Systematic identification of mutations and copy number alterations associated with cancer patient prognosis

Aneuploidy as a cancer vulnerability

Aneuploidy is prevalent in cancer and has complicated roles in tumorigenesis. Paradoxically, artificially engineered aneuploidy in normal cells reduces cellular fitness by inducing proteotoxic and genotoxic stresses. A better molecular understanding of the multifaceted roles of aneuploidy in cancer evolution offers promising avenues for future cancer therapies. Here, we discuss the patterns and consequences of aneuploidy in human cancer. We highlight recent efforts to explore aneuploidy as a cancer vulnerability and new interventions that exploit this vulnerability for cancer treatment.

Salivary biomarkers: a promising approach for predicting immunotherapy response in head and neck cancers

Head and neck cancers, including cancers of the mouth, throat, voice box, salivary glands, and nose, are a significant global health issue. Radiotherapy and surgery are commonly used treatments. However, due to treatment resistance and disease recurrence, new approaches such as immunotherapy are being explored. Immune checkpoint inhibitors (ICIs) have shown promise, but patient responses vary, necessitating predictive markers to guide appropriate treatment selection. This study investigates the potential of non-invasive biomarkers found in saliva, oral rinses, and tumor-derived exosomes to predict ICI response in head and neck cancer patients. The tumor microenvironment significantly impacts immunotherapy efficacy. Oral biomarkers can provide valuable information on composition, such as immune cell presence and checkpoint expression. Elevated tumor mutation load is also associated with heightened immunogenicity and ICI responsiveness. Furthermore, the oral microbiota may influence treatment outcomes. Current research aims to identify predictive salivary biomarkers. Initial studies indicate that tumor-derived exosomes and miRNAs present in saliva could identify immunosuppressive pathways and predict ICI response. While tissue-based markers like PD-L1 have limitations, combining multiple oral fluid biomarkers could create a robust panel to guide treatment decisions and advance personalized immunotherapy for head and neck cancer patients.

Comprehensive analysis of prognosis markers with molecular features derived from pan-cancer whole-genome sequences

Cancer prognosis markers are useful for treatment decisions; however, the omics-level landscape is not well understood across multiple cancer types. Pan-Cancer Analysis of Whole Genomes (PCAWG) provides unprecedented access to various types of molecular data, ranging from typical DNA mutations and RNA expressions to more deeply analyzed or whole-genomic features, such as HLA haplotypes and structural variations. We analyzed the PCAWG data of 13 cancer types from 1,514 patients to identify prognosis markers belonging to 17 molecular features in the survival analysis based on the Cox and Lasso regression methods. We found that germline features including HLA haplotypes, neoantigens, and the number of structural variations were associated with overall survival; however, mutational signatures were not. Measuring a few markers provided a sufficient prognostic performance evaluated by c-index for each cancer type. DNA markers demonstrated better or comparable prognostic performance compared to RNA markers in some cancer types. "Universal" markers strongly associated with overall survival across cancer types were not identified. These findings will give insights into the clinical implementation of prognosis markers.

Cytosolic and endoplasmic reticulum chaperones inhibit wt-p53 to increase cancer cells' survival by refluxing ER-proteins to the cytosol

The endoplasmic reticulum (ER) is an essential sensing organelle responsible for the folding and secretion of almost one-third of eukaryotic cells' total proteins. However, environmental, chemical, and genetic insults often lead to protein misfolding in the ER, accumulating misfolded proteins, and causing ER stress. To solve this, several mechanisms were reported to relieve ER stress by decreasing the ER protein load. Recently, we reported a novel ER surveillance mechanism by which proteins from the secretory pathway are refluxed to the cytosol to relieve the ER of its content. The refluxed proteins gain new prosurvival functions in cancer cells, thereby increasing cancer cell fitness. We termed this phenomenon ER to CYtosol Signaling (or 'ERCYS'). Here, we found that in mammalian cells, ERCYS is regulated by DNAJB12, DNAJB14, and the HSC70 cochaperone SGTA. Mechanistically, DNAJB12 and DNAJB14 bind HSC70 and SGTA - through their cytosolically localized J-domains to facilitate ER-protein reflux. DNAJB12 is necessary and sufficient to drive this phenomenon to increase AGR2 reflux and inhibit wt-p53 during ER stress. Mutations in DNAJB12/14 J-domain prevent the inhibitory interaction between AGR2-wt-p53. Thus, targeting the DNAJB12/14-HSC70/SGTA axis is a promising strategy to inhibit ERCYS and impair cancer cell fitness.

Clinical outcomes and molecular characteristics of lung-only and liver-only metastatic pancreatic cancer: results from a real-world evidence database

BACKGROUND

Previous research demonstrates longer survival for patients with lung-only metastatic pancreatic adenocarcinoma (mPDAC) compared to liver-only mPDAC. The objective of this study is to understand the survival differences, impact of chemotherapy, and associated genomic features of mPDAC that is isolated to either the liver or lung.

PATIENTS AND METHODS

Longitudinal clinical outcomes and molecular sequencing data were retrospectively analyzed across 831 patients with PDAC across all stages whose tumors first metastasized to the liver or lung. Survival differences were evaluated using Cox regression. Mutational frequency differences were evaluated using Fisher's exact test.

RESULTS

Median overall survival (mOS) was shorter in patients with liver-only metastasis (1.3y [1.2-1.4], n = 689) compared to lung-only metastasis (2.1y [1.9-2.5], n = 142) (P = .000000588, HR = 2.00 [1.53-2.63]. Survival differences were observed regardless of choice of 1st-line standard-of-care therapy. For 5-fluorouracil-based therapies, mOS for liver-only mPDAC was 1.4y [1.3-1.6] (n = 211) compared to 2.1y [1.8-3.3] for lung-only mPDAC (n = 175) (P = .008113, HR = 1.75 [1.16-2.65]). For gemcitabine/nab-paclitaxel therapy, mOS for liver-only mPDAC was 1.2y [1.1-1.5] (n = 175) compared to 2.1y [1.6-3.4] for lung-only disease (n = 32) (P = .01863, HR = 1.84 [1.11-3.06]). PDAC tumors with liver-only metastases were modestly enriched (unadjustable P < .05) for: TP53 mutations, MYC amplifications, inactivating CDK2NA alterations, inactivating SMAD alterations, and SWI/SWF pathway mutations. PDAC tumors with lung-only metastases were enriched for: STK11 mutations, CCND1 amplifications, and GNAS alterations.

