Purpose:

Cyclin-dependent kinase 12 (CDK12) aberrations have been reported as a biomarker of response to immunotherapy for metastatic castration-resistant prostate cancer (mCRPC). Herein, we characterize CDK12-mutated mCRPC, presenting clinical, genomic, and tumor-infiltrating lymphocyte (TIL) data.

Experimental Design:

Patients with mCRPC consented to the molecular analyses of diagnostic and mCRPC biopsies. Genomic analyses involved targeted next-generation (MiSeq; Illumina) and exome sequencing (NovaSeq; Illumina). TILs were assessed by validated immunocytochemistry coupled with deep learning–based artificial intelligence analyses including multiplex immunofluorescence assays for CD4, CD8, and FOXP3 evaluating TIL subsets. The control group comprised a randomly selected mCRPC cohort with sequencing and clinical data available.

Results:

Biopsies from 913 patients underwent targeted sequencing between February 2015 and October 2019. Forty-three patients (4.7%) had tumors with CDK12 alterations. CDK12-altered cancers had distinctive features, with some revealing high chromosomal break numbers in exome sequencing. Biallelic CDK12-aberrant mCRPCs had shorter overall survival from diagnosis than controls [5.1 years (95% confidence interval (CI), 4.0–7.9) vs. 6.4 years (95% CI, 5.7–7.8); hazard ratio (HR), 1.65 (95% CI, 1.07–2.53); P = 0.02]. Median intratumoral CD3+ cell density was higher in CDK12 cancers, although this was not statistically significant (203.7 vs. 86.7 cells/mm2; P = 0.07). This infiltrate primarily comprised of CD4+FOXP3 cells (50.5 vs. 6.2 cells/mm2; P < 0.0001), where high counts tended to be associated with worse survival from diagnosis (HR, 1.64; 95% CI, 0.95–2.84; P = 0.077) in the overall population.

Conclusions:

CDK12-altered mCRPCs have worse prognosis, with these tumors surprisingly being primarily enriched for CD4+FOXP3 cells that seem to associate with worse outcome and may be immunosuppressive.

See related commentary by Lotan and Antonarakis, p. 380

Translational Relevance

Prostate cancers harboring cyclin-dependent kinase 12 alterations have a worse prognosis and are enriched primarily in CD4+FOXP3 tumor-infiltrating lymphocytes that associate with a worse prognosis.

Cyclin-dependent kinase 12 (CDK12) is a transcription-associated CDK, which forms a heterodimeric complex with cyclin K (CycK), implicated in DNA repair (DDR), splicing, and differentiation (1–6). CDK12 maps on chromosome 17q12 RNA and generates two mRNA splice isoforms, differing only in the last exon, with both having a central kinase region flanked by large N- and C-terminal extensions with arginine-serine–rich (RS) domains (3). Recent genomic analyses of primary (7–11) and metastatic castration-resistant prostate cancer (mCRPC; refs. 11–15) have identified recurrent, deleterious CDK12 alterations in 2%–4% of primary prostate cancers (9, 11) and in 4.7%–11% of mCRPCs (11, 14, 15). Integrative genomic analysis of 360 mCRPC samples has demonstrated that CDK12-mutated prostate cancer is genetically, transcriptionally, and phenotypically distinct from tumors with homologous recombination repair defects and mismatch repair deficiency (MMRd; ref. 16), being reported to have innumerable tandem duplications (TD) and genomic rearrangements, high neoantigen burdens, and increased tumor-infiltrating lymphocytes (TIL; ref. 16). This is postulated to confer a vulnerability to immunotherapy similar to cancers with MMRd (16, 17), with this genomic profile probably also acquiring immune cell evasive strategies to allow tumor growth (18–20).

Understanding the clinical, molecular, and immune characteristics of these tumors is critically important for the development of successful clinical therapeutic strategies for this disease subclass (20–22). Herein, we characterize CDK12-mutated prostate cancer, with both biallelic and monoallelic aberrations, describing their genomic, clinical, histopathologic, and immune infiltrate features.

Patient population

This retrospective study included patients with mCRPC who consented for molecular characterization of their prostate cancer biopsies at The Institute of Cancer Research (Sutton, London, United Kingdom). Consent was obtained either within a Royal Marsden Hospital (Sutton, London, United Kingdom)-specific protocol (Ethics Review Committee reference no. 04/Q0801/60) and/or the phase II trial TOPARP-B (ClinicalTrials.gov, NCT01682772). These studies were conducted in accordance with the Declaration of Helsinki. Both studies allowed acquisition, whenever possible, of a mCRPC biopsy and the diagnostic hormone-sensitive prostate cancer (HSPC) biopsy.

Demographic and clinical data for each patient were collected from the hospital electronic patient record or the clinical trial database. Clinical outcomes included overall survival (OS) from diagnosis, OS from castration resistance, and time to CRPC, respectively, defined as the time between diagnosis of prostate cancer and death or last follow-up, between castration resistance and death or last follow-up, and between diagnosis and castration resistance. Furthermore, time-on-treatment with abiraterone/enzalutamide (whichever came first) was investigated separately before and after treatment with docetaxel.

Tumor tissue samples

Formalin-fixed, paraffin-embedded (FFPE) prostate cancer tissue was obtained from prostate needle biopsies, transurethral resections of the prostate, prostatectomies, or prostate cancer metastases within bone (bone marrow trephine), lymph node, soft tissue, or visceral metastases (needle biopsies). A subset of patients had matching, same patient, primary tissue and CRPC biopsies available. All tissue blocks were freshly sectioned, and tumor content was confirmed by examination of hematoxylin and eosin–stained samples sections by a trained pathologist (B. Gurel).

Next-generation sequencing

Targeted next-generation sequencing (NGS) was performed on diagnostic and mCRPC samples. DNA was extracted from FFPE blocks positive for tumor content using the FFPE Tissue DNA Kit (Qiagen) and quantified with the Quant-iT High-sensitivity PicoGreen Double-stranded DNA Assay Kit (Invitrogen). The Illumina FFPE QC Kit (WG-321-1001) was used for DNA quality control tests according to the manufacturer's protocol. Libraries for targeted NGS were constructed from 40 ng of DNA using a customized panel (GeneRead DNAseq Mix-n-Match Panel v2; Qiagen) covering 6,025 amplicons across 113 genes. Libraries were run using the MiSeq Sequencer (Illumina). FASTQ files were generated using the Illumina MiSeq Reporter v2.5.1.3. Sequence alignment and mutation calling were performed using BWA tools and the Genome Analysis Toolkit (GATK) variant annotator by the Qiagen GeneRead Targeted Exon Enrichment Panel Data Analysis web portal. Biallelic alterations in CDK12 were prospectively defined as (i) a deleterious mutation with LOH at the wild-type allele, (ii) copy-number loss (homozygous deletion), and (iii) ≥2 CDK12 deleterious genomic alterations in a given sample. Mutations with >5% allele frequency were reported.

