A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters.

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.

ctGAN: combined transformation of gene expression and survival data with generative adversarial network.

Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.

Evaluating the impact of a street outreach intervention on participant involvement in gun violence.

The past several years have witnessed increased calls for community violence interventions (CVIs) that address firearm violence while centering local expertise and avoiding the criminal legal system. Currently, little evidence exists on CVI effectiveness at the individual level. This study presents an evaluation of the impact of a street outreach-based CVI [Chicago CRED (Create Real Economic Destiny)] on participant involvement in violence. We used a quasiexperimental design with a treatment sample of 324 men recruited by outreach staff from 2016 to 2021 and a balanced comparison sample of 2,500 men from a network of individuals arrested in CRED's service areas. We conducted a Bayesian survival analysis to evaluate CRED's effect on individual violence-related outcomes on three levels of treatment: All enrolled participants, a subsample that made it through the initial phase, and those who completed programming. The intervention had a strong favorable effect on the probability of arrest for a violent crime for those completing the program: After 24 mo, CRED alumni experienced an 11.3 percentage point increase in survival rates of arrest for a violent crime relative to their comparisons (or, stated differently, a 73.4% reduction in violent crime arrests). The other two treatment levels experienced nontrivial declines in arrests but did not reach statistical significance. No statistically significant reduction in victimization risk was detected for any of the treatment levels. Results demonstrate that completion of violence intervention programming reduces the likelihood of criminal legal involvement for participants, despite the numerous systemic and environmental factors that impede personal success.

Longer CAG repeat length is associated with shorter survival after disease onset in Huntington disease.

It is well known that the length of the CAG trinucleotide expansion of the huntingtin gene is associated with many aspects of Huntington disease progression. These include age of clinical onset and rate of initial progression of disease severity. The relationship between CAG length and survival in Huntington disease is less studied. To address this, we obtained the complete Registry HD database from the European Huntington Disease Network and reanalyzed the time from reported age of disease onset until death. We conducted semiparametric proportional hazards modeling of 8,422 participants who had experienced onset of clinical Huntington disease, either retrospectively or prospectively. Of these, 826 had a recorded age of death. To avoid biased model estimates, retrospective onset ages were represented by left truncation at study entry. After controlling for onset age, which tends to be younger in those with longer CAG repeat lengths, we found that CAG length had a substantial and highly significant influence upon survival time after disease onset. For a fixed age of onset, longer CAG expansions were predictive of shorter survival. This is consistent with other known relationships between CAG length and disease severity. We also show that older onset age predicts shorter lifespan after controlling for CAG length and that the influence of CAG on survival length is substantially greater in women. We demonstrate that apparent contradictions between these and previous analyses of the same data are primarily due to the question of whether to control for clinical onset age in the analysis of time until death.

Bayesian semiparametric model for sequential treatment decisions with informative timing.

We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT is not randomized, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semiparametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time. G-computation is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using our approach, we estimate the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.

Characterizing quantile-varying covariate effects under the accelerated failure time model.

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia.

AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration.

Vertical federated learning has gained popularity as a means of enabling collaboration and information sharing between different entities while maintaining data privacy and security. This approach has potential applications in disease healthcare, cancer prognosis prediction, and other industries where data privacy is a major concern. Although using multi-omics data for cancer prognosis prediction provides more information for treatment selection, collecting different types of omics data can be challenging due to their production in various medical institutions. Data owners must comply with strict data protection regulations such as European Union (EU) General Data Protection Regulation. To share patient data across multiple institutions, privacy and security issues must be addressed. Therefore, we propose an adaptive optimized vertical federated-learning-based framework adaptive optimized vertical federated learning for heterogeneous multi-omics data integration (AFEI) to integrate multi-omics data collected from multiple institutions for cancer prognosis prediction. AFEI enables participating parties to build an accurate joint evaluation model for learning more information related to cancer patients from different perspectives, based on the distributed and encrypted multi-omics features shared by multiple institutions. The experimental results demonstrate that AFEI achieves higher prediction accuracy (6.5% on average) than using single omics data by utilizing the encrypted multi-omics data from different institutions, and it performs almost as well as prognosis prediction by directly integrating multi-omics data. Overall, AFEI can be seen as an efficient solution for breaking down barriers to multi-institutional collaboration and promoting the development of cancer prognosis prediction.

