Challenges and opportunities in cancer immunotherapy: a Society for Immunotherapy of Cancer (SITC) strategic vision.

Cancer immunotherapy has flourished over the last 10-15 years, transforming the practice of oncology and providing long-term clinical benefit to some patients. During this time, three distinct classes of immune checkpoint inhibitors, chimeric antigen receptor-T cell therapies specific for two targets, and two distinct classes of bispecific T cell engagers, a vaccine, and an oncolytic virus have joined cytokines as a standard of cancer care. At the same time, scientific progress has delivered vast amounts of new knowledge. For example, advances in technologies such as single-cell sequencing and spatial transcriptomics have provided deep insights into the immunobiology of the tumor microenvironment. With this rapid clinical and scientific progress, the field of cancer immunotherapy is currently at a critical inflection point, with potential for exponential growth over the next decade. Recognizing this, the Society for Immunotherapy of Cancer convened a diverse group of experts in cancer immunotherapy representing academia, the pharmaceutical and biotechnology industries, patient advocacy, and the regulatory community to identify current opportunities and challenges with the goal of prioritizing areas with the highest potential for clinical impact. The consensus group identified seven high-priority areas of current opportunity for the field: mechanisms of antitumor activity and toxicity; mechanisms of drug resistance; biomarkers and biospecimens; unique aspects of novel therapeutics; host and environmental interactions; premalignant immunity, immune interception, and immunoprevention; and clinical trial design, endpoints, and conduct. Additionally, potential roadblocks to progress were discussed, and several topics were identified as cross-cutting tools for optimization, each with potential to impact multiple scientific priority areas. These cross-cutting tools include preclinical models, data curation and sharing, biopsies and biospecimens, diversification of funding sources, definitions and standards, and patient engagement. Finally, three key guiding principles were identified that will both optimize and maximize progress in the field. These include engaging the patient community; cultivating diversity, equity, inclusion, and accessibility; and leveraging the power of artificial intelligence to accelerate progress. Here, we present the outcomes of these discussions as a strategic vision to galvanize the field for the next decade of exponential progress in cancer immunotherapy.

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.

Chordoma cancer stem cell subpopulation characterization may guide targeted immunotherapy approaches to reduce disease recurrence.

Introduction: Cancer stem cells (CSCs), a group of tumor-initiating and tumor-maintaining cells, may be major players in the treatment resistance and recurrence distinctive of chordoma. Characterizing CSCs is crucial to better targeting this subpopulation. Methods: Using flow cytometry, six chordoma cell lines were evaluated for CSC composition. In vitro, cell lines were stained for B7H6, HER2, MICA-B, ULBP1, EGFR, and PD-L1 surface markers. Eighteen resected chordomas were stained using a multispectral immunofluorescence (mIF) antibody panel to identify CSCs in vivo. HALO software was used for quantitative CSC density and spatial analysis. Results: In vitro, chordoma CSCs express more B7H6, MICA-B, and ULBP1, assessed by percent positivity and mean fluorescence intensity (MFI), as compared to non-CSCs in all cell lines. PD- L1 percent positivity is increased by >20% in CSCs compared to non-CSCs in all cell lines except CH22. In vivo, CSCs comprise 1.39% of chordoma cells and most are PD-L1+ (75.18%). A spatial analysis suggests that chordoma CSCs cluster at an average distance of 71.51 mm (SD 73.40 mm) from stroma. Discussion: To our knowledge, this study is the first to identify individual chordoma CSCs and describe their surface phenotypes using in vitro and in vivo methods. PD-L1 is overexpressed on CSCs in chordoma human cell lines and operative tumor samples. Similarly, potential immunotherapeutic targets on CSCs, including B7H6, MICA-B, ULBP1, EGFR, and HER2 are overexpressed across cell lines. Targeting these markers may have a preferential role in combating CSCs, an aggressive subpopulation likely consequential to chordoma's high recurrence rate.

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

PURPOSE: Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. METHODS: A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. RESULTS: Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). CONCLUSION: The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

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.

Efficacy, safety, and biomarker analyses of bintrafusp alfa, a bifunctional fusion protein targeting TGF-ß and PD-L1, in patients with advanced non-small cell lung cancer.

