Translational studies have shown that CDK12 mutations may delineate an immunoresponsive subgroup of prostate cancer, characterized by high neo-antigen burden. Given that these mutations may define a clinically distinct subgroup, we sought to describe outcomes to standard drugs and checkpoint inhibitors (CPI).

Clinical data from consecutive patients with CDK12 mutations were retrospectively collected from 7 centers. Several clinical-grade sequencing assays were used to assess CDK12 status. Descriptive statistics included PSA50 response rate (≥ 50% decline in prostate-specific antigen from baseline) and clinical/radiographic progression-free survival (PFS).

Of 52 patients with CDK12-mutated prostate cancer, 27 (52%) had detected biallelic CDK12 alterations. At diagnosis, 44 (88%) had Gleason grade group 4-5, 52% had T3-T4, and 14 (27%) had M1 disease. Median follow-up was 8.2 years (95% CI, 5.6 to 11.1 years), and 49 (94%) developed metastatic disease. Median overall survival from metastasis was 3.9 years (95% CI, 3.2 to 8.1 years). Unconfirmed PSA50 response rates to abiraterone and enzalutamide in the first-line castration-resistant prostate cancer setting were 11 of 17 (65%) and 9 of 12 (75%), respectively. Median PFS on first-line abiraterone and enzalutamide was short, at 8.2 months (95% CI, 6.6 to 12.6 months) and 10.6 months (95% CI, 10.2 months to not reached), respectively. Nineteen patients received CPI therapy. PSA50 responses to CPI were noted in 11%, and PFS was short; however, the estimated 9-month PFS was 23%. PFS was higher in chemotherapy-naïve versus chemotherapy-pretreated patients (median PFS: not reached v 2.1 months, P = .004).

CDK12 mutations define an aggressive prostate cancer subgroup, with a high rate of metastases and short overall survival. CPI may be effective in a minority of these patients, and exploratory analysis supports using anti–programmed cell death protein 1 drugs early. Prospective studies testing CPI in this subset of patients with prostate cancer are warranted.

To date, immune checkpoint inhibitors (CPI) have demonstrated limited clinical benefit in patients with unselected metastatic castration-resistant prostate cancer (mCRPC), with responses to the programmed cell death protein 1 pathway inhibitor pembrolizumab documented in < 10% of patients with mCRPC.1-4 One explanation for this lack of clinical efficacy may relate to relatively few tumor-specific antigens (ie, neoantigens) being expressed on prostate cancer cells, which is likely a consequence of the low somatic mutational burden in most prostate cancers.5,6 Consistent with the hypothesis that mutational burden correlates to response to immune checkpoint blockade, pembrolizumab has been shown to be highly active in tumors with mismatch repair (MMR) deficiency or microsatellite instability, molecular features that are associated with a high mutational load.7-9 Indeed, in the subset of patients with prostate cancer with MMR deficiency, responses to pembrolizumab have been reported in upwards of 50% of patients.10,11 Interestingly, some patients with mCRPC seem to respond favorably to CPI even in the absence of MMR deficiency or hypermutation, raising the possibility that other factors, including novel mechanisms leading to tumor neo-antigen formation, may mediate responses to CPI.

CONTEXT
  • Key Objective

  • To provide detailed clinical data to standard therapies as well as immune checkpoint blockade in patients with prostate cancer with CDK12 alterations.

  • Knowledge Generated

  • CDK12-mutated prostate cancers demonstrate an aggressive clinical course, and a minority of men have favorable responses to immune checkpoint blockade.

  • Relevance

  • CDK12 mutations have been proposed as a potential biomarker for response to immune checkpoint blockade, and prospective studies to formally evaluate this are underway. This analysis provides a framework for developing future trials targeting this potentially actionable genomic subgroup.

