Temozolomide

SignatureAnalyzer uses a Bayesian variant of NMF that infers the number of signatures through the automatic relevance determination technique and delivers highly interpretable and sparse representations for both signature profiles and attributions that strike a balance between data fitting and model complexity. Further details of the actual implementation of the computational approach have previously been published^{9 (link),27 (link),64 (link)}. SignatureAnalyzer was applied by using a two-step signature extraction strategy using 1,536 pentanucleotide contexts for SBSs, 83 indel features and 78 DBS features. In addition to the separate extraction of SBS, indel and DBS signatures, we performed a ‘COMPOSITE’ signature extraction based on all 1,697 features (1,536 SBS + 78 DBS + 83 indel). For SBSs, the 1,536 SBS COMPOSITE signatures are preferred; for DBSs and indels, the separately extracted signatures are preferred.
In step 1 of the two-step extraction process, global signature extraction was performed for the samples with a low mutation burden (n = 2,624). These excluded hypermutated tumours: those with putative polymerase epsilon (POLE) defects or mismatch repair defects (microsatellite instable tumours), skin tumours (which had intense UV-light mutagenesis) and one tumour with temozolomide (TMZ) exposure. Because the underlying algorithm of SignatureAnalyzer performs a stochastic search, different runs can produce different results. In step 1, we ran SignatureAnalyzer 10 times and selected the solution with the highest posterior probability. In step 2, additional signatures unique to hypermutated samples were extracted (again selecting the highest posterior probability over ten runs) while allowing all signatures found in the samples with low mutation burden, to explain some of the spectra of hypermutated samples. This approach was designed to minimize a well-known ‘signature bleeding’ effect or a bias of hyper- or ultramutated samples on the signature extraction. In addition, this approach provided information about which signatures are unique to the hypermutated samples, which was later used when attributing signatures to samples.
A similar strategy was used for signature attribution: we performed a separate attribution process for low- and hypermutated samples in all COMPOSITE, SBS, DBS and indel signatures. For downstream analyses, we preferred to use the COMPOSITE attributions for SBSs and the separately calculated attributions for DBSs and indels. Signature attribution in samples with a low mutation burden was performed separately in each tumour type (for example, Biliary–AdenoCA, Bladder–TCC, Bone–Osteosarc, and so on). Attribution was also performed separately in the combined microsatellite instable tumours (n = 39), POLE (n = 9), skin melanoma (n = 107) and TMZ-exposed samples (syn11738314). In both groups, signature availability (which signatures were active, or not) was primarily inferred through the automatic relevance determination process applied to the activity matrix H only, while fixing the signature matrix W. The attribution in samples with a low mutation burden was performed using only signatures found in the step 1 of the signature extraction. Two additional rules were applied in SBS signature attribution to enforce biological plausibility and minimize a signature bleeding: (i) allow SBS4 (smoking signature) only in lung, head and neck cases; and (ii) allow SBS11 (TMZ signature) in a single GBM sample. This was enforced by introducing a binary, signature-by-sample signature indicator matrix Z (1, allowed; 0, not allowed), which was multiplied by the H matrix in every multiplication update of H. No additional rules were applied to indel or DBS signature attributions, except that signatures found in hypermutated samples were not allowed in samples with a low mutation burden.

To determine the cytotoxicity of chemotherapy drugs, cell growth/viability was measured using an acid phosphatase assay; 1.5–3 × 10³ cells were seeded in flat-bottomed 96-well plates and incubated overnight prior to addition of drug. Chemotherapeutics were obtained from St Vincent’s University Hospital, Dublin, Ireland. Lapatinib was purchased from Sequoia. Temozolomide was obtained from the National Cancer Institute. Other inhibitors and modulators were obtained from Sigma. Drug-free controls were included in each assay. Plates were incubated for a further 5 (HCC1954, Malme-3M and HT144) or 7 days (H1299 and H460) at 37°C in a humidified atmosphere with 5% CO₂ and cell viability was determined using an acid phosphatase assay (97 (link)). Growth of drug-treated cells was calculated relative to control untreated cells in biological triplicate.

A retrospective analysis was made of 26 patients who were newly diagnosed or had recurrent high-grade glioma treated in the Department of Radiotherapy of Jinling Hospital from 2017 to 2022.
Inclusion criteria: 1) Karnofsky Performance Scale (KPS) ≥60; 2) age ≥ 18 years old; 3) pathologically confirmed as World Health Organization Level IV glioblastoma (GBM); 4) recurrence after magnetic resonance imaging (MRI) review (including MR spectroscopy (MRS) and perfusion imaging) is assessed by surgeons, radiologists, and oncologists according to Response Assessment in Neuro-Oncology (RANO) criteria; 5) measurable lesions; 6) good bone marrow, liver, and kidney functions; 7) anlotinib combined with dose-intensive temozolomide (TMZ) in the treatment of relapse; 8) previous standard concurrent chemoradiotherapy (CCRT) and adjuvant chemotherapy (AC).
Exclusion criteria: 1) complications of other malignant tumors or serious diseases; 2) patients with hypertension whose blood pressure still cannot be reduced to the normal range after treatment with antihypertensive drugs, patients with grade I and above myocardial ischemia or myocardial infarction, and arrhythmias (including QT interval ≥440 ms), patients with grade II cardiac insufficiency, and patients with arteriovenous thrombosis, such as cerebrovascular accident (including transient ischemic attack), deep vein thrombosis, and pulmonary embolism within 6 months; 3) lack of follow-up data.
All the patients were younger than 75 years old and their KPS scores were above 60 points. Anlotinib was taken orally during postoperative concurrent chemoradiotherapy or after relapse. It was used as 12 mg once a day for 2 weeks and stopped for 1 week. Efficacy was evaluated according to the RANO criteria, and the cranial MR examination was conducted every 1–3 months. According to the MR results and clinical symptoms, the efficacy was divided into complete remission (CR), partial response (PR), stable disease (SD), and progressive disease (PD). The overall response rate (ORR) means CR and PR. The DCR means CR, PR, and SD. The primary study endpoints were PFS at 6 months and OS at 1 year (both were calculated at the start of anlotinib treatment).