Citations

Abstract The T-cell non-Hodgkin lymphomas continue to be an area of unmet need for new therapies that offer novel mechanisms of action. Tipifarnib is a potent and selective inhibitor of the enzyme farnesyltransferase (FT). Tipifarnib inhibits farnesylation of key regulatory proteins involved in CXCL12 production (Gualberto et al, EHA 2019). CXCL12 is a chemokine that is essential for T-cell homing to lymphoid organs and bone marrow, and for the maintenance of immune cell progenitors via its receptor CXCR4 (Susek et al, Front Immunology, 2018). Angioimmunoblastic T-cell lymphoma (AITL) is associated with high levels of CXCL12 expression, which is also a negative prognostic factor (Witzig et al, Blood, 2019). Additionally, Peripheral T-Cell lymphoma Not Otherwise Specified (PTCL-NOS) patients with the CXCL12 rs2839695 A/A wildtype genotype (PTCL-CXCL12+) have also been found to have elevated levels of CXCL12. Therefore, there is strong rationale for targeting these subsets of PTCL with tipifarnib. This Phase 2 study (NCT02464228) was a multi-institutional, single-arm, open-label trial determining the efficacy, safety, and potential predictive biomarkers for tipifarnib in adult patients with relapsed or refractory (R/R) PTCL. Patients received tipifarnib 300 mg orally twice daily on Days 1-21 of 28-day treatment cycles until progression of disease or unacceptable toxicity. The primary objective of the study was to determine the antitumor activity by overall response rate (ORR) per Lugano Classification. Secondary objectives included safety, duration of response (DoR) and progression-free survival (PFS), and exploratory analyses included overall survival (OS) and identification of potential biomarkers associated with tipifarnib activity. Sixty-five R/R PTCL patients (38 AITL, 14 PTCL-NOS, 11 PTCL-CXCL12+, 2 other (1 ALCL-ALK negative, 1 PTCL-subtype not specified) were treated with tipifarnib. The population was heavily pretreated with a median of 3 prior regimens (range 1-8). All patients had at least one treatment-emergent adverse event (TEAE); most patients (87.7%) had at least one treatment-related TEAE, and 18 (27.7%) patients had at least one treatment-related serious TEAE. The most frequently observed (≥20%) treatment-related TEAEs (all grades) were hematological (neutropenia, thrombocytopenia, anemia,) and gastrointestinal (nausea, diarrhea), as well as fatigue, which is consistent with the known safety profile of tipifarnib. Fifty-eight patients were efficacy evaluable and had an associated ORR of 39.7% (23/58) with most responses observed in the AITL and PTCL-CXCL12+ cohorts. In the 32 patients with AITL, the ORR was 56.3% (18/32), including 9 complete responses (CR) and 9 partial responses (PR). In the 10 patients with PTCL-CXCL12+, the ORR was 40.0% (4/10), including 1 CR and 3 PR. The median OS for patients with AITL was 32.8 months, and the median OS had not been reached for patients with PTCL-CXCL12+. Previously, a strong association between KIR3DL2 C336R/Q386E mutation and the activity of tipifarnib was observed and reported in AITL patients (Witzig et al, EHA 2020). Extensive gene expression and gene variant analyses of samples from the study population is currently ongoing to evaluate this correlation and explore other potential predictors of response. Final data will be presented at the time of the conference. Overall, the results from this Phase 2 study with tipifarnib demonstrated very encouraging efficacy in patients with CXCL12-expressing subtypes of PTCL (AITL and PTCL-CXCL12+) and showed a tolerable safety profile. Disclosures Witzig: Karyopharm Therapeutics, Abbvie/Genentech, Seattle Genetics, Celgene/BMS, Incyte, Epizyme, Cellectar, Tessa Therapeutics, Portola Pharmaceuticals, MorphoSys, ADC Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Sokol: Dren Bio: Membership on an entity's Board of Directors or advisory committees; Kyowa-Kirin: Membership on an entity's Board of Directors or advisory committees. Kim: IGM Biosciences: Research Funding; Kyowa Kirin: Research Funding; Roche: Research Funding; Sanofi: Research Funding; Celltrion: Research Funding; Dong-A Pharmaceutical: Research Funding; Johnson & Johnson: Research Funding; Eisai: Research Funding. de la Cruz Vicente: Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Eusapharma: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Advani: Astellas/Agensys: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees; Bristol Myer Squibb: Membership on an entity's Board of Directors or advisory committees; Cell Medica: Membership on an entity's Board