Recent work conducted through the Stand Up to Cancer-Prostate Cancer Foundation Prostate Cancer International Dream Team has found that CDK12 loss of function mutations delineates a distinct immunoresponsive subset of prostate cancer, which is characterized by increased focal tandem duplications (TD), gene fusions, and a high neo-antigen burden.12,13 Its associated immune phenotype is characterized by T-cell clonal expansion and increased tumor T-cell infiltration. The incidence of CDK12 mutations seems to be enriched in patients with castration-resistant prostate cancer (CRPC) compared with patients with unselected primary prostate cancer (6.9% v 1.2%). However, the incidence of CDK12 mutations in primary prostate specimens from men who subsequently developed CPRC is comparable to that observed in metastatic tissue from men with CPRC, indicating that CDK12 mutations are likely early (ie, truncal) events.14 In the aforementioned Stand Up to Cancer-Prostate Cancer Foundation Prostate Cancer International Dream Team report, 2 out of 4 patients with CDK12 loss-of-function mutations were reported to have responded favorably to CPI.12 On the basis of these findings, studies evaluating immune CPI in men with CDK12-inactivating mutations have been launched (ClinicalTrials.gov identifiers: NCT03570619 and NCT04104893). It is notable that CDK12 mutations also occur across a variety of other malignancies, which raises the possibility that CDK12 alterations could be predictive for immune responses regardless of tumor type.15

Although preclinical work supports CDK12-mutated prostate cancer as being a prime subgroup for deploying precision immuno-oncology strategies, little is known about the clinical outcomes of men with mCRPC who harbor CDK12 loss-of-function alterations. We sought to conduct a multi-institution retrospective study to determine the clinical behavior of CDK12-mutated prostate cancer. Our goal was to evaluate the clinical outcomes of patients harboring CDK12 alterations after the use of standard prostate cancer therapies, as well as the outcomes to off-label immune CPI.

This was a multi-institution, retrospective analysis aimed at understanding the clinical behavior of men with CDK12-mutated prostate cancer. Participating centers included University of Washington (UW), University of Michigan (UM), Duke, City of Hope, Medical College of Wisconsin, University of California San Diego, and Mount Sinai. Consecutive patients with evidence of a pathogenic CDK12 mutation affecting at least 1 allele were included in this study. Appropriate institutional review board approval was obtained at each participating center before data collection.

A variety of clinical-grade sequencing assays was used to determine CKDK12 status; these included Foundation One, Tempus xT, Guardant360, Mi-OncoSeq, and UW-OncoPlex.16-19 Patients with CDK12 alterations that were reported as potentially pathogenic on the clinical report were included, with the exception being uncharacterized missense mutations outside the CDK12 kinase domain and those near splice sites that were not predicted to affect splicing. Tumor DNA analysis was performed on primary tumor samples, cell-free circulating tumor DNA, and/or metastatic tissue. Baseline demographic, pathologic, and clinical characteristics were captured for all patients. We also performed a genomic signature analysis to assess for the presence of TD using previously described methods, and adapted for use on whole-exome (Mi-OncoSeq cases) and targeted cancer gene panel data (UW-OncoPlex cases).20 For the Mi-OncoSeq analysis, we applied TITAN to generate genome-wide allelic copy number profiles with a resolution of approximately 50 kb.21 For the UW OncoPlex analysis, we modified an existing analysis pipeline to generate a genome-wide copy number profile at a resolution of approximately 500 kb using sequencing reads of nonspecifically captured DNA molecules that mapped to off-target genomic regions.22 We analyzed the predicted copy number gains to assess the number, size, and degree of genome-wide TD dispersions using a Nearest Neighbor Index (NNI) metric, which allowed us to distinguish TD clusters within discrete genomic regions versus dispersion throughout the genome.

