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BACKGROUND: Prostate cancers featuring an expansile cribriform (EC) pattern are associated with worse clinical outcomes following radical prostatectomy (RP). However, studies of the genomic characteristics of Gleason pattern 4 subtypes are limited. OBJECTIVE: To explore transcriptomic characteristics and heterogeneity within Gleason pattern 4 subtypes (fused/poorly formed, glomeruloid, small cribriform, EC/intraductal carcinoma [IDC]) and the association with biochemical recurrence (BCR)-free survival. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study including 165 men with grade group 2-4 prostate cancer who underwent RP at a single academic institution (2016-2020) and Decipher testing of the RP specimen. Patients with Gleason pattern 5 were excluded. IDC and EC patterns were grouped. Median follow-up was 2.5 yr after RP for patients without BCR. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS: Prompted by heterogeneity within pattern 4 subtypes identified via exploratory analyses, we investigated transcriptomic consensus clusters using partitioning around medoids and hallmark gene set scores. The primary clinical outcome was BCR, defined as two consecutive prostate-specific antigen measurements >0.2 ng/ml at least 8 wk after RP, or any additional treatment. Multivariable Cox proportional-hazards models were used to determine factors associated with BCR-free survival. RESULTS AND LIMITATIONS: In this cohort, 99/165 patients (60%) had EC and 67 experienced BCR. Exploratory analyses and clustering demonstrated transcriptomic heterogeneity within each Gleason pattern 4 subtype. In the multivariable model controlled for pattern 4 subtype, margin status, Cancer of the Prostate Risk Assessment Post-Surgical score, and Decipher score, a newly identified steroid hormone-driven cluster (hazard ratio 2.35 95% confidence interval 1.01-5.47) was associated with worse BCR-free survival. The study is limited by intermediate follow-up, no validation cohort, and lack of accounting for intratumoral and intraprostatic heterogeneity. CONCLUSIONS: Transcriptomic heterogeneity was present within and across each Gleason pattern 4 subtype, demonstrating there is additional biologic diversity not captured by histologic subtypes. This heterogeneity can be used to develop novel signatures and to classify transcriptomic subtypes, which may help in refining risk stratification following RP to further guide decision-making on adjuvant and salvage treatments. PATIENT SUMMARY: We studied prostatectomy specimens and found that tumors with similar microscopic appearance can have genetic differences that may help to predict outcomes after prostatectomy for prostate cancer. Our results demonstrate that further gene expression analysis of prostate cancer subtypes may improve risk stratification after prostatectomy. Future studies are needed to develop novel gene expression signatures and validate these findings in independent sets of patients.

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BACKGROUND: Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked. RESEARCH QUESTION: Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing? STUDY DESIGN AND METHODS: Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome. RESULTS: In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not. INTERPRETATION: The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

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Radical prostatectomy (RP) is one of the primary treatment options for localised prostate cancer (PCa). Despite its curativeintent, 1/3 of patients will experience biochemical recurrence (BCR) during follow-up. Experts have devoted efforts to associatethe influence of each individual factor with the risk of BCR to select the optimal treatment for each patient. Optimal managementmust aim to find a balance between delaying the onset of metastatic disease and overtreating an indolent disease with treatmentsthat can affect quality of life of the patients. Thus far, effective treatment options for men with BCR remain controversial interms of ideal treatment timing (adjuvant vs. salvage), radiotherapy (RT) fields and doses, selection and duration of systemictherapy and potential synergies between treatments and their therapeutic sequencing. Next-generation imaging techniques, suchas Prostate-Specific Membrane Antigen Positron Emission Tomography, are used for early detection of disease progression andexact site of recurrence or progression, thereby enhancing decision making for future disease management. In this review, weevaluate available evidence of controversial topics regarding BCR after RP and explore future directions, such as prognosticand/or predictive factors of response, genetic panels, second-generation hormone treatments, ultra-hypofractionated RT andongoing clinical trials in this clinical scenario. (AU)

