Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future

Exploring the potential of the TCR repertoire as a tumor biomarker (Review)

T cells play an important role in adaptive immunity. Mature T cells specifically recognize antigens on major histocompatibility complex molecules through T-cell receptors (TCRs). As the TCR repertoire is highly diverse, its analysis is vital in the assessment of T cells. Advances in sequencing technology have provided convenient methods for further investigation of the TCR repertoire. In the present review, the TCR structure and the mechanisms by which TCRs function in tumor recognition are described. In addition, the potential value of the TCR repertoire in tumor diagnosis is reviewed. Furthermore, the role of the TCR repertoire in tumor immunotherapy is introduced, and the relationships between the TCR repertoire and the effects of different tumor immunotherapies are discussed. Based on the reviewed literature, it may be concluded that the TCR repertoire has the potential to serve as a biomarker for tumor prognosis. However, a wider range of cancer types and more diverse subjects require evaluation in future research to establish the TCR repertoire as a biomarker of tumor immunity.

Relationship between clonal evolution and drug resistance in bladder cancer: A genomic research review

Bladder cancer stands as a prevalent global malignancy, exhibiting notable sex-based variations in both incidence and prognosis. Despite substantial strides in therapeutic approaches, the formidable challenge of drug resistance persists. The genomic landscape of bladder cancer, characterized by intricate clonal heterogeneity, emerges as a pivotal determinant in fostering this resistance. Clonal evolution, encapsulating the dynamic transformations within subpopulations of tumor cells over time, is implicated in the emergence of drug-resistant traits. Within this review, we illuminate contemporary insights into the role of clonal evolution in bladder cancer, elucidating its influence as a driver in tumor initiation, disease progression, and the formidable obstacle of therapy resistance.

Multi-omics analysis unveils immunosuppressive microenvironment in the occurrence and development of multiple pulmonary lung cancers

Multiple pulmonary lung cancers (MPLCs) are frequently encountered on computed tomography (CT) scanning of chest, yet their intrinsic characteristics associated with genomic features and radiological or pathological textures that may lead to distinct clinical outcomes remain largely unexplored. A total of 27 pulmonary nodules covering different radiological or pathological textures as well as matched adjacent normal tissues and blood samples were collected from patients diagnosed with MPLCs. Whole-exome sequencing (WES) and whole-transcriptome sequencing were performed. The molecular and immune features of MPLCs associated with distinct radiological or pathological textures were comprehensively investigated. Genomics analysis unveiled the distinct branches of pulmonary nodules originating independently within the same individual. EGFR and KRAS mutations were found to be prevalent in MPLCs, exhibiting mutual exclusivity. The group with KRAS mutations exhibited stronger immune signatures compared to the group with EGFR mutations. Additionally, MPLCs exhibited a pronounced immunosuppressive microenvironment, which was particularly distinct when compared with normal tissues. The expression of the FDSCP gene was specifically observed in MPLCs. When categorizing MPLCs based on radiological or pathological characteristics, a progressive increase in mutation accumulation was observed, accompanied by heightened chromatin-level instability as ground-glass opacity component declined or invasive progression occurred. A close association with the immunosuppressive microenvironment was also observed during the progression of pulmonary nodules. Notably, the upregulation of B cell and regulatory T cell marker genes occurred progressively. Immune cell abundance analysis further demonstrated a marked increase in exhausted cells and regulatory T cells during the progression of pulmonary nodules. These results were further validated by independent datasets including nCounter RNA profiling, single-cell RNA sequencing, and spatial transcriptomic datasets. Our study provided a comprehensive representation of the diverse landscape of MPLCs originating within the same individual and emphasized the significant influence of the immunosuppressive microenvironment in the occurrence and development of pulmonary nodules. These findings hold great potential for enhancing the clinical diagnosis and treatment strategies for MPLCs.

The COMPASS complex maintains the metastatic capacity imparted by a subpopulation of cells in UPS

Intratumoral heterogeneity is common in cancer, particularly in sarcomas like undifferentiated pleomorphic sarcoma (UPS), where individual cells demonstrate a high degree of cytogenic diversity. Previous studies showed that a small subset of cells within UPS, known as the metastatic clone (MC), as responsible for metastasis. Using a CRISPR-based genomic screen in-vivo, we identified the COMPASS complex member Setd1a as a key regulator maintaining the metastatic phenotype of the MC in murine UPS. Depletion of Setd1a inhibited metastasis development in the MC. Transcriptome and chromatin sequencing revealed COMPASS complex target genes in UPS, such as Cxcl10, downregulated in the MC. Deleting Cxcl10 in non-MC cells increased their metastatic potential. Treating mice with human UPS xenografts with a COMPASS complex inhibitor suppressed metastasis without affecting tumor growth in the primary tumor. Our data identified an epigenetic program in a subpopulation of sarcoma cells that maintains metastatic potential.

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Spatial intra-tumour heterogeneity and treatment-induced genomic evolution in oesophageal adenocarcinoma: implications for prognosis and therapy

BACKGROUND

Oesophageal adenocarcinoma (OAC) is a highly heterogeneous cancer with poor survival. Standard curative treatment is chemotherapy with or without radiotherapy followed by oesophagectomy. Genomic heterogeneity is a feature of OAC and has been linked to treatment resistance.

METHODS

Whole-genome sequencing data from 59 treatment-naïve and 18 post-treatment samples from 29 OAC patients was analysed. Twenty-seven of these were enrolled in the DOCTOR trial, sponsored by the Australasian Gastro-Intestinal Trials Group. Two biopsies from each treatment-naïve tumour were assessed to define 'shared' (between both samples) and 'private' (present in one sample) mutations.

