Loss of Fhit expression in non-small-cell lung cancer: correlation with molecular genetic abnormalities and clinicopathological features

The FHIT gene is located at a chromosomal site (3p14.2) which is commonly affected by translocations and deletions in human neoplasia. Although FHIT alterations at the DNA and RNA level are frequent in many types of tumours, the biological and clinical significance of these changes is not clear. In this study we aimed at correlating loss of Fhit protein expression with a large number of molecular genetic and clinical parameters in a well-characterized cohort of non-small-cell lung cancers (NSCLCs). Paraffin sections of 99 non-small-cell carcinomas were reacted with an anti-Fhit polyclonal antibody in a standard immunohistochemical reaction. Abnormal cases were characterized by complete loss of cytoplasmic Fhit staining. The Fhit staining results were then correlated with previously obtained clinical and molecular data. Fifty-two of 99 tumours lacked cytoplasmic Fhit staining, with preserved reactivity in adjacent normal cells. Lack of Fhit staining correlated with: loss of heterozygosity (LOH) at the FHIT 3p14.2 locus, but not at other loci on 3p; squamous histology; LOH at 17p13 and 5q but not with LOH at multiple other suspected tumour suppressor gene loci; and was inversely correlated with codon 12 mutations in K- ras. Fhit expression was not correlated overall with a variety of clinical parameters including survival and was not associated with abnormalities of immunohistochemical expression of p53, RB, and p16. All of these findings are consistent with loss of Fhit protein expression being as frequent an abnormality in lung cancer pathogenesis as are p53 and p16 protein abnormalities and that such loss occurs independently of the commitment to the metastatic state and of most other molecular abnormalities. © 2000 Cancer Research Campaig

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

Rational Manual and Automated Scoring Thresholds for the Immunohistochemical Detection of TP53 Missense Mutations in Human Breast Carcinomas

Missense mutations in TP53 are common in human breast cancer, have been associated with worse prognosis, and may predict therapy effect. TP53 missense mutations are associated with aberrant accumulation of p53 protein in tumor cell nuclei. Previous studies have used relatively arbitrary cutoffs to characterize breast tumors as positive for p53 staining by immunohistochemical assays. This study aimed to objectively determine optimal thresholds for p53 positivity by manual and automated scoring methods utilizing whole tissue sections from the Carolina Breast Cancer Study. P53 immunostained slides were available for 564 breast tumors previously assayed for TP53 mutations. Average nuclear p53 staining intensity was manually scored as negative, borderline, weak, moderate, or strong and percentage of positive tumor cells was estimated. Automated p53 signal intensity was measured using the Aperio nuclear v9 algorithm combined with the Genie® histology pattern recognition tool and tuned to achieve optimal nuclear segmentation. ROC curve analysis was performed to determine optimal cutoffs for average staining intensity and percent cells positive to distinguish between tumors with and without a missense mutation. ROC curve analysis demonstrated a threshold of moderate average nuclear staining intensity as a good surrogate for TP53 missense mutations in both manual (AUC=0.87) and automated (AUC=0.84) scoring systems. Both manual and automated immunohistochemical scoring methods predicted missense mutations in breast carcinomas with high accuracy. Validation of the automated intensity scoring threshold suggests a role for such algorithms in detecting TP53 missense mutations in high throughput studies

Why bother teaching entrepreneurship? : a field quasi-experiment on the behavioral outcomes of compulsory entrepreneurship education

The proliferation of entrepreneurship education in business schools suggests that it is commonly believed to foster venture creation. At the same time, research on entrepreneurship education is growing. However, further studies are needed to determine the effectiveness of compulsory entrepreneurship education (CEE) by providing evidence on the specific type of entrepreneurial behavior it elicits and when these effects occur. To address this gap, this study evaluates different behavioral outcomes of CEE over time while building on social cognitive career theory to account for mediating effects of entrepreneurial intentions and entrepreneurial self-efficacy. We conduct a field quasi-experiment by following university business students (1,387 observations for 450 individuals) over 24 months post-treatment. Our findings reveal that CEE effectively increases entrepreneurial behavior in the short term but does not extend much beyond that. A follow-up study (N = 395) adds confidence to the generalizability of the results. We contribute to research on entrepreneurship education and policy

G1 checkpoint protein and p53 abnormalities occur in most invasive transitional cell carcinomas of the urinary bladder

