Cellular senescence is an essential tumor suppressive mechanism that prevents the propagation of oncogenically activated, genetically unstable, and/or damaged cells. Induction of tumor cell senescence is also one of the underlying mechanisms by which cancer therapies exert antitumor activity. However, an increasing body of evidence from preclinical studies demonstrates that radiation and chemotherapy cause accumulation of senescent cells (SnCs) both in tumor and normal tissue. SnCs in tumors can, paradoxically, promote tumor relapse, metastasis, and resistance to therapy, in part, through expression of the senescence-associated secretory phenotype. In addition, SnCs in normal tissue can contribute to certain radiation- and chemotherapy-induced side effects. Because of its multiple roles, cellular senescence could serve as an important target in the fight against cancer. This commentary provides a summary of the discussion at the National Cancer Institute Workshop on Radiation, Senescence, and Cancer (August 10-11, 2020, National Cancer Institute, Bethesda, MD) regarding the current status of senescence research, heterogeneity of therapy-induced senescence, current status of senotherapeutics and molecular biomarkers, a concept of “one-two punch” cancer therapy (consisting of therapeutics to induce tumor cell senescence followed by selective clearance of SnCs), and its integration with personalized adaptive tumor therapy. It also identifies key knowledge gaps and outlines future directions in this emerging field to improve treatment outcomes for cancer patients.

Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose. We used a cohort-based registry of 849 patients with cancer in the lung treated with high dose radiotherapy using stereotactic body radiotherapy. We input pre-therapy lung CT images into a multi-task deep neural network, Deep Profiler, to generate an image fingerprint that primarily predicts time to event treatment outcomes and secondarily approximates classical radiomic features. We validated our findings in an independent study population (n = 95). Deep Profiler was combined with clinical variables to derive iGray, an individualized dose that estimates treatment failure probability to be <5%. Radiation treatments in patients with high Deep Profiler scores fail at a significantly higher rate than in those with low scores. The 3-year cumulative incidences of local failure were 20.3% (95% CI: 16.0–24.9) and 5.7% (95% CI: 3.5–8.8), respectively. Deep Profiler independently predicted local failure (hazard ratio 1.65, 95% 1.02–2.66, p = 0.04). Models that included Deep Profiler and clinical variables predicted treatment failures with a concordance index of 0.72 (95% CI: 0.67–0.77), a significant improvement compared to classical radiomics or clinical variables alone (p = <0.001 and <0.001, respectively). Deep Profiler performed well in an external study population (n = 95), accurately predicting treatment failures across diverse clinical settings and CT scanner types (concordance index = 0.77 [95% CI: 0.69–0.92]). iGray had a wide dose range (21.1–277 Gy, BED), suggested dose reductions in 23.3% of patients and can be safely delivered in the majority of cases. Our results indicate that there are image-distinct subpopulations that have differential sensitivity to radiotherapy. The image-based deep learning framework proposed herein is the first opportunity to use medical images to individualize radiotherapy dose.

Radiotherapy is not currently informed by the genetic composition of an individual patient’s tumour. To identify genetic features regulating survival after DNA damage, here we conduct large-scale profiling of cellular survival after exposure to radiation in a diverse collection of 533 genetically annotated human tumour cell lines. We show that sensitivity to radiation is characterized by significant variation across and within lineages. We combine results from our platform with genomic features to identify parameters that predict radiation sensitivity. We identify somatic copy number alterations, gene mutations and the basal expression of individual genes and gene sets that correlate with the radiation survival, revealing new insights into the genetic basis of tumour cellular response to DNA damage. These results demonstrate the diversity of tumour cellular response to ionizing radiation and establish multiple lines of evidence that new genetic features regulating cellular response after DNA damage can be identified.