Orchestrating information across tissues via a novel multitask GAT framework to improve quantitative gene regulation relation modeling for survival analysis.

Survival analysis is critical to cancer prognosis estimation. High-throughput technologies facilitate the increase in the dimension of genic features, but the number of clinical samples in cohorts is relatively small due to various reasons, including difficulties in participant recruitment and high data-generation costs. Transcriptome is one of the most abundantly available OMIC (referring to the high-throughput data, including genomic, transcriptomic, proteomic and epigenomic) data types. This study introduced a multitask graph attention network (GAT) framework DQSurv for the survival analysis task. We first used a large dataset of healthy tissue samples to pretrain the GAT-based HealthModel for the quantitative measurement of the gene regulatory relations. The multitask survival analysis framework DQSurv used the idea of transfer learning to initiate the GAT model with the pretrained HealthModel and further fine-tuned this model using two tasks i.e. the main task of survival analysis and the auxiliary task of gene expression prediction. This refined GAT was denoted as DiseaseModel. We fused the original transcriptomic features with the difference vector between the latent features encoded by the HealthModel and DiseaseModel for the final task of survival analysis. The proposed DQSurv model stably outperformed the existing models for the survival analysis of 10 benchmark cancer types and an independent dataset. The ablation study also supported the necessity of the main modules. We released the codes and the pretrained HealthModel to facilitate the feature encodings and survival analysis of transcriptome-based future studies, especially on small datasets. The model and the code are available at http://www.healthinformaticslab.org/supp/.

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy.

Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.

Application of the ERS/ATS Spirometry Standards and Race-Neutral Equations in the COPDGene Study.

RATIONALE: The European Respiratory Society (ERS) and the American Thoracic Society (ATS) recommend using z-scores, and the ATS has recommended using Global Lung Initiative (GLI)- "Global" race-neutral reference equations for spirometry interpretation. However, these recommendations have been variably implemented and the impact has not been widely assessed, both in clinical and research settings. OBJECTIVES: We evaluated the ERS/ATS airflow obstruction severity classification. METHODS: In the COPDGene Study (n = 10,108), airflow obstruction has been defined as a forced expiratory volume in one second to forced vital capacity (FEV1/FVC) ratio <0.70, with spirometry severity graded from class 1 to 4 based on race-specific percent predicted (pp) FEV1 cut-points as recommended by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We compared the GOLD approach, using NHANES III race-specific equations, to the application of GLI-Global equations using the ERS/ATS definition of airflow obstruction as FEV1/FVC ratio < lower limit of normal (LLN) and z-FEV1 cut-points of -1.645, -2.5, and -4 ("zGLI Global"). We tested the four-tier severity scheme for association with COPD outcomes. MEASUREMENTS AND MAIN RESULTS: The lowest agreement between ERS/ATS with zGLI Global and the GOLD classification was observed in individuals with milder disease (56.9% and 42.5% in GOLD 1 and 2) and race was a major determinant of redistribution. After adjustment for relevant covariates, zGLI Global distinguished all-cause mortality risk between normal spirometry and the first grade of COPD (Hazard Ratio 1.23, 95% CI 1.04-1.44, p=0.014), and showed a linear increase in exacerbation rates with increasing disease severity, in comparison to GOLD. CONCLUSIONS: The zGLI Global severity classification outperformed GOLD in the discrimination of survival, exacerbations, and imaging characteristics.

Risk of Malaria Following Untreated Subpatent Plasmodium falciparum Infections: Results Over 4 Years From a Cohort in a High-Transmission Area in Western Kenya.

