The utilization of archived, formalin-fixed paraffin-embedded (FFPE) tumor samples for massive parallel sequencing has been challenging due to DNA damage and contamination with normal stroma. Here, we perform whole genome sequencing of DNA isolated from two triple-negative breast cancer tumors archived for >11 years as 5 µm FFPE sections and matched germline DNA. The tumor samples show differing amounts of FFPE damaged DNA sequencing reads revealed as relatively high alignment mismatch rates enriched for C · G > T · A substitutions compared to germline samples. This increase in mismatch rate is observable with as few as one million reads, allowing for an upfront evaluation of the sample integrity before whole genome sequencing. By applying innovative quality filters incorporating global nucleotide mismatch rates and local mismatch rates, we present a method to identify high-confidence somatic mutations even in the presence of FFPE induced DNA damage. This results in a breast cancer mutational profile consistent with previous studies and revealing potentially important functional mutations. Our study demonstrates the feasibility of performing genome-wide deep sequencing analysis of FFPE archived tumors of limited sample size such as residual cancer after treatment or metastatic biopsies.

In the I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis 2), the pan-erythroblastic oncogene B inhibitor neratinib was available to all hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) subtypes and graduated in the HR-negative/HER2-positive signature. We hypothesized that neratinib response may be predicted by baseline HER2 epidermal growth factor receptor (EGFR) signaling activation/phosphorylation levels independent of total levels of HER2 or EGFR proteins. Complete experimental and response data were available for between 130 and 193 patients. In qualifying analyses, which used logistic regression and treatment interaction analysis, 18 protein/phosphoprotein, 10 mRNA, and 12 DNA biomarkers that related to HER family signaling were evaluated. Exploratory analyses used Wilcoxon rank sum and t tests without multiple comparison correction. HER pathway DNA biomarkers were either low prevalence or nonpredictive. In expression biomarker analysis, only one gene (STMN1) was specifically associated with response to neratinib in the HER2-negative subset. In qualifying protein/phosphoprotein analyses that used reverse phase protein microarrays, six HER family markers were associated with neratinib response. After analysis was adjusted for HR/HER2 status, EGFR Y1173 (pEGFR) showed a significant biomarker-by-treatment interaction (P = .049). Exploratory analysis of HER family signaling in patients with triple-negative (TN) disease found that activation of EGFR Y1173 (P = .005) and HER2 Y1248 (pHER2) (P = .019) were positively associated with pathologic complete response. Exploratory analysis in this pEGFR/pHER2-activated TN subgroup identified elevated levels of estrogen receptor α (P < .006) in these patients. Activation of HER family phosphoproteins associates with response to neratinib, but only EGFR Y1173 and STMN1 appear to add value to the graduating signature. Activation of HER2 and EGFR in TN tumors may identify patients whose diseases respond to neratinib and implies that there is a subset of patients with TN disease who paradoxically exhibit HER family signaling activation and may achieve clinical benefit with neratinib; this concept must be validated in future studies.

Rapid and affordable tumor molecular profiling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostication and treatment of cancer. A critical point has now been reached at which the analysis and storage of annotated clinical and genomic information in unconnected silos will stall the advancement of precision cancer care. Information systems must be harmonized to overcome the multiple technical and logistical barriers to data sharing. Against this backdrop, the Global Alliance for Genomic Health (GA4GH) was established in 2013 to create a common framework that enables responsible, voluntary and secure sharing of clinical and genomic data. This Perspective from the GA4GH Clinical Working Group Cancer Task Team highlights the data-aggregation challenges faced by the field, suggests potential collaborative solutions and describes how GA4GH can catalyze a harmonized data-sharing culture.

Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12^-/- and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12^-/- and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups.