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

Opportunities and Challenges of Small Molecule Inhibitors in Glioblastoma Treatment: Lessons Learned from Clinical Trials

Glioblastoma (GBM) is the most prevalent central nervous system tumour (CNS). Patients with GBM have a dismal prognosis of 15 months, despite an intensive treatment schedule consisting of surgery, chemoradiation and concurrent chemotherapy. In the last decades, many trials have been performed investigating small molecule inhibitors, which target specific genes involved in tumorigenesis. So far, these trials have been unsuccessful, and standard of care for GBM patients has remained the same since 2005. This review gives an overview of trials investigating small molecule inhibitors on their own, combined with chemotherapy or other small molecule inhibitors. We discuss possible resistance mechanisms in GBM, focussing on intra- and intertumoral heterogeneity, bypass mechanisms and the influence of the tumour microenvironment. Moreover, we emphasise how combining inhibitors can help overcome these resistance mechanisms. We also address strategies for improving trial outcomes through modifications to their design. In summary, this review aims to elucidate different resistance mechanisms against small molecule inhibitors, highlighting their significance in the search for novel therapeutic combinations to improve the overall survival of GBM patients.

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

BACKGROUND

Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts.

RESULTS

We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining.

CONCLUSIONS

A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

Molecular tumour boards - current and future considerations for precision oncology

Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can provide useful information. However, greater funding, resources and expertise are needed to ensure the sustainability of MTBs and expand their outreach to underserved populations. Harmonization between practice and policy will be required to optimally implement precision oncology. Herein, we discuss the evolving role of MTBs and current and future considerations for their use in precision oncology.

Genomic Profiling and Clinical Outcomes of Targeted Therapies in Adult Patients with Soft Tissue Sarcomas

Genomic profiling has improved our understanding of the pathogenesis of different cancers and led to the development of several targeted therapies, especially in epithelial tumors. In this review, we focus on the clinical utility of next-generation sequencing (NGS) to inform therapeutics in soft tissue sarcoma (STS). The role of NGS is still controversial in patients with sarcoma, given the low mutational burden and the lack of recurrent targetable alterations in most of the sarcoma histotypes. The clinical impact of genomic profiling in STS has not been investigated prospectively. A limited number of retrospective, mainly single-institution, studies have addressed this issue using various NGS technologies and platforms and a variety of criteria to define a genomic alteration as actionable. Despite the detailed reports on the different gene mutations, fusions, or amplifications that were detected, data on the use and efficacy of targeted treatment are very scarce at present. With the exception of gastrointestinal stromal tumors (GISTs), these targeted therapies are administered either through off-label prescription of an approved drug or enrollment in a matched clinical trial. Based mainly on anecdotal reports, the outcome of targeted therapies in the different STS histotypes is discussed. Prospective studies are warranted to assess whether genomic profiling improves the management of STS patients.

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.