RESULTS

Mutational signatures SBS2/13 (APOBEC) and SBS3 (BRCA) were almost exclusively detected in private mutation populations of treatment-naïve tumours. Patients presenting these signatures had significantly worse disease specific survival. Furthermore, mutational signatures associated with platinum-based chemotherapy treatment as well as high platinum enrichment scores were only detected in post-treatment samples. Additionally, clones with high putative neoantigen binding scores were detected in some treatment-naïve samples suggesting immunoediting of clones.

CONCLUSIONS

This study demonstrates the high intra-tumour heterogeneity in OAC, as well as indicators for treatment-induced changes during tumour evolution. Intra-tumour heterogeneity remains a problem for successful treatment strategies in OAC.

Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer

Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34-7.3; HR = 2.24 and 95% CI = 1.28-3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11-4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution.

Targeting of human cancer stem cells predicts efficacy and toxicity of FDA-approved oncology drugs

Cancer remains the leading cause of death worldwide with approved oncology drugs continuing to have heterogenous patient responses and accompanied adverse effects (AEs) that limits effectiveness. Here, we examined >100 FDA-approved oncology drugs in the context of stemness using a surrogate model of transformed human pluripotent cancer stem cells (CSCs) vs. healthy stem cells (hSCs) capable of distinguishing abnormal self-renewal and differentiation. Although a proportion of these drugs had no effects (inactive), a larger portion affected CSCs (active), and a unique subset preferentially affected CSCs over hSCs (selective). Single cell gene expression and protein profiling of each drug's FDA recognized target provided a molecular correlation of responses in CSCs vs. hSCs. Uniquely, drugs selective for CSCs demonstrated clinical efficacy, measured by overall survival, and reduced AEs. Our findings reveal that while unintentional, half of anticancer drugs are active against CSCs and associated with improved clinical outcomes. Based on these findings, we suggest ability to target CSC targeting should be included as a property of early onco-therapeutic development.

Off-the-shelf CAR-NK cells targeting immunogenic cell death marker ERp57 execute robust antitumor activity and have a synergistic effect with ICD inducer oxaliplatin

BACKGROUND

Chimeric antigen receptor natural killer (CAR-NK) therapy holds great promise for treating hematologic tumors, but its efficacy in solid tumors is limited owing to the lack of suitable targets and poor infiltration of engineered NK cells. Here, we explore whether immunogenic cell death (ICD) marker ERp57 translocated from endoplasmic reticulum to cell surface after drug treatment could be used as a target for CAR-NK therapy.

METHODS

To target ERp57, a VHH phage display library was used for screening ERp57-targeted nanobodies (Nbs). A candidate Nb with high binding affinity to both human and mouse ERp57 was used for constructing CAR-NK cells. Various in vitro and in vivo studies were performed to assess the antitumor efficacy of the constructed CAR-NK cells.

RESULTS

We demonstrate that the translocation of ERp57 can not only be induced by low-dose oxaliplatin (OXP) treatment but also is spontaneously expressed on the surface of various types of tumor cell lines. Our results show that G6-CAR-NK92 cells can effectively kill various tumor cell lines in vitro on which ERp57 is induced or intrinsically expressed, and also exhibit potent antitumor effects in cancer cell-derived xenograft and patient-derived xenograft mouse models. Additionally, the antitumor activity of G6-CAR-NK92 cells is synergistically enhanced by the low-dose ICD-inducible drug OXP.

CONCLUSION

Collectively, our findings suggest that ERp57 can be leveraged as a new tumor antigen for CAR-NK targeting, and the resultant CAR-NK cells have the potential to be applied as a broad-spectrum immune cell therapy for various cancers by combining with ICD inducer drugs.

From genetic mosaicism to tumorigenesis through indirect genetic effects

Genetic mosaicism has long been linked to aging, and several hypotheses have been proposed to explain the potential connections between mosaicism and susceptibility to cancer. It has been proposed that mosaicism may disrupt tissue homeostasis by affecting intercellular communications and releasing microenvironmental constraints within tissues. The underlying mechanisms driving these tissue-level influences remain unidentified, however. Here, we present an evolutionary perspective on the interplay between mosaicism and cancer, suggesting that the tissue-level impacts of genetic mosaicism can be attributed to Indirect Genetic Effects (IGEs). IGEs can increase the level of cellular stochasticity and phenotypic instability among adjacent cells, thereby elevating the risk of cancer development within the tissue. Moreover, as cells experience phenotypic changes in response to challenging microenvironmental conditions, these changes can initiate a cascade of nongenetic alterations, referred to as Indirect non-Genetic Effects (InGEs), which in turn catalyze IGEs among surrounding cells. We argue that incorporating both InGEs and IGEs into our understanding of the process of oncogenic transformation could trigger a major paradigm shift in cancer research with far-reaching implications for practical applications.

Cellular adaptation to cancer therapy along a resistance continuum

Advancements in precision oncology over the past decades have led to new therapeutic interventions, but the efficacy of such treatments is generally limited by an adaptive process that fosters drug resistance1. In addition to genetic mutations2, recent research has identified a role for non-genetic plasticity in transient drug tolerance3 and the acquisition of stable resistance4,5. However, the dynamics of cell-state transitions that occur in the adaptation to cancer therapies remain unknown and require a systems-level longitudinal framework. Here we demonstrate that resistance develops through trajectories of cell-state transitions accompanied by a progressive increase in cell fitness, which we denote as the 'resistance continuum'. This cellular adaptation involves a stepwise assembly of gene expression programmes and epigenetically reinforced cell states underpinned by phenotypic plasticity, adaptation to stress and metabolic reprogramming. Our results support the notion that epithelial-to-mesenchymal transition or stemness programmes-often considered a proxy for phenotypic plasticity-enable adaptation, rather than a full resistance mechanism. Through systematic genetic perturbations, we identify the acquisition of metabolic dependencies, exposing vulnerabilities that can potentially be exploited therapeutically. The concept of the resistance continuum highlights the dynamic nature of cellular adaptation and calls for complementary therapies directed at the mechanisms underlying adaptive cell-state transitions.

