scholarly journals Capturing colorectal cancer inter-tumor heterogeneity in patient-derived xenograft (PDX) models

2018 ◽  
Vol 144 (2) ◽  
pp. 366-371 ◽  
Author(s):  
Pramudita R. Prasetyanti ◽  
Sander R. van Hooff ◽  
Tessa van Herwaarden ◽  
Nathalie de Vries ◽  
Kieshen Kalloe ◽  
...  
2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Vanessa F. Bonazzi ◽  
Olga Kondrashova ◽  
Deborah Smith ◽  
Katia Nones ◽  
Asmerom T. Sengal ◽  
...  

Abstract Background Endometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprises four molecular subtypes with differing etiology, prognoses, and responses to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. As pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models. Methods Here, we report whole exome or whole genome sequencing of 11 PDX models and their matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the copy number high/p53-mutant and mismatch-repair deficient molecular subtypes of EC. Results PDX models were successfully generated from grade 2/3 tumors, including three uterine carcinosarcomas. The models showed similar histomorphology to the primary tumors and represented all four molecular subtypes of EC, including five mismatch-repair deficient models. The different PDX lineages showed a wide range of inter-tumor and intra-tumor heterogeneity. However, for most PDX models, one arm recapitulated the molecular landscape of the primary tumor without major genomic drift. An in vivo response to talazoparib was detected in four copy number high models. Two models (carcinosarcomas) showed a response consistent with stable disease and two models (one copy number high serous EC and another carcinosarcoma) showed significant tumor growth inhibition, albeit one consistent with progressive disease; however, all lacked the HR deficiency genomic signature. Conclusions EC PDX models represent the four molecular subtypes of disease and can capture intra-tumor heterogeneity of the original primary tumor. PDXs of the copy number high molecular subtype showed sensitivity to PARPi; however, deeper and more durable responses will likely require combination of PARPi with other agents.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Deepak Vangala ◽  
Swetlana Ladigan ◽  
Sven T. Liffers ◽  
Soha Noseir ◽  
Abdelouahid Maghnouj ◽  
...  

Abstract Background The development of secondary resistance (SR) in metastatic colorectal cancer (mCRC) treated with anti-epidermal growth factor receptor (anti-EGFR) antibodies is not fully understood at the molecular level. Here we tested in vivo selection of anti-EGFR SR tumors in CRC patient-derived xenograft (PDX) models as a strategy for a molecular dissection of SR mechanisms. Methods We analyzed 21 KRAS, NRAS, BRAF, and PI3K wildtype CRC patient-derived xenograft (PDX) models for their anti-EGFR sensitivity. Furthermore, 31 anti-EGFR SR tumors were generated via chronic in vivo treatment with cetuximab. A multi-omics approach was employed to address molecular primary and secondary resistance mechanisms. Gene set enrichment analyses were used to uncover SR pathways. Targeted therapy of SR PDX models was applied to validate selected SR pathways. Results In vivo anti-EGFR SR could be established with high efficiency. Chronic anti-EGFR treatment of CRC PDX tumors induced parallel evolution of multiple resistant lesions with independent molecular SR mechanisms. Mutations in driver genes explained SR development in a subgroup of CRC PDX models, only. Transcriptional reprogramming inducing anti-EGFR SR was discovered as a common mechanism in CRC PDX models frequently leading to RAS signaling pathway activation. We identified cAMP and STAT3 signaling activation, as well as paracrine and autocrine signaling via growth factors as novel anti-EGFR secondary resistance mechanisms. Secondary resistant xenograft tumors could successfully be treated by addressing identified transcriptional changes by tailored targeted therapies. Conclusions Our study demonstrates that SR PDX tumors provide a unique platform to study molecular SR mechanisms and allow testing of multiple treatments for efficient targeting of SR mechanisms, not possible in the patient. Importantly, it suggests that the development of anti-EGFR tolerant cells via transcriptional reprogramming as a cause of anti-EGFR SR in CRC is likely more prevalent than previously anticipated. It emphasizes the need for analyses of SR tumor tissues at a multi-omics level for a comprehensive molecular understanding of anti-EGFR SR in CRC.