CONCLUSION

Patients with lung-only mPDAC demonstrate an improved prognosis relative to those with liver-only mPDAC. Responses to chemotherapy do not explain these differences. Organotropic metastatic tumor diversity is mirrored at the molecular level in PDAC.

Chromosomal instability as a driver of cancer progression

Chromosomal instability (CIN) refers to an increased propensity of cells to acquire structural and numerical chromosomal abnormalities during cell division, which contributes to tumour genetic heterogeneity. CIN has long been recognized as a hallmark of cancer, and evidence over the past decade has strongly linked CIN to tumour evolution, metastasis, immune evasion and treatment resistance. Until recently, the mechanisms by which CIN propels cancer progression have remained elusive. Beyond the generation of genomic copy number heterogeneity, recent work has unveiled additional tumour-promoting consequences of abnormal chromosome segregation. These mechanisms include complex chromosomal rearrangements, epigenetic reprogramming and the induction of cancer cell-intrinsic inflammation, emphasizing the multifaceted role of CIN in cancer.

Synthesis and characterization of a novel Naphthalimide-Selenium based Temozolomide drug conjugate in glioma cells

Temozolomide (TMZ)2 is the frontline chemotherapeutic drug against glioblastoma. As chemoresistance is a severe limitation of TMZ therapy, we aimed to synthesize a novel drug to improve its efficacy. This was achieved by conjugating TMZ with Naphthalimide (known DNA intercalator) via selenourea linkage (redox regulator). The synthesized Naphthalimide-selenourea-TMZ (Naph-Se-TMZ)3 exhibited heightened cell death in TMZ-sensitive and TMZ-resistant glioma cells compared to an equivalent dose of TMZ. Diminished cell viability was concomitant with heightened reactive oxygen species (ROS)4 levels in Naph-Se-TMZ treated cells. Docking simulations and in vitro studies attributed the improved cytotoxicity of Naph-Se-TMZ to its ability to inhibit HDAC1. A ROS-dependent decrease in HDAC1 expression and total HDAC activity was observed in Naph-Se-TMZ treated cells. We report the heightened cytotoxicity of synthesized novel Naph-Se-TMZ over TMZ in TMZ-resistant and TMZ-sensitive glioma cells through its ability to serve as a ROS generator and HDAC inhibitor. Importantly, TCGA dataset analysis indicating the association of heightened HDAC1 expression with poor prognosis and elevated antioxidant enzyme levels in glioma patients points towards the likely involvement of HDAC1 in protecting glioma cells from oxidative stress-induced damage. Taken together, our findings underscore the potential of Naph-Se-TMZ as a more effective therapeutic alternative to TMZ for glioblastoma treatment.

Elements of liquid biopsies: isolation, analysis, and clinical application in cancer diagnosis to prognosis

INTRODUCTION

The liquid biopsy is a breakthrough in the field of medical diagnostics. It serves as a sentinel that can quietly detect even the subtlest aberrations that indicate the presence of disease. They make it possible to uncover relevant genetic factors of tumors with minimal to no risk to cancer patients. Liquid biopsies allow detailed diagnosis, dynamic treatment monitoring, and accurate prognosis. They are also invaluable in diagnosing other diseases such as infectious diseases and aberrant gene mutations.

AREAS COVERED

The present review undertakes an in-depth analysis of the existing status of liquid biopsy diagnostic tools, focusing on their principal components. Furthermore, the review highlights pertinent and recent research in this field to provide a comprehensive understanding of the current state of this technology and its prospects.

EXPERT OPINION

Despite new and upcoming research in liquid biopsies, multiple areas need to be further explored before the viable transition into the clinical arena. With the advancements in tools such as artificial intelligence and machine learning and the integration of these technologies with liquid biopsies, these challenges are being addressed and will eventually lead to the development of a highly evolved liquid biopsy diagnostic tools.

Evidence of RNA polymerase III recruitment and transcription at protein-coding gene promoters

The transcriptional interplay of human RNA polymerase I (RNA Pol I), RNA Pol II, and RNA Pol III remains largely uncharacterized due to limited integrative genomic analyses for all three enzymes. To address this gap, we applied a uniform framework to quantify global RNA Pol I, RNA Pol II, and RNA Pol III occupancies and identify both canonical and noncanonical patterns of gene localization. Most notably, our survey captures unexpected RNA Pol III recruitment at promoters of specific protein-coding genes. We show that such RNA Pol III-occupied promoters are enriched for small nascent RNAs terminating in a run of 4 Ts-a hallmark of RNA Pol III termination indicative of constrained RNA Pol III transcription. Taken further, RNA Pol III disruption generally reduces the expression of RNA Pol III-occupied protein-coding genes, suggesting RNA Pol III recruitment and transcription enhance RNA Pol II activity. These findings resemble analogous patterns of RNA Pol II activity at RNA Pol III-transcribed genes, altogether uncovering a reciprocal form of crosstalk between RNA Pol II and RNA Pol III.

