Citations

7529 Background: iAEs, such as cytokine release syndrome (CRS) and macrophage activation syndrome (MAS), can occur after infusion of chimeric antigen receptor T cell therapies such as ide-cel, but are typically low-grade and manageable with tocilizumab and corticosteroids. Although high-grade iAEs are rare with ide-cel, identification of patients at increased risk may improve management and remains an unmet need. Three composite biomarkers (multivariate models) of iAE risk were previously developed in a discovery cohort from the KarMMa and KarMMa-2 studies of ide-cel in RRMM (Mashadi-Hossein A, et al. J Clin Oncol 2023;41(16_suppl):e20005). Here, we validate these 3 prototype models for risk of developing high-grade iAEs after ide-cel infusion. Methods: Two models used pretreatment patient and routine clinical laboratory parameters; the third model also included change in 6 exploratory cytokines from baseline to 1 day following ide-cel infusion. To validate, each model was retrospectively applied to KarMMa-3 (NCT03651128) data in a blinded fashion, and sensitivity and specificity to identify grade ≥3 CRS and any-grade MAS were determined. Model performance thresholds used for sensitivity and specificity were 90% and 50%, respectively. Performance for identifying grade 2 CRS was also analyzed post hoc. Results: The first and simplest model comprised a manual algorithm with 7 features; it identified patients with grade ≥3 CRS or MAS with 85% sensitivity and 60% specificity. The second model comprised 19 routinely measured pretreatment features; it identified grade ≥3 CRS with 91% sensitivity and 59% specificity, and any-grade MAS with 60% sensitivity and 88% specificity. The third and most complex model comprised 7 pretreatment clinical features and 6 exploratory cytokines; it identified grade ≥3 CRS with 100% sensitivity and 51% specificity, and any-grade MAS with 100% sensitivity and 81% specificity. None of the models identified grade ≥2 CRS with passable sensitivity or specificity. Clustering analyses across all features showed that only a subset of grade 2 CRS cases was within the previously identified high-risk iAE cluster while the remaining grade 2 CRS cases were spread across other clusters. Conclusions: Two of the 3 composite biomarkers met validation criteria for grade ≥3 CRS risk. Criteria were not met for grade 2 CRS, suggesting these models are specific to higher-grade events and that grade 2 CRS represents a more heterogeneous group of patients. While high-grade iAEs are rare with ide-cel, these models may further optimize the robust benefit–risk profile that ide-cel has demonstrated by helping identify, prior to or shortly after infusion, patients most at risk for severe events. Clinical trial information: NCT03651128 .

e20005 Background: iAEs such as cytokine release syndrome (CRS) and neurotoxicity (NT) are common with CAR T cell therapies such as ide-cel; CRS and NT observed with ide-cel are generally low grade and manageable with tocilizumab (toci) and corticosteroids. Identifying iAE risk with sensitivity and specificity is challenging, suggesting multifactorial etiology and limitations evaluating dynamic processes at specific timepoints. Macrophage activation syndrome (MAS; per CTCAE) has a specific molecular profile representing a homogeneous subgroup within CRS, tending to be more severe/refractory to front-line management. Using longitudinal data on post-infusion soluble factors we aimed to develop a candidate post-infusion inflammatory risk score based on MAS molecular signature and determine associations with pt baseline characteristics. Methods: Statistical learning tools were applied to the longitudinal profile of 34 factors measured over 28 d after ide-cel infusion in 291 pts from KarMMa (NCT03361748) and KarMMa-2 (NCT03601078) to extract features describing the profiles. Unsupervised machine learning-based clustering was applied to these features and enrichment for MAS cases within each cluster was assessed. A metric describing membership in a cluster of interest was used as a candidate post-infusion inflammatory risk score for further correlative analyses. Results: Pts with MAS had cytokine profiles consistent with high levels of pro-inflammatory signalling across multiple inflammatory cell types. Cytokine profiles of MAS were qualitatively similar to those of CRS/NT; however, unsupervised clustering showed a distinct group of pts with MAS. A metric of similarity to the MAS-enriched cluster was defined as a candidate post-infusion inflammatory risk score, posited to identify pts more likely to have more severe/refractory iAEs. This inflammatory risk score positively correlated with CRS and NT grades and frequency of toci usage (Kruskal-Wallis test; all P < 0.001). A prototype predictive model based on baseline pt and clinical characteristics strongly correlated with post-infusion inflammatory risk score (Pearson; ρ = 0.88). Low blood counts, higher tumor burden, and elevated levels of inflammatory markers on the day of ide-cel infusion were associated with a higher inflammatory risk score. Conclusions: A molecular profile of severe/refractory iAEs was preliminarily identified in ide-cel treated pts with MM. This profile enabled derivation of a candidate post-infusion inflammatory risk score and a prototype predictive model of this risk score based on pt baseline characteristics was developed. These results may help identify pts at risk for severe/refractory iAEs, who may be potential candidates for closer monitoring or novel interventions, and warrant further study. Clinical trial information: NCT03361748 , NCT03601078 .