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BACKGROUND: Molecular testing aids in the work up and management of patients with pancreatic cysts. This study reports on the value of PancreaSeq® in the evaluation of pancreatic cyst aspirates. METHODS: PancreaSeq® testing at our institution was implemented June 1, 2022. Over a 7-month period (June 1, 2022 through December 31, 2022) 50 cyst aspirates of which 26 (52%) were non-diagnostic 4 (8%) negative 1 (2%) atypical, 17 (34%) suspicious for a mucinous cystic neoplasm (MCN) and 2 (4%) positive for a MCN on cytology were sent for testing. RESULTS: KRAS/GNAS gene mutations were present in 15 non-diagnostic cases and 5 cases suspicious for an MCN. The type of cyst was interpreted as mucinous (IPMN) and the risk of progression to high grade dysplasia/adenocarcinoma as low. KRAS mutations were present in 8 non-diagnostic cases, 1 atypical case, 8 cases suspicious for a MCN and one case positive for an MCN; findings interpreted as a mucinous cyst (IPMN/MCN) with a low risk of progression. BRAF mutations were present in 2 cases; one suspicious and the second positive for an MCN; both interpreted as a mucinous cyst (IPMN) with a low risk of progression. One non-diagnostic case was positive for several gene mutations and copy number alterations; findings interpreted as a mucinous (IPMN) cyst with an elevated risk of progression. VHL mutations were present in 2 negative cases interpreted as serous cystadenomas. Two non-diagnostic, 2 negative and 2 cases suspicious for a MCN were negative for gene mutations. CONCLUSION: Implementation of PancreaSeq® has led to improvements in clinical management of patients with pancreatic cysts.

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BACKGROUND: Most prostate cancer (PC) active surveillance (AS) protocols recommend "Per Protocol" surveillance biopsy (PPSBx) every 1-3 years, even if clinical and imaging parameters remained stable. Herein, we compared the incidence of upgrading on biopsies that met criteria for "For Cause" surveillance biopsy (FCSBx) versus PPSBx. METHODS: We retrospectively reviewed men with GG1 PC on AS in the Michigan Urological Surgery Improvement Collaborative (MUSIC) registry. Surveillance prostate biopsies obtained 1 year after diagnosis were classified as either PPSBx or FCSBx. Biopsies were retrospectively deemed FCSBx if any of these criteria were met: PSA velocity > 0.75 ng/mL/year; rise in PSA > 3 ng from baseline; surveillance magnetic resonance imaging (MRI) (sMRI) with a PIRADS ≥ 4; change in DRE. Biopsies were classified PPSBx if none of these criteria were met. The primary outcome was upgrading to ≥GG2 or ≥GG3 on surveillance biopsy. The secondary objective was to assess for the association of reassuring (PIRADS ≤ 3) confirmatory or surveillance MRI findings and upgrading for patients undergoing PPSBx. Proportions were compared with the chi-squared test. RESULTS: We identified 1773 men with GG1 PC in MUSIC who underwent a surveillance biopsy. Men meeting criteria for FCSBx had more upgrading to ≥GG2 (45%) and ≥GG3 (12%) compared with those meeting criteria for PPSBx (26% and 4.9%, respectively, p < 0.001 and p < 0.001). Men with a reassuring confirmatory or surveillance MRI undergoing PPSBx had less upgrading to ≥GG2 (17% and 17%, respectively) and ≥GG3 (2.9% and 1.8%, respectively) disease compared with men without an MRI (31% and 7.4%, respectively). CONCLUSIONS: Patients undergoing PPSBx had significantly less upgrading compared with men undergoing FCSBx. Confirmatory and surveillance MRI seem to be valuable tools to stratify the intensity of surveillance biopsies for men on AS. These data may help inform the development of a risk-stratified, data driven AS protocol.

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We present a genome polymorphisms/machine learning approach for severe COVID-19 prognosis. Ninety-six Brazilian severe COVID-19 patients and controls were genotyped for 296 innate immunity loci. Our model used a feature selection algorithm, namely recursive feature elimination coupled with a support vector machine, to find the optimal loci classification subset, followed by a support vector machine with the linear kernel (SVM-LK) to classify patients into the severe COVID-19 group. The best features that were selected by the SVM-RFE method included 12 SNPs in 12 genes: PD-L1, PD-L2, IL10RA, JAK2, STAT1, IFIT1, IFIH1, DC-SIGNR, IFNB1, IRAK4, IRF1, and IL10. During the COVID-19 prognosis step by SVM-LK, the metrics were: 85% accuracy, 80% sensitivity, and 90% specificity. In comparison, univariate analysis under the 12 selected SNPs showed some highlights for individual variant alleles that represented risk (PD-L1 and IFIT1) or protection (JAK2 and IFIH1). Variant genotypes carrying risk effects were represented by PD-L2 and IFIT1 genes. The proposed complex classification method can be used to identify individuals who are at a high risk of developing severe COVID-19 outcomes even in uninfected conditions, which is a disruptive concept in COVID-19 prognosis. Our results suggest that the genetic context is an important factor in the development of severe COVID-19.

