A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors.

Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.

A biological age model based on physical examination data to predict mortality in a Chinese population.

Biological age could be reflective of an individual's health status and aging degree. Limited estimations of biological aging based on physical examination data in the Chinese population have been developed to quantify the rate of aging. We developed and validated a novel aging measure (Balanced-AGE) based on readily available physical health examination data. In this study, a repeated sub-sampling approach was applied to address the data imbalance issue, and this approach significantly improved the performance of biological age (Balanced-AGE) in predicting all-cause mortality with a 10-year time-dependent AUC of 0.908 for all-cause mortality. This mortality prediction tool was found to be effective across different subgroups by age, sex, smoking, and alcohol consumption status. Additionally, this study revealed that individuals who were underweight, smokers, or drinkers had a higher extent of age acceleration. The Balanced-AGE may serve as an effective and generally applicable tool for health assessment and management among the elderly population.

Combining Classic and Novel Neutrophil-Related Biomarkers to Identify Non-Small-Cell Lung Cancer.

BACKGROUND: Recent studies have revealed that neutrophils play a crucial role in cancer progression. This study aimed to explore the diagnostic value of neutrophil-related biomarkers for non-small-cell lung cancer (NSCLC). METHODS: We initially assessed the associations between classic neutrophil-related biomarkers (neutrophil-to-lymphocyte ratio (NLR), absolute neutrophil counts (NEU), absolute lymphocyte counts (LYM)) and NSCLC in 3942 cases and 6791 controls. Then, we measured 11 novel neutrophil-related biomarkers via Luminex Assays in 132 cases and 66 controls, individually matching on sex and age (±5 years), and evaluated their associations with NSCLC risk. We also developed the predictive models by sequentially adding variables of interest and assessed model improvement. RESULTS: Interleukin-6 (IL-6) (odds ratio (OR) = 10.687, 95% confidence interval (CI): 3.875, 29.473) and Interleukin 1 Receptor Antagonist (IL-1RA) (OR = 8.113, 95% CI: 3.182, 20.689) shows strong associations with NSCLC risk after adjusting for body mass index, smoking status, NLR, and carcinoembryonic antigen. Adding the two identified biomarkers to the predictive model significantly elevated the model performance from an area under the receiver operating characteristic curve of 0.716 to 0.851 with a net reclassification improvement of 97.73%. CONCLUSIONS: IL-6 and IL-1RA were recognized as independent risk factors for NSCLC, improving the predictive performance of the model in identifying disease.

Big data and artificial intelligence in cancer research.

The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.

Association of fat mass and fat-free mass with all-cause and cause-specific mortality in Asian individuals: A prospective cohort study.

OBJECTIVE: The study's objective was to investigate the association of fat mass index (FMI) and fat-free mass index (FFMI) with all-cause mortality and cause-specific mortality in the Chinese population. METHODS: A total of 422,430 participants (48.1% men and 51.9% women) from the Taiwan MJ Cohort with an average follow-up of 9 years were included. RESULTS: The lowest (Q1) and highest (Q5) quintiles of FMI and FFMI were associated with increased all-cause mortality. Compared with those in the third quintile (Q3) group of FMI, participants in Q1 and Q5 groups of FMI had hazard ratios and 95% CI of 1.32 (1.24-1.40) and 1.13 (1.06-1.20), respectively. Similarly, compared with those in Q3 group of FFMI, people in Q1 and Q5 groups of FFMI had hazard ratios of 1.14 (1.06-1.23) and 1.16 (1.10-1.23), respectively. In the restricted cubic spline models, both FMI and FFMI showed a J-shaped association with all-cause mortality. People in Q5 group of FFMI had a hazard ratio of 0.72 (0.58-0.89) for respiratory disease. CONCLUSIONS: The mortality risk increases in those with excessively high or low FMI and FFMI, yet the associations between FMI, FFMI, and the risk of death varied across subgroups and causes of death.

Association between plant-based dietary pattern and biological aging trajectory in a large prospective cohort.

