Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions.

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.

Current treatment and recent progress in gastric cancer.

Gastric cancer is not a top-10 malignancy in the United States but represents one of the most common causes of cancer death worldwide. Biological differences between tumors from Eastern and Western countries add to the complexity of identifying standard-of-care therapy based on international trials. Systemic chemotherapy, radiotherapy, surgery, immunotherapy, and targeted therapy all have proven efficacy in gastric adenocarcinoma; therefore, multidisciplinary treatment is paramount to treatment selection. Triplet chemotherapy for resectable gastric cancer is now accepted and could represent a plateau of standard cytotoxic chemotherapy for localized disease. Classification of gastric cancer based on molecular subtypes is providing an opportunity for personalized therapy. Biomarkers, in particular microsatellite instability (MSI), programmed cell death ligand 1 (PD-L1), human epidermal growth factor receptor 2 (HER2), tumor mutation burden, and Epstein-Barr virus, are increasingly driving systemic therapy approaches and allowing for the identification of populations most likely to benefit from immunotherapy and targeted therapy. Significant research opportunities remain for the less differentiated histologic subtypes of gastric adenocarcinoma and those without markers of immunotherapy activity.

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer.

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Small cells - big issues: biological implications and preclinical advancements in small cell lung cancer.

Current treatment guidelines refer to small cell lung cancer (SCLC), one of the deadliest human malignancies, as a homogeneous disease. Accordingly, SCLC therapy comprises chemoradiation with or without immunotherapy. Meanwhile, recent studies have made significant advances in subclassifying SCLC based on the elevated expression of the transcription factors ASCL1, NEUROD1, and POU2F3, as well as on certain inflammatory characteristics. The role of the transcription regulator YAP1 in defining a unique SCLC subset remains to be established. Although preclinical analyses have described numerous subtype-specific characteristics and vulnerabilities, the so far non-existing clinical subtype distinction may be a contributor to negative clinical trial outcomes. This comprehensive review aims to provide a framework for the development of novel personalized therapeutic approaches by compiling the most recent discoveries achieved by preclinical SCLC research. We highlight the challenges faced due to limited access to patient material as well as the advances accomplished by implementing state-of-the-art models and methodologies.

A novel mitochondrial unfolded protein response-related risk signature to predict prognosis, immunotherapy and sorafenib sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common cause of cancer-associated death worldwide. The mitochondrial unfolded protein response (UPRmt) not only maintains mitochondrial integrity but also regulates cancer progression and drug resistance. However, no study has used the UPRmt to construct a prognostic signature for HCC. This work aimed to establish a novel signature for predicting patient prognosis, immune cell infiltration, immunotherapy, and chemotherapy response based on UPRmt-related genes (MRGs). Transcriptional profiles and clinical information were obtained from the TCGA and ICGC databases. Cox regression and LASSO regression analyses were applied to select prognostic genes and develop a risk model. The TIMER algorithm was used to investigate immunocytic infiltration in the high- and low-risk subgroups. Here, two distinct clusters were identified with different prognoses, immune cell infiltration statuses, drug sensitivities, and response to immunotherapy. A risk score consisting of seven MRGs (HSPD1, LONP1, SSBP1, MRPS5, YME1L1, HDAC1 and HDAC2) was developed to accurately and independently predict the prognosis of HCC patients. Additionally, the expression of core MRGs was confirmed by immunohistochemistry (IHC) staining, single-cell RNA sequencing, and spatial transcriptome analyses. Notably, the expression of prognostic MRGs was significantly correlated with sorafenib sensitivity in HCC and markedly downregulated in sorafenib-treated HepG2 and Huh7 cells. Furthermore, the knockdown of LONP1 decreased the proliferation, invasion, and migration of HepG2 cells, suggesting that upregulated LONP1 expression contributed to the malignant behaviors of HCC cells. To our knowledge, this is the first study to investigate the consensus clustering algorithm, prognostic potential, immune microenvironment infiltration and drug sensitivity based on the expression of MRGs in HCC. In summary, the UPRmt-related classification and prognostic signature could assist in determining the prognosis and personalized therapy of HCC patients from the perspectives of predictive, preventative and personalized medicine.