Exome sequencing and copy-number break analysis

Libraries for whole-exome sequencing were performed using Kapa Hyper Plus Library Prep Kits and the Agilent SureSelectXT V6 Target Enrichment Kit. Paired-end sequencing was performed using the NovaSeq 6000 S2 Flow Cell (2 × 100 cycles, Illumina). FASTQ files were generated from the sequencer's output using Illumina bcl2fastq2 Software (v.2.17.1.14, Illumina) with the default chastity filter to select sequence reads for subsequent analyses. All reads were aligned to the human genome reference sequence (GRCh37-hg19) using the BWA-MEM algorithm (v. 0.7.12). Picard tools (v.2.8.2) were used to mark PCR duplicates and to calculate sequencing metrics for quality control check. The GATK (v. 3.5-0) was applied to realign local insertions/deletions (indels), recalibrate base scores, and identify point mutations, small insertions, and deletions. Somatic point mutations and indels were called using MuTect2 by comparing tumor DNA with germline DNA control. Copy-number estimation was obtained through a modified ASCAT2 package. Copy Number Alterations (CNA) segmentation file was used to define the CNA breakpoint when there was a change of segment and/or CNA.

Immunohistochemistry (IHC)

Three-micron-thick sections from FFPE tissue blocks were obtained using a microtome, floated in a water bath, and mounted in Superfrost slides. Slides were baked and dried overnight at 37°C. CD3 IHC was performed using a rabbit anti-CD3 antibody (#A0452; polyclonal; Dako; Agilent Technologies) and an automated Staining Platform (Bond-RX, Leica Microsystems). Antigen retrieval was achieved by using Bond Epitope Retrieval Solution 1 (#AR9961, Leica Biosystems) for 30 minutes prior to incubation with anti-CD3 antibody (1:150 dilution) for 15 minutes at room temperature. The reaction was visualized using the Bond Polymer Refine Kit (#DS9800, Leica Biosystems). Antibody specificity was confirmed in human appendix and prostate tissues (positive controls) and LNCaP cell pellets (negative control).

PTEN and ATM protein expression studies were conducted as described previously (23, 24).

Nuclear staining (and cytoplasmic staining for PTEN) was semiquantitatively assessed using an H-score (3× % of strongly stained cells and 2× the % of moderately stained cells, and the % of weakly stained cells, for a range of 0–300). Cases were considered as PTEN or ATM loss if they had an H-score ≤ 10.

Slide digitization and computer-assisted image analysis for IHC

CD3 IHC-stained slides were scanned at high resolution (200×) using the ZEISS Axio Scan.Z1 Digital Slide Scanner (Carl Zeiss AG). The digitized slides were then analyzed with the HALO image analysis suite (Indica Labs). A supervised machine learning algorithm was trained to recognize prostate cancer foci and surrounding stroma. Color deconvolution for DAB and hematoxylin stains was performed, cell recognition and nuclear segmentation were optimized for hematoxylin stain, and recognition of CD3 staining was optimized for the membranous and cytoplasmic compartments. The analysis algorithm was adjusted to provide optical density data for intensity of CD3 membranous and cytoplasmic staining in the automatically annotated tumor and stromal regions. A threshold was set to categorize each detected cell into CD3 positive or negative. The number of intratumoral and stromal CD3-positive cells was then divided by the total area of prostate tumor and stroma, respectively, providing intratumoral and stromal CD3 density values (CD3+/mm²) for each sample. Each stained sample was also visually assessed and assigned an intratumoral and stromal chronic inflammation score, using a modified version of the histopathologic classification system developed by Nickel and colleagues (25).

Multiplex immunofluorescence assay

Intratumoral TILs were determined by immunofluorescence (IF) for T-cell subpopulations as described previously (26). Briefly, multiplex IF staining was performed on 3-μm-thick FFPE tissue sections using an automated staining platform (BOND RX, Leica Microsystems). Antigen retrieval was achieved using BOND Epitope Retrieval Solution 2 (#AR9640, Leica Microsystems). Endogenous peroxidase was inactivated in 3% H2O2. Tissue sections were incubated with antibodies against CD4 (#ab133616, clone EPR6855, 1:100, Abcam) and CD8 (#M7103, clone C8/144B, 1:200, Dako, Agilent Technologies), and detected with Alexa Fluor 555–conjugated IgG (H+L) goat anti-rabbit (#A21429, Invitrogen) and Alexa Fluor 488–conjugated IgG (H+L) goat anti-mouse (#A-11029, Invitrogen) secondary antibodies, respectively. Endogenous biotin was blocked with Avidin/Biotin Blocking Kit according to the manufacturer's protocol (#ab64212, Abcam). Next, tissue sections were incubated with antibodies against FOXP3 conjugated to biotin (#13-4777-82, clone 236A/E7, 1:100, eBioscience) and pan-cytokeratin (PanCK) conjugated to Alexa Fluor 647 (#4528S, clone C11, 1:100, Cell Signaling Technology), followed by streptavidin peroxidase (horseradish peroxidase) (#K5001, Dako, Agilent Technologies) and TSA Coumarin Detection System (#NEL703001KT, Akoya Biosciences). Nuclei were counterstained with DRAQ 7 (#DR71000, BioStatus) and tissue sections were mounted with ProLong Gold Antifade Reagent (#P36930, Molecular Probes).

Slides were scanned using the Vectra Automated Multispectral Imaging System (Akoya Biosciences) and analyzed using inForm v2.2.1 (Akoya Biosciences). Tissue and nuclear cell segmentation were performed using methods described previously (1). TIL phenotype determination was based on staining for CD8, CD4, and FOXP3, and were separated into bins as follows: CD8+, CD4+FOXP3+, and CD4+FOXP3 T cells. Quantification of the immune cell densities are presented as number of cells per mm2.