BACKGROUND: Aging is a dynamic and heterogeneous process that may better be captured by trajectories of aging biomarkers. Biological age has been advocated as a better biomarker of aging than chronological age, and plant-based dietary patterns have been found to be linked to aging. However, the associations of biological age trajectories with mortality and plant-based dietary patterns remained unclear. METHODS: Using group-based trajectory modeling approach, we identified distinctive aging trajectory groups among 12,784 participants based on a recently developed biological aging measure acquired at four-time points within an 8-year period. We then examined associations between aging trajectories and quintiles of plant-based dietary patterns assessed by overall plant-based diet index (PDI), healthful PDI (hPDI), and unhealthful PDI (uPDI) among 10,191 participants who had complete data on dietary intake, using multivariable multinomial logistics regression adjusting for sociodemographic and lifestyles factors. Cox proportional hazards regression models were applied to investigate the association between aging trajectories and all-cause mortality. RESULTS: We identified three latent classes of accelerated aging trajectories: slow aging, medium-degree, and high-degree accelerated aging trajectories. Participants who had higher PDI or hPDI had lower odds of being in medium-degree (OR = 0.75, 95% CI: 0.65, 0.86 for PDI; OR = 0.73, 95% CI: 0.62, 0.85 for hPDI) or high-degree (OR = 0.63, 95% CI: 0.46, 0.86 for PDI; OR = 0.62, 95% CI: 0.44, 0.88 for hPDI) accelerated aging trajectories. Participants in the highest quintile of uPDI were more likely to be in medium-degree (OR = 1.72, 95% CI: 1.48, 1.99) or high-degree (OR = 1.70, 95% CI: 1.21, 2.38) accelerated aging trajectories. With a mean follow-up time of 8.40 years and 803 (6.28%) participants died by the end of follow-up, we found that participants in medium-degree (HR = 1.56, 95% CI: 1.29, 1.89) or high-degree (HR = 3.72, 95% CI: 2.73, 5.08) accelerated aging trajectory groups had higher risks of death than those in the slow aging trajectory. CONCLUSIONS: We identified three distinctive aging trajectories in a large Asian cohort and found that adopting a plant-based dietary pattern, especially when rich in healthful plant foods, was associated with substantially lowered pace of aging.

6-Year trajectory of fasting plasma glucose (FPG) and mortality risk among individuals with normal FPG at baseline: a prospective cohort study.

BACKGROUND: Higher fasting plasma glucose (FPG) levels were associated with an increased risk of all-cause mortality; however, the associations between long-term FPG trajectory groups and mortality were unclear, especially among individuals with a normal FPG level at the beginning. The aims of this study were to examine the associations of FPG trajectories with the risk of mortality and identify modifiable lifestyle factors related to these trajectories. METHODS: We enrolled 50,919 individuals aged ≥ 20 years old, who were free of diabetes at baseline, in the prospective MJ cohort. All participants completed at least four FPG measurements within 6 years after enrollment and were followed until December 2011. FPG trajectories were identified by group-based trajectory modeling. We used Cox proportional hazards models to examine the associations of FPG trajectories with mortality, adjusting for age, sex, marital status, education level, occupation, smoking, drinking, physical activity, body mass index, baseline FPG, hypertension, dyslipidemia, cardiovascular disease or stroke, and cancer. Associations between baseline lifestyle factors and FPG trajectories were evaluated using multinomial logistic regression. RESULTS: We identified three FPG trajectories as stable (n = 32,481), low-increasing (n = 17,164), and high-increasing (n = 1274). Compared to the stable group, both the low-increasing and high-increasing groups had higher risks of all-cause mortality (hazard ratio (HR) = 1.18 (95% CI 0.99-1.40) and 1.52 (95% CI 1.09-2.13), respectively), especially among those with hypertension. Compared to participants with 0 to 1 healthy lifestyle factor, those with 6 healthy lifestyle factors were more likely to be in the stable group (ORlow-increasing = 0.61, 95% CI 0.51-0.73; ORhigh-increasing = 0.20, 95% CI 0.13-0.32). CONCLUSIONS: Individuals with longitudinally increasing FPG had a higher risk of mortality even if they had a normal FPG at baseline. Adopting healthy lifestyles may prevent individuals from transitioning into increasing trajectories.

