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A path to translation: How 3D patient tumor avatars enable next generation precision oncology

2022; Cell Press; Volume: 40; Issue: 12 Linguagem: Inglês

10.1016/j.ccell.2022.09.017

1878-3686

Shree Bose, Margarida Barroso, Milan G. Chheda, Hans Clevers, Elena Élez, Salma Kaochar, Scott Kopetz, Xiao‐Nan Li, Funda Meric‐Bernstam, Clifford A. Meyer, Haiwei Mou, Kristen M. Naegle, Martín F. Pera, Zinaida Perova, Katerina Politi, Benjamin J. Raphael, Paul Robson, Rosalie C. Sears, Josep Tabernero, David A. Tuveson, Alana L. Welm, Bryan E. Welm, Christopher D. Willey, Konstantin Salnikow, Jeffrey H. Chuang, Xiling Shen,

Advanced Radiotherapy Techniques

3D patient tumor avatars (3D-PTAs) hold promise for next-generation precision medicine. Here, we describe the benefits and challenges of 3D-PTA technologies and necessary future steps to realize their potential for clinical decision making. 3D-PTAs require standardization criteria and prospective trials to establish clinical benefits. Innovative trial designs that combine omics and 3D-PTA readouts may lead to more accurate clinical predictors, and an integrated platform that combines diagnostic and therapeutic development will accelerate new treatments for patients with refractory disease. 3D patient tumor avatars (3D-PTAs) hold promise for next-generation precision medicine. Here, we describe the benefits and challenges of 3D-PTA technologies and necessary future steps to realize their potential for clinical decision making. 3D-PTAs require standardization criteria and prospective trials to establish clinical benefits. Innovative trial designs that combine omics and 3D-PTA readouts may lead to more accurate clinical predictors, and an integrated platform that combines diagnostic and therapeutic development will accelerate new treatments for patients with refractory disease. Precision medicine, as defined in modern oncology, has focused on the development of therapies that target specific genetic alterations in cancer. Imatinib (Gleevec) for leukemias with BCL-ABL mutations, Trastuzumab (Herceptin) for HER2-overexpressing cancers, and others were promising early demonstrations of this vision. In 2006, the National Institutes of Health (NIH) launched The Cancer Genome Atlas, a landmark cancer genomics program that sequenced over 11,000 primary cancer samples. The precision medicine approach was simple: sequence a patient’s tumor, identify driver mutations, and administer therapies to target those mutations. With tumors that were dependent on the targeted oncogenes, early successes bolstered collaborations between pharmaceutical companies and academic research for rapid drug development. With only a small subset of targetable mutations, a minority of cancer patients are thought to actually benefit from genome-guided therapies (Marquart et al., 2018Marquart J. Chen E.Y. Prasad V. Estimation of the percentage of US patients with cancer who benefit from genome-driven oncology.JAMA Oncol. 2018; 4: 1093-1098Crossref PubMed Scopus (207) Google Scholar). Given our growing appreciation for other drivers of neoplastic behaviors (metabolic, phenotypic, epigenetic, and microenvironmental) and tumor evolution during treatment, the need for an approach that integrates more biology into therapeutic decision making is evident. With the support of key national funding entities, including the NIH, biorepositories of patient-derived models have been developed; however, their application in precision medicine has been largely dependent on their ability to adequately recapitulate the clinical response of patient tumors in a laboratory setting. The advantages and limitations of each of these models have been extensively reviewed (Figure 1A). Although they have been a mainstay of cancer research over several decades, 2D cell cultures are not ideal models of tumors because they often represent only the most rapid-growing cells in a plate rather than the diversity of the neoplastic growth (Letai, 2017Letai A. Functional precision cancer medicine—moving beyond pure genomics.Nature medicine. 2017; 23: 1028Crossref PubMed Scopus (203) Google Scholar). Patient-derived xenograft (PDX) models, or murine models of cancer that use patient tumor tissue engrafted into immune-compromised mice, have been a critical component of translational modeling. Although it is valuable in its ability to capture genetic diversity and other features of patient tumor physiology, serial PDX modeling through patient treatment and tumor evolution is often not feasible due to the substantial cost and time requirements of PDXs. Additionally, the murine stromal tumor microenvironment (TME) and the need for immune reconstitution in PDXs make immuno-oncology studies challenging, thus obligating the further development of other preclinical models. Ex vivo explants, or cultures of whole tissue, capture the 3D architecture and cellular organization of tumor samples, although the amount of required tissue, low throughput, and difficulty in reproducibility limit their scalability for clinical implementation. In recent years, 3D patient tumor avatars (3D-PTAs) have rapidly emerged as a new model system for exploring tumor behaviors. These models, ranging from patient-derived organoids (PDOs) to microscale models like organotypic tumor spheroids (PDOTs), 3D bioprinting, organoids-on-a-chip, and micro-organospheres (MOS), can model cellular behaviors while capturing characteristics true to the source tissue (Jenkins et al., 2018Jenkins R.W. Aref A.R. Lizotte P.H. Ivanova E. Stinson S. Zhou C.W. Bowden M. Deng J. Liu H. Miao D. He M.X. et al.Ex vivo Profiling of PD-1 Blockade Using Organotypic Tumor Spheroids.Cancer Discovery. 