Prognostic value of routine laboratory variables in prediction of breast cancer recurrence

The Importance of Hypoxia-Related to Hemoglobin Concentration in Breast Cancer

The importance of hemoglobin (Hgb) as a novel prognostic biomarker in predicting clinical features of cancers has been the subject of intense interest. Anemia is common in various types of cancer including breast cancer (BC) and is considered to be attributed to tumoral hypoxia. Cancer microenvironments are hypoxic compared with normal tissues, and this hypoxia is associated with Hgb concentration. Recent preclinical documents propose a direct or indirect correlation of intratumoral hypoxia, specifically along with acidity, with Hgb concentration and anemia. Analysis of the prognostic value of Hgb in BC patients has demonstrated increased hypoxia in the intratumoral environment. A great number of studies demonstrated that lower concentrations of Hgb before or during common cancer treatments, such as radiation and chemotherapy, is an essential risk factor for poor prognostic and survival, as well as low quality of life in BC patients. This data suggests a potential correlation between anemia and hypoxia in BC. While low Hgb levels are detrimental to BC invasion and survival, identification of a distinct and exact threshold for low Hgb concentration is challenging and inaccurate. The optimal thresholds for Hgb and partial pressure of oxygen (pO2) vary based on different factors including age, gender, therapeutic approaches, and tumor types. While necessitating further investigations, understanding the correlation of Hgb levels with tumoral hypoxia and oxygenation could improve exploring strategies to overcome radio-chemotherapy related anemia in BC patients. This review highlights the collective association of Hgb concentration and hypoxia condition in BC progression.

Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients

OBJECTIVES

 While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk.

METHODS

 This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions.

RESULTS

 Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p < 0.0001). Integrating clinician predictions with the model's estimates further increased the AUC to 0.853 (p < 0.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities.

CONCLUSION

 The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.

Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data

Background

Epidermal growth factor receptor (EGFR) T790M mutation is the standard predictive biomarker for third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment. While not all T790M-positive patients respond to third-generation EGFR-TKIs and have a good prognosis, it necessitates novel tools to supplement EGFR genotype detection for predicting efficacy and stratifying EGFR-mutant patients with various prognoses. Mixture-of-experts (MoE) is designed to disassemble a large model into many small models. Meanwhile, it is also a model ensembling method that can better capture multiple patterns of intrinsic subgroups of enrolled patients. Therefore, the combination of MoE and Cox algorithm has the potential to predict efficacy and stratify survival in non-small cell lung cancer (NSCLC) patients with EGFR mutations.

Methods

We utilized the electronic medical record (EMR) and pharmacokinetic parameters of 326 T790M-mutated NSCLC patients, including 283 patients treated with Abivertinib in phase I (n=177, for training) and II (n=106, for validation) clinical trials and an additional validation cohort 2 comprising 43 patients treated with BPI-7711. Furthermore, 18 patients underwent whole-exome sequencing for biological interpretation of CoxMoE. We evaluated the predictive performance for therapeutic response using the area under the curve (AUC) and the Concordance index (C-index) for progression-free survival (PFS).

Results

CoxMoE exhibited AUCs of 0.73-0.83 for predicting efficacy defined by best overall response (BoR) and achieved C-index values of 0.64-0.65 for PFS prediction in training and validating cohorts. The PFS of 198 patients with a low risk [median, 6.0 (range, 1.0-23.3) months in the abivertinib treated cohort; median 16.5 (range, 1.4-27.4) months in BPI-7711 treated cohort] of being non-responder increased by 43% [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.40-0.78; P=0.0013] and 50% (HR, 0; 95% CI, 0-0; P=0.01) compared to those at high-risk [median, 4.2 (range, 1.0-35) months in the abivertinib treated cohort; median, 11.0 (range, 1.4-25.1) months in BPI-7711 treated cohort]. Additionally, activated partial thromboplastin time (APTT), creatinine clearance (Ccr), monocyte, and steady-state plasma trough concentration utilited to construct model were found significantly associated with drug resistance and aggressive tumor pathways. A robust correlation was observed between APTT and Ccr with PFS (log-rank test; P<0.01) and treatment response (Wilcoxon test; P<0.05), respectively.

Conclusions

CoxMoE offers a valuable approach for patient selection by forecasting therapeutic response and PFS utilizing laboratory tests and pharmacokinetic parameters in the setting of early-phase clinical trials. Simultaneously, CoxMoE could predict the efficacy of third-generation EGFR-TKI non-invasively for T790M-positive NSCLC patients, thereby complementing existing EGFR genotype detection.