FPR3 reprograms glycolytic metabolism and stemness in gastric cancer via calcium-NFATc1 pathway

Aerobic glycolysis accelerates tumor proliferation and progression, and inhibitors or drugs targeting abnormal cancer metabolism have been developing. Cancer stem-like cells (CSCs) significantly contribute to tumor initiation, metastasis, therapy resistance, and recurrence. Formyl peptide receptor 3 (FPR3), a member of FPR family, involves in inflammation, tissue repair, and angiogenesis. However, studies in exploring the regulatory mechanisms of aerobic glycolysis and CSCs by FPR3 in gastric cancer (GC) remain unknown. Here, we demonstrated that overexpressed FPR3 suppressed glycolytic capacity and stemness of tumor cells, then inhibited GC cells proliferation. Mechanistically, FPR3 impeded cytoplasmic calcium ion flux and hindered nuclear factor of activated T cells 1 (NFATc1) nuclear translocation, leading to the transcriptional inactivation of NFATc1-binding neurogenic locus notch homolog protein 3 (NOTCH3) promoter, subsequently obstructing NOTCH3 expression and the AKT/mTORC1 signaling pathway, and ultimately downregulating glycolysis. Additionally, NFATc1 directly binds to the sex determining region Y-box 2 (SOX2) promoter and modifies stemness in GC. In conclusion, our work illustrated that FPR3 played a negative role in GC progression by modulating NFATc1-mediated glycolysis and stemness in a calcium-dependent manner, providing potential insights into cancer therapy.

Beyond typical histology of BAP1-inactivated melanocytoma

BAP1-inactivated melanocytoma (BIM) is a novel subgroup of melanocytic neoplasm listed in the 5th edition of WHO classification of skin tumor. BIM is characterized by two molecular alterations, including a mitogenic driver mutation (usually BRAF gene) and the loss of function of BAP1, a tumor suppressor gene located on chromosome 3p21, which encodes for BRCA1-associated protein (BAP1). The latter represents a nuclear-localized deubiquitinase involved in several cellular processes including cell cycle regulation, chromatin remodeling, DNA damage response, differentiation, senescence and cell death. BIMs are histologically characterized by a population of large epithelioid melanocytes with well-demarcated cytoplasmic borders and copious eosinophilic cytoplasm, demonstrating loss of BAP1 nuclear expression by immunohistochemistry. Recently, we have published a series of 50 cases, extending the morphological spectrum of the neoplasm and highlighting some new microscopic features. In the current article, we focus on some new histological features, attempting to explain and link them to certain mechanisms of tumor development, including senescence, endoreplication, endocycling, asymmetric cytokinesis, entosis and others. In light of the morphological and molecular findings observed in BIM, we postulated that this entity unmasks a fine mechanism of tumor in which both clonal/stochastic and hierarchical model can be unified.

Targeting Tumor Heterogeneity by Breaking a Stem Cell and Epithelial Niche Interaction Loop

Tumor heterogeneity, the presence of multiple distinct subpopulations of cancer cells between patients or among the same tumors, poses a major challenge to current targeted therapies. The way these different subpopulations interact among themselves and the stromal niche environment, and how such interactions affect cancer stem cell behavior has remained largely unknown. Here, it is shown that an FGF-BMP7-INHBA signaling positive feedback loop integrates interactions among different cell populations, including mammary gland stem cells, luminal epithelial and stromal fibroblast niche components not only in organ regeneration but also, with certain modifications, in cancer progression. The reciprocal dependence of basal stem cells and luminal epithelium is based on basal-derived BMP7 and luminal-derived INHBA, which promote their respective expansion, and is regulated by stromal-epithelial FGF signaling. Targeting this interaction loop, for example, by reducing the function of one or more of its components, inhibits organ regeneration and breast cancer progression. The results have profound implications for overcoming drug resistance because of tumor heterogeneity in future targeted therapies.

GWAS advancements to investigate disease associations and biological mechanisms

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

Mimicking Tumor Metastasis Using a Transwell-Integrated Organoids-On-a-Chip Platform

The mortality rate among cancer patients is primarily attributed to tumor metastasis. The evaluation of metastasis potential provides a powerful framework for personalized therapies. However, little work has so far been undertaken to precisely model tumor metastasis in vitro, hindering the development of preventive and therapeutic interventions. In this work, a tumor-metastasis-mimicked Transwell-integrated organoids-on-a-chip platform (TOP) for precisely evaluating tumor metastatic potential is developed. Unlike the conventional Transwell device for detecting cell migration, the engineered device facilitates the assessment of metastasis in patient-derived organoids (PDO). Furthermore, a novel Transwell chamber with a hexagon-shaped structure is developed to mimic the migration of tumor cells into surrounding tissues, allowing for the evaluation of tumor metastasis in a horizontal direction. As a proof-of-concept demonstration, tumor organoids and metastatic clusters are further evaluated at the protein, genetic, and phenotypic levels. In addition, preliminary drug screening is undertaken to highlight the potential for using the device to combat cancers. In summary, the tumor-metastasis-mimicked TOP offers unique capabilities for evaluating the metastasis potential of tumor organoids and contributes to the development of personalized cancer therapies.

