Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer.

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

A phase Ib/II study of cadonilimab (PD-1/CTLA-4 bispecific antibody) plus anlotinib as first-line treatment in patients with advanced non-small cell lung cancer.

BACKGROUND: Cadonilimab is a bispecific antibody that simultaneously targets programmed cell death receptor-1 and cytotoxic T lymphocyte-associated antigen-4. This study aimed to assess the safety and efficacy of cadonilimab plus anlotinib for the first-line treatment of advanced non-small cell lung cancer (NSCLC) without sensitizing EGFR/ALK/ROS1 mutations. METHODS: Patients received cadonilimab 15 mg/kg and 10 mg/kg every three weeks (Q3W) plus anlotinib at doses of 10 or 12 mg once daily for two weeks on a one-week-off schedule. The primary endpoints included safety and objective response rate (ORR). RESULTS: Sixty-nine treatment-naïve patients received cadonilimab 15 mg/kg Q3W combination (n = 49) and 10 mg/kg Q3W combination (n = 20). Treatment-related adverse events (TRAEs) were reported in 48 (98.0%) and 19 (95.0%) patients, with grade ≥3 TRAEs occurring in 29 (59.2%) and five (25.0%) patients, respectively. TRAEs leading to cadonilimab discontinuation occurred in eight (16.3%) and one (5.0%) patients in the cadonilimab 15 mg/kg Q3W and 10 mg/kg Q3W dosing groups. The confirmed ORRs were 51.0% (25/49) and 60.0% (12/20) accordingly. CONCLUSIONS: Cadonilimab 10 mg/kg Q3W plus anlotinib showed manageable safety and promising efficacy as a first-line chemo-free treatment for advanced NSCLC. GOV IDENTIFIER: NCT04646330.

Oligo-residual disease in PD-1/PD-L1 inhibitor-treated metastatic non-small cell lung cancer: incidence, pattern of failure, and clinical value of local consolidative therapy.

OBJECTIVES: To investigate the feasibility and potential clinical value of local consolidative therapy (LCT) in PD-1/PD-L1 inhibitor-treated metastatic non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: PD-1/PD-L1 inhibitor-treated metastatic NSCLC patients with measurable disease in three academic centers were screened and those with adequate follow-up were included. Oligo-residual disease (ORD) was defined as residual tumors limited to three organs and five lesions evaluated at the best response among patients with partial response or stable disease after PD-1/PD-L1 inhibitors. Oligometastatic and multiple-metastatic disease (OMD/MMD) were similarly classified at baseline. Locoregional interventions, administered after effective treatment of PD-1/PD-L1 inhibitors and before initial disease progression, were defined as LCT. Patterns of initial progressive disease (PD) were classified as involving only residual sites (RP), only new sites (NP), or a combination of both (BP). RESULTS: Among the 698 patients included, ORD was documented in 73 (47.1%) of 155 patients with baseline OMD and 60 (11.0%) of 543 patients with baseline MMD. With a median follow-up of 31.0 (range, 6.0-53.0) months, 108 patients with ORD developed initial PD, with RP, NP, and BP occurring in 51 (47%), 23 (21.3%), and 34 (31.5%), respectively. Among the 133 patients with ORD, those receiving LCT (n = 43) had longer progression-free survival (HR = 0.58, 95% CI 0.40-0.85, p = 0.01) and overall survival (HR = 0.49, 95% CI 0.30-0.79, p < 0.0001). CONCLUSION: ORD occurs with a clinically relevant frequency among PD-1/PD-L1 inhibitor-treated metastatic NSCLC patients and LCT may provide extra survival benefits in those with ORD.

Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network.

The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.

Atroposelective Synthesis of Axially Chiral Diaryl Ethers by N-Heterocyclic-Carbene-Catalyzed Sequentially Desymmetric/Kinetic Resolution Process.

We describe herein an N-heterocyclic-carbene-catalyzed atroposelective synthesis of axially chiral diaryl ethers. Through a sequentially enantioselective desymmetric process and a kinetic resolution process, the products could be constructed in good yields with excellent enantiopurities. Both alcohols and phenols were compatible with this catalytic system. The axially chiral carboxylic acids derived from the esters were proven to be potential chiral ligands for asymmetric synthesis, for example, Rh(III)-catalyzed enantioselective C-H functionalization.