Importance: Artificial intelligence (AI) large language models (LLMs) demonstrate potential in simulating human-like dialogue. Their efficacy in accurate patient-clinician communication within radiation oncology has yet to be explored.Objective: To determine an LLM’s quality of responses to radiation oncology patient care questions using both domain-specific expertise and domain-agnostic metrics.Design, Setting, and Participants: This cross-sectional study retrieved questions and answers from websites (accessed February 1 to March 20, 2023) affiliated with the National Cancer Institute and the Radiological Society of North America. These questions were used as queries for an AI LLM, ChatGPT version 3.5 (accessed February 20 to April 20, 2023), to prompt LLM-generated responses. Three radiation oncologists and 3 radiation physicists ranked the LLM-generated responses for relative factual correctness, relative completeness, and relative conciseness compared with online expert answers. Statistical analysis was performed from July to October 2023.Main Outcomes and Measures: The LLM’s responses were ranked by experts using domain-specific metrics such as relative correctness, conciseness, completeness, and potential harm compared with online expert answers on a 5-point Likert scale. Domain-agnostic metrics encompassing cosine similarity scores, readability scores, word count, lexicon, and syllable counts were computed as independent quality checks for LLM-generated responses.Results: Of the 115 radiation oncology questions retrieved from 4 professional society websites, the LLM performed the same or better in 108 responses (94%) for relative correctness, 89 responses (77%) for completeness, and 105 responses (91%) for conciseness compared with expert answers. Only 2 LLM responses were ranked as having potential harm. The mean (SD) readability consensus score for expert answers was 10.63 (3.17) vs 13.64 (2.22) for LLM answers (P < .001), indicating 10th grade and college reading levels, respectively. The mean (SD) number of syllables was 327.35 (277.15) for expert vs 376.21 (107.89) for LLM answers (P = .07), the mean (SD) word count was 226.33 (191.92) for expert vs 246.26 (69.36) for LLM answers (P = .27), and the mean (SD) lexicon score was 200.15 (171.28) for expert vs 219.10 (61.59) for LLM answers (P = .24).Conclusions and Relevance: In this cross-sectional study, the LLM generated accurate, comprehensive, and concise responses with minimal risk of harm, using language similar to human experts but at a higher reading level. These findings suggest the LLM’s potential, with some retraining, as a valuable resource for patient queries in radiation oncology and other medical fields.

Yeast VPS13 is the founding member of a eukaryotic gene family of growing interest in cell biology and medicine. Mutations in three of four human VPS13 genes cause autosomal recessive neurodegenerative or neurodevelopmental disease, making yeast Vps13p an important structural and functional model. Using cell-free reconstitution with purified Vps13p, we show that Vps13p is directly required both for transport from the trans-Golgi network (TGN) to the late endosome/prevacuolar compartment (PVC) and for TGN homotypic fusion. Vps13p must be in complex with the small calcium-binding protein Cdc31p to be active. Single-particle electron microscopic analysis of negatively stained Vps13p indicates that this 358-kD protein is folded into a compact rod-shaped density (20 × 4 nm) with a loop structure at one end with a circular opening ∼6 nm in diameter. Vps13p exhibits ATP-stimulated binding to yeast membranes and specific interactions with phosphatidic acid and phosphorylated forms of phosphatidyl inositol at least in part through the binding affinities of conserved N- and C-terminal domains.

Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable patients with early-stage NSCLC. However, NSCLC is composed of several histological subtypes and the impact of this heterogeneity on SBRT treatments has yet to be established. We analyzed 740 patients with early-stage NSCLC treated definitively with SBRT from 2003 through 2015. We calculated cumulative incidence curves using the competing risk method and identified predictors of local failure using Fine and Gray regression. Overall, 72 patients had a local failure, with a cumulative incidence of local failure at 3 years of 11.8%. On univariate analysis, squamous histological subtype, younger age, fewer medical comorbidities, higher body mass index, higher positron emission tomography standardized uptake value, central tumors, and lower radiation dose were associated with an increased risk for local failure. On multivariable analysis, squamous histological subtype (hazard ratio = 2.4 p = 0.008) was the strongest predictor of local failure. Patients with squamous cancers fail SBRT at a significantly higher rate than do those with adenocarcinomas or NSCLC not otherwise specified, with 3-year cumulative rates of local failure of 18.9% (95% confidence interval [CI]: 12.7-25.1), 8.7% (95% CI: 4.6-12.8), and 4.1% (95% CI: 0-9.6), respectively. Our results demonstrate an increased rate of local failure in patients with squamous cell carcinoma. Standard approaches for radiotherapy that demonstrate efficacy for a population may not achieve optimal results for individual patients. Establishing the differential dose effect of SBRT across histological groups is likely to improve efficacy and inform ongoing and future studies that aim to expand indications for SBRT.