Research progress on factors affecting the sensitivity of breast cancer to radiotherapy: a narrative review.

Background and Objective: Radiation therapy (RT) is one of the important components of comprehensive treatment for breast cancer and has important value in improving the control rate of local areas, reducing the chance of recurrence and metastasis after breast cancer surgery, delaying disease progression, and improving the survival of breast cancer patients. The factors that affect the RT sensitivity of breast cancer are important. The above potential predictors of radiation efficacy can provide patients with a predictive method and therefore have significant value in clinical therapy. In this paper, we have summarised the predictive factors of radiotherapy sensitivity by reviewing recent research on breast cancer and focused on the following areas: tumor immune microenvironment (TIME), cancer stem cells, noncoding RNAs, signal transduction pathways, genes, etc. This review aims to provide theoretical basis and reference for improving the efficacy of radiotherapy and experimental individualized treatment of breast cancer. Methods: We searched the Web of Science database to identify clinical studies published between 2010 and January 2024 that investigated radiotherapy sensitivity. The main findings of the validated studies were summarised. Key Content and Findings: Improving the radiosensitivity of breast cancer is essential in the treatment of breast cancer. The radiosensitivity can be improved by modulating immune cells or immunomodulatory factors in the TIME, modulating signal transduction pathways, and other innovative combination therapy strategies. And we also summarized the predictive markers of breast cancer radiosensitivity. Conclusions: In this paper, we reviewed the literature and summarized the newest research advances on the radiosensitivity of breast cancer patients. This review paper includes the following six aspects: the immune microenvironment, tumor stem cells, signaling pathways, regulation of gene/protein expression, small molecule drugs, and predictive markers for radiosensitivity.

Neural activity during inhibitory control predicts suicidal ideation with machine learning.

Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.

This study achieves a high-accuracy machine learning model that can classify an individual as having suicidal ideation or not from source-localized EEG signals captured during an inhibitory control task. In addition, we have identified key brain regions that drive this model.

Potential diagnostic, prognostic, and predictive biomarkers of gastric cancer.

Background: Gastric cancer (GC), a malignant epithelial tumor, is the fourth leading cause of cancer-related death worldwide. Therapeutic strategies for GC, despite the biggest challenges, can significantly improve survival rates through early detection and effective screening methods. Aim: To provide brief information on the necessity of multiple specific diagnostic, prognostic, and predictive markers for GC. Methods: This review was conducted using a variety of search engines, including PubMed Central, Scopus, Web of Science, Google Scholar, and others. Results: Some potential biomarkers that provide essential information include circulating tumor cells (CTCs), DNA methylation, claudin 18.2, fibroblast growth factor receptor 2 (FGFR2), long noncoding RNAs (lncRNAs), cell-free DNA (cfDNA), microRNAs, and serum pepsinogens. Conclusion: Multiple tumor markers are essential for screening, tumor identification, staging, prognostic assessment, and monitoring recurrence after therapy due to the absence of a single tumor indicator for diagnosing, prognosticating, and predicting GC.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Current status of molecular diagnostics for lung cancer.

The management of lung cancer (LC) requires the analysis of a diverse spectrum of molecular targets, including kinase activating mutations in EGFR, ERBB2 (HER2), BRAF and MET oncogenes, KRAS G12C substitutions, and ALK, ROS1, RET and NTRK1-3 gene fusions. Administration of immune checkpoint inhibitors (ICIs) is based on the immunohistochemical (IHC) analysis of PD-L1 expression and determination of tumor mutation burden (TMB). Clinical characteristics of the patients, particularly age, gender and smoking history, significantly influence the probability of finding the above targets: for example, LC in young patients is characterized by high frequency of kinase gene rearrangements, while heavy smokers often have KRAS G12C mutations and/or high TMB. Proper selection of first-line therapy influences overall treatment outcomes, therefore, the majority of these tests need to be completed within no more than 10 working days. Activating events in MAPK signaling pathway are mutually exclusive, hence, fast single-gene testing remains an option for some laboratories. RNA next-generation sequencing (NGS) is capable of detecting the entire repertoire of druggable gene alterations, therefore it is gradually becoming a dominating technology in LC molecular diagnosis.

High viral loads combined with inflammatory markers predict disease severity in hospitalized COVID-19 patients: Results from the NOR-Solidarity trial.

