Snail promotes resistance to enzalutamide through regulation of androgen receptor activity in prostate cancer

The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.

Background: Osteosarcoma is a rare but aggressive bone cancer that occurs primarily in children. Like other rare cancers, treatment advances for osteosarcoma have stagnated, with little improvement in survival for the past several decades. Developing new treatments has been hampered by extensive genomic heterogeneity and limited access to patient samples to study the biology of this complex disease. Methods: To overcome these barriers, we combined the power of comparative oncology with patient-derived models of cancer and high-throughput chemical screens in a cross-species drug discovery pipeline. Results: Coupling in vitro high-throughput drug screens on low-passage and established cell lines with in vivo validation in patient-derived xenografts we identify the proteasome and CRM1 nuclear export pathways as therapeutic sensitivities in osteosarcoma, with dual inhibition of these pathways inducing synergistic cytotoxicity. Conclusions: These collective efforts provide an experimental framework and set of new tools for osteosarcoma and other rare cancers to identify and study new therapeutic vulnerabilities.

Epithelial-to-mesenchymal transition (EMT) and its reverse mesenchymal-to-epithelial transition (MET) have been often suggested to play crucial roles in metastatic dissemination of carcinomas. Recent studies have revealed that neither of these processes is binary. Instead, carcinoma cells often exhibit a spectrum of epithelial/mesenchymal phenotype(s). While epithelial-mesenchymal plasticity has been observed pre-clinically and clinically, whether any of these phenotypic transitions are indispensable for metastatic outgrowth remains an unanswered question. Here, we focus on epithelial-mesenchymal plasticity in metastatic dissemination and propose alternative mechanisms for successful dissemination and metastases beyond the traditional EMT-MET view. We highlight multiple hypotheses that can help reconcile conflicting observations, and outline the next set of key questions that can offer valuable insights into mechanisms of metastasis in multiple tumor models.

Cancer drug discovery is an inefficient process, with more than 90% of newly-discovered therapies failing to gain regulatory approval. Patient-derived models of cancer offer a promising new approach to identifying personalized treatments; however, for rare cancers, such as sarcomas, access to patient samples can be extremely limited, which precludes development of patient-derived models. To address the limited access to patient samples, we have turned to pet dogs with naturally-occurring sarcomas. Although sarcomas make up less than 1% of all cancers in humans, sarcomas represent at least 15% of all cancers in dogs. Dogs with naturally-occurring sarcomas also have intact immune systems, an accelerated pace of cancer progression, and share the same environment as humans, making them ideal models that bridge key gaps between mouse models and human sarcomas. Here, we develop a framework for a personalized medicine pipeline that integrates drug screening, validation, and genomics to identify new therapies. We tested this paradigm through the study of a pet dog, Teddy, who presented with six synchronous leiomyosarcomas. By integrating patient-derived cancer models, in vitro drug screens, and in vivo validation we identified proteasome inhibitors as a potential therapy for Teddy. After showing an initial response to the proteasome inhibitor, bortezomib, Teddy developed rapid resistance, and tumor growth resumed. Whole exome sequencing revealed substantial genetic heterogeneity across Teddy’s multiple recurrent tumors and metastases, suggesting that intra-patient heterogeneity was responsible for the heterogeneous clinical response. Ubiquitin proteomics coupled with exome sequencing revealed multiple candidate driver mutations in proteins related to the proteasome pathway. Together, our results demonstrate how the comparative study of canine sarcomas can offer rapid insights into the process of developing personalized medicine approaches that can lead to new treatments for sarcomas in both humans and canines.

Authors: Kathryn E. Ware1,2, Santosh Gupta1,2, Jared Eng1,2, Gabor Kemeny1, Bhairavy J. Puviindran1, Wen-Chi Foo3, Lorin A. Crawford4, R. Garland Almquist1, Daniella Runyambo1, Beatrice C. Thomas1, Maya U. Sheth1, Anika Agarwal1, Mariaelena Pierobon5, Emanuel F. Petricoin5, David L. Corcoran1, Jennifer Freedman1,2, Steven R. Patierno1,2, Tian Zhang1,2, Simon Gregory1, Zoi Sychev6,