Opposing Functions of Interferon Coordinate Adaptive and Innate Immune Responses to Cancer Immune Checkpoint Blockade.

Interferon-gamma (IFNG) augments immune function yet promotes T cell exhaustion through PDL1. How these opposing effects are integrated to impact immune checkpoint blockade (ICB) is unclear. We show that while inhibiting tumor IFNG signaling decreases interferon-stimulated genes (ISGs) in cancer cells, it increases ISGs in immune cells by enhancing IFNG produced by exhausted T cells (TEX). In tumors with favorable antigenicity, these TEX mediate rejection. In tumors with neoantigen or MHC-I loss, TEX instead utilize IFNG to drive maturation of innate immune cells, including a PD1+TRAIL+ ILC1 population. By disabling an inhibitory circuit impacting PD1 and TRAIL, blocking tumor IFNG signaling promotes innate immune killing. Thus, interferon signaling in cancer cells and immune cells oppose each other to establish a regulatory relationship that limits both adaptive and innate immune killing. In melanoma and lung cancer patients, perturbation of this relationship is associated with ICB response independent of tumor mutational burden.

Tumor Interferon Signaling Regulates a Multigenic Resistance Program to Immune Checkpoint Blockade.

Therapeutic blocking of the PD1 pathway results in significant tumor responses, but resistance is common. We demonstrate that prolonged interferon signaling orchestrates PDL1-dependent and PDL1-independent resistance to immune checkpoint blockade (ICB) and to combinations such as radiation plus anti-CTLA4. Persistent type II interferon signaling allows tumors to acquire STAT1-related epigenomic changes and augments expression of interferon-stimulated genes and ligands for multiple T cell inhibitory receptors. Both type I and II interferons maintain this resistance program. Crippling the program genetically or pharmacologically interferes with multiple inhibitory pathways and expands distinct T cell populations with improved function despite expressing markers of severe exhaustion. Consequently, tumors resistant to multi-agent ICB are rendered responsive to ICB monotherapy. Finally, we observe that biomarkers for interferon-driven resistance associate with clinical progression after anti-PD1 therapy. Thus, the duration of tumor interferon signaling augments adaptive resistance and inhibition of the interferon response bypasses requirements for combinatorial ICB therapies.

Exosome transfer from stromal to breast cancer cells regulates therapy resistance pathways.

Stromal communication with cancer cells can influence treatment response. We show that stromal and breast cancer (BrCa) cells utilize paracrine and juxtacrine signaling to drive chemotherapy and radiation resistance. Upon heterotypic interaction, exosomes are transferred from stromal to BrCa cells. RNA within exosomes, which are largely noncoding transcripts and transposable elements, stimulates the pattern recognition receptor RIG-I to activate STAT1-dependent antiviral signaling. In parallel, stromal cells also activate NOTCH3 on BrCa cells. The paracrine antiviral and juxtacrine NOTCH3 pathways converge as STAT1 facilitates transcriptional responses to NOTCH3 and expands therapy-resistant tumor-initiating cells. Primary human and/or mouse BrCa analysis support the role of antiviral/NOTCH3 pathways in NOTCH signaling and stroma-mediated resistance, which is abrogated by combination therapy with gamma secretase inhibitors. Thus, stromal cells orchestrate an intricate crosstalk with BrCa cells by utilizing exosomes to instigate antiviral signaling. This expands BrCa subpopulations adept at resisting therapy and reinitiating tumor growth.

Adjuvant Therapy After Neoadjuvant Therapy for Esophageal Cancer: Who Needs It?