Proposing a novel molecular subtyping scheme for predicting distant recurrence-free survival in breast cancer post-neoadjuvant chemotherapy with close correlation to metabolism and senescence

Background

High relapse rates remain a clinical challenge in the management of breast cancer (BC), with distant recurrence being a major driver of patient deterioration. To optimize the surveillance regimen for distant recurrence after neoadjuvant chemotherapy (NAC), we conducted a comprehensive analysis using bioinformatics and machine learning approaches.

Materials and methods

Microarray data were retrieved from the GEO database, and differential expression analysis was performed with the R package 'Limma'. We used the Metascape tool for enrichment analyses, and 'WGCNA' was utilized to establish co-expression networks, selecting the soft threshold power with the 'pickSoftThreshold' algorithm. We integrated ten machine learning algorithms and 101 algorithm combinations to identify key genes associated with distant recurrence in BC. Unsupervised clustering was performed with the R package 'ConsensusCluster Plus'. To further screen the key gene signature of residual cancer burden (RCB), multiple knockdown studies were analyzed with the Genetic Perturbation Similarity Analysis (GPSA) database. Single-cell RNA sequencing (scRNA-seq) analysis was conducted through the Tumour Immune Single-cell Hub (TISCH) database, and the XSum algorithm was used to screen candidate small molecule drugs based on the Connectivity Map (CMAP) database. Molecular docking processes were conducted using Schrodinger software. GMT files containing gene sets associated with metabolism and senescence were obtained from GSEA MutSigDB database. The GSVA score for each gene set across diverse samples was computed using the ssGSEA function implemented in the GSVA package.

Results

Our analysis, which combined Limma, WGCNA, and machine learning approaches, identified 16 RCB-relevant gene signatures influencing distant recurrence-free survival (DRFS) in BC patients following NAC. We then screened GATA3 as the key gene signature of high RCB index using GPSA analysis. A novel molecular subtyping scheme was developed to divide patients into two clusters (C1 and C2) with different distant recurrence risks. This molecular subtyping scheme was found to be closely associated with tumor metabolism and cellular senescence. Patients in cluster C2 had a poorer DRFS than those in cluster C1 (HR: 4.04; 95% CI: 2.60-6.29; log-rank test p < 0.0001). High GATA3 expression, high levels of resting mast cell infiltration, and a high proportion of estrogen receptor (ER)-positive patients contributed to better DRFS in cluster C1. We established a nomogram based on the N stage, RCB class, and molecular subtyping. The ROC curve for 5-year DRFS showed excellent predictive value (AUC=0.91, 95% CI: 0.95-0.86), with a C-index of 0.85 (95% CI: 0.81-0.90). Entinostat was identified as a potential small molecule compound to reverse high RCB after NAC. We also provided a comprehensive review of the EDCs exposures that potentially impact the effectiveness of NAC among BC patients.

Conclusion

This study established a molecular classification scheme associated with tumor metabolism and cancer cell senescence to predict RCB and DRFS in BC patients after NAC. Furthermore, GATA3 was identified and validated as a key gene associated with BC recurrence.

Prognostic value of alkaline phosphatase and bone-specific alkaline phosphatase in breast cancer: A systematic review and meta-analysis

Numerous studies have reported the clinical value of alkaline phosphatase (ALP) and its bone-specific isoforms (bone-specific alkaline phosphatase (BAP)) in breast cancer. The purpose of this meta-analysis was to summarize the prognostic value of serum ALP and BAP in breast cancer, especially focused on bone metastasis and survival. PRISMA guidelines were followed to conduct this review. Observational studies were searched in PubMed, Cochcrane Library and EMBASE to January 1, 2022. Data were extracted to explore the prognostic value of ALP and BAP. The quality of the included studies was assessed and the outcome effects were evaluated. Subgroup and sensitivity analyses were performed to explore the potential sources of heterogeneity. Publication bias was assessed. There was a total of 53 studies with 22,436 patients included. For the primary outcome of survival, high levels of both ALP and BAP were associated with short survival time. The hazard ratio of high ALP level on overall survival was 1.72 (95% CI 1.37, 2.16, P < 0.001). For the secondary outcomes, a high ALP level (not BAP) was detected in breast cancer compared with healthy controls, and high levels of both ALP and BAP were risk factors for bone metastasis, while ALP (not BAP) was a risk factor for non-bone metastasis. This study showed that high levels of both serum ALP and BAP were associated with metastasis (BAP was associated with bone metastasis) and survival in breast cancer. The biomarkers could provide useful information for the early diagnostic assessment and monitoring in the follow-up of breast cancer patients.

