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Prostate cancer

Novel Hormonal Therapies for nmCRPC Underused, Data Show

Use of androgen deprivation therapy (ADT) in combination with novel hormonal therapies (NHTs) improves metastasis-free and overall survival among men with high-risk nonmetastatic castration-resistant prostate cancer (nmCRPC), but these regimens are underused in this patient population, according to study findings presented at the European Society for Medical Oncology 2022 (ESMO 2022) Congress in Paris, France.

The findings are from a retrospective study of 2007-2020 data from the Optum electronic health records database. Sumati Gupta, MD, of Huntsman Cancer Institute at the University of Utah in Salt Lake City, and colleagues studied 1572 men with high-risk nmCRPC. As first-line treatment, 48.2% received ADT only, 32.9% received ADT plus first-generation nonsteroidal antiandrogens (NSAAs), 8.8% received ADT plus NHTs, and 10.1% received other regimens. As of 2018-2020, only 21% of patients received ADT plus NHTs (including those with a PSA doubling time of 4 months or less), whereas 44.5% received ADT alone and 26.2% received ADT plus NSAAs. Patients with a PSA doubling time of 10 months or less are at high risk for metastatic disease.

“The study emphasizes the need for real-world data to help highlight deviations from guidelines and quality metrics,” Dr Gupta’s team concluded in a poster presentation.


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Novel hormonal therapies included enzalutamide, apalutamide, and darolutamide, which are androgen receptor inhibitors indicated for treating nmCRPC, as well as abiraterone, which inhibits androgen production in the testes, adrenal glands, and prostate cancer tumors. “Although abiraterone is not indicated for nmCRPC, it was included for completeness and due to the evidence from the IMAAGEN nmCRPC phase 2 trial,” the investigators explained.

In that trial, men with high-risk nmCRPC treated with abiraterone plus prednisone had significant decreases in PSA, with 86.9% experiencing a 50% or greater decline in PSA, according to study findings reported in The Journal of Urology. The median time to radiographic evidence of disease progression was not reached.

Disclosure: The ESMO research was supported by Astellas Pharma and Pfizer. Please see the original reference for a full list of disclosures.

References

Gupta S, Hong A, El-Chaar NN, et al. Real-world first-line (1L) treatment patterns in patients (pt) with high-risk nonmetastatic castration-resistant prostate cancer (nmCRPC). Presented at: ESMO 2022, September 9-13, Paris, France. Abstract 1410P.

Ryan CJ, Crawford ED, Shore ND, et al. The IMAAGEN Study: Effect of abiraterone acetate and prednisone on prostate specific antigen and radiographic disease progression in patients with nonmetastatic castration resistant prostate cancer. J Urol. 2018;200:344-352. doi:10.1016/j.uro.2018.03.125

Categories
Prostate cancer

Celastrol recruits UBE3A to recognize and degrade the DNA binding domain of steroid receptors

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    Prostate cancer

    Active Surveillance Best Suited for Older Men With Low-Risk Prostate Cancer

    Prostate cancer active surveillance is most appropriate for older men with low-risk disease, whereas younger men with intermediate-risk disease derive less benefit, investigators report.

    Eugenio Ventimiglia, MD, of IRCCS Ospedale San Raffaele, in Milan, Italy, and colleagues created a state transition model using 1992-2014 data from Prostate Cancer data Base Sweden involving 23,655 men with very low-risk to intermediate-risk prostate cancer. Of these, 16,177 men received active surveillance and 7478 received watchful waiting. The team simulated prostate cancer trajectories up to 30 years by age group at diagnosis, prostate cancer risk category, and Charlson Comorbidity Index.

    Younger men diagnosed at age 55 years who died of prostate cancer before age 85 years had the highest prostate cancer death rates: 9%, 13%, and 15% among those with very low-, low-, and intermediate-risk disease, respectively, Dr Ventimiglia’s team reported in JAMA Network Open. Older men diagnosed at age 70 years had lower prostate cancer death rates: 3%, 6%, and 7% among those with very low-, low-, and intermediate-risk disease, respectively,


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    The investigators also estimated patients’ remaining treatment-free life-years by age group and prostate cancer risk category. The mean proportion of remaining life-years without treatment for men diagnosed at age 55 years was 48%, 36%, and 29% for very low-, low-, and intermediate-risk prostate cancer, respectively. Men aged 70 years at diagnosis had higher mean proportions of remaining life-years without treatment: 77%, 66%, and 60% for those with very low-, low-, and intermediate-risk prostate cancer, respectively.

    “The findings of this Swedish cohort study suggest that men older than 65 years with low-risk [prostate cancer] had a high proportion of treatment-free years (53%-70%) and a low risk of [prostate cancer] death (6%-8%), hence [active surveillance] was indicated among men in this subgroup,” Dr Ventimiglia’s team wrote. “In contrast, in men younger than 65 years, [active surveillance] appeared to be indicated only in those with very low-risk [prostate cancer].”

    In an accompanying editorial, Ahmed O. Elmehrath, MD, of Cairo University in Egypt commented: “These results may be useful in informing clinical practice with regard to disease management and follow-up of men with [prostate cancer] regarding the optimal selection of treatment strategies and their allocation to patient populations that will benefit most from their implementation.”

