Cancer Medicine最新文献

Purpose: To evaluate the predictive value of pre-treatment histogram analysis using APTWI, diffusion-weighted imaging (DWI), and early contrast-enhanced silhouette imaging in determining pathological complete response (pCR) post-NAC in breast cancer, and to investigate whether combining immunohistochemical indicators enhances predictive accuracy.

Materials and methods: A retrospective continuous collection of 108 females with breast cancer who underwent NAC and pre-treatment APTWI, DWI, and dynamic contrast-enhanced imaging at our hospital. Clinical, MRI imaging, and pathological characteristics were analyzed for patients. NAC response was divided into pCR and non-pCR. Tumor segmentation and histogram feature extraction were performed on APT, ADC, and early contrast-enhanced silhouette images, and combined them with clinical features to construct an NAC efficacy prediction model. Diagnostic performance was assessed using receiver operating characteristic curves, with DeLong's test employed to compare areas under the curve (AUC).

Results: In pCR group, mean, root-mean-square deviation, and 5th, 10th, 15th, 25th, 50th, 75th, 85th percentile of MTRasym, along with 1st percentiles of ADC were significantly higher in the pCR group than in the non-pCR group (p < 0.05). Conversely, the interquartile range of MTRasym and 10th percentiles of ADC were significantly lower in the pCR group (p < 0.05). ER-negative, HER2-positive expression, and 5th percentile MTRasym value were identified as independent predictors of pCR post-NAC (odds ratios, 0.16, 7.25, and 1.35, respectively). The combined diagnostic model demonstrated an AUC of 0.844, significantly outperforming individual parameters (p < 0.05).

Conclusion: Pre-treatment histogram analysis of MTRasym values derived from APTWI provides significant predictive value for pCR post-NAC in breast cancer. The combined diagnostic model incorporating APTWI with ER and HER2 expression status further enhances diagnostic performance.

Background: Lung cancer (LC) is a leading cause of morbidity and mortality worldwide. Cardiovascular disease is the primary cause of non-cancer-related death among cancer survivors. Frailty, characterized by a decline in physiological reserves, has been identified as a predictor of poor outcomes in cancer. However, the relationship between frailty, pre-frailty, and long-term cardiovascular outcomes in LC patients remains insufficiently explored.

Methods: This retrospective analysis of a cohort study utilized prospectively collected data from the UK Biobank, with baseline assessment between 2006 and 2010 and follow-up until October 31, 2022. Frailty was defined using the frailty phenotype according to five components (weight loss, exhaustion, low physical activity, slow gait speed, and low grip strength). Participants were categorized as non-frail, pre-frail or frail. The outcome was defined as major adverse cardiac and cerebrovascular events (MACCE). Cox proportional hazards models adjusted for confounders including age, sex, obesity, smoking status, socioeconomic status, diabetes, hypertension, COPD, and tumor type were employed to estimate hazard ratios (HR) for MACCE.

Results: Of the 500,530 participants in this cohort, 6095 were diagnosed with LC after recruitment. Among LC patients, 43.79% were non-frail, 48.20% pre-frail, and 8.01% frail. Frail individuals had a significantly higher risk of MACCE (HR = 1.21, 95% CI: 1.07-1.38, p = 0.002) compared to non-frail patients, while pre-frail individuals also exhibited an elevated risk (HR = 1.10, 95% CI: 1.02-1.18, p = 0.010). Specific frailty components, particularly low physical activity and slow gait speed, were strongly associated with increased risks of both MACCE and all-cause mortality. In contrast, low grip strength did not show a significant association with adverse outcomes.

Conclusions: LC participants had a higher prevalence of pre-frailty and frailty. The presence of frailty and pre-frailty significantly increased the risk of MACCE in long-term LC survivors. Notably, slow gait speed and low physical activity were strongly associated with MACCE compared to other frailty components.

Background: Comprehensive genomic profiling (CGP) is a time- and tissue- efficient method to help guide precision oncology. To enhance the clinical utility of CGP, we investigated the performance of a novel strategy integrating tumor DNA and mRNA profiling, together with liquid biopsy ctDNA monitoring.

Methods: Genomic DNA and mRNA simultaneously extracted from 604 archived tissue samples of 12 cancer types were used. Tumor DNA was subjected to targeted sequencing using a 504-gene panel with high-density probes (HDP), and shallow whole genome sequencing to profile genomic biomarkers. mRNA transcriptome profiling was performed to further capture fusion variants, and to predict tissue of origin (TOO) using our ensemble model OriCUP, an algorithm trained on 9889 samples and independently validated on 731 samples. In a cohort of 55 metastatic lung cancer patients, longitudinal plasma ctDNA was analyzed using a hybrid tumor-informed and tumor-agnostic approach to predict progression-free survival (PFS).

