Brian M. Bot

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.

Molecular Markers Identify Subtypes of Stage III Colon Cancer Associated with Patient Outcomes

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 polymerase chain reaction-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 BRAF or KRAS mutations which were mutually exclusive. Three subtypes were MMR proficient: those with mutations in BRAF (6.9% of samples), mutations in KRAS (35%), or tumors lacking either BRAF or KRAS mutations (49%). Two subtypes were MMR deficient: the sporadic type (6.8%) with BRAF mutation and/or or hypermethylation of MLH1 and the familial type (2.6%), which lacked BRAF(V600E) or hypermethylation of MLH1. A higher percentage of MMR-proficient tumors with BRAF(V600E) were proximal (76%), high-grade (44%), N2 stage (59%), and detected in women (59%), compared with MMR-proficient tumors without BRAF(V600E) or KRAS mutations (33%, 19%, 41%, and 42%, respectively; all P < .0001). A significantly lower proportion of patients with MMR-proficient tumors with mutant BRAF (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 with patients whose MMR-proficient tumors lacked mutations in either gene. Disease-free survival rates of patients with MMR-deficient sporadic or familial subtypes was similar to those of patients with MMR-proficient tumors without BRAF or KRAS mutations. The observed differences in survival rates of patients with different tumor subtypes were 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 BRAF or KRAS mutations 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 BRAF(V600E) or KRAS mutations) had similar outcomes to those of patients with MMR-deficient cancers.

Integrated Analysis of Gene Expression, CpG Island Methylation, and Gene Copy Number in Breast Cancer Cells by Deep Sequencing

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.

Pooled Safety and Efficacy Analysis Examining the Effect of Performance Status on Outcomes in Nine First-Line Treatment Trials Using Individual Data From Patients With Metastatic Colorectal Cancer

PURPOSE Performance status (PS) is a prognostic factor in patients with metastatic colorectal cancer. Clinical trials typically enroll less than 10% of patients with a PS of 2 (PS2); thus, the benefit of systemic chemotherapy in PS2 patients is uncertain. PATIENTS AND METHODS Individual data from 6,286 patients (509 PS2 patients) from nine clinical trials were used to compare treatment efficacy by PS. Progression-free survival (PFS), grade > or = 3 adverse events, 60-day all-cause mortality, overall survival (OS), and response rate (RR) were explored in the full set of nine trials and in the five trials comparing first-line monotherapy with combination therapy. RESULTS Compared with patients with PS of 0 or 1, PS2 patients had significantly higher rates of grade > or = 3 nausea (8.5% v 16.4%, respectively; P < .0001) and vomiting (7.6% v 11.9%, respectively; P = .006) and 60-day all-cause mortality (2.8% v 12.0%, respectively; P < .0001). PS2 was prognostic for PFS (hazard ratio [HR] = 1.52; P < .0001; median PFS, 7.6 months for PS 0 or 1 v 4.9 months for PS2), OS (HR = 2.18; P < .0001; median OS, 17.3 months for PS 0 or 1 v 8.5 months for PS2), and RR (odds ratio = 0.61; P < .0001; 43.8% for PS 0 or 1 v 32.0% for PS2). The relative benefit and toxicity of experimental versus control treatment and monotherapy versus combination therapy were not different in PS 0 or 1 patients versus PS2 patients. CONCLUSION In clinical trials, PS2 patients derive similar benefit from superior treatment as patients with PS of 0 to 1 but with an increased risk of toxicities and 12% 60-day mortality. Although current treatment provides benefit, new approaches are required to approach 1-year median survival for PS2 patients.

The mPower study, Parkinson disease mobile data collected using ResearchKit

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.

Two or three year disease-free survival (DFS) as a primary end-point in stage III adjuvant colon cancer trials with fluoropyrimidines with or without oxaliplatin or irinotecan: data from 12,676 patients from MOSAIC, X-ACT, PETACC-3, C-06, C-07 and C89803.

