Impact of Family History and Germline Genetic Risk Single Nucleotide Polymorphisms on Long-Term Outcomes of Favorable-Risk Prostate Cancer.

PURPOSE: Family history and germline genetic risk single nucleotide polymorphisms (SNPs) have been separately shown to stratify lifetime risk of prostate cancer. Here, we evaluate the combined prognostic value of family history of prostate and other related cancers and germline risk SNPs among patients with favorable-risk prostate cancer. MATERIALS AND METHODS: A total of 1367 participants from the prospective Health Professionals Follow-up Study diagnosed with low- or favorable intermediate-risk prostate cancer from 1986 to 2017 underwent genome-wide SNP genotyping. Multivariable Cox regression was used to estimate the association between family history, specific germline risk variants, and a 269 SNP polygenic risk score with prostate cancer‒specific death. RESULTS: Family history of prostate, breast, and/or pancreatic cancer was observed in 489 (36%) participants. With median follow-up from diagnosis of 14.9 years, participants with favorable-risk prostate cancer with a positive family history had a significantly higher risk of prostate cancer‒specific death (HR 1.95, 95% CI 1.15-3.32, P = .014) compared to those without any family history. The rs2735839 (19q13) risk allele was associated with prostate cancer‒specific death (HR 1.81 per risk allele, 95% CI 1.04-3.17, P = .037), whereas the polygenic risk score was not. Combined family history and rs2735839 risk allele were each associated with an additive risk of prostate cancer‒specific death (HR 1.78 per risk factor, 95% CI 1.25-2.53, P = .001). CONCLUSIONS: Family history of prostate, breast, or pancreatic cancer and/or a 19q13 germline risk allele are associated with an elevated risk of prostate cancer‒specific death among favorable-risk patients. These findings have implications for how family history and germline genetic risk SNPs should be factored into clinical decision-making around favorable-risk prostate cancer.

Magnetic Resonance Imaging-based Prostate Cancer Screening in Carriers of Pathogenic Germline Mutations: Interim Results from the Initial Screening Round of the Prostate Cancer Genetic Risk Evaluation and Screening Study.

BACKGROUND: The risk of early-onset and clinically aggressive prostate cancer is elevated in carriers of certain rare pathogenic germline mutations. The utility of augmenting traditional prostate-specific antigen (PSA)-based screening measures with multiparametric magnetic resonance imaging (MRI) in this population is not yet known. OBJECTIVE: To evaluate MRI-based screening in comparison with traditional PSA-based screening among individuals at an elevated genetic risk for prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: Male germline carriers of pathogenic/likely pathogenic variants in any of 19 prostate cancer risk genes between the ages of 35 and 74 yr with no prior history of prostate cancer were recruited. Intervention Enrolled participants underwent screening with annual PSA, digital rectal examination (DRE), and triennial multiparametric MRI. Individuals with abnormal DRE, elevated age-adjusted PSA (>1.5 ng/ml for 35-49 yr, >2.0 ng/ml for 50-54 yr, and >3.0 ng/ml for 55-74 yr), or suspicious multiparametric MRI (Prostate Imaging Reporting and Data System [PI-RADS] ≥3 lesion) were offered prostate biopsy. Outcome measurements and statistical analysis Endpoints were diagnosis of any and clinically significant prostate cancer, and alternative screening strategies were compared by a decision curve analysis. RESULTS AND LIMITATIONS: To date, 101 males have completed the first round of screening. The greatest proportion of participants are carriers of BRCA2 (n = 44), BRCA1 (n = 35), and ATM (n = 7) variants. Twenty-one have undergone biopsy, resulting in the detection of nine cases of cancer (seven clinically significant). For the detection of clinically significant prostate cancer, abnormal MRI (PI-RADS ≥3) demonstrated 100% sensitivity (7/7) with a negative predictive value (NPV) of 100%, whereas PSA-based screening alone had 57% (4/7) sensitivity with an NPV of 73%. Of six screening strategies evaluated in the decision curve analysis, MRI-based screening alone achieved superior net benefit at all threshold probabilities compared with PSA screening-detecting one additional cancer case per 7.5 patients, while avoiding more unnecessary biopsies at the same threshold probability. CONCLUSIONS: Disease prevalence is high among carriers of prostate cancer-associated pathogenic germline mutations. Early results suggest that MRI-based screening enhances early detection of clinically significant disease beyond PSA screening alone. PATIENT SUMMARY: In this study, we present the interim results from the PROGRESS prostate cancer screening trial. We found that in certain germline carriers of prostate cancer risk mutations, magnetic resonance imaging-based screening enhances detection of prostate cancer while reducing biopsies triggered, in comparison with traditional prostate-specific antigen screening strategies.

