Biology subjects

Kather, J. N.

Publications and source records attributed to Kather, J. N..

23 records · Page 2Linked to original sources

bioRxiv2020

Nerve fibers in the Tumor Microenvironment are co-localized with Tertiary Lymphoid Structures

BackgroundB cells and tertiary lymphoid structures (TLS) are reported to be important in the improvement of survival of cancer patients. These secondary lymphoid organs have been associated with the generation of an anti-tumor response. Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types and the stromal architecture shapes the intratumoral heterogeneity. The stroma of PDAC is a complex system in which crosstalk takes place between cancer-associated fibroblasts, immune cells, endothelial cells and the cancer cells. Besides immune cells and fibroblasts, there is some limited data about the influence of nerve fibers on cancer progression. Patients and methodsNerve Fiber Density (NFD) was analysed in our cohort of 188 patients with Pancreatic Ductal Adenocarcinoma who underwent pancreatic surgery. We used immunohistochemistry and multiplex imaging to phenotype the immune cell infiltrate. The cell detection classifier measured distance from immune cell to cancer gland and with a heat map we could count TLS. By using Machine learning we were able to define the spatial distribution and counting Tertiary Lymphoid Structures. ResultsHigh NFD is significantly associated with prolonged overall survival (HR 1.676 (95%CI 1.126,2.495) for low vs. high NFD, p-value 0.0109). The immune cells surrounding the nerve fibers were phenotyped in B cells, T cells and dendritic follicular cells, matching a TLS. Here we show that small nerve fibers are located at the TLS in Pancreatic Cancer and a high Nerve Fiber Density combined with more than 5 TLS is associated with a better survival (HR 0.388 (95%CI 0.218, 0.689). ConclusionThe co-localization of small nerve fibers with TLS is a new finding which has not been described before. However the precise roles of these TLS and nerve fibers remains unknown. These findings unravel future pathways and has the potential to reach new directions into already existing targeted therapy.

cancer biology↗

bioRxiv2019

Pan-cancer image-based detection of clinically actionable genetic alterations

Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays.1 These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs.2 Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures3,4 directly from routine histological images of tumor tissue. We developed and systematically optimized a one-stop-shop workflow and applied it to more than 4000 patients with breast5, colon and rectal6, head and neck7, lung8,9, pancreatic10, prostate11 cancer, melanoma12 and gastric13 cancer. Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data. Our method can be implemented on mobile hardware14, potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients.

genetics↗

bioRxiv2019

Deep learning detects virus presence in cancer histology

Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.\n\nWe hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.\n\nThis workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.

cancer biology↗

bioRxiv2019

Spatial structure governs the mode of tumour evolution

Characterizing the mode - the way, manner, or pattern - of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. DNA sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour.1, 2 Here we propose that differences in tumour architecture alone can explain the variety of observed patterns. We examine this hypothesis using spatially explicit population genetic models and demonstrate that, within biologically relevant parameter ranges, human tumours are expected to exhibit four distinct onco-evolutionary modes (oncoevotypes): rapid clonal expansion (predicted in leukaemia); progressive diversification (in colorectal adenomas and early-stage colorectal carcinomas); branching evolution (in invasive glandular tumours); and effectively almost neutral evolution (in certain non-glandular and poorly differentiated solid tumours). We thus provide a simple, mechanistic explanation for a wide range of empirical observations. Oncoevotypes are governed by the mode of cell dispersal and the range of cell-cell interaction, which we show are essential factors in accurately characterizing, forecasting and controlling tumour evolution.

cancer biology↗

bioRxiv2019

Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt

Renal cell carcinoma (RCC) is one of the ten most common and lethal cancers in the United States. Tumor heterogeneity and development of resistance to treatment suggest that patient-specific evolutionary therapies may hold the key to better patients prognosis. Mathematical models are a powerful tool to help develop such strategies; however, they depend on reliable biomarker information. In this paper, we present a dynamic model of tumor-immune interactions, as well as the treatment effect on tumor cells and the tumor-immune environment. We hypothesize that the neutrophil-to-lymphocyte ratio (NLR) is a powerful biomarker that can be used to predict an individual patients response to treatment. Using randomly sampled virtual patients, we show that the model recapitulates patient outcomes from clinical trials in RCC. Finally, we use in silico patient data to recreate realistic tumor behaviors and simulate various treatment strategies to find optimal treatments for each virtual patient.

cancer biology↗