OBJECTIVES: To investigate temporal changes in the association between SARS-CoV2 viral load (VL) and markers of inflammation during hospitalization, as well as the ability of these markers alone or in combination to predict severe outcomes. METHODS: Serial oropharyngeal and blood samples were obtained from hospitalized COVID-19 patients (n = 160). Levels of inflammatory markers and oropharyngeal VL were measured during hospitalization (admission, days 3-5, and days 7-10) and related to severe outcomes (respiratory failure/intensive care unit admission). RESULTS: Elevated admission levels of IL (interleukin)-6, IL-33, IL-8, monocyte chemoattractant protein-1 (MCP-1), interferon-γ-induced protein 10 (IP-10), IL-1ß, and IL-1Ra were associated with severe outcomes during hospitalization. Although no inflammatory markers correlated with VL at baseline, there was a significant correlation between VL and levels of IP-10 and MCP-1 at days 3-5, accompanied by IL-8 and IL-6 at days 7-10. Finally, there was a seemingly additive effect of IP-10, MCP-1, and IL-6 in predicting severe outcomes when combined with high VL at baseline. CONCLUSIONS: An increasing number of inflammatory markers were associated with VL during the first 10 days of hospitalization, and several of these markers were associated with severe outcomes, in particular when combined with elevated VL. Future studies should assess the potential for combining antiviral and immunomodulatory treatment, preferably guided by viral and inflammatory biomarkers, for the selection of high-risk patients.

Findings of PTSD-specific deficits in default mode network strength following a mild experimental stressor.

Reductions in default mode (DMN) connectivity strength have been reported in posttraumatic stress disorder (PTSD). However, the specificity of DMN connectivity deficits in PTSD compared to major depressive disorder (MDD), and the sensitivity of these alterations to acute stressors are not yet known. 52 participants with a primary diagnosis of PTSD (n = 28) or MDD (n = 24) completed resting-state functional magnetic resonance imaging immediately before and after a mild affective stressor. A 2 × 2 design was conducted to determine the effects of group, stress, and group*stress on DMN connectivity strength. Exploratory analyses were completed to identify the brain region(s) underlying the DMN alterations. There was significant group*stress interaction (p = 0.03), reflecting stress-induced reduction in DMN strength in PTSD (p = 0.02), but not MDD (p = 0.50). Nodal exploration of connectivity strength in the DMN identified regions of the ventromedial prefrontal cortex and the precuneus potentially contributing to DMN connectivity deficits. The findings indicate the possibility of distinct, disease-specific, patterns of connectivity strength reduction in the DMN in PTSD, especially following an experimental stressor. The identified dynamic shift in functional connectivity, which was perhaps induced by the stressor task, underscores the potential utility of the DMN connectivity and raises the question whether these disruptions may be inversely affected by antidepressants known to treat both MDD and PTSD psychopathology.

To study any differences between PTSD and depression in the way the brain talks with itself in its default mode when not doing any particular thing, we did MRI brain scans with 52 people with Depression, but only some had PTSD. We found that mild emotional stress may briefly reduce default mode strength in PTSD, but not in depression. This might help researchers better understand the impact of stress and trauma on the brain.

Diagnostic, predictive and prognostic molecular biomarkers in clear cell renal cell carcinoma: A retrospective study.

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of kidney cancer. Many patients are diagnosed at advanced stages, making early detection crucial. Unfortunately, there are currently no noninvasive tests for ccRCC, emphasizing the need for new biomarkers. Additionally, ccRCC often develops resistance to treatments like radiotherapy and chemotherapy. Identifying biomarkers that predict treatment outcomes is vital for personalized care. The integration of artificial intelligence (AI), multi-omics analysis, and computational biology holds promise in bolstering detection precision and resilience, opening avenues for future investigations. The amalgamation of radiogenomics and biomaterial-basedimmunomodulation signifies a revolutionary breakthrough in diagnostic medicine. This review summarizes existing literature and highlights emerging biomarkers that enhance diagnostic, predictive, and prognostic capabilities for ccRCC, setting the stage for future clinical research.

Predictive value of the Naples prognostic score on postoperative delirium in the elderly with gastrointestinal tumors: a retrospective cohort study.