Incorporating Genetic Risk Into Prostate Cancer Care: Implications for Early Detection and Precision Oncology.

The availability and cost of germline and somatic genetic testing have dramatically improved over the past two decades, enabling precision medicine approaches in oncology, with significant implications for prostate cancer (PCa) care. Roughly 12% of individuals with advanced disease are carriers of rare pathogenic germline variants that predispose to particularly aggressive and earlier-onset disease. Several of these variants are already established as clinically actionable by modern precision oncology therapeutics, while others may come to aid the selection of active surveillance, definitive local therapies, and systemic therapies. Concurrently, the number of common variants (ie, incorporated into polygenic risk scores) associated with PCa risk has continued to grow, but with several important considerations both at the intersection of race and ancestry and for early detection of aggressive disease. Family history has historically been used as a proxy for this inherited genetic risk of PCa, but recently emerging evidence examining this relation has shifted our understanding of how best to leverage this tool in PCa care. This review seeks to clarify and contextualize the existing and emerging precision oncology paradigms that use inherited genetic risk in PCa care, for both early detection and localized disease management.

Single-cell analysis of immune and stroma cell remodeling in clear cell renal cell carcinoma primary tumors and bone metastatic lesions.

BACKGROUND: Despite therapeutic advances, once a cancer has metastasized to the bone, it represents a highly morbid and lethal disease. One third of patients with advanced clear cell renal cell carcinoma (ccRCC) present with bone metastasis at the time of diagnosis. However, the bone metastatic niche in humans, including the immune and stromal microenvironments, has not been well-defined, hindering progress towards identification of therapeutic targets. METHODS: We collected fresh patient samples and performed single-cell transcriptomic profiling of solid metastatic tissue (Bone Met), liquid bone marrow at the vertebral level of spinal cord compression (Involved), and liquid bone marrow from a different vertebral body distant from the tumor site but within the surgical field (Distal), as well as bone marrow from patients undergoing hip replacement surgery (Benign). In addition, we incorporated single-cell data from primary ccRCC tumors (ccRCC Primary) for comparative analysis. RESULTS: The bone marrow of metastatic patients is immune-suppressive, featuring increased, exhausted CD8 + cytotoxic T cells, T regulatory cells, and tumor-associated macrophages (TAM) with distinct transcriptional states in metastatic lesions. Bone marrow stroma from tumor samples demonstrated a tumor-associated mesenchymal stromal cell population (TA-MSC) that appears to be supportive of epithelial-to mesenchymal transition (EMT), bone remodeling, and a cancer-associated fibroblast (CAFs) phenotype. This stromal subset is associated with poor progression-free and overall survival and also markedly upregulates bone remodeling through the dysregulation of RANK/RANKL/OPG signaling activity in bone cells, ultimately leading to bone resorption. CONCLUSIONS: These results provide a comprehensive analysis of the bone marrow niche in the setting of human metastatic cancer and highlight potential therapeutic targets for both cell populations and communication channels.

Anticoagulation prophylaxis patterns following retroperitoneal lymph node dissection for testis cancer.