The G1 cell cycle checkpoint regulates entry into S phase for normal cells. Components of the G1 checkpoint, including retinoblastoma (Rb) protein, cyclin D1 and p16INK4a, are commonly altered in human malignancies, abrogating cell cycle control. Using immunohistochemistry, we examined 79 invasive transitional cell carcinomas of the urinary bladder treated by cystectomy, for loss of Rb or p16INK4a protein and for cyclin D1 overexpression. As p53 is also involved in cell cycle control, its expression was studied as well. Rb protein loss occurred in 23/79 cases (29%); it was inversely correlated with loss of p16INK4a, which occurred in 15/79 cases (19%). One biphenotypic case, with Rb+p16– and Rb-p16+ areas, was identified as well. Cyclin D1 was overexpressed in 21/79 carcinomas (27%), all of which retained Rb protein. Fifty of 79 tumours (63%) showed aberrant accumulation of p53 protein; p53 staining did not correlate with Rb, p16INK4a, or cyclin D1 status. Overall, 70% of bladder carcinomas showed abnormalities in one or more of the intrinsic proteins of the G1 checkpoint (Rb, p16INK4a and cyclin D1). Only 15% of all bladder carcinomas (12/79) showed a normal phenotype for all four proteins. In a multivariate survival analysis, cyclin D1 overexpression was linked to less aggressive disease and relatively favourable outcome. In our series, Rb, p16INK4a and p53 status did not reach statistical significance as prognostic factors. In conclusion, G1 restriction point defects can be identified in the majority of bladder carcinomas. Our findings support the hypothesis that cyclin D1 and p16INK4a can cooperate to dysregulate the cell cycle, but that loss of Rb protein abolishes the G1 checkpoint completely, removing any selective advantage for cells that alter additional cell cycle proteins. © 1999 Cancer Research Campaig

Reproducibility and intratumoral heterogeneity of the PAM50 breast cancer assay

Background: The PAM50 assay is used routinely in clinical practice to determine breast cancer prognosis and management; however, research assessing how technical variation and intratumoral heterogeneity contribute to misclassification and reproducibility of these tests is limited. Methods: We evaluated the impact of intratumoral heterogeneity on the reproducibility of results for the PAM50 assay by testing RNA extracted from formalin-fixed paraffin embedded breast cancer blocks sampled at distinct spatial locations. Samples were classified according to intrinsic subtype (Luminal A, Luminal B, HER2-enriched, Basal-like, or Normal-like) and risk of recurrence with proliferation score (ROR-P, high, medium, or low). Intratumoral heterogeneity and technical reproducibility (replicate assays on the same RNA) were assessed as percent categorical agreement between paired intratumoral and replicate samples. Euclidean distances between samples, calculated across the PAM50 genes and the ROR-P score, were compared for concordant vs. discordant samples. Results: Technical replicates (N = 144) achieved 93% agreement for ROR-P group and 90% agreement on PAM50 subtype. For spatially distinct biological replicates (N = 40 intratumoral replicates), agreement was lower (81% for ROR-P and 76% for PAM50 subtype). The Euclidean distances between discordant technical replicates were bimodal, with discordant samples showing higher Euclidian distance and biologic heterogeneity. Conclusion: The PAM50 assay achieved very high technical reproducibility for breast cancer subtyping and ROR-P, but intratumoral heterogeneity is revealed by the assay in a small proportion of cases

Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype

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

Prediagnostic smoking is associated with binary and quantitative measures of ER protein and ESR1 mRNA expression in breast tumors

Background: Smoking is a possible risk factor for breast cancer and has been linked to increased risk of estrogen receptor-positive (ER+) disease in some epidemiologic studies. It is unknown whether smoking has quantitative effects on ER expression. Methods: We examined relationships between smoking and ER expression from tumors of 1,888 women diagnosed with invasive breast cancer from a population-based study in North Carolina. ER expression was characterized using binary (±) and continuous measures for ER protein, ESR1 mRNA, and a multigene luminal score (LS) that serves as a measure of estrogen signaling in breast tumors. We used logistic and linear regression models to estimate temporal and dose-dependent associations between smoking and ER measures. Results: The odds of ER+, ESR1+, and LS+ tumors among current smokers (at the time of diagnosis), those who smoked 20 or more years, and those who smoked within 5 years of diagnosis were nearly double those of nonsmokers. Quantitative levels of ESR1 were highestamong current smokers compared with never smokers overall [mean (log2) = 9.2 vs. 8.7, P > 0.05] and among ER+ cases; however, we did not observe associations betweensmokingmeasures and continuous ER protein expression. Conclusions: In relationship to breast cancer diagnosis, recent smoking was associated with higher odds of the ER+, ESR1+, and LS+ subtype. Current smoking was associated with elevated ESR1 mRNA levels and an elevated LS, but not with altered ER protein. Impact: A multigene LS and single-gene ESR1 mRNA may capture tumor changes associated with smoking