The prognostic significance of positive pelvic washings in endometrial cancer (EC) remains unknown, and little data exist regarding washings as a source of genetic information in relation to a patient's tumor. We sought to assess the feasibility of identifying EC mutations in peritoneal washings.

Peritoneal washings from 21 biopsy-confirmed newly diagnosed patients with EC across disease stages between 09/2018 and 07/2019 were collected. Peritoneal washings, primary EC, and normal DNA samples were subjected to next-generation sequencing targeting 468 cancer-related genes. Sequencing results were compared to cytological analysis.

For the 21 EC cases included, cytology found 8 (38 %) of the peritoneal washings as positive, 7 (33 %) as negative, and 6 (29 %) as suspicious or rare-atypical cells. Based on molecular analysis, tumor mutations (TMs) were detected in 18/21 (86 %) of peritoneal washings. Overall, 11/21 (52 %) samples demonstrated concordant results between cytologic and molecular analysis, and all positive cytologic results were confirmed with molecular analysis. However, of cases with negative or suspicious cytology results, 77 % (10/13) were found to have TMs in washings. Five patients with negative cytology were positive on molecular analysis (5/7, 71 %), and 5 patients with suspicious washings demonstrated TMs (5/6, 83 %). Of the 10 EC patients who developed recurrences, regardless of stage, 5/10 (50 %) patients had positive cytology, whereas 9/10 (90 %) had TMs based on molecular analysis.

Mutational analysis of peritoneal washings using panel sequencing in EC is feasible. A substantial subset of patients with cytology-negative or suspicious washings had TMs detected.

Aneuploidy is observed as gains or losses of whole chromosomes or chromosome arms and is a common hallmark of cancer. Whereas models for the generation of aneuploidy in cancer invoke mitotic chromosome segregation errors, whole-arm losses might occur simply as a result of centromere breakage. We recently showed that elevated RNA Polymerase II level over the S-phase-dependent histone genes predicts rapid recurrence of human meningioma and is correlated with total whole-arm losses relative to gains. To explain this imbalance in arm losses over gains, we have proposed that histone overexpression at S-phase competes with the histone H3 variant CENP-A, resulting in centromere breaks and whole-arm losses. To test whether centromere breaks alone can drive aneuploidy, we ask whether total whole-arm aneuploids can predict outcomes across different cancer types in large RNA and whole-genome sequencing databanks. We find that total whole-arm losses generally predict outcome, suggesting that centromere breakage is a major initiating factor leading to aneuploidy and the resulting changes in the selective landscape that drive most cancers. We also present evidence that centromere breakage alone is sufficient to account for whole-arm losses and gains, contrary to mitotic spindle error models for the generation of aneuploidy. Our results suggest that therapeutic intervention targeting histone overexpression has the potential to reduce aneuploidy and slow cancer progression.

Solid tumors are spatially heterogeneous in their genetic, molecular, and cellular composition, but recent spatial profiling studies have mostly charted genetic and RNA variation in tumors separately. To leverage the potential of RNA to identify copy number alterations (CNAs), we develop SlideCNA, a computational tool to extract CNA signals from sparse spatial transcriptomics data with near single cellular resolution. SlideCNA uses expression-aware spatial binning to overcome sparsity limitations while maintaining spatial signal to recover CNA patterns. We test SlideCNA on simulated and real Slide-seq data of (metastatic) breast cancer and demonstrate its potential for spatial subclone detection.

Neoadjuvant chemotherapy (NAC) is a standard treatment for breast cancer (BC) to shrink tumors and facilitate surgery. However, the molecular underpinnings of response to NAC and prognosis have not been well characterized.

We enrolled 73 stage II/III BC patients who received NAC followed by surgery. Tumor tissue samples were available from 36 patients at baseline and 38 at the time of surgery. Plasma circulating tumor DNA (ctDNA) was collected at three time points: before NAC (n = 63), during NAC (n = 42), and after NAC (n = 40). Comprehensive genomic, transcriptomic, and ctDNA analyses were performed to identify biomarkers associated with pathological complete response (pCR) and survival outcomes.