Statistical analysis

In this analysis, the prevalence of CDK12 aberrations observed in our cohort was reported. In a subset of patients with available clinical (n = 36, 28 biallelic and eight monoallelic) and TIL data (n = 24, 17 biallelic and seven monoallelic), tumors with CDK12 aberrations were compared with a control group that comprised a random selection of patients concurrently sequenced at our institution without such aberrations or mismatch repair alterations. Comparison of lymphocyte infiltration between CDK12 biallelic cases, CDK12 monoallelic cases, and controls was performed using a t test after density per mm2 was log10 transformed, whereas Wilcoxon rank-sum test assessed differences between diagnostic and mCRPC samples in CDK12 cases. Wilcoxon rank-sum test was also used for copy-number breaks (CNB) comparison between CDK12 biallelic cases, CDK12 monoallelic cases, and controls. Kaplan–Meier estimator and Cox regression models were used for time-to-event analyses comparing controls and biallelic CDK12 cases only. In a sensitivity analysis, all time-to-event analyses were repeated with mono- and biallelic CDK12 cases being pooled into one group and compared with the control group. The significance level was set to 5% for all analyses and there was no multiplicity adjustment made as to the exploratory nature of this work. Analyses were carried out with R version 3.6.2.

CDK12-altered prostate cancers

Between March 2015 and October 2019, 913 patients with prostate cancer were analyzed by targeted NGS after passing quality control. All patients had a histologic diagnosis of prostate cancer and either diagnostic tissue and/or a mCRPC biopsy sample available for analysis; the patient characteristics and their treatment histories are described in Table 1 and Supplementary Table S1. We identified 43 patients with potentially pathogenic alterations of CDK12 (4.7%), of which 31 (3.4%) had biallelic genomic aberrations. One tumor had a concomitant MSH6 somatic mutation with associated MSH6 loss of expression on IHC and was not included in subsequent analyses, being deemed to be an MMRd prostate cancer. The remaining 11 patients were considered to have monoallelic CDK12 alterations (Supplementary Fig. S1).

Table 1.

Patients characteristics.

OverallControlMonoallelicBiallelicP
n 180 144 28  
Age at diagnosis [median (IQR)] 61.0 (56.0–64.2) 61.0 (55.8–64.0) 59.0 (55.0–63.5) 62.5 (60.0–65.0) 0.401a 
T group (%)     0.281b 
T1 2 (1.1) 1 (0.7) 1 (12.5) 0 (0.0)  
T2 20 (11.1) 18 (12.5) 0 (0.0) 2 (7.1)  
T3 76 (42.2) 57 (39.6) 4 (50.0) 15 (53.6)  
T4 34 (18.9) 26 (18.1) 2 (25.0) 6 (21.4)  
TX 48 (26.7) 42 (29.2) 1 (12.5) 5 (17.9)  
T3/4 = yes (%) 110 (83.3) 83 (81.4) 6 (85.7) 21 (91.3) 0.623b 
N (%)     0.197b 
N0 42 (36.8) 28 (33.3) 5 (83.3) 9 (37.5)  
N1 65 (57.0) 51 (60.7) 1 (16.7) 13 (54.2)  
N2 7 (6.1) 5 (6.0) 0 (0.0) 2 (8.3)  
N1/2 = yes (%) 72 (63.2) 56 (66.7) 1 (16.7) 15 (62.5) 0.059b 
M = M1 (%) 96 (56.5) 76 (56.3) 2 (28.6) 18 (64.3) 0.231b 
Total Gleason score (G) [median (IQR)] 9.0 (7.0–9.0) 8.0 (7.0–9.0) 9.0 (7.8–9.0) 9.0 (9.0–9.0) <0.001a 
G ≤ 7 vs. G > 8 = yes (%) 117 (72.2) 84 (66.1) 6 (75.0) 27 (100.0) <0.001b 
PSA at diagnosis mcg/L [median (IQR)] 63.0 (15.4–176.1) 53.5 (10.9–162.0) 106.0 (34.5–137.0) 72.0 (25.5–229.2) 0.449a 
Prostatectomy Y/N = yes (%) 22 (12.2) 18 (12.5) 2 (25.0) 2 (7.1) 0.318b 
Radiotherapy Y/N = yes (%) 67 (37.2) 57 (39.6) 2 (25.0) 8 (28.6) 0.450b 
OverallControlMonoallelicBiallelicP
n 180 144 28  
Age at diagnosis [median (IQR)] 61.0 (56.0–64.2) 61.0 (55.8–64.0) 59.0 (55.0–63.5) 62.5 (60.0–65.0) 0.401a 
T group (%)     0.281b 
T1 2 (1.1) 1 (0.7) 1 (12.5) 0 (0.0)  
T2 20 (11.1) 18 (12.5) 0 (0.0) 2 (7.1)  
T3 76 (42.2) 57 (39.6) 4 (50.0) 15 (53.6)  
T4 34 (18.9) 26 (18.1) 2 (25.0) 6 (21.4)  
TX 48 (26.7) 42 (29.2) 1 (12.5) 5 (17.9)  
T3/4 = yes (%) 110 (83.3) 83 (81.4) 6 (85.7) 21 (91.3) 0.623b 
N (%)     0.197b 
N0 42 (36.8) 28 (33.3) 5 (83.3) 9 (37.5)  
N1 65 (57.0) 51 (60.7) 1 (16.7) 13 (54.2)  
N2 7 (6.1) 5 (6.0) 0 (0.0) 2 (8.3)  
N1/2 = yes (%) 72 (63.2) 56 (66.7) 1 (16.7) 15 (62.5) 0.059b 
M = M1 (%) 96 (56.5) 76 (56.3) 2 (28.6) 18 (64.3) 0.231b 
Total Gleason score (G) [median (IQR)] 9.0 (7.0–9.0) 8.0 (7.0–9.0) 9.0 (7.8–9.0) 9.0 (9.0–9.0) <0.001a 
G ≤ 7 vs. G > 8 = yes (%) 117 (72.2) 84 (66.1) 6 (75.0) 27 (100.0) <0.001b 
PSA at diagnosis mcg/L [median (IQR)] 63.0 (15.4–176.1) 53.5 (10.9–162.0) 106.0 (34.5–137.0) 72.0 (25.5–229.2) 0.449a 
Prostatectomy Y/N = yes (%) 22 (12.2) 18 (12.5) 2 (25.0) 2 (7.1) 0.318b 
Radiotherapy Y/N = yes (%) 67 (37.2) 57 (39.6) 2 (25.0) 8 (28.6) 0.450b 

Abbreviations: IQR, interquartile range T size and extent of the main tumor; M, presence of metastatic disease; N, presence of nodal disease.

aKruskal–Wallis test.

bFisher exact test.

