News

CompCancer Seminar 31.03.2021 - Samantha Praktiknjo

The upcoming CompCancer Seminar will be hosted by Clemens Messerschmidt from the Leser group. Find the invitation below. The link is available from compcancer at charite dot de.

Dear compcancer members,

Next Wednesday (2021-03-31) at 10:00, Samantha Praktiknjo will join us for our seminar to present her paper

"Tracing tumorigenesis in a solid tumor model at single-cell resolution".

https://www.nature.com/articles/s41467-020-14777-0

The publication describes work done as a senior scientist in Nikolaus Rajewsky's group at the BIMSB.

Recently, she has joined the the newly established Single-Cell Research Focus Area as an Associate Group Leader at the BIH, Charité and MDC .

https://www.mdc-berlin.de/content/single-cell-approaches-personalized-medicine

For this reason, they are eager to learn what others in Berlin are doing in the field, and are looking forward to identifying potentially interesting collaboration opportunities.

Best regards and hope to see you next week,

Clemens

Wanja Kassuhn published his work on Classification of High-Grade Serous Ovarian Cancer

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

CompCancer Seminar 10.03.2021 - Vito Zanotelli

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

Clemens Messerschmidt contributed to work on YAP and beta-catenin in TNBC

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

Lorenz Rumberger shared his work on arXiv.org:

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

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About

The research training group CompCancer (RTG2424) is a DFG funded PhD programme in Berlin, focussing on computational aspects of cancer research.

Contact: compcancer at charite dot de