We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. Significance: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB. See related commentary by Letai, p. 290. This article is highlighted in the In This Issue feature, p. 275

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75–78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET, and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

Antibody phage display technology is well established and widely used for selecting specific antibodies against desired targets. Using conventional manual methods, it is laborious to perform multiple selections with different antigens simultaneously. Furthermore, manual screening of the positive clones requires much effort. The authors describe optimized and automated procedures of these processes using a magnetic bead processor for the selection and a robotic station for the screening step. Both steps are performed in a 96-well microplate format. In addition, adopting the antibody phage display technology to automated platform polyethylene glycol precipitation of the enriched phage pool was unnecessary. For screening, an enzyme-linked immunosorbent assay protocol suitable for a robotic station was developed. This system was set up using human γ-globulin as a model antigen to select antibodies from a VTT naive human single-chain antibody (scFv) library. In total, 161 γ-globulin-selected clones were screened, and according to fingerprinting analysis, 9 of the 13 analyzed clones were different. The system was further tested using testosterone bovine serum albumin (BSA) and β-estradiol-BSA as antigens with the same library. In total, 1536 clones were screened from 4 rounds of selection with both antigens, and 29 different testosterone-BSA and 23 β-estradiol-BSA binding clones were found and verified by sequencing. This automated antibody phage display procedure increases the throughput of generating wide panels of target-binding antibody candidates and allows the selection and screening of antibodies against several different targets in parallel with high efficiency. (Journal of Biomolecular Screening 2009:282-293)

Machine learning and single-cell data inform personalized drug combinations that selectively co-inhibit cancer cell populations.

Viral diseases remain serious threats to public health because of the shortage of effective means of control. To combat the surge of viral diseases, new treatments are urgently needed. Here we show that small-molecules, which inhibit cellular anti-apoptotic Bcl-2 proteins (Bcl-2i), induced the premature death of cells infected with different RNA or DNA viruses, whereas, at the same concentrations, no toxicity was observed in mock-infected cells. Moreover, these compounds limited viral replication and spread. Surprisingly, Bcl-2i also induced the premature apoptosis of cells transfected with viral RNA or plasmid DNA but not of mock-transfected cells. These results suggest that Bcl-2i sensitizes cells containing foreign RNA or DNA to apoptosis. A comparison of the toxicity, antiviral activity, and side effects of six Bcl-2i allowed us to select A-1155463 as an antiviral lead candidate. Thus, our results pave the way for the further development of Bcl-2i for the prevention and treatment of viral diseases.

In vitro cancer drug testing carries a low predictive value. We developed the human leiomyoma–derived matrix “Myogel” to better mimic the human tumor microenvironment (TME). We hypothesized that Myogel could provide an appropriate microenvironment for cancer cells, thereby allowing more in vivo–relevant drug testing. We screened 19 anticancer compounds, targeting the epidermal growth factor receptor (EGFR), MEK, and PI3K/mTOR on 12 head and neck squamous cell carcinoma (HNSCC) cell lines cultured on plastic, mouse sarcoma–derived Matrigel (MSDM), and Myogel. We applied a high-throughput drug screening assay under five different culturing conditions: cells in two-dimensional (2D) plastic wells and on top or embedded in Matrigel or Myogel. We then compared the efficacy of the anticancer compounds to the response rates of 19 HNSCC monotherapy clinical trials. Cancer cells on top of Myogel responded less to EGFR and MEK inhibitors compared to cells cultured on plastic or Matrigel. However, we found a similar response to the PI3K/mTOR inhibitors under all culturing conditions. Cells grown on Myogel more closely resembled the response rates reported in EGFR-inhibitor monotherapy clinical trials. Our findings suggest that a human tumor matrix improves the predictability of in vitro anticancer drug testing compared to current 2D and MSDM methods.