For the 31 biallelic cases identified, a total of 34 diagnostic and mCRPC samples were sequenced by NGS; 3 patients had matched primary and metastatic samples available for analyses, which were concordant with the presence of the same CDK12 genomic alterations. The biallelic group comprised of 11 missense alterations, 13 nonsense alterations, 32 frameshift mutations or indels, and two splice site mutations. We discovered a CDK12 deep deletion on copy-number analysis in one case (Fig. 1A; Supplementary Table S2). The majority of these aberrations were truncating mutations, predominantly located in the region between the amino terminus and the kinase domain or within the kinase domain, whereas the missense mutations were predominantly clustered in the CDK12 kinase domain. Two patients had three CDK12 aberrations, including V463G, one of the missense mutations not located in the kinase domain (lollipop plot shown in Fig. 1A).

Figure 1.

A, Lollipop plot depicting mutations in CDK12 in this prostate cancer cohort with biallelic alterations (n = 31); missense mutations are depicted in green and frameshift and nonsense mutations in black. B, OncoPrint figure representing CDK12 mutations for the biallelic tumors in HSPC and mCRPC samples (top). Concomitant alterations in other DDR genes and other relevant pathways in prostate cancer, detected on targeted and exome sequencing in diagnostic and/or mCRPC samples (middle). The available IHC data for PTEN and ATM for the samples sequenced (bottom). PTEN and ATM data were, respectively, available for 16 and 17 samples of the 31 CDK12 biallelic cases. Gray boxes indicate sample with no available PTEN or ATM results by IHC.

Figure 1.

A, Lollipop plot depicting mutations in CDK12 in this prostate cancer cohort with biallelic alterations (n = 31); missense mutations are depicted in green and frameshift and nonsense mutations in black. B, OncoPrint figure representing CDK12 mutations for the biallelic tumors in HSPC and mCRPC samples (top). Concomitant alterations in other DDR genes and other relevant pathways in prostate cancer, detected on targeted and exome sequencing in diagnostic and/or mCRPC samples (middle). The available IHC data for PTEN and ATM for the samples sequenced (bottom). PTEN and ATM data were, respectively, available for 16 and 17 samples of the 31 CDK12 biallelic cases. Gray boxes indicate sample with no available PTEN or ATM results by IHC.

Close modal

Beyond the MMRd cancer with a concomitant CDK12 deleterious alteration, we identified multiple prostate cancers with biallelic CDK12 deleterious alterations contemporaneously harboring other DDR defects (Fig. 1B). The prostate cancer with CDK12 deep deletion also had an ATM alteration and ATM loss detected by IHC. Other samples presented with pathogenic PALB2 alteration (I265Tfs*12 in diagnostic and CRPC biopsies), BRCA2 (S744*) and CHEK2 (L464fs*16) mutations, FANCA deep deletion, and FANCL frameshift mutation (T372Ifs*2). Other alterations in CDK12 biallelic tumors included PTEN mutations, the androgen receptor (AR) L702H point mutation, PIK3CA activating mutation (E545K), and SPOP alterations (Supplementary Table S2). We had IHC data for PTEN and ATM available, respectively, for 16 and 17 of the 31 CDK12 biallelic cases. Interestingly, none of the cancers with biallelic CDK12 alterations had PTEN loss by IHC; conversely, four of the biallelic CDK12 tumors (4/17, 23.5%) had ATM loss, detected by IHC.

Clinical outcomes of prostate cancer with CDK12 alterations

Clinical data were available for 36 of the 42 patients with CDK12 aberrations (28 biallelic and eight monoallelic) and were compared with the data of 144 controls. Patient characteristics and treatment histories are described in Table 1 and Supplementary Table S1. The two groups were balanced for age, staging, and presence of metastatic disease, as well as for PSA levels and initial treatment received. However, CDK12 tumors had higher Gleason scores at diagnosis (P < 0.001). Biallelic CDK12 cases experienced worse survival than the control group with a median OS from diagnosis, 6.4 years [95% confidence interval (CI), 5.7–7.8] for the control group and 5.1 years (95% CI, 4.0–7.9) for the biallelic CDK12 group (HR, 1.65; 95% CI, 1.07–2.53; P = 0.02). Similar results were obtained when monoallelic and biallelic cases were combined into one group and compared with controls (HR, 1.50; 95% CI, 1.02–2.20; P = 0.04; Fig. 2A and B) and when OS was estimated from the date of CRPC (Fig. 2C and D). However, in the multivariable analysis, CDK12 mutational status was no longer independently prognostic (HR, 0.94; 95% CI, 0.6–1.6; P = 0.81; Supplementary Table S3). Median time to CRPC diagnosis was not different between biallelic and control cohorts, being 1.9 years (95% CI, 1.4–2.6) in the control group and 2.2 years (95% CI, 1.5–2.7) in the biallelic CDK12 group (HR, 1.25; 95% CI, 0.82–1.91; P = 0.3; Supplementary Fig. S2A). Almost all patients had received docetaxel chemotherapy and abiraterone/enzalutamide; 61% of patients in the control group received cabazitaxel, whereas only 36% patients in the combined mono/biallelic CDK12 group received cabazitaxel (Supplementary Table S1). There was no statistically significant difference in time on abiraterone/enzalutamide treatment between the groups, with median duration of treatment in the pre-docetaxel setting being 7.6 months (95% CI, 6.0–9.3) in the control group and 8.2 months (95% CI, 4.7, NA) in the biallelic CDK12 group (HR, 1.07; 95% CI, 0.58–2.11; P = 0.75), with similar findings in the post-docetaxel setting (Supplementary Fig. S2B and S2C) and when monoallelic and biallelic cases were combined into one group (data not shown).

Figure 2.

Survival curves. A, Kaplan–Meier curves for OS from date of diagnosis. CDK12 biallelic-mutated cases in yellow and controls in blue. B, Kaplan–Meier curves for OS from date of diagnosis. CDK12 biallelic- and monoallelic-mutated cases in yellow and controls in blue. C, Kaplan–Meier curves for OS from date of CRPC diagnosis. CDK12 biallelic-mutated cases in yellow and controls in blue. D, Kaplan–Meier curves for OS from date of CRPC diagnosis. CDK12 biallelic- and monoallelic-mutated cases in yellow and controls in blue.

Figure 2.

