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.

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.

Background & Aims Categorization of colon cancers into distinct subtypes using a combination of pathway-based biomarkers could provide insight into stage-independent variability in outcomes. Methods We used a PCR-based assay to detect mutations in BRAF (V600E) and in KRAS in 2720 stage III cancer samples, collected prospectively from patients participating in an adjuvant chemotherapy trial (NCCTG N0147). Tumors deficient or proficient in DNA mismatch repair (MMR) were identified based on detection of MLH1, MSH2, and MSH6 proteins and methylation of the MLH1 promoter. Findings were validated using tumor samples from a separate set of patients with stage III cancer (n=783). Association with 5-year disease-free survival was evaluated using Cox proportional hazards models. Results Tumors were categorized into 5 subtypes based on MMR status and detection of BRAFV600E or mutations in KRAS, which were mutually exclusive. Three subtypes were MMR proficient: those with BRAFV600E (6.9% of samples), mutations in KRAS (35%), or tumors lacking either BRAFV600E or mutations in KRAS (49%). Two subtypes were MMR deficient: the sporadic type (6.8%) with BRAFV600E or hypermethylation of MLH1, and the familial type (2.6%), which lacked BRAFV600E or hypermethylation of MLH1. A higher percentage of MMR-proficient tumors with BRAFV600E were proximal (76%), high grade (44%), N2 stage (59%), and detected in women (59%), compared to MMR-proficient tumors without BRAFV600E or mutations in KRAS (33%, 19%, 41%, and 42%, respectively; all P<.0001). A significantly lower proportion of patients with MMR-proficient tumors with BRAFV600E (hazard ratio, 1.43; 95% confidence interval, 1.11–1.85, Padjusted=.0065) or mutant KRAS (hazard ratio, 1.48; 95% confidence interval, 1.27–1.74, Padjusted<.0001) survived disease free for 5 years compared to patients whose MMR-proficient tumors lacked mutations in either gene. Disease-free survival of patients with MMR-deficient sporadic or familial subtypes was similar to that of patients with MMR-proficient tumors without BRAFV600E or mutations in KRAS. The observed differences in survival of patients with different tumor subtypes was validated in an independent cohort. Conclusions We identified subtypes of stage III colon cancer, based on detection of mutations in BRAF (V600E) or KRAS, and MMR status that show differences in clinical and pathologic features and disease-free survival. Patients with MMR-proficient tumors and BRAFV600E or mutations in KRAS had statistically shorter survival times than patients whose tumors lacked these mutations. The tumor subtype found in nearly half of the study cohort (MMR-proficient without BRAFV600E or KRAS mutations) had similar outcomes to those of patients with MMR-deficient cancers.

BackgroundTNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation.Patients and methodsAfter imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n = 3016)—N0147 (NCT00079274) and PETACC3 (NCT00026273)—was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n = 1499), and also externally in different population cohorts of chemotherapy-treated (n = 949) or -untreated (n = 1080) CC patients, and an additional series without treatment annotation (n = 782).ResultsTNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61–0.68 in the TNM alone model to 0.63–0.71 in models with added molecular markers, 0.65–0.73 with clinicopathological features and 0.66–0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively.ConclusionsIncorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients.

Purpose RECIST evaluation does not take into account the pre-treatment tumor kinetics and may provide incomplete information regarding experimental drug activity. Tumor Growth Rate (TGR) allows for a dynamic and quantitative assessment of the tumor kinetics. How TGR varies along the introduction of experimental therapeutics and is associated with outcome in phase I patients remains unknown. Experimental designs Medical records from all patients (n=253) prospectively treated in 20 phase I trials were analyzed. TGR was computed during the pre-treatment period (REFERENCE) and the EXPERIMENTAL period. Associations between TGR, standard prognostic scores (RMH score) and outcome (PFS, OS) were computed (multivariate analysis). Results We observed a reduction of TGR between the REFERENCE vs. EXPERIMENTAL periods (38% vs. 4.4%, P<.00001). Although most patients were classified as stable disease (65%) or progressive disease (25%) by RECIST at the first evaluation, 82% and 65% of them exhibited a decrease in TGR, respectively. In a multivariate analyses, only the decrease of TGR was associated with PFS (P=.004), whereas the RMH score was the only variable associated with OS (P=.0008). Only the investigated regimens delivered were associated with a decrease of TGR (P<.00001, multivariate analysis). Computing TGR profiles across different clinical trials reveals specific patterns of antitumor activity. Conclusions Exploring TGR in phase I patients is simple and provides clinically relevant information: (i) an early and subtle assessment of signs of antitumor activity; (ii) indpendent association with PFS; and (iii) It reveals drug-specific profiles; suggesting potential utility for guiding the further development of the investigational drugs.

We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER− cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER− cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5′ end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER− breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.

Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...

Protein stability is a major regulatory principle of protein function and cellular homeostasis. Despite limited understanding on mechanisms, disruption of protein turnover is widely implicated in diverse pathologies from heart failure to neurodegenerations. Information on global protein dynamics therefore has the potential to expand the depth and scope of disease phenotyping and therapeutic strategies. Using an integrated platform of metabolic labeling, high-resolution mass spectrometry and computational analysis, we report here a comprehensive dataset of the in vivo half-life of 3,228 and the expression of 8,064 cardiac proteins, quantified under healthy and hypertrophic conditions across six mouse genetic strains commonly employed in biomedical research. We anticipate these data will aid in understanding key mitochondrial and metabolic pathways in heart diseases, and further serve as a reference for methodology development in dynamics studies in multiple organ systems.