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Background:Tipifarnib is a potent and selective inhibitor of the enzyme farnesyltransferase (FT). FT catalyzes post‐translational attachment of farnesyl groups required for localization of signaling molecules to the inner cell membrane. CXCL12 is a chemokine that is essential for T cell homing to lymphoid organs and the bone marrow, and for the maintenance of immune cell progenitors.Aims:Herein we report preliminary efficacy, safety and biomarker data from a Phase 2 study of tipifarnib in angioimmunoblastic T‐cell lymphoma (AITL) and CXCL12+ peripheral T‐cell lymphoma (PTCL) patients (pts).Methods:This Phase 2 study (NCT02464228) is a multi‐institutional, single‐arm, open‐label trial initially designed as a two‐stage (11+7 pts) design to determine the efficacy, safety and biomarkers of tipifarnib in pts with relapsed/refractory (R/R) PTCL age >/ = 18 years and a performance status of 0–2. Based on initial findings, the study was amended to include a cohort of AITL (n = 12) and PTCL (n = 12) pts with the CXCL12 rs2839695 A/A genotype (CXCL12+ cohort). Pts received tipifarnib 300 mg administered orally twice daily on days 1–21 of 28‐day treatment cycles until progression of disease (PD) or unacceptable toxicity. The primary endpoint of the study is overall response rate (ORR). Ancillary studies are also ongoing to investigate the prognostic value of CXCL12 expression in pts who received tipifarnib and standard of care treatment.Results:As of 20 February 2019, 43 PTCL pts (20 AITL, 21 PTCL‐NOS, 1 ALK‐ ALCL, 1 gamma‐delta TCL) have been treated with tipifarnib, 19 pts in stages 1 and 2, and 24 pts in the ongoing AITL histology and CXCL12 cohort. Median number of prior treatment regimens was 3; 19 pts had a prior stem cell transplant. All pts had at least one treatment‐emergent adverse event (TEAE); 39 (90%) had at least 1 study drug‐related TEAE and 12 (28%) at least 1 drug related SAE. The most frequently observed drug‐related TEAEs of Grade ≥3 occurring in 10% or more of pts were blood and lymphatic system disorders, including neutropenia (44%), thrombocytopenia (37%), leukopenia (28%), anemia (21%) and febrile neutropenia (21%). There have been 14 deaths on study; none were related to study drug. Of 18 evaluable pts enrolled in Stages 1 and 2 of the trial, 3 partial responses (PR), 2 of them in pts with AITL histology, and 4 best responses of stable disease (SD) were observed. In the AITL cohort (11 evaluable pts), a 45% ORR and 73% clinical benefit rate (CBR; 3 CR, 2 PR and 3 SD) was observed. In the CXCL12+ cohort (n = 3 evaluable pts), 1 PR and 2 SD have been observed, with 4 pts pending cycle 2 response evaluation. CXCL12 expression correlated with favorable outcome to tipifarnib treatment. Pre‐treatment high CXCL12 expression had 90% sensitivity and 93% specificity to identify pts who experienced CR/PR/SD on study in a preliminary analysis of 24 pts. The prognosis of CXCL12 was also retrospectively investigated in 73 pts who received standard of care therapy (AITL N = 50, PTCL NOS = 23). A trend for poor prognosis (22 vs 40 months median OS from diagnosis, HR = 1.8, p = 0.09) was observed using the high CXCL12 subset using the cut‐off point that predicted clinical benefit under tipifarnib therapy. Fifty percent of AITL and 35% of non‐AITL samples overexpressed CXCL12.Summary/Conclusion:Preliminary activity of tipifarnib was observed in PTCL pts, particularly in those with tumors of AITL histology and high CXCL12 expression and enrollment in the CXCL12+ cohort continues.

