Gift Nyamundanda

The consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor–β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC—with clear biological interpretability—and the basis for future clinical stratification and subtype-based targeted interventions.

A Low-Cost Multiplex Biomarker Assay Stratifies Colorectal Cancer Patient Samples into Clinically-Relevant Subtypes

Previously, we classified colorectal cancers (CRCs) into five CRCA subtypes with different prognoses and potential treatment responses, using a 786-gene signature. We merged our subtypes and those described by five other groups into four consensus molecular subtypes (CMS) that are similar to CRCA subtypes. Here we demonstrate the analytical development and application of a custom NanoString platform-based biomarker assay to stratify CRC into subtypes. To reduce costs, we switched from the standard protocol to a custom modified protocol (NanoCRCA) with a high Pearson correlation coefficient (>0.88) between protocols. Technical replicates were highly correlated (>0.96). The assay included a reduced robust 38-gene panel from the 786-gene signature that was selected using an in-laboratory developed computational pipeline of class prediction methods. We applied our NanoCRCA assay to untreated CRCs including fresh-frozen and formalin-fixed paraffin-embedded (FFPE) samples (n=81) with matched microarray or RNA-Seq profiles. We further compared the assay results with CMS classification, different platforms (microarrays/RNA-Seq) and gene-set classifiers (38 and 786 genes). NanoCRCA classified fresh-frozen samples (n=39; not including those showing a mixture of subtypes) into all five CRCA subtypes with overall high concordance across platforms (89.7%) and with CMS subtypes (84.6%), independent of tumour cellularity. This analytical validation of the assay shows the association of subtypes with their known molecular, mutational and clinical characteristics. Overall, our modified NanoCRCA assay with further clinical assessment may facilitate prospective validation of CRC subtypes in clinical trials and beyond. Novelty and Impact We previously identified five gene expression-based CRC subtypes with prognostic and potential predictive differences using a 786-gene signature and microarray platform. Subtype-driven clinical trials require a validated assay suitable for routine clinical use. This study demonstrates, for the first time, how molecular CRCA subtype can be detected using NanoString Technology-based biomarker assay (NanoCRCA) suitable for clinical validation. NanoCRCA is suitable for analysing FFPE samples, and this assay may facilitate patient stratification within clinical trials.Objective: In order to personalise standard therapies based on molecular profiles, we previously classified colorectal cancers (CRCs) into five distinct subtypes (CRCAssigner) and later into four consensus molecular subtypes (CMS) with different prognoses and treatment responses. For clinical application, here we developed a low-cost multiplex biomarker assay. Design: Three cohorts of untreated fresh frozen CRC samples (n=57) predominantly from primary tumours and profiled by microarray/RNA-Seq were analysed. A reduced 38-gene panel (CRCAssigner-38) was selected from the published 786-gene CRCAssigner signature (CRCAssigner-786) using an in-house gene selection approach. A customised NanoString Technologies nCounter platform-based assay (NanoCRCAssigner) was developed for comparison with different classifiers (CMS subtypes), platforms (microarrays and RNA-Seq), and gene sets (CRCAssigner-38 and CRCAssigner-786). Results: NanoCRCAssigner classified samples (n=48; except those showing a mixture of subtypes) into all five CRCAssigner subtypes with overall high concordance across platforms (> 87%) and with CMS subtypes (81%) irrespective of variable tumour cellularity. The association of subtypes with their known molecular (microsatellite-instable and stemness), mutational (KRAS/BRAF), and clinical characteristics (including overall survival) further demonstrated assay validity. To reduce costs, we switched from the standard protocol to a low-cost protocol with a high Pearson correlation co-efficient (0.9) between protocols. Technical replicates were highly correlated (0.98). Conclusion: Here we developed a low-cost and potentially clinically deployable NanoCRCAssigner assay to facilitate prospective validation of (CRCAssigner and potentially CMS) subtypes in clinical trials and beyond.

A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies

Genome projects now generate large-scale data often produced at various time points by different laboratories using multiple platforms. This increases the potential for batch effects. Currently there are several batch evaluation methods like principal component analysis (PCA; mostly based on visual inspection), and sometimes they fail to reveal all of the underlying batch effects. These methods can also lead to the risk of unintentionally correcting biologically interesting factors attributed to batch effects. Here we propose a novel statistical method, finding batch effect (findBATCH), to evaluate batch effect based on probabilistic principal component and covariates analysis (PPCCA). The same framework also provides a new approach to batch correction, correcting batch effect (correctBATCH), which we have shown to be a better approach to traditional PCA-based correction. We demonstrate the utility of these methods using two different examples (breast and colorectal cancers) by merging gene expression data from different studies after diagnosing and correcting for batch effects and retaining the biological effects. These methods, along with conventional visual inspection-based PCA, are available as a part of an R package exploring batch effect (exploBATCH; https://github.com/syspremed/exploBATCH).

polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance

BackgroundTo ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce “polyClustR”, a tool that reconciles clusters identified by different methods into subtype “communities” using a hypergeometric test or a measure of relative proportion of common samples.ResultsThe polyClustR pipeline was initially tested using a breast cancer dataset to demonstrate how results are compatible with and add to the understanding of this well-characterised cancer. Two uveal melanoma datasets were then utilised to identify and validate novel subtype communities with significant metastasis-free prognostic differences and associations with known chromosomal aberrations.ConclusionWe demonstrate the value of the polyClustR approach of applying multiple consensus clustering algorithms and systematically reconciling the results in identifying novel subtype communities of two cancer types, which nevertheless are compatible with established understanding of these diseases. An R implementation of the pipeline is available at: https://github.com/syspremed/polyClustR

A Next Generation Clustering Tool Enables Identification of Functional Cancer Subtypes with Associated Biological Phenotypes

One of the major challenges faced in defining clinically applicable and homogeneous molecular tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. The conventional approach involves at least three steps: Firstly, identify subtypes using unsupervised clustering of patient tumours with molecular (etiological) profiles; secondly associate the subtypes with clinical or phenotypic information (covariates) to infer some biological meaning to the redefined subtypes; and thirdly, identify clinically relevant biomarkers associated with the subtypes. Here, we report the implementation of a tool, phenotype mapping (phenMap), which combines these three steps to define functional subtypes with associated phenotypic information and molecular signatures. phenMap models meta (unobserved) variables as a function of covariates to expose any underlying clustering structure within the data and discover associations between subtypes and phenotypes. We demonstrate how this tool can more avidly identify functional subtypes that are an improvement over already existing etiological subtypes by analysing published breast cancer gene expression data.

Revealing unidentified heterogeneity in different epithelial cancers using heterocellular subtype classification

Cancers are currently diagnosed, categorised, and treated based on their tissue of origin. However, how different cellular compartments of tissues (e.g., epithelial, immune and stem cells) are similar across cancer types is unknown. Here we used colorectal cancer subtypes and their signatures representing different colonic crypt cell types as surrogates to classify different epithelial cancers into five heterotypic cellular (heterocellular) subtypes. The stem-like and inflammatory heterocellular subtypes are ubiquitous across epithelial cancers so capture intrinsic, tissue-independent properties. Conversely, well-differentiated/specialized goblet-like/enterocyte heterocellular subtypes differ across cancer types due to their colorectum-specific genes. The transit-amplifying heterocellular subtype shows a dynamic range of cellular differentiation with shared common pathways (Wnt, FGFR) in certain cancer types. Importantly, this approach revealed previously unrecognised heterogeneity in pancreatic, breast, microsatellite-instability enriched and KRAS mutation-dependent cancers. Immune cell-type differences are common and useful for patient stratification for immunotherapy. This unique approach identifies cell type-dependent but tissue-independent heterogeneity in different cancers for precision medicine.