Survival curves. A, Kaplan–Meier curves for OS from date of diagnosis. CDK12 biallelic-mutated cases in yellow and controls in blue. B, Kaplan–Meier curves for OS from date of diagnosis. CDK12 biallelic- and monoallelic-mutated cases in yellow and controls in blue. C, Kaplan–Meier curves for OS from date of CRPC diagnosis. CDK12 biallelic-mutated cases in yellow and controls in blue. D, Kaplan–Meier curves for OS from date of CRPC diagnosis. CDK12 biallelic- and monoallelic-mutated cases in yellow and controls in blue.

Close modal

Exome sequencing data

Overall, of the 913 patients with prostate cancer evaluated by targeted NGS, 211 patients (diagnostic = 93 and CRPC = 118) had samples suitable for exome sequencing including 26 samples harboring CDK12 genomic aberrations, 23 of which were biallelic and three monoallelic. Although 73% of the CDK12-aberrant cases (19/26) had CNB values higher than the median, which was 513 (Q1, 405.5 and Q3, 617.5), only 10 of the 26 (38.5%) CDK12-aberrant cases were above the CNB top quartile (Fig. 3A). In general, however, CDK12 tumors had significantly higher CNBs than the controls (P = 0.01; Fig. 3B).

Figure 3.

Genomic analyses. A, CNBs determined from exome sequencing data in n = 211 patients. CDK12 biallelic mutation cases (n = 23) in red, monoallelic cases (n = 3) in yellow, and cancers with no CDK12 alterations (controls) in blue. The black line indicates the median number of CNBs (median = 513) in the whole cohort. B, Violin plot depicting CNB numbers in CDK12-mutated cases (red) and in controls (blue).

Figure 3.

Genomic analyses. A, CNBs determined from exome sequencing data in n = 211 patients. CDK12 biallelic mutation cases (n = 23) in red, monoallelic cases (n = 3) in yellow, and cancers with no CDK12 alterations (controls) in blue. The black line indicates the median number of CNBs (median = 513) in the whole cohort. B, Violin plot depicting CNB numbers in CDK12-mutated cases (red) and in controls (blue).

Close modal

TIL analyses

For the analysis of intratumor CD3+ TIL density (D-TIL), there were 122 samples for controls as well as 17 samples with biallelic CDK12 alterations and seven monoallelic CDK12 alteration cases (total N = 146; Fig. 4A and B). Median intratumoral total CD3+ cell density was higher in CDK12 biallelic loss samples than in controls, although this was not statistically significant (203.7 vs. 86.7 CD3+/mm2; P = 0.073; Fig. 4B). Two of the CDK12 biallelic loss prostate cancer cases had more than 1,200 CD3+ cells per mm2, which was above the 90% centile (Fig. 4A); however, multiple samples with biallelic CDK12 aberrations had CD3+ counts that were below the median. D-TIL was not statistically different between diagnostic and mCRPC samples for all CDK12 cases (Supplementary Fig. S3). Interestingly, there was no association found in our analyses between CNBs and D-TIL, neither in diagnostic nor CRPC samples. The samples with the highest CD3+ density also had the highest CD4+ and CD8+ infiltrates. The two samples with the highest CD3+ density had modest CNB scores of 613 and 600 (just above the median of 513).

Figure 4.

TILs analyses. A, Intratumoral CD3+ TILs per mm2. Bar chart depicting intratumoral CD3+/mm2 density (y-axis) in CDK12 biallelic cases (red), monoallelic cases (yellow), and controls (blue). B, Box and whisker plot of log10-transformed intratumoral CD3+ cell density per mm2 depicting CDK12 biallelic cases (red), monoallelic cases (yellow), and control cases (blue).

Figure 4.

TILs analyses. A, Intratumoral CD3+ TILs per mm2. Bar chart depicting intratumoral CD3+/mm2 density (y-axis) in CDK12 biallelic cases (red), monoallelic cases (yellow), and controls (blue). B, Box and whisker plot of log10-transformed intratumoral CD3+ cell density per mm2 depicting CDK12 biallelic cases (red), monoallelic cases (yellow), and control cases (blue).

Close modal

We next quantified D-TILs, on the basis of T-cell subtypes (Fig. 5A and B; Supplementary Fig. S4A–S4C); tissue sites included prostate tissue (n = 9), lymph node biopsies (n = 31), bone (n = 10), liver (n = 1), and soft tissue metastases (n = 1; Supplementary Table S4). In the overall population, total CD3+ cell numbers were correlated with CD4+FOXP3 (R = 0.51; P < 0.001) and CD8+ cell numbers (R = 0.45; P < 0.001; Fig. 5A). Moreover, our analyses revealed that CDK12 cancers were significantly enriched for CD4+FOXP3 cells (median CD4+FOXP3/mm2 50.5 in CDK12 biallelic cases vs. 6.2 in controls; P < 0.001), with a higher CD4/CD8 ratio (r) compared with controls (CD4/CD8 r = 1.22 for CDK12 biallelic cases vs. 0.4 for controls), without significantly higher CD8+ or CD4+FOXP3+ cells (Fig. 5B). Higher CD4+FOXP3 infiltration appeared to be associated with shorter OS (HR, 1.64; 95% CI, 0.95–2.84; P = 0.077), with similar trends for CD4+FOXP3+ and CD8+ cells (HR, 1.14; 95% CI, 0.73–1.76; P = 0.6 and HR, 1.42; 95% CI, 0.94–2.16; P = 0.0967, respectively).

Figure 5.

TIL subset analyses. A, Scatter plots depicting associations between log-transformed CD3+ cells/mm2 TILs and CD4+FOXP3 cells/mm2 (i), CD3+ cells/mm2 and CD8+cells/mm2 (ii), and CD3+ cells/mm2 and CD4+FOXP3+cells/mm2 (iii). B, Box and whisker plots representing intratumoral infiltrate density as log10-transformed CD4+FOXP3 cells/mm2 (i), CD8+cells/mm2 (ii), CD4+ FOXP3+cells/mm2 (iii) in CDK12 biallelic (red), monoallelic (yellow), and control cases (blue); and (iv) tumor CD4+FOXP3/CD8+ ratio between biallelic cases (red) and controls (blue).

Figure 5.

TIL subset analyses. A, Scatter plots depicting associations between log-transformed CD3+ cells/mm2 TILs and CD4+FOXP3 cells/mm2 (i), CD3+ cells/mm2 and CD8+cells/mm2 (ii), and CD3+ cells/mm2 and CD4+FOXP3+cells/mm2 (iii). B, Box and whisker plots representing intratumoral infiltrate density as log10-transformed CD4+FOXP3 cells/mm2 (i), CD8+cells/mm2 (ii), CD4+ FOXP3+cells/mm2 (iii) in CDK12 biallelic (red), monoallelic (yellow), and control cases (blue); and (iv) tumor CD4+FOXP3/CD8+ ratio between biallelic cases (red) and controls (blue).