BACKGROUND: People with suspected malaria may harbor Plasmodium falciparum undetected by rapid diagnostic test (RDT). The impact of these subpatent infections on the risk of developing clinical malaria is not fully understood. METHODS: We analyzed subpatent P. falciparum infections using a longitudinal cohort in a high-transmission site in Kenya. Weighted Kaplan-Meier models estimated the risk difference (RD) for clinical malaria during the 60 days following a symptomatic subpatent infection. Stratum-specific estimates by age and transmission season assessed modification. RESULTS: Over 54 months, we observed 1128 symptomatic RDT-negative suspected malaria episodes, of which 400 (35.5%) harbored subpatent P. falciparum. Overall, the 60-day risk of developing clinical malaria was low following all episodes (8.6% [95% confidence interval, 6.7%-10.4%]). In the low-transmission season, the risk of clinical malaria was slightly higher in those with subpatent infection, whereas the opposite was true in the high-transmission season (low-transmission season RD, 2.3% [95% confidence interval, .4%-4.2%]; high-transmission season RD, -4.8% [-9.5% to -.05%]). CONCLUSIONS: The risk of developing clinical malaria among people with undetected subpatent infections is low. A slightly elevated risk in the low-transmission season may merit alternate management, but RDTs identify clinically relevant infections in the high-transmission season.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.

BACKGROUND: Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information. RESULTS: The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study. CONCLUSIONS: Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .

Estimating and Presenting Hazard Ratios and Absolute Risks from a Cox Model with Complex Non-linear Interactions.

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model, and to the presentation of stratified results over levels of additional covariates. Results stratification often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product terms in a Cox regression model, disregarding the clinically relevant information that are captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scale in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a non-linear fashion, we provide software material to replicate our procedure, and discuss different approaches to derive confidence intervals. The presented approach will allow clinical and public health researchers assessing complex relationships between multiple covariates as they relate to a clinical endpoint, and providing a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification.

Data-Driven Simulations to Assess the Impact of Study Imperfections in Time-to-Event Analyses.

Quantitative bias analysis (QBA) permits assessment of the expected impact of various imperfections of the available data on the results and conclusions of a particular real-world study. This article extends QBA methodology to multivariable time-to-event analyses with right-censored endpoints, possibly including time-varying exposures or covariates. The proposed approach employs data-driven simulations, which preserve important features of the data at hand while offering flexibility in controlling the parameters and assumptions that may affect the results. First, the steps required to perform data-driven simulations are described, and then two examples of real-world time-to-event analyses illustrate their implementation and the insights they may offer. The first example focuses on the omission of an important time-invariant predictor of the outcome in a prognostic study of cancer mortality, and permits separating the expected impact of confounding bias from non-collapsibility. The second example assesses how imprecise timing of an interval-censored event - ascertained only at sparse times of clinic visits - affects its estimated association with a time-varying drug exposure. The simulation results also provide a basis for comparing the performance of two alternative strategies for imputing the unknown event times in this setting. The R scripts that permit the reproduction of our examples are provided.

Risk Factors for Second Primary Cancer in a Prospective Cohort of Endometrial Cancer Survivors: an Alberta Endometrial Cancer Cohort Study.

We examined associations between modifiable and non-modifiable cancer-related risk factors measured at endometrial cancer diagnosis and during early survivorship (~3 years post-diagnosis) with second primary cancer (SPC) risk among 533 endometrial cancer survivors in the Alberta Endometrial Cancer Cohort using Fine and Gray sub-distribution hazard models. During a median follow-up of 16.7 years (interquartile range (IQR)=12.2-17.9), 89 (17%) participants developed a SPC with breast (29%), colorectal (13%) and lung (12%) cancers being the most common. Dietary glycemic load before endometrial cancer diagnosis (≥90.4 vs. <90.4 g/day: sub-hazard ratios (sHR)=1.71, 95% confidence intervals (CI)=1.09-2.69) as well as older age (≥60 vs. <60: sHR=2.48, 95% CI=1.34-4.62) and alcohol intake (≥2 drink/week vs. none: sHR=3.81, 95% CI=1.55-9.31) during early survivorship were associated with increased SPC risk. Additionally, reductions in alcohol consumption from prediagnosis to early survivorship significantly reduced SPC risk (sHR=0.34, 95% CI=0.14-0.82). With one-in-six survivors developing a SPC, further investigation of SPC risk factors and targeted surveillance options for high-risk survivors could improve long-term health outcomes in this population. Reductions in dietary glycemic load and alcohol intake from prediagnosis to early survivorship showed promising risk reductions for SPCs and could be important modifiable risk factors to target among endometrial cancer survivors.

