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.

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 xenograft (PDX) tumor models are essential for identifying new biomarkers, signaling pathways and novel targets, to better define key factors of therapy response and resistance mechanisms. Therefore, this study aimed at establishing pancreas carcinoma (PC) PDX models with thorough molecular characterization, and the identification of signatures defining responsiveness toward drug treatment. In total, 45 PC-PDXs were generated from 120 patient tumor specimens and the identity of PDX and corresponding patient tumors was validated. The majority of engrafted PDX models represent ductal adenocarcinomas (PDAC). The PDX growth characteristics were assessed, with great variations in doubling times (4 to 32 days). The mutational analyses revealed an individual mutational profile of the PDXs, predominantly showing alterations in the genes encoding KRAS, TP53, FAT1, KMT2D, MUC4, RNF213, ATR, MUC16, GNAS, RANBP2 and CDKN2A. Sensitivity of PDX toward standard of care (SoC) drugs gemcitabine, 5-fluorouracil, oxaliplatin and abraxane, and combinations thereof, revealed PDX models with sensitivity and resistance toward these treatments. We performed correlation analyses of drug sensitivity of these PDX models and their molecular profile to identify signatures for response and resistance. This study strongly supports the importance and value of PDX models for improvement in therapies of PC.

Colorectal carcinomas (CRC) are one of the most common malignant tumors. Due to its heterogeneity and high probability to metastasize, the disease is often incurable and novel, improved therapies are needed. Enhanced, physiologically relevant in-vitro tumor models significantly increase the success rate in new drug development, reduce the number of animal experiments and result finally in better therapies for cancer patients. Furthermore, they have the potential to offer new approaches to personalized oncology. In recent years, 3D cell cultures were established as relevant preclinical cancer models, suitable for high-throughput-screening. However, their capacity to model and/or measure physiological processes can still be enhanced. In this project, we developed a tumor organoid-on-chip platform (TumOC) by combining most recent technological advances: We integrated patient-derived 3D cell cultures (organoids) from colorectal carcinomas and microsensor particles measuring oxygen concentration and therefore allowing assessment of cell vitality in real time in a microfluidic system. The resulting TumOC platform allows for defined exposition of cytostatic drugs including dynamic treatments. Measurements of cell vitality in real time enables analysis of not only the final effect of a drug treatment, but also the kinetics of drug response. At the same time, utilizing organoids allows for recapitulating tumor architecture and heterogeneity. Here we report the first proof of principle results generated with this system. We measured the cell vitality in real time over 72 hours during treatment with classic chemotherapeutics or targeted cancer drugs and compared the results to end-point measurements on the same organoids in a static system. In conclusion, our TumOC platform with its ability to recapitulate tumor heterogeneity in combination with dynamic treatments and real-time cell vitality assessment is an in-vitro tumor model closely recapitulating the physiological situation in a patient. TumOC provides an impressive opportunity to test and, consequently, predict the effectiveness of anti-cancer therapies. Therefore, this system is of interest not only for pre-clinical drug development but also for personal oncology. Citation Format: Marie Flechner, Juergen Loskutov, Ulrike Pfohl, Katja Osman, Christian R. Regenbrecht, Katja Uhlig, Lena Wedeken. TumOC: A tumor organoid-on-chip platform for real-time cell vitality measurements [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 6209.

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.