Dual-stream cortical pathways mediate sensory prediction.

Predictions are constantly generated from diverse sources to optimize cognitive functions in the ever-changing environment. However, the neural origin and generation process of top-down induced prediction remain elusive. We hypothesized that motor-based and memory-based predictions are mediated by distinct descending networks from motor and memory systems to the sensory cortices. Using functional magnetic resonance imaging (fMRI) and a dual imagery paradigm, we found that motor and memory upstream systems activated the auditory cortex in a content-specific manner. Moreover, the inferior and posterior parts of the parietal lobe differentially relayed predictive signals in motor-to-sensory and memory-to-sensory networks. Dynamic causal modeling of directed connectivity revealed selective enabling and modulation of connections that mediate top-down sensory prediction and ground the distinctive neurocognitive basis of predictive processing.

A short ORF-encoded transcriptional regulator.

Recent technological advances have expanded the annotated protein coding content of mammalian genomes, as hundreds of previously unidentified, short open reading frame (ORF)-encoded peptides (SEPs) have now been found to be translated. Although several studies have identified important physiological roles for this emerging protein class, a general method to define their interactomes is lacking. Here, we demonstrate that genetic incorporation of the photo-crosslinking noncanonical amino acid AbK into SEP transgenes allows for the facile identification of SEP cellular interaction partners using affinity-based methods. From a survey of seven SEPs, we report the discovery of short ORF-encoded histone binding protein (SEHBP), a conserved microprotein that interacts with chromatin-associated proteins, localizes to discrete genomic loci, and induces a robust transcriptional program when overexpressed in human cells. This work affords a straightforward method to help define the physiological roles of SEPs and demonstrates its utility by identifying SEHBP as a short ORF-encoded transcription factor.

Biochemistry and Protein Interactions of the CYREN Microprotein.

Over the past decade, advances in genomics have identified thousands of additional protein-coding small open reading frames (smORFs) missed by traditional gene finding approaches. These smORFs encode peptides and small proteins, commonly termed micropeptides or microproteins. Several of these newly discovered microproteins have biological functions and operate through interactions with proteins and protein complexes within the cell. CYREN1 is a characterized microprotein that regulates double-strand break repair in mammalian cells through interaction with Ku70/80 heterodimer. Ku70/80 binds to and stabilizes double-strand breaks and recruits the machinery needed for nonhomologous end join repair. In this study, we examined the biochemical properties of CYREN1 to better understand and explain its cellular protein interactions. Our findings support that CYREN1 is an intrinsically disordered microprotein and this disordered structure allows it to enriches several proteins, including a newly discovered interaction with SF3B1 via a distinct short linear motif (SLiMs) on CYREN1. Since many microproteins are predicted to be disordered, CYREN1 is an exemplar of how microproteins interact with other proteins and reveals an unknown scaffolding function of this microprotein that may link NHEJ and splicing.

Exploiting innate immunity for cancer immunotherapy.

Immunotherapies have revolutionized the treatment paradigms of various types of cancers. However, most of these immunomodulatory strategies focus on harnessing adaptive immunity, mainly by inhibiting immunosuppressive signaling with immune checkpoint blockade, or enhancing immunostimulatory signaling with bispecific T cell engager and chimeric antigen receptor (CAR)-T cell. Although these agents have already achieved great success, only a tiny percentage of patients could benefit from immunotherapies. Actually, immunotherapy efficacy is determined by multiple components in the tumor microenvironment beyond adaptive immunity. Cells from the innate arm of the immune system, such as macrophages, dendritic cells, myeloid-derived suppressor cells, neutrophils, natural killer cells, and unconventional T cells, also participate in cancer immune evasion and surveillance. Considering that the innate arm is the cornerstone of the antitumor immune response, utilizing innate immunity provides potential therapeutic options for cancer control. Up to now, strategies exploiting innate immunity, such as agonists of stimulator of interferon genes, CAR-macrophage or -natural killer cell therapies, metabolic regulators, and novel immune checkpoint blockade, have exhibited potent antitumor activities in preclinical and clinical studies. Here, we summarize the latest insights into the potential roles of innate cells in antitumor immunity and discuss the advances in innate arm-targeted therapeutic strategies.