Unfolding the mysteries of heterogeneity from a high-resolution perspective: integration analysis of single-cell multi-omics and spatial omics revealed functionally heterogeneous cancer cells in ccRCC

The genomic landscape of clear cell renal cell carcinoma (ccRCC) has a considerable intra-tumor heterogeneity, which is a significant obstacle in the field of precision oncology and plays a pivotal role in metastasis, recurrence, and therapeutic resistance of cancer. The mechanisms of intra-tumor heterogeneity in ccRCC have yet to be fully established. We integrated single-cell RNA sequencing (scRNA-seq) and transposase-accessible chromatin sequencing (scATAC-seq) data from a single-cell multi-omics perspective. Based on consensus non-negative matrix factorization (cNMF) algorithm, functionally heterogeneous cancer cells were classified into metabolism, inflammatory, and EMT meta programs, with spatial transcriptomics sequencing (stRNA-seq) providing spatial information of such disparate meta programs of cancer cells. The bulk RNA sequencing (RNA-seq) data revealed high clinical prognostic values of functionally heterogeneous cancer cells of three meta programs, with transcription factor regulatory network and motif activities revealing the key transcription factors that regulate functionally heterogeneous ccRCC cells. The interactions between varying meta programs and other cell subpopulations in the microenvironment were investigated. Finally, we assessed the sensitivity of cancer cells of disparate meta programs to different anti-cancer agents. Our findings inform on the intra-tumor heterogeneity of ccRCC and its regulatory networks and offers new perspectives to facilitate the designs of rational therapeutic strategies.

Visual Intratumor Heterogeneity and Breast Tumor Progression

High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or 'visual' heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity. We used VGG16 to train an image classifier to identify unique, patient-specific visual features in 1655 breast tumors (5907 core images) from the Carolina Breast Cancer Study (CBCS). Extracted features for images, as well as the epithelial and stromal image components, were hierarchically clustered, and visual heterogeneity was defined as a greater distance between images from the same patient. We assessed the association between visual heterogeneity, clinical features, and DNA-based molecular heterogeneity using generalized linear models, and we used Cox models to estimate the association between visual heterogeneity and tumor recurrence. Basal-like and ER-negative tumors were more likely to have low visual heterogeneity, as were the tumors from younger and Black women. Less heterogeneous tumors had a higher risk of recurrence (hazard ratio = 1.62, 95% confidence interval = 1.22-2.16), and were more likely to come from patients whose tumors were comprised of only one subclone or had a TP53 mutation. Associations were similar regardless of whether the image was based on stroma, epithelium, or both. Histologic heterogeneity adds complementary information to commonly used molecular indicators, with low heterogeneity predicting worse outcomes. Future work integrating multiple sources of heterogeneity may provide a more comprehensive understanding of tumor progression.

Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas

OBJECTIVE

High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.

METHODS

We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes.

RESULTS

The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes.

INTERPRETATION

The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

ScRNA-seq revealed the tumor microenvironment heterogeneity related to the occurrence and metastasis in upper urinary tract urothelial carcinoma

Metastasis is the greatest clinical challenge for UTUCs, which may have distinct molecular and cellular characteristics from earlier cancers. Herein, we provide single-cell transcriptome profiles of UTUC para cancer normal tissue, primary tumor lesions, and lymphatic metastases to explore possible mechanisms associated with UTUC occurrence and metastasis. From 28,315 cells obtained from normal and tumor tissues of 3 high-grade UTUC patients, we revealed the origin of UTUC tumor cells and the homology between metastatic and primary tumor cells. Unlike the immunomicroenvironment suppression of other tumors, we found no immunosuppression in the tumor microenvironment of UTUC. Moreover, it is imperative to note that stromal cells are pivotal in the advancement of UTUC. This comprehensive single-cell exploration enhances our comprehension of the molecular and cellular dynamics of metastatic UTUCs and discloses promising diagnostic and therapeutic targets in cancer-microenvironment interactions.

Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors

Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors.

SIGNIFICANCE

A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.

Single-cell analysis of tumor microenvironment and cell adhesion reveals that interleukin-1 beta promotes cancer cell proliferation in breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC), which is so called because of the lack of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) receptors on the cancer cells, accounts for 10%-15% of all breast cancers. The heterogeneity of the tumor microenvironment is high. However, the role of plasma cells controlling the tumor migration progression in TNBC is still not fully understood.

METHODS

We analyzed single-cell RNA sequencing data from five HER2 positive, 12 ER positive/PR positive, and nine TNBC samples. The potential targets were validated by immunohistochemistry.

RESULTS

Plasma cells were enriched in TNBC samples, which was consistent with validation using data from The Cancer Genome Atlas. Cell communication analysis revealed that plasma cells interact with T cells through the intercellular adhesion molecule 2-integrin-aLb2 complex, and then release interleukin 1 beta (IL1B), as verified by immunohistochemistry, ultimately promoting tumor growth.

CONCLUSION

Our results revealed the role of plasma cells in TNBC and identified IL1B as a new prognostic marker for TNBC.

Whole-tumoral metabolic heterogeneity in 18F-FDG PET/CT is a novel prognostic marker for neuroblastoma

BACKGROUND

Neuroblastoma (NB) is a highly heterogeneous tumor, and more than half of newly diagnosed NB are associated with extensive metastases. Accurately characterizing the heterogeneity of whole-body tumor lesions remains clinical challenge. This study aims to quantify whole-tumoral metabolic heterogeneity (WMH) derived from whole-body tumor lesions, and investigate the prognostic value of WMH in NB.