Close modal

Deleterious CDK12 mutations have been reported in multiple tumor types, but are not common, although they may have special clinical importance in view of their immunogenomic properties. A subset of mCRPC has deleterious CDK12 aberrations (16, 27, 28), resulting in characteristic genomic profiles with innumerable focal TDs, gene fusions, and neoantigens (16, 27). These prostate cancer CDK12 alterations, like MMRd, have been postulated to represent a predictive biomarker of response to immunotherapy (17), making their study of huge interest (29, 30). Our analysis is arguably one of the largest integrated efforts interrogating their genomic, pathologic, and clinical characteristics, and also investigating their TIL landscape.

We confirm that approximately 5% of mCRPCs have CDK12 deleterious aberrations (11, 16), although fewer (3.4%) are definitely biallelic with the majority being frameshift or nonsense mutations and a small minority single-nucleotide aberrations of the kinase domain with a high likelihood of impact on protein function. Interestingly, these alterations were invariably present at diagnosis in matched same patient HSPC and mCRPC biopsies, suggesting that they are clonal events. Exome analyses have identified a specific genomic pattern of CDK12-aberrant cancers characterized by a high number of CNBs, and this highly fragmented genome has been shown to be correlated with a tandem duplicator phenotype (16). In our analysis, although high CNBs were characteristic of these tumors, not all prostate cancers with biallelic CDK12 alterations had this genomic signature, indicating that this may not be useful as the sole test to identify this subset. However, in keeping with the PROfound olaparib phase III genomic analyses data, we did observe concomitant mutations in other DDR genes in CDK-altered tumors including PALB2, BRCA2, FANCA, ATM, and MSH6 and in AR, PTEN, and PI3K/AKT pathway genes, with 23.5% of these cancers presenting concomitant ATM loss, but no PTEN loss by IHC (31). We also showed that prostate cancer with CDK12 biallelic alterations associate with higher Gleason scores and shorter survival from diagnosis, although we did not observe any differences in outcome on abiraterone/enzalutamide treatment, which has been previously reported, maybe due to the retrospective nature of our investigation (29, 30).

We also show for the first time that prostate cancer with biallelic CDK12 aberrations are predominantly enriched for CD4+FOXP3 cells with these tumors surprisingly not having significantly higher CD4+FOXP3+ or CD8+ TILs compared with controls. Interestingly, multiple studies have reported on immunosuppressive CD4+FOXP3 TIL subsets (32, 33), with some of these cells being reported to express high PD-1 and being able to inhibit cytotoxic T-cell function in a PD-1/PD-L1–dependent fashion as well as limit immunotherapy antitumor activity (32). The data, herein, suggest that CD4+FOXP3 cells, which CDK12-altered prostate cancer seem to be enriched for, might have an immunosuppressive role, associating with worse survival and might be relevant to immunotherapy trials as a biomarker of interest (30, 34, 35).

We acknowledge that significant limitations of our data include the fact that this was a single-institution retrospective study, and that, while we screened almost a thousand patients, our CDK12 analyses are based on a relatively small cohort, even though this comprises one of the largest studies of mCRPC subjects. Furthermore, we acknowledge that more effort is now needed to elucidate why not all prostate cancers with biallelic CDK12 alterations have the TD signature, and studies need to evaluate whether monoallelic loss can result in any biological relevance perhaps due to haploinsufficiency and a gene dose effect. Moreover, further studies to characterize the CD4+FOXP3 TIL subset are now crucial to drive therapeutic advances for this subset.

In conclusion, our data indicate that biallelic CDK12 aberrations in prostate cancer are associated with higher Gleason grade and poorer prognosis, and are infiltrated by CD4+FOXP3 T lymphocytes that appear to be associated with worse outcome and are likely to be immunosuppressive, with this being relevant to immunotherapy approaches.

J. Mateo reports grants, personal fees, and nonfinancial support from AstraZeneca (advisory board, grant to institution as principal investigator, speakers bureau), grants from Pfizer Oncology (grant to institution as principal investigator), personal fees and nonfinancial support from Clovis Oncology (advisory board, material and samples for research), Janssen (advisory board, travel to conference support, speakers bureau), and personal fees from Amgen (advisory board), Roche (advisory board), Merck-MSD (advisory board), and Astellas (Speakers bureau) outside the submitted work. A. Sharp reports personal fees from Sanofi (travel), Roche-Genentech (travel), and Astellas (speaker) outside the submitted work and reports employment with The Institute of Cancer Research (ICR), which has a commercial interest in abiraterone. W. Yuan reports receiving a meeting travel grant from Jilin Huarui Gene Technology Ltd. M.D. Fenor de la Maza has received grants and fees from “Fundación Cris contra el Cáncer”; travel fees from Astellas, AstraZeneca, Pfizer, Pierre Fabre, Roche, Bristol Meiers Squibb, Novartis, MSD, Janssen, and Bayer Pharmamar; and personal fees from Janssen, Pierre Fabre, and Roche outside the submitted work. J.S. de Bono reports grants, personal fees, and other from AstraZeneca (honorarium, travel expenses) during the conduct of the study, grants, personal fees, and other from Astellas (honorarium, travel expenses), Bayer (honorarium, travel expenses), BioXCell Therapeutics (honorarium, travel expenses), CellCentric (honorarium, travel expenses), Daiichi Sankyo (honorarium, travel expenses), Genentech/Roche (honorarium, travel expenses), Genmab (honorarium, travel expenses), GlaxoSmithKline (honorarium, travel expenses), Janssen (honorarium, travel expenses), Merck Serono (honorarium, travel expenses), Merck Sharp & Dohme (honorarium, travel expenses), Menarini/Silicon Biosystems (honorarium, travel expenses), Orion (honorarium, travel expenses), Pfizer (honorarium, travel expenses), Sanofi Aventis (honorarium, travel expenses), Sierra Oncology (honorarium, travel expenses), Taiho (honorarium, travel expenses), and Vertex Pharmaceuticals (honorarium, travel expenses), personal fees and other from Boehringer Ingelheim (honorarium, travel expenses), Eisai (honorarium, travel expenses), and Qiagen (honorarium, travel expenses) outside of the submitted work, and is listed as an inventor on a patent regarding PARP inhibition in DNA repair defective cancers owned by ICR and licensed to AstraZeneca. No disclosures were reported by the other authors.

