Utility of bioselection with neoadjuvant chemotherapy for organ preservation in patients with T4 laryngeal cancer.

BACKGROUND: Neoadjuvant chemotherapy for induction selection of definitive treatment (IS) protocols have shown excellent outcomes for organ preservation and survival in patients with T3 laryngeal squamous cell carcinoma (LSCC). We seek to evaluate survival and organ preservation outcomes in T4 LSCC patients treated with IS protocols. METHODS: Retrospective cohort of advanced T3 and T4 LSCC patients who underwent IS protocols based upon potential for preserving a functional larynx. Patients received one neoadjuvant cycle of platinum-based chemotherapy with either 5-fluorouracil or docetaxel or with two cycles of platinum-based chemotherapy with docetaxel and a Bcl-2 inhibitor. Patients who achieved ≥ 50 % response as determined by radiographic review and/or endoscopic evaluation received definitive chemoradiation. Patients who had < 50 % response after IS underwent total laryngectomy (TL) followed by post-operative radiation +/- chemotherapy. RESULTS: Amongst T4 patients, 114 met inclusion criteria including 89 who underwent IS protocols and 25 who received an upfront TL. In total, 76.0 % of T3 patients and 71.9 % of T4 patients responded to IS and underwent definitive chemoradiation. There was no significant difference in hazard of death between T4 IS and T4 TL patients (HR: 0.9, p = 0.86). Among responders, there was no significant difference in 5-year laryngectomy-free survival (T3 - 59.6 %, T4 44.3 %, p = 0.15) or laryngeal preservation by T stage (T3 - 72.8 %, T4 - 73.0 %, p = 0.84). CONCLUSIONS: Select T4 patients may benefit from organ preservation using IS protocols with similar response rates to patients with T3 tumors, without compromising survival when compared to upfront TL.

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017.

PURPOSE: Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period. METHODS: Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks. RESULTS: Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival. CONCLUSION: These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.

Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy Registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that prostate-specific antigen is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal prostate-specific antigen history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2.

Robust data integration from multiple external sources for generalized linear models with binary outcomes.

We aim to estimate parameters in a generalized linear model (GLM) for a binary outcome when, in addition to the raw data from the internal study, more than 1 external study provides summary information in the form of parameter estimates from fitting GLMs with varying subsets of the internal study covariates. We propose an adaptive penalization method that exploits the external summary information and gains efficiency for estimation, and that is both robust and computationally efficient. The robust property comes from exploiting the relationship between parameters of a GLM and parameters of a GLM with omitted covariates and from downweighting external summary information that is less compatible with the internal data through a penalization. The computational burden associated with searching for the optimal tuning parameter for the penalization is reduced by using adaptive weights and by using an information criterion when searching for the optimal tuning parameter. Simulation studies show that the proposed estimator is robust against various types of population distribution heterogeneity and also gains efficiency compared to direct maximum likelihood estimation. The method is applied to improve a logistic regression model that predicts high-grade prostate cancer making use of parameter estimates from 2 external models.

Aspirin use and head and neck cancer survival and recurrence.

BACKGROUND: Head and neck cancer (HNC) has low 5-year survival, and evidence-based recommendations for tertiary prevention are lacking. Aspirin improves outcomes for cancers at other sites, but its role in HNC tertiary prevention remains understudied. METHODS: HNC patients were recruited in the University of Michigan Head and Neck Cancer Specialized Program of Research Excellence (SPORE) from 2003 to 2014. Aspirin data were collected through medical record review; outcomes (overall mortality, HNC-specific mortality, and recurrence) were collected through medical record review, Social Security Death Index, or LexisNexis. Cox proportional hazards models were used to evaluate the associations between aspirin use at diagnosis (yes/no) and HNC outcomes. RESULTS: We observed no statistically significant associations between aspirin and cancer outcome in our HNC patient cohort (n = 1161) (HNC-specific mortality: HR = 0.91, 95% CI = 0.68-1.21; recurrence: HR = 0.94, 95% CI = 0.73-1.19). In analyses stratified by anatomic site, HPV status, and disease stage, we observed no association in any strata examined with the possible exception of a lower risk of recurrence in oropharynx patients (HR = 0.60, 95% CI 0.35-1.04). CONCLUSIONS: Our findings do not support a protective association between aspirin use and cancer-specific death or recurrence in HNC patients, with the possible exception of a lower risk of recurrence in oropharynx patients.

Surrogacy validation for time-to-event outcomes with illness-death frailty models.

A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the causal association paradigm assesses the relationship of the treatment effect on the surrogate outcome with the treatment effect on the true, primary outcome. In particular, we propose illness-death models to accommodate the censored and semicompeting risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multistate models, we characterize what a valid surrogate would look like using a causal effect predictiveness plot. We evaluate the estimation properties of a Bayesian method using Markov chain Monte Carlo and assess the sensitivity of our model assumptions. Our motivating data source is a localized prostate cancer clinical trial where the two survival outcomes are time to distant metastasis and time to death.