Baseline C-reactive protein predicts efficacy of the first-line immune checkpoint inhibitors plus chemotherapy in advanced lung squamous cell carcinoma: a retrospective, multicenter study.

AIMS: To investigate the predictive value of baseline C-reactive protein (CRP) levels on the efficacy of chemotherapy plus immune checkpoint inhibitors (ICI) in patients with advanced lung squamous cell carcinoma (LSCC). MATERIALS AND METHODS: In this retrospective multicenter study spanning from January 2016 to December 2020, advanced LSCC patients initially treated with chemotherapy or a combination of chemotherapy and ICI were categorized into normal and elevated CRP subgroups. The relationship between CRP levels and treatment outcomes was analyzed using multivariate Cox proportional hazards models and multivariate logistic regression, focusing primarily on the progression-free survival (PFS) endpoint, and secondarily on overall survival (OS) and objective response rate (ORR) endpoints. Survival curves were generated using the Kaplan-Meier method, with the log-rank test used for comparison between groups. RESULTS: Of the 245 patients evaluated, the 105 who received a combination of chemotherapy and ICI with elevated baseline CRP levels exhibited a significant reduction in PFS (median 6.5 months vs. 11.8 months, HR, 1.78; 95% CI: 1.12-2.81; p = 0.013) compared to those with normal CRP levels. Elevated CRP was identified as an independent risk factor for poor PFS through multivariate-adjusted analysis. However, among the 140 patients receiving chemotherapy alone, baseline CRP levels did not significantly influence PFS. Furthermore, within the combination therapy group, there was a notable decrease in the ORR (51% vs. 71%, p = 0.035), coupled with a significantly shorter OS (median 20.9 months vs. 31.5 months, HR, 2.24; 95% CI: 1.13-4.44; p = 0.033). CONCLUSION: In patients with advanced LSCC, elevated baseline CRP levels were identified as an independent predictive factor for the efficacy of combination therapy with chemotherapy and ICI, but not in chemotherapy alone. This suggests that CRP may be a valuable biomarker for guiding treatment strategies.

Repurposed Tocilizumab in Patients with Severe COVID-19.

The coronavirus disease 2019 (COVID-19) has caused a global pandemic, resulting in considerable morbidity and mortality. Tocilizumab, an inhibitor of IL-6, has been widely repurposed as a treatment of severely ill patients without robust evidence supporting its use. In this study, we aimed to systematically describe the effectiveness of treatment and prevention of the cytokine storms in COVID-19 patients with tocilizumab. In this multicentered retrospective and observational cohort study, 65 patients with COVID-19 receiving tocilizumab and 130 not receiving tocilizumab were propensity score matched at a ratio of 2:1 based on age, sex, and comorbidities from January 20, 2020 to March 18, 2020 in Wuhan, China. After adjusting for confounding, the detected risk for in-hospital death was lower in the tocilizumab group versus nontocilizumab group (hazard ratio = 0.47; 95% confidence interval = 0.25-0.90; p = 0.023). Moreover, use of tocilizumab was associated with a lower risk of acute respiratory distress syndrome (odds ratio = 0.23; 95% confidence interval = 0.11-0.45; p < 0.0001). Furthermore, patients had heightened inflammation and more dysregulated immune cells before treatment, which might aggravate disease progression. After tocilizumab administration, abnormally elevated IL-6, C-reactive protein, fibrinogen, and activated partial thromboplastin time decreased. Tocilizumab may be of value in prolonging survival in patients with severe COVID-19, which provided a novel strategy for COVID-19-induced cytokine release syndrome. Our findings could inform bedside decisions until data from randomized, controlled clinical trials become available.

Investigating the Physical and Mental Health Nexus: a Network Analysis of Depression, Cardiometabolic Health, Bone Mass, and Perceived Health Status Among Filipino Domestic Workers.

