Augmentation of tumor expression of HLA-DR, CXCL9, and CXCL10 may improve olfactory neuroblastoma immunotherapeutic responses.

BACKGROUND: Olfactory neuroblastoma is a rare malignancy of the anterior skull base typically treated with surgery and adjuvant radiation. Although outcomes are fair for low-grade disease, patients with high-grade, recurrent, or metastatic disease oftentimes respond poorly to standard treatment methods. We hypothesized that an in-depth evaluation of the olfactory neuroblastoma tumor immune microenvironment would identify mechanisms of immune evasion in high-grade olfactory neuroblastoma as well as rational targetable mechanisms for future translational immunotherapeutic approaches. METHODS: Multispectral immunofluorescence and RNAScope evaluation of the tumor immune microenvironment was performed on forty-seven clinically annotated olfactory neuroblastoma samples. A retrospective chart review was performed and clinical correlations assessed. RESULTS: A significant T cell infiltration was noted in olfactory neuroblastoma samples with a stromal predilection, presence of myeloid-derived suppressor cells, and sparse natural killer cells. A striking decrease was observed in MHC-I expression in high-grade olfactory neuroblastoma compared to low-grade disease, representing a mechanism of immune evasion in high-grade disease. Mechanistically, the immune effector stromal predilection appears driven by low tumor cell MHC class II (HLA-DR), CXCL9, and CXCL10 expression as those tumors with increased tumor cell expression of each of these mediators correlated with significant increases in T cell infiltration. CONCLUSION: These data suggest that immunotherapeutic strategies that augment tumor cell expression of MHC class II, CXCL9, and CXCL10 may improve parenchymal trafficking of immune effector cells in olfactory neuroblastoma and augment immunotherapeutic responses.

Utility of Large Language Models for Health Care Professionals and Patients in Navigating Hematopoietic Stem Cell Transplantation: Comparison of the Performance of ChatGPT-3.5, ChatGPT-4, and Bard.

BACKGROUND: Artificial intelligence is increasingly being applied to many workflows. Large language models (LLMs) are publicly accessible platforms trained to understand, interact with, and produce human-readable text; their ability to deliver relevant and reliable information is also of particular interest for the health care providers and the patients. Hematopoietic stem cell transplantation (HSCT) is a complex medical field requiring extensive knowledge, background, and training to practice successfully and can be challenging for the nonspecialist audience to comprehend. OBJECTIVE: We aimed to test the applicability of 3 prominent LLMs, namely ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Bard (Google AI), in guiding nonspecialist health care professionals and advising patients seeking information regarding HSCT. METHODS: We submitted 72 open-ended HSCT-related questions of variable difficulty to the LLMs and rated their responses based on consistency-defined as replicability of the response-response veracity, language comprehensibility, specificity to the topic, and the presence of hallucinations. We then rechallenged the 2 best performing chatbots by resubmitting the most difficult questions and prompting to respond as if communicating with either a health care professional or a patient and to provide verifiable sources of information. Responses were then rerated with the additional criterion of language appropriateness, defined as language adaptation for the intended audience. RESULTS: ChatGPT-4 outperformed both ChatGPT-3.5 and Bard in terms of response consistency (66/72, 92%; 54/72, 75%; and 63/69, 91%, respectively; P=.007), response veracity (58/66, 88%; 40/54, 74%; and 16/63, 25%, respectively; P<.001), and specificity to the topic (60/66, 91%; 43/54, 80%; and 27/63, 43%, respectively; P<.001). Both ChatGPT-4 and ChatGPT-3.5 outperformed Bard in terms of language comprehensibility (64/66, 97%; 53/54, 98%; and 52/63, 83%, respectively; P=.002). All displayed episodes of hallucinations. ChatGPT-3.5 and ChatGPT-4 were then rechallenged with a prompt to adapt their language to the audience and to provide source of information, and responses were rated. ChatGPT-3.5 showed better ability to adapt its language to nonmedical audience than ChatGPT-4 (17/21, 81% and 10/22, 46%, respectively; P=.03); however, both failed to consistently provide correct and up-to-date information resources, reporting either out-of-date materials, incorrect URLs, or unfocused references, making their output not verifiable by the reader. CONCLUSIONS: In conclusion, despite LLMs' potential capability in confronting challenging medical topics such as HSCT, the presence of mistakes and lack of clear references make them not yet appropriate for routine, unsupervised clinical use, or patient counseling. Implementation of LLMs' ability to access and to reference current and updated websites and research papers, as well as development of LLMs trained in specialized domain knowledge data sets, may offer potential solutions for their future clinical application.