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer.

Background: In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages. Methods: We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness. Results: We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM. Conclusions: Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods' ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models.

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

Integrating Information from Existing Risk Prediction Models with No Model Details.

Consider the setting where (i) individual-level data are collected to build a regression model for the association between an event of interest and certain covariates, and (ii) some risk calculators predicting the risk of the event using less detailed covariates are available, possibly as algorithmic black boxes with little information available about how they were built. We propose a general empirical-likelihood-based framework to integrate the rich auxiliary information contained in the calculators into fitting the regression model, to make the estimation of regression parameters more efficient. Two methods are developed, one using working models to extract the calculator information and one making a direct use of calculator predictions without working models. Theoretical and numerical investigations show that the calculator information can substantially reduce the variance of regression parameter estimation. As an application, we study the dependence of the risk of high grade prostate cancer on both conventional risk factors and newly identified molecular biomarkers by integrating information from the Prostate Biopsy Collaborative Group (PBCG) risk calculator, which was built based on conventional risk factors alone.

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Oral Microbiome Community Composition in Head and Neck Squamous Cell Carcinoma.

The impact of the oral microbiome on head and neck cancer pathogenesis and outcomes requires further study. 16s rRNA was isolated and amplified from pre-treatment oral wash samples for 52 cases and 102 controls. The sequences were binned into operational taxonomic units (OTUs) at the genus level. Diversity metrics and significant associations between OTUs and case status were assessed. The samples were binned into community types using Dirichlet multinomial models, and survival outcomes were assessed by community type. Twelve OTUs from the phyla Firmicutes, Proteobacteria, and Acinetobacter were found to differ significantly between the cases and the controls. Beta-diversity was significantly higher between the cases than between the controls (p < 0.01). Two community types were identified based on the predominant sets of OTUs within our study population. The community type with a higher abundance of periodontitis-associated bacteria was more likely to be present in the cases (p < 0.01), in older patients (p < 0.01), and in smokers (p < 0.01). Significant differences between the cases and the controls in community type, beta-diversity, and OTUs indicate that the oral microbiome may play a role in HNSCC.

Increased Nerve Density Adversely Affects Outcome in Oral Cancer.

PURPOSE: Perineural invasion (PNI) in oral cavity squamous cell carcinoma (OSCC) is associated with poor survival. Because of the risk of recurrence, patients with PNI receive additional therapies after surgical resection. Mechanistic studies have shown that nerves in the tumor microenvironment promote aggressive tumor growth. Therefore, in this study, we evaluated whether nerve density (ND) influences tumor growth and patient survival. Moreover, we assessed the reliability of artificial intelligence (AI) in evaluating ND. EXPERIMENTAL DESIGN: To investigate whether increased ND in OSCC influences patient outcome, we performed survival analyses. Tissue sections of OSCC from 142 patients were stained with hematoxylin and eosin and IHC stains to detect nerves and tumor. ND within the tumor bulk and in the adjacent 2 mm was quantified; normalized ND (NND; bulk ND/adjacent ND) was calculated. The impact of ND on tumor growth was evaluated in chick chorioallantoic-dorsal root ganglia (CAM-DRG) and murine surgical denervation models. Cancer cells were grafted and tumor size quantified. Automated nerve detection, applying the Halo AI platform, was compared with manual assessment. RESULTS: Disease-specific survival decreased with higher intratumoral ND and NND in tongue SCC. Moreover, NND was associated with worst pattern-of-invasion and PNI. Increasing the number of DRG, in the CAM-DRG model, increased tumor size. Reduction of ND by denervation in a murine model decreased tumor growth. Automated and manual detection of nerves showed high concordance, with an F1 score of 0.977. CONCLUSIONS: High ND enhances tumor growth, and NND is an important prognostic factor that could influence treatment selection for aggressive OSCC. See related commentary by Hondermarck and Jiang, p. 2342.

Whole blood transcriptome analysis in dairy calves experimentally challenged with bovine herpesvirus 1 (BoHV-1) and comparison to a bovine respiratory syncytial virus (BRSV) challenge.

