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KRAS mutations play a critical role in the development and progression of several cancers, including non-small cell lung cancer and pancreatic cancer. Despite advancements in targeted therapies, the management of KRAS-mutant tumors remains challenging. This study leverages bibliometric analysis and a comprehensive review of clinical trials to identify emerging immunotherapies and potential treatments for KRAS-related cancers. Using the Web of Science Core Collection and Citespace, we analyzed publications from January 2008 to March 2023 alongside 52 clinical trials from ClinicalTrials.gov and WHO's registry, concentrating on immune checkpoint blockades (ICBs) and novel therapies. Our study highlights an increased focus on the tumor immune microenvironment and precision therapy. Clinical trials reveal the effectiveness of ICBs and the promising potential of T-cell receptor T-cell therapy and vaccines in treating KRAS-mutant cancers. ICBs, particularly in combination therapies, stand out in managing KRAS-mutant tumors. Identifying the tumor microenvironment and gene co-mutation profiles as key research areas, our findings advocate for multidisciplinary approaches to advance personalized cancer treatment. Future research should integrate genetic, immunological, and computational studies to unveil new therapeutic targets and refine treatment strategies for KRAS-mutant cancers.

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In recent years, there has been significant research interest in the field of immunotherapy for non-small cell lung cancer (NSCLC) within the academic community. Given the observed variations in individual responses, despite similarities in histopathologic type, immunohistochemical index, TNM stage, or mutation status, the identification of a reliable biomarker for early prediction of therapeutic responses is of utmost importance. Conventional medical imaging techniques primarily focus on macroscopic tumor monitoring, which may no longer adequately fulfill the requirements of clinical diagnosis and treatment. CT (computerized tomography) or PEF/CT-based radiomics has the potential to investigate the molecular-level biological attributes of tumors, such as PD-1/PD-L1 expression and tumor mutation burden, which offers a novel approach to assess the effectiveness of immunotherapy and forecast patient prognosis. The utilization of cutting-edge radiological imaging techniques, including radiomics, PET/CT, machine learning, and artificial intelligence, demonstrates significant potential in predicting diagnosis, treatment response, immunosuppressive characteristics, and immune-related adverse events. The current review highlights that CT scan-based radiomics is a reliable and feasible way to predict the benefits of immunotherapy in patients with advanced NSCLC.

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This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78-0.92), 0.76 (95% CI 0.68-0.84), and 0.89 (95% CI 0.84-0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86-1.00), 0.79 (95% CI 0.68-0.91), and 0.96 (95% CI 0.90-1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72-0.96), 0.75 (95% CI 0.62-0.87), and 0.86 (95% CI 0.75-0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.

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PURPOSE: Non-small cell lung cancer (NSCLC) is a highly fatal malignancy. The Kirsten rat sarcoma viral oncogene (KRAS) gene profoundly impacts patient prognosis. This study aims to explore the correlation between KRAS mutation subtypes, clinical data, and the impact of these subtypes on immunotherapy. MATERIALS AND METHODS: Tumor samples from 269 NSCLC patients at the Affiliated Cancer Hospital of Xinjiang Medical University were analyzed. Patients received first- or second-line therapy without targeted therapy. Molecular and clinical data were used to analysis KRAS mutation subtypes and treatment outcomes. RESULTS: KRAS mutations predominantly included G12C, G12D, and G12V subtypes. TP53 had the highest mutation frequency among KRAS mutations, followed by MST1, STK11, and KMT2C. Gender differences were noted among KRAS mutation subtypes, with G12C and G12V mutations prevalent in males, while G12D mutations were less common among males. Smokers exhibited varied KRAS mutation subtypes, with G12C and G12V prevalent in smokers and G12D in nonsmokers. KRAS mutations were mainly in lung adenocarcinoma. TTF-1 and PD-L1 expression differed significantly among KRAS mutations. Patients with G12C and G12V mutations showed higher TMB levels and better immunotherapy outcomes compared to those without KRAS mutations. Conversely, patients with G12D mutations had poorer immunotherapy responses. CONCLUSIONS: KRAS mutation subtypes exhibit distinct clinical and molecular characteristics and varying responses to immunotherapy. G12C and G12V mutations correlate with better immunotherapy outcomes, while G12D mutations are associated with poorer responses.