Impact of presentation timing in metastatic hormone-sensitive prostate cancer: Characterization of patients and identification of prognostic factors.

BACKGROUND: The treatment and surveillance of metastatic hormone-sensitive prostate cancer (mHSPC) has evolved since the introduction of several treatment intensification options associated with hormonal blockade and classifications based on the timing of metastatic disease presentation and disease volume. Using a hospital-based registry, we aimed to assess whether these new classifications are applicable to our population, as few studies have demonstrated their prognostic value for overall survival (OS) and time to development of castration-resistant prostate cancer (CRPC), and to establish prognostic factors in our population. METHODS: A retrospective cohort of mHSPC patients who were attended at an oncology referral hospital in Bogota between 2017 and 2021 were included in this study. The primary and secondary endpoints were OS and time to CRPC. The distribution of outcome measures was estimated using the Kaplan-Meier method. Proportional hazard models were constructed using the Cox regression approach and stratified according to risk factors. RESULTS: The study cohort included 373 patients. The median castration resistance-free survival was 48 months (CI: 32-73 months), and OS was 43 months (CI: 37-48 months). In multivariate analysis, nodal staging, ECOG status, and surgical castration were independent prognostic factors. CONCLUSION: In our hospital-based registry, the independent impact of the time of presentation on castration-resistant-free survival or OS could not be demonstrated, nor could the grouping of prognostic categories based on metastatic presentation temporality and volume. Other independent prognostic factors have been proposed.

Reappraisal of prognostic factors used in the European Pediatric Soft Tissue Sarcoma Study Group RMS 2005 study for localized rhabdomyosarcoma to optimize risk stratification and generate a prognostic nomogram.

BACKGROUND: The objective of this study was to investigate the role of clinical factors together with FOXO1 fusion status in patients with nonmetastatic rhabdomyosarcoma (RMS) to develop a predictive model for event-free survival and provide a rationale for risk stratification in future trials. METHODS: The authors used data from patients enrolled in the European Pediatric Soft Tissue Sarcoma Study Group (EpSSG) RMS 2005 study (EpSSG RMS 2005; EudraCT number 2005-000217-35). The following baseline variables were considered for the multivariable model: age at diagnosis, sex, histology, primary tumor site, Intergroup Rhabdomyosarcoma Studies group, tumor size, nodal status, and FOXO1 fusion status. Main effects and significant second-order interactions of candidate predictors were included in a multiple Cox proportional hazards regression model. A nomogram was generated for predicting 5-year event-free survival (EFS) probabilities. RESULTS: The EFS and overall survival rates at 5 years were 70.9% (95% confidence interval, 68.6%-73.1%) and 81.0% (95% confidence interval, 78.9%-82.8%), respectively. The multivariable model retained five prognostic factors, including age at diagnosis interacting with tumor size, tumor primary site, Intergroup Rhabdomyosarcoma Studies clinical group, and FOXO1 fusion status. Based on each patient's total score in the nomogram, patients were stratified into four groups. The 5-year EFS rates were 94.1%, 78.4%, 65.2%, and 52.1% in the low-risk, intermediate-risk, high-risk, and very-high-risk groups, respectively, and the corresponding 5-year overall survival rates were 97.2%, 91.5%, 74.3%, and 60.8%, respectively. CONCLUSIONS: The results presented here provide the rationale to modify the EpSSG stratification, with the most significant change represented by the replacement of histology with fusion status. This classification was adopted in the new international trial launched by the EpSSG.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome.

