Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors – Dependencies and Novel Pitfalls

1.AbstractPersonalized Oncology is a rapidly evolving area and offers cancer patients therapy options more specific than ever. Yet, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Approaching this question, we used two different unsupervised dimension reduction methods – t-SNE and UMAP – on three different metastases datasets – prostate cancer, neuroendocrine prostate cancer, and skin cutaneous melanoma – including 682 different samples, with three different underlying data transformations – unprocessed FPKM values, log10 transformed FPKM values, and log10+1 transformed FPKM values – to visualize potential underlying clusters. The approaches resulted in formation of different clusters that were independent of respective resection sites. Additionally, data transformation critically affected cluster formation in most cases. Of note, our study revealed no tight link between the metastasis resection site and specific transcriptomic features. Instead, our analysis demonstrates the dependency of cluster formation on the underlying data transformation and the dimension reduction method applied. These observations propose data transformation as another key element in the interpretation of visual clustering approaches apart from well-known determinants such as initialization and parameters. Furthermore, the results show the need for further evaluation of underlying data alterations based on the biological question and subsequently used methods and applications.

N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine—A Review

Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the “evidence puzzle”.

Deep molecular characterization linked to drug response profiling of pancreatic ductal adenocarcinoma using patient-derived organoids.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by high drug resistance and poor prognosis. Novel therapeutic and stratification strategies are urgently needed. Here, we present an integration of in-depth genomic and transcriptomic characterization with drug screening and clinical outcome based on a catalogue of 51 patient-derived tumor organoids (PDOs) from resected PDAC. Known PDAC molecular subtypes and their prognostic value are conserved in organoids. Integration of transcriptomic and drug response profiles suggest a metabolism-mediated modulations of drug resistance. Copy number alterations on chromosome 13q and wild-type status of TP53 emerged as potential novel genomic biomarkers for sensitivity to 5-FU and oxaliplatin treatment, respectively. Functional testing of targeted drugs in PDOs revealed its additional value for genome-driven personalized oncology. Co-deletion of TP53/POLR2A increased vulnerability to RNA polymerase II inhibition, pointing to a promising target for personalized treatment in PDAC.

Patient-Derived Organoids of Cholangiocarcinoma

Cholangiocarcinoma (CC) is an aggressive malignancy with an inferior prognosis due to limited systemic treatment options. As preclinical models such as CC cell lines are extremely rare, this manuscript reports a protocol of cholangiocarcinoma patient-derived organoid culture as well as a protocol for the transition of 3D organoid lines to 2D cell lines. Tissue samples of non-cancer bile duct and cholangiocarcinoma were obtained during surgical resection. Organoid lines were generated following a standardized protocol. 2D cell lines were generated from established organoid lines following a novel protocol. Subcutaneous and orthotopic patient-derived xenografts were generated from CC organoid lines, histologically examined, and treated using standard CC protocols. Therapeutic responses of organoids and 2D cell lines were examined using standard CC agents. Next-generation exome and RNA sequencing was performed on primary tumors and CC organoid lines. Patient-derived organoids closely recapitulated the original features of the primary tumors on multiple levels. Treatment experiments demonstrated that patient-derived organoids of cholangiocarcinoma and organoid-derived xenografts can be used for the evaluation of novel treatments and may therefore be used in personalized oncology approaches. In summary, this study establishes cholangiocarcinoma organoids and organoid-derived cell lines, thus expanding translational research resources of cholangiocarcinoma.

The Personalized Cancer Network Explorer (PeCaX) as a visual analytics tool to support molecular tumor boards

Personalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process. The Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene- drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker.

Role of Long Non-Coding RNA Polymorphisms in Cancer Chemotherapeutic Response

Genetic polymorphisms are defined as the presence of two or more different alleles in the same locus, with a frequency higher than 1% in the population. Since the discovery of long non-coding RNAs (lncRNAs), which refer to a non-coding RNA with a length of more than 200 nucleotides, their biological roles have been increasingly revealed in recent years. They regulate many cellular processes, from pluripotency to cancer. Interestingly, abnormal expression or dysfunction of lncRNAs is closely related to the occurrence of human diseases, including cancer and degenerative neurological diseases. Particularly, their polymorphisms have been found to be associated with altered drug response and/or drug toxicity in cancer treatment. However, molecular mechanisms are not yet fully elucidated, which are expected to be discovered by detailed studies of RNA–protein, RNA–DNA, and RNA–lipid interactions. In conclusion, lncRNAs polymorphisms may become biomarkers for predicting the response to chemotherapy in cancer patients. Here we review and discuss how gene polymorphisms of lncRNAs affect cancer chemotherapeutic response. This knowledge may pave the way to personalized oncology treatments.

Targeted Therapies for Pancreatic Cancer: Overview of Current Treatments and New Opportunities for Personalized Oncology

Cytotoxic chemotherapy remains the only treatment option for most pancreatic ductal adenocarcinoma patients. Currently, the median overall survival of patients with advanced disease rarely exceeds 1 year. The complex network of pancreatic cancer composed of immune cells, endothelial cells, and cancer-associated fibroblasts confers intratumoral and intertumoral heterogeneity with distinct proliferative and metastatic propensity. This heterogeneity can explain why tumors do not behave uniformly and are able to escape therapy. The advance in technology of whole-genome sequencing has now provided the possibility of identifying every somatic mutation, copy-number change, and structural variant in a given cancer, giving rise to personalized targeted therapies. In this review, we provide an overview of the current and emerging treatment strategies in pancreatic cancer. By highlighting new paradigms in pancreatic ductal adenocarcinoma treatment, we hope to stimulate new thoughts for clinical trials aimed at improving patient outcomes.