Robert Wieder

Fibroblast growth factors (FGFs) are ubiquitous growth factors, with functions in all organs and cells of the embryo and the post-partum mammal, and roles in all aspects of normal and pathologic physiology. They belong to a large family of proteins categorized into seven subfamilies with 23 members. FGF molecules initiate signaling through varied and interacting mechanisms that result in a highly diverse spectrum of multifunctional effects. FGF molecules induce their effects by binding to transmembrane tyrosine kinase receptors to initiate canonical signal transduction pathways, and by internalization of the FGF/FGF receptor (FGFR) complexes and directing intracellular non-canonical signaling in the cytoplasm and the nucleus. FGF proteins expressed by a cell can also engage in non-canonical intracellular signaling in the cytoplasm, in the nucleus, or by undergoing extracellular export and initiating autocrine, receptor-mediated signaling. In the mammalian breast, FGF family members play significant roles in embryonic and postnatal mammary gland development and the normal female mammalian life cycle. In breast cancer (BC), the intracellular detection of FGF family members has been associated with both cancer-promoting and cancer-inhibitory effects. The goal of this review is to deconvolute the often contradictory published data resulting in these differing impacts of FGF expression on BC. Within the context of these variables, there is a potential for an extraordinarily high number of combinatorial interstudy scenarios, interactive variables, experimental designs, and circumstance-specific responses to potential signal-initiating scenarios, rendering generalizations difficult. However, the available evidence suggests that pro-malignant FGF effects in BC are generally mediated by the combined effects of intracellularly initiated signaling and cell membrane FGFR-mediated autocrine or paracrine canonical signaling, together with crosstalk with other external factors. In contrast, the anti-cancer effects of intracellular FGF molecules are primarily mediated by intracellular signaling that results in the redifferentiation of cancer cells and adhesion-mediated crosstalk with the stroma.

Importance. Up to 50% of patients with localized breast cancer (BC) continue to recur after initial treatment for more than 20 years. Recurrent cancer is not curable, and the goal of therapy is the prolongation of life. Many variables, including adrenergic stimuli associated with anxiety and/or depression (anxiety/depression), have been linked to recurrence. Demonstrating this effect in predisposing circumstances could guide investigations of prevention.Objective. This study aims to develop predictive modeling of breast cancer recurrence to identify specific circumstances in which anxiety/depression are associated with breast cancer recurrence.Design, Setting, and Participants. We used the SEER-Medicare dataset linked dataset to investigate women diagnosed with stage I, II, and III breast cancer enrolled at 65 years or older for age eligibility. We collected data on the date of diagnosis, patient-, cancer-, treatment- and adverse events-associated variables, and diagnosis of anxiety and/or depression. We identified recurrence by a documented new diagnosis of recurrent, contralateral, or stage IV BC, new chemo-, bio-, hormone, or radiotherapy outside the 4-month window after completion of initial therapy. We extended four deep learning (DL)-based predictive survival models (Deep-24 Surv and DeepHit. Nnet-survival and Cox-Time) that deal with right-censored time-to-event data and found greater than 95% concordance in each hyperparameter to demonstrate prediction validity. We generated 96 hypothetical patient recurrence curves distributed into categories of stage I or III, White (W), or African American (AA) without or with a diagnosis of anxiety and/or depression.ResultsThe patient's age, race and comorbidities, cancer stage, and hormone status distributions mirrored previously published values. Recurrence rates and time to recurrence were worse in patients who were in the AA group, ER-/PR-, and higher stage. Approximately 36% of the patients had anxiety and/or depression, with higher prevalence in W patients. Time to diagnosis ranged from 23-35 months. Recurrence rates in these patients were 27.9% higher than in patients without these diagnoses, with greater impact in Stage I, ER+/PR+, W, and Hispanic patients. The hypothetical patient recurrent survival curves we generated by DL-based predictive modeling demonstrated wide ranges of predicted recurrence-free survival curves within each grouping, with median survivals varying by more than a decade within each group. There were some long-term survivors in many of the categories, but most patients with the anxiety/depression group had far fewer long-term recurrence-free survival curves than patients without the diagnosis. These results demonstrate the exceptional variability of median predicted survival rates and their modifications by chronic adrenergic stimuli.Conclusions and Relevance.This study demonstrates, for the first time, that the adrenergic stressors anxiety and depression increase the average population recurrence rate of dormant breast cancer in elderly SEER-Medicare patients with stage I, II, and III disease. The predictive impact of population-derived data is limited in individual patients. However, DL-based predictive modeling that accounts for unique circumstance-specific scenarios is able to model individual patient predictive recurrence and generate predicted survival probabilities and responses to psychological stressors with a 95% degree of confidence. The modeling demonstrates the exceptional variability of individual recurrence probabilities and will enable clinical investigation of the impact of variables in specific scenarios.

Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events. Objective: Our goal is to develop high-confidence deep learning (DL) models to predict survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment, and adverse event variables. We previously showed that predictive DL survival modeling of potentially curable stage I–III patients can be improved by combining time-fixed and time-varying covariates. Here, we demonstrate that DL-based predictive survival modeling in stage IV patients, where treatment does not offer a cure, can generate accurate individual survival predictions by considering subsequent lines of potential treatment to guide therapy. This guidance is rarely obtainable in the nearly limitless scenarios of metastatic disease. Design, Setting, and Participants: We applied the SEER-Medicare linked dataset from 1991 to 2016 to investigate 14,312 unique stage IV patients with 1,880,153 entries. We used DeepSurv- and DeepHit-, Nnet-survival- and Cox-Time DL-based predictive models to consider the combination of time-fixed and time-varying covariates at each visit for each patient. We adopted random sampling to divide the input dataset into training, validation, and testing sets. We verified the models’ implementation using the pycox package and fine-tuned the models using the open-source library Amazon SageMaker Python SDK 2.232.2 (software development kit). Our results demonstrated the proof of principle of the models by generating individual patients’ survival curves. Conclusions and Relevance: By extending the survival prediction models to consider stage IV BC patients’ time-fixed and time-varying covariates, we achieved a prediction error below 10%. Based on their circumstance-specific situations, these models can predict survival in individual stage IV patients with high confidence. The models will serve as an important adjunct to treatment decisions in patients with stage IV BC and test what-if scenarios of treatment or no treatment options to optimize therapy for extending patient lives and minimizing adverse events.

Importance. Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. These investigations will identify individual circumstances where appropriate therapy will extend survival while minimizing adverse events.Objective. Our goal is to develop high confidence deep learning (DL) models for predicting survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment and adverse events variables. Our plan is to improve the predictive accuracy of deep learning (DL)-based predictive models by considering time-fixed covariates, i.e., patient and cancer data at the time of diagnosis, together with time-varying events that occur after initial diagnosis. Design, Setting, and Participants. We used the SEER-Medicare linked dataset from 1991-2016 to investigate women diagnosed with stage IV BC who enrolled at 65 years or older for age eligibility. We outlined time-fixed variables, including date of diagnosis, age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status and comorbidity index, prior therapy, adverse events, and changes in comorbidity. We delineated the time-varying covariates at each visit, including administered treatments, adverse events, comorbidity index, and age. We extended four DL-based predictive survival models (DeepSurv and DeepHit. Nnet-survival and Cox-Time) that deal with right-censored time-to-event data to consider both a patient's time-fixed covariates and a patient's time-varying covariates. We predicted the survival of five hypothetical patients to demonstrate the model's utility. We found high concordance between the performance metric, time-dependent concordance, and each of the model's hyperparameters to demonstrated prediction validity.Results. By incorporating time-varying variables with the time-fixed variables, the models reduced the error rates of the concordance index, the most commonly applied evaluation metric in survival analysis, from 28-38% to 2-12%, and significantly improved the integrated Brier score, a metric of the model's discrimination and calibration. Conclusions and Relevance. By combining the consideration of time-fixed with those time-varying variables in our predictive models, we decreased the predictive error rate to under 10% in the predicted survival of stage IV BC patients. Our established models can predict survival in individual stage IV patients with high confidence based on their circumstance-specific situations generated by considering their unique patient, cancer, treatment and adverse events variabels. These models will serve as an important adjunct to treatment decisions in patients with stage IV BC to optimize therapy for extending patient lives and minimizing adverse events.

Background and objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider time-varying events that occur after initial diagnosis. Our objective is to improve the predictive modeling of survival of patients with localized breast cancer to consider both time-fixed and time-varying events; thus, we take into account the progression of a patient’s health status over time. Methods: We extended four DL-based predictive survival models (DeepSurv, DeepHit, Nnet-survival, and Cox-Time) that deal with right-censored time-to-event data to consider not only a patient’s time-fixed covariates (patient and cancer data at diagnosis) but also a patient’s time-varying covariates (e.g., treatments, comorbidities, progressive age, frailty index, adverse events from treatment). We utilized, as our study data, the SEER-Medicare linked dataset from 1991 to 2016 to study a population of women diagnosed with stage I–III breast cancer (BC) enrolled in Medicare at 65 years or older as qualified by age. We delineated time-fixed variables recorded at the time of diagnosis, including age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status, and comorbidity index. We analyzed six distinct prognostic categories, cancer stages I–III BC, and each stage’s ER/PR+ or ER/PR− status. At each visit, we delineated the time-varying covariates of administered treatments, induced adverse events, comorbidity index, and age. We predicted the survival of three hypothetical patients to demonstrate the model’s utility. Main Outcomes and Measures: The primary outcomes of the modeling were the measures of the model’s prediction error, as measured by the concordance index, the most commonly applied evaluation metric in survival analysis, and the integrated Brier score, a metric of the model’s discrimination and calibration. Results: The proposed extended patients’ covariates that include both time-fixed and time-varying covariates significantly improved the deep learning models’ prediction error and the discrimination and calibration of a model’s estimates. The prediction of the four DL models using time-fixed covariates in six different prognostic categories all resulted in approximately a 30% error in all six categories. When applying the proposed extension to include time-varying covariates, the accuracy of all four predictive models improved significantly, with the error decreasing to approximately 10%. The models’ predictive accuracy was independent of the differing published survival predictions from time-fixed covariates in the six prognostic categories. We demonstrate the utility of the model in three hypothetical patients with unique patient, cancer, and treatment variables. The model predicted survival based on the patient’s individual time-fixed and time-varying features, which varied considerably from Social Security age-based, and stage and race-based breast cancer survival predictions. Conclusions: The predictive modeling of the survival of patients with early-stage breast cancer using DL models has a prediction error of around 30% when considering only time-fixed covariates at the time of diagnosis and decreases to values under 10% when time-varying covariates are added as input to the models, regardless of the prognostic category of the patient groups. These models can be used to predict individual patients’ survival probabilities based on their unique repertoire of time-fixed and time-varying features. They will provide guidance for patients and their caregivers to assist in decision making.