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BACKGROUND: Diagnosing interstitial lung disease (ILD) remains challenging. Guidelines recommend utilizing a multidisciplinary discussion (MDD) to review clinical and radiographic data and if diagnostic uncertainty persists, then to obtain histopathology. Surgical lung biopsy and transbronchial lung cryobiopsy (TBLC) are acceptable methods, but risks of complications may be prohibitive. The Envisia genomic classifier (EGC) represents another option to determine a molecular usual interstitial pneumonia (UIP) signature to facilitate an ILD diagnosis at MDD with high sensitivity and specificity. We evaluated the concordance between TBLC and EGC at MDD and the safety of this procedure. METHODS: Demographic data, pulmonary function values, chest imaging pattern, procedural information, and MDD diagnosis were recorded. Concordance was defined as agreement between the molecular EGC results and histopathology from TBLC in the context of the patient's High Resolution CT pattern. RESULTS: 49 patients were enrolled. Imaging demonstrated a probable (n = 14) or indeterminate (n = 7) UIP pattern in 43% and an alternative pattern in 57% (n = 28). EGC results were positive for UIP in 37% (n = 18) and negative in 63% (n = 31). MDD diagnosis was obtained in 94% (n = 46) with fibrotic hypersensitivity pneumonitis (n = 17, 35%) and IPF (n = 13, 27%) most common. The concordance between EGC and TBLC at MDD was 76% (37/49) with discordant results seen in 24% (12/49) of patients. CONCLUSIONS: There appears to be reasonable concordance between EGC and TBLC results at MDD. Efforts clarifying the contributions of these tools to an ILD diagnosis may help identify specific patient populations that may benefit from a tailored diagnostic approach.

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INTRODUCTION: A detectable and rising PSA following radical prostatectomy is indicative of recurrent prostate cancer. Salvage radiotherapy (SRT) with/without androgen deprivation therapy represents the main treatment option for these patients and has been historically associated with a biochemical control rate of ~70%. To determine the optimal timing, diagnostic workup, radiotherapy dosefractionation, treatment volume, and use of systemic therapy, several informative studies have been conducted in the last decade. AREAS COVERED: This review examines the recent evidence to guide radiotherapy decision making in the SRT setting. Key topics include adjuvant vs salvage RT, utilization of molecular imaging and genomic classifiers, length of androgen deprivation therapy, inclusion of elective pelvic volume, and emerging role for hypofractionation. EXPERT OPINION: Recently reported trials, conducted in an era prior to the routine use of molecular imaging and genomic classifiers, have been pivotal in establishing the current standard of care for SRT in prostate cancer. However, decisions about radiation treatment and systemic therapy may be tailored based on available prognostic and predictive biomarkers. Data from contemporary clinical trials are awaited to define and establish individualized, biomarker-driven approaches for SRT.

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Radical prostatectomy (RP) is one of the primary treatment options for localised prostate cancer (PCa). Despite its curative intent, 1/3 of patients will experience biochemical recurrence (BCR) during follow-up. Experts have devoted efforts to associate the influence of each individual factor with the risk of BCR to select the optimal treatment for each patient. Optimal management must aim to find a balance between delaying the onset of metastatic disease and overtreating an indolent disease with treatments that can affect quality of life of the patients. Thus far, effective treatment options for men with BCR remain controversial in terms of ideal treatment timing (adjuvant vs. salvage), radiotherapy (RT) fields and doses, selection and duration of systemic therapy and potential synergies between treatments and their therapeutic sequencing. Next-generation imaging techniques, such as Prostate-Specific Membrane Antigen Positron Emission Tomography, are used for early detection of disease progression and exact site of recurrence or progression, thereby enhancing decision making for future disease management. In this review, we evaluate available evidence of controversial topics regarding BCR after RP and explore future directions, such as prognostic and/or predictive factors of response, genetic panels, second-generation hormone treatments, ultra-hypofractionated RT and ongoing clinical trials in this clinical scenario.