PURPOSE Multiparametric magnetic resonance imaging (mpMRI) is used widely for prostate cancer (PCa) evaluation. Approximately 35% of aggressive tumors, however, are not visible on mpMRI. We sought to identify the molecular alterations associated with mpMRI-invisible tumors and determine whether mpMRI visibility is associated with PCa prognosis. METHODS Discovery and validation cohorts included patients who underwent mpMRI before radical prostatectomy and were found to harbor both mpMRI-visible (Prostate Imaging and Reporting Data System 3 to 5) and -invisible (Prostate Imaging and Reporting Data System 1 or 2) foci on surgical pathology. Next-generation sequencing was performed to determine differential gene expression between mpMRI-visible and -invisible foci. A genetic signature for tumor mpMRI visibility was derived in the discovery cohort and assessed in an independent validation cohort. Its association with long-term oncologic outcomes was evaluated in a separate testing cohort. RESULTS The discovery cohort included 10 patients with 26 distinct PCa foci on surgical pathology, of which 12 (46%) were visible and 14 (54%) were invisible on preoperative mpMRI. Next-generation sequencing detected prioritized genetic mutations in 14 (54%) tumor foci (n = 8 mpMRI visible, n = 6 mpMRI invisible). A nine-gene signature (composed largely of cell organization/structure genes) associated with mpMRI visibility was derived (area under the curve = 0.89), and the signature predicted MRI visibility with 75% sensitivity and 100% specificity (area under the curve = 0.88) in the validation cohort. In the testing cohort (n = 375, median follow-up 8 years) there was no significant difference in biochemical recurrence, distant metastasis, or cancer-specific mortality in patients with predicted mpMRI-visible versus -invisible tumors (all P > .05). CONCLUSION Compared with mpMRI-invisible disease, mpMRI-visible tumors are associated with underexpression of cellular organization genes. mpMRI visibility does not seem to be predictive of long-term cancer outcomes, highlighting the need for biopsy strategies that detect mpMRI-invisible tumors.

AbstractBackground: We studied the utility of the tumor suppressor Tristetraprolin (TTP, ZFP36) as a clinically relevant biomarker of aggressive disease in prostate cancer patients after radical prostatectomy (RP).Methods: TTP RNA expression was measured in an RP cohort of patients treated at Moffitt Cancer Center (MCC) and obtained from six publically available RP datasets with biochemical recurrence (BCR; total n = 1,394) and/or metastatic outcome data (total n = 1,222). TTP protein expression was measured by immunohistochemistry in a tissue microarray of 153 MCC RP samples. The time to BCR or metastasis based on TTP RNA or protein levels was calculated using the Kaplan–Meier analysis. Univariable and multivariable Cox proportional hazard models were performed on multiple cohorts to evaluate if TTP is a clinically relevant biomarker and to assess if TTP improves upon the Cancer of the Prostate Risk Assessment postsurgical (CAPRA-S) score for predicting clinical outcomes.Results: In all of the RP patient cohorts, prostate cancer with low TTP RNA or protein levels had decreased time to BCR or metastasis versus TTP-high tumors. Further, the decreased time to BCR in TTP-low prostate cancer was more pronounced in low-grade tumors. Finally, pooled survival analysis suggests that TTP RNA expression provides independent information beyond CAPRA-S to predict BCR.Conclusions: TTP is a promising prostate cancer biomarker for predicting which RP patients will have poor outcomes, especially for low-grade prostate cancer patients.Impact: This study suggests that TTP RNA expression can be used to enhance the accuracy of CAPRA-S to predict outcomes in patients treated with RP. Cancer Epidemiol Biomarkers Prev; 27(11); 1376–83. ©2018 AACR.