    The study is limited by use of a Swedish national population health registry, which unlike tertiary centers did not have stringent criteria for adoption of prostate cancer active surveillance. During the 1992-2014 study period, prostate cancer classification and indications for deferred treatment also changed.

    References

    Ventimiglia E, Bill-Axelson A, Bratt O, et al. Long-term outcomes among men undergoing active surveillance for prostate cancer in Sweden. JAMA Netw Open. Published online September 14, 2022. doi:10.1001/jamanetworkopen.2022.31015

    Elmehrath AO. Exploring the long-term outcomes of active surveillance among men with prostate cancer—Best for whom? JAMA Netw Open. Published online September 14, 2022. doi:10.1001/jamanetworkopen.2022.31024

    Categories
    Prostate cancer

    Survival Improving in Metastatic Castration-Sensitive Prostate Cancer Population

    Overall survival (OS) improved among men with metastatic castration-sensitive prostate cancer (mCSPC) after the introduction of docetaxel and novel hormonal therapies (NHT; abiraterone, enzalutamide, apalutamide) for treating this disease state, according to study findings presented at the European Society for Medical Oncology 2022 Congress (ESMO 2022) in Paris, France.

    Daniel J. George, MD, of Duke University Medical Center in Durham, North Carolina, and colleagues studied 39,292 men with mCSPC (33,641 with Medicare coverage and 5651 receiving care at Veterans Affairs [VA] medical facilities). The investigators divided patients into 3 cohorts of patients who received first-line treatment for mCSPC: those treated during 2010-2011 (prior to the introduction of NHT for metastatic castration-resistant prostate cancer); 2012-2014 (to reflect the introduction of NHT for metastatic castration-resistant prostate cancer); and 2015-2018 (for Medicare patients) and 2015-2019 (for VA patients) to capture the effects of the introduction of NHT and docetaxel for treating mCSPC.

    Compared with Medicare and VA patients treated during 2010-2011, Medicare patients treated during 2015-2018 and VA patients treated during 2015-2019 had a significant 12% and 15% lower risk for death, respectively, the investigators reported in a poster presentation. OS survival did not improve during 2012-2014 compared with 2010-2011.


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    Although treatment intensification with NHT or doctaxel for mCSPC is standard of care, the investigators found that as of 2018-2019, less than one-third of men with mCSPC received this regimen as first-line therapy. “The underutilization of these agents in mCSPC suggests that further OS improvements may be possible,” the authors concluded.

    Disclosures: The study was funded by Pfizer and Astellas Pharma.

    Reference

    George DJ, Sandin R, Agarwal N, et al. Treatment patterns and overall survival (OS) in metastatic castration-sensitive prostate cancer (mCSPC) from 2010 to 2019. Presented at ESMO 2022, September 9-13, Paris, France. Abstract 1384P.

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    Prostate cancer

    Transcriptional profiling of matched patient biopsies clarifies molecular determinants of enzalutamide-induced lineage plasticity

    Heterogenous effects of enzalutamide treatment on the tumor transcriptome across matched biopsy samples

    By examining the Stand Up to Cancer Foundation/Prostate Cancer Foundation West Coast Dream Team (WCDT) prospective cohort, we identified 21 patients with CRPC who underwent a metastatic tumor biopsy prior to enza and a repeat biopsy at the time of progression and whose tumor cells underwent RNA-sequencing after laser capture microdissection. All progression biopsies were performed prior to discontinuing enza, enabling us to identify resistance mechanisms induced by continued enza treatment.

    The study design is shown in Fig. 1a. Patient demographic information and prior treatments are shown in Supplementary Table 1. Bone was the most common site for both pre-treatment and progression biopsies. Eighteen of 21 patients had the same tissue type biopsied at progression. In eight patients, the exact same lesion was biopsied at baseline and progression (Fig. 1b, Supplementary Table 2). The median time on enza treatment was 226 days, shorter than previous trials conducted in this same disease state6,25. PSA response at 12 weeks and the time between biopsies for each patient are shown in Fig. 1c.

    Fig. 1: Study biopsy and clinical information.
    figure 1

    a Study schematic. b Sankey diagram showing site of biopsy at baseline (left) and at progression (right). Values indicate number of biopsies performed on each type of tissue at baseline (left) or progression (right). c Left panel shows PSA change at 12 weeks for each patient. Right panel shows time between biopsies for each patient. Response indicates whether a patient experienced a ≥ 50% reduction in PSA level at 12 weeks vs. baseline. For subject 022, PSA information was not available. Source data are provided as a Source Data file.

    To understand sample-to-sample differences, we performed unsupervised hierarchical clustering and found the nearest neighbor of 13/21 (62%) baseline samples was their matched progression sample pair (Fig. 2a). Samples did not cluster together based solely on the site of biopsy, indicating laser capture microdissection removed much of the microenvironment from these samples. Furthermore, whether the same lesion was biopsied did not impact how samples clustered.

    Fig. 2: The effect of enzalutamide on tumor transcriptome is heterogenous across patients.
    figure 2

    a Similarity heatmap for all samples clustered by variance-stabilization transformation (vst). Hashes through biopsy site indicate that the same lesion was biopsied at baseline and progression. Bracket on right axis indicates that baseline and progression samples from the same patient are nearest neighbors. b Clinical and gene expression data for each matched pair ordered on x-axis by time between biopsies. TOS is time on study in months. AR expression is by log2(TPM + 1). Clusters for each sample were assigned based on classifications from Aggarwal et al.24 and Labrecque et al.23. AR VIPER Score is the predicted AR activity score based on the AR regulon in the VIPER package26. Source data are provided as a Source Data file.