Results: Among all biomarkers, DNA sequencing using HDP achieved higher sensitivity than the standard panel design to identify copy number variations at chromosome-, gene-, and exon- levels. The detection rate of fusion variants using DNA sequencing alone was 20% lower than mRNA sequencing in reference samples, while the combination of both methods was essential to maximize fusion detection in clinical FFPE samples. For TOO, our OriCup model achieved prediction accuracy of 87.7% for primary tumors and 81.4% for metastatic tumors. In 55 lung cancer patients, ctDNA profiling identified additional 11.5% tumor-agnostic actionable and resistance mutations. Patients having more than 50% ctDNA decrease from baseline were classified as molecular responders and showed significantly longer PFS than those classified as molecular non-responders (HR = 9.42, 95% CI: 3.33-26.67, p < 0.0001, 12-month PFS: 95.5% vs. 31.7%).

Conclusions: Comprehensive genomic and transcriptomic profiling could reliably unveil genetic details not provided by DNA-only CGP. The integration of ctDNA detection further helped detect tumor-agnostic mutations and monitor treatment response.

Background: Immunostimulatory effects of PARP inhibitors could increase sensitivity to immune checkpoint inhibitors. The previous Phase II trial (MEDIOLA) reported clinical benefits of durvalumab and olaparib (D + O) in patients with germline BRCA-mutated (gBRCAm) HER2-negative metastatic breast cancer. Yet, the clinical activity of D + O in germline BRCA wild-type (gBRCAwt) triple-negative breast cancer (TNBC) remains unknown.

Methods: This single-arm Phase II study tested D + O in patients with metastatic TNBC. The primary objective was overall response rate (ORR). Secondary objectives were safety, disease control rate (DCR), progression-free survival (PFS) and overall survival (OS). Based on gBRCA status, patients were assigned to either gBRCAwt or gBRCAm cohort and were treated with D (1500 mg iv q4w) and O (300 mg twice a day orally). Pretreatment fresh tissues and serial blood samples were collected for correlative studies.

Results: Fifteen patients (12 gBRCAwt and 3 gBRCAm) were enrolled. gBRCAm and gBRCAwt cohorts are reported as a combined dataset because of small sample size due to COVID-19 and slow accrual. The median number of prior therapies was three (range 0-8). Among 14 RECIST-evaluable patients (11 gBRCAwt and 3 gBRCAm), ORR was 28.6% (3 gBRCAm and 1 gBRCAwt). DCR was 64.3% (3 gBRCAm and 6 gBRCAwt). The median PFS and OS were 3.6 months (95% confidence interval [CI]: 1.8-5.7) and 10.7 months (95% CI: 5.9-38.9), respectively. There is one gBRCAm patient with ongoing durable PR (67.4+ months). There was no new safety concern. CD83 expression on Types 1 and 2 conventional dendritic cells in blood at baseline was low in the patients with PFS ≥ 4 months compared to those with PFS < 4 months.

Conclusion: Our study demonstrated modest clinical benefits of D + O with ORR of 28.6% in subsets of heavily pretreated TNBC. Further detailed classification of DCs to understand the predictive role of DCs and prospective validation in a large cohort is required.

Trial registration: ClinicalTrials.gov identifier: NCT02484404.

Background: Despite the proven benefits of early breast cancer detection in reducing mortality, adherence to breast cancer screening guidelines in the United States is suboptimal. In this study, we assessed the prevalence and factors associated with adherence to breast cancer screening guidelines.

Methods: Using nationally representative data from the National Health Interview Survey 2021, we included breast cancer screening-eligible women (aged 50-74). Descriptive statistics and population-weighted multivariable logistic regression were employed to examine breast cancer screening adherence per the United States Preventive Services Task Force guidelines, and its determinants in this population.

Results: Of the 6814 screening-eligible women included in this study, 76.4% adhered to breast cancer screening guidelines. Asians (adjusted odds ratio [AOR]: 0.59 (95% confidence interval [95% CI]: 0.42-0.83), p = 0.003), current cigarette smokers (AOR: 0.62 (95% CI: 0.50-0.76), p < 0.0001), individuals with a precollege educational attainment (less than high school [AOR: 0.60 (95% CI: 0.45-0.80), p = 0.0005] and high school or GED [AOR: 0.79 (95% CI: 0.66-0.94), p < 0.001]), those who reported a poor/fair health status (AOR: 0.71 (95% CI: 0.58-0.86), p = 0.0006), single/widowed/separated/divorced (AOR: 0.71 (95% CI: 0.61-0.82), p < 0.0001), and uninsured (AOR: 0.24 (95% CI: 0.18-0.32), p < 0.0001) women had lower odds of being adherent to breast cancer screening. Non-Hispanic Blacks (AOR: 1.81 (95% CI: 1.38-2.36), p < 0.0001), women who had a routine checkup in the past 2 years (AOR: 11.24 (95% CI: 7.31-17.29), p < 0.0001), and those with a personal cancer history (AOR: 1.44 (95% CI: 1.18-1.76), p = 0.0003) had higher odds of being adherent to breast cancer screening guidelines.