INTRODUCTION: Retroperitoneal lymph node dissection (RPLND) is the standard of care for testicular cancer in various disease settings. Deep vein thrombosis (DVT) complications have been reported to occur in <1% of primary RPLND cases and up to 3% of postchemotherapy (PC-RPLND) cases. While prophylactic anticoagulation (AC) has been well-documented to reduce DVT rates in patients undergoing surgery in general, the benefit of prophylactic AC in RPLND has not been assessed. In this retrospective cohort study, we seek to address this unmet need by evaluating the rates and associated risk factors of DVT and pulmonary embolism (PE) with a national and institutional database, assess the changing patterns in DVT prophylaxis with postoperative AC following RPLND, and quantify the potential benefit of prophylactic AC in patients who have undergone RPLND using a risk-stratified approach. METHODS: The National Surgical Quality Improvement Program (NSQIP) database was queried for patients who underwent RPLND during the 10-year period from 2011 to 2021. An institutional database was queried for all patients undergoing RPLND from 2013 to 2022. Patient characteristics and operative outcomes were compared between the NSQIP and the institutional database. The institutional database was stratified by prior oncologic treatment (i.e., primary RPLND vs. PC-RPLND) and outcomes were compared. Postoperative AC rate was determined and trended by year. The use of postoperative AC and PE events were stratified by clinical stage. The absolute risk reduction (ARR) of AC prophylaxis on PE events and the number needed to treat (NNT) with AC prophylaxis to prevent a single PE event was determined. RESULTS: In total, the NSQIP database query resulted in 779 patients and our institutional database query resulted in 188 patients. The rate of DVT and PE was 1.2% and 0.5% vs. 2.1% and 1.6% in the NSQIP and institutional cohort, respectively. The rate of postoperative AC following RPLND in patients from the institutional database increased from 5% in 2013 to 43% in 2022 (P = 0.01). There were no statistically significant differences in complication rates, including bleeding events, chyle leaks, or hospital readmissions amongst patients who were prescribed AC at discharge and those who were not. No stage I patients developed PEs and no stage I patients were prescribed AC. The ARR for AC prophylaxis for development of PE was found to be 0.023 for the clinical stage II and stage III cohorts. The NNT to prevent a single PE with AC was 44 and 43 for the stage II and stage III cohorts, respectively. CONCLUSIONS: AC appears beneficial with minimal risk of harm after RPLND, especially in patients with higher risk of developing DVT/PE, highlighting the safety and efficacy of this regimen. There was a significant increase in the rate of AC prophylaxis at discharge amongst patients undergoing RPLND in the institutional database from 2013 to 2022. A risk-stratified protocol of postoperative AC following RPLND appears reasonable, and further prospective trials are warranted to formally confirm this recommendation.

Sequential intravesical gemcitabine/docetaxel provides a durable remission in recurrent high-risk NMIBC following BCG therapy.

PURPOSE: Bacillus Calmette-Guerin (BCG) is the standard of care for high-risk nonmuscle invasive bladder cancer (NMIBC), but half of patients develop disease recurrence. Intravesical regimens for BCG unresponsive NMIBC are limited. We report the safety, efficacy, and differential response of sequential gemcitabine/docetaxel (gem/doce) depending on BCG failure classification. METHODS: Multi-institutional retrospective analysis of patients treated with induction intravesical gem/doce (≥5/6 instillations) for recurrent high-risk NMIBC after BCG therapy from May 2018 to December 2021. Maintenance therapy was provided to those without high-grade (HG) recurrence on surveillance cystoscopy. Kaplan-Meier curves and Cox regression analyses were utilized to assess survival and risk factors for disease recurrence. RESULTS: Our cohort included 102 patients with BCG-unresponsive NMIBC. Median age was 72 years and median follow-up was 18 months. Six-, 12-, and 24-month high-grade recurrence-free survival was 78%, 65%, and 49%, respectively. Twenty patients underwent radical cystectomy (median 15.5 months from induction). Six patients progressed to muscle invasive disease. Fifty-seven percent of patients experienced mild/moderate adverse effects (AE), but only 6.9% experienced a delay in treatment schedule. Most common AE were urinary frequency/urgency (41%) and dysuria (21%). Patients with BCG refractory disease were more likely to develop HG recurrence when compared to patients with BCG relapsing disease (HR 2.14; 95% CI 1.02-4.49). CONCLUSIONS: In patients with recurrence after BCG therapy, sequential intravesical gem/doce is an effective and well-tolerated alternative to early cystectomy. Patients with BCG relapsing disease are more likely to respond to additional intravesical gem/doce. Further investigation with a prospective trial is imperative.