BACKGROUND: Postoperative delirium (POD) is a common complication among elderly patients after surgery. The Naples Prognostic Score (NPS), a novel prognostic marker based on immune-inflammatory and nutritional status, was widely used in the assessment of the prognosis of surgical patients. However, no study has evaluated the relationship between NPS and POD. The aim of this article was to investigate the association between NPS and POD and test the predictive efficacy of preoperative NPS for POD in elderly patients with gastrointestinal tumors. MATERIALS AND METHODS: In the present study, we retrospectively collected perioperative data of 176 patients (≥ 60 years) who underwent elective gastrointestinal tumor surgery from June 2022 to September 2023. POD was defined according to the chart-based method and the NPS was calculated for each patient. We compared all the demographics and laboratory data between POD and non-POD groups. Univariate and multivariate logistic regression analysis was used to explore risk factors of POD. Moreover, the accuracy of NPS in predicting POD was further assessed by utilizing receiver operating characteristic (ROC) curves. RESULTS: 20 had POD (11.4%) in a total of 176 patients, with a median age of 71 (65-76). The outcomes by univariate analysis pointed out that age, ASA status ≥ 3, creatinine, white blood cell count, fasting blood glucose (FBG), and NPS were associated with the risk of POD. Multivariate logistic regression analysis further showed that age, ASA grade ≥ 3, FBG and NPS were independent risk factors of POD. Additionally, the ROC curves revealed that NPS allowed better prognostic capacity for POD than other variables with the largest area under the curve (AUC) of 0.798, sensitivity of 0.800 and specificity of 0.667, respectively. CONCLUSION: Age, ASA grade ≥ 3, and FBG were independent risk factors for POD in the elderly underwent gastrointestinal tumor surgery. Notably, the preoperative NPS was a more effective tool in predicting the incidence of POD, but prospective trials were still needed to further validate our conclusion. TRIAL REGISTRATION: The registration information for the experiment was shown below. (date: 3rd January 2024; number: ChiCTR2400079459).

KRAS as a Prognostic and Predictive Marker in Metastatic Non-Small Cell Lung Carcinoma: A Systematic Review.

Metastatic non-small cell lung cancer (NSCLC) poses a significant clinical challenge, prompting a focused investigation into the role of KRAS mutations in prognosis and treatment response. Targeted therapies offer promising avenues for intervention, motivating a comprehensive analysis of existing evidence. Conducted in June 2023, our review delved into MEDLINE (Medical Literature Analysis and Retrieval System Online), Embase, Scopus, and the Cochrane Register of Controlled Trials. Rigorous inclusion and exclusion criteria guided the selection of 12 articles, comprising two randomized controlled trials (RCTs) and 10 observational studies. Multiple investigators independently executed data extraction, evaluating prognostic factors (overall and progression-free survival) and predictive outcomes (treatment and objective response). The Newcastle-Ottawa Scale (NOS) and modified Jadad scores were used for study quality assessment of observational studies and RCTs, respectively. From an initial pool of 120 articles, the 12 selected studies, spanning 2013 to 2022, encompassed 2,845 metastatic NSCLC patients. KRAS mutations, particularly the G12C variant, emerged as a pivotal factor influencing treatment response. Notably, KRAS wild type patients displayed enhanced responses to platinum-based chemotherapy, while those with KRAS mutations exhibited favourable outcomes with immune checkpoint inhibitors (ICIs). The role of KRAS mutations as prognostic indicators in metastatic NSCLC is underscored by this systematic review, with implications for both survival and treatment response. The discernment between KRAS wild type and mutant patients offers insights into tailored therapeutic strategies, with platinum-based chemotherapy and immune checkpoint inhibitors emerging as context-dependent options. Nevertheless, more research is required to solidify the predictive role of KRAS and explore the efficacy of KRAS inhibitors and other targeted therapies, paving the way for refined and personalized interventions in the management of metastatic NSCLC.

The lymphocyte/monocyte ratio predicts the efficacy of isatuximab plus pomalidomide in multiple myeloma patients.

