News

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

https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-020-00844-x

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

https://arxiv.org/abs/2008.10678

Epithelial ovarian cancer (EOC) is the leading cause of death within gynecological cancers in the developed countries. Due to the lack of specific symptoms, EOC is often detected at an advanced stage with a five-year survival rate less than 40%. However, 25% of EOC patients are diagnosed in early stage (I-II), where the disease is often cured by surgery alone, or in combination with platinum-based chemotherapy. Even though the prognosis of patients with FIGO stage I-II increases dramatically with treatment, with five-year survival rates between 80–90%, some subgroups of early-stage EOC will relapse and 20–30% of these patients will finally succumb to the disease. Nevertheless, the optimal clinical management is still a controversial debate and patients with early-stage high-grade serous EOC might be over-treated which could potentially result in complications after radical surgical management and an increase in toxicity of chemotherapy. Hence, it is of utmost importance to identify novel diagnostic markers for this patient cohort in order to improve the risk assessment of tumor recurrence. Here, we have applied MALDI-imaging mass spectrometry (MALDI-IMS), a new method to identify distinct mass profiles including protein signatures on paraffin embedded tissue sections. In search of prognostic biomarker candidates, we compared proteomic profiles of primary tumor section from early-stage HGSOC patients with either recurrent or non-recurrent disease, and were able to identify a discriminative peptide signature to predict clinical outcome and treatment extent for patients with early-stage HGSOC.

Kulbe H., ... , Kassuhn W., et al., Discovery of Prognostic Markers for Early-Stage High-Grade Serous Ovarian Cancer by Maldi-Imaging, Cancers 2020

https://www.mdpi.com/2072-6694/12/8/2000/htm

we would like to announce our next "LAP-Life after PhD" seminar which will take place on July, 21 at 4:30 p.m. as an online seminar.

Our speaker will be Dr. Claudia Reschke, a former „Personal Assistant to the Scientific Director of DKFZ“ and now „Adviser Helmholtz Information & Data Science Academy (HIDA)“, the Helmholtz Information and Data Science Academy. She will talk about both her career path from her PhD to Science Management and the activities of the HIDA which offers extensive training in Information and Data Science to doctoral researchers and postdocs.

Date: July, 21st
Time: 4:30 p.m.
Venue: Zoom

Room and password upon reasonable request (contact: compcancer at charite dot de). You will find regular updates at https://www.regulatory-genome.hu-berlin.de/en/events/lectures/lap-series.

We are delighted to announce our next LAP seminar. This time Wiebke Skeffington, who is coordinating the IRTG2403 will speak about her career. Wiebke studied biology and during her career spent time in the US and UK and by now switched from research to research management.

Date: June, 23rd
Time: 4:30 p.m.
Venue: Zoom (https://zoom.us/j/878362909)
Speaker: Wiebke Skeffington - program coordinator

In addition to speaking about her own career, Wiebke will also give tips and tricks how to apply successfully for a postdoc position.

Password upon reasonable request (contact: compcancer at charite dot de). You will find regular updates at https://www.regulatory-genome.hu-berlin.de/en/events/lectures/lap-series.