Transcriptome-wide subtyping of pediatric and adult T cell acute lymphoblastic leukemia in an international study of 707 cases.

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy of T cell progenitors, known to be a heterogeneous disease in pediatric and adult patients. Here we attempted to better understand the disease at the molecular level based on the transcriptomic landscape of 707 T-ALL patients (510 pediatric, 190 adult patients, and 7 with unknown age; 599 from published cohorts and 108 newly investigated). Leveraging the information of gene expression enabled us to identify 10 subtypes (G1­G10), including the previously undescribed one characterized by GATA3 mutations, with GATA3R276Q capable of affecting lymphocyte development in zebrafish. Through associating with T cell differentiation stages, we found that high expression of LYL1/LMO2/SPI1/HOXA (G1­G6) might represent the early T cell progenitor, pro/precortical/cortical stage with a relatively high age of disease onset, and lymphoblasts with TLX3/TLX1 high expression (G7­G8) could be blocked at the cortical/postcortical stage, while those with high expression of NKX2-1/TAL1/LMO1 (G9­G10) might correspond to cortical/postcortical/mature stages of T cell development. Notably, adult patients harbored more cooperative mutations among epigenetic regulators, and genes involved in JAK-STAT and RAS signaling pathways, with 44% of patients aged 40 y or above in G1 bearing DNMT3A/IDH2 mutations usually seen in acute myeloid leukemia, suggesting the nature of mixed phenotype acute leukemia.

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma.

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Molecular classification and biomarkers of outcome with immunotherapy in extensive-stage small-cell lung cancer: analyses of the CASPIAN phase 3 study.

BACKGROUND: We explored potential predictive biomarkers of immunotherapy response in patients with extensive-stage small-cell lung cancer (ES-SCLC) treated with durvalumab (D) + tremelimumab (T) + etoposide-platinum (EP), D + EP, or EP in the randomized phase 3 CASPIAN trial. METHODS: 805 treatment-naïve patients with ES-SCLC were randomized (1:1:1) to receive D + T + EP, D + EP, or EP. The primary endpoint was overall survival (OS). Patients were required to provide an archived tumor tissue block (or ≥ 15 newly cut unstained slides) at screening, if these samples existed. After assessment for programmed cell death ligand-1 expression and tissue tumor mutational burden, residual tissue was used for additional molecular profiling including by RNA sequencing and immunohistochemistry. RESULTS: In 182 patients with transcriptional molecular subtyping, OS with D ± T + EP was numerically highest in the SCLC-inflamed subtype (n = 10, median 24.0 months). Patients derived benefit from immunotherapy across subtypes; thus, additional biomarkers were investigated. OS benefit with D ± T + EP versus EP was greater with high versus low CD8A expression/CD8 cell density by immunohistochemistry, but with no additional benefit with D + T + EP versus D + EP. OS benefit with D + T + EP versus D + EP was associated with high expression of CD4 (median 25.9 vs. 11.4 months) and antigen-presenting and processing machinery (25.9 vs. 14.6 months) and MHC I and II (23.6 vs. 17.3 months) gene signatures, and with higher MHC I expression by immunohistochemistry. CONCLUSIONS: These findings demonstrate the tumor microenvironment is important in mediating better outcomes with D ± T + EP in ES-SCLC, with canonical immune markers associated with hypothesized immunotherapy mechanisms of action defining patient subsets that respond to D ± T. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03043872.

Multi-omics analysis reveals epigenetically regulated processes and patient classification in lung adenocarcinoma.