Predictors of response to immunotherapy in colorectal cancer.

Colorectal cancer (CRC) is a major cause of cancer-related deaths globally. While treatment advancements have improved survival rates, primarily through targeted therapies based on KRAS, NRAS, and BRAF mutations, personalized treatment strategies for CRC remain limited. Immunotherapy, mainly immune checkpoint blockade, has shown efficacy in various cancers but is effective in only a small subset of patients with CRC with deficient mismatch repair (dMMR) proteins or high microsatellite instability (MSI). Recent research has challenged the notion that CRC is immunologically inert, revealing subsets with high immunogenicity and diverse lymphocytic infiltration. Identifying precise biomarkers beyond dMMR and MSI is crucial to expanding immunotherapy benefits. Hence, exploration has extended to various biomarker sources, such as the tumor microenvironment, genomic markers, and gut microbiota. Recent studies have introduced a novel classification system, consensus molecular subtypes, that aids in identifying patients with CRC with an immunogenic profile. These findings underscore the necessity of moving beyond single biomarkers and toward a comprehensive understanding of the immunological landscape in CRC, facilitating the development of more effective, personalized therapies.

Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma.

Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.

Tumor microenvironment phenotypes and prognostic evaluation tools for osteosarcoma characterized by different prognostic outcomes and immunotherapy responses.

BACKGROUND: The physiological and immunological characteristics of the tumor microenvironment (TME) have a profound impact on the effectiveness of immunotherapy. The present study aimed to define the TME subtype of osteosarcoma according to the signatures representing the global TME of the tumor, as well as create a new prognostic assessment tool to monitor the prognosis, TME activity and immunotherapy response of patients with osteosarcoma. METHODS: The enrichment scores of 29 functional gene expression signatures in osteosarcoma samples were calculated by single sample gene set enrichment analysis (ssGSEA). TME classification of osteosarcoma was performed and a prognostic assessment tool was created based on 29 ssGSEA scores to comprehensively correlate them with TME components, immunotherapy efficacy and prognosis of osteosarcoma. RESULTS: Three TME subtypes were generated that differed in survival, TME activity and immunotherapeutic response. Four differentially expressed genes between TME subtypes were involved in the development of prognostic assessment tools. The established prognosis assessment tool had strong performance in both training and verification cohorts, could be effectively applied to the survival prediction of samples of different ages, genders and transfer states, and could well distinguish the TME status of different samples. CONCLUSIONS: The present study describes three different TME phenotypes in osteosarcoma, provides a risk stratification tool for osteosarcoma prognosis and TME status assessment, and provides additional information for clinical decision-making of immunotherapy.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification.

BACKGROUND: The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC. METHODS: The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic. RESULTS: Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events. CONCLUSION: In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

Lifetime alcohol consumption patterns and young-onset breast cancer by subtype among Non-Hispanic Black and White women in the Young Women's Health History Study.

PURPOSE: The role of alcohol in young-onset breast cancer (YOBC) is unclear. We examined associations between lifetime alcohol consumption and YOBC in the Young Women's Health History Study, a population-based case-control study of breast cancer among Non-Hispanic Black and White women < 50 years of age. METHODS: Breast cancer cases (n = 1,812) were diagnosed in the Metropolitan Detroit and Los Angeles County SEER registry areas, 2010-2015. Controls (n = 1,381) were identified through area-based sampling and were frequency-matched to cases by age, site, and race. Alcohol consumption and covariates were collected from in-person interviews. Weighted multivariable logistic regression was conducted to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for associations between alcohol consumption and YOBC overall and by subtype (Luminal A, Luminal B, HER2, or triple negative). RESULTS: Lifetime alcohol consumption was not associated with YOBC overall or with subtypes (all ptrend ≥ 0.13). Similarly, alcohol consumption in adolescence, young and middle adulthood was not associated with YOBC (all ptrend ≥ 0.09). An inverse association with triple-negative YOBC, however, was observed for younger age at alcohol use initiation (< 18 years vs. no consumption), aOR (95% CI) = 0.62 (0.42, 0.93). No evidence of statistical interaction by race or household poverty was observed. CONCLUSIONS: Our findings suggest alcohol consumption has a different association with YOBC than postmenopausal breast cancer-lifetime consumption was not linked to increased risk and younger age at alcohol use initiation was associated with a decreased risk of triple-negative YOBC. Future studies on alcohol consumption in YOBC subtypes are warranted.

