Christoph Reinhard

Background: In colorectal cancer (CRC), mutations of genes associated with the TGF-β/BMP signaling pathway, particularly affecting SMAD4, are known to correlate with decreased overall survival and it is assumed that this signaling axis plays a key role in chemoresistance. Methods: Using CRISPR technology on syngeneic patient-derived organoids (PDOs), we investigated the role of a loss-of-function of SMAD4 in sensitivity to MEK-inhibitors. CRISPR-engineered SMAD4^R361H PDOs were subjected to drug screening, RNA-Sequencing, and multiplex protein profiling (DigiWest^®). Initial observations were validated on an additional set of 62 PDOs with known mutational status. Results: We show that loss-of-function of SMAD4 renders PDOs sensitive to MEK-inhibitors. Multiomics analyses indicate that disruption of the BMP branch within the TGF-β/BMP pathway is the pivotal mechanism of increased drug sensitivity. Further investigation led to the identification of the SFAB-signature (SMAD4, FBXW7, ARID1A, or BMPR2), coherently predicting sensitivity towards MEK-inhibitors, independent of both RAS and BRAF status. Conclusion: We identified a novel mutational signature that reliably predicts sensitivity towards MEK-inhibitors, regardless of the RAS and BRAF status. This finding poses a significant step towards better-tailored cancer therapies guided by the use of molecular biomarkers.

Patient-derived 3D cell culture models (PD3D®) developed as a powerful tool for disease modelling, biomarker and drug discovery. Currently, they are gaining increasing significance in the field of personalized oncology, as they recapitulate the histopathology of the original tumor tissue and preserve its genetic markup. PD3D® can be used to model intratumoral heterogeneity and for medium and high throughput drug screens. Using a reverse clinical engineering approach, PD3D® models allow identification of chemoresistance/sensitivity signatures (i.e., biomarkers) and can be applied in personalized oncology to identify treatment for an individual patient. We successfully established PD3D® models from more than 300 tumor tissue samples, ranging from more prevalent cancers like colorectal, breast and pancreas carcinoma, to rare tumor entities including various sarcoma types and thymoma. PD3D® models from different tumor entities differ in morphology and culture media requirements. When treating PD3D® from the same tumor entity with standard of care drugs, we found that their response differed, as does clinical response of patients. Furthermore, we successfully used PD3D® models to identify a biomarker for predicting chemosensitivity towards a targeted drug. For application of PD3D® in truly personalized oncology, we developed a protocol that allows us to generate a PD3D® culture and perform a drug sensitivity assay for an individual patient within a therapy-relevant timeframe. Using this protocol, we identified a combination therapy for a pretreated, metastasized appendix carcinoma within 29 days, that resulted in stable disease of the patient. In conclusion, PD3D® models can be derived from various cancer entities and used to analyze drug response in cohorts of models for drug development or identification of signatures related to drug resistance or sensitivity. Furthermore, PD3D® models can be used to predict a patient tumor’s drug response in a personalized manner, supporting the oncologist to identify the best treatment option for the patient. Citation Format: Alina Pflaume, Samantha Exner, Katja Herrera-Glomm, Jürgen Loskutov, Ulrike Pfohl, Manuela Regenbrecht, Sushmitha Sankarasubramanian, Lena Wedeken, Sabine Finkler, Larissa Ruhe, Quirin Graf Adelmann, Christoph Reinhard, Philipp Stroebel, Christian R. Regenbrecht. PD3D^®models: New age in cancer research and clinical diagnostics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6223.

Cancer represents a huge health problem worldwide and is well-recognized as an extremely heterogeneous disease affecting all the tissues and organs. The incidence of the particular cancer type is directly connected to the availability of specific medication and amount of the research focused on it, resulting in a high unmet medical need for treatment options for rare cancers. Currently, NCI defines “rare cancer” as cancer with an incidence rate below 15 per 10^5 people per year and recently the term “ultra-rare cancer” was defined as cancer with an incidence rate below 1 per 10^6 people per year. These encompass drastically understudied entities, usually with poor prognosis and grim outlook for an improvement in treatment. Low incidence of such cancers makes development of targeted therapies not interesting from a commercial point of view and completely abolishes the possibility of large-scale clinical trials. Therefore, a personalized medicine approach appears to be the most promising strategy for the patients to get adequate care. Here we report our experience with personalized oncology solutions for rare and ultra-rare cancers. We utilized patient derived 3D (PD3D) cultures to evaluate prospective therapeutic options in these exceptional cases to support the oncologists in providing personalized care to the patients. Fresh surgical specimens underwent several steps of mechanical and chemical dissociation. Subsequently, cell aggregates were seeded into 24 well plates in matrix-like scaffolds and allowed to grow until they started forming colonies. After harvesting, the cells underwent pathology evaluation to confirm origin and diagnosis. Therapies, recommended by the case leading oncologist, were used for drug sensitivity testing after transferring cells semi- automatically to 384-well plates. Over the last 18 months, we handled 6 cases classified as rare or ultra-rare cancers. The diagnoses included: clear cell sarcoma, extra-skeletal myxoid chondrosarcoma, CIC-rearranged round cell sarcoma, pleomorphic liposarcoma, cardiac angiosarcoma and clear cell endometrial carcinoma. In all cases we were able to successfully establish PD3D cultures and perform a drug screen, identifying a potential treatment for the patient. Overall, our results indicate that it is feasible to utilize our testing strategy for rare and ultra-rare cancer entities. Further research and rigorous follow up is required to confirm the benefit of the personalized approach vs current strategies. However, a demand for personalized care in such cases is clearly visible. Citation Format: Juergen Loskutov, Manuela Regenbrecht, Rica Sauer, Sabine Finkler, Maya Niethard, Christoph Reinhard, Christian Regenbrecht. The bad, the ugly and the ultra-rare: All cancers are equal in the face of personalized medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6224.

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient’s tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient’s treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.