    We next examined measurements of interest in all the matched samples (Fig. 2b). To estimate AR transcriptional activity, we used Virtual Inference of Protein-activity by Enriched Regulon (VIPER) master regulator analysis26. Nine (43%) patients did not have a marked difference in inferred AR activity. Nine (43%) patients had decreased AR activity, and three (14%) patients had increased AR activity at progression (Supplementary Fig. 1a). We used a second method to measure AR activity—the ARG10 signature27. ARG10 strongly correlated with the VIPER results (Supplementary Fig. 1b). Though AR-V7 expression increased in several samples at progression, the difference in expression using the entire 21-patient cohort was not statistically significant (Supplementary Fig. 1c).

    Previously, Aggarwal et al. identified five clusters of CRPC tumors by RNA-sequencing analysis24. Cluster 2 was enriched for tumors with loss of AR activity and increased E2F1 activity and contained a preponderance of tumors that had lost AR expression24, consistent with lineage plasticity. A subset of cluster 2 tumor samples was labeled NEPC based upon their morphologic appearance resembling small cell prostate cancer, though many of these tumor samples did not express canonical NEPC markers such as chromogranin A (CHGA) or synaptophysin (SYP)24.

    In examining the RNA-sequencing results from the baseline tumors, four of the five Aggarwal clusters were represented (clusters 1, 3, 4, and 5) in at least one sample, while no baseline sample harbored a cluster 2 program. We also applied the Labrecque transcription-based classifier that was developed on rapid autopsy CRPC samples and identified five subsets of prostate cancer: AR-driven prostate cancer (ARPC), amphicrine prostate cancer with neuroendocrine gene expression concomitant with AR signaling, AR-activity low prostate cancer, DNPC, and NEPC23. The Labrecque classifier designated all the baseline samples in our cohort as ARPC.

    To determine if any of the progression tumors in our cohort underwent lineage plasticity after enza, we determined the Aggarwal cluster and Labrecque classifier designation. Twelve of 21 matched pairs did not change their Aggarwal cluster designation. However, three of the 21 progression tumors (hereafter referred to as converters) had gene expression profiles consistent with cluster 2, suggesting enza-induced conversion to an alternate lineage. We also examined the Labrecque classifier on the progression samples. The three converter samples designated as Aggarwal cluster 2 at progression were most consistent with DNPC by the Labrecque classifier, corroborating lineage plasticity in these tumors (Fig. 2b).

    We next examined additional gene signatures linked previously to lineage plasticity in progression vs. baseline biopsies. Comparing samples from the three converter patients, signature scores for genes upregulated in NEPC tumors described by Beltran et al.21 were increased (Supplementary Fig. 1d). A previously described basal stemness signature28 was also activated in these three progression samples (Supplementary Fig. 1e). We previously identified a 76 gene AR-repressed gene signature that was activated in a CRPC cell line that undergoes enza-induced lineage plasticity29. This 76 gene signature was also increased in the progression samples from the three converters (Supplementary Fig. 1f). Finally, predicted AR activity was significantly decreased in the progression samples from the converters by both VIPER and ARG10 signatures (Supplementary Fig. 1a, g). In examining pre- and post-treatment samples using the entire 21-patient cohort, none of these signatures was significantly changed, demonstrating that activation of these lineage plasticity signatures was not a generalized effect of enza treatment. Altogether, these results suggest that enza-induced lineage plasticity and conversion to an AR-independent program occurs in a subset of tumors (3/21 or 14%), similar to the frequency of cluster 2 tumors (10%) described by Aggarwal previously24.

    Notably, the baseline tumors from the three converter patients did not fall into the same Aggarwal cluster (cluster 4 for sample 80 and cluster 5 for samples 135, 210). Therefore, it was not surprising that the baseline tumors from these three patients did not cluster together using unsupervised clustering (Supplementary Fig. 1h, i). These data suggest that there may be different starting points to lineage plasticity with enza treatment.

    Clarification of a baseline transcriptional program linked to lineage plasticity risk

    To identify genes linked with risk of lineage plasticity after enza, we examined the differentially expressed genes between the three baseline samples from converters vs. the 18 non-converters. Pathway analysis implicated activation of MYC targets, E2F targets, and allograft rejection in baseline tumors from converters (Fig. 3a). There were no significantly downregulated pathways in baseline tumors from converters. To identify differentially activated transcription factors, we performed master regulator analysis. E2F1 was the top transcription factor predicted to be activated in the baseline tumors from converters (Fig. 3b, full list Supplementary Data 1), corroborating pathway analysis and our prior work demonstrating that high E2F1 activity is linked to lineage plasticity risk29. Additionally, we found that there was an upward trend in a previously described RB1 loss signature30 in the progression samples from converters, further suggesting E2F1 activation contributes to the lineage switch (Supplementary Fig. 1j). Other highly activated transcription factors in the baseline samples from converters include MYC family members and E2F4. Conversely, TP53—whose loss has been linked to lineage plasticity27,31,32—was predicted to be the most deactivated transcription factor (Fig. 3b).

