottom quartile or with Medical as primary insurance. The frequency of OncotypeDX testing, result, therapy prescribed, relapse and cancer specific death rates are reported. Statistical analysis was performed by unpaired t-test; p values≤0.05 were considered significant. Results: 2,192 early-stage breast cancer patients were identified; 167 met criteria. Patients were younger (p<0.0001) and a lower rate of OncotypeDX testing was noted in the low SES group (p=0.028); no difference in stage was observed. All patients were prescribed anti hormone therapy but the low SES group received chemotherapy more frequently (p=0.0075). No difference in relapse rate and no deaths attributable to cancer were noted. Conclusions: We observed a lower rate of OncotypeDX use in the low SES cohort. While the cause is unclear, the higher rate of chemotherapy use in this group may represent overtreatment. Determining a survival impact attributable to this difference requires longer follow-up. Our data demonstrate the need for prospective analysis of tumor profiling use in low SES individuals to ensure early stage breast cancer patients receive optimal therapy. [Table: see text]

Abstract 3664: Breast cancer companion diagnostic platform based on objectively defined tumor co-expression patterns stratifies multiple clinical and therapeutic endpoints comparison to existing molecular subtyping definitions

Abstract Gene expression profiles of human breast tumors have greatly expanded our understanding of the genes and pathways that underlie breast cancer. Profiling studies have also supported a molecular classification of breast cancer. The resulting molecular subtypes Luminal, Basal-like, ERBB2+, and Normal-like were shown to have different prognostic and predictive characteristics. Related studies have led to a proliferation of multigene prognostic and predictive diagnostic tests. Two independent multigene tests, OncoType Dx and MammaPrint, have been shown to be helpful in predicting the risk of recurrence of patients with early stage breast cancer. Current multigene tests consistently prioritize the proliferation, estrogen receptor (ER), and ERBB2 pathways. An alternative approach to identifying key molecular variables within breast cancer is based on a definition of objectively defined tumor co-expression patterns. To this end, we defined co-expression patterns within 56 independent breast cancer molecular profiling datasets representing >5,000 unique patients. We then performed a meta-analysis across datasets to define the most robust, consistently occurring co-expression patterns. These patterns, termed modules, recapitulate the proliferation, ER, and ERBB2 pathways, but also monitor expression of other important variables including core cancer cell growth pathways, immune signaling and microenvironment, and hallmark genomic aberrations. An important feature of co-expression patterns is that a small number of genes serve as an effective surrogate for each module. Thus, we created a single multigene qPCR test that measures the expression of 18 distinct breast cancer modules and validated the test for use with formalin-fixed paraffin-embedded (FFPE) tumor samples. In retrospective microarray scoring analyses with key clinical datasets, and with analysis of FFPE specimens from breast cancer cohorts, we demonstrate that breast cancer modules can be used to recapitulate the molecular subtypes of breast cancer and to have prognostic and predictive properties similar to the current multigene tests. Because they recapitulate existing molecular tests, while also reading out many additional axes of molecular variability, breast cancer modules provide a universal assay with broad application to companion diagnostics development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3664. doi:1538-7445.AM2012-3664

A Retrospective Study of Treatment Outcomes in Patients With Carcinoma of Unknown Primary Site and a Colorectal Cancer Molecular Profile