Nine baseline mutations, including DNHD1 and PLEC, along with HIPPO pathway alterations, were associated with pCR. Responsive tumors exhibited immune activation and downregulated PI3K-Akt and AGE-RAGE pathways, while non-pCR tumors showed reduced cytokine and immune receptor activity. Undetectable ctDNA during and after NAC was predictive of treatment efficacy and correlated with improved survival. Baseline mutations in USH2A were associated with shorter disease-free survival (hazard ratio: 11.9; 95% confidence interval: 2.8-50.8; P < 0.001), with a consistent trend observed for overall survival. Elevated NHSL1 expression in baseline tumors indicated an initial treatment response but was later associated with tumor relapse and poor overall survival (P = 0.026 and P = 0.023, respectively), findings that were validated in an independent clinical cohort (N = 30) through immunohistochemistry staining.

Our comprehensive multi-omics analysis identified promising biomarkers predictive of treatment response and survival in BC patients receiving NAC followed by surgery. These findings underscore the importance of early tumor assessment for improved patient stratification and prognostication.

Wilms tumor (WT) is the most common kidney cancer in infants and young children. The determination of the clonality of bilateral WTs is critical to the treatment, because lineage-independent and metastatic tumors may require different treatment strategies. Here we found synchronous bilateral WT (n = 24 tumors from 12 patients) responded differently to preoperative chemotherapy. Transcriptome, whole-exome and whole-genome analysis (n = 12 tumors from 6 patients) demonstrated that each side of bilateral WT was clonally independent in terms of somatic driver mutations, copy number variations and transcriptomic profile. Molecular timing analysis revealed distinct timing and patterns of chromosomal evolution and mutational processes between the two sides of WT. Mutations in WT1, CTNNB1 and copy-neutral loss of heterozygosity of 11p15.5 provide possible genetic predisposition for the early initiation of bilateral WT. Our results provide comprehensive evidence and new insights regarding the separate initiation and early embryonic development of bilateral WT, which may benefit clinical practices in treating metastatic or refractory bilateral WT.

Uterine serous carcinoma (USC) typically arises from atrophic endometrium but may be associated with hyperplasia in 5% to 10% of cases. We sought to identify USC with concurrent hyperplasia and (i) define if these are clonally related, and (ii) determine if USC associated with hyperplasia is genetically distinct from USC without hyperplasia. Patients diagnosed with USC and hyperplasia from their hysterectomy specimen between January 1, 2014 and February 29, 2022 were identified. Hyperplasia and carcinoma were separately subjected to tumor-normal panel sequencing. Their repertoire of genetic alterations was compared with that of a separate cohort of atrophy-associated USCs. Of 267 USCs with clinical sequencing and slides available for review, 8 with concurrent carcinoma and hyperplasia had sufficient tissue for molecular studies. In 7 (87.5%) of these 8 cases, USC and hyperplasia were clonally related and shared multiple mutations, including TP53 in 4 cases (57%). In 1 case (USC4), USC and hyperplasia were unrelated at the genetic level, and the hyperplasia was TP53 wild-type. In another case (USC5), USC and TP53 wild-type hyperplasia shared 1 of 11 mutations while being distinct at the copy number level. The prevalence of ARID1A mutations was higher in hyperplasia-associated USC compared with atrophy-associated USC (43% vs. 0%, respectively; P=0.02). USC and co-occurring hyperplasia were clonally related in most cases, commonly harboring TP53 hotspot mutations in both components. These results suggest an alternative origin of tumorigenesis in this rare subset of endometrial cancers.