Diabetes and incidence of breast cancer and its molecular subtypes: A systematic review and meta-analysis.

Diabetes mellitus (DM) has been proposed to be positively associated with breast cancer (BCa) risk due to shared risk factors, metabolic dysfunction, and the use of antidiabetic medications. We conducted a systematic review and meta-analysis to evaluate the association between DM and BCa risk. We searched PubMed, Embase, and Web of Science for cohort and case-control studies assessing the association between DM and BCa published before 10 December 2021. Two reviewers independently screened the studies for inclusion, abstracted article data, and rated study quality. Random effects models were used to estimate summary risk ratios (RRs) and 95% confidence intervals (CIs). From 8396 articles identified in the initial search, 70 independent studies were included in the meta-analysis. DM was associated with an overall increased risk of BCa (RR = 1.20, 95% CI: 1.11-1.29). The 24 case-control studies demonstrated a stronger association (RR = 1.26, 95% CI: 1.13-1.40) than the 46 cohort studies (RR = 1.15, 95% CI: 1.05-1.27). Studies reporting risk by menopausal status found that postmenopausal women had an elevated risk of developing BCa (RR = 1.12, 95% CI: 1.07-1.17). No association between DM and BCa risk was observed among premenopausal women (RR = 0.95, 95% CI: 0.85-1.05). In addition, DM was associated with significantly increased risks of oestrogen receptor (ER)+ (RR = 1.09, 95% CI: 1.00-1.20), ER- (RR = 1.16, 95% CI: 1.04-1.30), and triple negative BCa (RR = 1.41, 95% CI: 1.01-1.96). The association estimate for human epidermal growth factor 2-positive BCa was also positive (RR = 1.21, 95% CI: 0.52-2.82), but the CI was wide and crossed the null. Our meta-analysis confirms a modest positive association between DM and BCa risk. In addition, our results suggest that the association between DM and BCa may be modified by menopausal status, and that DM may be differentially associated with BCa subtypes defined by receptor status. Additional studies are warranted to investigate the mechanisms underlying these associations and any influence of DM on BCa receptor expression.

Comment on "A novel super-enhancer-related gene signature predicts prognosis and immune microenvironment for breast cancer".

The primary aim of this study is to critically evaluate and comment on the research presented in the article titled "A Novel Super-Enhancer-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer" by Wu et al. Our specific objectives include assessing the methodology employed by the authors, particularly in regard to the utilization of a super-enhancer-related gene signature for breast cancer prognosis prediction. We propose the necessity of subgroup analysis to effectively address the heterogeneity in breast cancer subtypes, which is crucial for the applicability of the SERGs across diverse breast cancer cases. Additionally, we suggest conducting a more comprehensive immune panel study to deepen the understanding of how the immune microenvironment impacts breast cancer prognosis. Our commentary seeks to provide valuable insights into the strengths and weaknesses of the study, contributing to a more comprehensive understanding of its findings and potential clinical implications.