Conclusion: Adherence to breast cancer screening in the United States remained below the Healthy People 2030 goal, with important variations across sociodemographic, behavioral, and health-related factors. Public health actions such as integrating breast cancer awareness activities into smoking cessation programs, encouraging and equipping healthcare professionals to use culturally tailored interventions, and reinforcing community education about breast cancer and its prevention will help increase breast cancer screening adherence among US women.

Background: Dysgeusia is a side effect of the anti-GPRC5DxCD3 bispecific antibody talquetamab (TAL), but other myeloma treatments, such as high-dose melphalan (MEL) with autologous stem cell transplantation (ASCT), are also known to alter taste perception in patients with multiple myeloma (MM). This study investigates the spectrum, prevalence and severity of dysgeusia in patients receiving TAL and MEL and compares the results with anti-BCMA bispecifics as a control for T-cell-engaging therapies.

Methods: Gustatory and olfactory performance was assessed in 87 MM patients divided into three treatment groups: TAL (n = 26), MEL/ASCT (n = 35), and BCMA bispecifics (n = 26). Evaluations included Taste Strips, Sniffin' Sticks Identification Test 16, and comprehensive questionnaires on taste perception, dietary issues, quality of life (QoL), mood, and treatment compliance.

Results: TAL-treated patients exhibited severe taste impairment, with 96.2% reporting marked declines. Taste alterations were also observed in patients receiving MEL/ASCT and BCMA bispecifics, though these were less pronounced, affecting 62.9% and 30.8% of cases, respectively. Xerostomia incidence was highest in the TAL group. Patients considering discontinuation of TAL (30%) cited taste alterations as the primary reason. MEL was associated with higher incidences of nausea, vomiting, and appetite loss.

Conclusion: TAL-associated taste disturbances have a major impact on patients and require further investigation and mitigation strategies. Enhanced patient support, proactive monitoring, and targeted interventions are critical to improving the well-being and adherence of MM patients.

Background: HER2-targeted antibody-drug conjugates (ADCs) have dramatically advanced breast cancer outcomes. Two HER2-targeted ADCs, trastuzumab deruxtecan and trastuzumab emtansine, are currently approved for use in breast cancer, with > 60 other candidates under ongoing investigation.

Methods: In this report, we provide a narrative review of existing data underlying the current understanding of HER2-targeted ADC toxicities in breast cancer, highlighting both common and serious adverse events.

Results: When used as an adjuvant or neoadjuvant, first- or second-line agent, alone or in combination with another agent, trastuzumab emtansine has been commonly associated with epistaxis, fatigue, headache, nausea, and pyrexia across several clinical trials. Comparatively, trastuzumab deruxtecan has been commonly associated with alopecia, cytopenias, decreased appetite, fatigue, and gastrointestinal adverse effects.

Conclusion: Despite the demonstrated clinical benefits in breast cancer, diverse systemic and organ-specific adverse events, including dose-limiting toxicities, have been reported even at suboptimal therapeutic doses and pose significant barriers to pursuing dose escalations for maximizing therapeutic efficacy.

Background: The High Mobility Group Box 1 (HMGB1) protein, a member of the HMG family, plays a crucial role in both cancer progression and inflammatory responses. HMGB1 can act as a damage-associated molecular pattern (DAMP) to activate immune responses and modulate inflammation. Its dualistic roles in promoting and inhibiting tumor growth, as well as its involvement in DNA repair and drug resistance, make it a key target for understanding and treating cancer and inflammatory diseases.

Objective: This review aims to explore the dualistic roles of HMGB1 in cancer and inflammation, focusing on its pro-inflammatory and anti-inflammatory functions in the tumor microenvironment, its impact on DNA damage repair and tumor drug resistance, and its potential as a therapeutic target for cancer and inflammatory diseases.

Methods: We conducted a comprehensive review of the literature on HMGB1, analyzing its structural features, biological functions, and mechanisms of action in various pathological contexts. We also examined the impact of HMGB1 on tumor progression, immune responses, and metabolic reprogramming in cancer cells, as well as its role in inflammatory signaling pathways.