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BACKGROUND: Immunotherapy represents a groundbreaking and monumental achievement in the field of cancer therapy, marking a significant advancement in fighting against this devastating disease. Lung cancer has showed consistent clinical improvements in response to immunotherapy treatments, yet, it is undeniable that challenges such as limited response rates acquire resistance, and the unclear fundamental mechanisms were inevitable problems. METHODS: The cellular composition was defined and distinguished through single-cell RNA sequencing (scRNA-seq) analysis of MPR (major pathologic response) and NMPR (non-major pathologic response) samples in GSE207422, including four primary MPR samples and eight primary NMPR samples. RESULTS: We found obvious difference in CD8+ T cell population between MPR and NMPR samples, with high expression of TYMS, RRM2, and BIRC5 in NPMR samples. Meanwhile, the proportion of macrophages and tumor epithelial cells infiltration increased in the NMPR samples. We discovered biomarkers (ACTN4, ATF3, BRD2, CDKN1A, and CHMP4B) in epithelial cells which were potentially represented worse outcomes. CONCLUSIONS: By exploring the difference of tumor microenvironment (TME) in samples with different corresponding degrees of neoadjuvant immunotherapy, this research introduces a number of novel biomarkers for predicting the response of treatment and a theoretical basis for overcoming immunotherapy resistance.

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BACKGROUND: In the pursuit of improved clinical outcomes and patient experience in health care, shared decision-making (SDM) stands as a pivotal concept garnering increasing attention, but SDM utilization varies widely, often leading to confusion regarding team members' roles. This study explores knowledge, skills, and attitudes of oncology clinicians engaged in a pioneering educational initiative at a comprehensive cancer care center, aimed at enhancing frontline SDM capabilities. METHODS: Utilizing a prospective cohort qualitative approach, the team conducted interviews with 6 clinicians in a multidisciplinary oncology program who were engaged in an SDM continuing education program. In the program, participants were immersed in experiential learning activities including standardized didactic sessions and simulation-based SDM case role-play activities. RESULTS: Thematic analysis of interview data revealed 5 major categories: 1) perceptions of SDM; 2) training; 3) patient-centered care; 4) challenges and constraints; and 5) leadership buy-in. Participants perceived benefits, including adopting a better approach to integrate SDM into their practice, heightened engagement, emphasizing team collaboration, and embracing a patient-centric care model. CONCLUSIONS: This study underscores the transformative impact of education and training on enhancing SDM capabilities among oncology clinicians and is not intended for generalizability. By promoting a basic understanding and application of SDM principles, practicing clinicians can be better empowered to improve health care outcomes and experience. Our findings contribute to the broader endeavor of embedding practical SDM principles within clinical practice, thereby fostering a more patient-centered and effective health care environment.

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BACKGROUND: There is a growing body of evidence on shared decision-making (SDM) training programs worldwide. However, there is wide variation in program design, duration, effectiveness, and evaluation in both academia (ie, medical school) and the practice setting. SDM training has been slow to integrate in practice settings. METHODS: A pilot study of 6 multidisciplinary clinicians was conducted using quantitative and qualitative methods to evaluate changes in participant understanding and implementation of SDM in the practice setting. A 2-rater criterion-based evaluation method was used to assess a simulation-based case study role-play program using 7 domains of SDM pre and post training. The authors assessed whether clinicians addressed each of the 7 domains or what fraction of each domain was addressed as part of their simulation case study role-play performance. Focus groups were conducted pre- and postintervention to provide feedback to participants and to understand the clinician experience in greater detail. RESULTS: The increase in improvement in SDM ranged from 17% to 37%, and 7 of 8 domains for which participants were rated showed significant improvement. The areas of greatest improvement were seen in determining a patient's goals/preferences, including risk tolerance regarding treatments (+37%) and values and self-efficacy (+37%). CONCLUSION: The results of this study reveal a significant shift in clinician awareness of a patient's goals, preferences, and values. Postintervention, clinicians began to understand the value of building a partnership with their patients whereby the patient becomes an active participant in their clinical care.