of Directors or advisory committees; Forty Seven: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genetech Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen Pharmaceutical: Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Kura: Research Funding; Kyowa: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Millenium: Research Funding; Pharmacyclics: Consultancy, Research Funding; Portola Pharmaceuticals: Consultancy; Regeneron: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees. Marin Niebla: Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kiowa Kirin: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BeiGene: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Speakers Bureau; Amgen: Speakers Bureau. Terol: BMS: Consultancy; Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Hospital Clinico Valencia: Current Employment; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Research Funding; Roche: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Travel, Research Funding. Eva: Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Bendris: Kura Oncology: Current Employment, Current equity holder in publicly-traded company. Ahsan: Gilead: Current equity holder in publicly-traded company, Ended employment in the past 24 months; Kura Oncology: Current Employment, Current equity holder in publicly-traded company. Leoni: Kura Oncology: Current Employment. Foss: Daiichi Sankyo: Honoraria; Mallinckrodt: Honoraria; Acrotech: Honoraria, Speakers Bureau; Seattle Genetics: Honoraria, Speakers Bureau; Kyowa: Honoraria; Kura: Honoraria.

Abstract While chronological age is one of the most influential determinants of post-stroke outcomes, little is known of the impact of neuroimaging-derived biological brain age. We here first examine whether radiomics analysis of the texture of brain T2-FLAIR MRI images can be used to predict brain age in stroke patients. We then assess the clinical determinants of accelerated brain aging and, finally, its impact on post-stroke functional outcomes. Leveraging a multisite cohort of 4,163 ischemic stroke patients, we show that older-appearing patients have more hypertension, diabetes mellitus, prior strokes, and smoking history and are more likely to develop worse post-stroke outcomes than their younger-appearing counterparts. Our results strengthen the importance of preventive medicine for maintaining brain health in stroke patients as they age and suggest a novel methodology to capture previously undescribed prognostic information available on commonly acquired MRI sequences during routine stroke care.

Abstract Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Background: Fundamental advances in stroke care will require pooling imaging phenotype data from multiple centers, to complement the current aggregation of genomic, environmental, and clinical information. Sharing clinically acquired MRI data from multiple hospitals is challenging due to inherent heterogeneity of clinical data, where the same MRI series may be labeled differently depending on vendor and hospital. Furthermore, the de-identification process may remove data describing the MRI series, requiring human review. However, manually annotating the MRI series is not only laborious and slow but prone to human error. In this work, we present a recurrent convolutional neural network (RCNN) for automated classification of the MRI series. Methods: We randomly selected 1000 subjects from the MRI-GENetics Interface Exploration study and partitioned them into 800 training, 100 validation and 100 testing subjects. We categorized the MRI series into 24 groups (see Table). The RCNN used a modified AlexNet to extract features from 2D slices. AlexNet was pretrained on ImageNet photographs. Since clinical MRI are 3D and 4D, a gated recurrent unit neural network was used to aggregate information from multiple 2D slices to make the final prediction. Results: We achieved a classification accuracy (correct/total cases) of 99.8%, 98.5% and 97.5% on the training, validation and testing set, respectively. The averaged F1-score (percent overlap between predicted cases and actual cases) over all categories were 99.8% 98.2% and 94.4% on the training, validation and testing set. Conclusion: We showed that automated annotation of MRI series by repurposing deep-learning techniques used for photographic image recognition tasks is feasible. Such methods can be used to facilitate high throughput curation of MRI data acquired across multiple centers and enable scientifically productive collaboration by researchers and, ultimately enhancing big data stroke research.