BACKGROUND: Isatuximab, an anti-CD38 antibody, has been widely used in treatments for patients with relapsed/refractory multiple myeloma (MM). Despite its high efficacy, not all patients achieve a lasting therapeutic response with isatuximab. OBJECTIVE: We tried to identify biomarkers to predict the effectiveness of isatuximab by focusing on the host's immune status before treatment. METHODS: We retrospectively analyzed the cases of 134 relapsed/refractory MM patients in the Kansai Myeloma Forum database who had received only a first isatuximab treatment. RESULTS: Among the 134 patients, an isatuximab, pomalidomide and dexamethasone (Isa-PD) regimen, isatuximab, carfilzomib and dexamethasone (Isa-KD) regimen and isatuximab and/or dexamethasone (Isa-D) regimen were used in 112, 15 and 7 patients, respectively. The median age at treatment, number of prior treatment regimens, and progression-free survival (PFS) were 71, 6, and 6.54 months, respectively. Multivariate analysis showed that the PFS under the Isa-PD regimen was longer in patients with higher lymphocyte/monocyte ratio (LMR ≥ 4), fewer prior treatment regimens (< 6), and no use of prior daratumumab treatment. The OS under the Isa-PD regimen was longer in patients with higher white blood cell counts (WBC counts ≥ 3000/µL) and higher LMR. The PFS under the Isa-D regimen was longer in patients with fewer prior treatment regimens in univariate analysis, but no parameters were correlated with PFS/OS under the Isa-KD regimen. CONCLUSION: We found that the patients with higher LMR (≥ 4) could obtain longer PFS and OS under the Isa-PD regimen. Other cohort studies of isatuximab treatment might be necessary to substantiate our results.

D2/D3 dopamine supports the precision of mental state inferences and self-relevance of joint social outcomes.

Striatal dopamine is important in paranoid attributions, although its computational role in social inference remains elusive. We employed a simple game-theoretic paradigm and computational model of intentional attributions to investigate the effects of dopamine D2/D3 antagonism on ongoing mental state inference following social outcomes. Haloperidol, compared with the placebo, enhanced the impact of partner behaviour on beliefs about the harmful intent of partners, and increased learning from recent encounters. These alterations caused substantial changes to model covariation and negative correlations between self-interest and harmful intent attributions. Our findings suggest that haloperidol improves belief flexibility about others and simultaneously reduces the self-relevance of social observations. Our results may reflect the role of D2/D3 dopamine in supporting self-relevant mentalising. Our data and model bridge theory between general and social accounts of value representation. We demonstrate initial evidence for the sensitivity of our model and short social paradigm to drug intervention and clinical dimensions, allowing distinctions between mechanisms that operate across traits and states.

Semimethylation is a feature of diffuse large B-cell lymphoma, and subgroups with poor prognosis are characterized by global hypomethylation and short telomere length.

BACKGROUND: Large B-cell lymphoma (LBCL) is the most common lymphoma and is known to be a biologically heterogeneous disease regarding genetic, phenotypic, and clinical features. Although the prognosis is good, one-third has a primary refractory or relapsing disease which underscores the importance of developing predictive biological markers capable of identifying high- and low-risk patients. DNA methylation (DNAm) and telomere maintenance alterations are hallmarks of cancer and aging. Both these alterations may contribute to the heterogeneity of the disease, and potentially influence the prognosis of LBCL. RESULTS: We studied the DNAm profiles (Infinium MethylationEPIC BeadChip) and relative telomere lengths (RTL) with qPCR of 93 LBCL cases: Diffuse large B-cell lymphoma not otherwise specified (DLBCL, n = 66), High-grade B-cell lymphoma (n = 7), Primary CNS lymphoma (n = 8), and transformation of indolent B-cell lymphoma (n = 12). There was a substantial methylation heterogeneity in DLBCL and other LBCL entities compared to normal cells and other B-cell neoplasms. LBCL cases had a particularly aberrant semimethylated pattern (0.15 ≤ ß ≤ 0.8) with large intertumor variation and overall low hypermethylation (ß > 0.8). DNAm patterns could not be used to distinguish between germinal center B-cell-like (GC) and non-GC DLBCL cases. In cases treated with R-CHOP-like regimens, a high percentage of global hypomethylation (ß < 0.15) was in multivariable analysis associated with worse disease-specific survival (DSS) (HR 6.920, 95% CI 1.499-31.943) and progression-free survival (PFS) (HR 4.923, 95% CI 1.286-18.849) in DLBCL and with worse DSS (HR 5.147, 95% CI 1.239-21.388) in LBCL. These cases with a high percentage of global hypomethylation also had a higher degree of CpG island methylation, including islands in promoter-associated regions, than the cases with less hypomethylation. Additionally, telomere length was heterogenous in LBCL, with a subset of the DLBCL-GC cases accounting for the longest RTL. Short RTL was independently associated with worse DSS (HR 6.011, 95% CI 1.319-27.397) and PFS (HR 4.689, 95% CI 1.102-19.963) in LBCL treated with R-CHOP-like regimens. CONCLUSION: We hypothesize that subclones with high global hypomethylation and hypermethylated CpG islands could have advantages in tumor progression, e.g. by inactivating tumor suppressor genes or promoting treatment resistance. Our findings suggest that cases with high global hypomethylation and thus poor prognosis could be candidates for alternative treatment regimens including hypomethylating drugs.

