Prolonged response to treatment based on cell-free DNA analysis and molecular profiling in three patients with metastatic cancer: a case series

Precision Oncology: Evolving Clinical Trials across Tumor Types

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.

Challenges and opportunities associated with the MD Anderson IMPACT2 randomized study in precision oncology

We investigated the challenges of conducting IMPACT2, an ongoing randomized study that evaluates molecular testing and targeted therapy (ClinicalTrials.gov: NCT02152254). Patients with metastatic cancer underwent tumor profiling and were randomized between the two arms when eligibility criteria were met (Part A). In Part B, patients who declined randomization could choose the study arm. In Part A, 69 (21.8%) of 317 patients were randomized; 78.2% were not randomized because of non-targetable alterations (39.8%), unavailability of clinical trial (21.8%), other reasons (12.6%), or availability of US Food and Drug Administration (FDA)-approved drugs for the indication (4.1%). In Part B, 32 (20.4%) of 157 patients were offered randomization; 16 accepted and 16 selected their treatment arm; 79.0% were not randomized (patient’s/physician’s choice, 29.3%; treatment selection prior to genomic reports, 16.6%; worsening performance status/death, 12.7%; unavailability of clinical trials, 6.4%; other, 6.4%; non-targetable alterations, 5.7%; or availability of FDA-approved drugs for the indication, 1.9%). In conclusion, although randomized controlled trials have been considered the gold standard for drug development, the execution of randomized trials in precision oncology in the advanced metastatic setting is complicated. We encountered various challenges conducting the IMPACT2 study, a large precision oncology trial in patients with diverse solid tumor types. The adaptive design of IMPACT2 enables patient randomization despite the continual FDA approval of targeted therapies, the evolving tumor biomarker landscape, and the plethora of investigational drugs. Outcomes for randomized patients are awaited.

Clinical trial design in the era of precision medicine

Recent rapid biotechnological breakthroughs have led to the identification of complex and unique molecular features that drive malignancies. Precision medicine has exploited next-generation sequencing and matched targeted therapy/immunotherapy deployment to successfully transform the outlook for several fatal cancers. Tumor and liquid biopsy genomic profiling and transcriptomic, immunomic, and proteomic interrogation can now all be leveraged to optimize therapy. Multiple new trial designs, including basket and umbrella trials, master platform trials, and N-of-1 patient-centric studies, are beginning to supplant standard phase I, II, and III protocols, allowing for accelerated drug evaluation and approval and molecular-based individualized treatment. Furthermore, real-world data, as well as exploitation of digital apps and structured observational registries, and the utilization of machine learning and/or artificial intelligence, may further accelerate knowledge acquisition. Overall, clinical trials have evolved, shifting from tumor type-centered to gene-directed and histology-agnostic trials, with innovative adaptive designs and personalized combination treatment strategies tailored to individual biomarker profiles. Some, but not all, novel trials now demonstrate that matched therapy correlates with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To further improve the precision medicine paradigm, the strategy of matching drugs to patients based on molecular features should be implemented earlier in the disease course, and cancers should have comprehensive multi-omic (genomics, transcriptomics, proteomics, immunomic) tumor profiling. To overcome cancer complexity, moving from drug-centric to patient-centric individualized combination therapy is critical. This review focuses on the design, advantages, limitations, and challenges of a spectrum of clinical trial designs in the era of precision oncology.

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis

Background

Necroptosis, a form of programmed cell death, is increasingly being investigated for its controversial role in tumorigenesis and progression. Necroptosis suppresses tumor formation and tumor development by killing tumor cells; however, the necrotic cells also promote tumor formation and tumor development via the immunosuppressive effect of necroptosis and inflammatory response caused by cytokine release. Thus, the exact mechanism of necroptosis in pan-cancer remains unknown.

Methods

The data of 11,057 cancer samples were downloaded from the TCGA database, along with clinical information, tumor mutation burden, and microsatellite instability information of the corresponding patients. We used the TCGA data in a pan-cancer analysis to identify differences in mRNA level as well as single nucleotide variants, copy number variants, methylation profiles, and genomic signatures of miRNA-mRNA interactions. Two drug datasets (from GDSC, CTRP) were used to evaluate drug sensitivity and resistance against necroptosis genes.

Results

Necroptosis genes were aberrantly expressed in various cancers. The frequency of necroptosis gene mutations was highest in lung squamous cell carcinoma. Furthermore, the correlation between necroptosis gene expression in the tumor microenvironment and immune cell infiltration varied for different cancers. High necroptosis gene expression was found to correlate with NK, Tfh, Th1, CD8_T, and DC cells. These can therefore be used as biomarkers to predict prognosis. By matching gene targets with drugs, we identified potential candidate drugs.

Conclusion

Our study showed the genomic alterations and clinical features of necroptosis genes in 33 cancers. This may help clarify the link between necroptosis and tumorigenesis. Our findings may also provide new approaches for the clinical treatment of cancer.