P. Rescigno: Conceptualization, data curation, investigation, visualization, writing-original draft, writing-review and editing. B. Gurel: Conceptualization, data curation, formal analysis, investigation, visualization, methodology, writing-original draft. R. Pereira: Data curation. M. Crespo: Formal analysis, investigation, visualization, methodology, writing-review and editing. J. Rekowski: Software, formal analysis, investigation, visualization, methodology, writing-review and editing. M. Rediti: Data curation. M. Barrero: Data curation. J. Mateo: Data curation. D. Bianchini: Data curation. C. Messina: Data curation. M.D. Fenor de la Maza: Data curation. K. Chandran: Data curation. J. Carmichael: Data curation. C. Guo: Data curation. A. Paschalis: Data curation. A. Sharp: Data curation. G. Seed: Data curation, software, formal analysis, visualization. I. Figueiredo: Data curation. M. Lambros: Data curation. S. Miranda: Data curation. A. Ferreira: Data curation. C. Bertan: Data curation. R. Riisnaes: Data curation. N. Porta: Formal analysis. W. Yuan: Software, investigation, methodology, writing-review and editing. S. Carreira: Software, investigation, visualization, methodology, writing-review and editing. J.S. de Bono: Resources, supervision, funding acquisition.

P. Rescigno and J. Mateo were supported by a Prostate Cancer Foundation Young Investigator Award. Work in the de Bono laboratory was supported by Prostate Cancer UK and the Movember Foundation through the London Movember Centre of Excellence (CEO13_2-002), the US Department of Defense, the Prostate Cancer Foundation (20131017 and 20131017-1), Stand Up To Cancer (SU2C-AACR-DT0712), Cancer Research UK (CRM108X-A25144), and the UK Department of Health through an Experimental Cancer Medicine Centre grant (ECMC-CRM064X). A. Sharp has been supported by the Medical Research Council, the Academy of Medical Sciences, Prostate Cancer UK, and the Prostate Cancer Foundation. TOPARP-B is an investigator-initiated study supported by Cancer Research UK (CRUK/11/029, C12540 A12829, C12540/A13230, and C12540/A20447) and conducted with support from the Investigator-Sponsored Study Collaboration between AstraZeneca and the National Institute for Health Research Cancer Research Network.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Blazek
D
,
Kohoutek
J
,
Bartholomeeusen
K
,
Johansen
E
,
Hulinkova
P
,
Luo
Z
, et al
The cyclin K/Cdk12 complex maintains genomic stability via regulation of expression of DNA damage response genes
.
Genes Dev
2011
;
25
:
2158
72
.
2.
Cheng
SW
,
Kuzyk
MA
,
Moradian
A
,
Ichu
TA
,
Chang
VC
,
Tien
JF
, et al
Interaction of cyclin-dependent kinase 12/CrkRS with cyclin K1 is required for the phosphorylation of the C-terminal domain of RNA polymerase II
.
Mol Cell Biol
2012
;
32
:
4691
704
.
3.
Chilà
R
,
Guffanti
F
,
Damia
G
. 
Role and therapeutic potential of CDK12 in human cancers
.
Cancer Treat Rev
2016
;
50
:
83
8
.
4.
Juan
HC
,
Lin
Y
,
Chen
HR
,
Fann
MJ
. 
Cdk12 is essential for embryonic development and the maintenance of genomic stability
.
Cell Death Differ
2016
;
23
:
1038
48
.
5.
Tien
JF
,
Mazloomian
A
,
Cheng
SG
,
Hughes
CS
,
Chow
CCT
,
Canapi
LT
, et al
CDK12 regulates alternative last exon mRNA splicing and promotes breast cancer cell invasion
.
Nucleic Acids Res
2017
;
45
:
6698
716
.
6.
Ekumi
KM
,
Paculova
H
,
Lenasi
T
,
Pospichalova
V
,
Bösken
CA
,
Rybarikova
J
, et al
Ovarian carcinoma CDK12 mutations misregulate expression of DNA repair genes via deficient formation and function of the Cdk12/CycK complex
.
Nucleic Acids Res
2015
;
43
:
2575
89
.
7.
Barbieri
CE
,
Baca
SC
,
Lawrence
MS
,
Demichelis
F
,
Blattner
M
,
Theurillat
JP
, et al
Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer
.
Nat Genet
2012
;
44
:
685
9
.
8.
Fraser
M
,
Sabelnykova
VY
,
Yamaguchi
TN
,
Heisler
LE
,
Livingstone
J
,
Huang
V
, et al
Genomic hallmarks of localized, non-indolent prostate cancer
.
Nature
2017
;
541
:
359
64
.
9.
Cancer Genome Atlas Research Network
. 
The molecular taxonomy of primary prostate cancer
.
Cell
2015
;
163
:
1011
25
.
10.
Linch
M
,
Goh
G
,
Hiley
C
,
Shanmugabavan
Y
,
McGranahan
N
,
Rowan
A
, et al
Intratumoral evolutionary landscape of high-risk prostate cancer: the PROGENY study of genomic and immune parameters
.
Ann Oncol
2017
;
28
:
2472
80
.
11.
Abida
W
,
Armenia
J
,
Gopalan
A
,
Brennan
R
,
Walsh
M
,
Barron
D
, et al
Prospective genomic profiling of prostate cancer across disease states reveals germline and somatic alterations that may affect clinical decision making
.
JCO Precis Oncol
2017
;
2017
:
PO.17.00029
.
12.
Beltran
H
,
Prandi
D
,
Mosquera
JM
,
Benelli
M
,
Puca
L
,
Cyrta
J
, et al
Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer
.
Nat Med
2016
;
22
:
298
305
.
13.
Grasso
CS
,
Wu
YM
,
Robinson
DR
,
Cao
X
,
Dhanasekaran
SM
,
Khan
AP
, et al
The mutational landscape of lethal castration-resistant prostate cancer
.
Nature
2012
;
487
:
239
43
.
14.
Robinson
D
,
Van Allen
EM
,
Wu
YM
,
Schultz
N
,
Lonigro
RJ
,
Mosquera
JM
, et al