Systematic Modeling of Risk-Associated Copy Number Alterations in Cancer

The determination of the cancer prognosis is paramount for patients and medical personnel so that they can devise treatment strategies. Transcriptional-based signatures and subtypes derived from cancer biopsy material have been used in clinical practice for several cancer types to aid in setting the patient prognosis and forming treatment strategies. Other genomic features in cancer biopsies, such as copy number alterations (CNAs), have been underused in clinical practice, and yet they represent a complementary source of molecular information that can add detail to the prognosis, which is supported by recent work in breast, ovarian, and lung cancers. Here, through a systematic strategy, we explored the prognostic power of CNAs in 37 cancer types. In this analysis, we defined two modes of informative features, deep and soft, depending on the number of alleles gained or lost. These informative modes were grouped by amplifications or deletions to form four single-data prognostic models. Finally, the single-data models were summed or combined to generate four additional multidata prognostic models. First, we show that the modes of features are cancer-type dependent, where deep alterations generate better models. Nevertheless, some cancers require soft alterations to generate a feasible model due to the lack of significant deep alterations. Then, we show that the models generated by summing coefficients from amplifications and deletions appear to be more practical for many but not all cancer types. We show that the CNA-derived risk group is independent of other clinical factors. Furthermore, overall, we show that CNA-derived models can define clinically relevant risk groups in 33 of the 37 (90%) cancer types analyzed. Our study highlights the use of CNAs as biomarkers that are potentially clinically relevant to survival in cancer patients.

An elevated rate of whole-genome duplications in cancers from Black patients

In the United States, Black individuals have higher rates of cancer mortality than any other racial group. Here, we examine chromosome copy number changes in cancers from more than 1800 self-reported Black patients. We find that tumors from self-reported Black patients are significantly more likely to exhibit whole-genome duplications (WGDs), a genomic event that enhances metastasis and aggressive disease, compared to tumors from self-reported white patients. This increase in WGD frequency is observed across multiple cancer types, including breast, endometrial, and lung cancer, and is associated with shorter patient survival. We further demonstrate that combustion byproducts are capable of inducing WGDs in cell culture, and cancers from self-reported Black patients exhibit mutational signatures consistent with exposure to these carcinogens. In total, these findings identify a type of genomic alteration that is associated with environmental exposures and that may influence racial disparities in cancer outcomes.

Proteogenomic insights into the biology and treatment of pan-melanoma

Melanoma is one of the most prevalent skin cancers, with high metastatic rates and poor prognosis. Understanding its molecular pathogenesis is crucial for improving its diagnosis and treatment. Integrated analysis of multi-omics data from 207 treatment-naïve melanomas (primary-cutaneous-melanomas (CM, n = 28), primary-acral-melanomas (AM, n = 81), primary-mucosal-melanomas (MM, n = 28), metastatic-melanomas (n = 27), and nevi (n = 43)) provides insights into melanoma biology. Multivariate analysis reveals that PRKDC amplification is a prognostic molecule for melanomas. Further proteogenomic analysis combined with functional experiments reveals that the cis-effect of PRKDC amplification may lead to tumor proliferation through the activation of DNA repair and folate metabolism pathways. Proteome-based stratification of primary melanomas defines three prognosis-related subtypes, namely, the ECM subtype, angiogenesis subtype (with a high metastasis rate), and cell proliferation subtype, which provides an essential framework for the utilization of specific targeted therapies for particular melanoma subtypes. The immune classification identifies three immune subtypes. Further analysis combined with an independent anti-PD-1 treatment cohort reveals that upregulation of the MAPK7-NFKB signaling pathway may facilitate T-cell recruitment and increase the sensitivity of patients to immunotherapy. In contrast, PRKDC may reduce the sensitivity of melanoma patients to immunotherapy by promoting DNA repair in melanoma cells. These results emphasize the clinical value of multi-omics data and have the potential to improve the understanding of melanoma treatment.

TMPRSS2 is a tumor suppressor and its downregulation promotes antitumor immunity and immunotherapy response in lung adenocarcinoma

BACKGROUND

TMPRSS2, a key molecule for SARS-CoV-2 invading human host cells, has an association with cancer. However, its association with lung cancer remains insufficiently unexplored.

METHODS

In five bulk transcriptomics datasets, one single-cell RNA sequencing (scRNA-seq) dataset and one proteomics dataset for lung adenocarcinoma (LUAD), we explored associations between TMPRSS2 expression and immune signatures, tumor progression phenotypes, genomic features, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we performed experimental validation of the bioinformatics findings.

RESULTS

TMPRSS2 expression levels correlated negatively with the enrichment levels of both immune-stimulatory and immune-inhibitory signatures, while they correlated positively with the ratios of immune-stimulatory/immune-inhibitory signatures. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor progression, and worse survival in LUAD. We further validated that TMPRSS2 was downregulated with tumor progression in the LUAD cohort we collected from Jiangsu Cancer Hospital, China. In vitro and in vivo experiments verified the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1.

CONCLUSIONS

TMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a potential link between lung cancer and pneumonia caused by SARS-CoV-2 infection.

Evidence of RNA polymerase III recruitment and transcription at protein-coding gene promoters

RNA polymerase (Pol) I, II, and III are most commonly described as having distinct roles in synthesizing ribosomal RNA (rRNA), messenger RNA (mRNA), and specific small noncoding (nc)RNAs, respectively. This delineation of transcriptional responsibilities is not definitive, however, as evidenced by instances of Pol II recruitment to genes conventionally transcribed by Pol III, including the co-transcription of RPPH1 - the catalytic RNA component of RNase P. A comprehensive understanding of the interplay between RNA polymerase complexes remains lacking, however, due to limited comparative analyses for all three enzymes. To address this gap, we applied a uniform framework for quantifying global Pol I, II, and III occupancies that integrates currently available human RNA polymerase ChIP-seq datasets. Occupancy maps are combined with a comprehensive multi-class promoter set that includes protein-coding genes, noncoding genes, and repetitive elements. While our genomic survey appropriately identifies recruitment of Pol I, II, and III to canonical target genes, we unexpectedly discover widespread recruitment of the Pol III machinery to promoters of specific protein-coding genes, supported by colocalization patterns observed for several Pol III-specific subunits. We show that Pol III-occupied Pol II promoters are enriched for small, nascent RNA reads terminating in a run of 4 Ts, a unique hallmark of Pol III transcription termination and evidence of active Pol III activity at these sites. Pol III disruption differentially modulates the expression of Pol III-occupied coding genes, which are functionally enriched for ribosomal proteins and genes broadly linked to unfavorable outcomes in cancer. Our map also identifies additional, currently unannotated genomic elements occupied by Pol III with clear signatures of nascent RNA species that are sensitive to disruption of La (SSB) - a Pol III-related RNA chaperone protein. These findings reshape our current understanding of the interplay between Pols II and III and identify potentially novel small ncRNAs with broad implications for gene regulatory paradigms and RNA biology.