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The applicability of the Oncotype DX® (Genomic Health, Inc., Redwood City, CA, USA) recurrence score (RS) in Asian populations is unclear. A 23-gene classifier, RecurIndex® (Amwise Diagnostics, Pte. Ltd., Singapore), has been developed based on the gene expression profiles of early-stage breast cancer patients of ethnic Han Chinese population in Taiwan. This study aimed to compare the performance of the Oncotype DX® RS with the RecurIndex® recurrence index (RI) for predicting relapse-free survival. Therefore, we calculated both the RI and RS for 110 early stage breast cancer patients, with the cut-off value for high-risk recurrence set at 26 and 29 for the RS and the RI, respectively. With relapse-free interval (RFI) as the primary endpoint, the concordance between RS and RI was 78.2% (Kappa value = 0.297). For a median follow-up interval of 27 months, there was a statistically significant difference in RFI between the high- and low-risk groups defined by the RI (p = 0.04) but not between risk groups defined by the RS (p = 0.66). In conclusion, whereas there was high concordance between the RecurIndex® RI and the Oncotype DX RS, the current data showed that the RI had a better discrimination for recurrence risk than the RS. Subsequent studies with larger sample sizes will be needed to confirm the superiority of the RI over the RS in the Asian population.

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INTRODUCTION: Bronchoscopic sampling of pulmonary lesions suspicious for lung cancer is frequently nondiagnostic. A genomic sequencing classifier utilizing bronchial brushings obtained at the time of the bronchoscopy has been shown to provide an accurate reclassification of the risk of malignancy (ROM) based on pre-procedure risk. Our objectives for this study were to determine the frequency with which the classifier up- or down-classifies risk in regular clinical practice and to model the potential clinical utility of that reclassification. METHODS: This observational study retrospectively assessed data from four clinical sites that regularly use the genomic classifier in the bronchoscopic evaluation of indeterminate lesions. Demographics and pre-bronchoscopy ROM were recorded. The frequency of up- and down-classification was calculated. Modeling based on reclassification rates and the performance characteristics of the classifier was performed to demonstrate the potential clinical utility of the result. RESULTS: 86 patients who underwent classifier testing following a nondiagnostic bronchoscopy were included. 45% of patients with high ROM prior to bronchoscopy were reclassified very high-risk. 38% of patients with intermediate ROM were up-or down-classified. 56% of patients with low ROM were reclassified to very low-risk. Overall, 42% of patients had a change in classification. 35% of the study cohort could potentially have avoided additional unnecessary procedures with subsequent guideline-adherent management. CONCLUSIONS: The classifier can guide decision-making following a nondiagnostic bronchoscopy, reclassifying risk in a significant percentage of cases. Use of the classifier should allow more patients with early-stage cancer to proceed directly to curative therapy while helping more patients with benign disease avoid further unnecessary procedures.

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BACKGROUND: ThyroSeq assesses the probability of malignancy (POM) in thyroid fine-needle aspiration cytology specimens diagnosed as atypia of undetermined significance (AUS). The authors investigated whether defined AUS subcategories are associated with specific molecular alterations, the molecular-derived risk of malignancy (MDROM), and the risk of malignancy (ROM). METHODS: Fine-needle aspiration cytology reports of AUS and corresponding results from the ThyroSeq version 3 genomic classifier results were retrieved and subcategorized as follicular cells with either cytologic atypia (FC-C), architectural atypia (FC-A), both cytologic and architectural atypia (FC-CA), or a predominance of Hurthle cells (PHC). The MDROM, ROM, and frequency of molecular alterations by subcategory were computed and analyzed, and p < .05 was considered significant. RESULTS: The final analysis included 541 cases subdivided into 233 with FC-A, 104 with FC-C, 116 with FC-CA, and 88 with PHC. The benign call rate and positive call rate for the AUS category were 72% and 28%, respectively, which varied between AUS subcategories. The MDROM by subcategory was 15.9% FC-A, 20.5% FC-C, 33.8% FC-CA, and 14.4% PHC. Histologic follow-up was available for 155 (28%) AUS cases with a follow-up period ≥12 months. The 95% confidence intervals of the MDROMs overlapped with the ROMs. The highest MDROM and ROM were in the FC-CA subcategory. RAS mutations were present in all subcategories. BRAF V600E mutations and papillary thyroid carcinoma were most frequent in the FC-CA subcategory. Noninvasive follicular thyroid neoplasm with papillary-like nuclear features was significantly more frequent in the FC-C subcategory. CONCLUSIONS: The current results demonstrated that AUS subcategories are associated with specific genetic alterations, the MDROM, and the ROM. Molecular results and an awareness of various cancer probabilities within AUS subcategories can allow for a more tailored management.