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OBJECTIVE: To explore the survival effect of thoracic gross tumor volume (GTV) in three-dimensional (3D) radiotherapy for stage IV non-small cell lung cancer (NSCLC). METHODS: The data cases were obtained from a single-center retrospective analysis. From May. From 2008 to August 2018, 377 treatment criteria were enrolled. GTV was defined as the volume of the primary lesion and the hilus as well as the mediastinal metastatic lymph node. Chemotherapy was a platinum-based combined regimen of two drugs. The number of median chemotherapy cycles was 4 (2-6), and the cut-off value of the planning target volume (PTV) dose of the primary tumor was 63 Gy (30-76.5 Gy). The cut-off value of GTV volume was 150 cm3 (5.83-3535.20 cm3). RESULTS: The survival rate of patients with GTV <150 cm3 is better than patients with GTV ≥150 cm3. Multivariate Cox regression analyses suggested that peripheral lung cancer, radiation dose ≥63 Gy, GTV <150 cm3, 4-6 cycles of chemotherapy, and CR + PR are good prognostic factors for patients with stage IV non-small cell lung cancer. The survival rate of patients with GTV <150 cm3 was longer than patients with ≥150 cm3 when they underwent 2 to 3 cycles of chemotherapy concurrent 3D radiotherapy (p < 0.05). When performing 4 to 6 cycles of chemotherapy concurrent 3D radiotherapy, there was no significant difference between <150 cm3 and ≥150 cm3. CONCLUSIONS: The volume of stage IV NSCLC primary tumor can affect the survival of patients. Appropriate treatment methods can be opted by considering the volume of tumors to extend patients' lifetime to the utmost.

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PURPOSE: Performance status (PS), an essential indicator of patients' functional abilities, is often documented in clinical notes of patients with cancer. The use of natural language processing (NLP) in extracting PS from electronic medical records (EMRs) has shown promise in enhancing clinical decision-making, patient monitoring, and research studies. We designed and validated a multi-institute NLP pipeline to automatically extract performance status from free-text patient notes. PATIENTS AND METHODS: We collected data from 19,481 patients in Harris Health System (HHS) and 333,862 patients from veteran affair's corporate data warehouse (VA-CDW) and randomly selected 400 patients from each data source to train and validate (50%) and test (50%) the proposed pipeline. We designed an NLP pipeline using an expert-derived rule-based approach in conjunction with extensive post-processing to solidify its proficiency. To demonstrate the pipeline's application, we tested the compliance of PS documentation suggested by the American Society of Clinical Oncology (ASCO) Quality Metric and investigated the potential disparity in PS reporting for stage IV non-small cell lung cancer (NSCLC). We used a logistic regression test, considering patients in terms of race/ethnicity, conversing language, marital status, and gender. RESULTS: The test results on the HHS cohort showed 92% accuracy, and on VA data demonstrated 98.5% accuracy. For stage IV NSCLC patients, the proposed pipeline achieved an accuracy of 98.5%. Furthermore, our analysis revealed a documentation rate of over 85% for PS among NSCLC patients, surpassing the ASCO Quality Metrics. No disparities were observed in the documentation of PS. CONCLUSION: Our proposed NLP pipeline shows promising results in extracting PS from free-text notes from various health institutions. It may be used in longitudinal cancer data registries.