Risk of Death Associated With Reversion From Prediabetes to Normoglycemia and the Role of Modifiable Risk Factors.

Importance: Individuals with prediabetes have a higher risk of death than healthy individuals. However, previous findings have suggested that individuals with reversion from prediabetes to normoglycemia may not have a lower risk of death compared with individuals with persistent prediabetes. Objectives: To investigate the associations between changes in prediabetes status and risk of death and to elucidate the roles of modifiable risk factors in these associations. Design, Setting, and Participants: This population-based prospective cohort study used data from 45 782 participants with prediabetes from the Taiwan MJ Cohort Study who were recruited between January 1, 1996, and December 31, 2007. Participants were followed up from the second clinical visit to December 31, 2011, with a median (IQR) follow-up of 8 (5-12) years. Participants were categorized into 3 groups according to changes in their prediabetes status within a 3-year period after initial enrollment: reversion to normoglycemia, persistent prediabetes, and progression to diabetes. Cox proportional hazards regression models were used to examine the associations between changes in prediabetes status at baseline (ie, the second clinical visit) and risk of death. Data analysis was performed between September 18, 2021, and October 24, 2022. Main Outcomes and Measures: All-cause mortality, cardiovascular disease (CVD)-related mortality, and cancer-related mortality. Results: Of 45 782 participants with prediabetes (62.9% male; 100% Asian; mean [SD] age, 44.6 [12.8] years), 1786 (3.9%) developed diabetes and 17 021 (37.2%) reverted to normoglycemia. Progression from prediabetes to diabetes within a 3-year period was associated with higher risks of all-cause death (hazard ratio [HR], 1.50; 95% CI, 1.25-1.79) and CVD-related death (HR, 1.61; 95% CI, 1.12-2.33) compared with persistent prediabetes, while reversion to normoglycemia was not associated with a lower risk of all-cause death (HR, 0.99; 95% CI, 0.88-1.10), cancer-related death (HR, 0.91; 95% CI, 0.77-1.08), or CVD-related death (HR, 0.97; 95% CI, 0.75-1.25). Among individuals who were physically active, reversion to normoglycemia was associated with a lower risk of all-cause death (HR, 0.72; 95% CI, 0.59-0.87) compared with those with persistent prediabetes who were physically inactive. Among individuals with obesity, risk of death varied between those who experienced reversion to normoglycemia (HR, 1.10; 95% CI, 0.82-1.49) and those who had persistent prediabetes (HR, 1.33; 95% CI, 1.10-1.62). Conclusions and Relevance: In this cohort study, although reversion from prediabetes to normoglycemia within a 3-year period did not mitigate the overall risk of death compared with persistent prediabetes, risk of death associated with reversion to normoglycemia varied based on whether individuals were physically active or had obesity. These findings highlight the importance of lifestyle modification among those with prediabetes status.

Development of a Novel Multidimensional Measure of Aging to Predict Mortality and Morbidity in the Prospective MJ Cohort.

BACKGROUND: Although biological aging has been proposed as a more accurate measure of aging, few biological aging measures have been developed for Asians, especially for young adults. METHODS: A total of 521 656 participants were enrolled in the MJ cohort (1996-2011) and were followed until death, loss-to-follow-up, or December 31, 2011, whichever came first. We selected 14 clinical biomarkers, including chronological age, using a random forest algorithm, and developed a multidimensional aging measure (MDAge). Model performance was assessed by area under the curve (AUC) and internal calibration. We evaluated the associations of MDAge and residuals from regressing MDAge on chronological age (MDAgeAccel) with mortality and morbidity, and assessed the robustness of our findings. RESULTS: MDAge achieved an excellent AUC of 0.892 in predicting all-cause mortality (95% confidence interval [CI]: 0.889-0.894). Participants with higher MDAge at baseline were at a higher risk of death (per 5 years, hazard ration [HR] = 1.671, 95% CI: 1.662-1.680), and the association remained after controlling for other variables and in different subgroups. Furthermore, participants with higher MDAgeAccel were associated with shortened life expectancy. For instance, compared to men who were biologically younger (MDAgeAccel ≤ 0) at baseline, men in the highest tertiles of MDAgeAccel had shortened life expectancy by 17.23 years. In addition, higher MDAgeAccel was associated with having chronic disease either cross-sectionally (per 1-standard deviation [SD], odds ratio [OR] = 1.564, 95% CI: 1.552-1.575) or longitudinally (per 1-SD, OR = 1.218, 95% CI: 1.199-1.238). CONCLUSION: MDAge accurately predicted mortality and morbidity, which has great potential in the early identification of individuals at higher risk and therefore promoting early intervention.