Aberrant DNA methylation is a hallmark of many cancer types. Despite our knowledge of epigenetic and transcriptomic alterations in lung adenocarcinoma (LUAD), we lack robust multi-modal molecular classifications for patient stratification. This is partly because the impact of epigenetic alterations on lung cancer development and progression is still not fully understood. To that end, we identified disease-associated processes under epigenetic regulation in LUAD. We performed a genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis by integrating DNA methylation and gene expression data from 453 patients in the TCGA cohort. Using a community detection algorithm, we identified distinct communities of CpG-gene associations with diverse biological processes. Interestingly, we identified a community linked to hormone response and lipid metabolism; the identified CpGs in this community were enriched in enhancer regions and binding regions of transcription factors such as FOXA1/2, GRHL2, HNF1B, AR, and ESR1. Furthermore, the CpGs were connected to their associated genes through chromatin interaction loops. These findings suggest that the expression of genes involved in hormone response and lipid metabolism in LUAD is epigenetically regulated through DNA methylation and enhancer-promoter interactions. By applying consensus clustering on the integrated expression-methylation pattern of the emQTL-genes and CpGs linked to hormone response and lipid metabolism, we further identified subclasses of patients with distinct prognoses. This novel patient stratification was validated in an independent patient cohort of 135 patients and showed increased prognostic significance compared to previously defined molecular subtypes.

Prognostic Value of P63 Expression in Muscle-Invasive Bladder Cancer and Association with Molecular Subtypes-Preliminary Report.

There is an ongoing need for biomarkers that could reliably predict the outcome of BC and that could guide the management of this disease. In this setting, we aimed to explore the prognostic value of the transcription factor P63 in patients with muscle-invasive bladder cancer (MIBC) having undergone radical cystectomy. The correlation between P63 expression and clinicopathological features (tumor stage, nodes involvement, patterns of muscularis propria invasion, papillary architecture, anaplasia, concomitant carcinoma in situ, lymphovascular invasion, perineural invasion, necrosis) and molecular subtyping (basal and luminal type tumors) was tested in 65 radical cystectomy specimens and matched with cancer-specific survival (CSS) and overall survival (OS). P63-negative tumors displayed significantly higher rates of pattern 2 of muscularis propria invasion (50% vs. 14%, p = 0.002) and variant histology (45% vs. 19%, p = 0.022) compared to P63-positive ones. According to the combined expression of CK5/6 and CK20 (Algorithm #1), P63-positive and P63-negative tumors were mostly basal-like and double-negative, respectively (p = 0.004). Using Algorithm #2, based on the combined expression of CK5/6 and GATA3, the vast majority of tumors were luminal overall and in each group (p = 0.003). There was no significant difference in CSS and OS between P63-positive and P63-negative tumors, but the former featured a trend towards longer OS. Though associated with pathological features harboring negative prognostic potential, P63 status as such failed to predict CSS and OS. That said, it may contribute to better molecular subtyping of MIBC.

Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination.

Understanding the molecular mechanisms underpinning diverse vaccination responses is critical for developing efficient vaccines. Molecular subtyping can offer insights into heterogeneous nature of responses and aid in vaccine design. We analyzed multi-omic data from 62 haemagglutinin seasonal influenza vaccine recipients (2019-2020), including transcriptomics, proteomics, glycomics, and metabolomics data collected pre-vaccination. We performed a subtyping analysis on the integrated data revealing five subtypes with distinct molecular signatures. These subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining the effectiveness of seasonal vaccines. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

Intertumoral heterogeneity of bifocal breast cancer: a morphological and molecular study.

PURPOSE: To analyze concordance rates between individual foci of bifocal BC for histological grade, type and intrinsic subtype based on immunohistochemical (IHC) and mRNA-testing using MammaTyper. METHODS: We evaluated histological grade and type as well as intrinsic subtype based on IHC status for estrogen and progesterone receptors, HER2 and the mitotic activity index in 158 individual foci of 79 bifocal BC. A subgroup of 31 cases additionally underwent mRNA-based subtyping using the MammaTyper (MT) test. We calculated concordance rates between individual foci, as well as Cohen's Kappa (K). RESULTS: For 79 bifocal BC, concordance rates between individual foci for grade, histological type, and IHC-based subtype were 69.6% (K=0.53), 92.4% (K=0.81), and 74.7% (K=0.62), respectively. In the MT subgroup of 31 bifocal BC, concordance rates between individual foci for grade, histological type, IHC-based and mRNA-based intrinsic subtype were 87.1% (K=0.78), 90.3% (K=0.73), 87.1% (K=0.82), and 87.1% (K=0.7), respectively. Overall concordance between IHC- and mRNA-based subtype in the MT subgroup was 79% (K=0.7). In 6/79 cases (7.6%), testing of the smaller focus added clinically relevant information either on IHC- or mRNA-level: four cases showed high hormonal receptor expression while the expression in the larger focus was negative or low, warranting additional endocrine treatment; two cases presented with higher proliferative activity in the smaller focus, warranting additional chemotherapy. CONCLUSION: In bifocal BC, intertumoral heterogeneity on the morphological, immunohistochemical and molecular level is common, with discordant intrinsic subtype in up to 25% between individual foci, with about 8% clinically relevant discordances.

Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model.

BACKGROUND: Molecular subtyping is expected to enable precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE), we aimed to develop a novel TIDE-based subtyping strategy to guide personalized immunotherapy in the bladder cancer (BC). METHODS: Transcriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. Subsequently, consensus clustering was applied to classify BC patients based on TIDE marker-genes. Patients' clinicopathological, molecular features and signaling pathways of the different TIDE subtypes were well characterized. We also utilize the deconvolution algorithms to analyze the tumor microenvironment, and further explore the sensitivity and mechanisms of each subtype to immunotherapy. Furthermore, BC patient clinical information, real-world BC samples and urine samples were collected for the validation of our findings, which were used for RNA-seq analysis, H&E staining, immunohistochemistry and immunofluorescence staining, and enzyme-linked immunosorbent assay. Finally, we also explored the conservation of our novel TIDE subtypes in pan-cancers. RESULTS: We identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples and collected patient clinical data. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors. CONCLUSIONS: Our novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.

The molecular subtyping and precision medicine in triple-negative breast cancer---based on Fudan TNBC classification.

Triple-negative breast cancer (TNBC) is widely recognized as the most aggressive form of breast cancer, occurring more frequently in younger patients and characterized by high heterogeneity, early distant metastases and poor prognosis. Multiple treatment options have failed to achieve the expected therapeutic effects due to the lack of clear molecular targets. Based on genomics, transcriptomics and metabolomics, the multi-omics analysis further clarifies TNBC subtyping, which provides a greater understanding of tumour heterogeneity and targeted therapy sensitivity. For instance, the luminal androgen receptor subtype (LAR) exhibits responsiveness to anti-AR therapy, and the basal-like immune-suppressed subtype (BLIS) tends to benefit from poly (ADP-ribose) polymerase inhibitors (PARPis) and anti-angiogenic therapy. The efficacy of multi-dimensional combination therapy holds immense importance in guiding personalized and precision medicine for TNBC. This review offers a systematic overview of recent FuDan TNBC molecular subtyping and its role in the instruction of clinical precision therapy.

Comprehensive Transcriptome Analysis Expands lncRNA Functional Profiles in Breast Cancer.

Breast cancer is a heterogeneous disease that arises as a multi-stage process involving multiple cell types. Patients diagnosed with the same clinical stage and pathological classification may have different prognoses and therapeutic responses due to alterations in molecular genetics. As an essential marker for the molecular subtyping of breast cancer, long non-coding RNAs (lncRNAs) play a crucial role in gene expression regulation, cell differentiation, and the maintenance of genomic stability. Here, we developed a modular framework for lncRNA identification and applied it to a breast cancer cohort to identify novel lncRNAs not previously annotated. To investigate the potential biological function, regulatory mechanisms, and clinical relevance of the novel lncRNAs, we elucidated the genomic and chromatin features of these lncRNAs, along with the associated protein-coding genes and putative enhancers involved in the breast cancer regulatory networks. Furthermore, we uncovered that the expression patterns of novel and annotated lncRNAs identified in breast cancer were related to the hormone response in the PAM50 subtyping criterion, as well as the immune response and progression states of breast cancer across different immune cells and immune checkpoint genes. Collectively, the comprehensive identification and functional analysis of lncRNAs revealed that these lncRNAs play an essential role in breast cancer by altering gene expression and participating in the regulatory networks, contributing to a better insight into breast cancer heterogeneity and potential avenues for therapeutic intervention.