Our primary objective was to evaluate the efficacy of standard drugs approved by the Food and Drug Administration for the treatment of advanced/metastatic prostate cancer. Several efficacy parameters were calculated, including PSA50 response rate (ie, ≥ 50% decline in prostate-specific antigen [PSA] from baseline), PSA30 response rate (ie, ≥ 30% decline in PSA from baseline), PSA progression-free survival (PFS), and clinical or radiographic PFS (crPFS). Per Prostate Cancer Working Group 3 recommendations, patients were required to have received an agent for a minimum of 8 weeks before assessing for disease progression, with PSA progression defined as a ≥ 25% and ≥ 2 ng/mL increase in PSA from baseline/nadir.23 Given the retrospective nature of these data, confirmatory PSA data were not consistently available. As such, a confirmatory PSA was not required to establish PSA progression, and the reported PSA responses are unconfirmed. Efficacy data were stratified on the basis of whether a given agent was received in the hormone-sensitive or castrate-resistant setting and were also analyzed according to CDK12 allelic status. Drugs used for the treatment of mCRPC were further stratified on the basis of their use as first-line treatment compared with other drugs within the same class (eg, first-line abiraterone indicates this was the first next-generation androgen receptor [AR]-signaling inhibitor used for mCRPC) or beyond and included enzalutamide, abiraterone, and taxane chemotherapy. Secondary objectives were to evaluate efficacy parameters of off-label CPI. We also analyzed the end points of time from initiation of androgen deprivation therapy (ADT) to development of CRPC and overall survival (OS) from the development of metastatic disease and castration resistance.

Statistical analyses of best PSA responses used Fisher’s exact tests, and analyses of survival end points (eg, PSA PFS and crPFS) used Kaplan-Meier estimation and log-rank tests. All statistical analyses were performed using R version 3.6.

Patients

We identified 52 patients with a CDK12 mutation affecting at least 1 allele. Twenty-seven patients (52%) had a second CDK12 alteration affecting the other allele (ie, biallelic inactivation; Data Supplement). Median follow-up for this cohort was 8.2 years (95% CI, 5.6 to 11.1 years), and baseline demographics are presented in Table 1. Forty-nine patients (94%) developed metastatic disease at some point during their care. Forty-five (94%) of 48 patients with sufficient data to determine castration-resistant status have developed CRPC, and the median time from initiating ADT to developing CRPC was 1.4 years (95% CI, 1.1 to 1.8 years). The median OS from the time of metastasis was 3.9 years (95% CI, 3.2 to 8.1 years) and from castration-resistance was 3 years (95% CI, 2.5 to 3.8 years; Fig 1). There were no clear differences in survival when the data were analyzed on the basis of allelic status. The median OS from the time of metastasis was 3.6 years (95% CI, 2.9 years to not reached [NR]) and 3.9 years (95% CI, 3.2 years to NR; P = .6) in men with mono-allelic and biallelic CDK12 mutations, respectively. The median OS from the time of castration resistance was 2.8 years (95% CI, 2.5 years to NR) and 3.4 years (95% CI, 2.3 years to NR; P = .8) in men with mono-allelic and biallelic CDK12 mutations, respectively.

Table

TABLE 1. Patient Demographics and Clinical Characteristics

Response to Standard Therapies

This cohort was heavily pretreated, with 43 patients (83%) having received a next-generation AR-signaling inhibitor (eg, abiraterone and/or enzalutamide) and 34 (65%) having received taxane-based chemotherapy during their course (Table 1). Baseline laboratory and clinical variables before initiating new therapies are presented in the Data Supplement. PSA50 response rates to abiraterone and enzalutamide when used as first-line therapy for CRPC (ie, before any other second-generation AR-signaling inhibitor) were 65% and 75%, respectively. The median PSA PFS and crPFS to abiraterone when used as first-line therapy for CRPC were 8.1 months (95% CI, 3.4 to 10.6 months) and 8.2 months (95% CI, 6.6 to 12.6 months), respectively. The median PSA PFS and crPFS to enzalutamide when used as first-line therapy for CRPC were 9.1 months (95% CI, 4.8 months to NR) and 10.6 months (95% CI, 10.2 months to NR), respectively. Eight patients (24%) treated with abiraterone and 8 patients (22%) treated with enzalutamide had received prior docetaxel. PSA50 response rate for docetaxel used as first-line treatment (ie, before cabazitaxel) for CRPC was 48%. The median PSA PFS and crPFS for docetaxel when used as first-line treatment of CRPC were 4.2 months (95% CI, 3.2 months to NR) and 5.8 months (95% CI, 4.5 to 12.2 months), respectively. Additional PSA response and PFS data are presented in Table 2, Figure 2, and the Data Supplement. There were no clear differences in survival outcomes when results were analyzed on the basis of allelic status (ie, mono-allelic v biallelic mutations; Data Supplement).