OBJECTIVE: We hypothesized that, on average, patients do not benefit from additional adjuvant therapy after neoadjuvant therapy for locally advanced esophageal cancer, although subsets of patients might. Therefore, we sought to identify profiles of patients predicted to receive the most survival benefit or greatest detriment from adding adjuvant therapy. BACKGROUND: Although neoadjuvant therapy has become the treatment of choice for locally advanced esophageal cancer, the value of adding adjuvant therapy is unknown. METHODS: From 1970 to 2014, 22,123 patients were treated for esophageal cancer at 33 centers on 6 continents (Worldwide Esophageal Cancer Collaboration), of whom 7731 with adenocarcinoma or squamous cell carcinoma received neoadjuvant therapy; 1348 received additional adjuvant therapy. Random forests for survival and virtual-twin analyses were performed for all-cause mortality. RESULTS: Patients received a small survival benefit from adjuvant therapy (3.2±10 months over the subsequent 10 years for adenocarcinoma, 1.8±11 for squamous cell carcinoma). Consistent benefit occurred in ypT3-4 patients without nodal involvement and those with ypN2-3 disease. The small subset of patients receiving most benefit had high nodal burden, ypT4, and positive margins. Patients with ypT1-2N0 cancers had either no benefit or a detriment in survival. CONCLUSIONS: Adjuvant therapy after neoadjuvant therapy has value primarily for patients with more advanced esophageal cancer. Because the benefit is often small, patients considering adjuvant therapy should be counseled on benefits versus morbidity. In addition, given that the overall benefit was meaningful in a small number of patients, emerging modalities such as immunotherapy may hold more promise in the adjuvant setting.

Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning.

BACKGROUND: Machine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk. METHODS: We used survival and ML models to identify lifetime predictors using the Cohort of Norway (n=173,275) and hospital diagnoses in a Saskatoon clinical sample (n=12,614). The mean follow-up times were 17 years and 3 years for the Cohort of Norway and Saskatoon respectively. People in the clinical sample had a longitudinal record of hospital visits grouped in six-month intervals. We developed models in a training set and these models predicted survival probabilities in held-out test data. RESULTS: In the general population, we found that a higher proportion of low-income residents in a county, mood symptoms, and daily smoking increased the risk of dying from suicide in both genders. In the clinical sample, the only predictors identified were male gender and older age. CONCLUSION: Suicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets.

Value of Lymphadenectomy in Patients Receiving Neoadjuvant Therapy for Esophageal Adenocarcinoma.

OBJECTIVE: The aim of this study was to assess the effect on survival of extent of lymphadenectomy during esophagectomy for patients undergoing multimodality (neoadjuvant) therapy for adenocarcinoma of the esophagus and esophagogastric junction using Worldwide Esophageal Cancer Collaboration data. SUMMARY BACKGROUND DATA: Previous worldwide data demonstrated that optimum lymphadenectomy during esophagectomy alone for esophageal cancer provides accurate staging and maximum survival. However, for patients undergoing neoadjuvant therapy for locally advanced adenocarcinoma, its value is unclear, leading to wide practice variability. METHODS: A total of 3859 patients with adenocarcinoma of the esophagus or esophagogastric junction received neoadjuvant therapy. The endpoint was all-cause mortality, reported as gain or loss of lifetime within 10 years. Lifetime predicted for each regional lymph node resected used quantile survival random forest methodology. RESULTS: Across all post-neoadjuvant ypTNM cancer categories, some degree of lymphadenectomy was associated with longer lifetime, but in a nonlinear fashion. For patients with ypN0 cancers, there was a modest gain in lifetime up to 25 lymph nodes resected and an incremental loss in lifetime as >25 were resected. For patients with ypN+ cancers, there was a robust gain in lifetime up to 30 lymph nodes resected and then an incremental loss in lifetime. CONCLUSIONS: Worldwide data for adenocarcinoma of the esophagus and esophagogastric junction demonstrate that lymphadenectomy during esophagectomy is a valuable component of neoadjuvant therapy. Survival is maximized when an optimum range of nodes is resected.

BOOSTED NONPARAMETRIC HAZARDS WITH TIME-DEPENDENT COVARIATES.

Given functional data from a survival process with time-dependent covariates, we derive a smooth convex representation for its nonparametric log-likelihood functional and obtain its functional gradient. From this we devise a generic gradient boosting procedure for estimating the hazard function nonparametrically. An illustrative implementation of the procedure using regression trees is described to show how to recover the unknown hazard. The generic estimator is consistent if the model is correctly specified; alternatively an oracle inequality can be demonstrated for tree-based models. To avoid overfitting, boosting employs several regularization devices. One of them is step-size restriction, but the rationale for this is somewhat mysterious from the viewpoint of consistency. Our work brings some clarity to this issue by revealing that step-size restriction is a mechanism for preventing the curvature of the risk from derailing convergence.

Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer.

Immune checkpoint inhibitors result in impressive clinical responses, but optimal results will require combination with each other and other therapies. This raises fundamental questions about mechanisms of non-redundancy and resistance. Here we report major tumour regressions in a subset of patients with metastatic melanoma treated with an anti-CTLA4 antibody (anti-CTLA4) and radiation, and reproduced this effect in mouse models. Although combined treatment improved responses in irradiated and unirradiated tumours, resistance was common. Unbiased analyses of mice revealed that resistance was due to upregulation of PD-L1 on melanoma cells and associated with T-cell exhaustion. Accordingly, optimal response in melanoma and other cancer types requires radiation, anti-CTLA4 and anti-PD-L1/PD-1. Anti-CTLA4 predominantly inhibits T-regulatory cells (Treg cells), thereby increasing the CD8 T-cell to Treg (CD8/Treg) ratio. Radiation enhances the diversity of the T-cell receptor (TCR) repertoire of intratumoral T cells. Together, anti-CTLA4 promotes expansion of T cells, while radiation shapes the TCR repertoire of the expanded peripheral clones. Addition of PD-L1 blockade reverses T-cell exhaustion to mitigate depression in the CD8/Treg ratio and further encourages oligoclonal T-cell expansion. Similarly to results from mice, patients on our clinical trial with melanoma showing high PD-L1 did not respond to radiation plus anti-CTLA4, demonstrated persistent T-cell exhaustion, and rapidly progressed. Thus, PD-L1 on melanoma cells allows tumours to escape anti-CTLA4-based therapy, and the combination of radiation, anti-CTLA4 and anti-PD-L1 promotes response and immunity through distinct mechanisms.

Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality.

The prelisting variables essential for creating an accurate heart transplant allocation score based on survival are unknown. To identify these we studied mortality of adults on the active heart transplant waiting list in the Scientific Registry of Transplant Recipients database from January 1, 2004 to August 31, 2015. There were 33 069 candidates awaiting heart transplantation: 7681 UNOS Status 1A, 13 027 Status 1B, and 12 361 Status 2. During a median waitlist follow-up of 4.3 months, 5514 candidates died. Variables of importance for waitlist mortality were identified by machine learning using Random Survival Forests. Strong correlates predicting survival were estimated glomerular filtration rate (eGFR), serum albumin, extracorporeal membrane oxygenation, ventricular assist device, mechanical ventilation, peak oxygen capacity, hemodynamics, inotrope support, and type of heart disease with less predictive variables including antiarrhythmic agents, history of stroke, vascular disease, prior malignancy, and prior tobacco use. Complex interactions were identified such as an additive risk in mortality based on renal function and serum albumin, and sex-differences in mortality when eGFR >40 mL/min/1.73 m. Most predictive variables for waitlist mortality are in the current tiered allocation system except for eGFR and serum albumin which have an additive risk and complex interactions.

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric measure of variable importance (VIMP). A current limitation of VIMP, however, is that no systematic method exists for estimating its variance. As a solution, we propose a subsampling approach that can be used to estimate the variance of VIMP and for constructing confidence intervals. The method is general enough that it can be applied to many useful settings, including regression, classification, and survival problems. Using extensive simulations, we demonstrate the effectiveness of the subsampling estimator and in particular find that the delete-d jackknife variance estimator, a close cousin, is especially effective under low subsampling rates due to its bias correction properties. These 2 estimators are highly competitive when compared with the .164 bootstrap estimator, a modified bootstrap procedure designed to deal with ties in out-of-sample data. Most importantly, subsampling is computationally fast, thus making it especially attractive for big data settings.

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting.

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes.

A Random Forests Quantile Classifier for Class Imbalanced Data.

Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where q* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a density-based approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to tt-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.

Esophageal Cancer: Associations With (pN+) Lymph Node Metastases.