Molecular tumor profiling is a new method of identifying the tissue of origin in patients with carcinoma of unknown primary (CUP) site. However the value of this information in improving treatment outcomes is undefined. We evaluated results of site-specific treatment in a group of patients with CUP in whom molecular profiling predicted a colorectal site of origin. Tissue of origin predictions by a 92-gene real-time polymerase chain reaction (RT-PCR) molecular profiling assay (CancerTYPE ID; bioTheranostics, Inc, San Diego, CA) from March 2008 to August 2009 were reviewed. One hundred twenty-five of 1544 patients (8%) assayed were predicted to have a colorectal tissue of origin with > 80% probability. Surveys were sent to the physicians of these 125 patients requesting deidentified patient information. Information was provided for 42 of 125 patients (34%). Thirty-two patients received either first- or second-line therapy with colorectal cancer regimens; the overall response rate was 50%. Patients who received first-line empirical therapy for CUP had an overall response rate of 17%. The median survival of patients who received site-specific therapy for colorectal cancer was 27 months. Patients predicted to have a colorectal site of origin by molecular tumor profiling had median survival when treated with site-specific regimens that was similar to survival in patients with known metastatic colon cancer. The median survival in this group was substantially better than the historical median survival for patients with CUP (range 8-11 months) when treated with empirical CUP regimens. Molecular tumor profiling seems to improve survival by allowing specific therapy in this patient subgroup; prospective trials are ongoing to confirm these observations.

A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithms.

Molecular Profiling of Cancer—The Future of Personalized Cancer Medicine: A Primer on Cancer Biology and the Tools Necessary to Bring Molecular Testing to the Clinic

Cancers arise as a result of an accumulation of genetic aberrations that are either acquired or inborn. Virtually every cancer has its unique set of molecular changes. Technologies have been developed to study cancers and derive molecular characteristics that increasingly have implications for clinical care. Indeed, the identification of key genetic aberrations (molecular drivers) may ultimately translate into dramatic benefit for patients through the development of highly targeted therapies. With the increasing availability of newer, more powerful, and cheaper technologies such as multiplex mutational screening, next generation sequencing, array-based approaches that can determine gene copy numbers, methylation, expression, and others, as well as more sophisticated interpretation of high-throughput molecular information using bioinformatics tools like signatures and predictive algorithms, cancers will routinely be characterized in the near future. This review examines the background information and technologies that clinicians and physician-scientists will need to interpret in order to develop better, personalized treatment strategies.

The anti-diabetic drug metformin suppresses the metastasis-associated protein CD24 in MDA-MB-468 triple-negative breast cancer cells

CD24, a mucin-like adhesion molecule that enhances the metastatic potential of malignant cells, has been suggested to be a marker of poor prognosis in breast carcinomas. The tumor-initiating potential of CD44posCD24pos cell populations has been recently recognized and, accordingly, distant metastases are largely composed of CD24-positive cells in breast cancer patients refractory to treatment. Therefore, new therapeutic strategies aimed at down-regulating CD24 may negatively regulate the dissemination of tumor cells and formation of metastasis. Here, we reveal that suppression of CD24 protein expression is a crucial event in the molecular mechanisms underlying the growth-inhibitory effects of the anti-diabetic drug metformin in MDA-MB-468 triple-negative (basal-like) breast cancer cells. First, we confirmed that, among the different molecular classes of breast cancer, basal-like breast cancer cells were significantly more sensitive to the growth-inhibitory effects of metformin. Second, we observed a positive correlation between the growth inhibitory activity of metformin and the relative enrichment in cells bearing the CD44posCD24pos immunophenotype. Third, high-content indirect immunofluorescence imaging assays revealed that CD24 protein levels were drastically decreased in the presence of growth-inhibitory concentrations of metformin. Fourth, to preliminary assess the clinical relevance of metformin's anti-CD24 effects we took advantage of the recently developed ROCK online interface (http://rock.icr.ac.uk/), a publicly accessible portal that allows rapid integration of breast cancer functional and molecular profiling datasets. When we evaluated the impact of CD24 expression on distant metastasis-free survival (DMFS) in microarray gene expression breast cancer datasets, Kaplan-Meier survival analyses and log-rank tests comparing DMSF for CD24-high and CD24-low breast carcinomas revealed that patients with CD24-high tumors tended to have a shorter DMFS. These findings, altogether, suggest that the ability of metformin to suppress the oncogene, metastasis promoter and breast cancer stem cell marker CD24 may open a novel molecular avenue in the therapeutic management of highly-metastastic subgroups of triple-negative (basal-like) breast cancers naturally enriched with CD44posCD24pos tumor-initiating cell populations.