2018; 8: 196-215Crossref PubMed Scopus (322) Google Scholar; Sato et al., 2011Sato T. Stange D.E. Ferrante M. Vries R.G. Van Es J.H. Van Den Brink S. Van Houdt W.J. Pronk A. Van Gorp J. Siersema P.D. et al.Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium.Gastroenterology. 2011; 141: 1762-1772Abstract Full Text Full Text PDF PubMed Scopus (2383) Google Scholar; Park et al., 2019Park S.E. Georgescu A. Huh D. Organoids-on-a-chip.Science. 2019; 364: 960-965Crossref PubMed Scopus (421) Google Scholar; Ding et al., 2022Ding S. Hsu C. Wang Z. Natesh N.R. Millen R. Negrete M. Giroux N. Rivera G.O. Dohlman A. Bose S. et al.Patient-derived micro-organospheres enable clinical precision oncology.Cell Stem Cell. 2022; 29: 905-917.e9Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar; Langer et al., 2019Langer E.M. Allen-Petersen B.L. King S.M. Kendsersky N.D. Turnidge M.A. Kuziel G.M. Riggers R. Samatham R. Amery T.S. Jacques S.L. et al.Modeling tumor phenotypes in vitro with three-dimensional bioprinting.Cell Rep. 2019; 26: 608-623.e6Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar). Several landmark studies have demonstrated that PDOs can predict patient tumor response to chemotherapy and radiation (Bose et al., 2021Bose S. Clevers H. Shen X. Promises and challenges of organoid-guided precision medicine.Med. 2021; 2: 1011-1026Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar; Vlachogiannis et al., 2018Vlachogiannis G. Hedayat S. Vatsiou A. Jamin Y. Fernández-Mateos J. Khan K. Lampis A. Eason K. Huntingford I. Burke R. et al.Patient-derived organoids model treatment response of metastatic gastrointestinal cancers.Science. 2018; 359: 920-926Crossref PubMed Scopus (1041) Google Scholar; Ganesh et al., 2019Ganesh K. Wu C. O’Rourke K.P. Szeglin B.C. Zheng Y. Sauvé C.E.G. Adileh M. Wasserman I. Marco M.R. Kim A.S. et al.A rectal cancer organoid platform to study individual responses to chemoradiation.Nature medicine. 2019; 25: 1607-1614Crossref PubMed Scopus (269) Google Scholar). Although PDX models often require 6–8 months for development and expansion, PDOs can reduce this time to weeks with higher throughput (Hidalgo et al., 2014Hidalgo M. Amant F. Biankin A.V. Budinská E. Byrne A.T. Caldas C. Clarke R.B. de Jong S. Jonkers J. Mælandsmo G.M. et al.Patient-derived xenograft models: an emerging platform for translational cancer research.Cancer Discov. 2014; 4: 998-1013Crossref PubMed Scopus (1197) Google Scholar). More recently, microscale 3D-PTA technologies which leverage microfabrication or microfluidics have achieved substantially faster establishment and higher throughput. Although they are promising, these efforts on 3D-PTAs will require standardization and concerted buy-in from regulatory bodies, clinicians, researchers, and patients to bridge the gap between bench and bedside, and key recommendations to achieve this goal are discussed here (Figure 1B). Although the expertise of select groups has demonstrated the viability of 3D-PTAs as tools for modeling cancer, the variability in their creation remains a hurdle for reproducibility and clinical adoption. The skill of the operator remains an important driver of success in establishing both PDOs and ex vivo explants, and the most successful biobanking efforts report establishment rates of 70–95%, which can decrease in other settings (Bose et al., 2021Bose S. Clevers H. Shen X. Promises and challenges of organoid-guided precision medicine.Med. 2021; 2: 1011-1026Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar; van Tienderen et al., 2022van Tienderen G.S. Li L. Broutier L. Saito Y. Inacio P. Huch M. Selaru F.M. van der Laan L.J. Verstegen M.M. Hepatobiliary tumor organoids for personalized medicine: a multicenter view on establishment, limitations, and future directions.Cancer Cell. 2022; 40: 226-230Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar). Defining and standardizing precise methods to validate whether 3D-PTAs adequately capture the significant inter- and intratumoral heterogeneity of source tissue will also be crucial to deriving insights from this data. Defining the extracellular matrix (ECM) scaffolds, media, and cocktails of growth factors that are best suited for different types of 3D-PTAs has been a key effort of many groups, including the National Cancer Institute (NCI)-sponsored Patient-Derived Models of Cancer (PDMC) Consortium; however, standard precise culture methods remain elusive, and different cancer types will likely require more specific tailoring. Derived from mouse sarcoma, Matrigel tends to have batch-to-batch