Survival Of Cancer Patients Research Articles

Criteria for Re-Irradiation.

The treatment options for patients with progressive malignant tumors despite primary radiotherapy are often limited. In selected cases, re-irradiation can be offered. This article concerns the selection criteria and results of re-irradiation for certain types of cancer. This review is based on pertinent publications retrieved by a selective search in PubMed, with particular attention to glioblastoma, head and neck tumors, and prostatic carcinoma. The published studies of re-irradiation are few in number and often of limited methodological quality. For glioblastoma, a randomized controlled trial (RCT) found that adding re-irradiation to treatment with bevacizumab yielded no significant improvement in either median progression-free survival or median overall survival (hazard ratio [HR] 0.73; p = 0.05 and HR 0.98; p = 0.46, respectively). Re-irradiation is a treatment option for locoregional recurrences of head and neck tumors after primary radiotherapy, but it carries a risk of serious side effects. For unresectable recurrences of nasopharyngeal carcinoma, an RCT has shown that hyperfractionated re-irradiation is more effective than normofractionated re-irradiation (overall survival: HR 0.54, p = 0.014). For locally recurrent prostatic carcinoma after radiotherapy, re-irradiation can yield good oncologic outcomes with an acceptable level of urogenital and gastrointestinal side effects (5-year recurrence-free survival: stereotactic body radiation therapy (SBRT), 58%; high dose rate (HDR) brachytherapy, 77%; versus salvage prostatectomy, 72%). RCTs on this topic are lacking. Re-irradiation is a treatment option for selected cancer patients. As the available scientific evidence is limited, multidisciplinary collaboration and participatory decision-making are particularly important.

Analyzing Cancer Survival Times Using the Exponentiated Fréchet-Gompertz Distribution

Understanding the survival of cancer patients is essential for determining optimal treatment strategies. This research introduces a robust distribution for analyzing survival data known as the Exponential Fr ́echet-Gompertz distribution (EFG). The EFG combines the unique characteristics of the Exponential Fr ́echet and Gompertz distributions, enhancing its efficiency and providing greater flexibility in representing complex datasets. The study specifically investigates the EFG distribution’s effectiveness in modeling cancer patients’ survival times. A comprehensive analysis of the distribution’s properties is presented, including the quantile and quartile functions, shape indices, moments, moment-generating function, characteristic function, mean residual life, mean waiting time, R ́enyi entropy, and order statistics. The parameters of the distribution are derivedusing five distinct methods: Maximum Likelihood Estimation (MLE), Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Cram ́er-von Mises (CVM), and Maximum Product of Spacings (MPS). A Monte Carlo simulation technique is employed to evaluate the performance of these estimation methods. The simulation results indicate that as sample size increases, the meansquare error (MSE) values for all estimators decrease. Notably, the MLE exhibits the lowest MSE, while the MPS has the highest MSE, particularly for smaller sample sizes. Furthermore, the study presents a comprehensive comparison of the effectiveness of these estimation methods in analyzing survival times for various cancer types, including bladder, bone, blood and brain cancer. The results indicate that the EFG distribution is an optimal model for representing the survival times of patients across these different cancers data. Furthermore, the W.LS method yielded superior estimations for most datasets concerning cancer patient survival. Overall, the EFG distribution demonstrates exceptional capability in accurately fitting survival times for cancer patients compared to other competing distributions.

Clinical and prognostic significance of Hec1 expression in patients with Cervical Cancer