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BACKGROUND: Cribriform (CF) and/or intraductal carcinoma (IDC) are associated with more aggressive prostate cancer (CaP) and worse outcomes. OBJECTIVE: The transcriptomic features that typify CF/IDC are not well described and the capacity for clinically utilized genomic classifiers to improve risk modeling for CF/IDC remains undefined. DESIGN, SETTING, AND PARTICIPANTS: We performed a retrospective review of CaP patients who had Decipher testing at a single high-volume institution. Index lesions from radical prostatectomy specimens were identified by genitourinary pathologists who simultaneously reviewed prostatectomy specimens for the presence of CF and IDC features. Patients were grouped based on pathologic features, specifically the absence of CF/IDC (CF-/IDC-), CF positive only (CF+/IDC-), and CF/IDC positive (CF+/IDC+). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Clinical, pathologic, and genomic categorical variables were assessed using the Pearson chi-square test, while quantitative variables were assessed with the Kruskal-Wallis test. Multivariable logistic regression was used to identify the predictors of high-risk Decipher scores (>0.60). A gene set enrichment analysis was performed to identify genes and gene networks associated with CF/IDC status. RESULTS AND LIMITATIONS: A total of 463 patients were included. Patients who were CF+/IDC+ had the highest Decipher risk scores (CF+/IDC+: 0.79 vs CF+/IDC-: 0.71 vs CF-/IDC-: 0.56, p < 0.001). On multivariate logistic regression, predictors of high-risk Decipher scores included the presence of CF, both alone (CF+/IDC-; odds ratio [OR]: 5.45, p < 0.001) or in combination with positive IDC status (CF+/IDC+; OR: 6.87, p < 0.001). On the gene set enrichment analysis, MYC pathway upregulation was significantly enriched in tumor samples from CF/IDC-positive patients (normalized enrichment score [NES]: 1.65, p = 0.046). Other enriched pathways included E2F targets (NES: 1.69, p = 0.031) and oxidative phosphorylation (NES: 1.68, =0 .033). CONCLUSIONS: This is the largest series identifying an association between a clinically validated genomic classifier and the presence of CF and IDC at radical prostatectomy. Tumors with CF and intraductal features were associated with aggressive transcriptomic signatures. PATIENT SUMMARY: Genomic-based tests are becoming readily available for the management of prostate cancer. We observed that Decipher, a commonly used genomic test in prostate cancer, correlates with unfavorable features in tissue specimens.

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Background: Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis (IPF), the prototypical interstitial lung disease (ILD). Diagnosis of IPF requires that a typical UIP pattern be identified by using high-resolution chest computed tomography or lung sampling. A genomic classifier for UIP has been developed to predict histopathologic UIP by using lung samples obtained through bronchoscopy. Objective: To perform a systematic review to evaluate genomic classifier testing in the detection of histopathologic UIP to inform new American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax guidelines. Data Sources: Medline, Embase, and the Cochrane Central Register of Controlled Trials were searched through June 2020. Data Extraction: Data were extracted from studies that enrolled patients with ILD and reported the use of genomic classifier testing. Synthesis: Data were aggregated across studies via meta-analysis. The quality of the evidence was appraised by using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: Genomic classifier testing had a sensitivity of 68% (95% confidence interval [CI], 55-73%) and a specificity of 92% (95% CI, 81-95%) in predicting the UIP pattern in ILD. Confidence in an IPF diagnosis increased from 43% to 93% in one cohort and from 59% to 89% in another cohort. Agreement levels in categorical IPF and non-IPF diagnoses measured by using a concordance coefficient were 0.75 and 0.64 in the two cohorts. The quality of evidence was moderate for test characteristics and very low for both confidence and agreement. Conclusions: Genomic classifier testing predicts histopathologic UIP in patients with ILD with a specificity of 92% and improves diagnostic confidence; however, sensitivity is only 68%, and testing is not widely available.

