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 (CMS) with distinguishing features: CMS1 (MSI Immune, 14%), hypermutated, microsatellite unstable, strong immune activation; CMS2 (Canonical, 37%), epithelial, chromosomally unstable, marked WNT and MYC signaling activation; CMS3 (Metabolic, 13%), epithelial, 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 intra-tumoral 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.

Metastatic cancer is a major cause of death and is associated with poor treatment efficacy. A better understanding of the characteristics of late-stage cancer is required to help adapt personalized treatments, reduce overtreatment and improve outcomes. Here we describe the largest, to our knowledge, pan-cancer study of metastatic solid tumour genomes, including whole-genome sequencing data for 2,520 pairs of tumour and normal tissue, analysed at median depths of 106× and 38×, respectively, and surveying more than 70 million somatic variants. The characteristic mutations of metastatic lesions varied widely, with mutations that reflect those of the primary tumour types, and with high rates of whole-genome duplication events (56%). Individual metastatic lesions were relatively homogeneous, with the vast majority (96%) of driver mutations being clonal and up to 80% of tumour-suppressor genes being inactivated bi-allelically by different mutational mechanisms. Although metastatic tumour genomes showed similar mutational landscape and driver genes to primary tumours, we find characteristics that could contribute to responsiveness to therapy or resistance in individual patients. We implement an approach for the review of clinically relevant associations and their potential for actionability. For 62% of patients, we identify genetic variants that may be used to stratify patients towards therapies that either have been approved or are in clinical trials. This demonstrates the importance of comprehensive genomic tumour profiling for precision medicine in cancer.In recent years, several large-scale whole-genome sequencing (WGS) analysis efforts have yielded valuable insights into the diversity of the molecular processes that drive different types of adult 1,2 and paediatric 3,4 cancer and have fuelled the promises of genome-driven oncology care 5 . However, most analyses were done on primary tumour material, whereas metastatic cancers-which cause the bulk of the disease burden and 90% of all cancer deaths-have been less comprehensively studied at the whole-genome level, with previous efforts focusing on tumourspecific cohorts 6-8 or at a targeted gene panel 9 or exome level 10 . As cancer genomes evolve over time, both in the highly heterogeneous primary tumour mass and as disseminated metastatic cells 11,12 , a better understanding of metastatic cancer genomes will be highly valuable to improve on adapting treatments for late-stage cancers.Here we describe the pan-cancer whole-genome landscape of metastatic cancers based on 2,520 paired tumour (106× average depth) and normal (blood, 38×) genomes from 2,399 patients ( Supplementary Tables 1 and 2, Extended Data Fig. 1). The sample distribution over age and primary tumour types broadly reflects the incidence of solid cancers in the Western world, including rare cancers (Fig. 1a). Sequencing data were analysed using an optimized bioinformatic pipeline based on open source tools (Methods, Supplementary Information) and identified a total of 59,472,629 single nucleotide varian...

ColoPrint significantly improves the prognostic accuracy of pathologic factors and MSI in patients with stage II and III CRC and facilitates the identification of patients with stage II disease who may be safely managed without chemotherapy.

Inhibitors of the ALK and EGF receptor tyrosine kinases provoke dramatic but short-lived responses in lung cancers harboring EML4-ALK translocations or activating mutations of EGFR, respectively. We used a large-scale RNAi screen to identify MED12, a component of the transcriptional MEDIATOR complex that is mutated in cancers, as a determinant of response to ALK and EGFR inhibitors. MED12 is in part cytoplasmic where it negatively regulates TGF-βR2 through physical interaction. MED12 suppression therefore results in activation of TGF-βR signaling, which is both necessary and sufficient for drug resistance. TGF-β signaling causes MEK/ERK activation, and consequently MED12 suppression also confers resistance to MEK and BRAF inhibitors in other cancers. MED12 loss induces an EMT-like phenotype, which is associated with chemotherapy resistance in colon cancer patients and to gefitinib in lung cancer. Inhibition of TGF-βR signaling restores drug responsiveness in MED12(KD) cells, suggesting a strategy to treat drug-resistant tumors that have lost MED12.

Metastasis is the process by which cancers spread to distinct sites in the body. It is the principal cause of death in individuals suffering from cancer. For some types of cancer, early detection of metastasis at lymph nodes close to the site of the primary tumor is pivotal for appropriate treatment. Because it can be difficult to detect lymph node metastases reliably, many individuals currently receive inappropriate treatment. We show here that DNA microarray gene-expression profiling can detect lymph node metastases for primary head and neck squamous cell carcinomas that arise in the oral cavity and oropharynx. The predictor, established with an 82-tumor training set, outperforms current clinical diagnosis when independently validated. The 102 predictor genes offer unique insights into the processes underlying metastasis. The results show that the metastatic state can be deciphered from the primary tumor gene-expression pattern and that treatment can be substantially improved.