Table

TABLE 2. Unconfirmed PSA Declines and PFS to Standard Therapies

Response to Immunotherapy

Overall, 19 patients received an immune CPI at some point. The majority (n = 15) received pembrolizumab monotherapy. Otherwise, the following therapies were received: atezolizumab monotherapy (n = 1), ipilimumab plus nivolumab (n = 1), tremelimumab plus durvalumab (n = 1), and atezolizumab plus radium-223 (n = 1). All of these patients had developed mCRPC, with the notable exception of patient UW11, who received pembrolizumab in the absence of ADT for metastatic hormone-sensitive prostate cancer. Overall, this cohort was heavily pretreated, with 17 (89%) receiving ≥ 2 prior lines of therapy beyond medical/surgical castration and 12 (63%) receiving prior chemotherapy. Eleven (58%) of 19 patients had a decline in PSA after initiation of immunotherapy, with 2 (11%) having a PSA50 response (both with 100% PSA declines). The median crPFS and PSA PFS were 2.7 months (range, 1.9 to NR) and 2.8 months (range, 2.1 to NR), respectively. When these results were analyzed on the basis of CDK12 allelic status, we did not observe any differences in PSA decline or crPFS in those with mono-allelic versus biallelic CDK12 mutations who received CPI. However, there was a small improvement in PSA PFS observed in those with biallelic compared with mono-allelic CDK12 alterations (median OS, 3.4 months [95% CI, 2.7 months to NR] v 1.7 months [95% CI, 1 month to NR]; P = .01).

Patients not previously treated with chemotherapy seemed to have improved clinical outcomes to immune checkpoint blockade. PSA50 response rates were not significantly improved in the chemotherapy-naïve group (29% v 0%, P = .12); however, PSA30 response rates were more frequent (71% v 0%, P = .002). The median PSA PFS in the chemotherapy-naïve group was 8 months (95% CI, 4.2 months to NR) compared with 2.1 months (95% CI, 1.4 months to NR; P = .003) in patients who received prior chemotherapy. The median crPFS was not reached (95% CI, 4.4 months to NR) in chemotherapy-naïve patients, whereas those treated with prior chemotherapy had a median crPFS of 2.1 months (95% CI, 1.7 months to NR; P = .004; Fig 3).

TD Signature Analysis

The genomic hallmark of CDK12 inactivation is the presence of genome-wide focal TD.12,20 Therefore, we sought to evaluate for the presence of TD signature in the subset of patients for whom raw sequencing data were available (ie, the UW and UM cohorts; n = 19). Overall, 15 tumor samples (79%) had evidence of focal TD. Of the 4 patients without evidence of TD, 2 had biallelic CDK12 mutations. Ten patients had data available for TD signature analysis and received a CPI. In this limited subset, there was no obvious association between the presence of a TD signature and response to immunotherapy, nor did we see an association between the NNI (a measure of TD dispersion across the genome) and the response to checkpoint blockade. However, we did observe decreased OS from the time of metastases in patients with evidence for genome-wide TD (ie, an NNI dispersion score above the median), with a median OS of 5.15 months (95% CI, 3.61 months to NR) versus 3.2 months (95% CI, 2.12 months to NR) in those with an NNI score below the median (P = .01; Fig 4).

We sought to describe the clinical behavior of CDK12-mutated advanced prostate cancer, with the goal of providing a clinical framework that can be used to inform the design of precision medicine studies targeting this molecular subgroup. Herein, we report real-world clinical data from 52 patients with CDK12 mutations. On the basis of our dataset, several key conclusions can be made: (1) these patients often present with high-risk features, including Gleason grade group 4-5 (88% of patients), T3-T4 disease (52% of patients), and de novo metastases (29% of patients); (2) although PSA50 response rates to standard therapies were comparable to historical controls, PFS on AR-signaling inhibitors was generally short; (3) responses to immune checkpoint blockade seem to be enriched in less heavily pretreated (ie, chemotherapy-naïve) patients; and (4) the degree of TD dispersion across the genome may correlate with decreased survival.