Background and Purpose— We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods— Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms’ performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results— The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P <0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm 3 (0.9–16.6 cm 3 ). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P <0.0001) and different topography compared with other stroke subtypes. Conclusions— Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

Introduction: Genome-wide association (GWA) studies have contributed substantially to the understanding of complex vascular traits including ischemic stroke; however, genetic determinants of acute cerebral ischemia remain to be elucidated. Objective: To investigate the genetic architecture of cerebral infarct lesion burden in patients with acute ischemic stroke (AIS). Methods: Twelve international sites contributed 3,301 AIS subjects with acute MRI and genome-wide genotyping to the MRI-GENIE study. In a preliminary analysis of 657 subjects of European ancestry, acute cerebral infarct lesions were outlined on diffusion-weighted images (DWI) using a semi-automated volumetric method. Standard quality control measures were performed per single nucleotide polymorphism (SNP) and per subject. SNPs were imputed to the Haplotype Reference Consortium v1.1 panel. Natural log-transformed DWI volume was used for GWA analysis with allelic dosage per SNP, age, sex and principal components 1-5 of genetic ancestry as covariates. Results: GWA testing for DWI volume in 7.7 million SNPs yielded no signal crossing the Bonferroni-corrected genome-wide significance threshold of p <5*10 -8 . However, several loci passed the nominal significance threshold of p <1*10 -6 (Figure 1), including a locus on chromosome 2 in the SPATA3-AS1 (spermatogenesis associated 3 antisense RNA 1) gene (lead SNP: rs2368999, MAF=36%, p =8.4*10 -7 ) and an intronic locus on chromosome 10 in the ATRNL1 (Attractin like 1) gene (lead SNP: rs592284, MAF=10%, p =9.1*10 -7 ). Conclusion: In this first-to-date, preliminary GWAS of acute cerebral infarct volume in AIS patients, we identified several new, nominally significant loci including a locus in ATRNL1, a gene previously linked to carotid plaque burden. Further analyses of the MRI-GENIE cohort to replicate these findings, to include stroke subtype specific analyses, and to increase the overall statistical power of the study are ongoing.

Background and Purpose— Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. Methods— The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10 296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 previously genotyped cases, for a total of 14 549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. Center for Inherited Diseases Research–generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. Conclusions— The Stroke Genetics Network, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.

The recent “Advanced Neuroimaging for Acute Stroke Treatment” meeting on September 7 and 8, 2007 in Washington DC, brought together stroke neurologists, neuroradiologists, emergency physicians, neuroimaging research scientists, members of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), industry representatives, and members of the US Food and Drug Administration (FDA) to discuss the role of advanced neuroimaging in acute stroke treatment. The goals of the meeting were to assess state-of-the-art practice in terms of acute stroke imaging research and to propose specific recommendations regarding: (1) the standardization of perfusion and penumbral imaging techniques, (2) the validation of the accuracy and clinical utility of imaging markers of the ischemic penumbra, (3) the validation of imaging biomarkers relevant to clinical outcomes, and (4) the creation of a central repository to achieve these goals. The present article summarizes these recommendations and examines practical steps to achieve them.

Abstract Rationale Transforming growth factor (TGF)-β has a central role in driving many of the pathological processes that characterize pulmonary fibrosis. Inhibition of the integrin αvβ6, a key activator of TGF-β in lung, is an attractive therapeutic strategy, as it may be possible to inhibit TGF-β at sites of αvβ6 up-regulation without affecting other homeostatic roles of TGF-β. Objectives To analyze the expression of αvβ6 in human pulmonary fibrosis, and to functionally test the efficacy of therapeutic inhibition of αvβ6-mediated TGF-β activation in murine bleomycin–induced pulmonary fibrosis. Methods Lung biopsies from patients with a diagnosis of systemic sclerosis or idiopathic pulmonary fibrosis were stained for αvβ6 expression. A range of concentrations of a monoclonal antibody that blocks αvβ6-mediated TGF-β activation was evaluated in murine bleomycin–induced lung fibrosis. Measurements and Main Results αvβ6 is overexpressed in human lung fibrosis within pneumocytes lining the alveolar ducts and alveoli. In the bleomycin model, αvβ6 antibody was effective in blocking pulmonary fibrosis. At high doses, there was increased expression of markers of inflammation and macrophage activation, consistent with the effects of TGF-β inhibition in the lung. Low doses of antibody attenuated collagen expression without increasing alveolar inflammatory cell populations or macrophage activation markers. Conclusions Partial inhibition of TGF-β using αvβ6 integrin antibodies is effective in blocking murine pulmonary fibrosis without exacerbating inflammation. In addition, the elevated expression of αvβ6, an activator of the fibrogenic cytokine, TGF-β, in human pulmonary fibrosis suggests that αvβ6 monoclonal antibodies could represent a promising new therapeutic strategy for treating pulmonary fibrosis.