Results: HMGB1 exhibits both oncogenic and tumor-suppressive effects in cancer. It promotes tumor growth, metastasis, and immune evasion through mechanisms such as shaping the tumor microenvironment, driving metabolic reprogramming, and inducing drug resistance. Conversely, HMGB1 can enhance anti-tumor immunity by activating dendritic cells and T cells. In inflammation, HMGB1 acts as a DAMP, activating immune responses via receptors like RAGE and TLR4. Its redox state and subcellular localization determine its proinflammatory or anti-inflammatory functions. Targeting HMGB1 has shown promise in preclinical and clinical studies, with potential applications in anti-cancer and anti-inflammatory therapies.

Conclusion: The dualistic roles of HMGB1 in cancer and inflammation highlight its complexity and potential as a therapeutic target. Future research should focus on elucidating the context-specific mechanisms of HMGB1, developing precision-targeted therapies to modulate its multifunctional activities, and translating these findings into clinical practice to improve patient outcomes.

Background: Pancreatic cancer (PC) remains one of the most lethal malignancies worldwide, characterized by late-stage diagnosis and a poor prognosis. This study explores the clinical, biochemical, and gut microbiota differences between PC patients and healthy controls (CTRL), as well as between subgroups of PC patients with pancreatic head cancer (PHC) and pancreatic body-tail cancer (PBTC).

Methods: A total of 72 PC patients and 37 CTRL subjects were included, with further stratification of PC patients into 45 PHC and 27 PBTC cases. Clinical and biochemical data were collected. Gut microbiota was analyzed by 16S rRNA gene sequencing. Alpha-diversity indices, Firmicutes/Bacteroidetes ratio and taxonomic composition were evaluated and compared in all the experimental group. Correlation analyses were performed between specific bacterial taxa and biochemical markers and a Random Forest algorithm was applied to identify taxa discriminating PC from CTRL and PHC from PBTC.

Results: Clinical and biochemical data revealed significant heterogeneity between groups, with PHC patients exhibiting higher markers of inflammation and liver dysfunction, while PBTC patients showed relatively preserved physiological status. Gut microbiota analysis revealed significant dysbiosis in PC patients compared to CTRL. Alpha-diversity indices demonstrated reduced species evenness in PC patients, while the Firmicutes/Bacteroidetes ratio was significantly lower. Taxonomic composition analysis indicated enrichment of pro-inflammatory taxa and depletion of beneficial SCFA-producing genera. However, subgroup comparisons revealed distinct microbial profiles, with PHC patients enriched in taxa associated with localized inflammation and PBTCs showing higher levels of anti-inflammatory and SCFA-producing bacteria. A correlation analysis linked specific bacteria to markers of liver dysfunction and systemic inflammation, such as GGT, ALP, and ESR, while SCFA-producing taxa correlated negatively with inflammatory markers. A Random Forest algorithm identified key microbial taxa discriminating PC patients from CTRL and PHC from PBTC.

Conclusions: These findings highlight the interplay between microbiota composition, tumor localization, and systemic inflammation, showing a potential for microbiota-based diagnostics and interventions in PC.

Introduction: Predictive models could support clinicians in identifying patients who may benefit from cancer investigations. We aimed to examine published evidence on machine learning models (ML) developed to estimate cancer risk based on symptoms and other patient characteristics.

Methods: Using MEDLINE, Scopus, and EMBASE, we performed a systematic review of studies published in 2014-2024, which included data on signs/symptoms for cancer risk prediction. We used the QUADAS-AI tools to assess study quality. We performed a quantitative synthesis of diagnostic performance, including accuracy, sensitivity, specificity, area under the curve (AUC). Adherence to TRIPOD guidelines was assessed.

Results: Among the 5646 initially identified articles, 34 met inclusion criteria. Included studies most frequently examined lung (n = 9 studies), mesothelioma (n = 7), and gastrointestinal cancers (n = 4) and used hospital electronic health records (n = 8) or publicly available online datasets (n = 13). In addition to signs/symptoms (n = 34), most models included sociodemographic characteristics (n = 27) and lifestyle factors (n = 20). In 70% of studies, internal validation was performed. ML models demonstrated variable performance, with AUC values ranging from 0.60 to 1 during validation. Random Forest, Support Vector Machine, Decision Tree, and Multilayer Perceptron showed the best predictive performance. Most of the studies (94.1%) had a high risk of bias for the index test.

Conclusion: ML models have been reported to demonstrate potential in managing complex data for cancer risk prediction. However, the current evidence is heterogeneous and frequently limited by bias and incomplete reporting. Further validation and thorough assessments of real-world performance are necessary before these models can be considered reliable for clinical use.

Trial registration: International Prospective Register of Systematic Reviews (PROSPERO) registration number: CRD42024548088.