Erratum: Protocol of the HISTOTHERM study: assessing the response to hyperthermia and hypofractionated radiotherapy in recurrent breast cancer.

[This corrects the article DOI: 10.3389/fonc.2023.1275222.].

T-cell subsets and cytokines are indicative of neoadjuvant chemoimmunotherapy responses in NSCLC.

PURPOSE: Neoadjuvant PD-1 blockade combined with chemotherapy is a promising treatment for resectable non-small cell lung cancer (NSCLC), yet the immunological mechanisms contributing to tumor regression and biomarkers corresponding to different pathological responses remain unclear. METHODS: Using dynamic and paired blood samples from NSCLC patients receiving neoadjuvant chemoimmunotherapy, we analyzed the frequencies of CD8 + T-cell and Treg subsets and their dynamic changes during neoadjuvant treatment through flow cytometry. Cytokine profiles and function-related gene expression of CD8 + T cells and Tregs were analyzed through flow cytometry and mRNA-seq. Infiltrating T-cell subsets in resected tissues from patients with different pathological responses were analyzed through multiplex immunofluorescence. RESULTS: Forty-two NSCLC patients receiving neoadjuvant chemoimmunotherapy were enrolled and then underwent surgical resection and pathological evaluation. Nineteen patients had pCR (45%), 7 patients had MPR (17%), and 16 patients had non-MPR (38%). In patients with pCR, the frequencies of CD137 + CD8 + T cells (P = 0.0475), PD-1 + Ki-67 + CD8 + T cells (P = 0.0261) and Tregs (P = 0.0317) were significantly different from those of non-pCR patients before treatment. pCR patients usually had low frequencies of CD137 + CD8 + T cells, PD-1 + Ki-67 + CD8 + T cells and Tregs, and their AUCs were higher than that of tissue PD-L1 expression. Neoadjuvant chemoimmunotherapy markedly improved CD8 + T-cell proliferation and activation, especially in pCR patients, as the frequencies of CD137 + CD8 + (P = 0.0136) and Ki-67 + CD8 + (P = 0.0391) T cells were significantly increased. The blood levels of cytokines such as IL-2 (P = 0.0391) and CXCL10 (P = 0.0195) were also significantly increased in the pCR group, which is consistent with the high density of activated cytotoxic T cells at the tumor site (P < 0.0001). CONCLUSION: Neoadjuvant chemoimmunotherapy drives CD8 + T cells toward a proliferative and active profile. The frequencies of CD137 + CD8 + T cells, PD-1 + Ki-67 + CD8 + T cells and Tregs at baseline might predict the response to neoadjuvant chemoimmunotherapy in NSCLC patients. The increase in IL-2 and CXCL10 might reflect the chemotaxis and enrichment of cytotoxic T cells at the tumor site and a better response to neoadjuvant chemoimmunotherapy.

Relevance of Comet Assay and Phosphorylated-Hsp90α in Cancer Patients' Peripheral Blood Leukocytes as Tools to Assess Cisplatin-based Chemotherapy Clinical Response and Disease Outcome.

Cisplatin (cPt) is a commonly used treatment for solid tumors. The main target of its cytotoxicity is the DNA molecule, which makes the DNA damage response (DDR) crucial for cPt-based chemotherapy. Therefore, it is essential to identify biomarkers that can accurately predict the individual clinical response and prognosis. Our goal was to assess the usefulness of alkaline comet assay and immunocytochemical staining of phosphorylated Hsp90α (p-Hsp90α), γH2AX, and 53BP1 as predictive/prognostic markers. Pre-chemotherapy peripheral blood leukocytes were exposed to cPt in vitro and collected at 0, 24 (T24), and 48 (T48) hr post-drug removal. Healthy subjects were also included. Baseline DNA damage was elevated in cancer patients (variability between individuals was observed). After cPt, patients showed increased γH2AX foci/nucleus (T24 and T48). Both in healthy persons and patients, the nuclear p-Hsp90α and N/C (nuclear/cytoplasmic) ratio augmented (T24), decreasing at T48. Favorable clinical response was associated with high DNA damage and p-Hsp90α N/C ratio following cPt. For the first time, p-Hsp90α significance as a predictive marker is highlighted. Post-cPt-DNA damage was associated with longer disease-free survival and overall survival. Our findings indicate that comet assay and p-Hsp90α (a marker of DDR) would be promising prognostic/predictive tools in cP-treated cancer patients.