Molecular subtyping of small cell lung cancer.

Small cell lung cancer (SCLC), originating from lung neuroendocrine stem cells, is a common pulmonary malignant tumor. SCLC, with poor prognoses, accounts for approximately 13-15% of all lung cancer cases. Due to the slow progress of clinical treatment, the 5-year survival rate of SCLC has remained below 7% for many years. In recent years, with the development and popularity of gene sequencing technologies, we were able to better grasp patterns of gene mutations and tumor evolution in SCLC. Thus, appropriate molecular subtyping strategies have been established to help predict patients' prognoses and develop the treatment regimen for SCLC more accurately. In this narrative review, we aim to summarize the evolution of mutation-based molecular subtyping of SCLC, as well as the trends in molecular targeting and immunotherapeutic for SCLC. Based on the latest sequencing data for SCLC, thereafter, we discuss therapeutic opinions of SCLC from basic to clinic. This review may provide a basis for guiding the development of subsequent individualized precision-targeted therapy for SCLC patients to improve their clinical prognoses.

BluePrint molecular subtypes predict response to neoadjuvant pertuzumab in HER2-positive breast cancer.

BACKGROUND: The introduction of pertuzumab has greatly improved pathological complete response (pCR) rates in HER2-positive breast cancer, yet effects on long-term survival have been limited and it is uncertain which patients derive most benefit. In this study, we determine the prognostic value of BluePrint subtyping in HER2-positive breast cancer. Additionally, we evaluate its use as a biomarker for predicting response to trastuzumab-containing neoadjuvant chemotherapy with or without pertuzumab. METHODS: From a cohort of patients with stage II-III HER2-positive breast cancer who were treated with neoadjuvant chemotherapy and trastuzumab with or without pertuzumab, 836 patients were selected for microarray gene expression analysis, followed by readout of BluePrint standard (HER2, Basal and Luminal) and dual subtypes (HER2-single, Basal-single, Luminal-single, HER2-Basal, Luminal-HER2, Luminal-HER2-Basal). The associations between subtypes and pathological complete response (pCR), overall survival (OS) and breast cancer-specific survival (BCSS) were assessed, and pertuzumab benefit was evaluated within the BluePrint subgroups. RESULTS: BluePrint results were available for 719 patients. In patients with HER2-type tumors, the pCR rate was 71.9% in patients who received pertuzumab versus 43.5% in patients who did not (adjusted Odds Ratio 3.43, 95% CI 2.36-4.96). Additionally, a significantly decreased hazard was observed for both OS (adjusted hazard ratio [aHR] 0.45, 95% CI 0.25-0.80) and BCSS (aHR 0.46, 95% CI 0.24-0.86) with pertuzumab treatment. Findings were similar in the HER2-single subgroup. No significant benefit of pertuzumab was seen in other subtypes. CONCLUSIONS: In patients with HER2-type or HER2-single-type tumors, pertuzumab significantly improved the pCR rate and decreased the risk of breast cancer mortality, which was not observed in other subtypes. BluePrint subtyping may be valuable in future studies to identify patients that are likely to be highly sensitive to HER2-targeting agents.

Genomic Profiling Reveals Differences in Primary Central Nervous System Lymphoma and Large B-Cell Lymphoma, With Subtyping Suggesting Sensitivity to BTK Inhibition.

