Francesco C. Stingo

Fludarabine, cyclophosphamide, and rituximab treatment achieves long-term disease-free survival in IGHV-mutated chronic lymphocytic leukemia.

Accurate identification of patients likely to achieve long-progression-free survival (PFS) after chemoimmunotherapy is essential given the availability of less toxic alternatives, such as ibrutinib. Fludarabine, cyclophosphamide, and rituximab (FCR) achieved a high response rate, but continued relapses were seen in initial reports. We reviewed the original 300 patient phase 2 FCR study to identify long-term disease-free survivors. Minimal residual disease (MRD) was assessed posttreatment by a polymerase chain reaction-based ligase chain reaction assay (sensitivity 0.01%). At the median follow-up of 12.8 years, PFS was 30.9% (median PFS, 6.4 years). The 12.8-year PFS was 53.9% for patients with mutated immunoglobulin heavy chain variable (IGHV) gene (IGHV-M) and 8.7% for patients with unmutated IGHV (IGHV-UM). 50.7% of patients with IGHV-M achieved MRD-negativity posttreatment; of these, PFS was 79.8% at 12.8 years. A plateau was seen on the PFS curve in patients with IGHV-M, with no relapses beyond 10.4 years in 42 patients (total follow-up 105.4 patient-years). On multivariable analysis, IGHV-UM (hazard ratio, 3.37 [2.18-5.21]; P < .001) and del(17p) by conventional karyotyping (hazard ratio, 7.96 [1.02-61.92]; P = .048) were significantly associated with inferior PFS. Fifteen patients with IGHV-M had 4-color MRD flow cytometry (sensitivity 0.01%) performed in peripheral blood, at a median of 12.8 years posttreatment (range, 9.5-14.7). All were MRD-negative. The high rate of very long-term PFS in patients with IGHV-M after FCR argues for the continued use of chemoimmunotherapy in this patient subgroup outside clinical trials; alternative strategies may be preferred in patients with IGHV-UM, to limit long-term toxicity.

Complex karyotype is a stronger predictor than del(17p) for an inferior outcome in relapsed or refractory chronic lymphocytic leukemia patients treated with ibrutinib-based regimens

Ibrutinib is active in patients with relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL). In patients treated with ibrutinib for R/R CLL, del(17p), identified by interphase fluorescence in situ hybridization (FISH), is associated with inferior progression‐free survival despite equivalent initial response rates. Del(17p) is frequently associated with a complex metaphase karyotype (CKT); the prognostic significance of CKT in ibrutinib‐treated patients has not been reported.

INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES.

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.

Atypical chronic myeloid leukemia is clinically distinct from unclassifiable myelodysplastic/myeloproliferative neoplasms

Atypical chronic myeloid leukemia (aCML) is a rare subtype of myelodysplastic/myeloproliferative neoplasm (MDS/MPN) largely defined morphologically. It is, unclear, however, whether aCML-associated features are distinctive enough to allow its separation from unclassifiable MDS/MPN (MDS/MPN-U). To study these 2 rare entities, 134 patient archives were collected from 7 large medical centers, of which 65 (49%) cases were further classified as aCML and the remaining 69 (51%) as MDS/MPN-U. Distinctively, aCML was associated with many adverse features and an inferior overall survival (12.4 vs 21.8 months, P = .004) and AML-free survival (11.2 vs 18.9 months, P = .003). The aCML defining features of leukocytosis and circulating myeloid precursors, but not dysgranulopoiesis, were independent negative predictors. Other factors, such as lactate dehydrogenase, circulating myeloblasts, platelets, and cytogenetics could further stratify MDS/MPN-U but not aCML patient risks. aCML appeared to have more mutated RAS (7/20 [35%] vs 4/29 [14%]) and less JAK2p.V617F (3/42 [7%] vs 10/52 [19%]), but was not statistically significant. Somatic CSF3R T618I (0/54) and CALR (0/30) mutations were not detected either in aCML or MDS/MPN-U. In conclusion, within MDS/MPN, the World Health Organization 2008 criteria for aCML identify a subgroup of patients with features clearly distinct from MDS/MPN-U. The MDS/MPN-U category is heterogeneous, and patient risk can be further stratified by a number of clinicopathological parameters.