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

Targeting chromosomal instability and aneuploidy in cancer

Cancer development and therapy resistance are driven by chromosomal instability (CIN), which causes chromosome gains and losses (i.e., aneuploidy) and structural chromosomal alterations. Technical limitations and knowledge gaps have delayed therapeutic targeting of CIN and aneuploidy in cancers. However, our toolbox for creating and studying aneuploidy in cell models has greatly expanded recently. Moreover, accumulating evidence suggests that seven conventional antimitotic chemotherapeutic drugs achieve clinical response by inducing CIN instead of mitotic arrest, although additional anticancer activities may also contribute in vivo. In this review, we discuss these recent developments. We also highlight new discoveries, which together show that 25 chromosome arm aneuploidies (CAAs) may be targetable by 36 drugs across 14 types of cancer. Collectively, these advances offer many new opportunities to improve cancer treatment.

Multi-omics prognostic signatures of IPO11 mRNA expression and clinical outcomes in colorectal cancer using bioinformatics approaches

The most prevalent malignant illness of the gastrointestinal system, colorectal cancer, is the third most prevalent cancer in males and the second most prevalent cancer in women. Importin-11 is a protein that acts as a regulator of cancer cell proliferation in colorectal tumours by conveying β -catenin to the cell nucleus. However, the IPO11 gene was found to encode a protein called Importin-11, which functions as a nucleus importer for the cell. As a result, preventing β -catenin from entering the nucleus requires blocking Importin-11. As a result, we conducted a multi-omics investigation to assess IPO11 gene potential as a therapeutic biomarker for human colorectal cancer (CC). Oncomine, GEPIA2, immunohisto-chemistry, and UALCAN databases were used to analyses the mRNA expression profiles of IPO11 in CC. The investigation has yielded clear evidence of the increase of IPO11 expression in CC subtypes, as indicated by the data acquired. Analysing CC research from the cBioPortal database, the study discovered three new missense mutations in the importin-11 protein sequence at a frequency of 0.00-1.50% copy number changes. Additionally, the Kaplan-Meier plots demonstrated a strong connection concerning IPO11 downregulation and a poorer CC patient survival rate. The co-expressed gene profile of IPO11 was likewise associated with the onset of CC. IPO11 co-expressed gene profile was also linked to CC development. Moreover, the correlation analysis using bc-GenExMiner and the UCSC Xena server identified KIF2A as the most positively co-expressed gene. The study found that KIF2A and its co-expressed genes were involved in a wide variety of cancer progression pathways using the Enrichr database. Cumulatively, this result will not only provide new information about the expression of IPO11 associated with CC progression and patient survival, but could also serve as a therapeutic biomarker for treating CC in a significant and worthwhile manner.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00259-2.

Aneuploidy in human cancer: new tools and perspectives

Chromosome copy number imbalances, otherwise known as aneuploidies, are a common but poorly understood feature of cancer. Here, we describe recent advances in both detecting and manipulating aneuploidies that have greatly advanced our ability to study their role in tumorigenesis. In particular, new clustered regularly interspaced short palindromic repeats (CRISPR)-based techniques have been developed that allow the creation of isogenic cell lines with specific chromosomal changes, thereby facilitating experiments in genetically controlled backgrounds to uncover the consequences of aneuploidy. These approaches provide increasing evidence that aneuploidy is a key driver of cancer development and enable the identification of multiple dosage-sensitive genes encoded on aneuploid chromosomes. Consequently, measuring aneuploidy may inform clinical prognosis, while treatment strategies that target aneuploidy could represent a novel method to counter malignant growth.

Completing a genomic characterisation of microscopic tumour samples with copy number

BACKGROUND

Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples.

RESULTS

Here, we complete this genomic characterisation with copy number. We present a novel protocol, PicoCNV, to call allele-specific somatic copy number alterations from picogram quantities of tumour DNA. We find that PicoCNV provides exactly accurate copy number in 84% of the genome for even the smallest samples, and demonstrate its clinical potential in maintenance therapy.

CONCLUSIONS

PicoCNV complements our existing platform, allowing for accurate and comprehensive genomic characterisations of cancers in settings where only microscopic samples are available.

K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach

Background:For years now, cancer treatments have entailed tried-and-true methods. Yet, oncologists and clinicians recommend a series of surgeries, chemotherapy, and radiation therapy. Yet, even amidst these treatments, the number of deaths due to cancer increases at an alarming rate. The prognosis of cancer patients is influenced by mutations, age, and various cancer stages. However, the association between these variables is unclear. Methods: The present work adopts a machine learning technique-k-nearest neighbor; for both regression and classification tasks, regression for predicting the survival time of oral cancer patients, and classification for classifying the patients into one of the predefined oral cancer stages. Two cross-validation approaches-hold-out and k-fold methods-have been used to examine the prediction results. Results: The experimental results show that the k-fold method performs better than the hold-out method, providing the least mean absolute error score of 0.015. Additionally, the model classifies patients into a valid group. Of the 429 records, 97 (out of 106), 99 (out of 119), 95 (out of 113), and 77 (out of 91) were classified to its correct label as stages - 1, 2, 3, and 4. The accuracy, recall, precision, and F-measure for each classification group obtained are 0.84, 0.85, 0.85, and 0.84. Conclusions: The study showed that aged patients with a higher number of mutations than young patients have a higher risk of short survival. Senior patients with a more significant number of mutations have an increased risk of getting into the last cancer stage.