The median OS in our cohort of CDK12-mutated prostate cancer was 3.9 years from the time of metastasis. Given that only 12 patients (23%) included in this analysis received intensified treatment (eg, docetaxel, abiraterone, enzalutamide) in the hormone-sensitive space, the ADT-alone arm (ie, the control arm) of the CHAARTED trial seems to be an apt comparator to put our data into context.24 In that study, the control group had a median OS of 3.93 years, and although a formal comparison across studies is not appropriate, it is notable that these survival estimates are similar. Focusing on the group that did receive early treatment intensification, median OS was not reached (95% CI, 3.6 years to NR) from the time of metastases, and the 3-year survival estimate was 90%. This is similar to, or perhaps slightly improved, compared with that which has been reported when enzalutamide (ENZAMET study), docetaxel (STAMPEDE and CHAARTED studies), or abiraterone (LATITUDE study) are used in the upfront treatment of metastatic hormone-sensitive prostate cancer.24-27 However, caution should be taken in overinterpreting these results, given that this group is relatively small, and we cannot account for missing follow-up data in this subset.

In a recent paper, Reimers et al28 described the clinical outcomes of men with CDK12-mutated prostate cancer (n = 46) compared with those with other genomic alterations (ie, homologous recombination genes, TP53, and other genes). In contrast to our study, their research did not assess OS outcomes and instead focused on reporting time to metastases in the subset of patients that presented with localized disease (n = 24, CDK12-mutated cohort) and time to castration resistance after the initiation of androgen deprivation. Like us, they found that patients with CDK12-mutated prostate cancer often presented with metastatic disease (44%) and with a Gleason score ≥ 8 (88%). Although univariate analyses did show a decreased time to metastasis and castration resistance for the CDK12-mutated cohort, multivariate analysis did not reveal a significant relationship between CDK12 mutational status and time to metastases or castration resistance. The modest sample size in this study may have limited the researchers’ ability to detect differences between genomic subgroups. Consistent with the aforementioned findings, Antonarakis et al29 found that patients with CDK12 alterations (n = 58) tend to present with aggressive clinical features (eg, young age, high Gleason grade, and de novo metastases). In addition, they reported outcomes to anti–programmed cell death protein 1 therapy in 8 men, with PSA50 responses observed in 3 patients (38%).

As noted earlier in the text, patients with CDK12 alterations have recently gained intense interest on the basis of work showing that these patients may represent an immuno-responsive subgroup.12,13 Overall, we observed a modest PSA50 response rate and PSA PFS/crPFS in men receiving immune CPI. However, it is important to note that the majority of these patients were heavily pretreated and may have not responded well because of a more immune-evasive tumor microenvironment present in heavily pretreated patients.30 Indeed, there was a trend toward improved PSA50 responses in the chemotherapy-naive group, with significantly prolonged PSA PFS and crPFS in these less heavily pretreated patients. These findings are consistent with post hoc analyses showing that the dendritic cell vaccine sipuleucel-t may also be more effective in less heavily pretreated patients or in those with lower disease burden.31,32 Ultimately, prospective studies are needed to determine the effects of immune checkpoint blockade in men with advanced prostate cancer and inactivating CDK12 mutations.