OBJECTIVES: To identify the associations of lymph node metastases (pN+), number of positive nodes, and pN subclassification with cancer, treatment, patient, geographic, and institutional variables, and to recommend extent of lymphadenectomy needed to accurately detect pN+ for esophageal cancer. SUMMARY BACKGROUND DATA: Limited data and traditional analytic techniques have precluded identifying intricate associations of pN+ with other cancer, treatment, and patient characteristics. METHODS: Data on 5806 esophagectomy patients from the Worldwide Esophageal Cancer Collaboration were analyzed by Random Forest machine learning techniques. RESULTS: pN+, number of positive nodes, and pN subclassification were associated with increasing depth of cancer invasion (pT), increasing cancer length, decreasing cancer differentiation (G), and more regional lymph nodes resected. Lymphadenectomy necessary to accurately detect pN+ is 60 for shorter, well-differentiated cancers (<2.5 cm) and 20 for longer, poorly differentiated ones. CONCLUSIONS: In esophageal cancer, pN+, increasing number of positive nodes, and increasing pN classification are associated with deeper invading, longer, and poorly differentiated cancers. Consequently, if the goal of lymphadenectomy is to accurately define pN+ status of such cancers, few nodes need to be removed. Conversely, superficial, shorter, and well-differentiated cancers require a more extensive lymphadenectomy to accurately define pN+ status.

Estimating the prevalence of atrial fibrillation from a three-class mixture model for repeated diagnoses.

Atrial fibrillation (AF) is an abnormal heart rhythm characterized by rapid and irregular heartbeat, with or without perceivable symptoms. In clinical practice, the electrocardiogram (ECG) is often used for diagnosis of AF. Since the AF often arrives as recurrent episodes of varying frequency and duration and only the episodes that occur at the time of ECG can be detected, the AF is often underdiagnosed when a limited number of repeated ECGs are used. In studies evaluating the efficacy of AF ablation surgery, each patient undergoes multiple ECGs and the AF status at the time of ECG is recorded. The objective of this paper is to estimate the marginal proportions of patients with or without AF in a population, which are important measures of the efficacy of the treatment. The underdiagnosis problem is addressed by a three-class mixture regression model in which a patient's probability of having no AF, paroxysmal AF, and permanent AF is modeled by auxiliary baseline covariates in a nested logistic regression. A binomial regression model is specified conditional on a subject being in the paroxysmal AF group. The model parameters are estimated by the Expectation-Maximization (EM) algorithm. These parameters are themselves nuisance parameters for the purpose of this research, but the estimators of the marginal proportions of interest can be expressed as functions of the data and these nuisance parameters and their variances can be estimated by the sandwich method. We examine the performance of the proposed methodology in simulations and two real data applications.

Recommendations for clinical staging (cTNM) of cancer of the esophagus and esophagogastric junction for the 8th edition AJCC/UICC staging manuals.

We report analytic and consensus processes that produced recommendations for clinical stage groups (cTNM) of esophageal and esophagogastric junction cancer for the AJCC/UICC cancer staging manuals, 8th edition. The Worldwide Esophageal Cancer Collaboration (WECC) provided data on 22,123 clinically staged patients with epithelial esophageal cancers. Risk-adjusted survival for each patient was developed using random survival forest analysis from which (1) data-driven clinical stage groups were identified wherein survival decreased monotonically and was distinctive between and homogeneous within groups and (2) data-driven anatomic clinical stage groups based only on cTNM. The AJCC Upper GI Task Force, by smoothing, simplifying, expanding, and assessing clinical applicability, produced (3) consensus clinical stage groups. Compared with pTNM, cTNM survival was "pinched," with poorer survival for early cStage groups and better survival for advanced ones. Histologic grade was distinctive for data-driven grouping of cT2N0M0 squamous cell carcinoma (SCC) and cT1-2N0M0 adenocarcinoma, but consensus removed it. Grouping was different by histopathologic cell type. For SCC, cN0-1 was distinctive for cT3 but not cT1-2, and consensus removed cT4 subclassification and added subgroups 0, IVA, and IVB. For adenocarcinoma, N0-1 was distinctive for cT1-2 but not cT3-4a, cStage II subgrouping was necessary (T1N1M0 [IIA] and T2N0M0 [IIB]), advanced cancers cT3-4aN0-1M0 plus cT2N1M0 comprised cStage III, and consensus added subgroups 0, IVA, and IVB. Treatment decisions require accurate cStage, which differs from pStage. Understaging and overstaging are problematic, and additional factors, such as grade, may facilitate treatment decisions and prognostication until clinical staging techniques are uniformly applied and improved.