An evaluation of three commercially available metal artifact reduction methods for CT imaging

Three commercial metal artifact reduction methods were evaluated for use in computed tomography (CT) imaging in the presence of clinically realistic metal implants: Philips O-MAR, GEs monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI monochromatic imaging with metal artifact reduction software applied (MARs). Each method was evaluated according to CT number accuracy, metal size accuracy, and streak artifact severity reduction by using several phantoms, including three anthropomorphic phantoms containing metal implants (hip prosthesis, dental fillings and spinal fixation rods). All three methods showed varying degrees of success for the hip prosthesis and spinal fixation rod cases, while none were particularly beneficial for dental artifacts. Limitations of the methods were also observed. MARs underestimated the size of metal implants and introduced new artifacts in imaging planes beyond the metal implant when applied to dental artifacts, and both the O-MAR and MARs algorithms induced artifacts for spinal fixation rods in a thoracic phantom. Our findings suggest that all three artifact mitigation methods may benefit patients with metal implants, though they should be used with caution in certain scenarios.

A Bayesian graphical modeling approach to microRNA regulatory network inference

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.

Bayesian Inference of Multiple Gaussian Graphical Models

In this article, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior, which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions.

Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data

MOTIVATION Discriminant analysis is an effective tool for the classification of experimental units into groups. Here, we consider the typical problem of classifying subjects according to phenotypes via gene expression data and propose a method that incorporates variable selection into the inferential procedure, for the identification of the important biomarkers. To achieve this goal, we build upon a conjugate normal discriminant model, both linear and quadratic, and include a stochastic search variable selection procedure via an MCMC algorithm. Furthermore, we incorporate into the model prior information on the relationships among the genes as described by a gene-gene network. We use a Markov random field (MRF) prior to map the network connections among genes. Our prior model assumes that neighboring genes in the network are more likely to have a joint effect on the relevant biological processes. RESULTS We use simulated data to assess performances of our method. In particular, we compare the MRF prior to a situation where independent Bernoulli priors are chosen for the individual predictors. We also illustrate the method on benchmark datasets for gene expression. Our simulation studies show that employing the MRF prior improves on selection accuracy. In real data applications, in addition to identifying markers and improving prediction accuracy, we show how the integration of existing biological knowledge into the prior model results in an increased ability to identify genes with strong discriminatory power and also aids the interpretation of the results.

Application of the international prognostic scoring System-Revised in therapy-related myelodysplastic syndromes and oligoblastic acute myeloid leukemia

Application of the International Prognostic Scoring System-Revised in therapy-related myelodysplastic syndromes and oligoblastic acute myeloid leukemia

Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set

BackgroundWhile clinical outcomes following immunotherapy have shown an association with tumor mutation load using whole exome sequencing (WES), its clinical applicability is currently limited by cost and bioinformatics requirements.MethodsWe developed a method to accurately derive the predicted total mutation load (PTML) within individual tumors from a small set of genes that can be used in clinical next generation sequencing (NGS) panels. PTML was derived from the actual total mutation load (ATML) of 575 distinct melanoma and lung cancer samples and validated using independent melanoma (n = 312) and lung cancer (n = 217) cohorts. The correlation of PTML status with clinical outcome, following distinct immunotherapies, was assessed using the Kaplan–Meier method.ResultsPTML (derived from 170 genes) was highly correlated with ATML in cutaneous melanoma and lung adenocarcinoma validation cohorts (R2 = 0.73 and R2 = 0.82, respectively). PTML was strongly associated with clinical outcome to ipilimumab (anti-CTLA-4, three cohorts) and adoptive T-cell therapy (1 cohort) clinical outcome in melanoma. Clinical benefit from pembrolizumab (anti-PD-1) in lung cancer was also shown to significantly correlate with PTML status (log rank P value < 0.05 in all cohorts).ConclusionsThe approach of using small NGS gene panels, already applied to guide employment of targeted therapies, may have utility in the personalized use of immunotherapy in cancer.