CD8 + T cell-based molecular subtypes with heterogeneous immune landscapes and clinical significance in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy. Although high-dose chemotherapy is the primary treatment option, it cannot cure the disease, and new approaches need to be developed. The tumor microenvironment (TME) plays a crucial role in tumor biology and immunotherapy. CD8 + T cells are the main anti-tumor immune effector cells, and it is essential to understand their relationship with the TME and the clinicopathological characteristics of AML. METHODS: In this study, we conducted a systematic analysis of CD8 + T cell infiltration through multi-omics data and identified molecular subtypes with significant differences in CD8 + T cell infiltration and prognosis. We aimed to provide a comprehensive evaluation of the pathological factors affecting the prognosis of AML patients and to offer theoretical support for the precise treatment of AML. RESULTS: Our results indicate that CD8 + T cell infiltration is accompanied by immunosuppression, and that there are two molecular subtypes, with the C2 subtype having a significantly worse prognosis than the C1 subtype, as well as less CD8 + T cell infiltration. We developed a signature to distinguish molecular subtypes using multiple machine learning algorithms and validated the prognostic predictive power of molecular subtypes in nine AML cohorts including 2059 AML patients. Our findings suggest that there are different immunosuppressive characteristics between the two subtypes. The C1 subtype has up-regulated expression of immune checkpoints such as CTLA4, PD-1, LAG3, and TIGITD, while the C2 subtype infiltrates more immunosuppressive cells such as Tregs and M2 macrophages. The C1 subtype is more responsive to anti-PD-1 immunotherapy and induction chemotherapy, as well as having higher immune and cancer-promoting variant-related pathway activity. Patients with the C2 subtype had a higher FLT3 mutation rate, higher WBC counts, and a higher percentage of blasts, as indicated by increased activity of signaling pathways involved in energy metabolism and cell proliferation. Analysis of data from ex vivo AML cell drug assays has identified a group of drugs that differ in therapeutic sensitivity between molecular subtypes. CONCLUSIONS: Our results suggest that the molecular subtypes we constructed have potential application value in the prognosis evaluation and treatment guidance of AML patients.

Overexpression of TSPAN8 in consensus molecular subtype 3 colorectal cancer.

BACKGROUND: Recently, consensus molecular subtypes (CMSs) have been proposed as a robust transcriptome-based classification system for colorectal cancer (CRC). Tetraspanins (TSPANs) are transmembrane proteins. They have been associated with the development of numerous malignancies, including CRC, through their role as "master organizers" for multi-molecular membrane complexes. No previous study has investigated the correlation between TSPANs and CMS classification. Herein, we investigated the expression of TSPANs in patient-derived primary CRC tissues and their CMS classifications. METHODS: RNA samples were derived from primary CRC tissues (n = 100 patients diagnosed with colorectal adenocarcinoma) and subjected to RNA sequencing for transcriptome-based CMS classification and TSPAN-relevant analyses. Immunohistochemistry (IHC) and immunofluorescence (IF) stains were conducted to observe the protein expression level. To evaluate the relative biological pathways, gene-set enrichment analysis was performed. RESULTS: Of the highly expressed TSPAN genes in CRC tissues (TSPAN8, TSPAN29, and TSPAN30), TSPAN8 was notably overexpressed in CMS3-classified primary tissues. The overexpression of TSPAN8 protein in CMS3 CRC was also observed by IHC and IF staining. As a result of gene-set enrichment analysis, TSPAN8 may potentially play a role in organizing signaling complexes for kinase-based metabolic deregulation in CMS3 CRC. CONCLUSIONS: The present study reports the overexpression of TSPAN8 in CMS3 CRC. This study proposes TSPAN8 as a subtype-specific biomarker for CMS3 CRC. This finding provides a foundation for future CMS-based studies of CRC, a complex disease and the second leading cause of cancer mortality worldwide.

Consensus molecular subtype transition during progression of colorectal cancer.