Recommendations for neoadjuvant pathologic staging (ypTNM) of cancer of the esophagus and esophagogastric junction for the 8th edition AJCC/UICC staging manuals.

We report analytic and consensus processes that produced recommendations for neoadjuvant pathologic stage groups (ypTNM) of esophageal and esophagogastric junction cancer for the AJCC/UICC cancer staging manuals, 8th edition. The Worldwide Esophageal Cancer Collaboration provided data for 22,654 patients with epithelial esophageal cancers; 7,773 had pathologic assessment after neoadjuvant therapy. Risk-adjusted survival for each patient was developed. Random forest analysis identified data-driven neoadjuvant pathologic stage groups wherein survival decreased monotonically with increasing group, was distinctive between groups, and homogeneous within groups. An additional analysis produced data-driven anatomic neoadjuvant pathologic stage groups based only on ypT, ypN, and ypM categories. The AJCC Upper GI Task Force, by smoothing, simplifying, expanding, and assessing clinical applicability, produced consensus neoadjuvant pathologic stage groups. Grade and location were much less discriminating for stage grouping ypTNM than pTNM. Data-driven stage grouping without grade and location produced nearly identical groups for squamous cell carcinoma and adenocarcinoma. However, ypTNM groups and their associated survival differed from pTNM. The need for consensus process was minimal. The consensus groups, identical for both cell types were as follows: ypStage I comprised ypT0-2N0M0; ypStage II ypT3N0M0; ypStage IIIA ypT0-2N1M0; ypStage IIIB ypT3N1M0, ypT0-3N2, and ypT4aN0M0; ypStage IVA ypT4aN1-2, ypT4bN0-2, and ypTanyN3M0; and ypStage IVB ypTanyNanyM1. Absence of equivalent pathologic (pTNM) categories for the peculiar neoadjuvant pathologic categories ypTisN0-3M0 and ypT0N0-3M0, dissimilar stage group compositions, and markedly different early- and intermediate-stage survival necessitated a unified, unique set of stage grouping for patients of either cell type who receive neoadjuvant therapy.

Signalling pathway for RKIP and Let-7 regulates and predicts metastatic breast cancer.

Tumour metastasis suppressors are inhibitors of metastasis but their mechanisms of action are generally not understood. We previously showed that the suppressor Raf kinase inhibitory protein (RKIP) inhibits breast tumour metastasis in part via let-7. Here, we demonstrate an integrated approach combining statistical analysis of breast tumour gene expression data and experimental validation to extend the signalling pathway for RKIP. We show that RKIP inhibits let-7 targets (HMGA2, BACH1) that in turn upregulate bone metastasis genes (MMP1, OPN, CXCR4). Our results reveal BACH1 as a novel let-7-regulated transcription factor that induces matrix metalloproteinase1 (MMP1) expression and promotes metastasis. An RKIP pathway metastasis signature (designated RPMS) derived from the complete signalling cascade predicts high metastatic risk better than the individual genes. These results highlight a powerful approach for identifying signalling pathways downstream of a key metastasis suppressor and indicate that analysis of genes in the context of their signalling environment is critical for understanding their predictive and therapeutic potential.

Random survival forests for competing risks.

We introduce a new approach to competing risks using random forests. Our method is fully non-parametric and can be used for selecting event-specific variables and for estimating the cumulative incidence function. We show that the method is highly effective for both prediction and variable selection in high-dimensional problems and in settings such as HIV/AIDS that involve many competing risks.

Pathway hunting by random survival forests.

MOTIVATION: Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered. RESULTS: In this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data. AVAILABILITY: The R code for the analysis used in this article is available upon request.