Proteomic characterization of esophageal squamous cell carcinoma response to immunotherapy reveals potential therapeutic strategy and predictive biomarkers.

Immunotherapy is the first-line therapy for esophageal squamous cell carcinoma (ESCC), yet many patients do not respond due to drug resistance and the lack of reliable predictive markers. We collected 73 ESCC patients (including discovery cohort and validation cohort) without immune thrombocytopenia and undergoing anti-PD1 immunotherapy. Proteomic and phosphoproteomic analysis of 73 ESCC treatment-naive samples by mass spectrometry-based label-free quantification were applied to explore the potential resistant and sensitive mechanisms, and identify predictive markers of ESCC immunotherapy. Comparative analysis found the pathways related to immune and mitochondrial functions were associated with ESCC immunotherapy sensitivity; while platelet activation bioprocess showed negative correlation with CD8+ T cells and related to ESCC immunotherapy non-sensitivity. Finally, we identified 10 ESCC immunotherapy predictive biomarkers with high accuracy (≥ 0.90) to predict the immunotherapeutic response, which was validated in the independent cohort.

Galectin-3 and cancer immunotherapy: a glycobiological rationale to overcome tumor immune escape.

Immunotherapy with checkpoint inhibitors (ICIs) has radically changed the landscape of therapeutic opportunities in oncology, but much still needs to be understood from a mechanistic point of view. There is space for further improving tumors' response to ICIs, as supported by a strong biological rationale. For this achievement a detailed analysis of tumor cell phenotype with functional dissection of the molecular interactions occurring in the TME is required. Galectin-3 is a pleiotropic tumor relevant molecule, which deserves particular attention in immuno-oncology. Due to its ability to finely modulate immune response in vivo, Galectin-3 is a potential target molecule to be considered for overcoming tumor immune escape.

Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study.

The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.

Gene Expression Signatures Predict First-Year Response to Somapacitan Treatment in Children With Growth Hormone Deficiency.

CONTEXT: The pretreatment blood transcriptome predicts growth response to daily growth hormone (GH) therapy with high accuracy. OBJECTIVE: Investigate response prediction using pretreatment transcriptome in children with GH deficiency (GHD) treated with once-weekly somapacitan, a novel long-acting GH. METHODS: REAL4 is a randomized, multinational, open-label, active-controlled parallel group phase 3 trial, comprising a 52-week main phase and an ongoing 3-year safety extension (NCT03811535). A total of 128/200 treatment-naïve prepubertal children with GHD consented to baseline blood transcriptome profiling. They were randomized 2:1 to subcutaneous somapacitan (0.16 mg/kg/week) or daily GH (0.034 mg/kg/day). Differential RNA-seq analysis and machine learning were used to predict therapy response. RESULTS: 121/128 samples passed quality control. Children treated with somapacitan (n = 76) or daily GH (n = 45) were categorized based on fastest and slowest growing quartiles at week 52. Prediction of height velocity (HV; cm/year) was excellent for both treatments (out of bag [OOB] area under curve [AUC]: 0.98-0.99; validation AUC: 0.83-0.84), as was prediction of secondary markers of growth response: HV standard deviation score (SDS) (0.99-1.0; 0.75-0.78), change from baseline height SDS (ΔHSDS) (0.98-1.0; 0.61-0.75), and change from baseline insulin-like growth factor-I SDS (ΔIGF-I SDS) (0.96-1.0; 0.85-0.88). Genes previously identified as predictive of GH therapy response were consistently better at predicting the fastest growers in both treatments in this study (OOB AUC: 0.93-0.97) than the slowest (0.67-0.85). CONCLUSION: Pretreatment transcriptome predicts first-year growth response in somapacitan-treated children with GHD. A common set of genes can predict the treatment response to both once-weekly somapacitan and conventional daily GH. This approach could potentially be developed into a clinically applicable pretreatment test to improve clinical management.