METHODS

We retrospectively enrolled 95 newly diagnosed pediatric NB patients in our department. Traditional semi-quantitative PET/CT parameters including the maximum standardized uptake value (SUVmax), the mean standardized uptake value (SUVmean), the peak standardized uptake value (SUVpeak), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. These PET/CT parameters were expressed as PSUVmax, PSUVmean, PSUVpeak, PMTV, PTLG for primary tumor, WSUVmax, WSUVmean, WSUVpeak, WMTV, WTLG for whole-body tumor lesions. The metabolic heterogeneity was quantified using the areas under the curve of the cumulative SUV-volume histogram index (AUC-CSH index). Intra-tumoral metabolic heterogeneity (IMH) and WMH were extracted from primary tumor and whole-body tumor lesions, respectively. The outcome endpoints were overall survival (OS) and progression-free survival (PFS). Survival analysis was performed utilizing the univariate and multivariate Cox proportional hazards regression. The optimal cut-off values for metabolic parameters were obtained by receiver operating characteristic curve (ROC).

RESULTS

During follow up, 27 (28.4%) patients died, 21 (22.1%) patients relapsed and 47 (49.5%) patients remained progression-free survival, with a median follow-up of 35.0 months. In survival analysis, WMTV and WTLG were independent indicators of PFS, and WMH was an independent risk factor of PFS and OS. However, IMH only showed association with PFS and OS. In addition to metabolic parameters, the International Neuroblastoma Staging System (INSS) was identified as an independent risk factor for PFS, and neuron-specific enolase (NSE) served as an independent predictor of OS.

CONCLUSION

WMH was an independent risk factor for PFS and OS, suggesting its potential as a novel prognostic marker for newly diagnosed NB patients.

Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma

In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

Suppressing mitochondrial inner membrane protein (IMMT) inhibits the proliferation of breast cancer cells through mitochondrial remodeling and metabolic regulation

Metabolic reprogramming is widely recognized as a hallmark of malignant tumors, and the targeting of metabolism has emerged as an appealing approach for cancer treatment. Mitochondria, as pivotal organelles, play a crucial role in the metabolic regulation of tumor cells, and their morphological and functional alterations are intricately linked to the biological characteristics of tumors. As a key regulatory subunit of mitochondria, mitochondrial inner membrane protein (IMMT), plays a vital role in degenerative diseases, but its role in tumor is almost unknown. The objective of this research was to investigate the roles that IMMT play in the development and progression of breast cancer (BC), as well as to elucidate the underlying biological mechanisms that drive these effects. In this study, it was confirmed that the expression of IMMT in BC tissues was significantly higher than that in normal tissues. The analysis of The Cancer Genome Atlas (TCGA) database revealed that IMMT can serve as an independent prognostic factor for BC patients. Additionally, verification in clinical specimens of BC demonstrated a positive association between high IMMT expression and larger tumor size (> 2 cm), Ki-67 expression (> 15%), and HER-2 status. Furthermore, in vitro experiments have substantiated that the suppression of IMMT expression resulted in a reduction in cell proliferation and alterations in mitochondrial cristae, concomitant with the liberation of cytochrome c, but it did not elicit mitochondrial apoptosis. Through Gene Set Enrichment Analysis (GSEA) analysis, we have predicted the associated metabolic genes and discovered that IMMT potentially modulates the advancement of BC through its interaction with 16 metabolic-related genes, and the changes in glycolysis related pathways have been validated in BC cell lines after IMMT inhibition. Consequently, this investigation furnishes compelling evidence supporting the classification of IMMT as prognostic marker in BC, and underscoring its prospective utility as a novel target for metabolic therapy.

Characterization of EpCAM in thyroid cancer biology by three-dimensional spheroids in vitro model

BACKGROUND

Thyroid cancer (TC) is the most common endocrine malignancy. Nowadays, undifferentiated thyroid cancers (UTCs) are still lethal, mostly due to the insurgence of therapy resistance and disease relapse. These events are believed to be caused by a subpopulation of cancer cells with stem-like phenotype and specific tumor-initiating abilities, known as tumor-initiating cells (TICs). A comprehensive understanding of how to isolate and target these cells is necessary. Here we provide insights into the role that the protein Epithelial Cell Adhesion Molecule (EpCAM), a known TICs marker for other solid tumors, may have in TC biology, thus considering EpCAM a potential marker of thyroid TICs in UTCs.

METHODS

The characterization of EpCAM was accomplished through Western Blot and Immunofluorescence on patient-derived tissue samples, adherent cell cultures, and 3D sphere cultures of poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC) cell lines. The frequency of tumor cells with putative tumor-initiating ability within the 3D cultures was assessed through extreme limiting dilution analysis (ELDA). EpCAM proteolytic cleavages were studied through treatments with different cleavages' inhibitors. To evaluate the involvement of EpCAM in inducing drug resistance, Vemurafenib (PLX-4032) treatments were assessed through MTT assay.

RESULTS

Variable EpCAM expression pattern was observed in TC tissue samples, with increased cleavage in the more UTC. We demonstrated that EpCAM is subjected to an intense cleavage process in ATC-derived 3D tumor spheres and that the 3D model faithfully mimics what was observed in patient's samples. We also proved that the integrity of the protein appears to be crucial for the generation of 3D spheres, and its expression and cleavage in a 3D system could contribute to drug resistance in thyroid TICs.

CONCLUSIONS

Our data provide novel information on the role of EpCAM expression and cleavage in the biology of thyroid TICs, and our 3D model reflects the variability of EpCAM cleavage observed in tissue samples. EpCAM evaluation could play a role in clinical decisions regarding patient therapy since its expression and cleavage may have a fundamental role in the switch to a drug-resistant phenotype of UTC cells.