Subclonal Somatic Copy-Number Alterations Emerge and Dominate in Recurrent Osteosarcoma

Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. We conducted longitudinal whole-genome sequencing of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. Subclonal copy-number alterations were identified in all patients except one. In 5 patients, subclones from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clones in 6 of 7 patients with multiple clones. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy-number clones. A chromosomal duplication timing analysis revealed that complex genomic rearrangements typically occurred prior to diagnosis, supporting a macroevolutionary model of evolution, where a large number of genomic aberrations are acquired over a short period of time followed by clonal selection, as opposed to ongoing evolution. A mutational signature analysis of recurrent tumors revealed that homologous repair deficiency (HRD)-related SBS3 increases at each time point in patients with recurrent disease, suggesting that HRD continues to be an active mutagenic process after diagnosis. Overall, by examining the clonal relationships between temporally and spatially separated samples from patients with relapsed/refractory osteosarcoma, this study sheds light on the intratumor heterogeneity and potential drivers of treatment resistance in this disease.

SIGNIFICANCE

The chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.

TP53 Induced Glycolysis and Apoptosis Regulator and Monocarboxylate Transporter 4 drive metabolic reprogramming with c-MYC and NFkB activation in breast cancer

Breast cancer is composed of metabolically coupled cellular compartments with upregulation of TP53 Induced Glycolysis and Apoptosis Regulator (TIGAR) in carcinoma cells and loss of caveolin 1 (CAV1) with upregulation of monocarboxylate transporter 4 (MCT4) in fibroblasts. The mechanisms that drive metabolic coupling are poorly characterized. The effects of TIGAR on fibroblast CAV1 and MCT4 expression and breast cancer aggressiveness was studied using coculture and conditioned media systems and in-vivo. Also, the role of cytokines in promoting tumor metabolic coupling via MCT4 on cancer aggressiveness was studied. TIGAR downregulation in breast carcinoma cells reduces tumor growth. TIGAR overexpression in carcinoma cells drives MCT4 expression and NFkB activation in fibroblasts. IL6 and TGFB drive TIGAR upregulation in carcinoma cells, reduce CAV1 and increase MCT4 expression in fibroblasts. Tumor growth is abrogated in the presence of MCT4 knockout fibroblasts and environment. We discovered coregulation of c-MYC and TIGAR in carcinoma cells driven by lactate. Metabolic coupling primes the tumor microenvironment allowing for production, uptake and utilization of lactate. In sum, aggressive breast cancer is dependent on metabolic coupling.

Radius additivity score: a novel combination index for tumour growth inhibition in fixed-dose xenograft studies

The effect of combination therapies in many cancers has often been shown to be superior to that of monotherapies. This success is commonly attributed to drug synergies. Combinations of two (or more) drugs in xenograft tumor growth inhibition (TGI) studies are typically designed at fixed doses for each compound. The available methods for assessing synergy in such study designs are based on combination indices (CI) and model-based analyses. The former methods are suitable for screening exercises but are difficult to verify in in vivo studies, while the latter incorporate drug synergy in semi-mechanistic frameworks describing disease progression and drug action but are unsuitable for screening. In the current study, we proposed the empirical radius additivity (Rad-add) score, a novel CI for synergy detection in fixed-dose xenograft TGI combination studies. The Rad-add score approximates model-based analysis performed using the semi-mechanistic constant-radius growth TGI model. The Rad-add score was compared with response additivity, defined as the addition of the two response values, and the bliss independence model in combination studies derived from the Novartis PDX dataset. The results showed that the bliss independence and response additivity models predicted synergistic interactions with high and low probabilities, respectively. The Rad-add score predicted synergistic probabilities that appeared to be between those predicted with response additivity and the Bliss model. We believe that the Rad-add score is particularly suitable for assessing synergy in the context of xenograft combination TGI studies, as it combines the advantages of CI approaches suitable for screening exercises with those of semi-mechanistic TGI models based on a mechanistic understanding of tumor growth.

Pan-cancer analysis of NFE2L2 mutations identifies a subset of lung cancers with distinct genomic and improved immunotherapy outcomes

BACKGROUND

Mutations in the KEAP1-NFE2L2 signaling pathway were linked to increased tumorigenesis and aggressiveness. Interestingly, not all hotspot mutations on NFE2L2 were damaging; some even were activating. However, there was conflicting evidence about the association between NFE2L2 mutation and Nrf2-activating mutation and responsiveness to immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and other multiple cancers.

METHODS

The study with the largest sample size (n = 49,533) explored the landscape of NFE2L2 mutations and their impact response/resistance to ICIs using public cohorts. In addition, the in-house WXPH cohort was used to validate the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC.

RESULTS

In two pan-cancer cohorts, Nrf2-activating mutation was associated with higher TMB value compared to wild-type. We identified a significant association between Nrf2-activating mutation and shorter overall survival in pan-cancer patients and NSCLC patients but not in those undergoing ICIs treatment. Similar findings were obtained in cancer patients carrying the NFE2L2 mutation. Furthermore, in NSCLC and other cancer cohorts, patients with NFE2L2 mutation demonstrated more objective responses to ICIs than patients with wild type. Our in-house WXPH cohort further confirmed the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC. Lastly, decreased inflammatory signaling pathways and immune-depleted immunological microenvironments were enriched in Nrf2-activating mutation patients with NSCLC.

CONCLUSIONS

Our study found that patients with Nrf2-activating mutation had improved immunotherapy outcomes than patients with wild type in NSCLC and other tumor cohorts, implying that Nrf2-activating mutation defined a distinct subset of pan-cancers and might have implications as a biomarker for guiding ICI treatment, especially NSCLC.