Development and Multicenter Case-Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction.

PURPOSE: Urinary comprehensive genomic profiling (uCGP) uses next-generation sequencing to identify mutations associated with urothelial carcinoma and has the potential to improve patient outcomes by noninvasively diagnosing disease, predicting grade and stage, and estimating recurrence risk. EXPERIMENTAL DESIGN: This is a multicenter case-control study using banked urine specimens collected from patients undergoing initial diagnosis/hematuria workup or urothelial carcinoma surveillance. A total of 581 samples were analyzed by uCGP: 333 for disease classification and grading algorithm development, and 248 for blinded validation. uCGP testing was done using the UroAmp platform, which identifies five classes of mutation: single-nucleotide variants, copy-number variants, small insertion-deletions, copy-neutral loss of heterozygosity, and aneuploidy. UroAmp algorithms predicting urothelial carcinoma tumor presence, grade, and recurrence risk were compared with cytology, cystoscopy, and pathology. RESULTS: uCGP algorithms had a validation sensitivity/specificity of 95%/90% for initial cancer diagnosis in patients with hematuria and demonstrated a negative predictive value (NPV) of 99%. A positive diagnostic likelihood ratio (DLR) of 9.2 and a negative DLR of 0.05 demonstrate the ability to risk-stratify patients presenting with hematuria. In surveillance patients, binary urothelial carcinoma classification demonstrated an NPV of 91%. uCGP recurrence-risk prediction significantly prognosticated future recurrence (hazard ratio, 6.2), whereas clinical risk factors did not. uCGP demonstrated positive predictive value (PPV) comparable with cytology (45% vs. 42%) with much higher sensitivity (79% vs. 25%). Finally, molecular grade predictions had a PPV of 88% and a specificity of 95%. CONCLUSIONS: uCGP enables noninvasive, accurate urothelial carcinoma diagnosis and risk stratification in both hematuria and urothelial carcinoma surveillance patients.

Impact of a negative confirmatory biopsy on risk of disease progression among men on active surveillance for prostate cancer.

OBJECTIVE: Most prostate cancer active surveillance (AS) protocols suggest a confirmatory biopsy within 12 to 18 months of diagnosis to mitigate the risk of unsampled high-grade disease. We investigate whether the results of confirmatory biopsy impact AS outcomes and could be used to tailor surveillance intensity. METHODS: We retrospectively reviewed our institutional database of prostate cancer patients managed by AS from 1997 to 2019 who underwent confirmatory biopsy and ≥3 biopsies overall. Biopsy progression was defined as either an increase in grade group or an increase in the proportion of positive biopsy cores to >34% and was compared between patients with a negative vs positive confirmatory biopsy using the Kaplan-Meier method and Cox proportional hazards regression. RESULTS: We identified 452 patients meeting inclusion criteria for this analysis, of whom 169 (37%) had a negative confirmatory biopsy. With a median follow-up of 6.8 years, 37% of patients progressed to treatment, most commonly due to biopsy progression. A negative confirmatory biopsy was significantly associated with biopsy progression-free survival in multivariable analysis (HR 0.54, 95% CI 0.34-0.88, P = 0.013), adjusting for known clinical and pathologic factors, including use of mpMRI prior to confirmatory biopsy. Negative confirmatory biopsy was also associated with an increased risk of adverse pathologic features at prostatectomy but not with biochemical recurrence among men who ultimately underwent definitive treatment. CONCLUSIONS: A negative confirmatory biopsy is associated with a lower risk of biopsy progression. While the increased risk of adverse pathology at time of definitive treatment sounds a small cautionary note regarding decreasing surveillance intensity, the majority of such patients have a favorable outcome on AS.

