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Treatment with anaplastic lymphoma kinase (ALK) inhibitors significantly improves outcome for non-small-cell lung cancer (NSCLC) patients with ALK-rearranged tumors. However, clinical resistance typically develops over time and, in the majority of cases, resistance mechanisms are ALK-independent. We generated tumor cell cultures from multiple regions of an ALK-rearranged clinical tumor specimen and deployed functional drug screens to identify modulators of ALK-inhibitor response. This identified a role for PI3Kβ and EGFR inhibition in sensitizing the response regulating resistance to ALK inhibition. Inhibition of ALK elicited activation of EGFR, and subsequent MAPK and PI3K-AKT pathway reactivation. Sensitivity to ALK targeting was enhanced by inhibition or knockdown of PI3Kβ. In ALK-rearranged primary cultures, the combined inhibition of ALK and PI3Kβ prevented the EGFR-mediated ALK-inhibitor resistance, and selectively targeted the cancer cells. The combinatorial effect was seen also in the background of TP53 mutations and in epithelial-to-mesenchymal transformed cells. In conclusion, combinatorial ALK- and PI3Kβ-inhibitor treatment carries promise as a treatment for ALK-rearranged NSCLC., (© 2022 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Drug sensitivity data acquired from solid tumor-derived cultures are often unsuitable for personalized treatment guidance due to the lengthy turnaround time. Here, we present a protocol for determining ex vivo drug sensitivities using fresh uncultured human lung tumor-derived EpCAM + epithelial cells (FUTCs). We describe steps for drug testing in FUTCs to identify tumor cell-selective single or combination therapy in 72 h of sample processing. The FUTC-based approach can also be used to predict in vivo resistance to known targeted therapies. For complete details on the use and execution of this protocol, please refer to Talwelkar et al. (2021)., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Preclinical tumor models with native tissue microenvironments provide essential tools to understand how heterogeneous tumor phenotypes relate to drug response. Here we present syngeneic graft models of aggressive, metastasis-prone histopathology-specific NSCLC tumor types driven by KRAS mutation and loss of LKB1 (KL): adenosquamous carcinoma (ASC) and adenocarcinoma (AC). We show that subcutaneous injection of primary KL; ASC cells results in squamous cell carcinoma (SCC) tumors with high levels of stromal infiltrates, lacking the source heterogeneous histotype. Despite forming subcutaneous tumors, intravenously injected KL;AC cells were unable to form lung tumors. In contrast, intravenous injection of KL;ASC cells leads to their lung re-colonization and lesions recapitulating the mixed AC and SCC histopathology, tumor immune suppressive microenvironment and oncogenic signaling profile of source tumors, demonstrating histopathology-selective phenotypic dominance over genetic drivers. Pan-ERBB inhibition increased survival, while selective ERBB1/EGFR inhibition did not, suggesting a role of the ERBB network crosstalk in resistance to ERBB1/EGFR. This immunocompetent NSCLC lung colonization model hence phenocopies key properties of the metastasis-prone ASC histopathology, and serves as a preclinical model to dissect therapy responses and metastasis-associated processes., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2022. Published by The Company of Biologists Ltd.)

Functional profiling of a cancer patient's tumor cells holds potential to tailor personalized cancer treatment. Here, we report the utility of fresh uncultured tumor-derived EpCAM + epithelial cells (FUTCs) for ex vivo drug-response interrogation. Analysis of murine Kras mutant FUTCs demonstrates pharmacological and adaptive signaling profiles comparable to subtype-matched cultured cells. By applying FUTC profiling on non-small-cell lung cancer patient samples, we report robust drug-response data in 19 of 20 cases, with cells exhibiting targeted drug sensitivities corresponding to their oncogenic drivers. In one of these cases, an EGFR mutant lung adenocarcinoma patient refractory to osimertinib, FUTC profiling is used to guide compassionate treatment. FUTC profiling identifies selective sensitivity to disulfiram and the combination of carboplatin plus etoposide, and the patient receives substantial clinical benefit from treatment with these agents. We conclude that FUTC profiling provides a robust, rapid, and actionable assessment of personalized cancer treatment options., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)