Body mass index and survival after cancer diagnosis: A pan-cancer cohort study of 114 430 patients with cancer.

The recommendation encouraging patients with cancer to keep a normal body mass index (BMI) is largely extrapolated from data on risk of developing cancer. We tested the prospective association between peri-diagnostic (within 1 year post-diagnosis) BMI and all-cause mortality in patients with incident cancers. During 7.2 years of follow-up, 42% (48,340) of the 114 430 patients with cancer died. Spline analysis revealed that compared with a BMI of 22.5, a BMI lower than 22.5 was associated with increased risk of all-cause mortality across 24 cancer types. A BMI higher than 22.5 was associated with reduced all-cause mortality, while a non-linear association was observed; the lowest risk was found at a BMI of 29.6-34.2, and the risk started to return to and above unity at very high BMI values. The reduced mortality risk of high BMI was observed in 23 of 24 cancer types and maintained after attempts to remove potential selection bias, confounding by smoking and comorbidities, and reserve causality. Compared with a normal BMI of 18.5-24.9, the hazard ratios were 0.85 (95% confidence interval [CI], 0.83-0.87) for an overweight BMI (25-29.9) and 0.82 (0.80-0.85) for an obese BMI (≥30), and the associations were generally consistent across cancer types and various subgroups. Obese BMI was associated with increased life expectancy, up to 6 years among men and 3 years among women. In conclusion, while overweight/obese BMI increases the risk of developing cancer in the general population, overweight/obese peri-diagnostic BMI was associated with longer survival in cancer patients.

Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers.

The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non-small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non-small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage-specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy. SIGNIFICANCE: The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Soluble Immune Checkpoint-Related Proteins in Blood Are Associated With Invasion and Progression in Non-Small Cell Lung Cancer.

Background: Immune checkpoint inhibition therapy has been achieved significant success in the treatment of non-small cell lung cancer (NSCLC). However, the role of soluble immune checkpoint- related proteins in NSCLC remains obscure. Methods: We evaluated the circulating levels of 14 immune checkpoint-related proteins panel (BTLA, LAG-3, GITR, IDO, PD-L2, PD-L1, PD-1, HVEM, Tim-3, CD28, CD27, CD80, CD137 and CTLA-4) and their associations with the risk of invasive disease and the risk of NSCLC in 43 pre-invasive (AIS), 81 invasive NSCLC (IAC) patients and matched 35 healthy donors using a multiplex Luminex assay. Gene expression in tumors from TCGA were analyzed to elucidate potential mechanisms. The multivariate logistic regression model was applied in the study. ROC(receiver operator characteristic) curve and calibration curve were used in the performance evaluation. Results: We found that sCD27, sCD80, CD137 and sPDL2 levels were significantly increased in IAC cases compared to AIS cases (P= 1.05E-06, 4.44E-05, 2.30E-05 and 1.16E-06, respectively), whereas sPDL1 and sPDL2 levels were significantly increased in NSCLC cases compared to healthy controls (P=3.25E-05 and 1.49E-05, respectively). Unconditional univariate logistic regression analysis indicated that increased sCD27, sCD80, sCD137, and sPDL2 were significantly correlated with the risk of invasive diseases. The model with clinical variables, sCD27 and sPDL2 demonstrated the best performance (AUC=0.845) in predicting the risk of IAC. CD27 and PDCD1LG2 (PDL2) showed significant association with cancer invasion signature in TCGA dataset. Conclusion: Our study provides evidence that soluble immune checkpoint-related proteins may associate with the risk of IAC, and we further established an optimized multivariate predictive model, which highlights their potential application in the treatment of NSCLC patients. Future studies may apply these biomarkers to test their predictive value of survival and treatment outcome during immunotherapy in NSCLC patients.