Introduction: White matter hyperintensity volume (WMHv) is an important and highly heritable cerebrovascular phenotype; however, manual or semi-automated approaches to clinically acquired MRI analysis hinder large-scale studies in acute ischemic stroke (AIS). In this work, we develop a high-throughput, fully automated WMHv analysis pipeline for clinical fluid-attenuated inversion recovery (FLAIR) images to facilitate rapid genetic discovery in AIS. Methods: Automated WMHv extraction from multiple subjects relies on significant pre-processing of medical scans, including co-registration of the images. To reduce the effects of anisotropic voxel sizes, each FLAIR image is upsampled using bi-cubic interpolation. Brain extraction is performed using RObust Brain EXtraction (ROBEX). Images are then registered to an in-house FLAIR template using Advanced Normalization Tools (ANTs). The spatial covariation of WMH is learned through principal component analysis (PCA) of manual outlines from 100 subjects. Areas of leukoaraiosis are identified and separated from other lesions using the PCA modes. Volumes are then computed using non-interpolated slices for each subject. Standard deviation (SD) in WMHv (9 subjects; 6 raters each) is calculated as a measure of variability. Good agreement between automated and manual outlines is assessed in 358 subjects (automated WMHv within 3SD of manual WMHv). Results: As part of the MRI - Gen etics I nterface E xploration (MRI-GENIE) study, WMHv were calculated on a set of 2703 FLAIR images of patients from 12 independent AIS cohorts (sites). Results are shown in Figure 1. Comparing manual and automated WMHv shows that 88% of the automated WMHv fall within 3 SD from the manual WMHv, suggesting good agreement. Conclusion: WMHv segmentation using a fully-automated pipeline for analysis of clinical MRIs is both feasible and accurate. Ongoing analysis of the extracted WMHv is expected to advance current knowledge of risks and outcomes in AIS.

Introduction: Even though neuroinflammation is increasingly recognized as an essential contributor to ischemic brain injury, the exact underlying mechanisms remain unclear. Higher expression of the vascular cell adhesion molecule 1 (VCAM-1) increases leukocyte-brain endothelium interaction and has been associated with larger infarct size following acute ischemic stroke (AIS). The activation of the TGF-β signaling pathways can down-regulate VCAM-1 expression and ameliorate deleterious tissue outcome during neuroinflammation. Hypothesis: We sought to investigate whether genetic variation in the TGF-beta pathway and adhesion molecule genes is associated with acute stroke lesion size. Methods: We completed genome-wide association (GWA) testing and diffusion-weighted imaging lesion volume (DWIv) analysis in a discovery cohort of 532 AIS patients of European ancestry enrolled within 48 hours of symptom onset. An independent European ancestry cohort of 724 AIS patients with GWA data and automated DWIv served as replication cohort. GWA testing per SNP was performed using linear regression modeling of natural log-transformed DWIv adjusted for age, sex and relevant principal components. We selected 42 inflammatory genes in the TGF-beta pathway for a gene-based analysis using VEGAS (Versatile Gene-based -Association Study) software. A pre-specified discovery phase Bonferroni-corrected threshold was set at p<0.001. In the replication phase, 14 SNPs overlapping were tested at the Bonferroni-corrected p-value threshold of p<0.004. Results: Of all genes in the TGF-beta pathway, VCAM1 (p=0.0006) was significantly associated with DWIv in the discovery AIS cohort (age: 66 ± 14.9 years, sex: 63.4% male, DWIv: 2.2cm 3 (IQR: 0.6-11.7cm 3 )). A single SNP within the VCAM1 gene boundaries (rs3176876 (BETA=0.2341, p=0.003)) was significantly associated with DWIv in the replication AIS cohort (age: 65 ± 14.1 years, sex: 68.6% male, DWIv: 4cm 3 (IQR: 1.3-17.2cm 3 )). Conclusion: The genetic variant rs3176876 in the VCAM1 gene is associated with larger infarct lesion size measured on brain MRI of AIS patients. These findings suggest genetic contribution to the pro-inflammatory mechanisms in acute cerebral ischemia and warrant further investigation.

