The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor’s biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.

Advances in molecular technologies and targeted therapeutics have accelerated the implementation of precision oncology, resulting in improved clinical outcomes in selected patients. The use of next-generation sequencing and assessments of immune and other biomarkers helps optimize patient treatment selection. In this review, selected precision oncology trials including the IMPACT, SHIVA, IMPACT2, NCI-MPACT, TAPUR, DRUP, and NCI-MATCH studies are summarized, and their challenges and opportunities are discussed. Brief summaries of the new ComboMATCH, MyeloMATCH, and iMATCH studies, which follow the example of NCI-MATCH, are also included. Despite the progress made, precision oncology is inaccessible to many patients with cancer. Some patients’ tumors may not respond to these treatments, owing to the complexity of carcinogenesis, the use of ineffective therapies, or unknown mechanisms of tumor resistance to treatment. The implementation of artificial intelligence, machine learning, and bioinformatic analyses of complex multi-omic data may improve the accuracy of tumor characterization, and if used strategically with caution, may accelerate the implementation of precision medicine. Clinical trials in precision oncology continue to evolve, improving outcomes and expediting the identification of curative strategies for patients with cancer. Despite the existing challenges, significant progress has been made in the past twenty years, demonstrating the benefit of precision oncology in many patients with advanced cancer.

Background: This was a first-in-human, phase I, dose-escalation study evaluating the safety, pharmacokinetics, and preliminary efficacy of zavondemstat (TACH101), an epigenetic targeting inhibitor of KDM4 histone demethylase, in patients with heavily pre-treated advanced/metastatic cancers. Patients and Methods: Patients received zavondemstat orally on a weekly schedule in 28-day cycles. Dose escalation followed a Bayesian optimal interval design and explored both intermittent and continuous dosing. The primary objectives were to assess safety, dose-limiting toxicities (DLTs), maximum tolerated dose (MTD), and recommended phase II dose (RP2D). Secondary objectives included pharmacokinetics and radiographic response per Response Evaluation Criteria in Solid Tumors, version 1.1. Results: Thirty patients were enrolled across 6 dose cohorts. MTD was not reached at the maximum dose tested. The most common treatment-related adverse events (TRAEs) were diarrhea (12%), fatigue (7%), decreased appetite (7%), nausea (7%), and hyponatremia (7%). All TRAEs were grade 1 or 2. No serious TRAEs or DLTs were reported. Of 23 response-evaluable patients, 10 (44%) achieved stable disease (SD). Two patients (9%) had SD ≥ 6 months, including a patient with castration-resistant prostate cancer and a patient with leiomyosarcoma. A third patient (leiomyosarcoma) receiving ongoing treatment with zavondemstat under compassionate use has had SD for 6+ months. Zavondemstat demonstrated a dose-proportional exposure profile with a half-life of about 1.5 hours. There was no to minimal drug accumulation observed. Conclusions: Zavondemstat was very well tolerated and showed encouraging preliminary clinical benefit in heavily pretreated patients with advanced cancer. Continued evaluation of zavondemstat is warranted.

Breast and ovarian cancers pose significant therapeutic challenges. We explored the synergistic cytotoxicity of histone deacetylase inhibitors (HDACis), poly(ADP-ribose) polymerase inhibitors (PARPis), and decitabine in breast (MDA-MB-231 and MCF-7) and ovarian (HEY-T30 and SKOV-3) cancer cell lines that were exposed to HDACi (panobinostat or vorinostat), PARPi (talazoparib or olaparib), decitabine, or their combinations. HDACi, PARPi, and decitabine combinations had synergistic cytotoxicity (assessed by MTT and clonogenic assays) in all cell lines (combination index < 1). Clonogenic assays confirmed the sensitivity of breast and ovarian cancer cell lines to the three-drug combinations (panobinostat, talazoparib, and decitabine; panobinostat, olaparib, and decitabine; vorinostat, talazoparib, and decitabine; vorinostat, olaparib, and decitabine). Cell proliferation was inhibited by 48–70%, and Annexin V positivity was 42–59% in all cell lines exposed to the three-drug combinations. Western blot analysis showed protein PARylation inhibition, caspase 3 and PARP1 cleavage, and c-MYC down-regulation. The three-drug combinations induced more DNA damage (increased phosphorylation of histone 2AX) than the individual drugs, impaired the DNA repair pathways, and altered the epigenetic regulation of gene expression. These results indicate that HDACi, PARPi, and decitabine combinations should be further explored in these tumor types. Further clinical validation is warranted to assess their safety and efficacy.

To overcome challenges associated with adoptive cell therapy (ACT), we developed a personalized autologous T-cell therapy program. Patients with advanced cancer with HLA-A *02:01 allele and tumor expression of PRAME, MAGEA1, MAGEA4, MAGEA8, NY-ESO-1, COL6A3 exon 6, MXRA5, and/or MMP1 underwent leukapheresis and T-cell product manufacturing. Patients received lymphodepletion, IMA101 infusion and interleukin 2 for 14 days. Of 214 screened patients, 14 were treated (6, IMA101; 8, IMA101 and atezolizumab). The most common adverse events were cytokine release syndrome (G1, n = 6; G2, n = 4) and cytopenia. At 6 weeks, 12 (85.7%) patients had stable disease. Three patients had prolonged disease stabilization for 12.9, 7.3, and 13.7 months, respectively. The median progression-free survival and overall survival were 3.4 months and 9.4 months, respectively. Target-specific T cells expanded to constitute up to 78.7% of CD8+ cells. In conclusion, IMA101 was feasible and well tolerated, leveraging the potential of multi-targeted ACT that warrants further investigation.