Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene “mountains” and a much larger number of gene “hills” that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.

The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.

Purpose: While various studies have highlighted the prognostic significance of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAT), the impact of additional adjuvant therapy after pCR is not known.Experimental Design: PubMed was searched for studies with NAT for breast cancer and individual patient-level data was extracted for analysis using plot digitizer software. HRs, with 95% probability intervals (PI), measuring the association between pCR and overall survival (OS) or event-free survival (EFS), were estimated using Bayesian piece-wise exponential proportional hazards hierarchical models including pCR as predictor.Results: Overall, 52 of 3,209 publications met inclusion criteria, totaling 27,895 patients. Patients with a pCR after NAT had significantly better EFS (HR = 0.31; 95% PI, 0.24–0.39), particularly for triple-negative (HR = 0.18; 95% PI, 0.10–0.31) and HER2+ (HR = 0.32; 95% PI, 0.21–0.47) disease. Similarly, pCR after NAT was also associated with improved survival (HR = 0.22; 95% PI, 0.15–0.30). The association of pCR with improved EFS was similar among patients who received subsequent adjuvant chemotherapy (HR = 0.36; 95% PI, 0.19–0.67) and those without adjuvant chemotherapy (HR = 0.36; 95% PI, 0.27–0.54), with no significant difference between the two groups (P = 0.60).Conclusions: Achieving pCR following NAT is associated with significantly better EFS and OS, particularly for triple-negative and HER2+ breast cancer. The similar outcomes with or without adjuvant chemotherapy in patients who attain pCR likely reflects tumor biology and systemic clearance of micrometastatic disease, highlighting the potential of escalation/deescalation strategies in the adjuvant setting based on neoadjuvant response.See related commentary by Esserman, p. 2771

Although it has been hypothesized that some of the somatic mutations found in tumors may occur before tumor initiation, there is little experimental or conceptual data on this topic. To gain insights into this fundamental issue, we formulated a mathematical model for the evolution of somatic mutations in which all relevant phases of a tissue’s history are considered. The model makes the prediction, validated by our empirical findings, that the number of somatic mutations in tumors of self-renewing tissues is positively correlated with the age of the patient at diagnosis. Importantly, our analysis indicates that half or more of the somatic mutations in certain tumors of self-renewing tissues occur before the onset of neoplasia. The model also provides a unique way to estimate the in vivo tissue-specific somatic mutation rates in normal tissues directly from the sequencing data of tumors. Our results have substantial implications for the interpretation of the large number of genome-wide cancer studies now being undertaken.

Purpose: Recent studies sought to refine lung cancer classification using gene expression microarrays. We evaluate the extent to which these studies agree and whether results can be integrated. Experimental Design: We developed a practical analysis plan for cross-study comparison, validation, and integration of cancer molecular classification studies using public data. We evaluated genes for cross-platform consistency of expression patterns, using integrative correlations, which quantify cross-study reproducibility without relying on direct assimilation of expression measurements across platforms. We then compared associations of gene expression levels to differential diagnosis of squamous cell carcinoma versus adenocarcinoma via reproducibility of the gene-specific t statistics and to survival via reproducibility of Cox coefficients. Results: Integrative correlation analysis revealed a large proportion of genes in which the patterns agreed across studies more than would be expected by chance. Correlation of t statistics for diagnosis of squamous cell carcinoma versus adenocarcinoma is high (0.85) and increases (0.925) when using only the most consistent genes identified by integrative correlation. Correlations of Cox coefficients ranged from 0.13 to 0.31 (0.33–0.49 with genes selected for consistency). Although we find genes that are significant in multiple studies but show discordant effects, their number is approximately that expected by chance. We report genes that are reproducible by integrative analysis, significant in all studies, and concordant in effect. Conclusions: Cross-study comparison revealed significant, albeit incomplete, agreement of gene expression patterns related to lung cancer biology and identified genes that reproducibly predict outcomes. This analysis approach is broadly applicable to cross-study comparisons of gene expression profiling projects.

Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.