Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Time Course of PR of UV-Induced Chromosomal Aberrations and Lethal Damage in S and G2 Xenopus Cells

Sand G2 phase cells were exposed to 150 ergs mm⁻² UV and their ability to photoreactivate the induced cell killing (loss of colony forming ability) and chromosomal aberrations was determined as a function of time following the UV exposure. In S phase cells, the lesions leading to cell death and those leading to aberrations were both converted to a non-photoreactivable state shortly after the UV exposure. A significant fraction of the lesions induced in G2 cells, that led to cell death, were converted to a non-photoreactivable state before the progeny of the exposed cells reached the next succeeding S phase. Few, if any, lesions were induced in G2 cells that were expressed as aberrations at the first mitosis following exposure. Some of the lesions induced in G2 cells led to aberrations that were observable in the progeny that progressed to the second mitosis following exposure. These lesions were converted to a nonphotoreactivable state as the progeny of the exposed G2 cells progressed through the first S phase following exposure

Expression profiling of migrated and invaded breast cancer cells predicts early metastatic relapse and reveals Krüppel-like factor 9 as a potential suppressor of invasive growth in breast cancer

Cell motility and invasion initiate metastasis. However, only a subpopulation of cancer cells within a tumor will ultimately become invasive. Due to this stochastic and transient nature, in an experimental setting, migrating and invading cells need to be isolated from the general population in order to study the gene expression profiles linked to these processes. This report describes microarray analysis on RNA derived from migrated or invaded subpopulations of triple negative breast cancer cells in a Transwell set-up, at two different time points during motility and invasion, pre-determined as “early” and “late” in real-time kinetic assessments. Invasion- and migration-related gene expression signatures were generated through comparison with non-invasive cells, remaining at the upper side of the Transwell membranes. Late-phase signatures of both invasion and migration indicated poor prognosis in a series of breast cancer data sets. Furthermore, evaluation of the genes constituting the prognostic invasion-related gene signature revealed Krüppel-like factor 9 (KLF9) as a putative suppressor of invasive growth in breast cancer. Next to loss in invasive vs non-invasive cell lines, KLF9 also showed significantly lower expression levels in the “early” invasive cell population, in several public expression data sets and in clinical breast cancer samples when compared to normal tissue. Overexpression of EGFP-KLF9 fusion protein significantly altered morphology and blocked invasion and growth of MDA-MB-231 cells in vitro. In addition, KLF9 expression correlated inversely with mitotic activity in clinical samples, indicating anti-proliferative effects

An integrative approach unveils FOSL1 as an oncogene vulnerability in KRAS-driven lung and pancreatic cancer

KRAS mutated tumours represent a large fraction of human cancers, but the vast majority remains refractory to current clinical therapies. Thus, a deeper understanding of the molecular mechanisms triggered by KRAS oncogene may yield alternative therapeutic strategies. Here we report the identification of a common transcriptional signature across mutant KRAS cancers of distinct tissue origin that includes the transcription factor FOSL1. High FOSL1 expression identifies mutant KRAS lung and pancreatic cancer patients with the worst survival outcome. Furthermore, FOSL1 genetic inhibition is detrimental to both KRAS-driven tumour types. Mechanistically, FOSL1 links the KRAS oncogene to components of the mitotic machinery, a pathway previously postulated to function orthogonally to oncogenic KRAS. FOSL1 targets include AURKA, whose inhibition impairs viability of mutant KRAS cells. Lastly, combination of AURKA and MEK inhibitors induces a deleterious effect on mutant KRAS cells. Our findings unveil KRAS downstream effectors that provide opportunities to treat KRAS-driven cancers

Augmented Mitotic Cell Count using Field Of Interest Proposal

Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we present an algorithmic approach that first calculates a mitotic cell map based upon a deep convolutional network. This map is in a second step used to construct a mitotic activity estimate. Lastly, we select the image segment representing the size of ten high power fields with the overall highest mitotic activity as a region proposal for an expert MC determination. We evaluate the approach using a dataset of 32 completely annotated whole slide images, where 22 were used for training of the network and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.Comment: 6 pages, submitted to BVM 2019 (bvm-workshop.org