Risk of recurrence after neoadjuvant chemotherapy and transoral robotic surgery in patients with oropharynx cancer that avoid adjuvant radiation.

BACKGROUND: De-escalation strategies for newly-diagnosed p16-positive oropharyngeal squamous cell carcinoma (p16+ OPSCC), aim to reduce treatment-related morbidity without compromising disease control. One strategy is neoadjuvant cisplatin and docetaxel chemotherapy (NAC + S) before transoral robotic surgery, with pathology-based risk-adapted adjuvant treatment. METHODS: We examined the recurrence-free survival (RFS) for patients who received NAC + S. RESULTS: Comparing outcomes in 103 patients between 2008 and 2023, 92% avoided adjuvant treatment and showed significantly higher 2-year recurrence-free survival (RFS) compared to those with adjuvant treatment (95.9% vs. 43.8%, p = 0.0049) CONCLUSION: Our findings suggest that pathology-based risk-adapted omission of adjuvant treatment following NAC + S does not appear to elevate recurrence risk and that NAC may identify patients with favorable tumor biology, yielding a 2-year RFS probability exceeding 95% without adjuvant treatment. Further, the study identifies a patient subset experiencing disease recurrence despite triple modality therapy. Despite limitations, including a retrospective design and modest sample size, the data advocate for controlled NAC + S studies.

Spatial Transcriptomic Analysis of Pituitary Corticotroph Tumors.

Context: Spatial transcriptomic (ST) analysis of tumors provides a novel approach to studying gene expression along with the localization of tumor cells in their environment to uncover spatial interactions. Design: We present ST analysis of corticotroph pituitary neuroendocrine tumors (PitNETs) from formalin-fixed, paraffin-embedded tissues. ST data were compared to immunohistochemistry results. Gene expression profiles were reviewed for cluster annotations, and differentially expressed genes were used for pathway analysis. Results: Seven tumors were used for ST analysis. In situ annotation of tumor tissue was inferred from the gene expression profiles and was in concordance with the annotation made by a pathologist. Furthermore, relative gene expression in the tumor corresponded to common protein staining used in the evaluation of PitNETs, such as reticulin and Ki-67 index. Finally, we identified intratumor heterogeneity; clusters within the same tumor may present with different transcriptomic profiles, unveiling potential intratumor cell variability. Conclusion: Together, our results provide the first attempt to clarify the spatial cell profile in PitNETs.

Comparison of Large Language Models in Answering Immuno-Oncology Questions: A Cross-Sectional Study.

BACKGROUND: The capability of large language models (LLMs) to understand and generate human-readable text has prompted the investigation of their potential as educational and management tools for patients with cancer and healthcare providers. MATERIALS AND METHODS: We conducted a cross-sectional study aimed at evaluating the ability of ChatGPT-4, ChatGPT-3.5, and Google Bard to answer questions related to 4 domains of immuno-oncology (Mechanisms, Indications, Toxicities, and Prognosis). We generated 60 open-ended questions (15 for each section). Questions were manually submitted to LLMs, and responses were collected on June 30, 2023. Two reviewers evaluated the answers independently. RESULTS: ChatGPT-4 and ChatGPT-3.5 answered all questions, whereas Google Bard answered only 53.3% (P < .0001). The number of questions with reproducible answers was higher for ChatGPT-4 (95%) and ChatGPT3.5 (88.3%) than for Google Bard (50%) (P < .0001). In terms of accuracy, the number of answers deemed fully correct were 75.4%, 58.5%, and 43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (P = .03). Furthermore, the number of responses deemed highly relevant was 71.9%, 77.4%, and 43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (P = .04). Regarding readability, the number of highly readable was higher for ChatGPT-4 and ChatGPT-3.5 (98.1%) and (100%) compared to Google Bard (87.5%) (P = .02). CONCLUSION: ChatGPT-4 and ChatGPT-3.5 are potentially powerful tools in immuno-oncology, whereas Google Bard demonstrated relatively poorer performance. However, the risk of inaccuracy or incompleteness in the responses was evident in all 3 LLMs, highlighting the importance of expert-driven verification of the outputs returned by these technologies.

Increasing diversity in clinical trials: demographic trends at the National Cancer Institute, 2005-2020.

