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The concordance probability is used to evaluate the discriminatory power and the predictive accuracy of nonlinear statistical models. We derive an analytical expression for the concordance probability in the Cox proportional hazards model. The proposed estimator is a function of the regression parameters and the covariate distribution only and does not use the observed event and censoring times. For this reason it is asymptotically unbiased, unlike Harrell's c-index based on informative pairs. The asymptotic distribution of the concordance probability estimate is derived using U-statistic theory and the methodology is applied to a predictive model in lung cancer.

Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose 1 . Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions. A combination of genomic and clinical features improves predictions of response to immune checkpoint blockade.

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.

BackgroundPancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients.MethodsA prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation.ResultsA total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data.ConclusionWe present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

Introduction Neoadjuvant imatinib (Neo-IM) therapy may facilitate R0 resection in primary gastrointestinal stromal tumors (GISTs) that are large or in difficult anatomic locations. While response to preoperative tyrosine kinase inhibitors is associated with better outcome in metastatic GIST, little is known about prognostic factors after Neo-IM in primary GIST. Study Design Patients with primary GIST with or without synchronous metastases who underwent Neo-IM were retrospectively analyzed from a prospective maintained institutional database for Response Evaluation Criteria in Solid Tumors (RECIST), tumor viability, and mitotic rate. Overall survival (OS) was estimated by Kaplan-Meier and compared by log-rank test. Cox proportionate hazard models were used for univariate and multivariate analysis. Results One hundred and fifty patients were treated for a median of 7.1 months (range 0.2–160). By RECIST, partial response, stable disease, and progressive disease were seen in 40%, 51%, and 9%, respectively. By pathologic analysis, ≤ 50% of the tumor was viable in 72%, and the mitotic rate was ≤ 5/50HPF in 74%. On multivariate analysis, RECIST response and tumor viability were not associated with OS, while post-treatment high mitotic rate (hazard ratio (HR) for death 5.3, CI 2.3–12.4), R2 margins (HR 6.0, CI 2.3–15.5), and adjuvant imatinib (HR 0.4, CI 0.2–0.9) were ( p < 0.05). Five-year OS was 81 vs. 38% for low vs. high mitotic rate; 81, 59, and 39% for R0, R1, and R2 margins; and 75 vs 61% for adjuvant vs. no adjuvant imatinib therapy ( p < 0.05). Conclusions In primary GIST undergoing Neo-IM therapy, progression was uncommon, but substantial down-sizing occurred in the minority. High tumor mitotic rate and incomplete resection following Neo-IM were associated with poor outcome, while adjuvant imatinib was associated with prolonged survival.

T cell discrimination of self and non-self is the foundation of the adaptive immune response, and is orchestrated by the interaction between T cell receptors (TCRs) and their cognate ligands presented by major histocompatibility (MHC) molecules. However, the impact of host immunogenetic variation on the diversity of the TCR repertoire remains unclear. Here, we analyzed a cohort of 666 individuals with TCR repertoire sequencing. We show that TCR repertoire diversity is positively associated with polymorphism at the human leukocyte antigen class I (HLA-I) loci, and diminishes with age and cytomegalovirus (CMV) infection. Moreover, our analysis revealed that HLA-I polymorphism and age independently shape the repertoire in healthy individuals. Our data elucidate key determinants of human TCR repertoire diversity, and suggest a mechanism underlying the evolutionary fitness advantage of HLA-I heterozygosity.

Purpose To describe imaging findings, detection rates, and tumor characteristics of breast cancers in a large series of patients with BRCA1 and BRCA2 mutations to potentially streamline screening strategies. Methods An IRB-approved, HIPAA-compliant retrospective analysis of 496 BRCA mutation carriers diagnosed with breast carcinoma from 1999 to 2013 was performed. Institutional database and electronic medical records were reviewed for mammography and MRI imaging. Patient and tumor characteristics including age at diagnosis, tumor histology, grade, receptor, and nodal status were recorded. Results Tumors in BRCA1 mutation carriers were associated exhibited significantly higher nuclear and histological grade compared to BRCA2 ( p  < 0.001). Triple-negative tumors were more frequent in BRCA1 mutation carriers, whereas hormone receptor-positive tumors were more frequent in BRCA2 mutation carriers ( p  < 0.001). BRCA2 mutation carriers more frequently presented with ductal carcinoma in situ (DCIS) alone 14% (35/246) and cancers more frequently exhibiting calcifications ( p  < 0.001). Mammography detected fewer cancers in BRCA1 mutation carriers compared to BRCA2 ( p  = 0.04): 81% (186/231) BRCA1 versus 89% (212/237) BRCA2. MRI detected 99% cancers in each group. Mammography detected cancer in two patients with false-negative MRI (1 invasive cancer, 1 DCIS). Detection rates on both mammography and MRI did not significantly differ for women over 40 years and women below 40 years. Conclusions Breast cancers in BRCA1 mutation carriers are associated with more aggressive tumor characteristics compared to BRCA2 and are less well seen on mammography. Mammography rarely identified cancers not visible on MRI. Thus, the omission of mammography in BRCA1 mutation carriers screened with MRI can be considered.

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.

Intratumor genetic heterogeneity is a key mechanism underlying tumor progression and therapeutic resistance. The prevailing model for explaining intratumor diversity, the clonal evolution model, has recently been challenged by proponents of the cancer stem cell hypothesis. To investigate this issue, we performed combined analyses of markers associated with cellular differentiation states and genotypic alterations in human breast carcinomas and evaluated diversity with ecological and evolutionary methods. Our analyses showed a high degree of genetic heterogeneity both within and between distinct tumor cell populations that were defined based on markers of cellular phenotypes including stem cell-like characteristics. In several tumors, stem cell-like and more-differentiated cancer cell populations were genetically distinct, leading us to question the validity of a simple differentiation hierarchy-based cancer stem cell model. The degree of diversity correlated with clinically relevant breast tumor subtypes and in some tumors was markedly different between the in situ and invasive cell populations. We also found that diversity measures were associated with clinical variables. Our findings highlight the importance of genetic diversity in intratumor heterogeneity and the value of analyzing tumors as distinct populations of cancer cells to more effectively plan treatments.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.