BACKGROUND: Migrant domestic workers are vulnerable to physical and mental health problems given the many challenges they experience while working abroad. Using network analysis, this study examined the structure of depression, cardiometabolic health indicators (BMI, waist-hip ratio (WHR), blood pressure, and heart rate), bone mass, and perceived health status in this population. The network model allowed for an examination of central symptoms or symptoms with the most direct connections with other symptoms; bridge symptoms, or symptoms that link two or more communities; and edges, or relationships among symptoms. METHOD: Cross-sectional data were gathered from 1375 Filipino domestic workers in Macao (SAR), China. Data from a subsample of 510 participants who met a cutoff indicating depression were analyzed. Anthropometric measurements and surveys were used to collect data, which was analyzed using R statistical software. RESULTS: Results showed four community clusters: three communities consisted of at least two depression symptoms each and the fourth community included physical health indicators. Strong edges were formed between BMI-bone mass, psychomotor-concentration, BMI-WHR, and sad mood-anhedonia. The node with the highest expected influence was BMI. There were three bridges: worthlessness, psychomotor difficulties, and concentration difficulties. CONCLUSION: The link between depression, cardiometabolic indicators, bone mass, and poor perceived health reinforces the need to address multimorbidity within migrant populations. Health promotion interventions that address mental and physical health may improve the health of this population.

A brief report on incidence, radiographic feature and prognostic significance of brain MRI changes after anti-PD-1/PD-L1 therapy in advanced non-small cell lung cancer.

INTRODUCTION: Neurologic immune-related adverse events (nirAEs) are uncommon but potentially lethal complications of immune checkpoint inhibitor (ICI) treatment. However, the incidence, radiographic features and prognostic significance of brain magnetic resonance imaging (MRI) changes after ICI treatment remain largely unknown. METHODS: Consecutive patients with advanced non-small cell lung cancer (NSCLC) at three participating institutions receiving anti-PD-1/PD-L1 therapy from June 2017 to September 2020 were screened, and those who received brain MRI within 6 weeks before ICI initiation and at least one follow-up brain MRI after ICI treatment were included. Serial brain MRI images were independently reviewed by two experienced radiologists. RESULTS: With a median follow-up of 13.2 months, 27 (20.0%) of the 135 enrolled patients developed certain kind of brain MRI aberration. The 1-, 2- and 3-year cumulative incidence of brain MRI aberration was 17.1%, 36.3% and 52.2%, respectively. Brain MRI aberration indicative of stroke, mimicking typical white matter lesions and presenting as T2-hyperintensity suggestive of CNS vasculitis or encephalitis, was documented in 11, 9 and 4 patients, respectively. Patients with brain MRI aberration had higher clinical benefit rate (p = 0.030), longer progression-free survival (p = 0.015) and a tendency of improved overall survival (p = 0.054). CONCLUSIONS: Brain MRI aberrations developed after ICI treatment are not uncommon, and their manifestations vary a lot. Patients developing brain MRI aberrations tended to have better prognosis, which needed to be further investigated.

Accurate annotation of human protein-coding small open reading frames.

Functional protein-coding small open reading frames (smORFs) are emerging as an important class of genes. However, the number of translated smORFs in the human genome is unclear because proteogenomic methods are not sensitive enough, and, as we show, Ribo-seq strategies require additional measures to ensure comprehensive and accurate smORF annotation. Here, we integrate de novo transcriptome assembly and Ribo-seq into an improved workflow that overcomes obstacles with previous methods, to more confidently annotate thousands of smORFs. Evolutionary conservation analyses suggest that hundreds of smORF-encoded microproteins are likely functional. Additionally, many smORFs are regulated during fundamental biological processes, such as cell stress. Peptides derived from smORFs are also detectable on human leukocyte antigen complexes, revealing smORFs as a source of antigens. Thus, by including additional validation into our smORF annotation workflow, we accurately identify thousands of unannotated translated smORFs that will provide a rich pool of unexplored, functional human genes.

Targeted ß-catenin ubiquitination and degradation by multifunctional stapled peptides.