    Fig. 3: Pathway and master regulator analysis implicate E2F1 in lineage plasticity risk, and a signature of lineage plasticity risk identifies tumors with poor outcomes after androgen receptor signaling inhibitor treatment.
    figure 3

    a Hallmark pathway analysis of activated pathways in baseline samples for the three patients whose tumors converted (underwent lineage plasticity) vs. those patients whose tumors did not upon progression. b Master regulator analysis identifies top activated and deactivated transcription factors between converters and non-converters using the baseline tumor samples. Activity scores (right) and p-values (left, calculated using a gene shuffling test of the enrichment scores) were generated in the VIPER R package26. c Dot plot showing lineage plasticity signature score for patients in this cohort, the International Dream Team dataset described in Abida et al.9 and unique patients not included in this matched biopsy cohort from Alumkal et al.17. d, e Kaplan-Meier survival curves for patients in the Alumkal et al. cohort (d) and Abida et al. cohort (e) stratified by high or low lineage plasticity risk score. p-values shown were determined using the log-rank test. f Dot plot showing lineage plasticity signature score for all castration naïve adenocarcinoma PDX models described by Lin et al.22 Source data are provided as a Source Data file.

    Next, we focused on identifying genes significantly upregulated in the baseline tumors from converters vs. non-converters. We identified a 14-gene signature highly activated in the three baseline tumors from converters (Supplementary Table 3). Genes in this signature include those linked to: the Wnt pathway [RNF4333 and TRABD2A34], the spliceosome [SNRPF35], and the electron transport chain [NDUFA1236 and ATP5B37]. This signature trended downwards in the progression vs. baseline biopsies from the three converters (Supplementary Fig. 2a). These results suggest that this signature is not simply identifying tumor cells that have already undergone lineage plasticity prior to enza treatment. Rather, these genes may be markers of a transition state in cells susceptible to transcriptional conversion and lineage plasticity.

    Dividing the baseline samples between converters and non-converters, we defined a cut off for this 14-gene lineage plasticity risk signature that separated the groups (Fig. 3c). Additional cohorts with matched biopsies before and after enza with lineage plasticity information are lacking. However, we hypothesized that patients whose baseline tumors had high scores for this lineage plasticity risk signature would have worse outcomes. Survival data from the time of ARSI treatment were available for several CRPC cohorts whose tumors had undergone RNA-sequencing—the International Dream Team dataset9 and a prior prospective enza clinical trial led by our group17. Because a subset of the patients in that latter enza clinical trial overlapped with the patients in this current report, we focused only on patients from that clinical trial not represented in this matched biopsy cohort. Using our pre-defined 14-gene signature score cut-off from the matched biopsy cohort, we determined that high scores were associated with worse overall survival from the time of ARSI treatment in both independent datasets (p = 0.076, p = 0.005; Fig. 3d, e). Thus, high expression of the 14-gene lineage plasticity risk signature is linked to poor patient outcomes after ARSI treatment in CRPC. To determine if the lineage plasticity risk signature was activated in primary tumors, we examined the TCGA dataset38. Importantly, only two of 495 patients had high risk scores (Supplementary Fig. 2b). The lower frequency in primary tumors vs. CRPC cohorts suggests that activation of this lineage plasticity risk program may be induced by castration.

    As stated previously, validation datasets with matched biopsies before and after ARSI treatment that include information on lineage at time of progression are lacking. However, previously we determined the impact of surgical castration on adenocarcinoma patient-derived xenografts (PDX)22. Nine PDXs do not undergo castration-induced lineage plasticity, while one PDX—LTL331—does and converts to a resistant version called LTL331R22. Importantly, the patient from whom the LTL331 PDX is derived had evidence of lineage plasticity in his tumor when it became castration-resistant, demonstrating this model’s fidelity22,39. Our lineage plasticity risk signature was highly activated in LTL331 vs. the other hormone-naïve PDXs that do not undergo castration-induced lineage plasticity (Fig. 3f, Supplementary Fig. 2c, d). Indeed, LTL331 was the only PDX whose lineage plasticity risk score was greater than the cut-off defined in our matched biopsy cohort (Fig. 3f). Prior work demonstrates that the exome of LTL331 is strikingly similar to its castration-induced lineage plasticity derivative, strongly suggesting that transdifferentiation—rather than clonal selection—may explain conversion in this tumor22. Finally, the lineage plasticity risk score decreased in LTL331R vs. LTL331 (Supplementary Fig. 2c)—similar to the pattern we observed in the progression vs. baseline samples from converters in our matched biopsy cohort (Supplementary Fig. 2a).