ObjectiveHec1 is a component of the Ndc80 kinetochore complex and is frequently upregulated in various cancers. However, the clinical significance of Hec1 in cervical cancer remains largely unknown. This study aimed to investigate the expression patterns of Hec1 in cervical cancer and its relationship with the clinicopathological characteristics of patients diagnosed with the disease.MethodsImmunohistochemistry was used to assess the expression of Hec1 in 136 cervical cancer tissue samples and 82 normal cervical tissue samples. The relationship between Hec1 protein expression and the clinicopathological characteristics of cervical cancer patients was analyzed using the Chi-square test. Additionally, the association between Hec1 protein expression and patient survival was examined using Kaplan-Meier survival curves. Independent risk factors affecting the prognosis of cervical cancer patients were analyzed using the Cox proportional hazards regression model.ResultsThe positive expression rate of Hec1 protein in cervical cancer tissues was 83.82%, significantly higher than the 7.31% in normal cervical tissues. Compared to patients with negative Hec1 expression, those with positive expression exhibited significantly higher FIGO staging, increased lymph node metastasis, greater depth of tumor stromal infiltration, and larger tumor diameter. Multivariable analysis using the Cox proportional hazards regression model indicated that Hec1 positive expression was an independent risk factor for both overall survival (HR = 2.79, 95% CI: 1.65–4.05, p = 0.012) and progression-free survival (HR = 1.81, 95% CI: 1.22-3.18, p = 0.002) in cervical cancer patients. Kaplan-Meier survival curve analysis showed that patients with positive Hec1 expression experienced a lower overall survival (HR: 2.72, 95% CI: 1.15–4.52, p = 0.004) and progression-free survival (HR: 3.12, 95% CI: 1.62–5.03, p = 0.002) when compared to those with negative Hec1 expression.ConclusionHec1 is significantly upregulated in cervical cancer tissues and associated with poor prognosis in cervical cancer patients. Therefore, Hec1 could be a novel biomarker, not only for the diagnosis and treatment evaluation of cervical cancer but also as an indicator for predicting the prognosis of cervical cancer patients.

Characterizing dynamic tumor-immune interactions in lung adenocarcinoma through orthotopic allograft modeling.

The major clinical challenge in lung cancer immunotherapy is drug resistance. Therefore, establishing efficient orthotopic lung cancer mouse models to explore the mechanisms of drug immunotherapy resistance is highly important. In this study, we generated multiple fluorescently labeled lung adenocarcinoma cell lines from agenetically engineered KPZ mice model. Orthotopic transplantation of the primary 1F3 cell line induced a strong immune response, causing many small tumors to disappear, but some tumors evaded the immune attack and eventually formed large tumors. Tumor microenvironment analysis demonstrated that M2 macrophages play key roles in the immune response. Further mechanistic studies revealed that the chemokine CCL7 promoted the infiltration of M2 macrophages to facilitate immune escape, thereby promoting tumor growth in the orthotopic mouse model. Moreover, CCL7 levels were elevated in human lung cancer biopsies and positively correlated with M2 macrophage infiltration, and high CCL7 levels predicted advanced pathological stage and poor survival in lung cancer patients. Overall, we established a visualized and orthotopic mouse model with fluorescently labeled cells to better dissect the tumor microenvironment of lung cancer and define the critical role of CCL7 in promoting M2 macrophage polarization and tumorigenesis, providing new preclinical tools and potential targets for lung cancer immunotherapy.

Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma.

A novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset. This prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells in vitro. We constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells in vitro, and increased the percentage of apoptotic W038 cells. The analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene GBP1P1 might serve as prognostic biomarker for OC.

Pharmacological USP2 targeting suppresses ovarian cancer growth by potentiating apoptosis and ferroptosis

Ovarian cancer is a frequently observed type of gynaecologic malignancy generally associated with poor prognosis around the world. Ubiquitin-specific proteases (USPs) form the largest subfamily of deubiquitylating enzymes and have emerged as potential therapeutic targets against human cancers. Through a systematic analysis of the prognostic significance of USP expression, USP2 was found to be inversely correlated with patient survival in ovarian cancer. Accordingly, we investigated the effects of pharmacological inhibition of USP2 on ovarian cancer by exploiting its small molecule inhibitor ML364. Our findings show that ML364 effectively hindered ovarian cancer growth and migration using a series of in vitro assays. In addition to apoptosis induction, ML364 also sensitized ovarian cancer cells to ferroptosis. Mechanistically, ML364 treatment resulted in cyclin D1 downregulation, increased poly (ADP-ribose) polymerase (PARP) cleavage, and elevated ROS levels in ovarian cancer cells. Collectively, our findings suggest USP2 as a potential therapeutic target in ovarian cancer, and hence, its pharmacological inhibition warrants further investigation.

Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.