Temporal changes in serum uric acid and risk for metabolic syndrome: a longitudinal cohort study.

BACKGROUND: Studies suggested elevated serum uric acid (SUA) levels are associated with metabolic syndrome (MetS). However, it remains unclear whether baseline SUA and temporal changes predict MetS. The study aimed to investigate the association of baseline SUA and its temporal longitudinal changes with subsequent risk of MetS. METHODS: We conducted a retrospective longitudinal cohort study among 44,176 healthy participants aged 18 years and older without MetS at enrollment. The baseline levels and longitudinal changes of SUA were categorized by gender-specific quintiles. Participants were followed to identify newly developed MetS. We employed Cox model to investigate the relationship between SUA and MetS in men and women separately. RESULTS: During a median follow-up of 2.4 years, 5461 (12.36%) participants developed MetS. After adjustment of demographic, major clinical factors, a higher level of baseline SUA was associated with a significant higher risk of MetS. The corresponding HRs (95% CIs) comparing participants at extreme quintiles were 2.59 (2.32, 2.88) in men and 2.87 (2.41, 3.43) in women. Larger longitudinal absolute increase in SUA was also related to an increases risk of MetS (top vs bottom quintile, 1.70 [1.53, 1.89] in men and 1.94 [1.65, 2.28] in women), regardless the level of baseline SUA. Similarly, the HRs about SUA longitudinal percentage changes were 1.74 (1.56, 1.94) in men and 2.01 (1.69, 2.39) in women, respectively. Moreover, we observed the highest risk of MetS among participants with both higher baseline SUA and larger longitudinal increase in SUA. CONCLUSION: Higher baseline SUA and larger temporal increase in SUA independently predicted risk of MetS, highlighting the importance of longitudinal SUA monitoring and management for primary prevention of MetS in the general population.

Real-World Study of Characteristics and Treatment Outcomes Among Patients with KRAS p.G12C-Mutated or Other KRAS Mutated Metastatic Colorectal Cancer.

BACKGROUND: The KRAS p.G12C mutation has recently become an actionable drug target. To further understand KRAS p.G12C disease, we describe clinicopathologic characteristics, treatment patterns, overall survival (OS), and real-world progression-free survival (rwPFS) in patients with metastatic colorectal cancer (mCRC), KRAS p.G12C mutations (KRAS G12C), and other KRAS mutations (KRAS non-G12C) using a de-identified database. PATIENTS AND METHODS: Clinical and tumor characteristics, including treatments received, genomic profile, and clinical outcomes were assessed for patients from a US clinical genomic database with mCRC diagnosed between January 1, 2011, and March 31, 2020, with genomic sequencing data available. RESULTS: Of 6477 patients with mCRC (mCRC cohort), 238 (3.7%) had KRAS G12C and 2947 (45.5%) had KRAS non-G12C mutations. Treatment patterns were generally comparable across lines of therapy (LOT) in KRAS G12C versus KRAS non-G12C cohorts. Median (95% CI) OS after the first LOT was 16.1 (13.0-19.0) months for the KRAS G12C cohort versus 18.3 (17.2-19.3) months for the KRAS non-G12C cohort, and 19.2 (18.5-19.8) months for the mCRC overall cohort; median (95% CI) rwPFS was 7.4 (6.3-9.5), 9.0 (8.2-9.7), and 9.2 (8.6-9.7) months, respectively. The different KRAS non-G12C mutations examined did not affect clinical outcomes. Median OS and rwPFS for all cohorts declined with each subsequent LOT. CONCLUSIONS: Patients with KRAS p.G12C-mutant mCRC have poor treatment outcomes, and outcomes appear numerically worse than for those without this mutation, indicating potential prognostic implications for KRAS p.G12C mutations and an unmet medical need in this population.