Integrative clinical genomics of advanced prostate cancer
.
Cell
2015
;
161
:
1215
28
.
15.
Armenia
J
,
Wankowicz
SAM
,
Liu
D
,
Gao
J
,
Kundra
R
,
Reznik
E
, et al
The long tail of oncogenic drivers in prostate cancer
.
Nat Genet
2018
;
50
:
645
51
.
16.
Wu
YM
,
Cieślik
M
,
Lonigro
RJ
,
Vats
P
,
Reimers
MA
,
Cao
X
, et al
Inactivation of CDK12 delineates a distinct immunogenic class of advanced prostate cancer
.
Cell
2018
;
173
:
1770
82
.
17.
Rodrigues
DN
,
Rescigno
P
,
Liu
D
,
Yuan
W
,
Carreira
S
,
Lambros
MB
, et al
Immunogenomic analyses associate immunological alterations with mismatch repair defects in prostate cancer
.
J Clin Invest
2018
;
128
:
4441
53
.
18.
Schreiber
RD
,
Old
LJ
,
Smyth
MJ
. 
Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion
.
Science
2011
;
331
:
1565
70
.
19.
Fridman
WH
,
Pagès
F
,
Sautès-Fridman
C
,
Galon
J
. 
The immune contexture in human tumors: impact on clinical outcome
.
Nat Rev Cancer
2012
;
12
:
298
306
.
20.
Hendry
S
,
Salgado
R
,
Gevaert
T
,
Russell
PA
,
John
T
,
Thapa
B
, et al
Assessing tumor-infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the International Immunooncology Biomarkers Working Group: part 1. Assessing the host immune response, TILs in invasive breast carcinoma and ductal carcinoma in situ, metastatic tumor deposits and areas for further research
.
Adv Anat Pathol
2017
;
24
:
235
51
.
21.
Hendry
S
,
Salgado
R
,
Gevaert
T
,
Russell
PA
,
John
T
,
Thapa
B
, et al
Assessing tumor-infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the International Immunooncology Biomarkers Working Group: part 2. TILs in melanoma, gastrointestinal tract carcinomas, non-small cell lung carcinoma and mesothelioma, endometrial and ovarian carcinomas, squamous cell carcinoma of the head and neck, genitourinary carcinomas, and primary brain tumors
.
Adv Anat Pathol
2017
;
24
:
311
35
.
22.
Barnes
TA
,
Amir
E
. 
HYPE or HOPE: the prognostic value of infiltrating immune cells in cancer
.
Br J Cancer
2017
;
117
:
451
60
.
23.
Sundar
R
,
Miranda
S
,
Rodrigues
DN
,
Chénard-Poirier
M
,
Dolling
D
,
Clarke
M
, et al
Ataxia telangiectasia mutated protein loss and benefit from oxaliplatin-based chemotherapy in colorectal cancer
.
Clin Colorectal Cancer
2018
;
17
:
280
4
.
24.
Rescigno
P
,
Lorente
D
,
Dolling
D
,
Ferraldeschi
R
,
Rodrigues
DN
,
Riisnaes
R
, et al
Docetaxel treatment in PTEN- and ERG-aberrant metastatic prostate cancers
.
Eur Urol Oncol
2018
;
1
:
71
7
.
25.
Nickel
JC
,
True
LD
,
Krieger
JN
,
Berger
RE
,
Boag
AH
,
Young
ID
. 
Consensus development of a histopathological classification system for chronic prostatic inflammation
.
BJU Int
2001
;
87
:
797
805
.
26.
Minchom
A
,
Yuan
W
,
Crespo
M
,
Gurel
B
,
Figueiredo
I
,
Wotherspoon
A
, et al
Molecular and immunological features of a prolonged exceptional responder with malignant pleural mesothelioma treated initially and rechallenged with pembrolizumab
.
J Immunother Cancer
2020
;
8
:
e000713
.
27.
Sokol
ES
,
Pavlick
D
,
Frampton
GM
,
Ross
JS
,
Miller
VA
,
Ali
SM
, et al
Pan-cancer analysis of CDK12 loss-of-function alterations and their association with the focal tandem-duplicator phenotype
.
Oncologist
2019
;
24
:
1526
33
.
28.
Marshall
CH
,
Imada
EL
,
Tang
Z
,
Ross
JS
,
Miller
VA
,
Ali
SM
, et al
CDK12 inactivation across solid tumors: an actionable genetic subtype
.
Oncoscience
2019
;
6
:
312
16
.
29.
Reimers
MA
,
Yip
SM
,
Zhang
L
,
Cieslik
M
,
Dhawan
M
,
Montgomery
B
, et al
Clinical outcomes in cyclin-dependent kinase 12 mutant advanced prostate cancer
.
Eur Urol
2020
;
77
:
333
41
.
30.
Antonarakis
ES
,
Isaacsson Velho
P
,
Fu
W
,
Wang
H
,
Agarwal
N
,
Santos
VS
, et al
CDK12-altered prostate cancer: clinical features and therapeutic outcomes to standard systemic therapies, poly (ADP-ribose) polymerase inhibitors, and PD-1 inhibitors
.
JCO Precis Oncol
2020
;
4
:
370
81
.
31.
de Bono
JS
,
Fizazi
K
,
Saad
F
,
Shore
N
,
Sandhu
SK
,
Mehra
N
, et al
Central prospective detection of homologous recombination repair gene mutations (HRRm) in tumor tissue from 4000 men with metastatic castration-resistant prostate cancer (mCRPC) screened for the PROfound study
.
Ann Oncol
2019
;
30
:
v325
v355
.
32.
Gagliani
N
,
Magnani
CF
,
Huber
S
,
Gianolini
ME
,
Pala
M
,
Licona-Limon
P
, et al
Coexpression of CD49b and LAG-3 identifies human and mouse T regulatory type 1 cells
.
Nat Med
2013
;
19
:
739
46
.
33.
Liu
Y
,
Carlsson
R
,
Comabella
M
,
Wang
J
,
Kosicki
M
,
Carrion
B
, et al
FoxA1 directs the lineage and immunosuppressive properties of a novel regulatory T cell population in EAE and MS
.
Nat Med
2014
;
20
:
272
82
.
34.
Zappasodi
R
,
Budhu
S
,
Hellmann
MD
,
Postow
MA
,
Senbabaoglu
Y
,
Manne
S
, et al
Non-conventional inhibitory CD4+Foxp3PD-1hi T cells as a biomarker of immune checkpoint blockade activity
.
Cancer Cell
2018
;
34
:
691
.
35.
Schweizer
MT
,
Ha
G
,
Gulati
R
,
Brown
LC
,
McKay
RR
,
Dorff
T
, et al
CDK12-mutated prostate cancer: clinical outcomes with standard therapies and immune checkpoint blockade
.
JCO Precis Oncol
2020
;
4
:
382
92
.