The downstream consequence of CDK12 inactivation is the formation of focal TD, which are thought to lead to immunogenic gene fusions and underlie the biologic rationale for deploying immunotherapies in this subgroup.12,13,20 Therefore, TD signature analysis may provide a functional means of assessing for CDK12 inactivation and provide insights into the relevance of mutational variants of uncertain significance or mono-allelic alterations. Overall, we found that the majority of patients analyzed demonstrated evidence of a TD phenotype, and there was no clear correlation of TD signature with response to immune checkpoint blockade. However, we did find that the degree of TD dispersion across the genome was associated with decreased survival from the time of metastases. It seems biologically plausible that the presence of TD across the entire genome (as opposed to in clustered regions of the genome) could reflect a greater degree of genomic instability and could potentially influence the overall clinical course. Furthermore, some patients harbor a combination of genome-wide patterns, in addition to the TD phenotype, which may constitute another distinct molecular subgroup of CDK12-loss–associated tumors. Given that this was an exploratory analysis in a small subset of patients, these findings need to be confirmed in a larger cohort. Future prospective studies would be well served to evaluate whether TD signatures could provide a more precise metric for assessing CDK12 loss of function and to evaluate this biomarker’s prognostic and predictive significance.

An important limitation of this study is the inclusion of both mono- and biallelic CDK12-mutated patients. Ultimately, we included patients with mono-allelic CDK12 alterations under the assumption that the majority of these patients simply failed to call the “second hit” because of limitations in the sequencing platforms used and/or low tumor content in the specimens analyzed. For example, some of the commercial platforms used do not routinely report on loss of heterozygosity (Data Supplement). This seems plausible given that mutations in biologically important genes (such as CDK12) have a presumably low likelihood of representing benign passenger mutations. Furthermore, when clinical outcomes to standard therapies or immune checkpoint blockade are stratified on the basis of mono- or biallelic CDK12 mutational status, we do not see any major differences in clinical outcomes. Likewise, survival from the time of metastasis and castration resistance were comparable between mono-allelic and biallelic mutated patients.

This study is subject to all of the limitations associated with retrospective analyses, and additional prospective studies to more accurately define the clinical behavior of this subgroup are warranted. However, the cohort reported herein is relatively large given that only approximately 7% of men with CRPC harbor a CDK12 alteration. To put this in context, one would have to screen > 740 patients to identify this many patients prospectively. Until more robust prospective data can be generated, this study should provide a clinical framework for designing future therapeutic trials targeting this aggressive molecular subset of patient.

© 2020 by American Society of Clinical Oncology

See accompanying editorial doi: 10.1200/PO.20.00080 and article doi: 10.1200/PO.19.00399

SUPPORT

Supported by National Cancer Institute (NCI) grant P30 CA015704, Pacific Northwest Prostate Cancer SPORE CA097186, and the Institute for Prostate Cancer Research; by a Prostate Cancer Foundation Young Investigator Award (M.T.S) and Department of Defense Award W81XWH-16-1-0484 (M.T.S.); by NCI grant R50CA221836 (R.G.); and by NCI grant 2 P50 CA186786-06 (A.A.).

Conception and design: Michael T.Schweizer, R. Bruce Montgomery, Ajjai Alva

Financial support: Michael T. Schweizer, Ajjai Alva

Administrative support: Michael T. Schweizer, Ajjai Alva

Provision of study material or patients: Michael T. Schweizer, Pankaj Vats, Deepak Kilari, William K. Oh, Colin C. Pritchard, Andrew J. Armstrong, R. Bruce Montgomery, Ajjai Alva

Collection and assembly of data: Michael T. Schweizer, Gavin Ha, Landon C. Brown, Tanya Dorff, Jonathan Reichel, Deepak Kilari, Vaibhav Patel, William K. Oh, Colin C. Pritchard, Andrew J. Armstrong, R. Bruce Montgomery, Ajjai Alva

Data analysis and interpretation: Michael T. Schweizer, Gavin Ha, Roman Gulati, Rana R. McKay, Tanya Dorff, Anna C.H. Hoge, Pankaj Vats, Deepak Kilari, William K. Oh, Arul Chinnaiyan, Colin C. Pritchard, Andrew J. Armstrong, R. Bruce Montgomery, Ajjai Alva

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

Michael T. Schweizer

Consulting or Advisory Role: Janssen

Research Funding: Janssen (Inst), AstraZeneca (Inst), Roche (Inst), Pfizer (Inst), Zenith Epigenetics (Inst), Madison Vaccines (Inst), Immunomedics (Inst)