Due to the heterogeneity in the effectiveness of immunotherapy for lung cancer, identifying predictors is crucial. This study aimed to develop a machine learning model to identify predictors of overall survival in lung cancer patients treated with immune checkpoint inhibitors (ICIs). A retrospective analysis was performed on data from 1314 lung cancer patients at the Chongqing University Cancer Hospital from September 2018 to September 2022. We used the random survival forest (RSF) model to identify survival-influencing factors, using backward elimination for variable selection. A Cox proportional hazards (CPH) model was constructed using the most significant predictors. We assessed model performance and generalizability using time-dependent receiver operating characteristics (ROC) and predictive error curves. The RSF model demonstrated better predictive accuracy than the CPH (IBS 0.17 vs. 0.17; C-index 0.91 vs. 0.68), with better discrimination and prediction performance. The influential variables identified included D-dimer, Karnofsky performance status, albumin, surgery, TNM stage, platelet count, and age. The RSF model, which incorporated these variables, achieved area under the curve (AUC) scores of 0.95, 0.94, and 0.98 for 1-, 3-, and 5-year survival predictions, respectively, in the training set. The validation set showed AUCs of 0.94, 0.90, and 0.95, respectively, exceeding the performance of the CPH model. The study successfully developed a machine learning model that accurately predicted the survival benefits of ICI therapy in lung cancer patients, supporting clinical decision-making in lung cancer treatment.

The Effect of Statin Usage on Survival in Metastatic Colorectal Cancer Patients Receiving Regorafenib.

Regorafenib is an oral multikinase inhibitor used in later lines for metastatic colorectal carcinoma (mCRC) treatment, but its efficacy and tolerability are low. To improve the response rates and ameliorate adverse effects, different strategies have been implemented. In our study, we examined the effect of statin usage in patients with mCRC treated with regorafenib. This single-center retrospective study included patients with mCRC who were treated with regorafenib between January 2015 and December 2023. The primary outcomes were progression-free survival (PFS) and overall survival (OS), and the secondary outcomes were adverse effects and the tolerability of regorafenib. The data of 105 patients were collected retrospectively. The median age of the patients was 66 years, and 60 patients were male. Seventeen patients (16.1%) were receiving statins. Statin-using patients were significantly older than non-users (72 years vs. 66.5 years, p=0.05). Comorbid diseases were more common in patients using statins. The median PFS was 1.9 months for statin users and 4.2 months for statin non-users (p<0.001), and the median OS was 4.7 vs. 6.7 months (p=0.01). Cox regression revealed that statin usage was significantly associated with a higher hazard ratio (HR) for PFS (2.53) and OS (2.06) (both p<0.01 and p=0.02, respectively). Statins are associated with decreased survival and response rates in patients with mCRC treated with regorafenib. However, further studies are needed to confirm these results.

Companion diagnostics and predictive biomarkers for PD-1/PD-L1 immune checkpoint inhibitors therapy in malignant melanoma

Programmed cell death receptor 1 (PD-1), when bound to the ligand programmed death-ligand 1 (PD-L1), can suppress cellular immunity and play a critical role in the initiation and development of cancer. Immune drugs targeting these two sites have been developed for different cancers, including malignant melanoma. The accompanying diagnostic method has been approved by the FDA to guide patient medication. However, the method of immunohistochemical staining, which varies widely due to the antibody and staining cut-off values, has certain limitations in application and does not benefit all patients. Increasing researches begin to focus on new biomarkers to improve objective response rates and survival in cancer patients. In this article, we enumerated three major groups, including tumour microenvironment, peripheral circulation, and gene mutation, which covered the current main research directions. In the future, we hope those biomarkers may be used to guide the treatment of patients with malignant melanoma.

Prognostic value of patient-reported outcomes in advanced or metastatic melanoma patients treated with immunotherapy: Findings from the CheckMate-067 study

ObjectivePatient-reported outcomes (PROs) that predict survival in cancer patients have yet to be realized as practical tools for clinicians to make better treatment decisions. To identify such PROs in adults with advanced melanoma treated with immunotherapy, this study used 7.5-year follow-up data from CheckMate-067, a phase 3, randomized, double-blind study of nivolumab or nivolumab plus ipilimumab versus ipilimumab. MethodsPRO data assessed using the European Organization of Research for the Treatment of Cancer Core-30 and EQ-5D-3L at baseline and during subsequent visits after treatment initiation were pooled across treatment arms. Associations between baseline PRO or change from baseline (CFB) scores with survival outcomes (progression-free survival [PFS], overall survival [OS], and melanoma-specific survival [MSS]) were examined using Cox proportional hazards models for PFS or OS and cause-specific hazard models for MSS. ResultsBaseline and CFB scores for most PRO domains, especially for physical functioning, global health status/quality of life (GHS/QoL), fatigue, and EQ-5D visual analog scale (VAS), were prognostic of all survival outcomes. Achieving meaningful improvement/maintenance of baseline PRO scores at 12 weeks following treatment initiation predicted better survival outcomes than with meaningful worsening from baseline. ConclusionsPROs at baseline and during treatment, particularly for physical functioning, GHS/QoL, fatigue, and EQ-VAS, were prognostic of survival outcomes. This knowledge may accelerate development of prognostic tools to manage treatment in patients with previously untreated unresectable or metastatic melanoma who undergo immunotherapy.