    Identification of transcriptional changes in tumors undergoing lineage plasticity

    Next, we sought to understand changes induced by enza between the baseline and progression samples from the three converters more deeply. The top differentially expressed genes are shown in Fig. 4a. The AR, AR target genes (KLK2, KLK3, and TMPRSS2), and the AR coactivator HOXB13 had markedly decreased expression (Fig. 4a, Supplementary Data 2). In keeping with this, progression biopsies from converters had significantly reduced expression of AR target genes from the ARG10 gene signature27 (Fig. 4b). Though we found that genes from the Beltran NEPC Upregulated signature were increased in progression samples from converters (Supplementary Fig. 1d), it is worth noting that this signature contains both canonical NEPC genes and genes not explicitly associated with acquisition of neuroendocrine features that are AR-repressed. Specifically, examining canonical NEPC markers such as SYP, CHGA, and NCAM1, we found that these genes were not highly upregulated at progression (Supplementary Data 3). Importantly, other genes linked to NEPC (SYT11, CIITA, and ETV5)21 or those normally repressed by the AR (CDCA7L, FRMD3, IKZF3, and TNFAIP2)29 were more highly expressed in the progression biopsies, suggesting that these three converter tumors may be farther along the lineage plasticity spectrum than the previously described non-neuroendocrine DNPC subtype but not as far along as de novo NEPC or NEPC found at rapid autopsy by Labrecque et al.23 that harbor a more complete neuroendocrine program.

    Fig. 4: Gene expression profiling identifies gene expression changes in tumors undergoing enzalutamide-induced lineage plasticity.
    figure 4

    a Volcano plot showing top up and down regulated genes in progression samples vs. baseline samples for the three patients whose tumors converted (n = 3 pairs, no replicates). Adjusted p-values were calculated using the Wald test in the DESeq2 R package55. b ARG10 gene signature heatmap for three converters at baseline and progression. The left shows the expression levels of individual genes in the ARG10 signature, and the right shows the ARG10 signature score. p-value shown is for a two-tailed paired t-test between baseline and progression ARG10 scores (n = 3 pairs, no replicates). c Hallmark pathway analysis shows the top up or down regulated pathways in progression vs. baseline samples for the three patients whose tumors converted. Source data are provided as a Source Data file.

    Pathway analysis between baseline and progression samples from the three converters demonstrated enrichment in several pathways, including: allograft rejection, interferon gamma response, interferon alpha response, and IL6/JAK/STAT signaling (Fig. 4c). Conversely, androgen and estrogen response—both linked to luminal differentiation—were the most downregulated, confirming loss of AR-dependence. We examined differences in gene expression between baseline and progression samples from the 18 patients whose tumors did not undergo lineage plasticity. Several of the pathways activated in the converter tumors were also activated in the non-converters—namely, interferon alpha response, interferon gamma response, and TNF-α signaling (Supplementary Fig. 3). Uniquely upregulated pathways in the converters include: allograft rejection, IL6-JAK-STAT3 signaling, inflammatory response, and complement. Uniquely downregulated pathways in the progression samples from non-converters included: E2F targets, G2M checkpoint, and hedgehog signaling. The only uniquely upregulated pathway in non-converters was protein secretion, while uniquely downregulated pathways included hedgehog signaling, G2M checkpoint, and E2F targets.

    Protein expression analysis demonstrates switch to double negative prostate cancer in samples undergoing lineage plasticity

    To understand the architecture of the tumors from the three converters, we used multiplex immunofluorescence (IF) with three luminal lineage markers (AR, NKX3.1, and HOXB13)—all downregulated at the mRNA level by RNA-sequencing (Fig. 4a)—and the NEPC marker INSM140. LuCaP PDX samples were used as positive and negative controls (Supplementary Fig. 4a). Matched tissue samples for multiplex IF were available for subjects 135 and 210 but not for subject 80 (Fig. 5). We identified one additional WCDT subject (103) with matched biopsies whose tumor underwent rapid clinical progression after enza treatment in the setting of a falling serum PSA—a clinical marker of AR-independence. Matched RNA-sequencing was not available for this subject, but his tumor exhibited evidence of lineage plasticity (Fig. 5). There was a spectrum of AR, NKX3.1, and HOXB13 expression in baseline samples with some cells expressing low levels of each marker, while other cells expressed higher levels. However, at progression, there was a convergence towards population-wide loss of AR, NKX3.1, and HOXB13 in each sample. We did not identify INSM1 upregulation in any of the baseline or progression tumors (Supplementary Fig. 4b). These results match our RNA-sequencing that failed to demonstrate upregulation of other canonical NEPC markers (Supplementary Data 3) and that characterized the three converter samples as DNPC by the Labrecque classifier, rather than NEPC (Fig. 2b).

    Fig. 5: Multiplex immunofluorescence demonstrates switch to double negative prostate cancer in samples undergoing lineage plasticity.
    figure 5

    Patients 135, 210, and an additional West Coast Dream Team patient whose tumor converted, patient 103, were stained for AR, NKX3.1, and HOXB13 expression (n = 6 biologically independent samples with no replicates). The scale bar represents 50 µm. Signal intensity values for each marker are shown with the median value indicated. Signal intensity values were compared between each matched pair using the Mann-Whitney two-tailed test with p < 1 × 10−15 for each comparison except DTB_103 HOXB13, which is p = 1.8 × 10−14. Source data are provided as a Source Data file.

    Conservation of DNA mutations in tumors undergoing lineage plasticity

    Finally, to determine if the progression samples from converters represented distinct clones with unique genetic alterations vs. baseline, we performed DNA mutation and copy number analysis. For subjects 80 and 103, the same tumor lesion was biopsied at baseline and progression. DNA-sequencing of these biopsies showed identical DNA mutations. For subjects 135 and 210, matched metastatic biopsy DNA-sequencing was unavailable. However, cell-free DNA was available. DNA-sequencing of cell-free DNA samples showed that mutations and copy number alterations were conserved between baseline and progression samples (Table 1).