Aberrant activation of the Wnt signaling pathway has been identified in numerous types of cancer. One common feature of oncogenic Wnt regulation involves an increase in the cellular levels of ß-catenin due to interference with its constitutive ubiquitin-dependent degradation. Targeting ß-catenin has therefore emerged as an appealing approach for the treatment of Wnt-dependent cancers. Here, we report a strategy that employs multifunctional stapled peptides to recruit an E3 ubiquitin ligase to ß-catenin, thereby rescuing ß-catenin degradation by hijacking the endogenous ubiquitin-proteasome pathway. Specifically, we designed, synthesized, and evaluated a panel of multifunctional stapled peptides that have a ß-catenin binding moiety (StAx-35) covalently linked to a second stapled peptide moiety (SAH-p53-8), which is capable to interact with the E3 ubiquitin ligase MDM2. We found that in vitro these multifunctional peptides can recruit the MDM2 protein to ß-catenin and induce poly-ubiquitination on ß-catenin. In cellulo, treatment of the human colorectal cancer cell line SW480 with the multifunctional stapled peptides showed dose-dependent degradation of endogenous ß-catenin levels. In addition, a luciferase reporter assay showed that the multifunctional stapled peptides can suppress ß-catenin-mediated gene expression via the Wnt signaling pathway. Therefore, these multifunctional stapled peptides provide a unique research tool for examining the Wnt signaling pathway by targeted knockdown of ß-catenin at the protein level, and may serve as leads for potential drug candidates in the treatment of Wnt-dependent cancers.

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study.

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.

Association between recent oncologic treatment and mortality among patients with carcinoma who are hospitalized with COVID-19: A multicenter study.

BACKGROUND: To the authors' knowledge, little is known regarding the association between recent oncologic treatment and mortality in patients with cancer who are infected with coronavirus disease 2019 (COVID-19). The objective of the current study was to determine whether recent oncologic treatment is associated with a higher risk of death among patients with carcinoma who are hospitalized with COVID-19. METHODS: Data regarding 248 consecutive patients with carcinoma who were hospitalized with COVID-19 were collected retrospectively from 33 hospitals in Hubei Province, China, from January 1, 2020, to March 25, 2020. The follow-up cutoff date was July 22, 2020. Univariable and multivariable logistic regression analyses were performed to identify variables associated with a higher risk of death. RESULTS: Of the 248 patients enrolled, the median age was 63 years and 128 patients (52%) were male. On admission, 147 patients (59%) did not undergo recent oncologic treatment, whereas 32 patients (13%), 25 patients (10%), 12 patients (5%), and 10 patients (4%), respectively, underwent chemotherapy, surgery, targeted therapy, and radiotherapy. At the time of last follow-up, 51 patients (21%) were critically ill during hospitalization, 40 of whom had died. Compared with patients without receipt of recent oncologic treatment, the mortality rate of patients who recently received oncologic treatment was significantly higher (24.8% vs 10.2%; hazard ratio, 2.010 [95% CI, 1.079-3.747; P = .027]). After controlling for confounders, recent receipt of chemotherapy (odds ratio [OR], 7.495; 95% CI, 1.398-34.187 [P = .015]), surgery (OR, 8.239; 95% CI, 1.637-41.955 [P = .012]), and radiotherapy (OR, 15.213; 95% CI, 2.091-110.691 [P = .007]) were identified as independently associated with a higher risk of death. CONCLUSIONS: The results of the current study demonstrated a possible association between recent receipt of oncologic treatment and a higher risk of death among patients with carcinoma who are hospitalized with COVID-19.

Immune signature-based risk stratification and prediction of immune checkpoint inhibitor's efficacy for lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a common pulmonary malignant disease with a poor prognosis. There were limited studies investigating the influences of the tumor immune microenvironment on LUAD patients' survival and response to immune checkpoint inhibitors (ICIs). METHODS: Based on TCGA-LUAD dataset, we constructed a prognostic immune signature and validated its predictive capability in the internal as well as total datasets. Then, we explored the differences of tumor-infiltrating lymphocytes, tumor mutation burden, and patients' response to ICI treatment between the high-risk score group and low-risk score group. RESULTS: This immune signature consisted of 17 immune-related genes, which was an independent prognostic factor for LUAD patients. In the low-risk score group, patients had better overall survival. Although the differences were non-significant, patients with low-risk scores had more tumor-infiltrating follicular helper T cells and fewer macrophages (M0), which were closely related to clinical outcomes. Additionally, the total TMB was markedly decreased in the low-risk score group. Using immunophenoscore as a surrogate of ICI response, we found that patients with low-risk scores had significantly higher immunophenoscore. CONCLUSION: The 17-immune-related genes signature may have prognostic and predictive relevance with ICI therapy but needs prospective validation.