Subpopulation commensalism promotes Rac1-dependent invasion of single cells via laminin-332

Phenotypic heterogeneity poses a significant hurdle for cancer treatment but is under-characterized in the context of tumor invasion. Amidst the range of phenotypic heterogeneity across solid tumor types, collectively invading cells and single cells have been extensively characterized as independent modes of invasion, but their intercellular interactions have rarely been explored. Here, we isolated collectively invading cells and single cells from the heterogeneous 4T1 cell line and observed extensive transcriptional and epigenetic diversity across these subpopulations. By integrating these datasets, we identified laminin-332 as a protein complex exclusively secreted by collectively invading cells. Live-cell imaging revealed that laminin-332 derived from collectively invading cells increased the velocity and directionality of single cells. Despite collectively invading and single cells having similar expression of the integrin α6β4 dimer, single cells demonstrated higher Rac1 activation upon laminin-332 binding to integrin α6β4. This mechanism suggests a novel commensal relationship between collectively invading and single cells, wherein collectively invading cells promote the invasive potential of single cells through a laminin-332/Rac1 axis.

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

Research autopsy programmes in oncology: shared experience from 14 centres across the world

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Epigenetic regulation of breast cancer metastasis

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of cancer-related mortality among women worldwide. Recurrent metastasis is associated with poor patient outcomes and poses a significant challenge in breast cancer therapies. Cancer cells adapting to a new tissue microenvironment is the key event in distant metastasis development, where the disseminating tumor cells are likely to acquire genetic and epigenetic alterations during the process of metastatic colonization. Despite several decades of research in this field, the exact mechanisms governing metastasis are not fully understood. However, emerging body of evidence indicates that in addition to genetic changes, epigenetic reprogramming of cancer cells and the metastatic niche are paramount toward successful metastasis. Here, we review and discuss the latest knowledge about the salient attributes of metastasis and epigenetic regulation in breast cancer and crucial research domains that need further investigation.

Chromatin as an old and new anticancer target

Recent genome-wide analyses identified chromatin modifiers as one of the most frequently mutated classes of genes across all cancers. However, chemotherapies developed for cancers involving DNA damage remain the standard of care for chromatin-deranged malignancies. In this review we address this conundrum by establishing the concept of 'chromatin damage': the non-genetic damage to protein-DNA interactions induced by certain small molecules. We highlight anthracyclines, a class of chemotherapeutic agents ubiquitously applied in oncology, as an example of overlooked chromatin-targeting agents. We discuss our current understanding of this phenomenon and explore emerging chromatin-damaging agents as a basis for further studies to maximize their impact in modern cancer treatment.

Clonal tracking in cancer and metastasis

The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.

Exploring tumor organoids for cancer treatment

As a life-threatening chronic disease, cancer is characterized by tumor heterogeneity. This heterogeneity is associated with factors that lead to treatment failure and poor prognosis, including drug resistance, relapse, and metastasis. Therefore, precision medicine urgently needs personalized tumor models that accurately reflect the tumor heterogeneity. Currently, tumor organoid technologies are used to generate in vitro 3D tissues, which have been shown to precisely recapitulate structure, tumor microenvironment, expression profiles, functions, molecular signatures, and genomic alterations in primary tumors. Tumor organoid models are important for identifying potential therapeutic targets, characterizing the effects of anticancer drugs, and exploring novel diagnostic and therapeutic options. In this review, we describe how tumor organoids can be cultured and summarize how researchers can use them as an excellent tool for exploring cancer therapies. In addition, we discuss tumor organoids that have been applied in cancer therapy research and highlight the potential of tumor organoids to guide preclinical research.

Development of new techniques and clinical applications of liquid biopsy in lung cancer management

Lung cancer is an exceedingly malignant tumor reported as having the highest morbidity and mortality of any cancer worldwide, thus posing a great threat to global health. Despite the growing demand for precision medicine, current methods for early clinical detection, treatment and prognosis monitoring in lung cancer are hampered by certain bottlenecks. Studies have found that during the formation and development of a tumor, molecular substances carrying tumor-related genetic information can be released into body fluids. Liquid biopsy (LB), a method for detecting these tumor-related markers in body fluids, maybe a way to make progress in these bottlenecks. In recent years, LB technology has undergone rapid advancements. Therefore, this review will provide information on technical updates to LB and its potential clinical applications, evaluate its effectiveness for specific applications, discuss the existing limitations of LB, and present a look forward to possible future clinical applications. Specifically, this paper will introduce technical updates from the prospectives of engineering breakthroughs in the detection of membrane-based LB biomarkers and other improvements in sequencing technology. Additionally, it will summarize the latest applications of liquid biopsy for the early detection, diagnosis, treatment, and prognosis of lung cancer. We will present the interconnectedness of clinical and laboratory issues and the interplay of technology and application in LB today.

In silico study of heterogeneous tumour-derived organoid response to CAR T-cell therapy

Chimeric antigen receptor (CAR) T-cell therapy is a promising immunotherapy for treating cancers. This method consists in modifying the patients' T-cells to directly target antigen-presenting cancer cells. One of the barriers to the development of this type of therapies, is target antigen heterogeneity. It is thought that intratumour heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model (ABM), built upon a previous ABM, to rationalise the outcomes of different CAR T-cells therapies strategies over heterogeneous tumour-derived organoids. We found that one dose of CAR T-cell therapy should be expected to reduce the tumour size as well as its growth rate, however it may not be enough to completely eliminate it. Moreover, the amount of free CAR T-cells (i.e. CAR T-cells that did not kill any cancer cell) increases as we increase the dosage, and so does the risk of side effects. We tested different strategies to enhance smaller dosages, such as enhancing the CAR T-cells long-term persistence and multiple dosing. For both approaches an appropriate dosimetry strategy is necessary to produce "effective yet safe" therapeutic results. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low antigen expression. This shield turns out to protect cells with high antigen expression. Finally we tested a multi-antigen recognition therapy to overcome antigen escape and heterogeneity. Our studies suggest that larger dosages can completely eliminate the organoid, however the multi-antigen recognition increases the risk of side effects. Therefore, an appropriate small dosages dosimetry strategy is necessary to improve the outcomes. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and to explore the characteristics of successful and unsuccessful treatments.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Dual targeting negative enrichment strategy for highly sensitive and purity detection of CTCs

