PURPOSE In prostate cancer, inactivating CDK12 mutations lead to gene fusion–induced neoantigens and possibly sensitivity to immunotherapy. We aimed to clinically, pathologically, and molecularly characterize CDK12-aberrant prostate cancers. METHODS We conducted a retrospective multicenter study to identify patients with advanced prostate cancer who harbored somatic loss-of-function CDK12 mutations. We used descriptive statistics to characterize their clinical features and therapeutic outcomes (prostate-specific antigen [PSA] responses, progression-free survival [PFS]) to various systemic therapies, including sensitivity to poly (ADP-ribose) polymerase and PD-1 inhibitors. RESULTS Sixty men with at least monoallelic (51.7% biallelic) CDK12 alterations were identified across nine centers. Median age at diagnosis was 60.5 years; 71.7% and 28.3% were white and nonwhite, respectively; 93.3% had Gleason grade group 4-5; 15.4% had ductal/intraductal histology; 53.3% had metastases at diagnosis; and median PSA was 24.0 ng/mL. Of those who underwent primary androgen deprivation therapy for metastatic hormone-sensitive disease (n = 59), 79.7% had a PSA response, and median PFS was 12.3 months. Of those who received first-line abiraterone and enzalutamide for metastatic castration-resistant prostate cancer (mCRPC; n = 34), 41.2% had a PSA response, and median PFS was 5.3 months. Of those who received a first taxane chemotherapy for mCRPC (n = 22), 31.8% had a PSA response, and median PFS was 3.8 months. Eleven men received a PARP inhibitor (olaparib [n = 10], rucaparib [n = 1]), and none had a PSA response (median PFS, 3.6 months). Nine men received a PD-1 inhibitor as fourth- to sixth-line systemic therapy (pembrolizumab [n = 5], nivolumab [n = 4]); 33.3% had a PSA response, and median PFS was 5.4 months. CONCLUSION CDK12-altered prostate cancer is an aggressive subtype with poor outcomes to hormonal and taxane therapies as well as to PARP inhibitors. A proportion of these patients may respond favorably to PD-1 inhibitors, which implicates CDK12 deficiency in immunotherapy sensitivity.

Mismatch repair (MMR) gene mutations are rare in prostate cancer, and their histological and clinical characteristics are largely unknown. We conducted a retrospective study to explore disease characteristics and treatment outcomes of men with metastatic prostate cancer harboring germline and/or somatic MMR mutations detected using clinical-grade genomic assays. Thirteen patients with a deleterious MMR gene mutation were identified. Median age was 64 yr, 75% had grade group 5 (Gleason sum 9 or 10), 23% had intraductal histology, 46% had metastatic disease at initial diagnosis, and 31% had visceral metastases. Most patients (46%) had MSH6 mutations,

Researchers in the area of grid/cloud computing perform many of their experiments using simulations that must capture network behavior. In this context, packet-level simulations, which are widely used to study network protocols, are too costly given the typical large scales of simulated systems and applications. An alternative is to implement network simulations with less costly flow-level models. Several flow-level models have been proposed and implemented in grid/cloud simulators. Surprisingly, published validations of these models, if any, consist of verifications for only a few simple cases. Consequently, even when they have been used to obtain published results, the ability of these simulators to produce scientifically meaningful results is in doubt. This work evaluates these state-of-the-art flow-level network models of TCP communication via comparison to packet-level simulation. While it is straightforward to show cases in which previously proposed models lead to good results, instead we follow the critical method, which places model refutation at the center of the scientific activity, and we systematically seek cases that lead to invalid results. Careful analysis of these cases reveal fundamental flaws and also suggest improvements. One contribution of this work is that these improvements lead to a new model that, while far from being perfect, improves upon all previously proposed models in the context of simulation of grids or clouds. A more important contribution, perhaps, is provided by the pitfalls and unexpected behaviors encountered in this work, leading to a number of enlightening lessons. In particular, this work shows that model validation cannot be achieved solely by exhibiting (possibly many) "good cases." Confidence in the quality of a model can only be strengthened through an invalidation approach that attempts to prove the model wrong.