Effects of Concurrent Administration of Spironolactone in Veterans with Metastatic Prostate Cancer Receiving Abiraterone: A Real-World Retrospective Study.

Prostate cancer is the most common cancer in men in the United States with low survival rates once metastasized. Abiraterone is approved for use in castrate-sensitive and castrate-resistant prostate cancer and is used extensively in the Veterans Affairs (VA) healthcare system. Spironolactone, a diuretic used to treat heart failure, edema, ascites, and hypertension, may increase androgen levels and reduce effectiveness of abiraterone when used concurrently to treat prostate cancer patients. While previous case studies support this, no large epidemiology studies have been conducted. The current study utilizes the large, VA prostate cancer data core and evaluates the effect of concomitant spironolactone on efficacy of abiraterone treatment in metastatic prostate cancer patients. The study selected 18,943 veterans with metastatic prostate cancer on abiraterone treatment. Of these, 581 patients (3.1%) were also on concomitant spironolactone. The concomitant treatment group, abiraterone + spironolactone, significantly differed from the abiraterone-only group in body mass index, prevalence rates of heart failure and liver disease, and being previously treated with docetaxel. A 1:1 propensity score matching method was used to balance sample sizes and baseline traits between the two treatment groups, abiraterone versus abiraterone + spironolactone. Kaplan-Meier curves and Cox proportional hazard model were used to compare 5-year overall survival and all-cause mortality outcomes, respectively, between the two groups. After propensity score matched, the abiraterone + spironolactone group was treated with abiraterone significantly longer than the abiraterone-only group (mean ± standard deviation days 549.0 ± 552.3 vs. 435.5 ± 474.1; p = 0.0002) and had a higher 5-year overall survival rate (44% vs. 37%; p = 0.0116). Veterans with metastatic prostate cancer treated with abiraterone + spironolactone also had a lower 5-year all-cause mortality compared to those only on abiraterone (hazard ratio 0.80, 95% confidence intervals 0.61-0.96; p = 0.012). This large VA observational study suggests that concomitant use of spironolactone does not compromise cancer control or survival of metastatic prostate cancer patients treated with abiraterone.

SGLT2 inhibitor empagliflozin prevents anthracycline-induced cardiotoxicity in a large-animal model

Abstract Background While the development of cancer therapies has significantly improved the survival of cancer patients, more than 35% of cancer survivors treated with anthracyclines will develop cardiotoxicity. There is currently a lack of preventative therapies against anthracycline-induced cardiotoxicity (AIC). AIC is characterised by a disruption in myocardial metabolism. Sodium-glucose co-transporter (SLGT2) inhibitors represent a potential therapy as they have been shown to modulate myocardial substrate utilisation in other heart-failure contexts. Purpose We developed a highly translational large-animal model to test the ability of SGLT2 inhibitor empagliflozin to prevent AIC at different doses. Methods Female Large-White pigs were randomised (2:1:1) to no treatment (AIC-control group) or empagliflozin at a daily oral dose of 10 mg or 20 mg, beginning at the start of the doxorubicin regimen and continuing to 21 weeks. All pigs received 6 doses of doxorubicin intravenous infusion (25 mg/m2/every 3 weeks) with 2 additional off-dose visits. Pigs were assessed by cardiac magnetic resonance (CMR) and MR spectroscopy (MRS) at baseline and every 3 weeks for 21 weeks. At the end of 21-week follow-up, the LV was collected and processed for analysis by transmission electron microscopy (TEM) and mitochondrial respirometry. Results The primary outcome, final LVEF, was significantly higher in pigs receiving 20 mg empagliflozin than in the AIC-control group (57.5% (IQR=55.5, 60.3) vs 47.0% (40.8, 47.8); p=0.027). Final LVEF in pigs receiving 10 mg empagliflozin was 51% (46.5, 55.5) (p=0.020 vs 20 mg empagliflozin). The 20 mg empagliflozin dose prevented the doxorubicin-induced change in cardiac metabolic substrate utilisation and was associated with preservation of myocardial energetics (ATP/PCr ratio on MRS). TEM revealed disrupted mitochondrial structure and cristae density in AIC-control pigs, while mitochondria respirometry detected deteriorated respiratory function. Both mitochondrial structure and function were preserved in empagliflozin-treated pigs, with the 20 mg dose having the strongest cardioprotective effect. Conclusion The results from our translational large-animal model provide strong evidence that SGLT2 inhibition with empagliflozin prevents AIC by preserving LV systolic function and energetics as well as mitochondrial structural integrity and function. Our results establish the basis for clinical trial testing SGLT2i therapy in patients at high risk of AIC.

MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy

ObjectiveThis study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment.MethodsWe performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA).ResultsModel 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792–0.874) in the training set and 0.789 (95% CI: 0.679–0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set.ConclusionsThe integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.

Personalized three-year survival prediction and prognosis forecast by interpretable machine learning for pancreatic cancer patients: a population-based study and an external validation.

The overall survival of patients with pancreatic cancer is extremely low. We aimed to establish machine learning (ML) based model to accurately predict three-year survival and prognosis of pancreatic cancer patients. We analyzed pancreatic cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2021. Univariate and multivariate logistic analysis were employed to select variables. Recursive Feature Elimination (RFE) method based on 6 ML algorithms was utilized in feature selection. To construct predictive model, 13 ML algorithms were evaluated by area under the curve (AUC), area under precision-recall curve (PRAUC), accuracy, sensitivity, specificity, precision, cross-entropy, Brier scores and Balanced Accuracy (bacc) and F Beta Score (fbeta). An optimal ML model was constructed to predict three-year survival, and the predictive results were explained by SHapley Additive exPlanations (SHAP) framework. Meanwhile, 101 ML algorithm combinations were developed to select the best model with highest C-index to predict prognosis of pancreatic cancer patients. A total of 20,064 pancreatic cancer patients from SEER database was consecutively enrolled. We utilized eight clinical variables to establish prediction model for three-year survival. CatBoost model was selected as the best prediction model, and AUC was 0.932 [0.924, 0.939], 0.899 [0.873, 0.934] and 0.826 [0.735, 0.919] in training, internal test and external test sets, with 0.839 [0.831, 0.847] accuracy, 0.872 [0.858, 0.887] sensitivity, 0.803 [0.784, 0.825] specificity and 0.832 [0.821, 0.853] precision. Surgery type had the greatest effects on three-year survival according to SHAP results. For prognosis prediction, "RSF+GBM" algorithm was the best prognostic model with C-index of 0.774, 0.722 and 0.674 in training, internal test and external test sets. Our ML models demonstrate excellent accuracy and reliability, offering more precise personalized prognostic prediction to pancreatic cancer patients.

Impact of PM2.5 exposure on mortality and tumor recurrence in resectable non-small cell lung carcinoma

This study aimed to explore the impact of PM 2.5 exposure on survival, post-operative outcomes, and tumor recurrence in resectable non-small cell lung cancer (NSCLC) patients. The study cohort comprised 587 patients at Chiang Mai University Hospital between January 1, 2010, and December 31, 2017. Patients were categorized based on their residents’ average PM 2.5 concentration into two groups: exposed (PM 2.5 ≥ 25 µg/m3 annual mean) and unexposed (PM 2.5 < 25 µg/m3 annual mean). The exposed group had 278 patients, while the unexposed group had 309 patients. Baseline differences in gender and surgical approach were observed between the groups. Multivariable regression analysis revealed that patients in the exposed group had a higher risk of death (HR 1.44, 95% CI, 1.08–1.89, p = 0.012). However, no significant associations were found between PM 2.5 and post-operative pulmonary complications (RR 1.12, 95% CI, 0.60–2.11, p = 0.718), in-hospital mortality (RR 1.98, 95% CI, 0.40–9.77, p = 0.401), and tumor recurrence (HR 1.12, 95% CI, 0.82–1.51, p = 0.483). In conclusion, a PM 2.5 concentration ≥ 25 µg/m3 annual mean was associated with decreased overall survival and a potential increase in in-hospital mortality among resectable NSCLC patients. Larger studies with extended follow-up periods are required to validate these findings.