Implementing Real-World RECIST-based Tumor Response Assessment in Patients With Metastatic Non-small Cell Lung Cancer.

BACKGROUND: To accelerate drug approvals while maintaining scientific rigor in the evaluation of a therapeutic's efficacy and safety, the United States Food and Drug Administration now considers real-world data (RWD) to support New Drug Applications and expanded indications. Response Evaluation Criteria in Solid Tumors (RECIST) are the gold standard in clinical trials, but the derivation of RECIST-based treatment response from RWD is unproven. This study investigated the feasibility of implementing RECIST criteria in RWD by comparing lung cancer response assessments from RECIST-based measurement of lesions on archived radiologic films with results from medical oncologist and radiologist narratives recorded in electronic health records (EHR). MATERIALS AND METHODS: Response to index treatment via different assessment approaches was compared among 30 metastatic non-small cell lung cancer (mNSCLC) patients receiving systemic treatment (index) after progression on a platinum or anti-PD(L)-1-containing regimen. Specifically, responses based on assessments documented in the medical oncologists' narratives were compared to a radiologist's assessments of archived images using RECIST v1.1 criteria. Each patient's best overall response was characterized as complete or partial (CR/PR), stable disease (SD), progressive disease (PD), or not evaluable (NE). RESULTS: Similar distributions of best overall response and substantial concordance (77%) between medical oncologist-reported and radiologist re-assessed responses were observed. There were no instances of CR/PR to PD or PD to CR/PR discordance. CONCLUSIONS: Results suggest that accurate treatment responses, similar to RECIST, may be derived using RWD. Further validation and improvement of real-world response assessment are needed to develop a scalable real-world approach for response assessment.

A retrospective observational study of the natural history of advanced non-small-cell lung cancer in patients with KRAS p.G12C mutated or wild-type disease.

INTRODUCTION: The KRAS p.G12C mutation, prevalent in non-small-cell lung cancer (NSCLC), has only recently become a viable target. Here we present results of the largest retrospective observational study analyzing KRAS p.G12C in patients with advanced NSCLC. MATERIALS AND METHODS: Adults with advanced NSCLC (All Advanced NSCLC cohort) and subcohorts with different mutation profiles (KRAS p.G12C [G12C] and KRAS/EGFR/ALK wild type [Triple WT]) diagnosed January 2011 to March 2019 were selected from a US clinico-genomic database; treatment-related characteristics, molecular profiles, real-world overall (rwOS) and progression-free survival (rwPFS) were analyzed. RESULTS: Demographics were similar across cohorts, with more smokers and nonsquamous cell carcinoma histology in the G12C cohort. KRAS p.G12C was nearly mutually exclusive (≤1.2 %) with known actionable driver mutations, but non-driver co-mutations were common (STK11, 21.5 %; KEAP1, 7.0 %; TP53, 48.0 %). Among G12C patients, 20 % had no documentation of receiving systemic therapy. Across treated G12C patients, 67 % received immune checkpoint inhibitors; first-line usage increased from 0% (2014) to 81 % (2019). Among G12C patients, median (95 % CI) rwOS was 12.0 (9.6-15.3), 9.5 (8.1-13.1), and 6.7 (5.9-10.7) months after first, second, and third line of therapy, respectively; median (95 % CI) rwPFS was 5.0 (4.4-5.8), 4.0 (2.8-5.3), and 3.1 (2.4-4.3) months. Outcomes for the G12C subcohort were similar to those for all patients (All Advanced NSCLC cohort). Mutations in STK11/KEAP1 were associated with poorer survival across all cohorts. CONCLUSION: The poor outcomes associated with KRAS p.G12C mutated advanced NSCLC indicate an unmet need for more effective novel treatments.

Immune checkpoint-related serum proteins and genetic variants predict outcomes of localized prostate cancer, a cohort study.