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Localized Myxofibrosarcoma: A Retrospective Analysis of Primary Therapy and Prognostic Factors in 134 Patients in a Single Institution.

BACKGROUND: Primary therapy of localized myxofibrosarcoma (MFS) remains controversial. Primary resection is complicated by a high rate of local recurrence, and the refractoriness to non-surgical treatment results in a higher risk of metastasis. The aim of the present study was to contribute the findings of a single sarcoma-specialized center and encourage investigating new treatment options. PATIENTS AND METHODS: We analyzed 134 patients treated with localized MFS in our center regarding prognostic factors defining overall survival, local recurrence, and metastasis. We focused on multimodal treatment of localized MFS: surgery, radiation, chemotherapy, hyperthermia, and isolated limb perfusion. RESULTS: The 5-year OS was 74.9%. From a total of 134 patients: 74 (55.2%) stayed disease free, 48 (35.8%) had a local recurrence (LR), and 23 (17.2%) developed a distant metastasis (DM). The 5-year LR-free survival (LRFS) and DM-free survival (DMFS) were 66.1% and 80.8%, respectively. Older age, tumor size (cT) cT ≥ 2, non-extremity localization, and distant metastasis were adverse predictive factors for OS. Performing an incision biopsy, surgery in a sarcoma-center, wide local excision or compartment-oriented excision, negative margins, and radiotherapy were positive predictive factors for LR. Tumor size cT ≥ 3 was a negative predictive factor for DM. Grading was a negative predictive factor for LR (G ≥ 2) and for DM (G3) in the multivariable analysis. CONCLUSION: Adjuvant radiation had a positive impact on LRFS in all localized tumor stages, even in cT1 tumors. Chemotherapy did not have a significant impact on DMFS, regardless of tumor stage. Our findings indicate that myxofibrosarcoma may be a chemotherapy-resistant entity and a much closer monitoring is required, in case of neoadjuvant treatment.

Safety, efficacy, and survival outcomes of immune checkpoint inhibitors rechallenge in patients with cancer: a systematic review and meta-analysis.

BACKGROUNDS: There is little evidence on the safety, efficacy, and survival benefit of restarting immune checkpoint inhibitors (ICI) in patients with cancer after discontinuation due to immune-related adverse events (irAEs) or progressive disease (PD). Here, we performed a meta-analysis to elucidate the possible benefits of ICI rechallenge in patients with cancer. METHODS: Systematic searches were conducted using PubMed, Embase, and Cochrane Library databases. The objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and incidence of irAEs were the outcomes of interest. RESULTS: Thirty-six studies involving 2026 patients were analyzed. ICI rechallenge was associated with a lower incidence of all-grade (OR, 0.05; 95%CI, 0.02-0.13, P < .05) and high-grade irAEs (OR, 0.37; 95%CI, 0.21-0.64, P < .05) when compared with initial ICI treatment. Though no significant difference was observed between rechallenge and initial treatment regarding ORR (OR, 0.69; 95%CI, 0.39-1.20, P = .29) and DCR (OR, 0.85; 95%CI, 0.51-1.40, P = 0.52), patients receiving rechallenge had improved PFS (HR, 0.56; 95%CI, 0.43-0.73, P < .05) and OS (HR, 0.55; 95%CI, 0.43-0.72, P < .05) than those who discontinued ICI therapy permanently. Subgroup analysis revealed that for patients who stopped initial ICI treatment because of irAEs, rechallenge showed similar safety and efficacy with initial treatment, while for patients who discontinued ICI treatment due to PD, rechallenge caused a significant increase in the incidence of high-grade irAEs (OR, 4.97; 95%CI, 1.98-12.5, P < .05) and a decrease in ORR (OR, 0.48; 95%CI, 0.24-0.95, P < .05). CONCLUSION: ICI rechallenge is generally an active and feasible strategy that is associated with relative safety, similar efficacy, and improved survival outcomes. Rechallenge should be considered individually with circumspection, and randomized controlled trials are required to confirm these findings.