Precision Oncology in Older Cancer Patients: A Single-Center Experience

In the last two decades, next-generation sequencing (NGS) has facilitated enormous progress in cancer medicine, in both diagnosis and treatment. However, the usefulness of NGS in older cancer patients is unclear. To determine the role of NGS in older cancer patients, we retrospectively assessed demographic, clinicopathologic, and disease-specific data from 100 randomly selected cancer patients (any subtype/stage) who underwent NGS testing in 2020 at our institution and compared the treatment outcomes (progression-free survival [PFS] and overall survival [OS]) in the younger and older patient cohorts (A [n = 34] and B [n = 66]: age &lt;70 and ≥70 years, respectively). Overall, 27% had targetable mutations, and 8% received NGS-determined targeted therapy (45% and 19% of patients with a targetable mutation in cohorts A and B, respectively; p = 0.2), of whom 38% (3% of the whole cohort) benefited from the therapy (PFS &gt; 6 months). The median OS (from diagnosis) was 192 and 197 weeks in cohorts A and B, respectively (p = 0.08). This pilot study revealed no significant age-stratified difference in the diagnostic approach and treatment strategy. A small, but relevant, proportion of the cohort (3%) benefited from NGS-determined treatment. Nevertheless, older cancer patients with targetable mutations less frequently received targetable therapies.

MECOM Locus classical transcript isoforms affect tumor immune microenvironment and different targets in ovarian cancer

The MECOM locus is a gene frequently amplified in high-grade serous ovarian carcinoma (HGSOC). Nevertheless, the body of research examining the associations among MECOM transcripts, patient prognosis, and their role in modulating the tumor immune microenvironment (TIME) remains sparse, particularly in large cohorts. This study assessed the expression of MECOM transcripts in 352 HGSOC patients and 88 normal ovarian tissues from the combined GTEx/TCGA database. Using resources such as the UCSC Genome Browser, Ensembl, and NextProt, two transcripts corresponding to classical protein isoforms from MECOM were identified. Cox proportional hazards regression analysis, Kaplan-Meier survival curves, and a comprehensive TIME evaluation algorithm were employed to elucidate the connections between the expression levels of these transcripts and both patient prognosis and TIME status. Chromatin Immunoprecipitation sequencing (ChIP-seq) data for the two protein isoforms, as well as RNA sequencing data post-targeted silencing, were analyzed to identify potential regulatory targets of the different transcription factors. Elevated expression of the MECOM isoform transcripts was correlated with poorer survival in HGSOC patients, potentially through the modulation of cancer-associated fibroblasts (CAFs) and immunosuppressive cell populations. In contrast, higher levels of EVI1 isoform transcripts were linked to enhanced survival, possibly due to the regulation of CD8+ T cells, macrophages, and a reduction in the expression of JUN protein, or its DNA-binding activity on downstream genes. Diverse protein isoforms derived from MECOM were found to differentially affect the survival and tumor development in ovarian cancer patients through specific mechanisms. Investigating the molecular mechanisms underlying disease pathogenesis and identifying potential drug target proteins at the level of splice variant isoforms were deemed crucial.

Abstract B011: MTA-cooperative PRMT5 inhibition (BMS-986504) induces MTAP del tumor cell-intrinsic immune evasion and regulation