Clinical implementation of the intrinsic subtypes of breast cancer

Our group has reported several intrinsic gene sets important for identifying subtypes of breast cancer with clinical significance. 1 Sørlie T Tibshirani R Parker J et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003; 100: 8418-8423 Crossref PubMed Scopus (4148) Google Scholar , 2 Hu Z Fan C Oh DS et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006; 7: 96 Crossref PubMed Scopus (1108) Google Scholar , 3 Parker JS Mullins M Cheang MC et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009; 27: 116-167 Crossref Scopus (11) Google Scholar In these studies we have explored and published methods for sample classification across different genomic platforms and tissue qualities. In 2009, we suggested the use of a standardised gene set (PAM50) for subtype classification to improve the classification concordance reported by investigators. 3 Parker JS Mullins M Cheang MC et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009; 27: 116-167 Crossref Scopus (11) Google Scholar However, a standardised gene set does not completely resolve discrepancies between researchers since the genes might be quantitatively measured using different platforms and normalisation methods. Weigelt and co-workers 4 Weigelt B Mackay A A'hern R et al. Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol. 2010; 11: 339-349 Summary Full Text Full Text PDF PubMed Scopus (277) Google Scholar applied three different intrinsic gene sets to four data sets using one prediction method and showed a range of agreement. Because of the level of discordance that was reported, they concluded that identification of the intrinsic subtypes is not ready for clinical implementation. We disagree with this interpretation.

ROCK: a breast cancer functional genomics resource

The clinical and pathological heterogeneity of breast cancer has instigated efforts to stratify breast cancer sub-types according to molecular profiles. These profiling efforts are now being augmented by large-scale functional screening of breast tumour cell lines, using approaches such as RNA interference. We have developed ROCK ( rock.icr.ac.uk ) to provide a unique, publicly accessible resource for the integration of breast cancer functional and molecular profiling datasets. ROCK provides a simple online interface for the navigation and cross-correlation of gene expression, aCGH and RNAi screen data. It enables the interrogation of gene lists in the context of statistically analysed functional genomic datasets, interaction networks, pathways, GO terms, mutations and drug targets. The interface also provides interactive visualisations of datasets and interaction networks. ROCK collates data from a wealth of breast cancer molecular profiling and functional screening studies into a single portal, where analysed and annotated results can be accessed at the level of a gene, sample or study. We believe that portals such as ROCK will not only afford researchers rapid access to profiling data, but also aid the integration of different data types, thus enhancing the discovery of novel targets and biomarkers for breast cancer.

Breast cancer molecular profiling with single sample predictors: a retrospective analysis

Microarray expression profiling classifies breast cancer into five molecular subtypes: luminal A, luminal B, basal-like, HER2, and normal breast-like. Three microarray-based single sample predictors (SSPs) have been used to define molecular classification of individual samples. We aimed to establish agreement between these SSPs for identification of breast cancer molecular subtypes. Previously described microarray-based SSPs were applied to one in-house (n=53) and three publicly available (n=779) breast cancer datasets. Agreement was analysed between SSPs for the whole classification system and for the five molecular subtypes individually in each cohort. Fair-to-substantial agreement between every pair of SSPs in each cohort was recorded (kappa=0.238-0.740). Of the five molecular subtypes, only basal-like cancers consistently showed almost-perfect agreement (kappa>0.812). The proportion of cases classified as basal-like in each cohort was consistent irrespective of the SSP used; however, the proportion of each remaining molecular subtype varied substantially. Assignment of individual cases to luminal A, luminal B, HER2, and normal breast-like subtypes was dependent on the SSP used. The significance of associations with outcome of each molecular subtype, other than basal-like and luminal A, varied depending on SSP used. However, different SSPs produced broadly similar survival curves. Although every SSP identifies molecular subtypes with similar survival, they do not reliably assign the same patients to the same molecular subtypes. For molecular subtype classification to be incorporated into routine clinical practice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtypes is needed. Breakthrough Breast Cancer, Cancer Research UK.