BACKGROUND: The clinical predictors and biological mechanisms for localized prostate cancer (PCa) outcomes remain mostly unknown. We aim to evaluate the role of serum immune-checkpoint-related (ICK) proteins and genetic variations in predicting outcomes of localized PCa. METHODS: We profiled the serum levels of 14 ICK-related proteins (BTLA, GITR, HVEM, IDO, LAG-3, PD-1, PD-L1, PD-L2, Tim-3, CD28, CD80, 4-1BB, CD27, and CTLA-4) in 190 patients with localized PCa. The genotypes of 97 single nucleotide polymorphisms (SNPs) from 19 ICK-related genes were analyzed in an extended population (N = 1762). Meta-data from ArrayExpress and TCGA was employed to validate and to probe functional data. Patients were enrolled and tumor aggressiveness, biochemical recurrence (BCR), and progression information were obtained. Statistical analyses were performed analyzing associations between serum biomarkers, genotypes, mRNA and outcomes. RESULTS: We showed that serum (s)BTLA and sTIM3 levels were associated with PCa aggressiveness (P < 0.05). sCD28, sCD80, sCTLA4, sGITR, sHVEM and sIDO correlated with both BCR and progression risks (all P < 0.05). We further identified ICK variants were significantly associated with aggressiveness, BCR and progression. Among them, 4 SNPs located in CD80 (rs7628626, rs12695388, rs491407, rs6804441) were not only associated with BCR and progression risk, but also correlated with sCD80 level (P < 0.01). rs491407 was further validated in an independent cohort. The CD80 mRNA expression was associated with BCR (HR, 1.85, 95% CI 1.06-3.22, P = 0.03) in meta-analysis of validation cohorts. CONCLUSION: We highlight the prognostic value of serum ICK-related proteins for predicting aggressiveness, BCR and progression of PCa. The genetic variations and mRNA expression in CD80 could be predictors and potential targets of localized PCa.

Soluble immune checkpoint-related proteins as predictors of tumor recurrence, survival, and T cell phenotypes in clear cell renal cell carcinoma patients.

BACKGROUND: Immune checkpoint inhibitors have achieved unprecedented success in cancer immunotherapy. With the exception of a few candidate biomarkers, the prognostic role of soluble immune checkpoint-related proteins in clear cell renal cell cancer (ccRCC) patients is largely uninvestigated. METHODS: We profiled the circulating levels of 14 immune checkpoint-related proteins panel (BTLA, GITR, HVEM, IDO, LAG-3, PD-1, PD-L1, PD-L2, Tim-3, CD28, CD80, CD137, CD27 and CTLA-4) and their associations with the risk of recurrence and death in 182 ccRCC patients using a multiplex Luminex assay. Gene expression in tumors from a subset of participating patients (n = 47) and another 533 primary ccRCC from TCGA were analyzed to elucidate potential mechanisms. Our primary endpoint is overall survival; secondary endpoint is recurrence-free survival. Multivariate Cox proportional hazard model, unconditional logistic regression model, and Kaplan-Meier analysis were applied in the study. RESULTS: sTIM3 and sLAG3 were significantly associated with advanced (stage III) disease (P < 0.05). sPD-L2 was the strongest predictor of recurrence (HR 2.51, 95%CI 1.46-4.34, P = 9.33E-04), whereas high sBTLA and sTIM3 was associated with decreased survival (HR 6.02, 95%CI 2.0-18.1, P = 1.39E-03 and HR 3.12, 95%CI 1.44-6.75, P = 3.94E-03, respectively). Risk scores based on sTIM3 and sBTLA indicated that the soluble immune checkpoint-related proteins jointly predicted recurrence and death risks of ccRCC (P = 0.01 and 4.44E-04, respectively). Moreover, sLAG3 and sCD28 were found negatively correlated with cytolytic activity of T cells in tumors (rho = -0.31 and - 0.33, respectively). CONCLUSIONS: Our study provides evidence that soluble immune checkpoint-related proteins may associate with advanced disease, recurrence and survival in ccRCC patients, which highlights the prognostic values of soluble immune checkpoint-related proteins. Future independent validation in prospective studies is warranted.