PURPOSE BRCA1 or BRCA2 ( BRCA) alterations are common in men with metastatic castration-resistant prostate cancer (mCRPC) and may confer sensitivity to poly(ADP-ribose) polymerase inhibitors. We present results from patients with mCRPC associated with a BRCA alteration treated with rucaparib 600 mg twice daily in the phase II TRITON2 study. METHODS We enrolled patients who progressed after one to two lines of next-generation androgen receptor–directed therapy and one taxane-based chemotherapy for mCRPC. Efficacy and safety populations included patients with a deleterious BRCA alteration who received ≥ 1 dose of rucaparib. Key efficacy end points were objective response rate (ORR; per RECIST/Prostate Cancer Clinical Trials Working Group 3 in patients with measurable disease as assessed by blinded, independent radiology review and by investigators) and locally assessed prostate-specific antigen (PSA) response (≥ 50% decrease from baseline) rate. RESULTS Efficacy and safety populations included 115 patients with a BRCA alteration with or without measurable disease. Confirmed ORRs per independent radiology review and investigator assessment were 43.5% (95% CI, 31.0% to 56.7%; 27 of 62 patients) and 50.8% (95% CI, 38.1% to 63.4%; 33 of 65 patients), respectively. The confirmed PSA response rate was 54.8% (95% CI, 45.2% to 64.1%; 63 of 115 patients). ORRs were similar for patients with a germline or somatic BRCA alteration and for patients with a BRCA1 or BRCA2 alteration, while a higher PSA response rate was observed in patients with a BRCA2 alteration. The most frequent grade ≥ 3 treatment-emergent adverse event was anemia (25.2%; 29 of 115 patients). CONCLUSION Rucaparib has antitumor activity in patients with mCRPC and a deleterious BRCA alteration, but with a manageable safety profile consistent with that reported in other solid tumor types.

This is a repository copy of Pan-cancer image-based detection of clinically actionable genetic alterations.

Retroviruses contain relatively large amounts of ubiquitin, but the significance of this finding has been unknown. Here, we show that drugs that are known to reduce the level of free ubiquitin in the cell dramatically reduced the release of Rous sarcoma virus, an avian retrovirus. This effect was suppressed by overexpressing ubiquitin and also by directly fusing ubiquitin to the C terminus of Gag, the viral protein that directs budding and particle release. The block to budding was found to be at the plasma membrane, and electron microscopy revealed that the reduced level of ubiquitin results in a failure of mature virus particles to separate from each other and from the plasma membrane during budding. These data indicate that ubiquitin is actually part of the budding machinery.

Association for Cancer Research. D. Campbell has nothing to disclose. A. Patnaik has served in a consulting or advisory role for Exelixis, Janssen, and Jounce Therapeutics; has received honoraria from Clovis Oncology, Merck, Prime Inc, and Roche; and has received research funding from Clovis Oncology, Bristol-Myers Squibb, and GlaxoSmithKline. J. Shapiro has served in a consulting or advisory role for Amgen, Astellas, Ipsen, Merck, and Roche and received financial support for travel from Amgen and Merck. B. Sautois has served a consulting or advisory role for Clovis Oncology, Astellas, Janssen, and Sanofi and received financial support for travel and/or accommodation from Janssen. N.J. Vogelzang has served in a consulting or advisory role for Clovis Oncology,

Immunotherapy has revolutionized cancer therapy, largely attributed to the success of immune-checkpoint blockade. However, there are subsets of patients across multiple cancers who have not shown robust responses to these agents. A major impediment to progress in the fi eld is the availability of faithful mouse models that recapitulate the complexity of human malignancy and immune contexture within the tumor microenvironment. These models are urgently needed across all malignancies to interrogate and predict antitumor immune responses and therapeutic effi cacy in clinical trials. Herein, we seek to review pros and cons of different cancer mouse models, and how they can be used as platforms to predict effi cacy and resistance to cancer immunotherapies. MURINE TUMOR MODELS Syngeneic Tumor Cell Lines Syngeneic tumor models are the oldest and most heavily utilized preclinical models to evaluate anticancer therapeutics. Using inbred strains such as C57BL/6, BALB/c, and FVB mice, it is possible to isolate spontaneous, carcinogeninduced, or transgenic tumor cell lines that can be expanded Research.

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Metformin inhibits cancer cell proliferation and epidemiology studies suggest an association with increased survival in cancer patients taking metformin, however, the mechanism by which metformin improves cancer outcomes remains controversial. To explore how metformin might directly affect cancer cells, we analyzed how metformin altered the metabolism of prostate cancer cells and tumors. We found that metformin decreased glucose oxidation and increased dependency on reductive glutamine metabolism in both cancer cell lines and in a mouse model of prostate cancer. Inhibition of glutamine anaplerosis in the presence of metformin further attenuated proliferation while increasing glutamine metabolism rescued the proliferative defect induced by metformin. These data suggest that interfering with glutamine may synergize with metformin to improve outcomes in patients with prostate cancer.

and tluedde@ukaachen.de 34 35 Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular 36 biology assays. 1 These tests can be a bottleneck in oncology workflows because of high turna-37 round time, tissue usage and costs. 2 Here, we show that deep learning can predict point muta-38 tions, molecular tumor subtypes and immune-related gene expression signatures 3,4 directly 39 from routine histological images of tumor tissue. We developed and systematically optimized 40 a one-stop-shop workflow and applied it to more than 4000 patients with breast 5 , colon and 41 rectal 6 , head and neck 7 , lung 8,9 , pancreatic 10 , prostate 11 cancer, melanoma 12 and gastric 13 can-42 cer. Together, our findings show that a single deep learning algorithm can predict clinically ac-43 tionable alterations from routine histology data. Our method can be implemented on mobile 44 hardware 14 , potentially enabling point-of-care diagnostics for personalized cancer treatment 45 in individual patients. 46 Clinical guidelines recommend molecular testing of tumor tissue for most patients with advanced 47 209 The results are in part based upon data generated by the TCGA Research Network: http://can-210 cergenome.nih.gov/. Our funding sources are as follows. J.N.K.: RWTH University Aachen (START 211