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INTRODUCTION: Immunotherapy has revolutionized the management of lung cancer and improved lung cancer survival in trials, but its real-world impact at the population level remains unclear. METHODS: Using data obtained from eight Surveillance, Epidemiology, and End Results (SEER) registries from 2004 through 2019, we addressed the long-term trends in the incidence, incidence-based mortality (IBM), and survival of lung cancer patients in the United States. RESULTS: The incidence and IBM of both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) all significantly decreased steadily from 2004 to 2019. The 1-year survival (1-YS) of both NSCLC and SCLC improved over time, with the best improvement observed for Stage 4 NSCLC. Two significant turning points of Stage 4 NSCLC 1-YS were observed over the years: 0.63% (95% confidence interval [CI]: 0.33%-0.93%) from 2004 to 2010, 0.81% (95% CI: 0.41%-1.21%) from 2010 to 2014 and a striking 2.09% (95% CI: 1.70%-2.47%) from 2014 to 2019. The same two turning points in 1-YS were pronounced for Stage 4 NSCLC in women, which were coincident with the introduction of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) and immunotherapy. However, for Stage 4 NSCLC in men, only one significant turning point in the 1-YS starting in 2014 was found, which might only correspond to immunotherapy. Significant period effects in reduced IBM were also observed for both Stage 4 AD and Stage 4 SQCC during the period. CONCLUSION: This SEER analysis found that immunotherapy improved the survival of Stage 4 NSCLC patients at the population level in the United States. This real-world evidence confirms that immunotherapy has truly revolutionized the management of lung cancer.

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The advent of immunotherapy has greatly improved the prognosis of non-small cell lung (NSCLC) patients. However, given its low response rate and high cost of treatment, the search for valuable predictive markers of treatment efficacy is necessary. Considering the complexity and heterogeneity of the tumour and tumour microenvironment, the construction of a multi-dimensional prediction model is necessary. Therefore, we aimed to integrate clinical parameters, radiomic features, and immune signature data from NSCLC patients receiving immunotherapy to construct a multi-dimensional prediction model to better predict the efficacy of immunotherapy. The current study enrolled 137 NSCLC patients who received immunotherapy. We collected baseline clinical information, CT images, and tumour tissue specimens. Using 3D-Slicer software, radiomic features were extracted from patient CT images, and tumor tissue samples obtained before immunotherapy were subjected to immunohistochemical staining. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to downscale the data, and the radiomic features and immune signatures associated with the prognosis of immunotherapy patients were identified. The modified lung immune predictive index (mLIPI), radiomics score (Radioscore), immune score and multi-dimensional model nomogram were constructed. The C-index and area under the curve (AUC) were applied to evaluate the predictive efficacy of the models. Three radiomic features and three immune signatures that could predict the efficacy of immunotherapy were eventually screened. Multivariate analysis showed that the mLIPI, Radioscore, and immune score were independent predictive factors for PFS and OS (P < 0.05 for all models). The multi-dimensional model combining the three models showed better predictive efficacy than the mLIPI, Radioscore, and immune score (PFS: 0.721 vs. 0.662 vs. 0.610 vs. 0.610; OS: 0.727 vs. 0.661 vs. 0.601 vs. 0.602 respectively). The multi-dimensional model showed the best predictive efficacy, with C-index for PFS and OS higher than mLIPI, radioscore and immune score: 0.721 vs. 0.662 vs. 0.610 vs. 0.610 for PFS and 0.727 vs. 0.661 vs. 0.601 vs. 0.602 for OS, respectively. The AUC for the multi-dimensional model also performed better than those of the individual models: 0.771 vs. 0.684 vs. 0.715 vs. 0.711 for PFS and 0.768 vs. 0.662 vs. 0.661 vs. 0.658 for OS, respectively. The multi-dimensional model combining the three models had better predictive efficacy than any single model and was more likely to help provide patients personalized and precision medicine.