BACKGROUND: We described participant demographics for National Cancer Institute (NCI) clinical trials at the clinical center (NCI-CC participants) of the National Institutes of Health to identify enrollment disparities. METHODS: We analyzed NCI-CC data from 2005 to 2020, calculated enrollment fractions, compared with the US cancer population represented by the Surveillance, Epidemiology, and End Results cancer incidence data (2018) and the Cancer in North America database (2018), and compared further with clinical trial disparities data from the NCI Community Oncology Research Program and National Clinical Trials Network (2005-2019), and from ClinicalTrials.gov (2003-2016). RESULTS: NCI-CC (38 531 participants) had higher enrollment fractions for older adults (8.5%), male (5.6%), non-Hispanic (5.1%), and Black or African American (5.3%) participants; lower women proportion across race and ethnicity; and fewer female sex-specific cancer (6.8%) than male sex-specific cancer (11.7%) participants. NCI-CC had lower median age than Surveillance, Epidemiology, and End Results (54.0 vs 65.4); more Black or African American participants (12.0% vs 11.1%); and fewer women (41.7% vs 49.5%), White (76.1% vs 80.5%), Asian or Pacific Islander (4.6% vs 6.0%), American Indian or Alaska Native (0.3% vs 0.5%), and Hispanic participants (7.1% vs 13%). NCI-CC had more Black or African American and Asian or Pacific Islander participants; fewer Hispanic participants than the NCI Community Oncology Research Program and National Clinical Trials Network; more Black or African American and Hispanic participants; fewer Asian or Pacific Islander participants than ClinicalTrials.gov data. Improvement was noted for NCI-CC (older adults, Black or African American, Asian or Pacific Islander, Hispanic participants). CONCLUSION: We found lower representation of older adults, women, Asian or Pacific Islander, American Indian or Alaska Native, and Hispanic participants vs the US cancer population and higher representation of Black or African American vs US cancer population and oncology clinical trials. Multifaceted efforts are underway to reduce disparities in cancer clinical trials at the NCI-CC.

Influence of TP53 Comutation on the Tumor Immune Microenvironment and Clinical Outcomes With Immune Checkpoint Inhibitors in STK11-Mutant Non-Small-Cell Lung Cancer.

PURPOSE: Non-small-cell lung cancer (NSCLC) with STK11mut has inferior outcomes to immune checkpoint inhibitors (ICIs). Using multiomics, we evaluated whether a subtype of STK11mut NSCLC with a uniquely inflamed tumor immune microenvironment (TIME) harboring TP53 comutations could have favorable outcomes to ICIs. PATIENTS AND METHODS: NSCLC tumors (N = 16,896) were analyzed by next-generation sequencing (DNA-Seq/592 genes). A subset (n = 5,034) underwent gene expression profiling (RNA-Seq/whole transcriptome). Exome-level neoantigen load for STK11mut NSCLC was obtained from published pan-immune analysis. Tumor immune cell content was obtained from transcriptome profiles using the microenvironment cell population (MCP) counter. ICI data from POPLAR/OAK (n = 34) and the study by Rizvi et al (n = 49) were used to model progression-free survival (PFS), and a separate ICI-treated cohort (n = 53) from Dana-Farber Cancer Institute (DFCI) was used to assess time to treatment failure (TTF) and tumor RECIST response for STK11mutTP53mut versus STK11mutTP53wt NSCLC. RESULTS: Overall, 12.6% of NSCLC tumors had a STK11mut with the proportions of tumor mutational burden (TMB)-high (≥10 mut/Mb), PD-L1 ≥50%, and microsatellite instability-high being 38.3%, 11.8%, and 0.72%, respectively. Unsupervised hierarchical clustering of STK11mut (n = 463) for stimulator of interferon-gamma (STING) pathway genes identified a STING-high cluster, which was significantly enriched in TP53mut NSCLC (P < .01). Compared with STK11mutTP53wt, tumors with STK11mutTP53mut had higher CD8+T cells and natural killer cells (P < .01), higher TMB (P < .001) and neoantigen load (P < .001), and increased expression of MYC and HIF-1A (P < .01), along with higher expression (P < .01) of glycolysis/glutamine metabolism genes. Meta-analysis of data from OAK/POPLAR and the study by Rizvi et al showed a trend toward improved PFS in patients with STK11mutTP53mut. In the DFCI cohort, compared with the STK11mut TP53wt cohort, the STK11mutTP53mut tumors had higher objective response rates (42.9% v 16.7%; P = .04) and also had longer TTF (14.5 v 4.5 months, P adj = .054) with ICI. CONCLUSION: STK11mut NSCLC with TP53 comutation is a distinct subgroup with an immunologically active TIME and metabolic reprogramming. These properties should be exploited to guide patient selection for novel ICI-based combination approaches.