Introduction: White matter hyperintensity (WMH) is a highly heritable trait and a significant contributor to stroke risk and severity. Vascular risk factors contribute to WMH severity; however, knowledge of the determinants of WMH in acute ischemic stroke (AIS) is still limited. Hypothesis: WMH volume (WMHv) varies across AIS subtypes and is modified by vascular risk factors. Methods: We extracted WMHv from the clinical MRI scans of 2683 AIS subjects from the MRI-Genetics Interface Exploration (MRI-GENIE) study using a novel fully-automated, volumetric analysis pipeline. Demographic data, stroke risk factors and stroke subtyping for the Causative Classification of Stroke (CCS) were performed at each of the 12 international study sites. WMHv was natural log-transformed for linear regression analyses. Results: Median WMHv was 5.7cm 3 (interquartile range (IQR): 2.2-12.8cm 3 ). In univariable analysis, age (63.1 ± 14.7 years, β=0.04, SE=0.002), prior stroke (10.2%, β=0.66, SE=0.08), hypertension (65.4%, β=0.75, SE=0.05), diabetes mellitus (23.1%, β=0.35, SE=0.06), coronary artery disease (17.6%, β=0.04, SE=0.002), and atrial fibrillation (14.6%, β=0.48, SE=0.07) were significant predictors of WMHv (all p<0.0001), as well as smoking status (52.2%, β=0.15, SE=0.05, p=0.005), race (16.5% Non-Caucasian, β=0.25, SE=0.07) and ethnicity (8.2% Hispanic, β=0.30, SE=0.11) (all p<0.01). In multivariable analysis, age (β=0.04, SE=0.002), prior stroke (β=0.56, SE=0.08), hypertension (β=0.33, SE=0.05), smoking status (β=0.16, SE=0.05), race (β=0.42, SE=0.06), and ethnicity (β=0.34, SE=0.09) were independent predictors of WMHv (all p<0.0001), as well as diabetes mellitus (β=0.13, SE=0.06, p=0.02). WMHv differed significantly (p<0.0001, unadjusted) across CCS stroke subtypes: cardioembolic stroke (8.0cm 3 , IQR: 4.2-15.4cm 3 ), large-artery stroke (6.9cm 3 , IQR: 3.1-14.7cm 3 ), small-vessel stroke (5.8cm 3 , IQR: 2.5-13.5cm 3 ), stroke of undetermined (4.7cm 3 , IQR: 1.6-11.0cm 3 ) or other (2.55cm 3 , IQR: 0.9-8.8cm 3 ) causes. Conclusion: In this largest-to-date, multicenter hospital-based cohort of AIS patients with automated WMHv analysis, common vascular risk factors contribute significantly to WMH burden and WMHv varies by CCS subtype.

Introduction: The MRI-Genetics Interface Exploration (MRI-GENIE) study is the first international collaboration that aims to facilitate genetic discoveries in clinical cohorts of patients with acute ischemic stroke (AIS). We have amassed the largest-to-date collection of AIS cases with brain MRI scans and genome-wide genotyping to test the role of genetic susceptibility in MRI-based cerebrovascular traits. Objective/Hypothesis: To elucidate the genetic architecture of white matter hyperintensity (WMH) burden in AIS patients. Methods: Using a novel automated algorithm, we extracted WMH volume (WMHv) from clinical MRI scans of 2704 AIS patients (age 63.1 ± 14.7 years, 60.6% male) of European ancestry. Quality control (QC) measures were undertaken per subject and per SNP, excluding subjects with non-European ancestry and poor genotyping, as well as SNPs deviating from Hardy-Weinberg equilibrium and high levels of missingness. Imputation to the Haplotype Reference Consortium (HRC version r1.1) was conducted for 1712 remaining subjects with 2.8 million SNPs on the Michigan Imputation Server. After exclusion of poorly imputed SNPs (R 2 <0.5) and SNPs with minor allele frequency < 1%, 7.7 million SNPs remained for further analysis. Genome-wide association testing of natural log-transformed WMHv on the allelic dosage per SNP was adjusted for age, sex and principal components 1-10. Results: Genome-wide association testing has identified a novel locus on chromosome 2 (T allele at rs72856504) near the LDL Receptor related Protein 1B gene (LRP1B) that was significantly associated with WMHv burden in AIS (β=0.54, SE=0.098, p=3.65*10 -8 ). Conclusion: We have identified a novel locus (T allele rs72856504) on chromosome 2 near the LRP1B gene, which is specific for WMH in AIS and has not been previosuly described in stroke-free WMH cohorts. A replication effort involving additional independent cohorts of AIS patients with brain MRI and genome-wide genotyping is ongoing.