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OBJECTIVE: To evaluate the potential utility of 18F-FDG PET/CT to assess response to neoadjuvant immunochemotherapy in patients with resectable NSCLC, and the ability to screen patients who may benefit from neoadjuvant immunochemotherapy. METHODS: Fifty one resectable NSCLC (stage IA-IIIB) patients were analyzed, who received two-three cycles neoadjuvant immunochemotherapy.18F-FDG PET/CT was carried out at baseline(scan-1) and prior to radical resection(scan-2). SULmax, SULpeak, MTV, TLG, T/N ratio, ΔSULmax%,ΔSULpeak%, ΔMTV%, ΔTLG%,ΔT/N ratio% were calculated. 18F-FDG PET/CT responses were classified using PERCIST. We then compared the RECIST 1.1 and PERCIST criteria for response assessment.With surgical pathology of primary lesions as the gold standard, the correlation between metabolic parameters of 18F-FDG PET/CT and major pathologic response (MPR) was analyzed. All metabolic parameters were compared to treatment response and correlated to PFS and OS. RESULTS: In total of fifty one patients, MPR was achieved in 25(49%, 25/51) patients after neoadjuvant therapy. The metabolic parameters of Scan-1 were not correlated with MPR.The degree of pathological regression was negatively correlated with SULmax, SULpeak, MTV, TLG, T/N ratio of scan-2, and the percentage changes of the ΔSULmax%, ΔSULpeak%, ΔMTV%,ΔTLG%,ΔT/N ratio% after neoadjuvant therapy (p < 0.05). According to PERCIST, 36 patients (70.6%, 36/51) showed PMR, 12 patients(23.5%, 12/51) had stable metabolic disease(SMD), and 3 patients(5.9%, 3/51) had progressive metabolic disease (PMD). ROC indicated that all of scan-2 metabolic parameters and the percentage changes of metabolic parameters had ability to predict MPR and non-MPR, SULmax and T/N ratio of scan-2 had the best differentiation ability.The accuracy of RECIST 1.1 and PERCIST criteria were no statistical significance(p = 0.91). On univariate analysis, ΔMTV% has the highest correlation with PFS. CONCLUSIONS: Metabolic response by 18F-FDG PET/CT can predict MPR to neoadjuvant immunochemotherapy in resectable NSCLC. ΔMTV% was significantly correlated with PFS.

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Background: Cerebral radiation necrosis (RN), a severe complication of stereotactic radiotherapy (SRT), has been shown to significantly decrease patient survival time and quality of life. The purpose of this study was to analyze whether bevacizumab can prevent or reduce the occurrence of SRT-induced cerebral RN in non-small cell lung cancer (NSCLC) patients with brain metastases. Materials and methods: We retrospectively reviewed the clinical records of NSCLC patients with brain metastases from March 2013 to June 2023 who were treated with SRT. Patients were divided into two groups: those in the bevacizumab group received SRT with four cycles of bevacizumab, and patients in the control group received SRT only. Inverse probability of treatment weighting (IPTW) was performed based on a multinomial propensity score model to balance the baseline characteristics. The chi-square test was used. A Cox model was used to evaluate overall survival (OS). Results: A total of 80 patients were enrolled, namely, 28 patients in the bevacizumab group and 52 patients in the control group. The possibility of developing cerebral RN and/or symptomatic edema (RN/SE) was significantly decreased in patients treated with bevacizumab compared to those who did not receive bevacizumab before IPTW (p=0.036) and after IPTW (p=0.015) according to chi-square analysis. The IPTW-adjusted median OS was 47.7 months (95% CI 27.4-80.8) for patients in the bevacizumab group and 44.1 months (95% CI 36.7-68.0) (p=0.364) for patients in the control group. Conclusion: The application of bevacizumab concurrent with SRT may prevent or reduce the occurrence of cerebral RN in NSCLC patients with brain metastases.

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PURPOSE: Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validate the application of an NLP model to an NSCLC cohort by extracting NSCLC concepts from free-text medical notes and converting them to structured, interpretable data. METHODS: Patients with a lung neoplasm, NSCLC histology, and treatment information in their notes were selected from a repository of over 27 million patients. From these, 200 were randomly selected for this study with the longest and the most recent note included for each patient. An NLP model developed and validated on a large solid and blood cancer oncology cohort was applied to this NSCLC cohort. Two certified tumor registrars and a curator abstracted concepts from the notes: neoplasm, histology, stage, TNM values, and metastasis sites. This manually abstracted gold standard was compared with the NLP model output. Precision and recall scores were calculated. RESULTS: The NLP model extracted the NSCLC concepts with excellent precision and recall with the following scores, respectively: Lung neoplasm 100% and 100%, NSCLC histology 99% and 88%, histology correctly linked to neoplasm 98% and 79%, stage value 98.8% and 92%, stage TNM value 93% and 98%, and metastasis site 97% and 89%. High precision is related to a low number of false positives, and therefore, extracted concepts are likely accurate. High recall indicates that the model captured most of the desired concepts. CONCLUSION: This study validates that Optum's oncology NLP model has high precision and recall with clinical real-world data and is a reliable model to support research studies and clinical trials. This validation study shows that our nonspecific solid tumor and blood cancer oncology model is generalizable to successfully extract clinical information from specific cancer cohorts.

