BACKGROUND: The purpose of this study was to develop a deep learning classification approach to distinguish cancerous from noncancerous regions within optical coherence tomography (OCT) images of breast tissue for potential use in an intraoperative setting for margin assessment. METHODS: A custom ultrahigh-resolution OCT (UHR-OCT) system with an axial resolution of 2.7 μm and a lateral resolution of 5.5 μm was used in this study. The algorithm used an A-scan-based classification scheme and the convolutional neural network (CNN) was implemented using an 11-layer architecture consisting of serial 3 × 3 convolution kernels. Four tissue types were classified, including adipose, stroma, ductal carcinoma in situ, and invasive ductal carcinoma. RESULTS: The binary classification of cancer versus noncancer with the proposed CNN achieved 94% accuracy, 96% sensitivity, and 92% specificity. The mean five-fold validation F1 score was highest for invasive ductal carcinoma (mean standard deviation, 0.89 ± 0.09) and adipose (0.79 ± 0.17), followed by stroma (0.74 ± 0.18), and ductal carcinoma in situ (0.65 ± 0.15). CONCLUSION: It is feasible to use CNN based algorithm to accurately distinguish cancerous regions in OCT images. This fully automated method can overcome limitations of manual interpretation including interobserver variability and speed of interpretation and may enable real-time intraoperative margin assessment.

Mechanisms underlying the poor breast cancer prognosis among obese women are unresolved. DNA methylation levels are linked to obesity and to breast cancer survival. We hypothesized that obesity may work in conjunction with the epigenome to alter prognosis. Using a population-based sample of women diagnosed with first primary breast cancer, we examined modification of the obesity-mortality association by DNA methylation. In-person interviews were conducted approximately 3 months after diagnosis. Weight and height were assessed [to estimate body mass index (BMI)], and blood samples collected. Promoter methylation of 13 breast cancer-related genes was assessed in archived tumor by methylation-specific PCR and Methyl Light. Global methylation in white blood cell DNA was assessed by analysis of long interspersed elements-1 (LINE-1) and with the luminometric methylation assay (LUMA). Vital status among 1308 patients (with any methylation biomarker and complete BMI assessment) was determined after approximately 15 years of follow-up (N = 194/441 deaths due to breast cancer-specific/all-cause mortality). We used Cox proportional hazards regression to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs) using two-sided p values of 0.05. Breast cancer-specific mortality was higher among obese (BMI ≥ 30) patients with promoter methylation in APC (HR = 2.47; 95 % CI = 1.43-4.27) and TWIST1 (HR = 4.25; 95 % CI = 1.43-12.70) in breast cancer tissue. Estimates were similar, but less pronounced, for all-cause mortality. Increased all-cause (HR = 1.81; 95 % CI = 1.19-2.74) and breast cancer-specific (HR = 2.61; 95 % CI = 1.45-4.69) mortality was observed among obese patients with the lowest LUMA levels. The poor breast cancer prognosis associated with obesity may depend on methylation profiles, which warrants further investigation.

This study was conducted to determine the safety and efficacy of the green tea-derived Polyphenon E (Poly E) in patients with Barrett's Esophagus (BE). Subjects were randomized to a 6-month, twice daily (BID) oral treatment of placebo or Poly E (200, 400, or 600 mg). Endoscopic evaluation, including biopsies, was performed before and after treatment. The primary objective was to demonstrate safety; secondary objectives investigated catechin accumulation and effects in clinical specimens. Of the 44 enrolled subjects, 11 received placebo, and 33 received Poly E. No dose-limiting toxicities were encountered, and a maximum tolerated dose (MTD) was not reached. The recommended phase II dose was 600 mg twice daily. The most common treatment-related adverse events (AE) in Poly E-treated subjects were grade I and II nausea, grade I belching, and grade I lactate dehydrogenase (LDH) elevation. No treatment-related AEs were reported in placebo-treated subjects, aside from grade I laboratory abnormalities. Pill counts and subject diaries were not consistently collected, and compliance was difficult to determine. However, on the basis of an intention-to-treat analysis, there was a significant relationship between Poly E dose and esophageal EGCG level--mean changes (pmol/g) of 0.79 (placebo), 6.06 (200 mg), 35.67 (400 mg), and 34.95 (600 mg); P = 0.005. There was a possible relationship between Poly E dose and urine PGE-M concentration. In conclusion, Poly E was well-tolerated, and treatment with Poly E (400 and 600 mg) but not Poly E (200 mg) or placebo resulted in clinically relevant and detectable EGCG accumulation in the target organ, esophageal mucosa.

[This corrects the article DOI: 10.1038/s41523-018-0074-6.].

Advances in the surgical management of the axilla in patients treated with neoadjuvant chemotherapy, especially those with node positive disease at diagnosis, have led to changes in practice and more judicious use of axillary lymph node dissection that may minimize morbidity from surgery. However, there is still significant confusion about how to optimally manage the axilla, resulting in variation among practices. From the viewpoint of drug development, assessment of response to neoadjuvant chemotherapy remains paramount and appropriate assessment of residual disease-the primary endpoint of many drug therapy trials in the neoadjuvant setting-is critical. Therefore decreasing the variability, especially in a multicenter clinical trial setting, and establishing a minimum standard to ensure consistency in clinical trial data, without mandating axillary lymph node dissection, for all patients is necessary. The key elements which include proper staging and identification of nodal involvement at diagnosis, and appropriately targeted management of the axilla at the time of surgical resection are presented. The following protocols have been adopted as standard procedure by the I-SPY2 trial for management of axilla in patients with node positive disease, and present a framework for prospective clinical trials and practice.

Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma; yet, our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBMs and 10 extracranial melanoma metastases (ECMs) and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX⁺CD8⁺ T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

(Cell Reports 23, 313–326; April 3, 2018) In the originally published version of this article, the author list contained two errors. Specifically, David J. Kwiatkowski was misspelled as David J. Kwaitkowski, and William Y. Kim was inadvertently written as William T. Kim. Both names have been corrected online. The authors regret this error.

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

(Cell 173, 371–385.e1–e9; April 5, 2018) It has come to our attention that we made two errors in preparation of this manuscript. First, in the STAR Methods, under the subheading of “Hypermutators and Immune Infiltrates” within the “Quantification and Statistical Analysis” section, we inadvertently referred to Figures S7A–S7C for data corresponding to sample stratification by hypermutator status alone in the last sentence. It should have referred to Figure S6A–S6C. Second, the lists of highly frequent missense mutations for COAD (colon adenocarcinoma) and READ (rectum adenocarcinoma) displayed in Figure S7 were incorrect because when we ordered the mutations in the initial analysis, we mistakenly combined the two cancer types COAD and READ for analysis, despite the fact that they were listed as two separate cancer types in the x-axis of the figure. After re-ordering the mutations by frequency for COAD and READ independently, information on highly frequent missense mutations for each of these cancer types is different and updated now in the revised Figure S7. These errors don't change the major conclusions of the paper and have been corrected online. We apologize for any confusion they may have caused. [Figure-presented]

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.