Molecular prognostication in metastatic castration prostate cancer (mCRPC) remains challenging due to the lack of validated biomarkers. This study developed a plasma cell-free DNA (cfDNA) methylation-based prognostic model in mCRPC. Targeted cfDNA methylation sequencing in 96 prostate cancer patients in different states of cancer progression revealed 78 methylation haplotype blocks (MHBs) differentially methylated from organ-confined prostate cancer to mCRPC states. Among these 78 MHBs, the top 20 MHBs were associated with mCRPC overall survival and most MHB methylation levels positively correlated with predicted circulating tumor DNA (ctDNA) fraction. By integrating the MHB-based risk score with currently available prognostic clinical variables and ctDNA fraction a prognostic nomogram was developed which showed high predictive performance for mCRPC survival (AUC = 0.99 for 6 months, AUC = 0.90 for 1 year, and AUC = 0.87 for 2 years). These findings demonstrate potential of cfDNA methylation as a molecular biology-driven biomarker for mCRPC prognosis.

Peripheral T cell lymphomas (PTCLs) comprise heterogeneous malignancies with limited therapeutic options. To uncover targetable vulnerabilities, we generate a collection of PTCL patient-derived tumor xenografts (PDXs) retaining histomorphology and molecular donor-tumor features over serial xenografting. PDX demonstrates remarkable heterogeneity, complex intratumor architecture, and stepwise trajectories mimicking primary evolutions. Combining functional transcriptional stratification and multiparametric imaging, we identify four distinct PTCL microenvironment subtypes with prognostic value. Mechanistically, we discover a subset of PTCLs expressing Epstein-Barr virus-specific T cell receptors and uncover the capacity of cancer-associated fibroblasts of counteracting treatments. PDXs' pre-clinical testing captures individual vulnerabilities, mirrors donor patients' clinical responses, and defines effective patient-tailored treatments. Ultimately, we assess the efficacy of CD5KO- and CD30- Chimeric Antigen Receptor T Cells (CD5KO-CART and CD30_CART, respectively), demonstrating their therapeutic potential and the synergistic role of immune checkpoint inhibitors for PTCL treatment. This repository represents a resource for discovering and validating intrinsic and extrinsic factors and improving the selection of drugs/combinations and immune-based therapies.

Analyzing gene expression data from the Cancer Genome Atlas (TCGA) and similar repositories often requires advanced coding skills, creating a barrier for many researchers. To address this challenge, we developed The Cancer Genome Explorer (TCGEx), a user-friendly, web-based platform for conducting sophisticated analyses such as survival modeling, gene set enrichment analysis, unsupervised clustering, and linear regression-based machine learning. TCGEx provides access to preprocessed TCGA data and immune checkpoint inhibition studies while allowing integration of user-uploaded data sets. Using TCGEx, we explore molecular subsets of human melanoma and identify microRNAs associated with intratumoral immunity. These findings are validated with independent clinical trial data on immune checkpoint inhibitors for melanoma and other cancers. In addition, we identify cytokine genes that can be used to predict treatment responses to various immune checkpoint inhibitors prior to treatment. Built on the R/Shiny framework, TCGEx offers customizable features to adapt analyses for diverse research contexts and generate publication-ready visualizations. TCGEx is freely available at https://tcgex.iyte.edu.tr , providing an accessible tool to extract insights from cancer transcriptomics data.

Protein Tyrosine Phosphatase Non-Receptor Type 6 (PTPN6) plays a crucial regulatory role in cellular processes and has been implicated in oncogenesis. This pan-cancer analysis aimed to elucidate PTPN6's involvement across various cancer types, with a particular emphasis on its association with tumor immunity. We analyzed PTPN6 expression data from open access databases using various statistical techniques, including survival analysis, genetic heterogeneity analysis, immune profiling, single-cell analysis, drug sensitivity analysis, and protein interaction analysis. We also conducted in vitro experiments utilizing colorectal cancer cell lines to validate PTPN6's functional role. PTPN6 exhibited distinct expression patterns across cancers, and its prognostic significance was apparent in several cancer types, particularly in glioblastoma, sarcoma, and melanoma. We observed correlations between PTPN6 and immune genes/cell infiltration in these cancers, suggesting a potential role in modulating the tumor immune microenvironment. Single-cell analysis revealed that PTPN6 is predominantly localized in macrophages, B cells, and dendritic cells within the tumor microenvironment, implying its involvement in regulating immune cell function. Enrichment analysis highlighted PTPN6's role in immune-related pathways. Drug sensitivity analysis identified specific drugs, including PAC-1, SNX-2112, BELINOSTAT, VORINOSTAT, TPCA-1, and PHA-893,888, whose efficacy may be influenced by PTPN6 expression. Knocking down PTPN6 expression inhibited the proliferation and migration of colorectal cancer cells in vitro, confirming its oncogenic role in this cancer type. This pan-cancer analysis establishes PTPN6's multifaceted influence on tumor immunity and its potential as a biomarker and therapeutic target.

Aneuploidy is observed as gains or losses of whole chromosomes or chromosome arms and is a common hallmark of cancer. Whereas models for the generation of aneuploidy in cancer invoke mitotic chromosome segregation errors, whole-arm losses might occur simply as a result of centromere breakage. We recently showed that elevated RNA Polymerase II (RNAPII) level over S-phase-dependent histone genes predicts rapid recurrence of human meningioma and is correlated with total whole-arm losses relative to gains. To explain this imbalance in arm losses over gains, we have proposed that histone overexpression at S-phase competes with the histone H3 variant CENP-A, resulting in centromere breaks and whole-arm losses. To test whether centromere breaks alone can drive aneuploidy, we ask whether total whole-arm aneuploids can predict outcome across different cancer types in large RNA and whole-genome sequencing databanks. We find that total whole-arm losses generally predict outcome, suggesting that centromere breakage is a major initiating factor leading to aneuploidy and the resulting changes in the selective landscape that drive most cancers. We also present evidence that centromere breakage alone is sufficient to account for whole-arm losses and gains, contrary to mitotic spindle error models for generation of aneuploidy. Our results suggest that therapeutic intervention targeting histone overexpression has the potential of reducing aneuploidy and slowing cancer progression.

Gain or loss of whole chromosome arms following centromere breaks is frequent in cancer, but whether or not there is a common initiating event is unknown. Here we show that the total number of whole-arm losses predicts patient outcomes across cancer types, suggesting a causal relationship. This general excess of losses over gains is not predicted by mitotic error models of aneuploidy but rather suggests that centromere breaks themselves initiate whole-arm aneuploidies. Insofar as aneuploidy reshapes the selective landscapes that drive most cancers, our results have potential clinical implications.

The study examines Opisthorchis viverrini (OV)-related cholangiocarcinoma (CCA), a serious malignancy common in Southeast Asia. Through multi-regional whole-exome sequencing of 52 tumor samples and 13 adjacent tissues from 13 patients, significant intratumoral heterogeneity (ITH) and inter-patient heterogeneity are shown. Chronic liver fluke infection induces a distinct mutational landscape, with 48-90% of mutations concentrated in each region of the tumor. The average mutation burden is 95 non-synonymous mutations per area, exceeding previous CCA investigations. Critical driver mutations in TP53, SMAD4, and other genes underscore their significance in pathogenesis. Mutational markers elucidate mechanisms including spontaneous deamination and impaired DNA repair. Unique mutation patterns distinguish OV-associated CCA from other variants. Chromosomal instability in patient K110 signifies aggressive tumor behavior and unfavorable prognosis. Targetable mutations such as ERBB2 underscore the possibility for personalized therapeutics. These findings underscore the necessity for personalized strategies for treatment that target both trunk and branch mutations in endemic areas.