BACKGROUND: Bintrafusp alfa, a first-in-class bifunctional fusion protein targeting transforming growth factor-ß (TGF-ß) and programmed cell death ligand 1, has demonstrated encouraging efficacy as second-line treatment in patients with non-small cell lung cancer (NSCLC) in a dose expansion cohort of the phase 1, open-label clinical trial (NCT02517398). Here, we report the safety, efficacy, and biomarker analysis of bintrafusp alfa in a second expansion cohort of the same trial (biomarker cohort). METHODS: Patients with stage IIIb/IV NSCLC who were either immune checkpoint inhibitor (ICI)-naïve (n=18) or ICI-experienced (n=23) were enrolled. The primary endpoint was the best overall response. Paired biopsies (n=9/41) and peripheral blood (n=14/41) pretreatment and on-treatment were studied to determine the immunological effects of treatment and for associations with clinical activity. RESULTS: Per independent review committee assessment, objective responses were observed in the ICI-naïve group (overall response rate, 27.8%). No new or unexpected safety signals were identified. Circulating TGF-ß levels were reduced (>97%; p<0.001) 2 weeks after initiation of treatment with bintrafusp alfa and remained reduced up to 12 weeks. Increases in lymphocytes and tumor-associated macrophages (TAMs) were observed in on-treatment biospies, with an increase in the M2 (tumor trophic TAMs)/M1 (inflammatory TAMs) ratio associated with poor outcomes. Specific peripheral immune analytes at baseline and early changes after treatment were associated with clinical response. CONCLUSIONS: Bintrafusp alfa was observed to have modest clinical activity and manageable safety, and was associated with notable immunologic changes involving modulation of the tumor immune microenvironment in patients with advanced NSCLC.

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

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). RESULTS: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). CONCLUSION: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

Cooperative Armoring of CAR and TCR T Cells by T Cell-Restricted IL15 and IL21 Universally Enhances Solid Tumor Efficacy.

PURPOSE: Chimeric antigen receptor (CAR) and T-cell receptor (TCR) T-cell therapies are effective in a subset of patients with solid tumors, but new approaches are needed to universally improve patient outcomes. Here, we developed a technology to leverage the cooperative effects of IL15 and IL21, two common cytokine-receptor gamma chain family members with distinct, pleiotropic effects on T cells and other lymphocytes, to enhance the efficacy of adoptive T cells. EXPERIMENTAL DESIGN: We designed vectors that induce the constitutive expression of either membrane-tethered IL15, IL21, or IL15/IL21. We used clinically relevant preclinical models of transgenic CARs and TCRs against pediatric and adult solid tumors to determine the effect of the membrane-tethered cytokines on engineered T cells for human administration. RESULTS: We found that self-delivery of these cytokines by CAR or TCR T cells prevents functional exhaustion by repeated stimulation and limits the emergence of dysfunctional natural killer (NK)-like T cells. Across different preclinical murine solid tumor models, we observed enhanced regression with each individual cytokine but the greatest antitumor efficacy when T cells were armored with both. CONCLUSIONS: The coexpression of membrane-tethered IL15 and IL21 represents a technology to enhance the resilience and function of engineered T cells against solid tumors and could be applicable to multiple therapy platforms and diseases. See related commentary by Ruffin et al., p. 1431.

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 cancer patients 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 four 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 30th, 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 <0.0001). The number of questions with reproducible answers was higher for ChatGPT-4 (95%) and ChatGPT3.5 (88.3%) than for Google Bard (50%) (p <0.0001). 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 = 0.03). 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 = 0.04). 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 = 0.02). 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 three LLMs, highlighting the importance of expert-driven verification of the outputs returned by these technologies.

A T cell receptor ß chain-directed antibody fusion molecule activates and expands subsets of T cells to promote antitumor activity.

Despite the success of programmed cell death-1 (PD-1) and PD-1 ligand (PD-L1) inhibitors in treating solid tumors, only a proportion of patients respond. Here, we describe a first-in-class bifunctional therapeutic molecule, STAR0602, that comprises an antibody targeting germline Vß6 and Vß10 T cell receptors (TCRs) fused to human interleukin-2 (IL-2) and simultaneously engages a nonclonal mode of TCR activation with costimulation to promote activation and expansion of αß T cell subsets expressing distinct variable ß (Vß) TCR chains. In solution, STAR0602 binds IL-2 receptors in cis with Vß6/Vß10 TCRs on the same T cell, promoting expansion of human Vß6 and Vß10 CD4+ and CD8+ T cells that acquire an atypical central memory phenotype. Monotherapy with a mouse surrogate molecule induced durable tumor regression across six murine solid tumor models, including several refractory to anti-PD-1. Analysis of murine tumor-infiltrating lymphocyte (TIL) transcriptomes revealed that expanded Vß T cells acquired a distinct effector memory phenotype with suppression of genes associated with T cell exhaustion and TCR signaling repression. Sequencing of TIL TCRs also revealed an increased T cell repertoire diversity within targeted Vß T cell subsets, suggesting clonal revival of tumor T cell responses. These immunological and antitumor effects in mice were recapitulated in studies of STAR0602 in nonhuman primates and human ex vivo models, wherein STAR0602 boosted human antigen-specific T cell responses and killing of tumor organoids. Thus, STAR0602 represents a distinct class of T cell-activating molecules with the potential to deliver enhanced antitumor activity in checkpoint inhibitor-refractory settings.