    Table 1 DNA sequencing of matched samples from converters demonstrates conserved alterations

    Loss of the tumor suppressor genes TP53, RB1, and PTEN has been linked to lineage plasticity risk in pre-clinical models31,32. However, we do not know if the presence of these genomic abnormalities in patient tumors is associated with the risk of lineage plasticity to DNPC. One of the three converter patients (subject 80) was found to have an inactivating PTEN mutation and a second patient (subject 103) had RB1 loss, but none were found to have compound TP53/RB1/PTEN loss. When available, we also examined TP53/RB1/PTEN status for tumors from the Abida et al.9 and Alumkal et al.17 cohorts that had high lineage plasticity risk scores. Of the seven high lineage plasticity risk score tumors examined from these two validation cohorts, only two tumors had loss of two or more of the genes TP53, RB1, and PTEN (Supplementary Table 4). DNA-sequencing of matched metastatic biopsies for the cohort as a whole is shown in Supplementary Table 5.

    Categories
    Prostate cancer

    Determination of pharmacokinetics and tissue distribution of a novel lutetium-labeled PSMA-targeted ligand, 177Lu-DOTA-PSMA-GUL, in rats by using LC–MS/MS

    Materials

    1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-PSMA-GUL (99.0%), 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA)-PSMA-GUL (99.3%), and 175LuCl3 (99.9%) were obtained from CellBion (Seoul, Korea). Esomeprazole, sodium acetate, and acetic acid were purchased from Sigma Aldrich Chemical Co. (Milwaukee, WI, USA). Hydrochloric acid was obtained from Samchun Chemical Co., Ltd (Seoul, Korea). High-performance liquid chromatography (HPLC) grade acetonitrile, methanol, and water were purchased from J.T. Baker Co. (Philipsburg, NJ, USA).

    Preparation of 175Lu-DOTA-PSMA-GUL solution

    The drug solution of 175Lu-DOTA-PSMA-GUL was prepared according to the method provided by Cellbion Co. (Seoul, Korea). Briefly, 175Lu-DOTA-PSMA-GUL was synthesized by mixing DOTA-PSMA-GUL solution and 175LuCl3 solution. DOTA-PSMA-GUL solution was prepared by dissolving 50 mg of DOTA-PSMA-GUL powder in 20 mL of 0.5 M sodium acetate buffer (pH 4.5). 175LuCl3 solution was prepared by dissolving 19 mg of 175LuCl3 in 10 mL of 0.04 M HCl. Then, 20 mL of DOTA-PSMA-GUL and 8.5 mL of 175LuCl3 were mixed for 20 min at 40 °C in a shaking water bath. The mixture was finally tested for the purity of 175Lu-DOTA-PSMA-GUL via HPLC–UV method using Waters 2695 separation module coupled with Waters 2487 dual wavelength absorbance detector (Waters, Milford, MA, USA). 175Lu-DOTA-PSMA-GUL was separated on an Agilent Zorbax 300SB-C18 (4.6 × 250 mm i.d., 5 μm, Agilent, Santa Clara, CA, USA) and detected at 244 nm.

    LC–MS/MS analysis condition

    Liquid chromatography-tandem mass spectrometry (LC–MS/MS) analysis was performed by an Agilent 6490 triple-quadrupole mass spectrometer coupled with an Agilent 1260 HPLC (Agilent Technologies, Santa Clara, CA, USA). 175Lu-DOTA-PSMA-GUL in the rat biometrics (plasma, urine, feces, and 12 different tissue samples) was separated on an Agilent Zorbax SB-Aq column (100 × 2.1 mm, i.d., 3.5 μm, Agilent). Chromatographic separations were performed by using a binary gradient mobile phase composed of mobile phase A (1% formic acid in distilled water) and mobile phase B (1% formic acid in methanol). The gradient elution profile and flow rate was set as: 0 min, A:B = 95:5 (v/v), 0.3 mL/min; 8 min, 0:100, 0.3 mL/min; 10 min, 0:100, 0.3 mL/min; 10.01 min, 95:5, 0.5 mL/min; 15 min, 100:0, 0.5 mL/min; 15.01 min, 95:5, 0.3 mL/min; 22 min, 95:5, 0.3 mL/min. The gradient profile was optimized to improve the peak response and achieve rapid wash-out interference and equilibrate the column with the initial mobile phase condition for the next injection. The total run time was 22 min, and the column oven temperature was 40 °C. The sample injection volume was 5 μL.

    The electrospray ionization (ESI) source was operated in positive mode, and the mass spectrometer was operated in the multiple reaction monitoring (MRM) mode. The observed MRM transitions and mass spectrometry settings are summarized in Supplementary Table 1.

    Preparation of stock solutions, calibration standards, and quality control samples

    Stock solutions

    The stock solutions of 175Lu-DOTA-PSMA-GUL were prepared by diluting 2.1 mg/mL synthesized solution in methanol to 400 μg/mL. The stock solutions of NOTA-PSMA-GUL (internal standard 1, IS1) and esomeprazole (internal standard 2, IS2) were prepared by separately dissolving 10 mg of each in 10 mL of methanol (1 mg/mL).