BACKGROUND: B-cell primary central nervous system (CNS) lymphoma (PCL) is diffuse large B-cell lymphoma (DLBCL) confined to the CNS. Less than 50% of patients with PCL achieve complete remission with current therapies. We describe the findings from comprehensive genomic profiling (CGP) of a cohort of 69 patients with PCL, 36 cases of secondary CNS lymphoma (SCL), and 969 cases of DLBCL to highlight their differences and characterize the PCL cohort. In addition, we highlight the differences in frequency of germinal center B-cell like (GCB) and non-GCB subtypes and molecular subtypes, particularly MCD and EZH subtypes, between PCL and DLBCL. MATERIALS AND METHODS: Sixty-nine cases of B-cell PCL, 36 cases of secondary CNS lymphoma (SCL), and 969 cases of DLBCL were evaluated by CGP of 405 genes via DNAseq and 265 genes via RNAseq for fusions (FoundationOne Heme). Tumor mutational burden (TMB) was calculated from 1.23 Mb of sequenced DNA. RESULTS: Genomic alterations with significant differences between PCL and DLBCL included MYD88, ETV6, PIM1, PRDM1, CXCR4, TP53, and CREBBP, while only MYD88 was significantly different between SCL and DLBCL. PCL cases were significantly enriched for the MCD molecular subtypes, which have an excellent response to BTKi. We report a patient with a durable complete response to BTKi consistent with their genomic profile. EBV status, CD274 amplification, and TMB status suggest that 38% of PCL patients may benefit from ICPI; however further study is warranted. CONCLUSION: CGP of PCLs reveals biomarkers, genomic alterations, and molecular classifications predictive of BTKi efficacy and potential ICPI efficacy. Given the limitations of standard of care for PCL, CGP is critical to identify potential therapeutic approaches for patients in this rare form of lymphoma.

p53 Expression in Luminal Breast Cancer Correlates With TP53 Mutation and Primary Endocrine Resistance.

TP53 mutation is associated with primary endocrine resistance in luminal breast cancer (BC). Nuclear accumulation of p53, as determined by immunohistochemistry (IHC), is a surrogate marker for TP53 mutation. The immunohistochemical p53 index that defines a p53-positive status is not well established. This study determined the optimal p53 index cutoff to identify luminal BCs harboring TP53 mutations. In total, 364 luminal BCs from the West German Study Group ADAPT trial (NCT01779206) were analyzed for TP53 mutations by next-generation sequencing and for p53 expression by IHC (DO-7 antibody). P53 indices were determined by automated image analysis. All tumors were from patients treated with short-term preoperative endocrine therapy (pET; tamoxifen or aromatase inhibitor) before tumor resection. IHC evaluation included needle biopsies before therapy (baseline) and resections specimens after therapy (post-pET). Optimal p53 index cutoffs were defined with Youden statistics. TP53 mutations were detected in 16.3% of BC cases. The median p53 indices were significantly higher in TP53-mutated BCs compared to BCs harboring wild-type TP53 (baseline: 47.0% vs 6.4%, P < .001; post-pET: 50.1% vs 1.1%, P < .001). Short-term pET decreased p53 indices in BCs harboring wild-type TP53 (P < .001) but not in TP53-mutated BCs (P = .102). For baseline biopsies, the optimal p53 index cutoff was ≥34.6% (specificity 0.92, sensitivity 0.63, Youden index 0.54, accuracy: 0.87). For post-pET specimens, the optimal cutoff was ≥25.3% (specificity 0.95, sensitivity 0.65, Youden index 0.60, accuracy: 0.90). Using these cutoffs to define the p53 status, p53-positive BCs were >2-fold more common in pET nonresponders compared to pET responders (baseline: 37/162, 22.8% vs 18/162, 11.1%, P = .007; post-pET: 36/179, 20.1% vs 16/179, 8.9%, P = .004). In summary, IHC for p53 identifies TP53-mutated luminal BCs with high specificity and accuracy. Optimal cutoffs are ≥35% and ≥25% for treatment-naïve and endocrine-pretreated patients, respectively.

Clinicopathological Features and Survival Analysis in Molecular Subtypes of Muscle-Invasive Bladder Cancer.

