Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

Rheumatoid Arthritis is a chronic disease leading to decreased quality of life with a rather variable response rate to Disease Modifying Anti Rheumatic Drugs. Methotrexate (MTX) is the gold standard therapy in Rheumatoid Arthritis. The Multidrug resistance Related Protein and Multi Drug Resistance protein 1, also called P-glycoprotein-170 transporters can alter the intracellular concentration of different drugs. Methotrexate is an MRP1 substrate and thus the functional activity of MRP1 might have a clinical impact on the efficiency of the Methotrexate-therapy in Rheumatoid Arthritis. We have compared the functional Multidrug Activity Factors (MAF) of the MDR1 and MRP1 transporters of Peripheral Blood Leukocytes of 59 Rheumatoid Arthritis patients with various response rate to MTX-therapy (MTX-responder, MTX-resistant and MTX-intolerant RA-groups) and 47 non-RA controls in six different leukocyte subpopulations (neutrophil leukocytes, monocytes, lymphocytes, CD4+, CD8+ and CD19+ cells). There was a decreased MAF of RA patients compared to non- Rheumatoid Arthritis patients and healthy controls in the leukocyte subpopulations. There was a significant difference between the MAF values of the MTX-responder and MTX intolerant groups. But we have not found significant differences between the MAF values of the MTX-responder and MTX-resistant Rheumatoid Arthritis -groups. Our results suggest that MDR1 and MRP1 functional activity does not seem to affect the response rate to MTX-therapy of Rheumatoid Arthritis-patients, but it might be useful in predicting MTX-side effects. We have demonstrated the decreased functional MDR-activity on almost 60 Rheumatoid Arthritis patients, which can be interpreted as a sign of the immune-suppressive effect of the MTX-treatment.

The poor prognosis of head and neck squamous cell carcinoma (HNSCC) is partly due to the lack of reliable predictive markers. Connexin 43 (Cx43) protein and its cell-communication channels have been assigned tumor suppressor functions while the anti-apoptotic Bcl-2 (B-cell lymphoma-2) protein has been associated with negative prognostic significance in cancer. This study aimed to test the role of Cx43 protein on Bcl-2 expression, tumor progression and response to taxane-based treatment in HNSCC. Human papillomavirus (HPV) negative HNSCC cell lines were tested for paclitaxel sensitivity through measuring apoptosis induction, cell viability and changes in Cx43 and Bcl-2 levels using flow cytometry, cell viability assay, immunocytochemistry and western blot. Inhibition of Cx43 expression using siRNA increased Bcl-2 protein levels in SCC25 (tongue squamous cell carcinoma) cells, while forced Cx43 expression reduced Bcl-2 levels and supported paclitaxel cytotoxicity in FaDu (hypopharynx squamous cell carcinoma) cells. In vitro results were in line with protein expression and clinicopathological features tested in tissue microarray samples of HNSCC patients. Our data demonstrate that elevated Cx43 and reduced Bcl-2 levels may indicate HNSCC sensitivity to taxane-based treatments. On the contrary, silencing of the Cx43 gene GJA1 (gap junction protein alpha-1) can result in increased Bcl-2 expression and reduced paclitaxel efficiency. Clinical tumor-based analysis also confirmed the inverse correlation between Cx43 and Bcl-2 expression.

Several promising anti-cancer drug–GnRH (gonadotropin-releasing hormone) conjugates have been developed in the last two decades, although none of them have been approved for clinical use yet. Crizotinib is an effective multi-target kinase inhibitor, approved against anaplastic lymphoma kinase (ALK)- or ROS proto-oncogene 1 (ROS-1)-positive non-small cell lung carcinoma (NSCLC); however, its application is accompanied by serious side effects. In order to deliver crizotinib selectively into the tumor cells, we synthesized novel crizotinib analogues and conjugated them to a [d-Lys⁶]–GnRH-I targeting peptide. Our most prominent crizotinib–GnRH conjugates, the amide-bond-containing [d-Lys⁶(crizotinib*)]–GnRH-I and the ester-bond-containing [d-Lys⁶(MJ55*)]–GnRH-I, were able to bind to GnRH-receptor (GnRHR) and exert a potent c-Met kinase inhibitory effect. The efficacy of compounds was tested on the MET-amplified and GnRHR-expressing EBC-1 NSCLC cells. In vitro pharmacological profiling led to the conclusion that that crizotinib–GnRH conjugates are transported directly into lysosomes, where the membrane permeability of crizotinib is diminished. As a consequence of GnRHR-mediated endocytosis, GnRH-conjugated crizotinib bypasses its molecular targets—the ATP-binding site of RTKs— and is sequestered in the lysosomes. These results explained the lower efficacy of crizotinib–GnRH conjugates in EBC-1 cells, and led to the conclusion that drug escape from the lysosomes is a major challenge in the development of clinically relevant anti-cancer drug–GnRH conjugates.