Quantitative Analysis of Serial Positron Emission Tomography Imaging in Men with Metastatic Castration-resistant Prostate Cancer Treated with Enzalutamide.

BACKGROUND: The emergence of positron emission tomography (PET) in prostate cancer is impacting clinical practice, but little is known about PET imaging as a tool to determine treatment failure in metastatic castration-resistant prostate cancer (mCRPC). OBJECTIVE: To evaluate PET imaging dynamics in mCRPC patients on enzalutamide with stable computed tomography (CT) and technetium-99m (Tc99) bone scans. DESIGN, SETTING, AND PARTICIPANTS: All patients were on treatment with enzalutamide for first-line mCRPC in a clinical trial at the National Cancer Institute (Bethesda, MD, USA). A volunteer sample had serial 18F-sodium fluoride (NaF) PET in parallel with CT and Tc99. Regions of interest (ROIs) on NaF were analyzed quantitatively for response. INTERVENTION: Patients were randomized to enzalutamide with/without a cancer immunotherapy, Prostvac. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A post hoc, descriptive analysis was performed comparing the changes seen on CT and Tc99 as per RECIST 1.1 with NaF PET scans including the use of a quantitative analysis. RESULTS AND LIMITATIONS: Eighteen mCRPC patients had 67 NaF scans. A total of 233 ROIs resolved after treatment, 52 (22%) of which eventually retuned while on therapy. In all, 394 new ROIs were seen, but 112(28%) resolved subsequently. Of 18 patients, 14 had new ROIs that ultimately resolved after appearing. Many patients experienced progression in a minority of lesions, and one patient with radiation intervention to oligoprogression had a remarkable response. This study is limited by its small number of patients and post hoc nature. CONCLUSIONS: These data highlight the dynamic nature of NaF PET in mCRPC patients treated with enzalutamide, where not all new findings were ultimately related to disease progression. This analysis also provides a potential strategy to identify and intervene in oligoprogression in prostate cancer. PATIENT SUMMARY: In this small analysis of patients with prostate cancer on enzalutamide, changes on 18F-sodium fluoride positron emission tomography (PET) imaging were not always associated with treatment failure. Caution may be indicated when using PET imaging to determine whether new therapy is needed.

The tumor microenvironment state associates with response to HPV therapeutic vaccination in patients with respiratory papillomatosis.

Recurrent respiratory papillomatosis (RRP) is a rare, debilitating neoplastic disorder caused by chronic infection with human papillomavirus (HPV) type 6 or 11 and characterized by growth of papillomas in the upper aerodigestive tract. There is no approved medical therapy, and patients require repeated debulking procedures to maintain voice and airway function. PRGN-2012 is a gorilla adenovirus immune-therapeutic capable of enhancing HPV 6/11-specific T cell immunity. This first-in-human, phase 1 study (NCT04724980) of adjuvant PRGN-2012 treatment in adult patients with severe, aggressive RRP demonstrates the overall safety and clinically meaningful benefit observed with PRGN-2012, with a 50% complete response rate in patients treated at the highest dose. Responders demonstrate greater expansion of peripheral HPV-specific T cells compared with nonresponders. Additional correlative studies identify an association between reduced baseline papilloma HPV gene expression, greater interferon responses and expression of CXCL9 and CXCL10, and greater papilloma T cell infiltration in responders. Conversely, nonresponders were characterized by greater HPV and CXCL8 gene expression, increased neutrophilic cell infiltration, and reduced T cell papilloma infiltration. These results suggest that papilloma HPV gene expression may regulate interferon signaling and chemokine expression profiles within the tumor microenvironment that cooperate to govern clinical response to therapeutic HPV vaccination in patients with respiratory papillomatosis.

Prediction of cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

Health diplomacy in action: The cancer legacy of the Good Friday Agreement.

2023 marks the 25th anniversary of the Good Friday Agreement, which led peace in Northern Ireland. As well as its impact on peace and reconciliation, the Good Friday Agreement has also had a lasting positive impact on cancer research and cancer care across the island of Ireland. Pursuant to the Good Friday Agreement, a Memorandum of Understanding (MOU) was signed between the respective Departments of Health in Ireland, Northern Ireland and the US National Cancer Institute (NCI), giving rise to the Ireland - Northern Ireland - National Cancer Institute Cancer Consortium, an unparalleled tripartite agreement designed to nurture and develop linkages between cancer researchers, physicians and allied healthcare professionals across Ireland, Northern Ireland and the US, delivering world class research and better care for cancer patients on the island of Ireland and driving research and innovation in the US.