Molecular subtyping of bladder cancer (BC) aims to capture the biological heterogeneity of this complex disease in order to provide better patient risk stratification. Immunohistochemical (IHC) markers are regarded as promising surrogates to classify BCs into luminal and basal subtypes in routine practice. We investigated the correlation between the molecular subclassification, assessed through IHC, and the conventional prognostic variables of a cohort of 93 muscle-invasive BCs (MIBCs), with a focus on the pattern of muscularis propria (MP) invasion, and evaluated their association with outcome. Basal, luminal, double-positive (DP), and double-negative (DN) phenotypes were identified according to the coordinate expression of 1 basal (CK5/6) and 2 luminal (CK20, GATA3) markers, and accounted for 33.3%, 32.3%, 3.2%, and 31.2% (Scheme #1) and 9.7%, 60.2%, 26.9%, and 3.2% (Scheme #2). There was a significant association between the pattern of MP invasion and the molecular subtypes according to Scheme #2, in that all 8 basal and DN cases, as well as 83% of DP cases, had a non-infiltrative invasion pattern. No consistent differences were observed in terms of OS and CSS between the molecular subtypes obtained through surrogate IHC markers. In keeping with previous studies, we report the correlation between the identification of BC subtypes and the presence of morphological prognostic factors, supporting the need for a comprehensive pathological evaluation, including clinicopathological and molecular parameters, in order to improve the diagnosis and management of MIBC.

Molecular classification of hormone-sensitive and castration-resistant prostate cancer, using nonnegative matrix factorization molecular subtyping of primary and metastatic specimens.

BACKGROUND: Despite the rapidly evolving therapeutic landscape, immunotherapy has demonstrated limited activity in prostate cancer. A greater understanding of the molecular landscape, particularly the expression of immune-related pathways, will inform future immunotherapeutic strategies. Consensus nonnegative matrix factorization (cNMF) is a novel model of molecular classification analyzing gene expression data, focusing on biological interpretation of metagenes and selecting meaningful clusters. OBJECTIVE: We aimed to identify molecular subtypes of prostate cancer using cNMF and correlate these with existing biomarkers to inform future immunotherapeutic strategies. METHODS: A cohort of archival tumor specimens from hormone-sensitive and castration-resistant disease was studied. Whole transcriptomic profiles were generated using TruSeq RNA Access technology and subjected to cNMF. Comprehensive genomic profiling was performed with the FoundationOne assay. NMF subtypes were characterized by gene expression pathways, genomic alterations and correlated with clinical data, then applied to The Cancer Genome Atlas data set. RESULTS: We studied 164 specimens, including 52 castration-resistant and 13 paired primary/metastatic specimens. cNMF identified four distinct subtypes. NMF1 (19%) is enriched for immune-related and stromal-related pathways with transforming growth factor ß (TGFß) signature. NMF2 (36%) is associated with FOXO-mediated transcription signature and AKT signaling, NMF3 (26%) is enriched for ribosomal RNA processing, while NMF4 (19%) is enriched for cell cycle and DNA-repair pathways. The most common gene alterations included TMPRSS22 (42%), TP53 (23%), and DNA-repair genes (19%), occurring across all subtypes. NMF4 is significantly enriched for MYC and Wnt-signaling gene alterations. TMB, CD8 density, and PD-L1 expression were low overall. NMF1 and NMF4 were NMF2 was associated with superior overall survival. CONCLUSIONS: Using cNMF, we identified four molecularly distinct subtypes which may inform treatment selection. NMF1 demonstrates the most inflammatory signature with asuppressive TGFß signature, suggesting potential benefit with immunotherapy combination strategies targeting TGFß and PD-(L)1. Prospective studies are required to evaluate the use of this novel model to molecularly stratify patients for optimal treatment selection.

Linear Regression Equations To Predict ß-Lactam, Macrolide, Lincosamide, and Fluoroquinolone MICs from Molecular Antimicrobial Resistance Determinants in Streptococcus pneumoniae.