Bovine herpesvirus 1 (BoHV-1), is associated with several clinical syndromes in cattle, among which bovine respiratory disease (BRD) is of particular significance. Despite the importance of the disease, there is a lack of information on the molecular response to infection via experimental challenge with BoHV-1. The objective of this study was to investigate the whole-blood transcriptome of dairy calves experimentally challenged with BoHV-1. A secondary objective was to compare the gene expression results between two separate BRD pathogens using data from a similar challenge study with BRSV. Holstein-Friesian calves (mean age (SD) = 149.2 (23.8) days; mean weight (SD) = 174.6 (21.3) kg) were either administered BoHV-1 inoculate (1 × 107/mL × 8.5 mL) (n = 12) or were mock challenged with sterile phosphate buffered saline (n = 6). Clinical signs were recorded daily from day (d) -1 to d 6 (post-challenge), and whole blood was collected in Tempus RNA tubes on d six post-challenge for RNA-sequencing. There were 488 differentially expressed (DE) genes (p < 0.05, False Discovery rate (FDR) < 0.10, fold change ≥2) between the two treatments. Enriched KEGG pathways (p < 0.05, FDR <0.05); included Influenza A, Cytokine-cytokine receptor interaction and NOD-like receptor signalling. Significant gene ontology terms (p < 0.05, FDR <0.05) included defence response to virus and inflammatory response. Genes that are highly DE in key pathways are potential therapeutic targets for the treatment of BoHV-1 infection. A comparison to data from a similar study with BRSV identified both similarities and differences in the immune response to differing BRD pathogens.

A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.

There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based methodology, where the goal is to fit a target regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The method allows for heterogeneity of covariate effects across the external populations. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression. This flexible and unified approach can improve statistical efficiency of the estimated coefficients in the internal study, improve predictions by utilizing even partial information available from models that use a subset of the full set of covariates used in the internal study, and provide statistical inference for the external population with potentially different covariate effects from the internal population.

Impact of monoclonal antibody therapy for head and neck cancer on end-of-life care utilization and costs.

BACKGROUND: The impact of monoclonal antibody therapy (mAB) for advanced head and neck cancer on end-of-life health care utilization and costs has yet to be adequately studied. METHODS: Retrospective cohort study of patients aged 65 and over with a diagnosis of head and neck cancer between 2007 and 2017 within the SEER-Medicare registry assessing the impact of mAB therapy (i.e., cetuximab, nivolumab, or pembrolizumab) on end-of-life health care utilization (ED visits, inpatient admissions, ICU admissions, and hospice claims) and costs. RESULTS: Of 12 544 patients with HNC, 270 (2.2%) utilized mAB therapy at the end-of-life period. On multivariable analyses adjusting for demographic and clinicopathologic characteristics, there was a significant association between mAB therapy and emergency department visits (OR: 1.38, 95% CI: 1.1-1.8, p = 0.01) and healthcare costs (ß: $9760, 95% CI: 5062-14 458, p < 0.01). CONCLUSIONS: mAB use is associated with higher emergency department utilization and health care costs potentially due to infusion-related and drug toxicity expenses.

Robotic Assisted Transplant Nephrectomy: Case Series and Training Model for Improving Adoption.

Introduction: Open transplant nephrectomy for failed renal allograft is an invasive procedure associated with significant perioperative morbidity and mortality. Minimally invasive surgical approaches have improved a variety of patient outcomes for many surgeries. Thus, robotic assisted transplant nephrectomy (RATN) potentially offers significant patient benefit. Although previously reported, there remains a paucity of data on RATN outcomes and techniques. Methods: Four perfused, high-fidelity hydrogel models were created using previously described techniques and used for simulated RATN. Subsequently performed institutional cases were included for analysis. Intra- and postoperative variables along with patient demographics were retrospectively obtained through parsing of patient records. Results: Simulated nephrectomy time was 67.33 minutes (35.75 - 98.91). Five patients underwent RATN. There were four male and one female patients. The average age was 47 years. The most common indication was abdominal pain secondary to rejection (3/5). Mean blood loss was 188 mL; mean operative time was 243 minutes, and mean length of stay was 4.5 days. Intraoperatively there were two incidences of small cystotomies. One patient was readmitted within 30 days for intraabdominal abscess. Conclusion: This study adds to the growing literature around RATN, demonstrating the feasibility of the technique and reporting good outcomes for this cohort.

Spatial and Transcriptomic Analysis of Perineural Invasion in Oral Cancer.

