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.

Squamous cell carcinomas (SCCs) are among the most frequent solid tumors in humans. SCCs, related or not to the human papillomavirus, share common molecular features. Immunotherapies, and specifically immune checkpoint inhibitors, have been shown to improve overall survival in multiple cancer types, including SCCs. However, only a minority of patients experience a durable response with immunotherapy. Epigenetic modulation plays a major role in escaping tumor immunosurveillance and confers resistance to immune checkpoint inhibitors. Preclinical evidence suggests that modulating the epigenome might improve the efficacy of immunotherapy. We herein review the preclinical and the clinical rationale for combining immunotherapy with an epidrug, and detail the design of PEVOsq, a basket clinical trial combining pembrolizumab with vorinostat, a histone deacetylase inhibitor, in patients with SCCs of different locations. Sequential blood and tumor sampling will be collected in order to identify predictive and pharmacodynamics biomarkers of efficacy of the combination. We also present how clinical and biological data will be managed with the aim to enable the development of a prospective integrative platform to allow secure and controlled access to the project data as well as further exploitations.

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.

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e13631 Background: In recent years, precision medicine has increasingly been integrated into routine clinical oncology care. However, interpretation of large amounts of biological information can be challenging in daily clinical practice settings. We previously demonstrated that digital drug assignment (DDA), an artificial intelligence-based computational method that ranks associated targeted therapies based on the totality of available genomic data rather than matching one drug to one biomarker, was predictive of relative benefit of the agents as used in the SHIVA01 trial (Petak et al., 2021, doi: 10.1038/s41698-021-00191-2). Here, we collected real-world clinical outcome data from patients with solid tumors who received decision support where DDA was integrated to aid a molecular tumor board (MTB) and investigated the effectiveness of recommended therapies. Methods: Between 2016 and 2021, 208 patients with solid tumors were involved in our precision oncology program (69% gastrointestinal, 14% gynecological, 9% breast, 8% other tumors). In most cases targeted panel sequencing was carried out (50-gene panel: 51%, 591-gene panel: 45%). Classification of the detected alterations and therapeutic ranking was performed by the DDA software tool as previously described. The output was assessed by the MTB that provided a strategy to the clinicians who made the final therapy decisions. DDA output scores were supportive for all molecularly targeted agents (MTAs) administered. Treatment lines after decision support with MTAs were compared with lines of standard agents (STs) retrospectively and evaluated by best overall response, log-rank test of progression-free survival (PFS), and durable clinical benefit (DCB). Results: Of all 208 patients, 81 were treated with MTAs and 59 with STs after DDA, implying 114 and 97 therapeutic lines, respectively. Disease control rate (DCR) of the suggested MTAs was 40% (0 CR, 9 PR, 37 SD, 43 PD, 25 lost to follow-up), while DCR for STs was 25% (1 CR, 3 PR, 20 SD, 48 PD, 25 lost) ( p=0.025). PFS value was available for 88 MTA and 71 ST lines. Log-rank test revealed a significantly longer median PFS for MTAs than STs: 4 vs. 2.5 months, respectively ( p<0.001). Proportion of patients who reached DCB of 6 months was 32% in the MTA and 11% in the ST group ( p<0.001). DCB at 9 months was 19% and 5% in the MTA and ST cohorts, respectively ( p=0.003). Conclusions: This study revealed clear clinical benefit for targeted therapies over conventional treatments used in daily practice, where MTB was aided by a computational tool to interpret complex molecular data. After the previous clinical validation, here we show that integration of DDA into real-world clinical setting is feasible and safe. The results fit well into the trend of targeted therapies becoming a routine procedure and underscore the importance of precision oncology decision support by advanced computational tools.