Backgrouds: Whether homologous recombination repair (HRR) mutation has a differential effect on the prognosis has not been confirmed by current studies. The purpose of this study was to explore the clinical importance, prognostic value, and frequency of pathogenic changes in HRR genes in patients with ovarian cancer (OC). Methods: We analyze information including HRR mutation and clinical prognosis of OC patients both in our cohort and in the TCGA-OV database. Blood and/or tumor samples from 98 women admitted to Shanghai General Hospital between January 2021 and May 2024, and DNA sequencing was performed on these samples for all patients included in this retrospective study. Testing was performed for HRR mutations, including germline BRCA1/2 mutations, and defects in HRR were defined as detrimental mutations within relevant genes. Comprehensive medical records were gathered for all patients, with a follow-up period recorded for 74 of them. Results: HRR pathway genes, including BRCA1/2, CDK12, RAD54L, RAD51, ATM, MRE11, and BRIP2, are highly expressed in FIGO Stages I-II OCs among 482 patients in the TCGA-OV database, and 95.06% samples presented mutations. The alignment diagram analyzed by logistic and Cox regression was derived to investigate HRR genes on overall survival (OS < 763 days) of OC patients. A total of 98 patients were enrolled in our study, with 70 harboring HRR mutations (HRRmt) and 28 having the HRR wild-type (HRRwt). The predominant pathological type across all four patient groups was high-grade serous adenocarcinoma, with similar prevalence in HRRmt (84.30%) versus HRRwt (75%, p=0.360) and BRCAmt (94.20%) versus BRCAwt (74.60%, p=0.151) groups. Survival prediction data were collected from 74 patients, and the HRRmt group (n = 50) exhibited a numerically longer PFS compared to the HRRwt group (n = 24), with 23 months versus 17 months, respectively. A significant disparity was noted in the percentage of patients administered PARPi medication between the HRRmt and HRRwt groups (58.00% vs. 20.20%; p=0.003). Patients in both the HRRmt group (p=0.049) and the BRCAwt group (p=0.046) receiving PARPi treatment have extended PFS. Significant differences were identified between HRRmt and HRRwt groups in the size of the initial debulking surgery achieving R0 status (p=0.005), low CA125 levels (< 1000 U/mL) at diagnosis (p=0.015), and the use of PARP inhibitors (PARPi) (p=0.024) and antiangiogenic drugs (p < 0.001). For patients with HRR mutations, the use of PARPi significantly impacted PFS (p=0.049), and achieving R0 status (p=0.005) significantly influenced PFS. Conclusions: This study indicates that mutations in the HRR gene possess significant potential as a prognostic marker in OC. Our aim was to comprehensively explore how HRR gene mutations, including but not limited to BRCA, might influence the clinical course and survival of patients, shedding light on potential new avenues for personalized treatment strategies.

Metastatic cancers arise from a decades-long succession of increasingly virulent precursor lesions, each of which represents prospective targets for therapeutic intervention. This evolutionary process has been particularly vivid in esophageal adenocarcinoma (EAC), as this cancer and associated precursor lesions, including Barrett's esophagus (BE), low-grade dysplasia (LGD), and high-grade dysplasia (HGD), coexist in an accessible, 2-dimensional pattern in esophageal mucosa. Given the durability of these precursor lesions, it is likely that they, like EAC, rely on stem cells for their regenerative growth. To assess the role of stem cells in the evolution of EAC, we apply technology that selectively clones stem cells from the gastrointestinal tract to patient-matched endoscopic biopsies from each of the precursor lesions implicated in EAC.

Histologically validated, endoscopic biopsy series including EAC, HGD, LGD, BE, and normal esophageal mucosa were obtained from patients presenting with EAC. Rare (1:1000) cells from each of these lesions proved clonogenic and were assessed by in vitro differentiation, tumorigenicity in mice, and by molecular genetics.

Each of the lesions in the evolution of EAC possesses a discrete set of clonogenic cells marked by immaturity, enormous proliferative potential, and lesion-specific differentiation fate. DNA sequencing of these clones reveals intralesional heterogeneity and clonal resolution of the mutation progression within a given patient from BE, LGD, HGD, and EAC. High-throughput chemical screens against BE stem cells reveal drug combinations that are similarly effective against stem cells of LGD, HGD, and EAC.

All lesions in the evolution of EAC possess discrete populations of stem cells that are potential therapeutic targets.