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 ligand for CXCR4 that is essential for T cell homing to lymphoid organs and the bone marrow, and for the maintenance of immune cell progenitors. We have previously shown that FT inhibition by tipifarnib downregulates CXCL12 secretion. 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 &gt;/=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 (wt CXCL12 3'UTR 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). Tumor Whole Exon Sequencing (WES) was generated by NGS and gene expression data generated by RNA Seq. Ancillary studies also investigated the prognostic value of CXCL12 expression in pts who received standard of care treatment. Results As of 24 May 2019, 50 PTCL pts (23 AITL, 25 PTCL-NOS, 1 ALK- ALCL, 1 gamma-delta TCL) have been treated with tipifarnib, 19 pts in stages 1 and 2, and 31 pts in the ongoing AITL histology and wt CXCL12 3'UTR cohorts. Median number of prior treatment regimens was 3; 19 pts had a prior stem cell transplant. All pts (n=48 with available safety data) had at least one treatment-emergent adverse event (TEAE); 42 (88%) had at least 1 study drug-related TEAE and 13 (27%) at least 1 drug related SAE. The most frequently observed drug-related TEAEs of Grade &gt;3 occurring in 10% or more of pts were blood and lymphatic system disorders, including neutropenia (40%), thrombocytopenia (33%), leukopenia (25%), anemia and febrile neutropenia (19% each). There have been 14 deaths on study; one related to study drug (lung infection). 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 5 best responses of stable disease (SD) were observed. In the AITL cohort (11 evaluable of 16 pts enrolled), a 45% ORR and 73% clinical benefit rate (CBR; 3 CR, 2 PR and 3 SD) was observed. In the wt CXCL12 3'UTR cohort (n=12 evaluable pts), a 42% ORR was observed (3 CR, 2 PR), with 2 of the 3 CRs observed in patients of AITL histology (n=4). A total of 23 AITL subjects were enrolled in the overall study of whom 16 had WES data. A strong association with the activity of tipifarnib was observed in 8 of the 16 (50%) carrying KIR3DL2 gene variants C336R/Q386E: 50% CR rate, 75% ORR, 100% clinical benefit rate. These tumors expressed also very low levels of CXCL5, a ligand for CXCR2, that may mediate resistance to tipifarnib. High Allele Frequency of KIR3DL2 variants predicted CR to tipifarnib treatment (ROC AUC=0.94, p&lt;0.0001) and AITL patients carrying KIR3DL2 gene variants experienced a better outcome with tipifarnib treatment than with prior SOC treatment. While AITL tumors expressed high levels of CXCL12 and responded to tipifarnib, a trend for poor prognosis (22 vs 40 months median OS from diagnosis, HR=1.8, p=0.09) was observed in a series of 50 PTCL subjects treated with SOC therapy. Fifty percent of AITL and 35% of non-AITL samples overexpressed CXCL12. Conclusion The AITL and wt CXCL12 3'UTR cohorts met pre-specified statistical hypotheses supporting proof-of-concept for tipifarnib in PTCL. AITL histology, KIR3DL2 and CXCL12 genotype provided robust tools for the selection/stratification of PTCL subjects treated with tipifarnib. Extended enrollment of AITL patients continues and an update on enrollment and outcomes will be provided at the time of the presentation. Disclosures Sokol: EUSA: Consultancy. Foss:Mallinckrodt: Consultancy; Eisai: Consultancy; Spectrum: Other: fees for non-CME/CE services ; Acrotech: Consultancy; Seattle Genetics: Consultancy, Other: fees for non-CME/CE services ; miRagen: Consultancy. Kim:Donga: Research Funding; Celltrion: Research Funding; J&J: Research Funding; Roche: Research Funding; Kyowa-Kirin: Research Funding; Novartis: Research Funding; Mundipharma: Research Funding. Jacobsen:Kura Oncology: Research Funding. Advani:Kyowa Kirin Pharmaceutical Developments, Inc.: Consultancy; Kura: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Research Funding; Agensys: Research Funding; Stanford University: Employment, Equity Ownership; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Celmed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Forty-Seven: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cell Medica, Ltd: Consultancy; Gilead Sciences, Inc./Kite Pharma, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharma: Research Funding; Millennium: Research Funding. Roncero:Kura Oncology: Research Funding. Terol:Janssen: Consultancy, Research Funding; Roche: Consultancy; Gilead: Research Funding; Astra Zeneca: Consultancy; Abbvie: Consultancy. Domingo-Domenech:Bristol-Myers Squibb: Other: Travel expenses; Roche: Other: Travel expenses; Seattle Genetics: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses. Piris:Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Lecture Fees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jansen: Membership on an entity's Board of Directors or advisory committees, Other: Lecture Fees; Nanostring: Membership on an entity's Board of Directors or advisory committees; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees; Kura: Research Funding. Rodriguez:Kura Oncology: Research Funding. Bolognese:Kura Oncology: Consultancy. Kessler:Kura Oncology: Employment. Mishra:Kura Oncology: Employment. Curry:Kura Oncology: Employment. Kurman:Kura Oncology: Employment. Scholz:Kura Oncology: Employment, Equity Ownership, Patents & Royalties. Gualberto:Kura Oncology: Employment, Equity Ownership, Patents & Royalties.

