CompCancer Seminar 20.01.2021 - Christian Schürch

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.

Lorenz Rumberger
Kainmüller Lab

Congratulations to Dr. Torsten Gross!






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!

Life after Phd seminar - with Anncharlott Berglar

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

Tincy Simon is co-author on work in colorectal cancer progression

Colorectal Cancer (CRC) is the 3rd most commonly occurring cancer world-wide. The past two decades of intense research have indeed advanced our understanding of the genetics underlying the formation of an adenoma (benign tissue) and carcinoma (cancerous tissue) of CRC, albeit utilizing mainly unmatched patient cohorts of adenoma and carcinoma. However, although key driver DNA variants that distinguishes both entities have been well established in the field, the determinant and earliest variant that selects an adenoma to progress to a carcinoma remains unknown. Mamlouk et. al. investigated this with a unique cohort of matched patient samples consisting of polyps carrying adenomas captured at the transition stage to carcinoma. We identified that key alterations in TP53 and chromosome 20 gain are early events driving the progression towards carcinoma. They were not only found shared between adenoma-carcinoma pairs, but also, distinguished low-grade from more high-grade adenoma. This highlights the major finding of the publication that the molecular progression, that is DNA alterations such as mutations and copy number changes, are uncoupled from the histological progression within these tumors. We further expanded on the heterogeneity present within the polyps by performing clonal deconvolution analysis using mutational data from multi-regional tissue isolation. We showed that selective pressure occurs at both adenoma and carcinoma tissue and subclonal populations are further evident within adenoma tissue long after its progression to a carcinoma.

Mamlouk, S., Simon, T. et al., Malignant transformation and genetic alterations are uncoupled in early colorectal cancer progression

Lorenz Rumbergers work on Instance Segmentation accepted at European conference on computer vision

In the field of computer vision, probabilistic convolutional neural networks, which predict distributions of predictions instead of point estimates led to many recent advances. Besides state of the art benchmark results, these networks made it possible to quantify local uncertainties in the predictions. These were used in active learning frameworks to target the labeling efforts of specialist annotators or to assess the quality of a prediction in a safety-critical environment. However, for instance segmentation problems, which aim at separating different objects (e.g. cells) from one another, these methods are not frequently used so far. We seek to close this gap by proposing a generic method to obtain model-inherent uncertainty estimates within proposal-free instance segmentation models. Furthermore, the quality of the uncertainty estimates is analyzed with a metric adapted from semantic segmentation, which seeks to separate objects based on their class (e.g. epithelial cells or other cells). The method is evaluated on a dataset that contains C.elegans brightfield microscopy images, where it yields competitive performance while also predicting uncertainty estimates that carry information about object-level inaccuracies like false splits and false merges. These uncertainty estimates are then used in a simulation study to guide proofreading efforts.

The manuscript was accepted for the Bio Image Computing Workshop within ECCV2020 and is available at arXiv

Rumberger, J.L., Mais, L., Kainmüller, D. Probabilistic Deep Learning for Instance Segmentation

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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