The cyclin-dependent kinases 12 (CDK12) and 13 (CDK13) govern several steps of gene expression, including transcription, RNA processing and translation. The main target of CDK12/13 is the serine 2 residue of the carboxy-terminal domain of RNA polymerase II (RNAPII), thus influencing the directionality, elongation rate and processivity of the enzyme. The CDK12/13-dependent regulation of RNAPII activity influences the expression of selected target genes with important functional roles in the proliferation and viability of all eukaryotic cells. Neuronal cells are particularly affected by the loss of CDK12/13, as result of the high dependency of neuronal genes on RNAPII processivity for their expression. Deregulation of CDK12/13 activity strongly affects brain physiology by influencing the stemness potential and differentiation properties of neuronal precursor cells. Moreover, mounting evidence also suggest the involvement of CDK12/13 in brain tumours. Herein, we discuss the functional role(s) of CDK12 and CDK13 in gene expression regulation and highlight similarities and differences between these highly homologous kinases, with particular attention to their impact on brain physiology and pathology. Lastly, we provide an overview of CDK12/13 inhibitors and of their efficacy in brain tumours and other neoplastic diseases.

Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the need for personalized treatment strategies based on genomic data.

We analyzed whole-genome and RNA sequences from biopsy specimens of 65 advanced gastric cancer patients before their chemotherapy treatment. Using machine learning techniques, we developed a model with 123 omics features, such as immune signatures and copy number variations, to predict their chemotherapy outcomes.

The model demonstrated a prediction accuracy of 70-80% in forecasting chemotherapy responses in both test and validation cohorts. Notably, tumor-associated neutrophils emerged as significant predictors of treatment efficacy. Further single-cell analyses from cancer tissues revealed different neutrophil subgroups with potential antitumor activities suggesting their usefulness as biomarkers for treatment decisions.

This study confirms the utility of machine learning in advancing personalized medicine for gastric cancer by identifying tumor-associated neutrophils and their subgroups as key indicators of chemotherapy response. These findings could lead to more tailored and effective treatment plans for patients.

Immune checkpoint inhibitor (ICI) therapy is a promising anti-tumor therapeutic strategy; however, its efficacy in solid tumors is limited. Chromosome missegregation is common in various solid tumors; however, its role in tumor progression remains poorly understood, and its correlation with ICI is yet to be explored. Here, it is found that increased chromosome missegregation promotes tumor immune microenvironment, and eventually immunotherapeutic efficacy, by triggering pyroptosis. yin yang 2 (YY2) is identified as a mitotic checkpoint regulator, which promotes chromosome missegregation by upregulating BUB1B transcription. Increased chromosome missegregation promoted the formation of micronuclei and release of double-stranded DNA (dsDNA) into the cytosol, triggering an AIM2-mediated cytosolic dsDNA response. The subsequent pyroptosis strengthened the tumor immune microenvironment, thereby enhancing immunoinfiltration and cytotoxicity of CD8+ T cells, while preventing their exhaustion. Finally, through in vitro and in vivo experiments, it is demonstrated that combining YY2 overexpression-induced chromosome missegregation/cytosolic dsDNA response and PD-1 inhibitor significantly enhanced the efficacy of ICI immunotherapy in microsatellite instable and microsatellite stable colorectal cancer cells. Together, these findings provide new insights on the role of chromosome missegregation in triggering cytosolic dsDNA response-mediated pyroptosis and modulating the tumor immune microenvironment, suggesting a novel strategy for improving ICI therapeutic efficacy in colorectal cancer.

Personalized cancer vaccines (PCVs) can generate circulating immune responses against predicted neoantigens1-6. However, whether such responses can target cancer driver mutations, lead to immune recognition of a patient's tumour and result in clinical activity are largely unknown. These questions are of particular interest for patients who have tumours with a low mutational burden. Here we conducted a phase I trial (ClinicalTrials.gov identifier NCT02950766) to test a neoantigen-targeting PCV in patients with high-risk, fully resected clear cell renal cell carcinoma (RCC; stage III or IV) with or without ipilimumab administered adjacent to the vaccine. At a median follow-up of 40.2 months after surgery, none of the 9 participants enrolled in the study had a recurrence of RCC. No dose-limiting toxicities were observed. All patients generated T cell immune responses against the PCV antigens, including to RCC driver mutations in VHL, PBRM1, BAP1, KDM5C and PIK3CA. Following vaccination, there was a durable expansion of peripheral T cell clones. Moreover, T cell reactivity against autologous tumours was detected in seven out of nine patients. Our results demonstrate that neoantigen-targeting PCVs in high-risk RCC are highly immunogenic, capable of targeting key driver mutations and can induce antitumour immunity. These observations, in conjunction with the absence of recurrence in all nine vaccinated patients, highlights the promise of PCVs as effective adjuvant therapy in RCC.