Gavin Ha

Patents, Royalties, Other Intellectual Property: Methods for genome characterization (US20190078232A1) patent application status is pending

Rana R. McKay

Consulting or Advisory Role: Janssen, Novartis, Tempus, Exelixis, Pfizer, Bristol-Myers Squibb, Astellas Medivation, Dendreon

Research Funding: Pfizer (Inst), Bayer (Inst)

Tanya Dorff

Consulting or Advisory Role: Bayer, Janssen Oncology, AstraZeneca, Roche, Seattle Genetics, Noxopharm, Bristol-Myers Squibb

Speakers' Bureau: Exelixis, Prometheus Laboratories

Research Funding: Bristol-Myers Squibb, Bayer

Deepak Kilari

Honoraria: Exelixis

Consulting or Advisory Role: Exelixis, Sanofi

Speakers' Bureau: Janssen, Exelixis, Genzyme (Inst), Genzyme

Research Funding: Astellas Pharma (Inst)

Travel, Accommodations, Expenses: Sanofi, Exelixis, Janssen, Bayer, Clovis Oncology

William K. Oh

Leadership: CheckPoint Sciences

Stock and Other Ownership Interests: Bellicum Pharmaceuticals

Consulting or Advisory Role: Sanofi, Janssen, AstraZeneca, Amgen, Bayer, TYME, CheckPoint Sciences, Sema4, Huya Biosciences, TeneoBio

Research Funding: Sotio (Inst), Constellation Pharmaceuticals

Arul Chinnaiyan

Stock and Other Ownership Interests: Oncopia, Tempus, Esanik, OncoFusion Therapeutics, Medsyn

Consulting or Advisory Role: Tempus

Patents, Royalties, Other Intellectual Property: University of Michigan royalties

Colin C. Pritchard

Consulting or Advisory Role: Promega

Andrew J. Armstrong

Honoraria: Dendreon, Janssen Oncology

Consulting or Advisory Role: Bayer, Sanofi, Dendreon, Medivation, Janssen Biotech, Pfizer, Astellas Scientific and Medical Affairs, Clovis Oncology, AstraZeneca

Speakers' Bureau: Dendreon, Bayer

Research Funding: Dendreon (Inst), Sanofi (Inst), Bayer (Inst), Pfizer (Inst), Novartis (Inst), Janssen Oncology (Inst), Medivation (Inst), Astellas Pharma (Inst), Gilead Sciences (Inst), Roche/Genentech (Inst), Active Biotech (Inst), Bristol-Myers Squibb (Inst), Constellation Pharmaceuticals (Inst), Merck (Inst)

Patents, Royalties, Other Intellectual Property: Circulating tumor cell novel capture technology (Inst)

Travel, Accommodations, Expenses: Dendreon, Janssen Biotech, Bayer, Astellas Scientific and Medical Affairs

R. Bruce Montgomery

Research Funding: AstraZeneca (Inst), Janssen Oncology (Inst), Clovis (Inst), Astellas Pharma (Inst), Beigene (Inst)

Ajjai Alva

Consulting or Advisory Role: AstraZeneca, Merck, Pfizer, Bristol-Myers Squibb

Speakers' Bureau: AstraZeneca

Research Funding: Genentech (Inst), Bristol-Myers Squibb (Inst), Merck Sharp & Dohme (Inst), Prometheus Laboratories (Inst), Mirati Therapeutics (Inst), AstraZeneca (Inst), Roche (Inst), Bayer (Inst), Progenics (Inst), Astellas Pharma (Inst), Arcus Biosciences (Inst), Harpoon Therapeutics (Inst), Progenics (Inst), Celgene (Inst), Janssen (Inst)

Travel, Accommodations, Expenses: Merck, BMS

No other potential conflicts of interest were reported.

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ARTICLE CITATION

DOI: 10.1200/PO.19.00383 JCO Precision Oncology no. 4 (2020) 382-392. Published online April 21, 2020.

PMID: 32671317

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