Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation

The size of nuclei in histological preparations from excised breast tumors is predictive of patient outcome (large nuclei indicate poor outcome). Pathologists take into account nuclear size when performing breast cancer grading. In addition, the mean nuclear area (MNA) has been shown to have independent prognostic value. The straightforward approach to measuring nuclear size is by performing nuclei segmentation. We hypothesize that given an image of a tumor region with known nuclei locations, the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model, without the intermediate step of nuclei segmentation. Towards this goal, we train a deep convolutional neural network model that is applied locally at each nucleus location, and can reliably measure the area of the individual nuclei and the MNA. Furthermore, we show how such an approach can be extended to perform combined nuclei detection and measurement, which is reminiscent of granulometry.Comment: Conditionally accepted for MICCAI 201

Morphometrical malignancy grading is a valuable prognostic factor in invasive ductal breast cancer

The aim of the present study is to augment the prognostic power of breast cancer grading by elaboration of quantitative histopathological methods. We focus on the recently introduced morphometrical grading system in which the three grading sub-features of the WHO grading system are evaluated with the help of computerised nuclear morphometry, and quantitative methods for assessing mitotic activity and tubular differentiation. The prognostic value of the morphometrical grading system is now confirmed in a material of 159 cases of invasive ductal breast cancer. In the current material the morphometrical grading system very efficiently predicted the prognosis of breast cancer by dividing the patients into favourable (grade I), intermediate (grade II), and unfavourable (grade III) outcome (P<0.0001). The morphometrical grading system was especially efficient in identifying patients with the most unfavourable outcome. In our material the morphometrical grade III was associated with a 5.4-fold risk of breast cancer death. In light of the present results, the morphometrical grading can be applied to clinical use as an aid in treatment decisions of patients with invasive ductal breast cancer

Proteomic maps of breast cancer subtypes

Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics

Proteomic maps of breast cancer subtypes

Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics.</p

Correlation of computed tomography with carotid plaque transcriptomes associates calcification with lesion-stabilization

Background and aims: Unstable carotid atherosclerosis causes stroke, but methods to identify patients and lesions at risk are lacking. We recently found enrichment of genes associated with calcification in carotid plaques from asymptomatic patients. Here, we hypothesized that calcification represents a stabilising feature of plaques and investigated how macro-calcification, as estimated by computed tomography (CT), correlates with gene expression profiles in lesions. Methods: Plaque calcification was measured in pre-operative CT angiographies. Plaques were sorted into high- and lowcalcified, profiled with microarrays, followed by bioinformatic analyses. Immunohistochemistry and qPCR were performed to evaluate the findings in plaques and arteries with medial calcification from chronic kidney disease patients. Results: Smooth muscle cell (SMC) markers were upregulated in high-calcified plaques and calcified plaques from symptomatic patients, whereas macrophage markers were downregulated. The most enriched processes in high-calcified plaques were related to SMCs and extracellular matrix (ECM) organization, while inflammation, lipid transport and chemokine signaling were repressed. These findings were confirmed in arteries with high medial calcification. Proteoglycan 4 (PRG4) was identified as the most upregulated gene in association with plaque calcification and found in the ECM, SMA+ and CD68+/TRAP + cells. Conclusions: Macro-calcification in carotid lesions correlated with a transcriptional profile typical for stable plaques, with altered SMC phenotype and ECM composition and repressed inflammation. PRG4, previously not described in atherosclerosis, was enriched in the calcified ECM and localized to activated macrophages and smooth muscle-like cells. This study strengthens the notion that assessment of calcification may aid evaluation of plaque phenotype and stroke risk.The European Union’s Horizon 2020/Marie Sklodowska-Curie grant agreement No 722609 (INTRICARE);Swedish Heart and Lung FoundationSwedish Research Council (K2009-65X-2233-01-3, K2013- 65X-06816-30-4, 349-2007-8703)Uppdrag Besegra Stroke (P581/ 2011-123)Stockholm County Council (ALF2011-0260, ALF-2011- 0279)Swedish Society for Medical ResearchTore Nilsson’s FoundationMagnus Bergvall’s FoundationKarolinska Institutet FoundationEuropean Commission (722609)Publishe