Circulating tumor cells (CTCs) have significant clinical value in early tumor detection, dynamic monitoring and immunotherapy. CTC detection stands out as a leading non-invasive approach for tumor diagnostics and therapeutics. However, the high heterogeneity of CTCs and the occurrence of epithelial-mesenchymal transition (EMT) during metastasis pose challenges to methods relying on EpCAM-positive enrichment. To address these limitations, a method based on negative enrichment of CTCs using specific leukocyte targets has been developed. In this study, aiming to overcome the low purity associated with immunomagnetic beads targeting solely the leukocyte common antigen CD45, we introduced CD66b-modified immunomagnetic beads. CD66b, a specific target for neutrophils with abundant residues, was chosen as a complementary approach. The process involved initial collection of nucleated cells from whole blood samples using density gradient centrifugation. Subsequently, magnetically labeled leukocytes were removed by magnetic field, enabling the capture of CTCs with higher sensitivity and purity while retaining their activity. Finally, we selected 20 clinical blood samples from patients with various cancers to validate the effectiveness of this strategy, providing a new generalized tool for the clinical detection of CTCs.

Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location

Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.

Nomograms Predict PFS and OS for SCLC Patients After Standardized Treatment: A Real-World Study

Purpose

This study aims to investigate the process of small cell lung cancer (SCLC) patients from achieving optimal efficacy to experiencing disease progression until death. It examines the predictive value of the treatment response on progression free survival (PFS) and overall survival (OS) of SCLC patients.

Patients and Methods

We conducted a retrospective analysis on 136 SCLC patients diagnosed from 1992 to 2018. Important prognostic factors were identified to construct nomogram models. The predictive performance of the models was evaluated using the receiver operating characteristic curves and calibration curves. Survival differences between groups were compared using Kaplan-Meier survival curves. Subsequently, an independent cohort consisting of 106 SCLC patients diagnosed from 2014 to 2021 was used for validation.

Results

We constructed two nomograms to predict first-line PFS (PFS1) and OS of SCLC. The area under the receiver operating characteristic curves for the PFS1 nomogram predicting PFS at 3-, 6-, and 12-months were 0.919 (95% CI: 0.867-0.970), 0.908 (95% CI: 0.860-0.956) and 0.878 (95% CI: 0.798-0.958), and for the OS nomogram predicting OS at 6-, 12-, and 24-months were 0.814 (95% CI: 0.736-0.892), 0.819 (95% CI: 0.749-0.889) and 0.809 (95% CI: 0.678-0.941), indicating those two models with a high discriminative ability. The calibration curves demonstrated the models had a high degree of consistency between predicted and observed values. According to the risk scores, patients were divided into high-risk and low-risk groups, showing a significant difference in survival rate. And these findings were validated in another independent validation cohort.

Conclusion

Based on the patients' treatment response after standardized treatment, we developed and validated two nomogram models to predict PFS1 and OS of SCLC. The models demonstrated good accuracy, reliability and clinical applicability by validating in an independent cohort.

Distinct molecular profiles drive multifaceted characteristics of colorectal cancer metastatic seeds

Metastasis of primary tumors remains a challenge for early diagnosis and prevention. The cellular properties and molecular drivers of metastatically competent clones within primary tumors remain unclear. Here, we generated 10-16 single cell-derived lines from each of three colorectal cancer (CRC) tumors to identify and characterize metastatic seeds. We found that intrinsic factors conferred clones with distinct metastatic potential and cellular communication capabilities, determining organ-specific metastasis. Poorly differentiated or highly metastatic clones, rather than drug-resistant clones, exhibited poor clinical prognostic impact. Personalized genetic alterations, instead of mutation burden, determined the occurrence of metastatic potential during clonal evolution. Additionally, we developed a gene signature for capturing metastatic potential of primary CRC tumors and demonstrated a strategy for identifying metastatic drivers using isogenic clones with distinct metastatic potential in primary tumors. This study provides insight into the origin and mechanisms of metastasis and will help develop potential anti-metastatic therapeutic targets for CRC patients.

Identification and classification of glioma subtypes based on RNA-binding proteins

BACKGROUND

Glioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy.

METHODS

RNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers.

RESULTS

Six tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways.

CONCLUSIONS

The study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Decoding the interplay between genetic and non-genetic drivers of metastasis

Metastasis is a multistep process by which cancer cells break away from their original location and spread to distant organs, and is responsible for the vast majority of cancer-related deaths. Preventing early metastatic dissemination would revolutionize the ability to fight cancer. Unfortunately, the relatively poor understanding of the molecular underpinnings of metastasis has hampered the development of effective anti-metastatic drugs. Although it is now accepted that disseminating tumour cells need to acquire multiple competencies to face the many obstacles they encounter before reaching their metastatic site(s), whether these competencies are acquired through an accumulation of metastasis-specific genetic alterations and/or non-genetic events is often debated. Here we review a growing body of literature highlighting the importance of both genetic and non-genetic reprogramming events during the metastatic cascade, and discuss how genetic and non-genetic processes act in concert to confer metastatic competencies. We also describe how recent technological advances, and in particular the advent of single-cell multi-omics and barcoding approaches, will help to better elucidate the cross-talk between genetic and non-genetic mechanisms of metastasis and ultimately inform innovative paths for the early detection and interception of this lethal process.