Analysis of evolutionary dynamics and clonal architecture in prostate cancer.

The extent to which clinical and genomic characteristics associate with prostate cancer clonal architecture, tumor evolution, and therapeutic response remains unclear. Here, we reconstructed the clonal architecture and evolutionary trajectories of 845 prostate cancer tumors with harmonized clinical and molecular data. We observed that tumors from patients who self-reported as Black had more linear and monoclonal architectures, despite these men having higher rates of biochemical recurrence. This finding contrasts with prior observations relating polyclonal architecture to adverse clinical outcomes. Additionally, we utilized a novel approach to mutational signature analysis that leverages clonal architecture to uncover additional cases of homologous recombination and mismatch repair deficiency in primary and metastatic tumors and link the origin of mutational signatures to specific subclones. Broadly, prostate cancer clonal architecture analysis reveals novel biological insights that may be immediately clinically actionable and provide multiple opportunities for subsequent investigation. Statement of significance: Tumors from patients who self-reported as Black demonstrate linear and monoclonal evolutionary trajectories yet experience higher rates of biochemical recurrence. In addition, analysis of clonal and subclonal mutational signatures identifies additional tumors with potentially actionable alterations such as deficiencies in mismatch repair and homologous recombination.

Surgical Management of Renal Cell Carcinoma with Inferior Vena Cava Tumor Thrombus.

Most kidney cancers are primary renal cell carcinomas (RCC) of clear cell histology. RCC is unique in its ability to invade into contiguous veins - a phenomenon terms venous tumor thrombus. Surgical resection is indicated for most patients with RCC and an inferior vena cava (IVC) thrombus in the absence of metastatic disease. Resection also has an important role in selected patients with metastatic disease. In this review, we discuss the comprehensive management of the patient with RCC with IVC tumor thrombus, emphasizing a multidisciplinary approach to the surgical techniques and perioperative management.

Impact of an Expanded Definition of Family History on Outcomes of Active Surveillance for Prostate Cancer.

PURPOSE: Despite family history being an established risk factor for prostate cancer, the role of a broader definition of family history inclusive of not just prostate cancer but other genetically related malignancies has not been investigated in the active surveillance population. Here, we evaluate the impact of an expanded definition of family history on active surveillance outcomes. MATERIALS AND METHODS: Patients undergoing active surveillance for prostate cancer at Massachusetts General Hospital from 1997-2019 with detailed data available on family cancer history were identified. Primary outcome was biopsy progression-free survival, and secondary outcomes were treatment-free survival, adverse pathological features at prostatectomy, and biochemical recurrence after treatment. Statistical analyses were conducted using the Kaplan-Meier method and Cox regression. RESULTS: Among 855 evaluable patients, 300 (35.1%) patients had any family history of prostate cancer, and 95 (11.1%) had a family history of related malignancies suggestive of a hereditary cancer syndrome. Family history of prostate cancer alone was not associated with biopsy progression, whereas family history suggestive of a hereditary cancer syndrome was associated with a significantly increased risk of biopsy progression (HR 1.43, 95%CI 1.01-2.02), independent of other known clinicopathological risk factors in multivariable analysis. Similarly, family history suggestive of a hereditary cancer syndrome was associated with significantly lower treatment-free survival (HR 1.58, 95%CI 1.14-2.18) in multivariable analysis. No significant association was found between family history and adverse features on surgical pathology or biochemical recurrence. CONCLUSIONS: An expanded family history suggestive of a hereditary cancer syndrome is an independent predictor of biopsy progression during active surveillance. Men with such a family history may still be offered active surveillance but should be counseled regarding the higher risk of disease progression.

Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses.

The treatment of low-risk primary prostate cancer entails active surveillance only, while high-risk disease requires multimodal treatment including surgery, radiation therapy, and hormonal therapy. Recurrence and development of metastatic disease remains a clinical problem, without a clear understanding of what drives immune escape and tumor progression. Here, we comprehensively describe the tumor microenvironment of localized prostate cancer in comparison with adjacent normal samples and healthy controls. Single-cell RNA sequencing and high-resolution spatial transcriptomic analyses reveal tumor context dependent changes in gene expression. Our data indicate that an immune suppressive tumor microenvironment associates with suppressive myeloid populations and exhausted T-cells, in addition to high stromal angiogenic activity. We infer cell-to-cell relationships from high throughput ligand-receptor interaction measurements within undissociated tissue sections. Our work thus provides a highly detailed and comprehensive resource of the prostate tumor microenvironment as well as tumor-stromal cell interactions.

Biomarkers in Testicular Cancer: Classic Tumor Markers and Beyond.

Biomarkers play a key role in patients with testicular germ cell tumors in a variety of clinical contexts, including initial diagnosis, prognostication, monitoring treatment response, and posttreatment surveillance. Although the classic serum tumor markers for testicular germ cell tumors are essential for clinical management, the low sensitivity (particularly for seminoma and teratoma) and potential for false positives has spurred novel biomarker discovery and validation efforts. Here, we review the current state of serum-based biomarkers for testicular germ cell tumors, with a focus on the classic serum tumor markers and emerging class of microRNA markers.

A machine learning approach to predict progression on active surveillance for prostate cancer.

PURPOSE: Robust prediction of progression on active surveillance (AS) for prostate cancer can allow for risk-adapted protocols. To date, models predicting progression on AS have invariably used traditional statistical approaches. We sought to evaluate whether a machine learning (ML) approach could improve prediction of progression on AS. PATIENTS AND METHODS: We performed a retrospective cohort study of patients diagnosed with very-low or low-risk prostate cancer between 1997 and 2016 and managed with AS at our institution. In the training set, we trained a traditional logistic regression (T-LR) classifier, and alternate ML classifiers (support vector machine, random forest, a fully connected artificial neural network, and ML-LR) to predict grade-progression. We evaluated model performance in the test set. The primary performance metric was the F1 score. RESULTS: Our cohort included 790 patients. With a median follow-up of 6.29 years, 234 developed grade-progression. In descending order, the F1 scores were: support vector machine 0.586 (95% CI 0.579 - 0.591), ML-LR 0.522 (95% CI 0.513 - 0.526), artificial neural network 0.392 (95% CI 0.379 - 0.396), random forest 0.376 (95% CI 0.364 - 0.380), and T-LR 0.182 (95% CI 0.151 - 0.185). All alternate ML models had a significantly higher F1 score than the T-LR model (all p <0.001). CONCLUSION: In our study, ML methods significantly outperformed T-LR in predicting progression on AS for prostate cancer. While our specific models require further validation, we anticipate that a ML approach will help produce robust prediction models that will facilitate individualized risk-stratification in prostate cancer AS.

Predicting survival after radical prostatectomy: Variation of machine learning performance by race.

