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Background: Tumor-promoting inflammation and inflammatory cytokines are linked to immune checkpoint blockade (ICB) resistance. Methods: We assessed the associations between pre-treatment Interleukin-6 (IL-6), Interleukin-8 (IL-8) levels and on-treatment changes in IL-6, IL-8 and C-reactive protein (CRP) with ICB trial end points. Results: 27 studies representing 6,719 patients were included. Low pre-treatment IL-6 levels were associated with improved objective response rate (ORR) (odds ratio (OR) = 0.31 [0.18-0.55]) and better progression-free survival (PFS) (hazard ratio (HR) = 0.59 [0.48-0.72]) and overall survival (OS) [95% confidence interval (CI)] (HR = 0.42 [0.35-0.50]). Low pre-treatment IL-8 levels were associated with improved ORR (OR = 0.47 [0.36-0.61]) and better PFS (HR = 0.65 [0.58-0.74]) and OS (HR = 0.44 [0.39-0.51]). On-treatment decline in CRP was associated with improved ORR (OR = 0.18 [0.11-0.20]), PFS (HR = 0.40 [0.31-0.91]) and OS (HR = 0.48 [0.40-0.58]). Conclusion: Peripheral blood cytokines warrant further evaluation as enrichment and pharmacodynamic biomarkers for strategies targeting tumor-promoting inflammation.
Measuring a substance called C-reactive protein (CRP) in the blood can help predict if cancer treatments that boost the immune system, like immune checkpoint blockers (ICB), will work. CRP levels are increased when there is inflammation in the body, helping cancer cells grow. IL-6 and IL-8 are related blood markers that are more specific to cancer cells and may improve our ability to predict if ICB will effectivity destroy cancer cells. Our study found that having lower levels of IL-6 and IL-8 before treatment and low levels of CRP during treatment might mean patients live longer and respond better to ICB treatments. Measuring IL-6 and IL-8 before treatment and CRP during treatment could help improve how doctors use ICB to treat cancer by managing inflammation that helps cancer grow.
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PURPOSE: One year of neratinib therapy is known to derive a significant invasive disease-free survival (iDFS) benefit in early-stage, hormone receptor-positive (HR +), HER2 + , node-positive breast cancer after trastuzumab-based adjuvant therapy. Limitations to neratinib use include significant gastrointestinal side effects, which often result in treatment discontinuation. In this study, we aimed to identify clinicopathologic features associated with adjuvant neratinib use and factors impacting treatment completion. METHODS: We performed a retrospective review of patients with early-stage HR + HER2 + breast cancer who were prescribed neratinib from 2017 to 2023 at our institution. We used the electronic medical record to extract information on patient characteristics, clinical features, and treatment information. Patients were identified as high risk based on definitions adapted from the standard high-risk definition in HR + HER2- breast cancer combined with studies correlating high Ki67 or high tumor grade with lower recurrence-free survival. Statistical analysis was performed using two-sided T-tests and chi-square tests. RESULTS: We identified 62 eligible patients of whom 55% completed 1 year of neratinib and 45% did not. Sixty percent (N = 37) of patients offered neratinib were considered high risk at diagnosis. The most common reason for neratinib discontinuation was inability to tolerate side effects (54%) followed by pill burden (18%). The most common side effect experienced by patients was diarrhea despite anti-diarrheal prophylaxis (56%), followed by rash (8%). Patients who received an up-titration of neratinib were more likely to complete the full course of neratinib when compared to those who did not (76% vs. 40.5% p = 0.013). The median starting dose of those who completed neratinib treatment was 140 vs. 240 mg in those who did not (p = 0.016). Neither group experienced a statistically significant greater likelihood of treatment holds or dose reductions. In terms of outcomes, 10 patients had progression of disease of whom 7 did not complete neratinib treatment (p = 0.169). Interestingly, those 7 patients developed metastatic disease and 57% (N = 4) had central nervous system metastases. CONCLUSION: Patients are more likely to complete 1 year of adjuvant neratinib with dose up-titration. Dose reductions and interruptions did not affect neratinib adherence in our patient population. Seven patients (11%) in our study developed metastatic disease, all of whom did not complete adjuvant neratinib treatment.
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Objective: National data have shown worse endometrial cancer (EC) outcomes among racial and ethnic minorities. We aimed to analyze EC patient outcomes within a large urban academic health system, with a focus on patterns of care and recurrence rates. Methods: This was a retrospective chart review of EC patients at three system hospitals from 1/1/07-12/31/17. Demographic and clinical factors, including time from EMB to surgery, rate of chemotherapy completion, persistent or recurrent disease, and palliative care referrals were extracted. Descriptive statistics and survival curves were generated. Analysis was done using SAS version 9.4. Results: Black patients had lower overall survival compared to all others on univariate analysis only (p < 0.0001). Hospital site was associated with OS, with the academic anchor and satellite 1 having higher rates of all-cause mortality compared to satellite 2 (HR 4.68 academic anchor, 95 % CI 1.72-12.76, HR 5.36 satellite 1, 95 % CI 1.85-15.52). Time from EMB to surgery and rates of persistent disease following primary treatment were higher in Black patients. After adjusting for stage and grade, chemotherapy completion rate was significantly associated with race. Palliative care was utilized more for Black than White patients after adjusting for stage and grade (p = 0.005). Conclusions: Racial disparities in EC are caused by a complex web of interconnected factors that ultimately lead to worse outcomes in Black women. While precision medicine has helped to close the gap, social determinants of health should be addressed, and models focusing on the complex interactions between biologic, genetic, and social factors should be utilized.
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Atypical regulation of inflammation has been proposed in the etiology of autism spectrum disorder (ASD); however, measuring the temporal profile of fetal inflammation associated with future ASD diagnosis has not been possible. Here, we present a method to generate approximately daily profiles of prenatal and early childhood inflammation as measured by developmentally archived C-reactive protein (CRP) in incremental layers of deciduous tooth dentin. In our discovery population, a group of Swedish twins, we found heightened inflammation in the third trimester in children with future ASD diagnosis relative to controls (n = 66; 14 ASD cases; critical window: -90 to -50 days before birth). In our replication study, in the US, we observed a similar increase in CRP in ASD cases during the third trimester (n = 47; 23 ASD cases; -128 to -21 days before birth). Our results indicate that the third trimester is a critical period of atypical fetal inflammatory regulation in ASD.
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Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden. The histopathological material was derived from 22 autopsy brains from patients with tauopathies. We used a custom web-based informatics platform integrated with an in-house information management system to manage whole slide images (WSI) and human expert annotations as ground truth. We utilized fully annotated regions to train a deep learning fully convolutional neural network (FCN) implemented in PyTorch against the human expert annotations. We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies. With our FCN model, we achieved high precision and recall in naive WSI semantic segmentation, correctly identifying tangle objects using a SegNet model trained for 200 epochs. Our FCN is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured performance on test data of 50 pre-annotated regions on eight naive WSI across various tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Machine learning is a useful tool for complex pathological assessment of AD and other tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-specific annotations with clinical, genetic, and molecular data, providing unbiased data for clinicopathological correlations that will enhance our knowledge of the neurodegeneration.