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.
e23538 Background: Compared to the progress in understanding and treating carcinomas in the past decade, the options and related clinical benefit in terms of response rates and overall survival for sarcoma patients lacks behind. Increasing incidence, low 5-year-survival rates and nearly endless heterogeneity of sarcomas are challenging oncologists every day. The necessity of so far missing representative preclinical models is obvious. PD3D cell culture models have already proven to be a useful tool to reverse clinical engineer patient outcome in carcinomas. Here we report the establishing and refining of PD3D models for the plethora of sarcoma entities. Methods: We obtained viable sarcoma tissue samples from incisional biopsies or tumor resections. We continuously optimized media conditions. For quality control and pathological evaluation, we embedded the cells in FFPE, stained patient-specific models according to original tumor samples and evaluated them pathologically. Upon histopathological evaluation, we performed semi-automated drug response assays on patient-specific models with up to 12 drugs and drug combinations, including standard of care drugs plus a selection of additional drugs. In the fashion of a prospective observatory study, we compared the results from the in vitro screen with the actual clinical outcome. Results: More than 25 patient derived 3D-cell culture models have been established from various subtypes of sarcomas. Optimized media conditions and sampling operation improved the take rates from ca. 10 to 80% irrespective of the tumor subtype. Pathological examination of the models confirmed original diagnoses and revealed that the patient-specific models recapitulate the key properties of the original tumor. Negative predictive value of drug sensitivity testing was close to 100 %, while the positive predictive value was > 80 %. These results in a limited number of cases puts the predictive value en par with recently published data about the predictive value of patient-derived organoids in carcinomas. Conclusions: Patient derived 3D-cell culture models of sarcomas can be routinely established, irrespective of subtype Models can be used for multi-omics analyses including drug sensitivity screenings Pretherapeutic drug sensitivity screenings could support clinical decision making Findings need to be confirmed in a prospective observatory trial.
Background Despite their heterogeneity, the current standard preoperative radiotherapy regimen for localized high-grade soft tissue sarcoma (STS) follows a one fits all approach for all STS subtypes. Sarcoma patient-derived three-dimensional cell culture models represent an innovative tool to overcome challenges in clinical research enabling reproducible subtype-specific research on STS. In this pilot study, we present our methodology and preliminary results using STS patient-derived 3D cell cultures that were exposed to different doses of photon and proton radiation. Our aim was: (i) to establish a reproducible method for irradiation of STS patient-derived 3D cell cultures and (ii) to explore the differences in tumor cell viability of two different STS subtypes exposed to increasing doses of photon and proton radiation at different time points. Methods Two patient-derived cell cultures of untreated localized high-grade STS (an undifferentiated pleomorphic sarcoma (UPS) and a pleomorphic liposarcoma (PLS)) were exposed to a single fraction of photon or proton irradiation using doses of 0 Gy (sham irradiation), 2 Gy, 4 Gy, 8 Gy and 16 Gy. Cell viability was measured and compared to sham irradiation at two different time points (four and eight days after irradiation). Results The proportion of viable tumor cells four days after photon irradiation for UPS vs. PLS were significantly different with 85% vs. 65% (4 Gy), 80% vs. 50% (8 Gy) and 70% vs. 35% (16 Gy). Proton irradiation led to similar diverging viability curves between UPS vs. PLS four days after irradiation with 90% vs. 75% (4 Gy), 85% vs. 45% (8 Gy) and 80% vs. 35% (16 Gy). Photon and proton radiation displayed only minor differences in cell-killing properties within each cell culture (UPS and PLS). The cell-killing effect of radiation sustained at eight days after irradiation in both cell cultures. Conclusions Pronounced differences in radiosensitivity are evident among UPS and PLS 3D patient-derived sarcoma cell cultures which may reflect the clinical heterogeneity. Photon and proton radiation showed similar dose-dependent cell-killing effectiveness in both 3D cell cultures. Patient-derived 3D STS cell cultures may represent a valuable tool to enable translational studies towards individualized subtype-specific radiotherapy in patients with STS.
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.
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