Breast cancer cells metastasize to the bone marrow before a primary tumor is detected. Most micrometastases die in this hostile microenvironment, but some survive and enter a state of dormancy and chemoresistance due to their close interaction with cells in the bone marrow hematopoietic niche. Over many years, some of the cells reawaken and result in metastatic disease that cannot be cured. Analyzing the cellular and molecular interactions between cancer and bone marrow niche cells requires relevant models that can be manipulated and studied. Generation of bone marrow stroma cultures in vitro permits the formation of cellular monolayers upon which breast cancer cells can be co-cultivated and their behavior interrogated using a variety of techniques. This manuscript describes the basic techniques for generating bone marrow stromal monolayers, co-incubating cancer cells and determining the effects on cancer cell proliferation and molecular signaling.

PURPOSE: Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER–Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. PATIENTS AN METHODS: We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model’s results demonstrated a high overlap between the model’s identified TR-AEs associated set and the actual set. RESULTS: Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. CONCLUSIONS: Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient’s health and life status change over time.

Despite lower incidence rates, African American (AA) patients have shorter survival from breast cancer (BC) than white (W) patients. Multiple factors contribute to decreased survival, including screening disparities, later presentation, and access to care. Disparities in adverse events (AEs) may contribute to delayed or incomplete treatment, earlier recurrence, and shortened survival. Here, we analyzed the SEER-Medicare dataset, which captures claims from a variety of venues, in order to determine whether the cancer care venues affect treatment and associated adverse events. We investigated a study population whose claims are included in the Outpatient files, consisting of hospital and healthcare facility venues, and a study population from the National Claims History (NCH) files, consisting of claims from physicians, office practices, and other non-institutional providers. We demonstrated statistically and substantively significant venue-specific differences in treatment rates, drugs administered, and AEs from treatments between AA and W patients. We showed that AA patients in the NCH dataset received lower rates of treatment, but patients in the Outpatient dataset received higher rates of treatment than W patients. The rates of recorded AEs per treatment were higher in the NCH setting than in the Outpatient setting in all patients. AEs were consistently higher in AA patients than in W patients. AA patients had higher comorbidity indices and were younger than W patients, but these variables did not appear to play roles in the AE differences. The frequency of specific anticancer drugs administered in cancer- and venue-specific circumstances and their associated AEs varied between AA and W patients. The higher AE rates were due to slightly higher frequencies in the administration of drugs with higher associated AE rates in AA patients than in W patients. Our investigations demonstrate significant differences in treatment rates and associated AEs between AA and W patients with BC, depending on the venues of care, likely contributing to differences in outcomes.

Up to 40% of patients with breast cancer (BC) have metastatic cells in the bone marrow (BM) at the initial diagnosis of localized disease. Despite definitive systemic adjuvant therapy, these cells survive in the BM microenvironment, enter a dormant state and recur stochastically for more than 20 years. Once they begin to proliferate, recurrent macrometastases are not curable, and patients generally succumb to their disease. Many potential mechanisms for initiating recurrence have been proposed, but no definitive predictive data have been generated. This manuscript reviews the proposed mechanisms that maintain BC cell dormancy in the BM microenvironment and discusses the data supporting specific mechanisms for recurrence. It addresses the well-described mechanisms of secretory senescence, inflammation, aging, adipogenic BM conversion, autophagy, systemic effects of trauma and surgery, sympathetic signaling, transient angiogenic bursts, hypercoagulable states, osteoclast activation, and epigenetic modifications of dormant cells. This review addresses proposed approaches for either eliminating micrometastases or maintaining a dormant state.