Neoadjuvant sipuleucel-T induces both Th1 activation and immune regulation in localized prostate cancer.

Sipuleucel-T is the only FDA-approved immunotherapy for metastatic castration-resistant prostate cancer. The mechanism by which this treatment improves survival is not fully understood. We have previously shown that this treatment can induce the recruitment of CD4 and CD8 T cells to the tumor microenvironment. In this study, we examined the functional state of these T cells through gene expression profiling. We found that the magnitude of T cell signatures correlated with the frequency of T cells as measured by immunohistochemistry. Sipuleucel-T treatment was associated with increased expression of Th1-associated genes, but not Th2-, Th17 - or Treg-associated genes. Post-treatment tumor tissues with high CD8+T cell infiltration was associated with high levels of CXCL10 expression. On in situ hybridization, CXCL10+ cells colocalized with CD8+T cells in post-treatment prostatectomy tumor tissue. Neoadjuvant sipuleucel-T was also associated with upregulation of immune inhibitory checkpoints, including CTLA4 and TIGIT, and downregulation of the immune activation marker, dipeptidylpeptidase, DPP4. Treatment-associated declines in serum PSA were correlated with induction of Th1 response. In contrast, rises in serum PSA while on treatment were associated with the induction of multiple immune checkpoints, including CTLA4, CEACAM6 and TIGIT. This could represent adaptive immune resistance mechanisms induced by treatment. Taken together, neoadjuvant sipuleucel-T can induce both a Th1 response and negative immune regulation in the prostate cancer microenvironment

Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI

Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. That is, where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can significantly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation

Development and pilot evaluation of a personalized decision support intervention for low risk prostate cancer patients.

ObjectivesDevelopment and pilot evaluation of a personalized decision support intervention to help men with early-stage prostate cancer choose among active surveillance, surgery, and radiation.MethodsWe developed a decision aid featuring long-term survival and side effects data, based on focus group input and stakeholder endorsement. We trained premedical students to administer the intervention to newly diagnosed men with low-risk prostate cancer seen at the University of California, San Francisco. Before the intervention, and after the consultation with a urologist, we administered the Decision Quality Instrument for Prostate Cancer (DQI-PC). We hypothesized increases in two knowledge items from the DQI-PC: How many men diagnosed with early-stage prostate cancer will eventually die of prostate cancer? How much would waiting 3 months to make a treatment decision affect chances of survival? Correct answers were: "Most will die of something else" and "A little or not at all."ResultsThe development phase involved 6 patients, 1 family member, 2 physicians, and 5 other health care providers. In our pilot test, 57 men consented, and 44 received the decision support intervention and completed knowledge surveys at both timepoints. Regarding the two knowledge items of interest, before the intervention, 35/56 (63%) answered both correctly, compared to 36/44 (82%) after the medical consultation (P = .04 by chi-square test).ConclusionsThe intervention was associated with increased patient knowledge. Data from this pilot have guided the development of a larger scale randomized clinical trial to improve decision quality in men with prostate cancer being treated in community settings

Incomplete inhibition of phosphorylation of 4E-BP1 as a mechanism of primary resistance to ATP-competitive mTOR inhibitors

The mammalian target of rapamycin (mTOR) regulates cell growth by integrating nutrient and growth factor signaling and is strongly implicated in cancer. But mTOR is not an oncogene, and which tumors will be resistant or sensitive to new ATP-competitive mTOR inhibitors now in clinical trials remains unknown. We screened a panel of over 600 human cancer cell lines to identify markers of resistance and sensitivity to the mTOR inhibitor PP242. RAS and PIK3CA mutations were the most significant genetic markers for resistance and sensitivity to PP242, respectively; colon origin was the most significant marker for resistance based on tissue type. Among colon cancer cell lines, those with KRAS mutations were most resistant to PP242, while those without KRAS mutations most sensitive. Surprisingly, cell lines with co-mutation of PIK3CA and KRAS had intermediate sensitivity. Immunoblot analysis of the signaling targets downstream of mTOR revealed that the degree of cellular growth inhibition induced by PP242 was correlated with inhibition of phosphorylation of the translational repressor 4E-BP1, but not ribosomal protein S6. In a tumor growth inhibition trial of PP242 in patient-derived colon cancer xenografts, resistance to PP242 induced inhibition of 4E-BP1 phosphorylation and xenograft growth was again observed in KRAS mutant tumors without PIK3CA co-mutation, compared to KRAS WT controls. We show that, in the absence of PIK3CA co-mutation, KRAS mutations are associated with resistance to PP242 and that this is specifically linked to changes in the level of phosphorylation of 4E-BP1

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

A 17-gene Assay to Predict Prostate Cancer Aggressiveness in the Context of Gleason Grade Heterogeneity, Tumor Multifocality, and Biopsy Undersampling

avai lable at www.sciencedirect.com journal homepage: www.europeanurology.com Genomic Prostate Score Outcome measures and statistical analysis: The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatec-predictive of aggressClinical validation Clinical utility tomy. Cox proportional hazards regressionmodels were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profileswere used together with clinical and pathologic characteristics to evaluate clinical utility. Results and limitations: Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) were ive disease after adjustment for prostate-specific antigen, GleasonArticle inf