OBJECTIVE: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination.
METHODS: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype.
RESULTS: There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI.
CONCLUSIONS: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.

BACKGROUND: Hormone receptor expression is a known positive prognostic and predictive factor in breast cancer; however, limited evidence exists on its prognostic impact on prognosis of young patients harboring a pathogenic variant (PV) in the BRCA1 and/or BRCA2 genes.
METHODS: This international, multicenter, retrospective cohort study included young patients (aged ≤40 years) diagnosed with invasive breast cancer and harboring germline PVs in BRCA genes. We investigated the impact of hormone receptor status on clinical behavior and outcomes of breast cancer. Outcomes of interest [disease-free survival (DFS), breast cancer-specific survival (BCSS), and overall survival (OS)] were first investigated according to hormone receptor expression (positive versus negative), and then according to breast cancer subtype [luminal A-like versus luminal B-like versus triple-negative versus human epidermal growth factor receptor 2 (HER2)-positive breast cancer].
RESULTS: From 78 centers worldwide, 4709 BRCA carriers were included, of whom 2143 (45.5%) had hormone receptor-positive and 2566 (54.5%) hormone receptor-negative breast cancer. Median follow-up was 7.9 years. The rate of distant recurrences was higher in patients with hormone receptor-positive disease (13.1% versus 9.6%, P < 0.001), while the rate of second primary breast cancer was lower (9.1% versus 14.7%, P < 0.001) compared to patients with hormone receptor-negative disease. The 8-year DFS was 65.8% and 63.4% in patients with hormone receptor-positive and negative disease, respectively. The hazard ratio of hormone receptor-positive versus negative disease changed over time for DFS, BCSS, and OS (P < 0.05 for interaction of hormone receptor status and survival time). Patients with luminal A-like breast cancer had the worst long-term prognosis in terms of DFS compared to all the other subgroups (8-year DFS: 60.8% in luminal A-like versus 63.5% in triple-negative versus 65.5% in HER2-positive and 69.7% in luminal B-like subtype).
CONCLUSIONS: In young BRCA carriers, differences in recurrence pattern and second primary breast cancer among hormone receptor-positive versus negative disease warrant consideration in counseling patients on treatment, follow-up, and risk-reducing surgery.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population.
Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation.
pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05).
Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.

BACKGROUND: Invasive stratified mucin-producing carcinoma is a recently recognized adenocarcinoma with distinctive features. It was first described in the cervix but similar tumors have since been reported in the penis, anus and prostate. In the gastrointestinal tract, the phenomenon of epithelial stratification has an interesting embryologic morphogenesis. Gastrointestinal mucosa starts off as nascent columnar epithelium that is subsequently patterned to confer regional specific functions. However, in disease states, normal architectural patterning can be disrupted by aberrant differentiation. Given this background and the phenotypic plasticity of neoplastic cells, we were interested in ascertaining whether invasive stratified mucin-producing carcinoma occurs in the colorectum.
METHODS: This was a retrospective study of all 584 cases of colorectal carcinoma accessioned at our institution over a 2-year period (January 2021- December 2022). Cases were analyzed to determine which fulfilled the criteria for invasive stratified mucin-producing carcinoma.
RESULTS: There were 9 cases of colorectal invasive stratified mucin-producing carcinoma-one pure form and 8 mixed. They showed the classic colorectal (CK20 + , CDX2 + , CK7-) immunostaining profile but, based on various morphologic criteria, they could be distinguished from conventional adenocarcinoma NOS, mucinous, signet ring cell, medullary, goblet cell and undifferentiated carcinomas. About half the cases were MLH1/PMS2 deficient and BRAF &/or PIK3CA mutated, which aligns with the hypermutated phenotype.
CONCLUSIONS: Colorectal invasive stratified mucin-producing carcinoma appears to be a real entity, best recognized in its early stages. It appears to be a high-grade carcinoma. With tumor progression, it evolves into a mucinous adenocarcinoma with a proclivity towards signet ring cells. In summary, the study of this tumor, particularly in its early stages, provides useful clues to further understanding the biology and progression of large bowel cancer. Further studies are required to learn more about this tumor.

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.
METHODS: 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.
METHODS: 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.
CONCLUSIONS: 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.
RESULTS: 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.

Background: Uveal Melanoma (UM) is the most prevalent primary intraocular malignancy in adults. This study assessed the importance of chromatin regulators (CRs) in UM and developed a model to predict UM prognosis. Methods: Gene expression data and clinical information for UM were obtained from public databases. Samples were typed according to the gene expression of CRs associated with UM prognosis. The prognostic key genes were further screened by the protein interaction network, and the risk model was to predict UM prognosis using the least absolute shrinkage and selection operator (LASSO) regression analysis and performed a test of the risk mode. In addition, we performed gene set variation analysis, tumor microenvironment, and tumor immune analysis between subtypes and risk groups to explore the mechanisms influencing the development of UM. Results: We constructed a signature model consisting of three CRs (RUVBL1, SIRT3, and SMARCD3), which was shown to be accurate, and valid for predicting prognostic outcomes in UM. Higher immune cell infiltration in poor prognostic subtypes and risk groups. The Tumor immune analysis and Tumor Immune Dysfunction and Exclusion (TIDE) score provided a basis for clinical immunotherapy in UM. Conclusion: The risk model has prognostic value for UM survival and provides new insights into the treatment of UM.