Aneuploidy is prevalent in cancer and associates with fitness advantage and poor patient prognosis. Yet, experimentally induced aneuploidy initially leads to adverse effects and impaired proliferation, suggesting that cancer cells must adapt to aneuploidy. We performed in vitro evolution of cells with extra chromosomes and obtained cell lines with improved proliferation and gene expression changes congruent with changes in aneuploid cancers. Integrated analysis of cancer multi-omics data and model cells revealed increased expression of DNA replicative and repair factors, reduced genomic instability, and reduced lysosomal degradation. We identified E2F4 and FOXM1 as transcription factors strongly associated with adaptation to aneuploidy in vitro and in cancers and validated this finding. The adaptation to aneuploidy also coincided with specific copy number aberrations that correlate with poor patient prognosis. Chromosomal engineering mimicking these aberrations improved aneuploid cell proliferation, while loss of previously present extra chromosomes impaired it. The identified common adaptation strategies suggest replication stress, genomic instability, and lysosomal stress as common liabilities of aneuploid cancers.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Cell division drives somatic evolution but is challenging to quantify. We developed a framework to count cell divisions with DNA replication-related mutations in polyguanine homopolymers. Analyzing 505 samples from 37 patients, we studied the milestones of colorectal cancer evolution. Primary tumors diversify at ~250 divisions from the founder cell, while distant metastasis divergence occurs significantly later, at ~500 divisions. Notably, distant but not lymph node metastases originate from primary tumor regions that have undergone surplus divisions, tying subclonal expansion to metastatic capacity. Then, we analyzed a cohort of 73 multifocal lung cancers and showed that the cell division burden of the tumors' common ancestor distinguishes independent primary tumors from intrapulmonary metastases and correlates with patient survival. In lung cancer too, metastatic capacity is tied to more extensive proliferation. The cell division history of human cancers is easily accessible using our simple framework and contains valuable biological and clinical information.

The use of poly(ADP-ribose) polymerase inhibitors (PARPi) revolutionized the treatment of BRCA-mutated cancers. Identifying patients exhibiting homologous recombination deficiency (HRD) has been proved useful to predict PARPi efficacy. However, obtaining HRD status remains an arduous task due to its evolution over the time. This causes HRD status to become obsolete when obtained from genomic scars, rendering PARPi ineffective for these patients. Only two HRD tests are currently FDA-approved, both based on genomic scars detection and BRCA mutations testing. Nevertheless, new technologies for obtaining an increasingly reliable HRD status continue to evolve. Application of these tests in clinical practice is an additional challenge due to the need for lower costs and shorter time to results delay.In this review, we describe the currently available methods for HRD testing, including the methodologies and corresponding tests for assessing HRD status, and discuss the clinical routine application of these tests and their technical validation.

Current breast cancer classification methods, particularly immunohistochemistry and PAM50, face challenges in accurately characterizing the HER2-low subtype, a therapeutically relevant entity with distinct biological features. This notable gap can lead to misclassification, resulting in inappropriate treatment decisions and suboptimal patient outcomes. Leveraging RNA-seq and machine-learning algorithms, we developed the Breast Cancer Classifier (BCC), a unique transcriptomic classifier for more precise breast cancer subtyping, specifically by delineating and incorporating HER2-low as a distinct subtype. BCC also redefined the PAM50 Normal subtype into other subtypes, disputing its classification as a unique molecular group. Our statistical analysis not only confirmed the reproducibility and accuracy of BCC, but also revealed similarities in prognostic characteristics between the HER2-low and Basal subtypes. Addressing this gap in breast cancer classification is clinically significant because it not only improves treatment stratification, but also uncovers novel molecular and immunohistochemical features associated with the HER2-low and HER2-high subtypes, thereby advancing our understanding of breast cancer heterogeneity and providing guidance in precision oncology.