BACKGROUND: Robust prediction of survival can facilitate clinical decision-making and patient counselling. Non-Caucasian males are underrepresented in most prostate cancer databases. We evaluated the variation in performance of a machine learning (ML) algorithm trained to predict survival after radical prostatectomy in race subgroups. METHODS: We used the National Cancer Database (NCDB) to identify patients undergoing radical prostatectomy between 2004 and 2016. We grouped patients by race into Caucasian, African-American, or non-Caucasian, non-African-American (NCNAA) subgroups. We trained an Extreme Gradient Boosting (XGBoost) classifier to predict 5-year survival in different training samples: naturally race-imbalanced, race-specific, and synthetically race-balanced. We evaluated performance in the test sets. RESULTS: A total of 68,630 patients met inclusion criteria. Of these, 57,635 (84%) were Caucasian, 8173 (12%) were African-American, and 2822 (4%) were NCNAA. For the classifier trained in the naturally race-imbalanced sample, the F1 scores were 0.514 (95% confidence interval: 0.513-0.511), 0.511 (0.511-0.512), 0.545 (0.541-0.548), and 0.378 (0.378-0.389) in the race-imbalanced, Caucasian, African-American, and NCNAA test samples, respectively. For all race subgroups, the F1 scores of classifiers trained in the race-specific or synthetically race-balanced samples demonstrated similar performance compared to training in the naturally race-imbalanced sample. CONCLUSIONS: A ML algorithm trained using NCDB data to predict survival after radical prostatectomy demonstrates variation in performance by race, regardless of whether the algorithm is trained in a naturally race-imbalanced, race-specific, or synthetically race-balanced sample. These results emphasize the importance of thoroughly evaluating ML algorithms in race subgroups before clinical deployment to avoid potential disparities in care.

Biologically informed deep neural network for prostate cancer discovery.

The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge1,2. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics3-5. Here we developed P-NET-a biologically informed deep learning model-to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.

Incorporating PSMA-Targeting Theranostics Into Personalized Prostate Cancer Treatment: a Multidisciplinary Perspective.

Recent developments in prostate-specific membrane antigen (PSMA) targeted diagnostic imaging and therapeutics (theranostics) promise to advance the management of primary, biochemically recurrent, and metastatic prostate cancer. In order to maximize the clinical impact of PSMA-targeted theranostics, a coordinated approach between the clinical stakeholders involved in prostate cancer management is required. Here, we present a vision for multidisciplinary use of PSMA theranostics from the viewpoints of nuclear radiology, medical oncology, urology, and radiation oncology. We review the currently available and forthcoming PSMA-based imaging and therapeutics and examine current and potential impacts on prostate cancer management from early localized disease to advanced treatment-refractory disease. Finally, we highlight the clinical and research opportunities related to PSMA-targeted theranostics and describe the importance of multidisciplinary collaboration in this space.

Transcriptional mediators of treatment resistance in lethal prostate cancer.

Metastatic castration-resistant prostate cancer is typically lethal, exhibiting intrinsic or acquired resistance to second-generation androgen-targeting therapies and minimal response to immune checkpoint inhibitors1. Cellular programs driving resistance in both cancer and immune cells remain poorly understood. We present single-cell transcriptomes from 14 patients with advanced prostate cancer, spanning all common metastatic sites. Irrespective of treatment exposure, adenocarcinoma cells pervasively coexpressed multiple androgen receptor isoforms, including truncated isoforms hypothesized to mediate resistance to androgen-targeting therapies2,3. Resistance to enzalutamide was associated with cancer cell-intrinsic epithelial-mesenchymal transition and transforming growth factor-ß signaling. Small cell carcinoma cells exhibited divergent expression programs driven by transcriptional regulators promoting lineage plasticity and HOXB5, HOXB6 and NR1D2 (refs. 4-6). Additionally, a subset of patients had high expression of dysfunction markers on cytotoxic CD8+ T cells undergoing clonal expansion following enzalutamide treatment. Collectively, the transcriptional characterization of cancer and immune cells from human metastatic castration-resistant prostate cancer provides a basis for the development of therapeutic approaches complementing androgen signaling inhibition.

Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma.

Importance: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection. Objective: To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer. Design, Setting, and Participants: A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017. Exposures: Germline variant detection using standard or deep learning methods. Main Outcomes and Measures: The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach. Results: The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, -1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, -2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]). Conclusions and Relevance: Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.