In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore, a better understanding of the CRC biology is required to identify those patients who will benefit from chemotherapy and to find a more tailored therapy plan for other patients. Based on unsupervised classification of whole genome data from 188 stages I–IV CRC patients, a molecular classification was developed that consist of at least three major intrinsic subtypes (A-, B- and C-type). The subtypes were validated in 543 stages II and III patients and were associated with prognosis and benefit from chemotherapy. The heterogeneity of the intrinsic subtypes is largely based on three biological hallmarks of the tumor: epithelial-to-mesenchymal transition, deficiency in mismatch repair genes that result in high mutation frequency associated with microsatellite instability and cellular proliferation. A-type tumors, observed in 22% of the patients, have the best prognosis, have frequent BRAF mutations and a deficient DNA mismatch repair system. C-type patients (16%) have the worst outcome, a mesenchymal gene expression phenotype and show no benefit from adjuvant chemotherapy treatment. Both A-type and B-type tumors have a more proliferative and epithelial phenotype and B-types benefit from adjuvant chemotherapy. B-type tumors (62%) show a low overall mutation frequency consistent with the absence of DNA mismatch repair deficiency. Classification based on molecular subtypes made it possible to expand and improve CRC classification beyond standard molecular and immunohistochemical assessment and might help in the future to guide treatment in CRC patients.

The large-scale genetic profiling of tumours can identify potentially actionable molecular variants for which approved anticancer drugs are available 1-3 . However, when patients with such variants are treated with drugs outside of their approved label, successes and failures of targeted therapy are not systematically collected or shared. We therefore initiated the Drug Rediscovery protocol, an adaptive, precision-oncology trial that aims to identify signals of activity in cohorts of patients, with defined tumour types and molecular variants, who are being treated with anticancer drugs outside of their approved label. To be eligible for the trial, patients have to have exhausted or declined standard therapies, and have malignancies with potentially actionable variants for which no approved anticancer drugs are available. Here we show an overall rate of clinical benefit-defined as complete or partial response, or as stable disease beyond 16 weeks-of 34% in 215 treated patients, comprising 136 patients who received targeted therapies and 79 patients who received immunotherapy. The overall median duration of clinical benefit was 9 months (95% confidence interval of 8-11 months), including 26 patients who were experiencing ongoing clinical benefit at data cut-off. The potential of the Drug Rediscovery protocol is illustrated by the identification of a successful cohort of patients with microsatellite instable tumours who received nivolumab (clinical benefit rate of 63%), and a cohort of patients with colorectal cancer with relatively low mutational load who experienced only limited clinical benefit from immunotherapy. The Drug Rediscovery protocol facilitates the defined use of approved drugs beyond their labels in rare subgroups of cancer, identifies early signals of activity in these subgroups, accelerates the clinical translation of new insights into the use of anticancer drugs outside of their approved label, and creates a publicly available repository of knowledge for future decision-making.The precision treatment of cancer holds great promise for patients in terms of life extension and quality of life 1,2,4-7 . However, early studies and experiences with genetically and molecularly informed decisions regarding treatment have also identified considerable hurdles, which may jeopardize the way in which we capitalize on precision medicine [8][9][10][11] . First, populations of patients who are eligible for specific treatments or trials become smaller and trials accrue slower, owing to pre-selection by targeted sequencing of candidate variants and to slow implementation of pre-selection tests. Second, these candidate variants can, in general, be appreciated only when their tissue context Online contentAny methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at

Classification of breast cancer into molecular subtypes maybe important for the proper selection of therapy, as tumors with seemingly similar histopathological features can have strikingly different clinical outcomes. Herein, we report the development of a molecular subtyping profile (BluePrint), that enables rationalization in patient selection for either chemotherapy or endocrine therapy prescription. An 80-Gene Molecular Subtyping Profile (BluePrint) was developed using 200 breast cancer patient specimens and confirmed on four independent validation cohorts (n = 784). Additionally, the profile was tested as a predictor of chemotherapy response in 133 breast cancer patients, treated with T/FAC neoadjuvant chemotherapy. BluePrint classification of a patient cohort that was treated with neoadjuvant chemotherapy (n = 133) shows improved distribution of pathological Complete Response (pCR), among molecular subgroups compared with local pathology: 56% of the patients had a pCR in the Basal-type subgroup, 3% in the MammaPrint Low-risk, Luminal-type subgroup, 11% in the MammaPrint High-risk, Luminal-type subgroup, and 50% in the HER2-type subgroup. The group of genes identifying Luminal-type breast cancer is highly enriched for genes having an Estrogen Receptor binding site proximal to the promoter-region, suggesting that these genes are direct targets of the Estrogen Receptor. Implementation of this profile may improve the clinical management of breast cancer patients, by enabling the selection of patients who are most likely to benefit from either chemotherapy or from endocrine therapy.