variability, whereas the effects of alternative scaffolds such as synthetic gel on 3D-PTA establishment and drug response have not been well characterized. Variations in adding common factors, whether fetal bovine serum (FBS) or antibiotics (penicillin, streptomycin, primocin, etc.) into culture media with supraphysiologic glucose are relatively commonplace. However, the consequent metabolic effects of these combinations are poorly understood but likely of importance. In 3D-PTAs, the need to harvest tissue from donors can also lead to variations in the time and amount of tissue without perfusion, leading to warm- vs. cold-ischemic changes that are difficult to characterize. Finally, the post-processing of these tissues—whether using clean-up procedures to remove necrotic tissue; applying specialized media to isolate different immune, stromal, or tumoral components; or mycoplasma surveillance methodologies—adds another layer of variability among studies. Because individual labs often optimize growth factor concentrations based on their own experience, results are sometimes hard to compare across publications because of the variabilities discussed above (van Tienderen et al., 2022van Tienderen G.S. Li L. Broutier L. Saito Y. Inacio P. Huch M. Selaru F.M. van der Laan L.J. Verstegen M.M. Hepatobiliary tumor organoids for personalized medicine: a multicenter view on establishment, limitations, and future directions.Cancer Cell. 2022; 40: 226-230Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar; Xie and Murphy, 2019Xie A.W. Murphy W.L. Engineered biomaterials to mitigate growth factor cost in cell biomanufacturing.Current Opinion in Biomedical Engineering. 2019; 10: 1-10Crossref Scopus (19) Google Scholar; Veninga and Voest, 2021Veninga V. Voest E.E. Tumor organoids: opportunities and challenges to guide precision medicine.Cancer Cell. 2021; 39: 1190-1201Abstract Full Text Full Text PDF PubMed Scopus (86) Google Scholar). As standardized protocols are developed for 3D-PTAs, these practices must be codified and shared across research groups. Following the establishment of and experimentation with 3D-PTAs, quantifying results with validated software pipelines is essential to establishing their reproducibility and functional readouts, as well (Kong et al., 2020Kong J. Lee H. Kim D. Han S.K. Ha D. Shin K. Kim S. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.Nat. Commun. 2020; 11: 1-13Crossref PubMed Scopus (82) Google Scholar). Further standardization and automation of these processes—handling of source material, culture conditions, validated reproduction of source characteristics, and measurements of endpoints like proliferation and survival—will enhance reproducibility. Together with independent replication studies, these factors will form an important foundation for using 3D-PTAs as companion support tools in research and clinical decision making. As the number and complexity of tumoral samples collected for large-scale 3D-PTA biobanks expand, the clinical factors that are captured should be well documented in order to ensure that analytical approaches can draw meaningful conclusions. Specifically, clinical parameters of race and ethnicity, body mass index, and socio-economic factors have come to be recognized as crucial to overall outcomes for patients, but they are not routinely available for large-scale biobanks. Other metadata to note in studies should include sex, age, sample collection dates, sample processing, cancer types and full history (initial presentation, metastases, stage/grade, treatment lines), family history, and tobacco exposure. Although these parameters have been reported in studies when relevant to the outcomes of interest, capturing that heterogeneity as a dimension of 3D-PTA libraries will enable a more holistic understanding of the predictive capabilities of these functional models. These requirements for patient data collection need to be established and supported by overseeing bodies—including the NIH, institutions, and journals which publish these studies. Key to achieving this will be the adoption of common regulatory frameworks for data exchange and access on a global scale. Building on the example of the PDX Minimal Information Standard (PDX-MI), standards around the clinical information, patient metadata collection, and patient informed consent for 3D-PTAs studies should be developed in the coming years, particularly as we find ourselves at the beginning of this confluence of big data and biorepository development. Although a well-established framework for genomic testing in accredited laboratories exists, the path for validation of 3D-PTAs for clinical decision making is yet to be established. As with genome-guided therapy, 3D-PTA-guided therapies must undergo rigorous prospective clinical trials to demonstrate clinical benefits for clinical adoption, regulatory approval, and eventually payer reimbursements. Currently, many clinical trials are seeking to assess the potential of 3D-PTAs to advance the management of patients with various tumor types in different settings. These clinical studies include observational (non-interventional) “co-clinical trials” aiming to evaluate the feasibility of deriving 3D-PTA-based assays from tissue biopsies in a turnaround time compatible with clinical workflows, as demonstrated by a recent clinical study using the MOS technology (Ding et al., 2022Ding S. Hsu C. Wang Z. Natesh N.R. Millen R. Negrete M. Giroux N. Rivera G.O. Dohlman A. Bose S. et al.Patient-derived micro-organospheres enable clinical precision oncology.Cell Stem Cell. 2022; 29: 905-917.e9Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar). As a next step, the potential of these platforms for predicting response rates and progression-free survival must be shown with either a prospective validation trial, where clinicians are informed of the 3D-PTA assay prediction when choosing between equipoised approved or investigational drugs, or in the setting of a randomized pilot study with an uninformed arm where 3D-PTA is not performed, and patients are managed as per available best practice. Quantitative measurements of immune, stromal, and tumor cell types over time could help define the temporal fidelity of 3D-PTAs and their utility in modeling tumor heterogeneity and evolution. Ultimately, results from these validation studies will buoy the efforts of early adopters while providing the basis for further clinical utility trials. These studies, with larger cohort sizes, randomized arms, and clinically meaningful endpoints of progression-free survival or overall survival, will be the foundation for adoption by the broader community and National Comprehensive Cancer Network (NCCN) guidelines. 3D-PTA-guided prospective trials can further leverage window-of-opportunity trial designs, which test experimental drugs before standard-of-care (SOC) regimens. Upon biopsy, 3D-PTA assays can be performed to test both experimental drugs and equipoised SOC regimens. During the short period of time prior to SOC treatment, typically ∼10–20 days, patients can be treated with experimental therapies and evaluated with a second biopsy and imaging (e.g., PET) or other assays (e.g., circulating tumor DNA) to assess drug on-target activity and early response as well as adaptive resistance programs. These window-of-opportunity trials allow 3D-PTA readouts to be correlated with experimental drug responses while guiding SOC decision making as well, thus providing valuable insights into the biological effects and mechanisms of action that accelerate development of new drugs and combination regimens. Further, 3D-PTAs may be used to evaluate the sensitivity of non-targeted chemotherapy-based regimens that lack molecular markers to guide patient care and offer additional treatment options to therapy-refractory patients. Furthermore, 3D-PTAs can serve as valuable tools to guide patient selection and optimize enrollment for specific experimental therapies. Although current clinical trials often provide limited benefit for a majority of enrollees, 3D-PTAs can be used to predict patient response and stratify treatment cohorts accordingly, similar to genome-guided umbrella trial designs. Such precision clinical trials could increase the benefits that enrollees derive from experimental therapies—enhancing patient survival, improving quality of life, encouraging accruement, reducing trial risk, and utilizing precious clinical resources more efficiently. Lastly, where current trials often fail to capture racial and socioeconomic diversity adequately, 3D-PTA may provide a more personalized platform to address such disparities and benefit minority and disadvantaged populations by treating them as unique individuals rather than relying on statistics from unrepresentative populations. Advances in genome- and function-based assays have occurred largely in parallel; however, the burgeoning confluence of the two has already begun to yield important insights—recapitulating associations with genetic mutations and targeted therapeutic sensitivities and pinpointing mutations which may be of interest for future investigation. A major question remains yet unanswered: can patient response be more accurately predicted by one or both in combination? Because a patient can only receive one treatment at a time, 3D-PTAs offer the opportunity to perform high-throughput screens with a library of drugs in parallel and drug combinations, while also guiding lower-throughput in vivo studies. When combined with molecular profiling, these functional models may provide much-needed training datasets to improve the performance of current omics-based predictors. Ultimately, as expanding clinical trials and growing repositories offer increasing statistical power, the combination of the molecular-guided and functional 3D-PTA-guided therapies will likely outperform either therapy alone while also providing the most predictive capabilities for future precision medicine efforts (Figure 1C). In this functional precision medicine approach, larger volumes of biopsied tissue can be both molecularly profiled and used to establish 3D-PTAs for functional drug response assays. A computational predictor trained using both omics and 3D-PTAs readouts will be used to predict patient response, which can be gauged against patient endpoints. This integrative approach may be particularly impactful in evaluating clinical approaches to overcome resistance to specific therapies. In addition to the clinical impact of these predictive computational models, the development of analytical pipelines which can integrate the metabolic, (phospho)proteomic, immune, morphological, and genetic data gathered from libraries of 3D-PTAs may also yield insights into previously unknown associations. Thus, further work on using 3D-PTAs and omics analyses in combination with well-defined computational pipelines to analyze them may better define the precise features of 3D-PTAs that contribute to their predictive value (Kong et al., 2020Kong J. Lee H. Kim D. Han S.K. Ha D. Shin K. Kim S. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.Nat. Commun. 2020; 11: 1-13Crossref PubMed Scopus (82) Google Scholar). To this end, federal funding agencies should leverage relevant consortia such as NCI PDMC, PDXnet, TEC, and CSBC to systematically compare omics-based biomarkers and 3D-PTA drug responses to quantify the predictive power of each alone and in combination. European academic research infrastructures dedicated to the development of preclinical models (i.e., INFRAFRONTIER, EuroPDX) are currently assessing the clinical value of pan-cancer 3D-PTA platforms for advancing precision oncology efforts, both at an academic and at a translational level. The European initiative HCA-Organoid is leveraging single-cell technologies in PDOs to enable therapeutic advances. Ultimately, an integrated approach will create synergy between these global scientific communities, improving omics and 3D-PTA models as the repositories continue to evolve with growing clinical specimens and data. 3D-PTAs pose the unique opportunity to perform de novo testing of new, experimental drugs or off-label drugs that lack existing clinical data, an impossible undertaking for clinical -omics predictors that require patient response data to be trained on. The importance of this capability for both drug testing and development is manifold. For one, next-generation precision medicine must be able to identify new treatments for patients who are refractory to the existing SOC. As new drugs are increasingly more specific and targeting smaller patient populations, pharmaceutical companies are facing the challenge of finding and accruing the right patients for clinical trials, and this increases the cost, time, and risk of new drug development. 3D-PTAs offer an opportunity to bring new drugs to market in a safe, expedited manner with preliminary clinical trials carried out using patient surrogates that can increase the likelihood of downstream success. Similarly, 3D-PTAs offer a unique opportunity to expand drug repurposing strategies that may provide solutions to unmet clinical needs. The next generation of precision medicine must incorporate the heterogeneity of the patient population into every step of the diagnosis-treatment cascade. Whereas current diagnostic and therapeutic developments are largely siloed, 3D-PTA functional precision medicine can serve as an integrated workflow to enhance cancer care and expedite clinical development of new drugs (Figure 1D). The clinical biomarkers captured by the omics profiling combined with the functional readout from the 3D-PTA can guide the patient to either SOC or new therapeutics in clinical trials. Compared to conventional clinical trial accrual, functional precision medicine may represent a more effective approach in selecting appropriate therapies, thereby improving quality and length of life while reducing clinical resource wastage and unnecessary toxicity from ineffective therapies. Furthermore, the 3D-PTAs from the diagnostic assays can be further passaged and preserved to form 3D-PTA biobanks with diverse clinical response data that capture patient heterogeneity. These are already becoming invaluable resources for preclinical drug and biomarker discovery as well as AI-based learning algorithms, which can then aid development of both new diagnostics and therapies. Finally, aggregating the data derived from these studies and making it accessible to the broader scientific community via resources like PDCM Finder (www.cancermodels.org) will maximize the utility of 3D-PTAs for precision oncology. 3D-PTAs hold tremendous promise for next-generation precision medicine, and they have support from initiatives around the globe. However, for these technologies to fulfill their envisioned goals as clinical decision-making tools, further work is necessary to build on what the scientific communities have accomplished so far. The remaining challenges to incorporation of 3D-PTAs include standardization of techniques and patient metadata collection, analytical tools, and the development of new clinical trial designs, all of which require a concerted community-wide effort guided by the best practices and standards proposed here. Physician scientists, or clinical key opinion leaders, from around the world will set the standards to develop 3D-PTAs and ensure that they can be incorporated into clinical practice and drive patient care that is truly personalized.