Discussion of advance care planning on end-of-life decisions with lung cancer patients in Wuhan, China: attitude, timing and future directions.

BACKGROUND: Progress in advance care planning (ACP) in China has been hindered for decades compared with other countries. AIMS: To describe knowledge of ACP, end-of-life (EOL) care preferences and the predictors of patients' preference for ACP, as well as who should mention ACP among Chinese lung cancer patients. METHODS: Questionnaire-based interviews were carried out. Two hundred and fifty-eight lung cancer patients were recruited when first admitted to Tongji Hospital from October 2017 to November 2018. Social-demographic factors, which might influence patients' preference on ACP decisions and who should mention ACP, were evaluated using multivariate logistic regression analysis. RESULTS: A total of 91.1% (n = 235) of patients favoured ACP on EOL issues. One hundred and sixty (60%) patients wanted to make EOL decisions on their own. Only 10% of patients were familiar with advance directions. Eighty-two (31.8%) patients were familiar with do not resuscitate/do not intubate (DNR/DNI) directions. ACP was not mentioned in 92.2% of patients. Gender (male, OR = 4.87 (2.16-5.83)), tumour stage (Stage III, OR = 0.108 (0.06-0.51); Stage IV, OR = 1.780 (1.02-2.11)) and number of children (every increase in the number of children, OR = 0.267 (0.09-0.93)) were the significant predictors of preference for autonomous ACP. Female patients and patients currently receiving treatment are 2.743 and 1.8 times, respectively, more willing to need ACP initiated by doctors. CONCLUSIONS: Chinese patients showed preferences towards ACP, but with inadequate knowledge. More assistance is needed with ACP for those patients, especially for females, patients with one child and those with early stage lung cancer. For female patients and patients receiving treatment, doctors may initiate ACP dialogue first.

A novel tumor mutational burden estimation model as a predictive and prognostic biomarker in NSCLC patients.

BACKGROUND: Tumor mutational burden (TMB) has both prognostic value in resected non-small cell lung cancer (NSCLC) patients and predictive value for immunotherapy response. However, TMB evaluation by whole-exome sequencing (WES) is expensive and time-consuming, hampering its application in clinical practice. In our study, we aimed to construct a mutational burden estimation model, with a small set of genes, that could precisely estimate WES-TMB and, at the same time, has prognostic and predictive value for NSCLC patients. METHODS: TMB estimation model was trained based on genomic data from 1056 NSCLC samples from The Cancer Genome Atlas (TCGA). Validation was performed using three independent cohorts, including Rizvi cohort and our own Asian cohorts, including 89 early-stage and n late-stage Asian NSCLC patients, respectively. TCGA data were obtained on September 3, 2018. The two Asian cohort studies were performed from September 1, 2018, to March 5, 2019. Pearson's correlation coefficient was used to assess the performance of estimated TMB with WES-TMB. The Kaplan-Meier survival analysis was applied to evaluate the association of estimated TMB with disease-free survival (DFS), overall survival (OS), and response to anti-programmed death-1 (PD-1) and anti-programmed death-ligand 1 (PD-L1) therapy. RESULTS: The estimation model, consisted of only 23 genes, correlated well with WES-TMB both in the training set of TCGA cohort and validation set of Rizvi cohort and our own Asian cohort. Estimated TMB by the 23-gene panel was significantly associated with DFS and OS in patients with early-stage NSCLC and could serve as a predictive biomarker for anti-PD-1 and anti-PD-L1 treatment response. CONCLUSIONS: The 23-gene panel, instead of WES or the currently used panel-based methods, could be used to assess the WES-TMB with a high relevance. This customized targeted sequencing panel could be easily applied into clinical practice to predict the immunotherapy response and prognosis of NSCLC.