Disentangling the roles of aneuploidy, chromosomal instability and tumour heterogeneity in developing resistance to cancer therapies

Aneuploidy is defined as the cellular state of having a number of chromosomes that deviates from a multiple of the normal haploid chromosome number of a given organism. Aneuploidy can be present in a static state: Down syndrome individuals stably maintain an extra copy of chromosome 21 in their cells. In cancer cells, however, aneuploidy is usually present in combination with chromosomal instability (CIN) which leads to a continual generation of new chromosomal alterations and the development of intratumour heterogeneity (ITH). The prevalence of cells with specific chromosomal alterations is further shaped by evolutionary selection, for example, during the administration of cancer therapies. Aneuploidy, CIN and ITH have each been individually associated with poor prognosis in cancer, and a wealth of evidence suggests they contribute, either alone or in combination, to cancer therapy resistance by providing a reservoir of potential resistant states, or the ability to rapidly evolve resistance. A full understanding of the contribution and interplay between aneuploidy, CIN and ITH is required to tackle therapy resistance in cancer patients. However, these characteristics often co-occur and are intrinsically linked, presenting a major challenge to defining their individual contributions. Moreover, their accurate measurement in both experimental and clinical settings is a technical hurdle. Here, we attempt to deconstruct the contribution of the individual and combined roles of aneuploidy, CIN and ITH to therapy resistance in cancer, and outline emerging approaches to measure and disentangle their roles as a step towards integrating these principles into cancer therapeutic strategy.

Oncogene-like addiction to aneuploidy in human cancers

Most cancers exhibit aneuploidy, but its functional significance in tumor development is controversial. Here, we describe ReDACT (Restoring Disomy in Aneuploid cells using CRISPR Targeting), a set of chromosome engineering tools that allow us to eliminate specific aneuploidies from cancer genomes. Using ReDACT, we created a panel of isogenic cells that have or lack common aneuploidies, and we demonstrate that trisomy of chromosome 1q is required for malignant growth in cancers harboring this alteration. Mechanistically, gaining chromosome 1q increases the expression of MDM4 and suppresses p53 signaling, and we show that TP53 mutations are mutually exclusive with 1q aneuploidy in human cancers. Thus, tumor cells can be dependent on specific aneuploidies, raising the possibility that these "aneuploidy addictions" could be targeted as a therapeutic strategy.

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

Multi-region DNA sequencing of primary tumors and metastases from individual patients helps identify somatic aberrations driving cancer development. However, most methods to infer copy-number aberrations (CNAs) analyze individual samples. We introduce HATCHet2 to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 introduces a novel statistic, the mirrored haplotype B-allele frequency (mhBAF), to identify mirrored-subclonal CNAs having different numbers of copies of parental haplotypes in different tumor clones. HATCHet2 also has high accuracy in identifying focal CNAs and extends the earlier HATCHet method in several directions. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 50 prostate cancer samples from 10 patients reveals previously-unreported mirrored-subclonal CNAs affecting cancer genes.

Characteristics of DNA macro-alterations in breast cancer with liver metastasis before treatment

BACKGROUND

Whole-genome doubling (WGD) has been observed in 30% of cancers, followed by a highly complex rearranged karyotype unfavourable to breast cancer's outcome. However, the macro-alterations that characterise liver metastasis in breast cancer(BC) are poorly understood. Here, we conducted a whole-genome sequencing analysis of liver metastases to explore the status and the time frame model of these macro-alterations in pre-treatment patients with metastatic breast cancer.

RESULTS

Whole-genome sequencing was conducted in 11 paired primary tumours, lymph node metastasis, and liver metastasis fresh samples from four patients with late-stage breast cancer. We also chose five postoperative frozen specimens from patients with early-stage breast cancer before any treatment as control. Surprisingly, all four liver metastasis samples were classified as WGD + . However, the previous study reported that WGD happened in 30% of cancers and 2/5 in our early-stage samples. WGD was not observed in the two separate primary tumours and one lymph node metastasis of one patient with metastatic BC, but her liver metastasis showed an early burst of bi-allelic copy number gain. The phylogenetic tree proves her 4 tumour samples were the polyclonal origin and only one WGD + clone metastasis to the liver. Another 3 metastatic BC patients' primary tumour and lymph node metastasis experienced WGD as well as liver metastasis, and they all showed similar molecular time-frame of copy number(CN) gain across locations within the same patient. These patients' tumours were of monoclonal origin, and WGD happened in a founding clone before metastasis, explaining that all samples share the CN-gain time frame. After WGD, the genomes usually face instability to evolve other macro-alterations. For example, a greater quantity and variety of complex structural variations (SVs) were detected in WGD + samples. The breakpoints were enriched in the chr17: 39 Mb-40 Mb tile, which contained the HER2 gene, resulting in the formation of tyfonas, breakage-fusion-bridge cycles, and double minutes. These complex SVs may be involved in the evolutionary mechanisms of the dramatic increase of HER2 copy number.

CONCLUSION

Our work revealed that the WGD + clone might be a critical evolution step for liver metastasis and favoured following complex SV of breast cancer.

Splicing Factor SRSF1 Promotes Pancreatitis and KRASG12D-Mediated Pancreatic Cancer

Inflammation is strongly associated with pancreatic ductal adenocarcinoma (PDAC), a highly lethal malignancy. Dysregulated RNA splicing factors have been widely reported in tumorigenesis, but their involvement in pancreatitis and PDAC is not well understood. Here, we report that the splicing factor SRSF1 is highly expressed in pancreatitis, PDAC precursor lesions, and tumors. Increased SRSF1 is sufficient to induce pancreatitis and accelerate KRASG12D-mediated PDAC. Mechanistically, SRSF1 activates MAPK signaling-partly by upregulating interleukin 1 receptor type 1 (IL1R1) through alternative-splicing-regulated mRNA stability. Additionally, SRSF1 protein is destabilized through a negative feedback mechanism in phenotypically normal epithelial cells expressing KRASG12D in mouse pancreas and in pancreas organoids acutely expressing KRASG12D, buffering MAPK signaling and maintaining pancreas cell homeostasis. This negative feedback regulation of SRSF1 is overcome by hyperactive MYC, facilitating PDAC tumorigenesis. Our findings implicate SRSF1 in the etiology of pancreatitis and PDAC, and point to SRSF1-misregulated alternative splicing as a potential therapeutic target.