The consensus molecular subtype (CMS) classification divides colorectal cancer (CRC) into four distinct subtypes based on RNA expression profiles. The biological differences between CMSs are already present in CRC precursor lesions, but not all CMSs pose the same risk of malignant transformation. To fully understand the path to malignant transformation and to determine whether CMS is a fixed entity during progression, genomic and transcriptomic data from two regions of the same CRC lesion were compared: the precursor region and the carcinoma region. In total, 24 patients who underwent endoscopic removal of T1-2 CRC were included. Regions were subtyped for CMS and DNA mutation analysis was performed. Additionally, a set of 85 benign adenomas was CMS-subtyped. This analysis revealed that almost all benign adenomas were classified as CMS3 (91.8%). In contrast, CMS2 was the most prevalent subtype in precursor regions (66.7%), followed by CMS3 (29.2%). CMS4 was absent in precursor lesions and originated at the carcinoma stage. Importantly, CMS switching occurred in a substantial number of cases and almost all (six out of seven) CMS3 precursor regions showed a shift to a different subtype in the carcinoma part of the lesion, which in four cases was classified as CMS4. In conclusion, our data indicate that CMS3 is related to a more indolent type of precursor lesion that less likely progresses to CRC and when this occurs, it is often associated with a subtype change that includes the more aggressive mesenchymal CMS4. In contrast, an acquired CMS2 signature appeared to be rather fixed during early CRC development. Combined, our data show that subtype changes occur during progression and that CMS3 switching is related to changes in the genomic background through acquisition of a novel driver mutation (TP53) or selective expansion of a clone, but also occurred independently of such genetic changes. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Assessing the Influence of B-US, CDFI, SE, and Patient Age on Predicting Molecular Subtypes in Breast Lesions Using Deep Learning Algorithms.

OBJECTIVES: Our study aims to investigate the impact of B-mode ultrasound (B-US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions. METHODS: Totally 2272 multimodal ultrasound imaging was collected from 198 patients. The ResNet-18 network was employed to predict four molecular subtypes from B-US imaging, CDFI, and SE of patients with different ages. All the images were split into training and testing datasets by the ratio of 80%:20%. The predictive performance on testing dataset was evaluated through 5 metrics including mean accuracy, precision, recall, F1-scores, and confusion matrix. RESULTS: Based on B-US imaging, the test mean accuracy is 74.50%, the precision is 74.84%, the recall is 72.48%, and the F1-scores is 0.73. By combining B-US imaging with CDFI, the results were increased to 85.41%, 85.03%, 85.05%, and 0.84, respectively. With the integration of B-US imaging and SE, the results were changed to 75.64%, 74.69%, 73.86%, and 0.74, respectively. Using images from patients under 40 years old, the results were 90.48%, 90.88%, 88.47%, and 0.89. When images from patients who are above 40 years old, they were changed to 81.96%, 83.12%, 80.5%, and 0.81, respectively. CONCLUSION: Multimodal ultrasound imaging can be used to accurately predict the molecular subtypes of breast lesions. In addition to B-US imaging, CDFI rather than SE contribute further to improve predictive performance. The predictive performance is notably better for patients under 40 years old compared with those who are 40 years old and above.

C-Myc protein expression indicates unfavorable clinical outcome in surgically resected small cell lung cancer.

BACKGROUND: By being highly involved in the tumor evolution and disease progression of small cell lung cancer (SCLC), Myc family members (C-Myc, L-Myc, and N-Myc) might represent promising targetable molecules. Our aim was to investigate the expression pattern and prognostic relevance of these oncogenic proteins in an international cohort of surgically resected SCLC tumors. METHODS: Clinicopathological data and surgically resected tissue specimens from 104 SCLC patients were collected from two collaborating European institutes. Tissue sections were stained by immunohistochemistry (IHC) for all three Myc family members and the recently introduced SCLC molecular subtype-markers (ASCL1, NEUROD1, POU2F3, and YAP1). RESULTS: IHC analysis showed C-Myc, L-Myc, and N-Myc positivity in 48%, 63%, and 9% of the specimens, respectively. N-Myc positivity significantly correlated with the POU2F3-defined molecular subtype (r = 0.6913, p = 0.0056). SCLC patients with C-Myc positive tumors exhibited significantly worse overall survival (OS) (20 vs. 44 months compared to those with C-Myc negative tumors, p = 0.0176). Ultimately, in a multivariate risk model adjusted for clinicopathological and treatment confounders, positive C-Myc expression was confirmed as an independent prognosticator of impaired OS (HR 1.811, CI 95% 1.054-3.113, p = 0.032). CONCLUSIONS: Our study provides insights into the clinical aspects of Myc family members in surgically resected SCLC tumors. Notably, besides showing that positivity of Myc family members varies across the patients, we also reveal that C-Myc protein expression independently correlates with worse survival outcomes. Further studies are warranted to investigate the role of Myc family members as potential prognostic and predictive markers in this hard-to-treat disease.