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Great progress has been made in utilizing immune checkpoint blockade (ICB) for the treatment of non-small-cell lung cancer (NSCLC). Therapies targeting programmed cell death protein 1 (PD-1) and its ligand PD-L1, expressed on tumor cells, have demonstrated potential in improving patient survival rates. An unresolved issue involves immune suppression induced by exosomal PD-L1 within the tumor microenvironment (TME), particularly regarding CD8+ T cells. Our study unveiled the crucial involvement of LAMTOR1 in suppressing the exosomes of PD-L1 and promoting CD8+ T cell infiltration in NSCLC. Through its interaction with HRS, LAMTOR1 facilitates PD-L1 lysosomal degradation, thereby reducing exosomal PD-L1 release. Notably, the ability of LAMTOR1 to promote PD-L1 lysosomal degradation relies on a specific ubiquitination site and an HRS binding sequence. The findings suggest that employing LAMTOR1 to construct peptides could serve as a promising strategy for bolstering the efficacy of immunotherapy in NSCLC. The discovery and comprehension of how LAMTOR1 inhibits the release of exosomal PD-L1 offer insights into potential therapeutic strategies for improving immunotherapy. It is imperative to conduct further research and clinical trials to investigate the feasibility and efficacy of targeting LAMTOR1 in NSCLC treatment.

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BACKGROUND: Budget impact models (BIMs) forecast the financial implications of adopting new technologies and the potential need for budget reallocation, thus playing a crucial role in reimbursement decisions. Despite the importance of accurate forecasts, studies indicate large discrepancies between estimates and reality. We are developing an artificial intelligence-based clinical decision tool to identify patients with non-small cell lung cancer who are most likely to benefit from immunotherapy. OBJECTIVE: To evaluate the budgetary implications and describe a systematic literature review of published lung cancer BIMs. METHODS: We searched PubMed and EMBASE for studies published between 2010 and 2023 that include BIMs that describe lung cancer interventions. Forward and backward reference searches were performed for all qualifying studies. We extracted author and publication year, country, interventions, disease stages, time horizon, analytical perspective, modeling methods used, types of costs included, sensitivity analyses conducted, and data sources used. We then evaluated adherence to the Professional Society for Health Economics and Pharmacoeconomics Research best-practice guidelines. RESULTS: A total of 25 BIMs were identified, spanning 14 different countries. Model structure could not be ascertained definitively for nearly half of the models. The cost calculator approach was most common among the others. Time horizons ranged from 1 to 5 years, in line with recommendations. Most models compared drugs, 4 compared nondrug interventions, and 7 compared diagnostic technologies. Assumptions about market uptake were poorly documented and poorly motivated. Inclusion of cancer-related costs was rare. Adherence to best practices was variable and did not appear to improve over time. CONCLUSIONS: The number of published BIMs for lung cancer exceeded expectations. There were modest trends toward publication frequency and model quality over time. Our analysis revealed variability across the models, as well as their adherence to best practices, indicating substantial room for improvement. Although none of the models were individually suitable for the purpose of evaluating an artificial intelligence-based treatment selection tool, some models provided valuable insights.

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BACKGROUND: Despite recent advances in lung cancer therapeutics and improving overall survival, disparities persist among socially disadvantaged populations. This study aims to determine the effects of neighborhood deprivation indices (NDI) on lung cancer mortality. This is a multicenter retrospective cohort study assessing the relationship between NDI and overall survival adjusted for age, disease stage, and DNA methylation among biopsy-proven lung cancer patients. State-specific NDI for each year of sample collection were computed at the U.S. census tract level and dichotomized into low- and high-deprivation. RESULTS: A total of 173 non small lung cancer patients were included, with n = 85 (49%) and n = 88 (51%) in the low and high-deprivation groups, respectively. NDI was significantly higher among Black patients when compared with White patients (p = 0.003). There was a significant correlation between DNA methylation and stage for HOXA7, SOX17, ZFP42, HOXA9, CDO1 and TAC1. Only HOXA7 DNA methylation was positively correlated with NDI. The high-deprivation group had a statistically significant shorter survival than the low-deprivation group (p = 0.02). After adjusting for age, race, stage, and DNA methylation status, belonging to the high-deprivation group was associated with higher mortality with a hazard ratio of 1.81 (95%CI: 1.03-3.19). CONCLUSIONS: Increased neighborhood-level deprivation may be associated with liquid biopsy DNA methylation, shorter survival, and increased mortality. Changes in health care policies that consider neighborhood-level indices of socioeconomic deprivation may enable a more equitable increase in lung cancer survival.