Applications of large language models in cancer care: current evidence and future perspectives.

The development of large language models (LLMs) is a recent success in the field of generative artificial intelligence (AI). They are computer models able to perform a wide range of natural language processing tasks, including content generation, question answering, or language translation. In recent months, a growing number of studies aimed to assess their potential applications in the field of medicine, including cancer care. In this mini review, we described the present published evidence for using LLMs in oncology. All the available studies assessed ChatGPT, an advanced language model developed by OpenAI, alone or compared to other LLMs, such as Google Bard, Chatsonic, and Perplexity. Although ChatGPT could provide adequate information on the screening or the management of specific solid tumors, it also demonstrated a significant error rate and a tendency toward providing obsolete data. Therefore, an accurate, expert-driven verification process remains mandatory to avoid the potential for misinformation and incorrect evidence. Overall, although this new generative AI-based technology has the potential to revolutionize the field of medicine, including that of cancer care, it will be necessary to develop rules to guide the application of these tools to maximize benefits and minimize risks.

Efficacy and safety of PARP inhibitors in metastatic castration-resistant prostate cancer: A systematic review and meta-analysis of clinical trials.

INTRODUCTION: PARP inhibitors (PARPi) are a standard-of-care (SoC) treatment option for patients with metastatic castration-resistant prostate cancer (mCRPC). Several clinical trials have shown the potential of combining PARPi with other anticancer agents. Therefore, we conducted a systematic review and meta-analysis to comprehensively evaluate the efficacy and safety of PARPi in patients with metastatic prostate cancer. METHODS: MEDLINE, Cochrane CENTRAL, EMBASE, CINAHL, and Web of Science were searched on March 22nd, 2023, for phase 2 or 3 clinical trials. Efficacy (progression-free survival [PFS], overall survival [OS], PSA decline >50% [PSA50], and objective response rate [ORR]) and safety outcomes were assessed in the included studies. RESULTS: Seventeen clinical trials (PARPi monotherapy [n = 7], PARPi + androgen-receptor signaling inhibitors [ARSI] [n = 6], and PARPi + immune checkpoint inhibitors [ICI] [n = 4]) were included in the quantitative analyses. PARPi monotherapy improved radiographic PFS and OS over SoC in mCRPC patients with alterations in BRCA1 or BRCA2 genes but not in those with alterations in the ATM gene. Higher rates of PSA50 and ORR were reported in participants treated with PARPi + ARSI than in single-agent PARPi or PARPi + ICI. Although the rate of high-grade adverse events was similar across all groups, treatment discontinuation was higher in patients treated with PARPi-based combinations than PARPi monotherapy. CONCLUSION: The efficacy of PARPi is not uniform across mCRPC patients with alterations in DNA damage repair genes, and optimal patient selection remains a clinical challenge. No unexpected safety signals for this class of agents emerged from this analysis.

Avelumab in Patients With Metastatic Colorectal Cancer.

BACKGROUND: Metastatic colorectal cancer (mCRC) is incurable, and median overall survival is less than 2½ years. Although monoclonal antibodies that block PD-1/PD-L1 interactions are active in microsatellite unstable/mismatch repair deficient tumors, a growing dataset shows that most patients with microsatellite stable/mismatch repair proficient tumors will not benefit from the blockade of PD-1/PD-L1 interactions. Here we present results from patients with mCRC (n = 22) treated with the anti-PD-L1 monoclonal antibody avelumab. METHODS: Patients received treatment on a phase I, open-label, dose-escalation trial via a consecutive parallel-group expansion in colorectal cancer. Patients aged 18 years and older with mCRC measurable by RECIST v1.1 who had received at least 1 line of systemic therapy for metastatic disease enrolled. Patients with prior immune checkpoint inhibitor treatment were excluded. Patients received avelumab 10 mg/kg intravenously every 2 weeks. The primary endpoint was the objective response rate. RESULTS: Twenty-two participants received treatment from July 2013 to August 2014. There were no objective responses and median progression-free survival was 2.1 months (95% CI: 1.4-5.5 months). There were 5 grade 3 treatment-related adverse events: GGT elevation (n = 2), PRESS (n = 1), lymphopenia (n = 1), and asymptomatic amylase/lipase elevation (n = 1). CONCLUSION: As demonstrated with other anti-PD-1/PD-L1 monoclonal antibodies, avelumab is not active in unselected patients with mCRC (ClinicalTrials.gov Identifier: NCT01772004).