    Calibration standards and quality control samples

    For drug analysis in the plasma, calibration curves were constructed by spiking 50 μL of working stock solutions to blank plasma (50 μL each) to provide 175Lu-DOTA-PSMA-GUL concentrations at 20,000, 10,000, 5000, 1000, 500, 100, 50, and 20 ng/mL. The plasma was spiked with 50 μL of IS1 solution and 150 μL of methanol and mixed on a vortex mixer. The mixture was then centrifuged for 10 min at 4,000 rpm (3220 × g), and 100 μL of the supernatant was transferred to a plastic vial. After 100 μL of distilled water was added to the supernatant, the mixture was vortex-mixed for 10 min and 5 μL of the mixture was injected onto the LC–MS/MS. Quality control (QC) samples were prepared by spiking the working drug solutions to blank rat plasma to provide high concentration QC (16,000 ng/mL), middle concentration QC (8,000 ng/mL), low concentration QC (80 ng/mL) and lower limit of quantification (LLOQ) QC (20 ng/mL).

    Similarly, calibration standards and QC samples were prepared for drug analysis in urine, feces, and twelve different tissues. Calibration ranges were 100–20,000 ng/mL for urine, 100–5000 ng/mL for feces and tissues. High, middle, and low QC sample concentrations were 16,000, 8000, and 400 ng/mL for urine, 4000, 1600, and 400 ng/mL for feces and tissue matrices.

    Sample preparation

    For plasma samples, NOTA-PSMA-GUL (internal standard 1, IS1) solution 50 μL was added to 50 μL of the rat plasma. As a precipitation solvent, 200 μL of methanol was added, and the mixture was mixed on a vortex mixer for 10 min, followed by centrifugation for 10 min at 4000 rpm (3220 × g). After taking 100 μL of the supernatant, 100 μL of distilled water was added, vortex-mixed for 10 min. Finally, 5 μL of the prepared mixture was injected onto the LC–MS/MS. Since several plasma samples showed concentrations above the ULOQ, those samples were diluted 10- or 20-fold for analysis.

    For urine and fecal homogenate samples, the working IS2 solution 50 μL was added to 50 μL of the homogenate samples. The samples were precipitated with methanol (900 μL) on a vortex mixer for 10 min, followed by centrifugation for 10 min at 4000 rpm (3220 × g). After taking 100 μL of the supernatant, 100 μL of distilled water was added, vortex-mixed for 10 min.

    For tissue homogenate samples, the working IS2 solution 50 μL was added to 50 μL of tissue homogenate samples. The samples were precipitated with methanol (400 μL) on a vortex mixer for 10 min, followed by centrifugation for 10 min at 4,000 rpm (3220 × g). After taking 200 μL of the supernatant, 200 μL of distilled water was added to the supernatant and centrifuged for 10 min again. After the second centrifugation, 100 μL of supernatant and the same volume of distilled water was mixed for 10 min. Finally, 5 μL of the prepared mixture was injected onto the LC–MS/MS.

    In vivo pharmacokinetic studies in rats

    Animals

    Male Sprague–Dawley rats (7 weeks, 190–210 g; DBL co., Eumsung, Korea) were kept in plastic cages with free access to a standard diet (Youngbio, Seong-nam, Korea) and water. All experiments were performed in accordance with the relevant guidelines and regulations. The animal study protocol was approved by the Institutional Animal Care and Use Committee of Sungkyunkwan University (SKKUIACUC2018-07–25-1). Studies involving animals are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org).

    Pharmacokinetics of 175Lu-DOTA-PSMA-GUL after I.V. bolus injection

    Freshly prepared 175Lu-DOTA-PSMA-GUL solution (2.1 mg/mL) was administered by i.v. bolus injection via the penile vein (n = 5–7) at three doses of 1, 2, and 5 mg/kg. Approximately 0.3 mL of the jugular venous blood samples were collected at predetermined times after i.v. injection. Plasma samples were harvested by centrifugation of the blood samples at 3220 × g for 10 min. Urine and feces samples were collected at 4, 8, 12, and 24 h after i.v. injection. All samples were stored at -70 °C until analysis.

    Tissue distribution of 175Lu-DOTA-PSMA-GUL after I.V. infusion

    Tissue distribution of 175Lu-DOTA-PSMA-GUL was examined under two different steady-state conditions after i.v. infusion of 175Lu-DOTA-PSMA-GUL. Two target steady-state plasma concentrations (Css) were set as 3000 and 6000 ng/mL. Rats were surgically cannulated with polyethylene tubing (0.58 mm i.d. and 0.96 mm o.d.; Natume, Tokyo, Japan) in the left jugular vein for blood sampling and femoral vein for i.v. injection and infusion. After one day of recovery, 175Lu-DOTA-PSMA-GUL was administered by i.v. injection as a loading dose (LD) and i.v. infusion for 3 h to achieve the target Css. The i.v. bolus LD and i.v. infusion rates (K0) were calculated by LD = Css,target·Vss and K0 = Css,target·CL, respectively24. The volume of distribution (Vss , 0.20 L/kg) and clearance (CL, 607.55 mL/h/kg) of 175Lu-DOTA-PSMA-GUL were obtained from the i.v. injection study. The calculated LD was 0.60 mg/kg and 1.20 mg/kg for the target Css of 3,000 ng/mL and 6,000 ng/mL, respectively. The calculated K0 was 1.82 mg/h/kg and 3.65 mg/h/kg for the target Css of 3,000 ng/mL and 6,000 ng/mL, respectively.

