News

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Wanja Kassuhn, Oliver Klein, Silvia Darb-Esfahani, Hedwig Lammert, Sylwia Handzik, Eliane T. Taube, Wolfgang D. Schmitt, Carlotta Keunecke, David Horst, Felix Dreher, Joshy George, David D. Bowtell, Oliver Dorigo, Michael Hummel, Jalid Sehouli, Nils Blüthgen, Hagen Kulbe, and Elena I. Braicu

Cancers, 2021

High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian carcinomas with sobering survival rates. The mechanisms mediating treatment efficacy are still poorly understood with no adequate biomarkers of response to treatment and risk assessment. This variability of treatment response might be due to its molecular heterogeneity. Therefore, identification of biomarkers or molecular signatures to stratify patients and offer personalized treatment is of utmost priority. Currently, comprehensive gene expression profiling is time- and cost-extensive and limited by tissue heterogeneity. Thus, it has not been implemented into clinical practice. This study demonstrates for the first time a spatially resolved, time- and cost-effective approach to stratifying HGSOC patients by combining novel matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) technology with machine-learning algorithms. Eventually, MALDI-derived predictive signatures for treatment efficacy, recurrent risk, or, as demonstrated here, molecular subtypes mightbe utilized for emerging clinical challenges to ultimately improve patient outcomes.

https://www.mdpi.com/2072-6694/13/7/1512/pdf

The upcoming CompCancer Seminar will be hosted by Rosario Astaburuaga García from the Blüthgen lab. Find the invitation below. The link is available from compcancer at charite dot de.

Dear all,

On March 10th at 4:30 pm, Vito Zanotelli will present his paper “A quantitative analysis of the interplay of environment, neighbourhood, and cell state in 3D spheroids" in our Journal Club. I invite you to read this interesting publication and collect your questions. 

Also, Vito is now working as Data Scientist in a company (D ONE, Data Driven Value Creation), so if you have questions regarding his “life after Ph.D.” or something like that, you are very welcome to ask.

Best,

Rosario Astaburuaga García

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