SIGNIFICANCE

We describe the regulation of splicing factor SRSF1 expression in the context of pancreas cell identity, plasticity, and inflammation. SRSF1 protein downregulation is involved in a negative feedback cellular response to KRASG12D expression, contributing to pancreas cell homeostasis. Conversely, upregulated SRSF1 promotes pancreatitis and accelerates KRASG12D-mediated tumorigenesis through enhanced IL1 and MAPK signaling. This article is highlighted in the In This Issue feature, p. 1501.

Extrachromosomal DNA (ecDNA) in cancer: mechanisms, functions, and clinical implications

Extrachromosomal DNA (ecDNA) is circular DNA that plays an important role in the development and heterogeneity of cancer. The rapid evolution of methods to detect ecDNA, including microscopic and sequencing approaches, has greatly enhanced our knowledge of the role of ecDNA in cancer development and evolution. Here, we review the molecular characteristics, functions, mechanisms of formation, and detection methods of ecDNA, with a focus on the potential clinical implications of ecDNA in cancer. Specifically, we consider the role of ecDNA in acquired drug resistance, as a diagnostic and prognostic biomarker, and as a therapeutic target in the context of cancer. As the pathological and clinical significance of ecDNA continues to be explored, it is anticipated that ecDNA will have broad applications in the diagnosis, prognosis, and treatment of patients with cancer.

Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data

Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods.

Utility of CYP2D6 copy number variants as prognostic biomarker in localized anal squamous cell carcinoma

BACKGROUND

Anal squamous cell carcinoma (ASCC) is an infrequent tumor whose treatment has not changed since the 1970s. The aim of this study is the identification of biomarkers allowing personalized treatments and improvement of therapeutic outcomes.

METHODS

Forty-six paraffin tumor samples from ASCC patients were analyzed by whole-exome sequencing. Copy number variants (CNVs) were identified and their relation to disease-free survival (DFS) was studied and validated in an independent retrospective cohort of 101 ASCC patients from the Multidisciplinary Spanish Digestive Cancer Group (GEMCAD). GEMCAD cohort proteomics allowed assessing the biological features of these tumors.

RESULTS

On the discovery cohort, the median age was 61 years old, 50% were males, stages I/II/III: 3 (7%)/16 (35%)/27 (58%), respectively, median DFS was 33 months, and overall survival was 45 months. Twenty-nine genes whose duplication was related to DFS were identified. The most representative was duplications of the CYP2D locus, including CYP2D6, CYP2D7P, and CYP2D8P genes. Patients with CYP2D6 CNV had worse DFS at 5 years than those with two CYP2D6 copies (21% vs. 84%; p < .0002, hazard ratio [HR], 5.8; 95% confidence interval [CI], 2.7-24.9). In the GEMCAD validation cohort, patients with CYP2D6 CNV also had worse DFS at 5 years (56% vs. 87%; p = .02, HR = 3.6; 95% CI, 1.1-5.7). Mitochondria and mitochondrial cell-cycle proteins were overexpressed in patients with CYP2D6 CNV.

CONCLUSIONS

Tumor CYP2D6 CNV identified patients with a significantly worse DFS at 5 years among localized ASCC patients treated with 5-fluorouracil, mitomycin C, and radiotherapy. Proteomics pointed out mitochondria and mitochondrial cell-cycle genes as possible therapeutic targets for these high-risk patients.

PLAIN LANGUAGE SUMMARY

Anal squamous cell carcinoma is an infrequent tumor whose treatment has not been changed since the 1970s. However, disease-free survival in late staged tumors is between 40% and 70%. The presence of an alteration in the number of copies of CYP2D6 gene is a biomarker of worse disease-free survival. The analysis of the proteins in these high-risk patients pointed out mitochondria and mitochondrial cell-cycle genes as possible therapeutic targets. Therefore, the determination of the number of copies of CYP2D6 allows the identification of anal squamous carcinoma patients with a high-risk of relapse that could be redirected to a clinical trial. Additionally, this study may be useful to suggest new treatment strategies to increase current therapy efficacy.

Systematic assessment of prognostic molecular features across cancers

Precision oncology promises accurate prediction of disease trajectories by utilizing molecular features of tumors. We present a systematic analysis of the prognostic potential of diverse molecular features across large cancer cohorts. We find that the mRNA expression of biologically coherent sets of genes (modules) is substantially more predictive of patient survival than single-locus genomic and transcriptomic aberrations. Extending our analysis beyond existing curated gene modules, we find a large novel class of highly prognostic DNA/RNA cis-regulatory modules associated with dynamic gene expression within cancers. Remarkably, in more than 82% of cancers, modules substantially improve survival stratification compared with conventional clinical factors and prominent genomic aberrations. The prognostic potential of cancer modules generalizes to external cohorts better than conventionally used single-gene features. Finally, a machine-learning framework demonstrates the combined predictive power of multiple modules, yielding prognostic models that perform substantially better than existing histopathological and clinical factors in common use.

Subclonal somatic copy number alterations emerge and dominate in recurrent osteosarcoma

Multiple large-scale tumor genomic profiling efforts have been undertaken in osteosarcoma, however, little is known about the spatial and temporal intratumor heterogeneity and how it may drive treatment resistance. We performed whole-genome sequencing of 37 tumor samples from eight patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. We identified subclonal copy number alterations in all but one patient. We observed that in five patients, a subclonal copy number clone from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clone in 6 out of 7 patients with more than one clone. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy number clones. Our study sheds light on intratumor heterogeneity and the potential drivers of treatment resistance in osteosarcoma.

Multiview Deep Forest for Overall Survival Prediction in Cancer

Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper. MVDF can learn the features of each view and fuse them with integrated learning and multiple kernel learning. Then, a gradient boost forest based on the information bottleneck theory is proposed to reduce redundant information and avoid overfitting. In addition, a pruning strategy for a cascaded forest is used to limit the impact of outlier data. Comprehensive experiments have been carried out on a data set from West China Hospital of Sichuan University and two public data sets. Results have demonstrated that our method outperforms the compared methods in predicting overall survival.