BACKGROUND The ACCENT group previously established disease-free survival (DFS) with 2 or 3 years median follow-up to predict 5 year overall survival (5 year OS) in stage II and III colon cancer. ACCENT further proposed (1) a stronger association between DFS and OS in stage III than II, and (2) 6 or 7 years necessary to demonstrate DFS/OS surrogacy in recent trials. The relationship between end-points in trials with oral fluoropyrimidines, oxaliplatin and irinotecan is unknown. METHODS Associations between the treatment effect hazard ratios (HRs) on 2 and 3 years DFS, and 5 and 6 years OS were examined in 6 phase III trials not included in prior analyses from 1997 to 2002. Individual data for 12,676 patients were analysed; two trials each tested oxaliplatin, irinotecan and oral treatment versus 5-FU/LV. FINDINGS Overall association between 2/3 year DFS and 5/6 year OS HRs was modest to poor (simple R² measures: 0.58-0.76, model-based R²: 0.17-0.49). In stage III patients, the association increased (model-based R² ≥ 0.79). Observed treatment effects on 2 year DFS accurately 5/6 year OS effects overall and in stage III patients. INTERPRETATION In recent trials of cytotoxic chemotherapy, 2 or 3 years DFS HRs are highly predictive of 5 and 6 years OS HRs in stage III but not stage II patients. In all patients the DFS/OS association is stronger for 6 year OS, thus at least 6 year follow-up is recommended to assess OS benefit. These data support DFS as the primary end-point for stage III colon cancer trials testing cytotoxic agents.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Database of Genomic Biomarkers for Cancer Drugs and Clinical Targetability in Solid Tumors

SUMMARY Comprehensive genomic profiling is expected to revolutionize cancer therapy. In this Prospective, we present the prevalence of mutations and copy-number alterations with predictive associations across solid tumors at different levels of stringency for gene-drug targetability. More than 90% of The Cancer Genome Atlas samples have potentially targetable alterations, the majority with multiple events, illustrating the challenges for treatment prioritization given the complexity of the genomic landscape. Nearly 80% of the variants in rarely mutated oncogenes are of uncertain functional significance, reflecting the gap in our understanding of the relevance of many alterations potentially linked to therapeutic actions. Access to targeted agents in early clinical trials could affect treatment decision in 75% of patients with cancer. Prospective implementation of large-scale molecular profiling and standardized reports of predictive biomarkers are fundamental steps for making precision cancer medicine a reality.

Association Between Disease-Free Survival and Overall Survival When Survival Is Prolonged After Recurrence in Patients Receiving Cytotoxic Adjuvant Therapy for Colon Cancer: Simulations Based on the 20,800 Patient ACCENT Data Set

PURPOSE We previously validated disease-free survival (DFS) as a surrogate for overall survival (OS) in fluorouracil-based adjuvant colon cancer clinical trials. New therapies have extended survival after recurrence from 1 to approximately 2 years. We examined the possible impact of this improvement on the DFS/OS association. METHODS The Adjuvant Colon Cancer Endpoints (ACCENT) data set of 20,898 patients was analyzed. In an exploratory fashion, time from recurrence to death in patients experiencing recurrence was extended using several algorithms, and the association of DFS after 3 years of median follow-up and OS after varying lengths of follow-up (median of 5, 6, and 7 years) was assessed. RESULTS Seven thousand four hundred two patients (35%) experienced recurrence. Median time from recurrence to death was 24 months in the hypothetical data sets. When times from recurrence to death were doubled, the association between treatment effects on DFS and 5-year OS was modest (R(2) = 0.51 for both 2- and 3-year DFS) but remained strong for DFS and 6-year OS (R(2) = 0.67 for both 2- and 3-year DFS) and 7-year OS (R(2) = 0.70 for both 2- and 3-year DFS). The reduced DFS/OS association with extended survival after recurrence was greater in stage II than stage III patients. Multiple simulations provided consistent findings. CONCLUSION Extended survival after recurrence reduces the association between treatment effects on 3-year DFS and 5-year OS, particularly in stage II patients; longer follow-up strengthens the association. In modern adjuvant trials, 6 or 7 years may be required to demonstrate OS improvements, further supporting DFS as the preferred primary end point for future adjuvant colon cancer clinical trials.

Developing predictive molecular maps of human disease through community-based modeling

1. The failure rate for drugs in clinical development is still startlingly high despite unprecedented investment in RD many compounds are shown to be safe and to engage the intended target but do not improve the primary indication. This failure stems from the simplistic ways in which we have historically studied potential drug targets for complex diseases and indicates a need for more innovative approaches to identify causal relationships between molecular entities and disease. Biology is rapidly changing and becoming a technology and data-intensive science with the development of new instrumentation to measure various molecular states in greater detail. Herein lays an opportunity to transform our understanding of the molecular underpinnings of disease and develop modeling frameworks that can describe complex systems and predict their behavior. Without these models acting as maps, biologists risk drowning in an ever-growing sea of data. This vision for biology, to use large-scale data to model disease, reflects parallel developments in other scientific disciplines: for example, modeling future trends in climate based on complex meteorological information in atmospheric science. The term fourth paradigm has been coined for this “data intensive” science discovery to distinguish it from empiric, theoretical and computational approaches 3