Elevated Cardiac Troponin to Detect Acute Cellular Rejection After Cardiac Transplantation: A Systematic Review and Meta-Analysis

Cardiac troponin is well known as a highly specific marker of cardiomyocyte damage, and has significant diagnostic accuracy in many cardiac conditions. However, the value of elevated recipient troponin in diagnosing adverse outcomes in heart transplant recipients is uncertain. We searched MEDLINE (Ovid), Embase (Ovid), and the Cochrane Library from inception until December 2020. We generated summary sensitivity, specificity, and Bayesian areas under the curve (BAUC) using bivariate Bayesian modelling, and standardised mean differences (SMDs) to quantify the diagnostic relationship of recipient troponin and adverse outcomes following cardiac transplant. We included 27 studies with 1,684 cardiac transplant recipients. Patients with acute rejection had a statistically significant late elevation in standardised troponin measurements taken at least 1 month postoperatively (SMD 0.98, 95% CI 0.33–1.64). However, pooled diagnostic accuracy was poor (sensitivity 0.414, 95% CrI 0.174–0.696; specificity 0.785, 95% CrI 0.567–0.912; BAUC 0.607, 95% CrI 0.469–0.723). In summary, late troponin elevation in heart transplant recipients is associated with acute cellular rejection in adults, but its stand-alone diagnostic accuracy is poor. Further research is needed to assess its performance in predictive modelling of adverse outcomes following cardiac transplant.

Systematic Review Registration: identifier CRD42021227861

Donor Cardiac Troponin for Prognosis of Adverse Outcomes in Cardiac Transplantation Recipients: a Systematic Review and Meta-analysis

BACKGROUND

Cardiac troponin is a highly specific and widely available marker of myocardial injury, and elevations in cardiac transplant donors may influence donor selection. We aimed to investigate whether elevated donor troponin has a role as a prognostic biomarker in cardiac transplantation.

METHODS

In a systematic review and meta-analysis, we searched MEDLINE, Embase, and the Cochrane Library, without language restriction, from inception to December 2020. We included studies reporting the association of elevated donor troponin with recipient outcome after cardiac transplant. We generated summary odds ratios and hazard ratios for the association of elevated donor troponin with short- and long-term adverse outcomes. Methodological quality was monitored using the Quality In Prognosis Studies tool, and interstudy heterogeneity was assessed using a series of sensitivity and subgroup analyses.

RESULTS

We included 17 studies involving 15 443 patients undergoing cardiac transplantation. Elevated donor troponin was associated with increased odds of graft rejection at 1 y (odds ratio, 2.54; 95% confidence interval, 1.22-5.28). No significant prognostic relationship was found between donor troponin and primary graft failure, short- to long-term mortality, cardiac allograft vasculopathy, and pediatric graft loss.

CONCLUSIONS

Elevated donor troponin is not associated with an increased short- or long-term mortality postcardiac transplant despite increasing the risk of graft rejection at 1 y. Accordingly, an elevated donor troponin in isolation should not exclude donation.

Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

BACKGROUND

Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients.

METHODS

In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS).

RESULTS

TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873-0.938]; 0.896, [0.860-0.931]; 0.882, [0.840-0.925] in the three cohorts).

CONCLUSION

These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

Several prognosis prediction models have been developed for breast cancer (BC) patients with curative surgery, but there is still an unmet need to precisely determine BC prognosis for individual BC patients in real time. This is a retrospectively collected data analysis from adjuvant BC registry at Samsung Medical Center between January 2000 and December 2016. The initial data set contained 325 clinical data elements: baseline characteristics with demographics, clinical and pathologic information, and follow-up clinical information including laboratory and imaging data during surveillance. Weibull Time To Event Recurrent Neural Network (WTTE-RNN) by Martinsson was implemented for machine learning. We searched for the optimal window size as time-stamped inputs. To develop the prediction model, data from 13,117 patients were split into training (60%), validation (20%), and test (20%) sets. The median follow-up duration was 4.7 years and the median number of visits was 8.4. We identified 32 features related to BC recurrence and considered them in further analyses. Performance at a point of statistics was calculated using Harrell's C-index and area under the curve (AUC) at each 2-, 5-, and 7-year points. After 200 training epochs with a batch size of 100, the C-index reached 0.92 for the training data set and 0.89 for the validation and test data sets. The AUC values were 0.90 at 2-year point, 0.91 at 5-year point, and 0.91 at 7-year point. The deep learning-based final model outperformed three other machine learning-based models. In terms of pathologic characteristics, the median absolute error (MAE) and weighted mean absolute error (wMAE) showed great results of as little as 3.5%. This BC prognosis model to determine the probability of BC recurrence in real time was developed using information from the time of BC diagnosis and the follow-up period in RNN machine learning model.

Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients

The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator. Methods: We observed five large-scale tumor-associated collagen signatures (TACS4-8) obtained by multiphoton microscopy at the invasion front of the breast primary tumor, which contrasted with the three tumor-associated collagen signatures (TACS1-3) discovered by Keely and coworkers at a smaller scale. Highly concordant TACS1-8 classifications were obtained by three independent observers. Using the ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8 and established a risk prediction model based on the TACS-score. In a blind fashion, consistent retrospective prognosis was obtained from 995 breast cancer patients in both a training cohort (n= 431) and an internal validation cohort (n = 300) collected from one clinical center, and in an external validation cohort (n = 264) collected from a different clinical center. Results: TACS1-8 model alone competed favorably with all reported models in predicting disease-free survival (AUC: 0.838, [0.800-0.872]; 0.827, [0.779-0.868]; 0.807, [0.754-0.853] in the three cohorts) and stratifying low- and high-risk patients (HR 7.032, [4.869-10.158]; 6.846, [4.370-10.726], 4.423, [2.917-6.708]). The combination of these factors with the TACS-score into a nomogram model further improved the prognosis (AUC: 0.865, [0.829-0.896]; 0.861, [0.816-0.898]; 0.854, [0.805-0.894]; HR 7.882, [5.487-11.323]; 9.176, [5.683-14.816], and 5.548, [3.705-8.307]). The nomogram identified 72 of 357 (~20%) patients with unsuccessful 5-year disease-free survival that might have been undertreated postoperatively. Conclusions: The risk prediction model based on TACS1-8 considerably outperforms the contextual clinical model and may thus convince pathologists to pursue a TACS-based breast cancer prognosis. Our methodology identifies a significant portion of patients susceptible to undertreatment (high-risk patients), in contrast to the multigene assays that often strive to mitigate overtreatment. The compatibility of our methodology with standard histology using traditional (non-tissue-microarray) formalin-fixed paraffin-embedded (FFPE) tissue sections could simplify subsequent clinical translation.

A Preliminary Cohort Study Assessing Routine Blood Analyte Levels and Neurological Outcome after Spinal Cord Injury

There is increasing interest in the identification of biomarkers that could predict neurological outcome following a spinal cord injury (SCI). Although initial American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade is a good indicator of neurological outcome, for the patient and clinicians, an element of uncertainty remains. This preliminary study aimed to assess the additive potential of routine blood analytes following principal component analysis (PCA) to develop prognostic models for neurological outcome following SCI. Routine blood and clinical data were collected from SCI patients (n = 82) and PCA used to reduce the number of blood analytes into related factors. Outcome neurology was obtained from AIS scores at 3 and 12 months post-injury, with motor (AIS and total including all myotomes) and sensory (AIS, touch and pain) abilities being assessed individually. Multiple regression models were created for all outcome measures. Blood analytes relating to “liver function” and “acute inflammation and liver function” factors were found to significantly increase prediction of neurological outcome at both 3 months (touch, pain, and AIS sensory) and at 1 year (pain, R² increased by 0.025 and total motor, R² increased by 0.016). For some models “liver function” and “acute inflammation and liver function” factors were both significantly predictive, with the greatest combined R² improvement of 0.043 occurring for 3 month pain prediction. These preliminary findings support ongoing research into the use of routine blood analytes in the prediction of neurological outcome in SCI patients.

Albumin/globulin ratio is negatively correlated with PD-1 and CD25 mRNA levels in breast cancer patients

Background

Previous studies have demonstrated the prognostic value of globulin (GLB), albumin (ALB), the ALB/GLB ratio (AGR), body mass index (BMI), hemoglobin (Hb), and prognostic nutritional index (PNI) in breast cancer. The underlying mechanism has been investigated by examining the impact of nutritional parameters on T cells, natural killer cells, and dendritic cells, but little is known about their effect on checkpoint molecules.

Methods

Here, we investigated the correlation of mRNA expression of programmed cell death protein 1 (PD-1), cluster of differentiation 28 (CD28), cytotoxic T-lymphocyte antigen-4 (CTLA-4), and cluster of differentiation 25 (CD25) with AGR, ALB, GLB, total protein, pre-ALB, Hb, BMI, and PNI in the peripheral blood of breast cancer patients. One hundred and three patients and 21 age- and sex-matched healthy controls were enrolled. Quantitative real-time PCR was used to test relative mRNA expression.

Results

The results indicated that the mRNA levels of PD-1 and CD25 were 5.2- and 3.3-fold higher in patients with low AGR than in those with high AGR (P < 0.05). The mRNA levels of PD-1 were 3.5-fold higher in patients with high GLB than in those with low GLB (P < 0.05). In addition, breast cancer patients had higher expression levels of PD-1, CD28, CTLA-4, and CD25 mRNA in their peripheral blood compared with healthy volunteers (P < 0.05).

Conclusion

These results suggest that AGR is negatively correlated with PD-1 and CD25 mRNA levels, while GLB is positively associated with PD-1 mRNA levels. Nutritional status in breast cancer patients may influence the PD-1 pathway and have implications for the optimization of cancer therapy.