2021 ◽  
Author(s):  
Vanessa F. Bonazzi ◽  
Olga Kondrashova ◽  
Deborah Smith ◽  
Katia Nones ◽  
Asmerom T. Sengal ◽  
...  

Background: Endometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprised of four molecular subtypes with differing etiology, prognoses, and response to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. Pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models. Methods: Here, we report whole exome or whole genome sequencing of 11 PDX models and the matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the different molecular subtypes of EC. Results: PDX models were successfully generated from all four molecular subtypes of EC and uterine carcinosarcomas, and they recapitulated morphology and the molecular landscape of primary tumors without major genomic drift. We also observed a wide range of inter-tumor and intra-tumor heterogeneity, well captured by different PDX lineages, which could lead to different treatment responses. An in vivo response to talazoparib was detected in two p53mut models consistent with stable disease, however both lacked the HR deficiency genomic signature. Conclusions: EC PDX models represent the four molecular subtypes of disease and can capture intra-tumoral heterogeneity of the original primary tumor. PDXs of the p53mut molecular subtype showed sensitivity to PARPi, however, deeper and more durable responses will likely require combination of PARPi with other agents.


2014 ◽  
Vol 80 (9) ◽  
pp. 873-877 ◽  
Author(s):  
Christopher J. Tignanelli ◽  
Silvia G. Herrera Loeza ◽  
Jen Jen Yeh

One obstacle in the translation of advances in cancer research into the clinic is a deficiency of adequate preclinical models that recapitulate human disease. Patient-derived xenograft (PDX) models are established by engrafting patient tumor tissue into mice and are advantageous because they capture tumor heterogeneity. One concern with these models is that selective pressure could lead to mutational drift and thus be an inaccurate reflection of patient tumors. Therefore, we evaluated if mutational frequency in PDX models is reflective of patient populations and if crucial mutations are stable across passages. We examined KRAS and PIK3CA gene mutations from pancreatic ductal adenocarcinoma (PDAC) (n = 30) and colorectal cancer (CRC) (n = 37) PDXs for as many as eight passages. DNA was isolated from tumors and target sequences were amplified by polymerase chain reaction. KRAS codons 12/13 and PIK3CA codons 542/545/1047 were examined using pyrosequencing. Twenty-three of 30 (77%) PDAC PDXs had KRAS mutations and one of 30 (3%) had PIK3CA mutations. Fifteen of 37 (41%) CRC PDXs had KRAS mutations and three of 37 (8%) had PIK3CA mutations. Mutations were 100 per cent preserved across passages. We found that the frequency of KRAS (77%) and PIK3CA (3%) mutations in PDAC PDX was similar to frequencies in patient tumors (71 to 100% KRAS, 0 to 11% PIK3CA). Similarly, KRAS (41%) and PIK3CA (8%) mutations in CRC PDX closely paralleled patient tumors (35 to 51% KRAS, 12 to 21% PIK3CA). The accurate mirroring and stability of genetic changes in PDX models compared with patient tumors suggest that these models are good preclinical surrogates for patient tumors.


Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2978
Author(s):  
Alice C. O’Farrell ◽  
Monika A. Jarzabek ◽  
Andreas U. Lindner ◽  
Steven Carberry ◽  
Emer Conroy ◽  
...  