Purpose Molecular characterization of prostate cancer, including The Cancer Genome Atlas, has revealed distinct subtypes with underlying genomic alterations. One of these core subtypes, SPOP (speckle-type POZ protein) mutant prostate cancer, has previously only been identifiable via DNA sequencing, which has made the impact on prognosis and routinely used risk stratification parameters unclear. Methods We have developed a novel gene expression signature, classifier (Subclass Predictor Based on Transcriptional Data), and decision tree to predict the SPOP mutant subclass from RNA gene expression data and classify common prostate cancer molecular subtypes. We then validated and further interrogated the association of prostate cancer molecular subtypes with pathologic and clinical outcomes in retrospective and prospective cohorts of 8,158 patients. Results The subclass predictor based on transcriptional data model showed high sensitivity and specificity in multiple cohorts across both RNA sequencing and microarray gene expression platforms. We predicted approximately 8% to 9% of cases to be SPOP mutant from both retrospective and prospective cohorts. We found that the SPOP mutant subclass was associated with lower frequency of positive margins, extraprostatic extension, and seminal vesicle invasion at prostatectomy; however, SPOP mutant cancers were associated with higher pretreatment serum prostate-specific antigen (PSA). The association between SPOP mutant status and higher PSA level was validated in three independent cohorts. Despite high pretreatment PSA, the SPOP mutant subtype was associated with a favorable prognosis with improved metastasis-free survival, particularly in patients with high-risk preoperative PSA levels. Conclusion Using a novel gene expression model and a decision tree algorithm to define prostate cancer molecular subclasses, we found that the SPOP mutant subclass is associated with higher preoperative PSA, less adverse pathologic features, and favorable prognosis. These findings suggest a paradigm in which the interpretation of common risk stratification parameters, particularly PSA, may be influenced by the underlying molecular subtype of prostate cancer.

Abstract Purpose: Currently, no genomic signature exists to distinguish men most likely to progress on adjuvant androgen deprivation therapy (ADT) after radical prostatectomy for high-risk prostate cancer. Here we develop and validate a gene expression signature to predict response to postoperative ADT. Experimental Design: A training set consisting of 284 radical prostatectomy patients was established after 1:1 propensity score matching metastasis between adjuvant-ADT (a-ADT)-treated and no ADT–treated groups. An ADT Response Signature (ADT-RS) was identified from neuroendocrine and AR signaling–related genes. Two independent cohorts were used to form three separate data sets for validation (set I, n = 232; set II, n = 435; set III, n = 612). The primary endpoint of the analysis was postoperative metastasis. Results: Increases in ADT-RS score were associated with a reduction in risk of metastasis only in a-ADT patients. On multivariable analysis, ADT-RS by ADT treatment interaction term remained associated with metastasis in both validation sets (set I: HR = 0.18, Pinteraction = 0.009; set II: HR = 0.25, Pinteraction = 0.019). In a matched validation set III, patients with Low ADT-RS scores had similar 10-year metastasis rates in the a-ADT and no-ADT groups (30.1% vs. 31.0%, P = 0.989). Among High ADT-RS patients, 10-year metastasis rates were significantly lower for a-ADT versus no-ADT patients (9.4% vs. 29.2%, P = 0.021). The marginal ADT-RS by ADT interaction remained significant in the matched dataset (Pinteraction = 0.035). Conclusions: Patients with High ADT-RS benefited from a-ADT. In combination with prognostic risk factors, use of ADT-RS may thus allow for identification of ADT-responsive tumors that may benefit most from early androgen blockade after radical prostatectomy. We discovered a gene signature that when present in primary prostate tumors may be useful to predict patients who may respond to early ADT after surgery. Clin Cancer Res; 24(16); 3908–16. ©2018 AACR.

