Abstract Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor in adults. Despite several investigations in this field, maximal safe resection followed by chemoradiotherapy and adjuvant temozolomide with or without tumor-treating fields remains the standard of care with poor survival outcomes. Many endeavors have failed to make a dramatic change in the outcomes of GBM patients. This study aimed to review the available strategies for newly diagnosed GBM in the neoadjuvant setting, which have been mainly neglected in contrast to other solid tumors.

State of the neoadjuvant therapy for glioblastoma multiforme—Where do we stand?

The identification of the cancer driver genes is essential for personalized therapy. The mutation frequency of most driver genes is in the middle (2-20%) or even lower range, which makes it difficult to find the driver genes with low-frequency mutations. Other forms of genomic aberrations, such as copy number variations (CNVs) and epigenetic changes, may also reflect cancer progression. In this work, a method for identifying the potential cancer driver genes (iPDG) based on molecular data integration is proposed. DNA copy number variation, somatic mutation, and gene expression data of matched cancer samples are integrated. In combination with the method of iKEEG, the "key genes" of cancer are identified, and the change in their expression levels is used for auxiliary evaluation of whether the mutated genes are potential drivers. For a mutated gene, the concept of mutational effect is defined, which takes into account the effects of copy number variation, mutation gene itself, and its neighbor genes. The method mainly includes two steps: the first step is data preprocessing. First, DNA copy number variation and somatic mutation data are integrated. Then, the integrated data are mapped to a given interaction network, and the diffusion kernel is used to form the mutation effect matrix. The second step is to obtain the key genes by using the iKGGE method, and construct the connection matrix by means of the gene expression data of the key genes and mutation impact matrix of the mutated genes. Experiments on TCGA breast cancer and Glioblastoma multiforme datasets demonstrate that iPDG is effective not only to identify the known cancer driver genes but also to discover the rare potential driver genes. When measured by functional enrichment analysis, we find that these genes are clearly associated with these two types of cancers.

A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration.

We retrospectively examined the potential effect on overall survival (OS) of delaying radiotherapy to administer neoadjuvant therapy in unresected glioblastoma patients. We compared OS in 119 patients receiving neoadjuvant therapy followed by standard treatment (NA group) and 96 patients receiving standard treatment without neoadjuvant therapy (NoNA group). The MaxStat package of R identified the optimal cut-off point for waiting time to radiotherapy. OS was similar in the NA and NoNA groups. Median waiting time to radiotherapy after surgery was 13 weeks for the NA group and 4.2 weeks for the NoNA group. The longest OS was attained by patients who started radiotherapy after 12 weeks and the shortest by patients who started radiotherapy within 4 weeks (12.3 vs 6.6 months) (P = 0.05). OS was 6.6 months for patients who started radiotherapy before the optimal cutoff of 6.43 weeks and 19.1 months for those who started after this time (P = 0.005). Patients who completed radiotherapy had longer OS than those who did not, in all 215 patients and in the NA and NoNA groups (P = 0.000). In several multivariate analyses, completing radiotherapy was a universally favorable prognostic factor, while neoadjuvant therapy was never identified as a negative prognostic factor. In our series of unresected patients receiving neoadjuvant treatment, in spite of the delay in starting radiotherapy, OS was not inferior to that of a similar group of patients with no delay in starting radiotherapy.

Delay in starting radiotherapy due to neoadjuvant therapy does not worsen survival in unresected glioblastoma patients

MGMT methylated (Met) patients (p) with glioblastoma (GBM) have a better prognosis with an earlier response (ER) than those who have a late response or pseudoprogression (LR/PsP). Results of the Gliocat study

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Delay in starting radiotherapy due to neoadjuvant therapy does not worsen survival in unresected glioblastoma patients

MGMT methylated (Met) patients (p) with glioblastoma (GBM) have a better prognosis with an earlier response (ER) than those who have a late response or pseudoprogression (LR/PsP). Results of the Gliocat study