Resistance to chemotherapy often results from dysfunctional apoptosis, however multiple proteins with overlapping functions regulate this pathway. We sought to determine whether an extensively validated, deterministic apoptosis systems model, ‘DR_MOMP’, could be used as a stratification tool for the apoptosis sensitiser and BCL-2 antagonist, ABT-199 in patient-derived xenograft (PDX) models of colorectal cancer (CRC). Through quantitative profiling of BCL-2 family proteins, we identified two PDX models which were predicted by DR_MOMP to be sufficiently sensitive to 5-fluorouracil (5-FU)-based chemotherapy (CRC0344), or less responsive to chemotherapy but sensitised by ABT-199 (CRC0076). Treatment with ABT-199 significantly improved responses of CRC0076 PDXs to 5-FU-based chemotherapy, but showed no sensitisation in CRC0344 PDXs, as predicted from systems modelling. 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) scans were performed to investigate possible early biomarkers of response. In CRC0076, a significant post-treatment decrease in mean standard uptake value was indeed evident only in the combination treatment group. Radiomic CT feature analysis of pre-treatment images in CRC0076 and CRC0344 PDXs identified features which could phenotypically discriminate between models, but were not predictive of treatment responses. Collectively our data indicate that systems modelling may identify metastatic (m)CRC patients benefitting from ABT-199, and that 18F-FDG-PET could independently support such predictions.


Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1321 ◽  
Author(s):  
Inoue ◽  
Deem ◽  
Kopetz ◽  
Heffernan ◽  
Draetta ◽  
...  

Our poor understanding of the intricate biology of cancer and the limited availability of preclinical models that faithfully recapitulate the complexity of tumors are primary contributors to the high failure rate of novel therapeutics in oncology clinical studies. To address this need, patient-derived xenograft (PDX) platforms have been widely deployed and have reached a point of development where we can critically review their utility to model and interrogate relevant clinical scenarios, including tumor heterogeneity and clonal evolution, contributions of the tumor microenvironment, identification of novel drugs and biomarkers, and mechanisms of drug resistance. Colorectal cancer (CRC) constitutes a unique case to illustrate clinical perspectives revealed by PDX studies, as they overcome limitations intrinsic to conventional ex vivo models. Furthermore, the success of molecularly annotated "Avatar" models for co-clinical trials in other diseases suggests that this approach may provide an additional opportunity to improve clinical decisions, including opportunities for precision targeted therapeutics, for patients with CRC in real time. Although critical weaknesses have been identified with regard to the ability of PDX models to predict clinical outcomes, for now, they are certainly the model of choice for preclinical studies in CRC. Ongoing multi-institutional efforts to develop and share large-scale, well-annotated PDX resources aim to maximize their translational potential. This review comprehensively surveys the current status of PDX models in translational CRC research and discusses the opportunities and considerations for future PDX development.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hua Sun ◽  
Song Cao ◽  
R. Jay Mashl ◽  
Chia-Kuei Mo ◽  
Simone Zaccaria ◽  
...  

AbstractDevelopment of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs’ recapitulation of human tumors.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Peter W. Eide ◽  
Seyed H. Moosavi ◽  
Ina A. Eilertsen ◽  
Tuva H. Brunsell ◽  
Jonas Langerud ◽  
...  

AbstractGene expression-based subtypes of colorectal cancer have clinical relevance, but the representativeness of primary tumors and the consensus molecular subtypes (CMS) for metastatic cancers is not well known. We investigated the metastatic heterogeneity of CMS. The best approach to subtype translation was delineated by comparisons of transcriptomic profiles from 317 primary tumors and 295 liver metastases, including multi-metastatic samples from 45 patients and 14 primary-metastasis sets. Associations were validated in an external data set (n = 618). Projection of metastases onto principal components of primary tumors showed that metastases were depleted of CMS1-immune/CMS3-metabolic signals, enriched for CMS4-mesenchymal/stromal signals, and heavily influenced by the microenvironment. The tailored CMS classifier (available in an updated version of the R package CMScaller) therefore implemented an approach to regress out the liver tissue background. The majority of classified metastases were either CMS2 or CMS4. Nonetheless, subtype switching and inter-metastatic CMS heterogeneity were frequent and increased with sampling intensity. Poor-prognostic value of CMS1/3 metastases was consistent in the context of intra-patient tumor heterogeneity.


2021 ◽  
Author(s):  
Sarah Elizabeth Woodfield ◽  
Roma H. Patel ◽  
Samuel R. Larson ◽  
Andrew Badachhape ◽  
Rohit K. Srivastava ◽  
...  

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