Immunologic tumor microenvironment modulators for turning cold tumors hot

Tumors can be classified into distinct immunophenotypes based on the presence and arrangement of cytotoxic immune cells within the tumor microenvironment (TME). Hot tumors, characterized by heightened immune activity and responsiveness to immune checkpoint inhibitors (ICIs), stand in stark contrast to cold tumors, which lack immune infiltration and remain resistant to therapy. To overcome immune evasion mechanisms employed by tumor cells, novel immunologic modulators have emerged, particularly ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1/programmed death-ligand 1(PD-1/PD-L1). These agents disrupt inhibitory signals and reactivate the immune system, transforming cold tumors into hot ones and promoting effective antitumor responses. However, challenges persist, including primary resistance to immunotherapy, autoimmune side effects, and tumor response heterogeneity. Addressing these challenges requires innovative strategies, deeper mechanistic insights, and a combination of immune interventions to enhance the effectiveness of immunotherapies. In the landscape of cancer medicine, where immune cold tumors represent a formidable hurdle, understanding the TME and harnessing its potential to reprogram the immune response is paramount. This review sheds light on current advancements and future directions in the quest for more effective and safer cancer treatment strategies, offering hope for patients with immune-resistant tumors.

The immunosuppressive landscape in tumor microenvironment

Recent advances in cancer immunotherapy, especially immune checkpoint inhibitors (ICIs), have revolutionized the clinical outcome of many cancer patients. Despite the fact that impressive progress has been made in recent decades, the response rate remains unsatisfactory, and many patients do not benefit from ICIs. Herein, we summarized advanced studies and the latest insights on immune inhibitory factors in the tumor microenvironment. Our in-depth discussion and updated landscape of tumor immunosuppressive microenvironment may provide new strategies for reversing tumor immune evasion, enhancing the efficacy of ICIs therapy, and ultimately achieving a better clinical outcome.

MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms

BACKGROUND AND OBJECTIVE

Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer respond better to different therapies.

METHODS

In this work, we propose a novel lightweight dual-channel attention-based deep learning model MOB-CBAM that utilizes the backbone of MobileNet-V3 architecture with a Convolutional Block Attention Module to make highly accurate and precise predictions about breast cancer. We used the CMMD mammogram dataset to evaluate the proposed model in our study. Nine distinct data subsets were created from the original dataset to perform coarse and fine-grained predictions, enabling it to identify masses, calcifications, benign, malignant tumors and molecular subtypes of cancer, including Luminal A, Luminal B, HER-2 Positive, and Triple Negative. The pipeline incorporates several image pre-processing techniques, including filtering, enhancement, and normalization, for enhancing the model's generalization ability.

RESULTS

While identifying benign versus malignant tumors, i.e., coarse-grained classification, the MOB-CBAM model produced exceptional results with 99 % accuracy, precision, recall, and F1-score values of 0.99 and MCC of 0.98. In terms of fine-grained classification, the MOB-CBAM model has proven to be highly efficient in accurately identifying mass with (benign/malignant) and calcification with (benign/malignant) classification tasks with an impressive accuracy rate of 98 %. We have also cross-validated the efficiency of the proposed MOB-CBAM deep learning architecture on two datasets: MIAS and CBIS-DDSM. On the MIAS dataset, an accuracy of 97 % was reported for the task of classifying benign, malignant, and normal images, while on the CBIS-DDSM dataset, an accuracy of 98 % was achieved for the classification of mass with either benign or malignant, and calcification with benign and malignant tumors.

CONCLUSION

This study presents lightweight MOB-CBAM, a novel deep learning framework, to address breast cancer diagnosis and subtype prediction. The model's innovative incorporation of the CBAM enhances precise predictions. The extensive evaluation of the CMMD dataset and cross-validation on other datasets affirm the model's efficacy.

Pathologic Assessment and Staging of Multiple Non-Small Cell Lung Carcinomas: A Paradigm Shift with the Emerging Role of Molecular Methods

Non-small cell lung carcinomas (NSCLCs) commonly present as 2 or more separate tumors. Biologically, this encompasses 2 distinct processes: separate primary lung carcinomas (SPLCs), representing independently arising tumors, and intrapulmonary metastases (IPMs), representing intrapulmonary spread of a single tumor. The advent of computed tomography imaging has substantially increased the detection of multifocal NSCLCs. The strategies and approaches for distinguishing between SPLCs and IPMs have evolved significantly over the years. Recently, genomic sequencing of somatic mutations has been widely adopted to identify targetable alterations in NSCLC. These molecular techniques have enabled pathologists to reliably discern clonal relationships among multiple NSCLCs in clinical practice. However, a standardized approach to evaluating and staging multiple NSCLCs using molecular methods is still lacking. Here, we reviewed the historical context and provided an update on the growing applications of genomic testing as a clinically relevant benchmark for determining clonal relationships in multiple NSCLCs, a practice we have designated "comparative molecular profiling." We examined the strengths and limitations of the morphology-based distinction of SPLCs vs IPMs and highlighted pivotal clinical and pathologic insights that have emerged from studying multiple NSCLCs using genomic approaches as a gold standard. Lastly, we suggest a practical approach for evaluating multiple NSCLCs in the clinical setting, considering the varying availability of molecular techniques.

RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation

BACKGROUND

Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg).

METHODS

Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets.

RESULTS

Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival.

CONCLUSION

The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.