Abstract Background Treatment-induced neuroendocrine prostate cancer (tNEPC) is an aggressive variant of late-stage metastatic castrate-resistant prostate cancer that commonly arises through neuroendocrine transdifferentiation (NEtD). Treatment options are limited, ineffective, and, for most patients, result in death in less than a year. We previously developed a first-in-field patient-derived xenograft (PDX) model of NEtD. Longitudinal deep transcriptome profiling of this model enabled monitoring of dynamic transcriptional changes during NEtD and in the context of androgen deprivation. Long non-coding RNA (lncRNA) are implicated in cancer where they can control gene regulation. Until now, the expression of lncRNAs during NEtD and their clinical associations were unexplored. Results We implemented a next-generation sequence analysis pipeline that can detect transcripts at low expression levels and built a genome-wide catalogue (n = 37,749) of lncRNAs. We applied this pipeline to 927 clinical samples and our high-fidelity NEtD model LTL331 and identified 821 lncRNAs in NEPC. Among these are 122 lncRNAs that robustly distinguish NEPC from prostate adenocarcinoma (AD) patient tumours. The highest expressed lncRNAs within this signature are H19, LINC00617, and SSTR5-AS1. Another 742 are associated with the NEtD process and fall into four distinct patterns of expression (NEtD lncRNA Class I, II, III, and IV) in our PDX model and clinical samples. Each class has significant (z-scores &gt;2) and unique enrichment for transcription factor binding site (TFBS) motifs in their sequences. Enriched TFBS include (1) TP53 and BRN1 in Class I, (2) ELF5, SPIC, and HOXD1 in Class II, (3) SPDEF in Class III, (4) HSF1 and FOXA1 in Class IV, and (5) TWIST1 when merging Class III with IV. Common TFBS in all NEtD lncRNA were also identified and include E2F, REST, PAX5, PAX9, and STAF. Interrogation of the top deregulated candidates (n = 100) in radical prostatectomy adenocarcinoma samples with long-term follow-up (median 18 years) revealed significant clinicopathological associations. Specifically, we identified 25 that are associated with rapid metastasis following androgen deprivation therapy (ADT). Two of these lncRNAs (SSTR5-AS1 and LINC00514) stratified patients undergoing ADT based on patient outcome. Discussion To date, a comprehensive characterization of the dynamic landscape of lncRNAs during the NEtD process has not been performed. A temporal analysis of the PDX-based NEtD model has for the first time provided this dynamic landscape. TFBS analysis identified NEPC-related TF motifs present within the NEtD lncRNA sequences, suggesting functional roles for these lncRNAs in NEPC pathogenesis. Furthermore, select NEtD lncRNAs appear to be associated with metastasis and patients receiving ADT. Treatment-related metastasis is a clinical consequence of NEPC tumours. Top candidate lncRNAs FENDRR, H19, LINC00514, LINC00617, and SSTR5-AS1 identified in this study are implicated in the development of NEPC. We present here for the first time a genome-wide catalogue of NEtD lncRNAs that characterize the transdifferentiation process and a robust NEPC lncRNA patient expression signature. To accomplish this, we carried out the largest integrative study that applied a PDX NEtD model to clinical samples. These NEtD and NEPC lncRNAs are strong candidates for clinical biomarkers and therapeutic targets and warrant further investigation.

Abstract Androgen receptor (AR) signaling is a key driver of prostate cancer, and androgen-deprivation therapy (ADT) is a standard treatment for patients with advanced and metastatic disease. However, patients receiving ADT eventually develop incurable castration-resistant prostate cancer (CRPC). Here, we report that the chromatin modifier LSD1, an important regulator of AR transcriptional activity, undergoes epigenetic reprogramming in CRPC. LSD1 reprogramming in this setting activated a subset of cell-cycle genes, including CENPE, a centromere binding protein and mitotic kinesin. CENPE was regulated by the co-binding of LSD1 and AR to its promoter, which was associated with loss of RB1 in CRPC. Notably, genetic deletion or pharmacological inhibition of CENPE significantly decreases tumor growth. Our findings show how LSD1-mediated epigenetic reprogramming drives CRPC, and they offer a mechanistic rationale for its therapeutic targeting in this disease. Cancer Res; 77(20); 5479–90. ©2017 AACR.