News

YAP and β-catenin cooperate to drive oncogenesis in basal breast cancer

Hazel M. Quinn,  Regina Vogel, Oliver Popp,  Philipp Mertins, Linxiang Lan,  Clemens Messerschmidt,  Alexandro Landshammer, Kamil Lisek, Sophie Chateau-Joubert,  Elisabetta Marangoni,  Elle Koren, Yaron Fuchs and Walter Birchmeier

Cancer Research, 2021

Targeting cancer stem cells (CSC) can serve as an effective approach toward limiting resistance to therapies. While basal-like (triple-negative) breast cancers encompass cells with CSC features, rational therapies remain poorly established. We show here that the receptor tyrosine kinase Met promotes YAP activity in basal-like breast cancer and find enhanced YAP activity within the CSC population. Interfering with YAP activity delayed basal-like cancer formation, prevented luminal to basal trans-differentiation, and reduced CSC. YAP knockout mammary glands revealed a decrease in β-catenin target genes, suggesting that YAP is required for nuclear β-catenin activity. Mechanistically, nuclear YAP interacted with β-catenin and TEAD4 at gene regulatory elements. Proteomic patient data revealed an upregulation of the YAP signature in basal-like breast cancers. Our findings demonstrate that in basal-like breast cancers, β-catenin activity is dependent on YAP signalling and controls the CSC program. These findings suggest that targeting the YAP/TEAD4/β-catenin complex offers a potential therapeutic strategy for eradicating CSCs in basal-like breast cancers.

https://cancerres.aacrjournals.org/content/early/2021/02/11/0008-5472.CAN-20-2801

How Shift Equivariance Impacts Metric Learning for Instance Segmentation

Josef Lorenz Rumberger, Xiaoyan Yu, Peter Hirsch, Melanie Dohmen, Vanessa Emanuela Guarino, Ashkan Mokarian, Lisa Mais, Jan Funke, and Dagmar Kainmueller

Metric learning has received conflicting assessments concerning its suitability for solving instance segmentation tasks. It has been dismissed as theoretically flawed due to the shift equivariance of the employed CNNs and their respective inability to distinguish same-looking objects. Yet it has been shown to yield state of the art results for a variety of tasks, and practical issues have mainly been reported in the context of tile-and-stitch approaches, where discontinuities at tile boundaries have been observed. To date, neither of the reported issues have undergone thorough formal analysis. In our work, we contribute a comprehensive formal analysis of the shift equivariance properties of encoder-decoder-style CNNs, which yields a clear picture of what can and cannot be achieved with metric learning in the face of same-looking objects. In particular, we prove that a standard encoder-decoder network that takes d-dimensional images as input, with l pooling layers and pooling factor f, has the capacity to distinguish at most fdl same-looking objects, and we show that this upper limit can be reached. Furthermore, we show that to avoid discontinuities in a tile-and-stitch approach, assuming standard batch size 1, it is necessary to employ valid convolutions in combination with a training output window size strictly greater than fl, while at test-time it is necessary to crop tiles to size n⋅fl before stitching, with n≥1. We complement these theoretical findings by discussing a number of insightful special cases for which we show empirical results on synthetic data.

https://arxiv.org/abs/2101.05846

The upcoming CompCancer Seminar will be hosted by Lorenz Rumberger from the Kainmüller lab. Find the invitation below. The link is available from compcancer at charite dot de.

Dear all,
I'd like to invite you to next week's CompCancer journal club, starting at 10.00am s.t. on Wednesday, 20.01.2021.
PD Dr. med. Christian M. Schürch, MD, PhD from the University Hospital Tübingen will present his recent publication 'Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front, Cell (2020)'. The publication presents a new method to obtain high-dimensional images from FFPE tissue samples. The method is showcased by obtaining data on 140 tissue samples from 35 colorectal cancer patients, sequentially stained with 56 IHC markers. The analysis reveals differences in the tissue organization and local immune cell abundance in patient subgroups. Besides this, Dr. Schürch will also present newly acquired data on the immune cell topography in cutaneous T cell lymphoma.

Best
Lorenz Rumberger
Kainmüller Lab

Torsten successfully defended his PhD on Thursday, November 12th with summa cum laude! In his PhD, Torsten developed two important methods for reverse engineering regulatory networks. One method, called response logic, allows to reverse engineer the topology of a network from perturbation data (Gross et al, 2019). A second method allows to identify which perturbation experiments would be optimal to quantiatively describe the network (Gross et al. 2020). The works were presented at the ISMB 2019 and 2020, and received prices for best student paper and best talk, respectively! Torsten will continue his career in London to work on machine learning application in health and biotechnology! We wish Torsten all the best for his future career!

we would like to announce our next "LAP-Life after PhD" seminar which will take place on November 17 at 4:30 p.m. as an online seminar via zoom.

Our speaker will be Dr. Anncharlott Berglar, who has done her PhD at Institut Pasteur followed by an MA in Scientific Illustration and is now a freelance scientific illustrator at SciVisLab.

Date: Nov, 17th

Time: 4:30 p.m.
Venue: Zoom

For more information please visit the IRTG2403 website. You will find regular updates at https://www.regulatory-genome.hu-berlin.de/en/events/lectures/lap-series.