Luminal breast cancer subtypes respond poorly to endocrine and trastuzumab treatments due to cellular heterogeneity arising from the phenotype transitions, accounted for mainly by the loss of receptor expression. The origins of basal-like and human epidermal growth factor receptor 2 (HER2)-overexpressing breast cancer subtypes have been attributed to genetic and protein modifications in stem-like cells and luminal progenitor cell populations, respectively. The post-transcriptional regulation of protein expression is known to be influenced by microRNAs (miRNAs) that are deemed to be master regulators of several biological processes in breast tumorigenesis and progression. Our objective was to identify the fractions of luminal breast cancer cells that share stemness potentials and marker profiles and to elucidate the molecular regulatory mechanism that drives transitions between fractions, leading to receptor discordances. Established breast cancer cell lines of all prominent subtypes were screened for the expression of putative cancer stem cell (CSC) markers and drug transporter proteins using a side population (SP) assay. Flow-cytometry-sorted fractions of luminal cancer cells implanted in immunocompromised mice generated a pre-clinical estrogen receptor alpha (ERα+) animal model with multiple tumorigenic fractions displaying differential expression of drug transporters and hormone receptors. Despite an abundance of estrogen receptor 1 (ESR1) gene transcripts, few fractions transitioned to the triple-negative breast cancer (TNBC) phenotype with a visible loss of ER protein expression and a distinct microRNA expression profile that is reportedly enriched in breast CSCs. The translation of this study has the potential to provide novel therapeutic miRNA-based targets to counter the dreaded subtype transitions and the failure of antihormonal therapies in the luminal breast cancer subtype.

tRNA-derived RNA fragments (tRFs) are a novel class of small ncRNA that are derived from precursor or mature tRNAs. Recently, the general relevance of their roles and clinical values in tumorigenesis, metastasis, and recurrence have been increasingly highlighted. However, there has been no specific systematic study to elucidate any potential clinical significance for these tRFs in prostate adenocarcinoma (PRAD), one of the most common and malignant cancers that threatens male health worldwide. Here, we investigate the clinical value of 5\'-tRFs in PRAD.
Small RNA sequencing data were analyzed to discover new 5\'-tRFs biomarkers for PRAD. Machine learning algorithms were used to identify 5\'-tRF classifiers to distinguish PRAD tumors from normal tissues. LASSO and Cox regression analyses were used to construct 5\'-tRF prognostic predictive models. NMF and consensus clustering analyses were performed on 5\'-tRF profiles to identify molecular subtypes of PRAD.
The overall levels of 5\'-tRFs were significantly upregulated in the PRAD tumor samples compared to their adjacent normal samples. tRF classifiers composed of 13 5\'-tRFs achieved AUC values as high as 0.963, showing high sensitivity and specificity in distinguishing PRAD tumors from normal samples. Multiple 5\'-tRFs were identified as being associated with the PRAD prognosis. The tRF score, defined by a set of eight 5\'-tRFs, was highly predictive of survival in PRAD patients. The combination of tRF and Gleason scores showed a significantly better performance than the Gleason score alone, suggesting that 5\'-tRFs can offer PRAD patients additional and improved prognostic information. Four molecular subtypes of the PRAD tumor were identified based on their 5\'-tRF expression profiles. Genetically, these 5\'-tRFs PRAD tumor subtypes exhibited distinct genomic landscapes in tumor cells. Clinically, they showed marked differences in survival and clinicopathological features.
5\'-tRFs are potential clinical biomarkers for the diagnosis, prognosis, and classification of tumor subtypes on a molecular level. These can help clinicians formulate personalized treatment plans for PRAD patients and may have similar potential applications for other disease types.

Neoadjuvant chemotherapy (NAC) is standard for many females with breast cancer (FBC). The efficacy of NAC in male breast cancer (MaBC) is unclear. The aim of this study was to compare proportions of pathologic complete response (pCR) between MaBC and FBC by tumor subtype (TS).
MaBC and FBC treated with NAC between 2010 and 2016, with known TS, were evaluated from the National Cancer Database. Proportions of pCR (ypT0/Tis ypN0) were compared between sexes within TS by Fisher test. Multivariable logistic regression assessed the independent association of sex with pCR. Overall survival (OS) was estimated by Kaplan-Meier.
A total of 385 MaBC and 68,065 FBC were included. Median time from initiation of NAC to surgery was 143 days in MaBC and 148 days in FBC. Proportions of pCR in MaBC and FBC by TS were: hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-): 4.9% vs 9.7%, p = .01; HR+/HER2+: 16.1% vs 33.6%, p < .001; HR-/HER2+: 44.0% vs 53.2%, p = .42; and HR-/HER2-: 21.4% vs 32.1%, p = .18, respectively. FBC had twice the odds of pCR than MaBC (adjusted odds ratio, 2.0; 95% CI, 1.5-2.8; p < .001). Five-year OS for MaBC with pCR vs not was 90% vs 64.7%; p = .02. Five-year OS for FBC with pCR vs not was 91.9% vs 75.3%; p < .01.
Proportions and odds of pCR to NAC were numerically lower in MaBC compared with FBC for each TS and statistically significant for HR+/HER2- and HR+/HER2+. The independent association of sex with pCR was confirmed in multivariable analysis. pCR is prognostic in both MaBC and FBC.