Abstract Homozygous deletion of MTAP gene (MTAP del) occurs in ∼10% of all cancers. BMS- 986504 is a second-generation MTA-cooperative PRMT5 inhibitor designed to preferentially bind to the PRMT5-MTA complex which accumulates in MTAP del cancer cells and has demonstrated clinical proof-of-concept (POC) for selectively inhibiting PRMT5 activity in MTAP del patients as monotherapy. While immunotherapy (IO) has increased patient survival in many cancers and is now the standard of care in first and later lines, most patients ultimately relapse. Thus, there remains an unmet need to improve response for these patients receiving IO. BMS-986504 in combination with anti- PD1 has the potential to increase efficacy, but there is a limited understanding of the interaction between MTAP/PRMT5 biology and PD-1 blockade. Here, we evaluated the BMS-986504-induced immunological and cell killing effects on MTAP del tumor cells and T cells to understand potential mechanisms to support BMS-986504 combination opportunities with anti-PD1 therapy. Using a panel of human PBMC-derived T cells, we first demonstrate that non-activated T cells and T cells activated via anti-CD3/CD28 are insensitive to BMS-986504-induced cell killing at in vitro concentrations equivalent to clinical exposure, supporting the significantly improved selectivity margin of BMS- 986504 relative to first-generation PRMT5 inhibitors that showed T cell toxicity in vitro and on-target dose-limiting hematological toxicity in the clinic. Since previous studies have suggested that MTAP wild-type cells may be affected by MTA secreted from surrounding MTAP del tumors, we assessed T cell viability in the presence of MTA. We found that inactivated T cells maintain insensitivity to BMS-986504 treatment with or without extracellular MTA, but T cells activated via anti-CD3/CD28 are partially sensitive to BMS-986504-induced killing in the presence of MTA only. Using flow cytometry, we characterized the surface expression of immune-related markers on tumor cells treated with BMS-986504 and identified that BMS-986504 dose-dependently increases PD-L1 expression on MTAP del tumor cells, suggesting a potential immune evasion mechanism. Next, using human PBMC-derived T cell and tumor cell co-culture assays, we evaluated the ability of BMS-986504 and anti-PD1 combination to mediate both tumor-intrinsic killing and anti-tumor immune activation in MTAP del cancer cell lines. Taken together, we provide translational proof-of-concept data demonstrating that BMS- 986504 induces tumor cell-intrinsic immunological effects in MTAP del tumors, while having limited impact on T cell viability. This data supports the rationale for BMS- 986504 and IO combination, which may potentially lead to anti-tumor immune activation and improved outcome for MTAP del patients. Citation Format: Josephine Hai, Stephanie Xavier, Sangeeth George, Lars Engstrom, Pete Olson, Tyler Simpson, Ming Lei, Benjamin Chen. MTA-cooperative PRMT5 inhibition (BMS-986504) induces MTAP del tumor cell-intrinsic immune evasion and regulation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr B011.

The relationship between the Prognostic Nutritional Index and lymphovascular and perineural invasion of the tumor in patients diagnosed with gastric cancer, and its effect on overall survival.

A low Prognostic Nutritional Index (PNI) value, lymphovascular invasion (LVI), and perineural invasion (PeNI) have been identified as indicators of poor prognosis for many malignancies. We aimed to evaluate the relationship between PNI and LVI/PeNI, their prognostic significance, and their effect on overall survival in gastric cancer patients who underwent curative gastrectomy. A cutoff value of 39.8 was taken for the PNI, and PNI < 39.8 was defined as moderate to severe malnutrition. Patients were grouped as PNI-low (PNI < 39.8) and PNI-high (PNI ≥ 39.8). Paraffin-embedded tissue sections of surgical specimens were used to evaluate PeNI as defined by previously reported criteria. The study included 270 patients with ages ranging from 23 to 90 years. The mean PNI was calculated as 39.8 ± 6.35. PeNI was detected in 232 patients (85.93%), and LVI was identified in 248 patients (91.85%). It was observed that the PNI value of patients with an expired status in the PNI < 39.8 group was lower compared to those who survived, and in patients with PNI > 39.8, those without PeNI had better survival. The presence of PeNI in patients with PNI > 39.8 increased the mortality risk by 2.088 units, while in patients with PNI > 39.8, it was found that those without LVI had better survival, and the presence of LVI increased the mortality risk by 3.171 units. Mortality developed in 166 patients (61.48%) during the five-year follow-up period. The five-year overall survival was found to be 31.02 ± 21.73 months. In patients with gastric cancer, the PNI, LVI, and PeNI are independent prognostic factors for overall survival in postoperative patients. A low PNI score is an inherently poor prognostic factor. In patients with a high PNI score, the presence of positive LVI and PeNI negatively impacts survival. We found that in patients with a low PNI, the rates of PeNI and LVI are higher compared to those with a high PNI, and this significantly affects mortality.

The role of NOP58 in prostate cancer progression through SUMOylation regulation and drug response.

Prostate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58. We conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses. The dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat. NOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology.