Cancer frequency and prevalence have been increasing in the past decades, with devastating impacts on patients and their families. Despite the great advances in targeted approaches, there is still a lack of methods to predict individual patient responses, and therefore treatments are tailored according to average response rates. "Omics" approaches are used for patient stratification and choice of therapeutic options towards a more precise medicine. These methods, however, do not consider all genetic and non-genetic dynamic interactions that occur upon drug treatment. Therefore, the need to directly challenge patient cells in a personalized manner remains. The present review addresses the state of the art of patient-derived in vitro and in vivo models, from organoids to mouse and zebrafish Avatars. The predictive power of each model based on the retrospective correlation with the patient clinical outcome will be considered. Finally, the review is focused on the emerging zebrafish Avatars and their unique characteristics allowing a fast analysis of local and systemic effects of drug treatments at the single-cell level. We also address the technical challenges that the field has yet to overcome.The arrival of precision medicine and immunotherapy are giving hope to finally manage cancer treatment. Many advances were made possible due to the discovery of molecular pathways in tumor cells and on their interaction with the surrounding tumor microenvironment (TME) [1], leading to the development of many new therapies. The latest successes in HER2-targeted therapy and the discovery of immune checkpoint inhibitors are great examples of this [2]. However, not all patients respond and many are not eligible for these "new therapies". Thus, nowadays, the majority of patients are still treated with traditional chemo/radiotherapy and surgery according to clinical guidelines, which are developed and approved based on average efficacy rates.As a result of this "one-size-fits-all" approach, treatments may prove to be efficient for some patients but not for others. For example, in the international therapeutic guidelines (NCCN and ESMO) for advanced colorectal cancer (CRC) there are two main chemotherapeutic arms (FOLFOX and FOLFIRI), which show very similar response rates of~50% [3,4]. In other words,~50% of patients respond to treatment while~50% do not. Clinicians do not know in which group patients will fall, as there is no predictive screening test to provide this information. Thus, patients that start with FOLFOX and do not respond change to FOLFIRI, and vice-versa. This applies for CRC and many other cancers, being especially relevant in the metastatic scenario when oncology therapy guidelines reach branch

Background: Whereas the role of neoadjuvant radiotherapy in rectal cancer is well-established, the ability to discriminate between radioresistant and radiosensitive tumors before starting treatment is still a crucial unmet need. Here we aimed to develop an in vivo test to directly challenge living cancer cells to radiotherapy, using zebrafish xenografts. Methods: We generated zebrafish xenografts using colorectal cancer cell lines and patient biopsies without in vitro passaging, and developed a fast radiotherapy protocol consisting of a single dose of 25 Gy. As readouts of the impact of radiotherapy we analyzed proliferation, apoptosis, tumor size and DNA damage. Findings: By directly comparing isogenic cells that only differ in the KRAS G13D allele, we show that it is possible to distinguish radiosensitive from radioresistant tumors in zebrafish xenografts, even in polyclonal tumors, in just 4 days. Most importantly, we performed proof-of-concept experiments using primary rectum biopsies, where clinical response to neoadjuvant chemoradiotherapy correlates with induction of apoptosis in their matching zebrafish Patient-Derived Xenografts-Avatars. Interpretation: Our work opens the possibility to predict tumor responses to radiotherapy using the zebrafish Avatar model, sparing valuable therapeutic time and unnecessary toxicity.

Tail-anchored (TA) proteins are anchored to their corresponding membrane via a single transmembrane segment (TMS) at their C-terminus. In yeast, the targeting of TA proteins to the endoplasmic reticulum (ER) can be mediated by the guided entry of TA proteins (GET) pathway, whereas it is not yet clear how mitochondrial TA proteins are targeted to their destination. It has been widely observed that some mitochondrial outer membrane (MOM) proteins are mistargeted to the ER when overexpressed or when their targeting signal is masked. However, the mechanism of this erroneous sorting is currently unknown. In this study, we demonstrate the involvement of the GET machinery in the mistargeting of suboptimal MOM proteins to the ER. These findings suggest that the GET machinery can, in principle, recognize and guide mitochondrial and non-canonical TA proteins. Hence, under normal conditions, an active mitochondrial targeting pathway must exist that dominates the kinetic competition against other pathways.

Background: Cancers of the pancreas and biliary tree remain one of the most aggressive oncological malignancies, with most patients relying on systemic chemotherapy. However, effective biomarkers to predict the best therapy option for each patient are still lacking. In this context, an assay able to evaluate individual responses prior to treatment would be of great value for clinical decisions. Here we aimed to develop such a model using zebrafish xenografts to directly challenge pancreatic cancer cells to the available chemotherapies. Methods: Zebrafish xenografts were generated from a Panc-1 cell line to optimize the pancreatic setting. Pancreatic surgical resected samples, without in vitro expansion, were used to establish zebrafish patient-derived xenografts (zAvatars). Upon chemotherapy exposure, zAvatars were analyzed by single-cell confocal microscopy. Results: We show that Panc-1 zebrafish xenografts are able to reveal tumor responses to both FOLFIRINOX and gemcitabine plus nanoparticle albumin-bound (nab)-paclitaxel in just 4 days. Moreover, we established pancreatic and ampullary zAvatars with patient-derived tumors representative of different histological types. Conclusion: Altogether, we provide a short report showing the feasibility of generating and analyzing with single-cell resolution zAvatars from pancreatic and ampullary cancers, with potential use for future preclinical studies and personalized treatment.