PURPOSE: Perineural invasion (PNI), a common occurrence in oral squamous cell carcinomas, is associated with poor survival. Consequently, these tumors are treated aggressively. However, diagnostic criteria of PNI vary and its role as an independent predictor of prognosis has not been established. To address these knowledge gaps, we investigated spatial and transcriptomic profiles of PNI-positive and PNI-negative nerves. EXPERIMENTAL DESIGN: Tissue sections from 142 patients were stained with S100 and cytokeratin antibodies. Nerves were identified in two distinct areas: tumor bulk and margin. Nerve diameter and nerve-to-tumor distance were assessed; survival analyses were performed. Spatial transcriptomic analysis of nerves at varying distances from tumor was performed with NanoString GeoMx Digital Spatial Profiler Transcriptomic Atlas. RESULTS: PNI is an independent predictor of poor prognosis among patients with metastasis-free lymph nodes. Patients with close nerve-tumor distance have poor outcomes even if diagnosed as PNI negative using current criteria. Patients with large nerve(s) in the tumor bulk survive poorly, suggesting that even PNI-negative nerves facilitate tumor progression. Diagnostic criteria were supported by spatial transcriptomic analyses of >18,000 genes; nerves in proximity to cancer exhibit stress and growth response changes that diminish with increasing nerve-tumor distance. These findings were validated in vitro and in human tissue. CONCLUSIONS: This is the first study in human cancer with high-throughput gene expression analysis in nerves with striking correlations between transcriptomic profile and clinical outcomes. Our work illuminates nerve-cancer interactions suggesting that cancer-induced injury modulates neuritogenesis, and supports reclassification of PNI based on nerve-tumor distance rather than current subjective criteria.

Impact of Nodal Metastases in HPV-Negative Oropharyngeal Cancer.

BACKGROUND: The updated American Joint Committee on Cancer (AJCC) 8th Edition staging manual restructured nodal classification and staging by placing less prognostic emphasis on nodal metastases for human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC). However, there was no change for HPV-negative OPSCC. The purpose of our study is to examine the impact of nodal metastases on survival in HPV-negative OPSCC. METHODS: HPV-negative OPSCC was queried from the National Cancer Database (NCDB) and Surveillance, Epidemiology and End Results program (SEER) databases. Univariable and multivariable models were utilized to determine the impact of nodal status on overall survival. These patients were reclassified according to AJCC 8 HPV-positive criteria (TNM8+) and risk stratification was quantified with C-statistic. RESULTS: There were 11,147 cases of HPV-negative OPSCC in the NCDB and 3,613 cases in SEER that were included in the nodal classification analysis. Unlike nonoropharyngeal malignancies, increased nodal stage is not clearly associated with survival for patients with OPSCC independent of HPV status. When the TNM8+ was applied to HPV-negative patients, there was improved concordance in the NCDB cohort, 0.561 (plus minus) 0.004 to 0.624 (plus minus) 0.004 (difference +0.063) and the SEER cohort, 0.561 (plus minus) 0.008 to 0.625 (plus minus) 0.008 (difference +0.065). CONCLUSIONS: We demonstrated a reduced impact of nodal metastasis on OPSCC survival, independent of HPV status and specific to OPSCC. IMPACT: We demonstrate, for the first time that when nodal staging is deemphasized as a part of overall staging, we see improved concordance and risk stratification for HPV-negative OPSCC. The exact mechanism of this differential impact remains unknown but offers a novel area of study.

Utility based approach in individualized optimal dose selection using machine learning methods.

The goal in personalized medicine is to individualize treatment using patient characteristics and improve health outcomes. Selection of optimal dose must balance the effect of dose on both treatment efficacy and toxicity outcomes. We consider a setting with one binary efficacy and one binary toxicity outcome. The goal is to find the optimal dose for each patient using clinical features and biomarkers from available dataset. We propose to use flexible machine learning methods such as random forest and Gaussian process models to build models for efficacy and toxicity depending on dose and biomarkers. A copula is used to model the joint distribution of the two outcomes and the estimates are constrained to have non-decreasing dose-efficacy and dose-toxicity relationships. Numerical utilities are elicited from clinicians for each potential bivariate outcome. For each patient, the optimal dose is chosen to maximize the posterior mean of the utility function. We also propose alternative approaches to optimal dose selection by adding additional toxicity based constraints and an approach taking into account the uncertainty in the estimation of the utility function. The proposed methods are evaluated in a simulation study to compare expected utility outcomes under various estimated optimal dose rules. Gaussian process models tended to have better performance than random forest. Enforcing monotonicity during modeling provided small benefits. Whether and how, correlation between efficacy and toxicity, was modeled, had little effect on performance. The proposed methods are illustrated with a study of patients with liver cancer treated with stereotactic body radiation therapy.

The association between inflammatory biomarkers and statin use among patients with head and neck squamous cell carcinoma.

BACKGROUND: Tumor-infiltrating lymphocytes (TILs) and cytokines are associated with prognosis among patients with head and neck squamous cell carcinoma (HNSCC). Statins (cholesterol-lowering drugs) may improve HNSCC prognosis, particularly in human papillomavirus (HPV)-positive cases, but the mechanism remains unclear. METHODS: Statin use was collected from medical records for HNSCC cases (2008-2014). TILs were counted in tumor tissue, and a total weighted score (TILws) was created. Cytokines were measured in blood. The associations between statins and biomarkers were estimated using logistic (biomarker categories:

Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients.

Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.