Intratumoral heterogeneity and clonal evolution are pivotal in the progression and metastasis of melanoma. However, when combined with variable tumor cellularity, intratumoral heterogeneity limits the sensitivity and accuracy of uncovering a cancer's clonal evolution. In this study, we combined fluorescence-activated cell sorting (FACS) with whole-exome sequencing (WES) to investigate the clonal composition and evolutionary patterns in seven melanoma biopsies obtained from three patients, each having both primary site and metastatic samples. We employed a multiparameter ploidy sorting approach to isolate tumor populations based on DNA ploidy and melanoma biomarkers (SOX10 or S100), enabling us to investigate clonal evolution with high resolution. Our approach increased the mean tumor purity from 70% (range 19-88%) in unsorted material to 91% (range 87-96%) post-sorting. Our findings revealed significant inter- and intratumor heterogeneity, with one patient exhibiting two genomically distinct clonal tumor populations within a single primary site biopsy, each giving rise to different metastases. Our findings highlight the critical role of intratumoral heterogeneity and clonal evolution in melanoma, especially when analyzing tumor trajectories. The unique combination of multiparameter FACS and WES provides a powerful method for identifying clonal populations and reconstructing clonal evolution. This study provides valuable insights into the clonal architecture of melanoma and lays the groundwork for future research with larger patient groups.

Osteosarcoma (OS) is the most common primary bone malignancy with variable molecular biology and prognosis. However, our understanding of the association between cell types and OS progression remains poor.

We generated a human OS cell atlas by integrating over 110,000 single cells from 17 samples. Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. The Scissor algorithm and gene enrichment analyses were conducted to delve into cell-intrinsic molecular characteristics linked to OS prognosis. Moreover, the study investigated the impact of ATF6α in OS aggressiveness through genetic and pharmacological loss of function analyses. Lastly, the CellChat algorithm was employed to investigate cell-cell communications.

Utilizing the high-quality human OS cell atlas, we identified tumor purity as a prognostic indicator and developed a robust tumor purity prediction model. We respectively delineated cancer cell- and immune cell-intrinsic molecular characteristics associated with OS prognosis at single-cell resolution. Interestingly, tumor cells with activated unfolded protein response (UPR) pathway were significantly associated with disease aggressiveness. Notably, ATF6α emerged as the top-activated transcription factor for this tumor subcluster. Subsequently, we confirmed that ATF6α was markedly associated with OS progression, while both genetic and pharmacological inhibition of ATF6α impaired the survival of HOS cells. Lastly, we depicted the landscape of signal crosstalk between the UPR-related subcluster and other cell types within the tumor microenvironment.

In summary, our work provides novel insights into the molecular biology of OS, and offers valuable resource for OS biomarker discovery and treatment strategy development.

Although some patients with metastatic melanoma experience durable responses to immune checkpoint inhibitors (ICIs), most exhibit intrinsic or acquired resistance to these therapies. Here, we compare somatic genomic profiles from matched pre-treatment and post-resistance tumor biopsies in patients (n = 25) with metastatic melanoma who exhibited heterogeneous ICI responses to nominate additional mediators of acquired resistance. We find that several acquired resistance tumors exhibit defects in B2M or JAK1/2, consistent with prior findings. We also discover resistance-associated mutations in SEC24C and SEC24D in 3 patients. SEC24 has an essential role in the trafficking of the dsDNA sensor STING and has been linked to interferonopathies. Melanoma cells engineered to express the SEC24C mutations observed in patients exhibit diminished STING signaling, including decreased type I interferon production, antigen presentation, and a reduced capacity to activate cytotoxic T cells. This study nominates a role for aberrant STING trafficking in acquired resistance to ICIs.