Background: The utility of routine extensive molecular profiling of pediatric tumors is a matter of debate due to the high number of genetic alterations of unknown significance or low evidence and the lack of standardized and personalized decision support methods. Digital drug assignment (DDA) is a novel computational method to prioritize treatment options by aggregating numerous evidence-based associations between multiple drivers, targets, and targeted agents. DDA has been validated to improve personalized treatment decisions based on the outcome data of adult patients treated in the SHIVA01 clinical trial. The aim of this study was to evaluate the utility of DDA in pediatric oncology.Methods: Between 2017 and 2020, 103 high-risk pediatric cancer patients (< 21 years) were involved in our precision oncology program, and samples from 100 patients were eligible for further analysis. Tissue or blood samples were analyzed by whole-exome (WES) or targeted panel sequencing and other molecular diagnostic modalities and processed by a software system using the DDA algorithm for therapeutic decision support. Finally, a molecular tumor board (MTB) evaluated the results to provide therapy recommendations.Results: Of the 100 cases with comprehensive molecular diagnostic data, 88 yielded WES and 12 panel sequencing results. DDA identified matching off-label targeted treatment options (actionability) in 72/100 cases (72%), while 57/100 (57%) showed potential drug resistance. Actionability reached 88% (29/33) by 2020 due to the continuous updates of the evidence database. MTB approved the clinical use of a DDA-top-listed treatment in 56 of 72 actionable cases (78%). The approved therapies had significantly higher aggregated evidence levels (AELs) than dismissed therapies. Filtering of WES results for targeted panels missed important mutations affecting therapy selection.Conclusions: DDA is a promising approach to overcome challenges associated with the interpretation of extensive molecular profiling in the routine care of high-risk pediatric cancers. Knowledgebase updates enable automatic interpretation of a continuously expanding gene set, a “virtual” panel, filtered out from genome-wide analysis to always maximize the performance of precision treatment planning.

Background: The anaplastic lymphoma kinase (ALK) gene fusion rearrangement is a potent oncogene, accounting for 2–7% of lung adenocarcinomas, with higher incidence (17–20%) in non-smokers. ALK-positive tumors are sensitive to ALK tyrosine kinase inhibitors (TKIs), thus ALK-positive non-small-cell lung cancer (NSCLC) is currently spearheading precision medicine in thoracic oncology, with three generations of approved ALK inhibitors in clinical practice. However, these treatments are eventually met with resistance. At the molecular level, ALK-positive NSCLC is of the lowest tumor mutational burden, which possibly accounts for the high initial response to TKIs. Nevertheless, TP53 co-mutations are relatively frequent and are associated with adverse outcome of crizotinib treatment, whereas utility of next-generation ALK inhibitors in TP53-mutant tumors is still unknown. Methods: We report the case of an ALK-positive, TP53-mutant NSCLC patient with about five years survival on ALK TKIs with continued next-generation regimens upon progression. Results: The tumor showed progression on crizotinib, but long tumor control was achieved following the incremental administration of next-generation ALK inhibitors, despite lack of evident resistance mechanisms. Conclusion: TP53 status should be taken into consideration when selecting ALK-inhibitor treatment for personalized therapies. In TP53-mutant tumors, switching TKI generations may overcome treatment exhaustion even in the absence of ALK-dependent resistance mechanisms.