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BACKGROUND: The multigene genomic classifier ThyroSeq V3 has proven to be an effective triage modality in the management of indeterminate thyroid nodules. This study reports on the clinical management of patients with indeterminate thyroid nodules. METHODS: ThyroSeq V3 testing at our institution was implemented April 1, 2019. Over a 17-month period (1 April 2019 through 31 August 2020). Thirty five indeterminate thyroid nodules were sent for testing; diagnoses included FLUS, suspicious for a follicular/Hurthle cell neoplasm (SFN/SHCN) and suspicious for papillary thyroid carcinoma (SPTC). There were 26 females, nine males; mean age 56 years. Aspirated nodules, mean size of 2 cm, were evaluated by rapid on site evaluation with adequacy assessed by the cytopathologist via telecytology. RESULTS: Of the 35 nodules, 17 (49%) were positive and 18 (51%) were negative on ThyroSeq Testing. All of the 17 positive nodules (four FLUS, 10 SFN/SHCN, three SPTC) showed neoplastic lesions; five adenomas, one NIFTP and 11 carcinomas on surgical resection. Only 4 (22%) of the 18 nodules that were negative were resected and showed two colloid/adenomatous nodules, one NIFTP and one follicular variant of papillary thyroid carcinoma. As FLUS FNAs were the majority of the indeterminate nodules, a comparison was made pre-and post ThyroSeq testing. Prior to ThyroSeq testing the majority (69%) of the nodules were resected as compared to 36% post implementation; a statistically significant value (P < .005). Surgical resection yielded a majority of benign nodules pre-ThyroSeq testing, 68%, as compared to post-ThyroSeq testing, 25%. CONCLUSION: Implementation of ThyroSeq V3 testing has led to improvements in clinical management of patients with indeterminate thyroid nodules.

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INTRODUCTION: The combined clinical cell-cycle risk (CCR) score is a validated model that combines the cell-cycle progression (CCP) score with the University of California San Francisco Cancer of the Prostate Risk Assessment (CAPRA) score. This score determines the risk of progressive disease for men with prostate cancer. Here, we further validate the prognostic ability of the CCR score and evaluate its ability to help determine which patients may safely forgo multimodality therapy. PATIENTS AND METHODS: We evaluated the CCR and a CCR-based multimodality threshold (2.112) in a retrospective, multi-institutional cohort of men with National Comprehensive Cancer Network intermediate- or high-risk localized disease (N = 718). These men received single or multimodality therapy (androgen deprivation with radiation [RT], or surgery with adjuvant RT or hormones). RESULTS: CCR score prognosticated metastasis for single-modality therapy, as a continuous variable (hazard ratio, 3.97; 95% confidence interval [CI], 2.61-6.06) and when dichotomized at the threshold (hazard ratio, 15.90; 95% CI, 5.43-46.52). The 10-year Kaplan-Meier risk for those receiving single-modality (RT or surgical) therapy with CCR scores below and above the threshold for single-modality treatment was 4.3% (95% CI, 1.0%-17.1%) and 20.4% (95% CI, 13.2%-30.7%), respectively. Using the threshold, 27% of men with newly diagnosed high-risk and 73% with unfavorable intermediate-risk disease could avoid multimodality therapy. CONCLUSIONS: Patients with CCR scores below the multimodality threshold (2.112) may safely forgo multimodality therapy. The CCR score can be used as a decision aid to counsel men whether or not single-modality therapy would be sufficient for their intermediate- or high-risk prostate cancer.

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INTRODUCTION: Prostate tumors with TP53 gene mutations are molecularly heterogenous, and the presence of TP53 gene mutations has been linked to inferior outcomes. We developed an RNA-based gene signature that detects underlying TP53 gene mutations and identifies wild-type prostate tumors that are analogous to TP53-mutant tumors. MATERIALS AND METHODS: Using genomic expression profiles from The Cancer Genome Atlas, we developed a mutation signature score to predict prostatic tumors with a molecular fingerprint similar to tumors with TP53 mutations. Area under the receiver operating characteristic curve assessed model accuracy in predicting TP53 mutations, and Cox regression models measured association between the signature and progression-free survival and metastasis-free survival (MFS). RESULTS: The TP53 signature score achieved an area under the receiver operating characteristic curve of 0.84 in the training and 0.82 in the validation cohorts for predicting an underlying mutation. In three retrospective cohorts, a high score was prognostic for poor 5-year MFS: 46% versus 81% (hazard ratio [HR], 3.05; P < .0001; Johns Hopkins University cohort), 64% versus 83% (HR, 2.77; P < .0001; Mayo Clinic cohort), and 71% versus 97% (HR, 6.8; P = .0001; Brigham and Women's Hospital cohort). The signature also identified TP53 wild-type tumors molecularly analogous to TP53 mutant tumors, wherein high signature score correlated with worse 5-year MFS (50% vs. 82%; HR, 3.05; P < .0001). CONCLUSIONS: This novel mutational signature predicted tumors with TP53 mutations, identified TP53 wild-type tumors analogous to mutant tumors, and was independently associated with poor MFS. This signature can therefore be used to strengthen existing clinical risk-stratification tools.