Antimicrobial resistance in Streptococcus pneumoniae represents a threat to public health, and monitoring the dissemination of resistant strains is essential to guiding health policy. Multiple-variable linear regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to antimicrobial MICs for penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim-sulfamethoxazole. Training data sets consisting of Canadian S. pneumoniae isolates obtained from 1995 to 2019 were used to generate multiple-variable linear regression equations for each antimicrobial. The regression equations were then applied to validation data sets of Canadian (n = 439) and U.S. (n = 607 and n = 747) isolates. The MICs for ß-lactam antimicrobials were fully explained by amino acid substitutions in motif regions of the penicillin binding proteins PBP1a, PPB2b, and PBP2x. Accuracies of predicted MICs within 1 doubling dilution to phenotypically determined MICs were 97.4% for penicillin, 98.2% for ceftriaxone, 94.8% for erythromycin, 96.6% for clarithromycin, 98.2% for clindamycin, 100% for levofloxacin, and 98.8% for trimethoprim-sulfamethoxazole, with an overall sensitivity of 95.8% and specificity of 98.0%. Accuracies of predicted MICs to the phenotypically determined MICs were similar to those of phenotype-only MIC comparison studies. The ability to acquire detailed antimicrobial resistance information directly from molecular determinants will facilitate the transition from routine phenotypic testing to whole-genome sequencing analysis and can fill the surveillance gap in an era of increased reliance on nucleic acid assay diagnostics to better monitor the dynamics of S. pneumoniae.

Annotated Whole-Genome Multilocus Sequence Typing Schema for Scalable High-Resolution Typing of Streptococcus pyogenes.

Streptococcus pyogenes is a major human pathogen with high genetic diversity, largely created by recombination and horizontal gene transfer, making it difficult to use single nucleotide polymorphism (SNP)-based genome-wide analyses for surveillance. Using a gene-by-gene approach on 208 complete genomes of S. pyogenes, a novel whole-genome multilocus sequence typing (wgMLST) schema was developed, comprising 3,044 target loci. The schema was used for core-genome MLST (cgMLST) analyses of previously published data sets and 265 newly sequenced draft genomes with other molecular and phenotypic typing data. Clustering based on cgMLST data supported the genetic heterogeneity of many emm types and correlated poorly with pulsed-field gel electrophoresis macrorestriction profiling, superantigen gene profiling, and MLST sequence type, highlighting the limitations of older typing methods. While 763 loci were present in all isolates of a data set representative of S. pyogenes genetic diversity, the proposed schema allows scalable cgMLST analysis, which can include more loci for an increased resolution when typing closely related isolates. The cgMLST and PopPUNK clusters were broadly consistent in this diverse population. The cgMLST analyses presented results comparable to those of SNP-based methods in the identification of two recently emerged sublineages of emm1 and emm89 and the clarification of the genetic relatedness among isolates recovered in outbreak contexts. The schema was thoroughly annotated and made publicly available on the chewie-NS online platform (https://chewbbaca.online/species/1/schemas/1), providing a framework for high-resolution typing and analyzing the genetic variability of loci of particular biological interest.

Functional characterization and clinical significance of super-enhancers in lung adenocarcinoma.

Super-enhancers (SEs) are important transcriptional regulators in tumorigenesis; however, the functional characterization and clinical significance of SEs in lung adenocarcinoma (LUAD) remain unclear. By using H3K27ac ChIP-seq data of two LUAD cell lines and eight lung tissues, we detected 1045 cancer-specific and 5032 normal-specific SEs. Compared to normal-specific SEs, cancer-specific SEs have different regulatory mechanisms where associated target genes were enriched in critical tumor-related pathways and tended to be regulated by transcription factors of Fos Proto-Oncogene, AP-1 Transcription Factor Subunit and Jun Proto-Oncogene, AP-1 Transcription Factor Subunit families. By using expression data of 513 LUAD and 57 adjacent samples from The Cancer Genome Atlas and 80 tumor-normal paired LUAD samples from the Nanjing Lung Cancer Cohort study, we performed differential expression analysis of target genes for SEs and defined 243 crucial SEs. Unsupervised clustering of crucial SEs revealed two subtypes with different levels of genomic aberrations (i.e., mutation and copy number alteration) and clinical outcomes (progression-free interval: p = 0.030; disease-free interval: p = 0.047). In addition, patients with adverse clinical outcomes were more sensitive to three small molecule inhibitors (bortezomib, doxorubicin, and etoposide), and their targets (PSMB5 and TOP2A) also have elevated expression levels among these patients. Taken together, our findings provided a comprehensive characterization of SEs in LUAD and emphasized their clinical significance in LUAD therapy.