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Lung cancer is the leading cause of cancer mortality worldwide. Fortunately, the advent of precision medicine, which includes targeted therapy and immunotherapy, offers hope. However, identifying specific mutations is imperative before initiating precise medications. Traditional methods, such as real-time PCR examination of individual mutations, are time-consuming. Contemporary techniques, such as tissue- and plasma-based next-generation sequencing (NGS), allow comprehensive genome analysis concurrently. Notably, plasma-based NGS has a shorter turnaround time (TAT) and thus a shorter time-to-treatment (TTT). In this case report, we demonstrate the benefits of plasma-based NGS before pathological diagnosis in a patient with image-suspected non-small cell lung cancer (NSCLC). An 82-year-old Taiwanese woman presented with lower back pain persisting for one month and left-sided weakness for two weeks. Whole-body computed tomography (CT) revealed lesions suspicious for brain and bone metastases, along with a mass consistent with a primary tumor in the left upper lobe, indicative of advanced NSCLC with T4N3M1c staging. The patient underwent a bronchoscopic biopsy on Day 0, and the preliminary report that came out on Day 1 was suggestive of metastatic NSCLC. Blood was also collected for plasma-based NGS on Day 0. The patient was Coronavirus disease 2019-positive and was treated with molnupiravir on Day 6. On Day 7, pathology confirmed pulmonary adenocarcinoma, and the results of plasma-based NGS included EGFR L858R mutation. The patient was started on targeted therapy (afatinib) on Day 9. Unfortunately, the patient died of hypoxic respiratory failure on Day 26, a complication of underlying viral infection. Plasma-based NGS offers a rapid and efficient means of mutation detection in NSCLC, streamlining treatment initiation and potentially improving the negative emotions of patients. Its utility, particularly in regions with a high prevalence of specific mutations, such as EGFR alterations in East Asian populations, highlights its relevance in guiding personalized therapy decisions.

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Adoptive transfer of tumor-infiltrating lymphocytes (TIL) has shown remarkable results in melanoma, but only modest clinical benefits in other cancers, even after TIL have been genetically modified to improve their tumor homing, cytotoxic potential or overcome cell exhaustion. The required ex vivo TIL expansion process may induce changes in the T cell clonal composition, which could likely compromise the tumor reactivity of TIL preparations and ultimately the success of TIL therapy. A promising approach based on the production of bispecific T cell-engagers (TCE) by engineered T cells (STAb-T therapy) improves the efficacy of current T cell redirection strategies against tumor-associated antigens in hematological tumors. We studied the TCRß repertoire in non-small cell lung cancer (NSCLC) tumors and in ex vivo expanded TIL from two unrelated patients. We generated TIL secreting anti-epidermal growth factor receptor (EGFR) × anti-CD3 TCE (TILSTAb) and tested their antitumor efficacy in vitro and in vivo using a NSCLC patient-derived xenograft (PDX) model in which tumor fragments and TIL from the same patient were transplanted into hIL-2 NOG mice. We confirmed that the standard TIL expansion protocol promotes the loss of tumor-dominant T cell clones and the overgrowth of virus-reactive TCR clonotypes that were marginally detectable in primary tumors. We demonstrated the antitumor activity of TILSTAb both in vitro and in vivo when administered intratumorally and systemically in an autologous immune-humanized PDX EGFR+ NSCLC mouse model, where tumor regression was mediated by TCE-redirected CD4+ TIL bearing non-tumor dominant clonotypes.

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