    Blood samples were collected at 1.5, 2, 2.5, and 3 h during i.v. infusion, and centrifuged at 3,200 × g for 10 min. At the end of the infusion, rats were sacrificed, and brain, lung, heart, spleen, small intestine, stomach, kidney, liver, prostate, fat, muscle, and testis were excised and immediately homogenized in normal saline. All samples were stored at -70 °C until analysis.

    Non-compartmental analysis

    The plasma concentration–time data were analyzed by non-compartmental method using Phoenix® WinNonlin® (Pharsight, NC, USA). The fraction of 175Lu-DOTA-PSMA-GUL excreted into urine (Furine) and feces (Ffeces) were calculated by the ratio of the total amount of drug excreted in the urine and feces to the fraction of the dose, respectively. The tissue-to-plasma partition coefficients (KP) were calculated as the tissue-to-plasma concentration ratios.

    Dose proportionality

    Dose proportionality was tested for Cmax, AUCall, and AUCinf based on power model. Assuming the natural logarithm of the pharmacokinetic parameter is linearly related to the natural logarithm of dose as in the following equation: ln(PK parameter) = β0 + β1 × ln(dose), the slope coefficient (β1) and its two-sided 95% confidence intervals (CI) were estimated.

    Statistical analysis

    The data were statistically tested by the unpaired t-test to compare between two means and by one-way analysis of variance (ANOVA) followed by scheffe or games-howell post hoc test. The statistical significance level was set at p < 0.05. All the statistical analyses were performed by using IBM® SPSS® Statistics 26 (IBM, Armonk, NY, USA).

    Categories
    Prostate cancer

    Longer Course of ADT With Postop Radiotherapy Improves Metastasis-Free Survival in Prostate Cancer

    Adding a longer course of androgen deprivation therapy (ADT) to postoperative radiotherapy can prolong metastasis-free survival (MFS) in patients with prostate cancer, according to data presented at ESMO Congress 2022.

    In the phase 3 RADICALS-HD trial, a 2-year course of ADT improved MFS when compared with 6 months of ADT. However, 6 months of ADT did not improve MFS when compared with no ADT.

    Researchers decided to test the efficacy of adding ADT to postoperative radiotherapy because the role of ADT in this setting “is uncertain, and current guidelines are largely silent on the matter,” said study presenter Chris Parker, MD, of the Royal Marsden Hospital NHS Foundation Trust in London, UK. 


    Continue Reading

    The RADICALS-HD trial included 2839 patients with prostate cancer who underwent radiotherapy after radical prostatectomy. Patients were randomly assigned to receive a short course of ADT (6 months), a long course of ADT (24 months), or no ADT. 

    For the comparison between long-course ADT and short-course ADT, 1523 patients were randomly assigned to short-course ADT (n=761) or long-course ADT (n=762). 

    For the comparison between no ADT and short-course ADT, 1480 patients were randomly assigned to short-course ADT (n=747) or no ADT (n=737).

    Dr Parker noted that adverse clinical factors were more common in the long-short ADT comparison than in the short-none comparison.

    Short vs None

    At a median follow-up of 9 years, short-course ADT did not prolong MFS compared with no ADT (hazard ratio [HR], 0.89; 95% CI, 0.69-1.14; P =.35). The 10-year MFS rate was 79% in the no-ADT arm and 80% in the short-course ADT arm.

    Likewise, there was no significant difference in freedom from distant metastases between the short-course and no-ADT groups (HR, 0.82; 95% CI, 0.58-1.15; P =.24). However, short-course ADT significantly delayed the time to salvage hormone therapy (HR, 0.54; 95% CI, 0.42-0.70; P <.0001). 

    Overall survival (OS) was similar between the short-course and no-ADT groups (HR, 0.88; 95% CI, 0.65-1.19; P =.42). The 10-years OS rate was 86% in the no-ADT arm and 85% in the short-course ADT arm.

    Long vs Short

    Long-course ADT significantly improved MFS compared with the shorter course (HR, 0.77; 95% CI, 0.61-0.97; P =.03). The 10-year MFS rate was 78% with long-course ADT and 72% with short-course ADT. 

    A long course of ADT also improved freedom from distant metastases (HR, 0.63; 95% CI, 0.47-0.85; P =.002) and time to salvage hormone therapy (HR, 0.73; 95% CI, 0.59-0.91; P =.005).

    However, OS was similar between the short-course and long-course ADT groups (HR, 0.88; 95% CI, 0.66-1.17; P =.38). The 10-year OS rate was 82% in the short-course group and 85% in the long-course group.

    Disclosures: Some of the study authors declared affiliations with biotech, pharmaceutical, and/or device companies. Please see the original reference for a full list of disclosures.

    Reference

    Parker CC, Clarke N, Cook A, et al. Duration of androgen deprivation therapy (ADT) with post-operative radiotherapy (RT) for prostate cancer: First results of the RADICALS-HD trial (ISRCTN40814031). Presented at ESMO 2022; September 9-13, 2022. Abstract LBA9.

    This article originally appeared on Cancer Therapy Advisor

    Categories
    Prostate cancer

    Characterization of prostate cancer adrenal metastases: dependence upon androgen receptor signaling and steroid hormones

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