Abstract 603: Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation

Background: Gene expression-based subtyping is widely accepted as a relevant source of disease stratification. Despite the widespread use, its translational and clinical utility is hampered by discrepant results, likely related to differences in data processing and algorithms applied to diverse patient cohorts, sample preparation methods, and gene expression platforms. In the absence of a clear methodological gold standard to perform such analyses, a more general framework that integrates and compares multiple strategies is needed to define common disease patterns in a principled, unbiased manner. Methods: We formed a consortium of 6 independent experts groups - each with a previously published CRC classifier, ranging from 3 to 6 subtypes - to understand similarities and differences of their subtyping systems. Sage Bionetworks functioned as neutral party to aggregate public and proprietary data (Synapse platform) and perform meta-analysis. Each group applied its CRC subtyping signature to the collection of data sets with gene expression (n = 4,151, predominantly stage II and III). Using the resulting subtype labels, we developed a network-based model and applied a Markov cluster algorithm to detect robust network substructures that would indicate recurring subtype patterns and therefore a consensus subtyping system. Correlative analyses using clinico-pathological, genomic and epigenomic features was performed to robustly characterize the identified subtypes. Results: This analytical framework revealed significant interconnectivity between the six independent classification systems, leading to the identification of four biologically distinct consensus molecular subtypes (CMS) enriched for key pathway traits: CMS1 (MSI Immune), hypermutated, microsatellite unstable, with strong immune activation; CMS2 (Canonical), epithelial, chromosomally unstable, with marked WNT and MYC signaling activation; CMS3 (Metabolic), epithelial, with evident metabolic dysregulation; and CMS4 (Mesenchymal), prominent TGFβ activation, angiogenesis, stromal invasion. Patients diagnosed with MSI Immune tumors had worse survival after relapse and those with mesenchymal tumors had increased risk of metastasis and worse overall survival. Discussion: We describe a novel methodological paradigm for deriving benchmarks of disease subtyping. Our work represents the first example of a community of experts identifying and advocating for a single reproducible model for cancer subtyping, effectively unifying previous classifiers. In the CRC domain, the uniformity afforded by this new classification system and its application to a large data set revealed important subtype-specific biological associations that were previously unnoticed or marginally significant, supporting a new taxonomy of the disease. Citation Format: Justin Guinney, Rodrigo Dienstmann, Xin Wang, Aurelien de Reynies, Andreas Schlicker, Charlotte Soneson, Laetitia Marisa, Paul Roepman, Gift Nyamundanda, Paolo Angelino, Brian Bot, Jeffrey S. Morris, Iris Simon, Sarah Gerster, Evelyn Fessler, Felipe de Sousa e Melo, Edoardo Missiaglia, Hena Ramay, David Barras, Krisztian Homicsko, Dipen Maru, Ganiraju Manyam, Bradley Broom, Valerie Boige, Ted Laderas, Ramon Salazar, Joe W. Gray, Josep Tabernero, Rene Bernards, Stephen Friend, Pierre Laurent-Puig, Jan P. Medema, Anguraj Sadanandam, Lodewyk Wessels, Mauro Delorenzi, Scott Kopetz, Louis Vermeulen, Sabine Tejpar. Consensus molecular subtyping through a community of experts advances unsupervised gene expression-based disease classification and facilitates clinical translation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 603. doi:10.1158/1538-7445.AM2015-603

Abstract 3083: Global gene expression profiling of mice tumor-derived organoids identifies key microRNAs and metabolic genes involved in CRC progression

Background. Progressive accumulation of mutations in oncogenic and tumor suppressor pathways are associated with colorectal cancer (CRC). Mutations in APC, KRAS and p53 represent key drivers events in CRC initiation and progression and are simultaneously mutated in about 30% of CRC. Tumor derived Organoids (TDO) are three-dimensional (3-D) structures composed of cells that are spatially organized like mini-guts and represent a useful ex vivo tool to study intestinal physiology and cancer progression. Here, we investigated the progressive deregulation of mRNA and microRNA (miRNA) genes in TDOs from intestines of three different Genetically Engineered Mouse models (GEMMs) harboring mutations in Apc, Apc plus Kras and Apc plus Kras and p53. Methods. Array analysis of protein coding and non-coding RNAs from mice TDOs was performed to identify mRNAs and miRNAs that were differentially expressed following Apc, Kras and p53 mutations. Agilent Feature Extraction software was used to analyze acquired array images and subsequent data processing was performed by the GeneSpring GX v11.5.1 software package. Results. Twenty-five% of mRNAs showed more than 2 fold changes in expression level and considered differentially expressed. Pathway analysis found that a great number of mRNAs, which were significantly differentially expressed (P-value 0.001), were involved in metabolic process. Of these, 10 were involved in metabolism of different amino acids or other compounds (e.g., arachidonic acid, ascorbate, alendrate, and linoleic acid) and 4 were encoding the cytochrome p450 family drug and xenobiotic metabolizing enzymes. In addition, 28 and 10% of miRNAs in Apc vs Apc/Kras and Apc/Kras vs Apc/Kras/p53 respectively were identified as differentially expressed miRNA. Twenty-two of these miRNAs were related to deregulated metabolic genes in this study. Selected metabolic genes (e.g., Pipox) were constantly down regulated from early (Apc only mutants) to advanced (Apc/Kras/p53 mutants) CRC, whereas their associated miRNAs (mir-883a-3p and mir-1943) were constantly up regulated. High expression level of mir-125 which was detected in Apc/Kras/p53 group was also associated to down regulation of associated metabolic genes: Pipox and Ggt7. Conclusions. Using 3-D TDOs, we have identified deregulated miRNAs that are involved in regulation of metabolic pathways associated with CRC progression. Further analysis of miRNA-mRNA interaction in these models may help identify metabolic vulnerabilities that may be exploited for CRC therapy. Citation Format: Mahnaz Darvish Damavandi, Chiara Braconi, Luciano Cascione, Andrea Lampis, Jens Hahne, Claudio Murgia, Michele Ghidini, Gift Nyamundanda, Anguraj Sadanandam, Carlo Croce, Owen Sansom, Nicola Valeri. Global gene expression profiling of mice tumor-derived organoids identifies key microRNAs and metabolic genes involved in CRC progression. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3083. doi:10.1158/1538-7445.AM2015-3083