Eboneé N. Butler

BACKGROUND: A new pandemic influenza A (H1N1) virus has emerged, causing illness globally, primarily in younger age groups. To assess the level of preexisting immunity in humans and to evaluate seasonal vaccine strategies, we measured the antibody response to the pandemic virus resulting from previous influenza infection or vaccination in different age groups. METHODS: Using a microneutralization assay, we measured cross-reactive antibodies to pandemic H1N1 virus (2009 H1N1) in stored serum samples from persons who either donated blood or were vaccinated with recent seasonal or 1976 swine influenza vaccines. RESULTS: A total of 4 of 107 persons (4%) who were born after 1980 had preexisting cross-reactive antibody titers of 40 or more against 2009 H1N1, whereas 39 of 115 persons (34%) born before 1950 had titers of 80 or more. Vaccination with seasonal trivalent inactivated influenza vaccines resulted in an increase in the level of cross-reactive antibody to 2009 H1N1 by a factor of four or more in none of 55 children between the ages of 6 months and 9 years, in 12 to 22% of 231 adults between the ages of 18 and 64 years, and in 5% or less of 113 adults 60 years of age or older. Seasonal vaccines that were formulated with adjuvant did not further enhance cross-reactive antibody responses. Vaccination with the A/New Jersey/1976 swine influenza vaccine substantially boosted cross-reactive antibodies to 2009 H1N1 in adults. CONCLUSIONS: Vaccination with recent seasonal nonadjuvanted or adjuvanted influenza vaccines induced little or no cross-reactive antibody response to 2009 H1N1 in any age group. Persons under the age of 30 years had little evidence of cross-reactive antibodies to the pandemic virus. However, a proportion of older adults had preexisting cross-reactive antibodies.

The surveillance of seasonal influenza virus susceptibility to neuraminidase (NA) inhibitors was conducted using an NA inhibition assay. The 50% inhibitory concentration values (IC(50)s) of 4,570 viruses collected globally from October 2004 to March 2008 were determined. Based on mean IC(50)s, A(H3N2) viruses (0.44 nM) were more sensitive to oseltamivir than A(H1N1) viruses (0.91 nM). The opposite trend was observed with zanamivir: 1.06 nM for A(H1N1) and 2.54 nM for A(H3N2). Influenza B viruses exhibited the least susceptibility to oseltamivir (3.42 nM) and to zanamivir (3.87 nM). To identify potentially resistant viruses (outliers), a threshold of a mean IC(50) value + 3 standard deviations was defined for type/subtype and drug. Sequence analysis of outliers was performed to identify NA changes that might be associated with reduced susceptibility. Molecular markers of oseltamivir resistance were found in six A(H1N1) viruses (H274Y) and one A(H3N2) virus (E119V) collected between 2004 and 2007. Some outliers contained previously reported mutations (e.g., I222T in the B viruses), while other mutations [e.g., R371K and H274Y in B viruses and H274N in A(H3N2) viruses) were novel. The R371K B virus outlier exhibited high levels of resistance to both inhibitors (>100 nM). A substantial variance at residue D151 was observed among A(H3N2) zanamivir-resistant outliers. The clinical relevance of newly identified NA mutations is unknown. A rise in the incidence of oseltamivir resistance in A(H1N1) viruses carrying the H274Y mutation was detected in the United States and in other countries in the ongoing 2007 to 2008 season. As of March 2008, the frequency of resistance among A(H1N1) viruses in the United States was 8.6% (50/579 isolates). The recent increase in oseltamivir resistance among A(H1N1) viruses isolated from untreated patients raises public health concerns and necessitates close monitoring of resistance to NA inhibitors.

Abstract RNA-based, multi-gene molecular assays are available and widely used for patients with ER-positive/HER2-negative breast cancers. However, RNA-based genomic tests can be costly and are not available in many countries. Methods for inferring molecular subtype from histologic images may identify patients most likely to benefit from further genomic testing. To identify patients who could benefit from molecular testing based on H&E stained histologic images, we developed an image analysis approach using deep learning. A training set of 571 breast tumors was used to create image-based classifiers for tumor grade, ER status, PAM50 intrinsic subtype, histologic subtype, and risk of recurrence score (ROR-PT). The resulting classifiers were applied to an independent test set ( n = 288), and accuracy, sensitivity, and specificity of each was assessed on the test set. Histologic image analysis with deep learning distinguished low-intermediate vs. high tumor grade (82% accuracy), ER status (84% accuracy), Basal-like vs. non-Basal-like (77% accuracy), Ductal vs. Lobular (94% accuracy), and high vs. low-medium ROR-PT score (75% accuracy). Sampling considerations in the training set minimized bias in the test set. Incorrect classification of ER status was significantly more common for Luminal B tumors. These data provide proof of principle that molecular marker status, including a critical clinical biomarker (i.e., ER status), can be predicted with accuracy >75% based on H&E features. Image-based methods could be promising for identifying patients with a greater need for further genomic testing, or in place of classically scored variables typically accomplished using human-based scoring.