Comparative expression of trophoblast cell-surface antigen 2 (TROP2) in the different molecular subtypes of invasive breast carcinoma: An immunohistochemical study of 94 therapy-naive primary breast tumors.

BACKGROUND: Sacituzumab govitecan, targeting trophoblast cell-surface antigen 2 (TROP2), is approved for the treatment of triple-negative and hormone receptor-positive/HER2-negative breast cancers. However, detailed studies comparing TROP2 protein expression in the different molecular subtypes of breast cancer are limited, and definitive evidence supporting the use of TROP2 as a biomarker for predicting response to this agent in patients with breast cancer is currently lacking. OBJECTIVE: To compare the expression of TROP2 in the different molecular subtypes of breast cancer. METHODS: Immunohistochemical staining for TROP2 was performed on 94 therapy-naive primary invasive breast carcinomas, including 25 luminal A-like, 25 luminal B-like, 19 HER2-like, and 25 triple-negative tumors. RESULTS: Intermediate to high levels of TROP2 expression were observed in the majority of carcinomas of each molecular subtype, with a wide range of expression in each subtype. Occasional tumors with low or absent TROP2 expression were encountered, including two metaplastic carcinomas which were completely negative for TROP2. CONCLUSIONS: Our observations support the continued investigation of the efficacy of sacituzumab govitecan in all molecular subtypes of breast carcinoma. Furthermore, the observed wide range of expression of TROP2 suggests that TROP2 may have potential utility as a biomarker for predicting responsiveness to sacituzumab govitecan. If this proves to be the case, then immunohistochemical staining for TROP2 would be critical for identifying those patients whose tumors are completely negative for TROP2, since these patients may be least likely or unlikely to respond to this agent, and alternative therapies may be more appropriate in such instances.

A novel model combining genes associated with disulfidptosis and glycolysis to predict breast cancer prognosis, molecular subtypes, and treatment response.

Breast cancer (BC) is a heterogeneous malignancy with a dismal prognosis. Disulfidptosis is a novel type of regulated cell death that happens in the presence of glucose deficiency and is linked to the metabolic process of glycolysis. However, the mechanism of action of disulfidptosis and glycolysis-related genes (DGRG) in BC, as well as their prognostic value in BC patients, remain unknown. After identifying the differentially expressed DGRG in normal and BC tissues, a number of machine learning algorithms were utilized to select essential prognostic genes to develop a model, including SLC7A11, CACNA1H, SDC1, CHST1, and TFF3. The expression characteristics of these genes were then examined using single-cell RNA sequencing, and BC was classified into three clusters using "ConsensusClusterPlus" based on these genes. The DGRG model's median risk score can categorize BC patients into high-risk and low-risk groups. Furthermore, we investigated variations in clinical landscape, immunoinvasion analysis, tumor immune dysfunction and rejection (TIDE), and medication sensitivity in patients in the DGRG model's high- and low-risk groups. Patients in the low-risk group performed better on immunological and chemotherapeutic therapies and had lower TIDE scores. In conclusion, the DGRG model we developed has significant clinical application potential because it can accurately predict the prognosis of BC, TME, and pharmacological treatment responses.