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BACKGROUND: Over 20% of men diagnosed with prostate cancer (PC) are ≥75 yr old. More objective disease-specific indices for predicting outcomes beyond chronological age are necessary. OBJECTIVE: To analyze age-related differences in clinical-genomic prognostic features of aggressiveness in localized PC. DESIGN, SETTING, AND PARTICIPANTS: A retrospective multicenter cross-sectional study reported the use of the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines. Clinical-genomic data of patients who underwent a prostate biopsy or radical prostatectomy (RP) were obtained from the Decipher Genomic Resource Information Database (NCT02609269). INTERVENTION: Our analyses focused on the 22-gene Decipher genomic classifier (GC) and 50-gene (PAM50) models in the biopsy and RP cohorts stratified by age. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was the impact of age on GC scores and PAM50 molecular subtypes. Prognostic indices including Decipher GC scores, PAM50 molecular subtypes, National Comprehensive Cancer Network risk categories, and ISUP grade groups (IGGs) were stratified by age using multivariable logistic regression analyses. RESULTS AND LIMITATIONS: Within histological low-risk IGGs, there were a higher proportion of patients with high-risk Decipher biopsy scores with age (age <60 yr: 10.1% IGG 1 and 29.9% IGG 2 vs age ≥80 yr: 22% IGG 1 and 37.7% IGG 2). The prevalence of the adverse phenotype luminal B (PAM50-defined) increased with age (age <60 yr: 22.7% and 40.2% vs age ≥80 yr: 29.7% and 49.1%, in patients with IGG 1 and IGG 2, respectively). In IGGs 3-5, no age differences were observed. Multivariable models demonstrated that each age decile entailed a 19% (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.10-1.29, p < 0.001) and a 10% (OR 1.1, 95% CI 1.05-1.16) increased probability for a high-risk Decipher biopsy and RP score, respectively. Aside from an obvious selection bias, data on race, family history, prostate volume, and long-term follow-up outcomes were unavailable. CONCLUSIONS: These data demonstrated that elderly men with favorable pathology (IGG 1-2), might harbor more aggressive disease than younger patients based on validated GC scores. PATIENT SUMMARY: The presented clinical-genomic data demonstrate that elderly patients with low-risk prostate cancer might harbor more aggressive disease than their younger counterparts. This suggests that standard well-accepted paradigm of elderly prostate cancer patients not being aggressively treated, based solely on their chronological age, might need to be reconsidered.

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Motivated by the problem of classifying individuals with a disease versus controls using a functional genomic attribute as input, we present relatively efficient general purpose inner product-based kernel classifiers to classify the test as a normal or disease sample. We encode each training sample as a string of 1 s (presence) and 0 s (absence) representing the attribute's existence across ordered physical blocks of the subdivided genome. Having binary-valued features allows for highly efficient data encoding in the computational basis for classifiers relying on binary operations. Given that a natural distance between binary strings is Hamming distance, which shares properties with bit-string inner products, our two classifiers apply different inner product measures for classification. The active inner product (AIP) is a direct dot product-based classifier whereas the symmetric inner product (SIP) classifies upon scoring correspondingly matching genomic attributes. SIP is a strongly Hamming distance-based classifier generally applicable to binary attribute-matching problems whereas AIP has general applications as a simple dot product-based classifier. The classifiers implement an inner product between N = 2 n dimension test and train vectors using n Fredkin gates while the training sets are respectively entangled with the class-label qubit, without use of an ancilla. Moreover, each training class can be composed of an arbitrary number m of samples that can be classically summed into one input string to effectively execute all test-train inner products simultaneously. Thus, our circuits require the same number of qubits for any number of training samples and are O ( log N ) in gate complexity after the states are prepared. Our classifiers were implemented on ibmqx2 (IBM-Q-team 2019b) and ibmq_16_melbourne (IBM-Q-team 2019a). The latter allowed encoding of 64 training features across the genome.