Abstract 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. 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 is a multi-institutional, single-arm, open-label trial initially designed as a two-stage (11+7 pts) cohort 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 patients who received standard of care treatment. Results As of 25 July 2018, 34 PTCL pts (13 AITL, 1 ALK- ALCL, 20 PTCL-NOS) have been treated with tipifarnib, 19 pts in stages 1 and 2, and 15 pts in the ongoing AITL histology and CXCL12 cohorts. Median number of prior treatment regimens was 3. The most common treatment-related adverse events (AE) (grade ≥ 3) are hematological, including neutropenia (50%), thrombocytopenia (43%), leukopenia (33%), febrile neutropenia (27%), and anemia (20%). Skin and subcutaneous tissue disorders were reported in 9 pts, 6 of them with AITL histology. One pt with AITL experienced an episode of possible Stevens Johnson Syndrome that resolved with dose discontinuation and did not recur upon re-challenge at one dose level reduction. Of 18 evaluable patients 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. Pre-treatment tumor tissue CXCL12 expression correlated with favorable pt outcomes. In the AITL cohort (10 evaluable pts), 1 PR and 1 SD have been observed so far, with 5 pts pending cycle 2 response evaluation. In the CXCL12+ cohort (n=3 evaluable pts), 1 SD has been observed, with 2 pts pending cycle 2 response evaluation. Plasma levels of CXCL12 decreased over time with tipifarnib treatment. Expression of CXCL12 mRNA and other biomarkers in pre-treatment biopsies of pts in the AITL and CXCL12+ cohorts are being evaluated using RT-PCR assays. In addition, the prognostic value of CXCL12 is being investigated in approximately 100 diagnostic specimens of PTCL pts who received standard therapy. Preliminary data suggest that CXCL12 overexpression is observed in approximately 25% of PTCL and negatively affects pt survival. Conclusion Preliminary activity of tipifarnib was observed in PTCL pts, particularly in those with tumors of AITL histology and high CXCL12 expression and enrollment continues. Disclosures Witzig: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Sokol:Mallinckrodt Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy; Spectrum Pharmaceuticals: Consultancy. Jacobsen:Merck: Consultancy; Seattle Genetics: Consultancy. Kim:Takeda: Research Funding; J&J: Research Funding; Mundipharma: Research Funding; Roche: Research Funding; Novartis: Research Funding; Kyowa-Kirin: Research Funding; Celltrion: Research Funding. Foss:Miragen: Consultancy, Speakers Bureau; Spectrum: Consultancy; Seattle genetics: Consultancy; Mallinkrodt: Consultancy. Advani:Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kura: Research Funding; Merck: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Kyowa: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Regeneron: Research Funding; Janssen: Research Funding; Bayer: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Cell Medica: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Astra Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Autolus: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Gilead/Kite: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Millenium: Research Funding; Agensys: Research Funding; Celgene: Research Funding; Forty Seven: Research Funding; Infinity: Research Funding. Marin Niebla:Amgen: Other: Medical education of Staff, Speakers Bureau; Roche: Consultancy, Other: Medical education of Staff, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Other: Medical education of Staff, Speakers Bureau; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Medical education of Staff, Speakers Bureau. Piris:Kura: Honoraria; Takeda: Honoraria; Janssen: Honoraria; Gilead: Honoraria. Curry:Kura Oncology: Employment, Equity Ownership. Gualberto:Kura Oncology: Employment, Equity Ownership.