Oncogene-like addiction to aneuploidy in human cancers

Most cancers exhibit aneuploidy, but its functional significance in tumor development is controversial. Here, we describe ReDACT (Restoring Disomy in Aneuploid cells using CRISPR Targeting), a set of chromosome engineering tools that allow us to eliminate specific aneuploidies from cancer genomes. Using ReDACT, we created a panel of isogenic cells that have or lack common aneuploidies, and we demonstrate that trisomy of chromosome 1q is required for malignant growth in cancers harboring this alteration. Mechanistically, gaining chromosome 1q increases the expression of MDM4 and suppresses TP53 signaling, and we show that TP53 mutations are mutually-exclusive with 1q aneuploidy in human cancers. Thus, specific aneuploidies play essential roles in tumorigenesis, raising the possibility that targeting these "aneuploidy addictions" could represent a novel approach for cancer treatment.

MACHETE identifies interferon-encompassing chromosome 9p21.3 deletions as mediators of immune evasion and metastasis

The most prominent homozygous deletions in cancer affect chromosome 9p21.3 and eliminate CDKN2A/B tumor suppressors, disabling a cell-intrinsic barrier to tumorigenesis. Half of 9p21.3 deletions, however, also encompass a type I interferon (IFN) gene cluster; the consequences of this co-deletion remain unexplored. To functionally dissect 9p21.3 and other large genomic deletions, we developed a flexible deletion engineering strategy, MACHETE (molecular alteration of chromosomes with engineered tandem elements). Applying MACHETE to a syngeneic mouse model of pancreatic cancer, we found that co-deletion of the IFN cluster promoted immune evasion, metastasis and immunotherapy resistance. Mechanistically, IFN co-deletion disrupted type I IFN signaling in the tumor microenvironment, leading to marked changes in infiltrating immune cells and escape from CD8+ T-cell surveillance, effects largely driven by the poorly understood interferon epsilon. These results reveal a chromosomal deletion that disables both cell-intrinsic and cell-extrinsic tumor suppression and provide a framework for interrogating large deletions in cancer and beyond.

Prognostic Value of Copy Number Alteration Burden in Early-Stage Breast Cancer and the Construction of an 11-Gene Copy Number Alteration Model

The increasing burden of breast cancer has prompted a wide range of researchers to search for new prognostic markers. Considering that tumor mutation burden (TMB) is low and copy number alteration burden (CNAB) is high in breast cancer, we built a CNAB-based model using a public database and validated it with a Chinese population. We collected formalin-fixed, paraffin-embedded (FFPE) tissue samples from 31 breast cancer patients who were treated between 2010 and 2014 at the National Cancer Center (CICAMS). METABRIC and TCGA data were downloaded via cBioPortal. In total, 2295 patients with early-stage breast cancer were enrolled in the study, including 1427 in the METABRIC cohort, 837 in the TCGA cohort, and 31 in the CICAMS cohort. Based on the ROC curve, we consider 2.2 CNA/MBp as the threshold for the CNAB-high and CNAB-low groupings. In both the TCGA cohort and the CICAMS cohort, CNAB-high had a worse prognosis than CNAB-low. We further simplified this model by establishing a prognostic nomogram for early breast cancer patients by 11 core genes, and this nomogram was highly effective in both the TCGA cohort and the CICAMS cohort. We hope that this model will subsequently help clinicians with prognostic assessments.

Concerted Regulation of Glycosylation Factors Sustains Tissue Identity and Function

Glycosylation is a fundamental cellular process affecting human development and health. Complex machinery establishes the glycan structures whose heterogeneity provides greater structural diversity than other post-translational modifications. Although known to present spatial and temporal diversity, the evolution of glycosylation and its role at the tissue-specific level is poorly understood. In this study, we combined genome and transcriptome profiles of healthy and diseased tissues to uncover novel insights into the complex role of glycosylation in humans. We constructed a catalogue of human glycosylation factors, including transferases, hydrolases and other genes directly involved in glycosylation. These were categorized as involved in N-, O- and lipid-linked glycosylation, glypiation, and glycosaminoglycan synthesis. Our data showed that these glycosylation factors constitute an ancient family of genes, where evolutionary constraints suppressed large gene duplications, except for genes involved in O-linked and lipid glycosylation. The transcriptome profiles of 30 healthy human tissues revealed tissue-specific expression patterns preserved across mammals. In addition, clusters of tightly co-expressed genes suggest a glycosylation code underlying tissue identity. Interestingly, several glycosylation factors showed tissue-specific profiles varying with age, suggesting a role in ageing-related disorders. In cancer, our analysis revealed that glycosylation factors are highly perturbed, at the genome and transcriptome levels, with a strong predominance of copy number alterations. Moreover, glycosylation factor dysregulation was associated with distinct cellular compositions of the tumor microenvironment, reinforcing the impact of glycosylation in modulating the immune system. Overall, this work provides genome-wide evidence that the glycosylation machinery is tightly regulated in healthy tissues and impaired in ageing and tumorigenesis, unveiling novel potential roles as prognostic biomarkers or therapeutic targets.

Genomic copy number alterations as biomarkers for triple negative pregnancy-associated breast cancer

Background

PABC, commonly defined as breast cancer diagnosed during or ≤ 1 year after pregnancy, accounts for 7% of all breast cancers in women ≤ 45 years. Compared to age-matched non-PABC patients, PABC is characterized by a particularly aggressive histopathologic profile with poorly differentiated and estrogen- and progesterone receptor negative tumors and associated high mortality rates. This study assessed the genomic background of triple-negative PABC tumors by detection of copy number alterations (CNAs).

Methods

MLPA was used to compare CNAs in breast cancer-associated chromosomal loci between triple-negative PABC- and subtype-matched non-PABC patients. Both CNA patterns were evaluated by cluster analysis; associations between individual gene CNAs, pathological characteristics and survival were explored.

Results

Triple-negative PABC tumors exhibited unique CNAs compared to non-PABC tumors, including enrichment for TOP2A copy number loss, an independent predictor of worse overall survival (HR 8.96, p = 0.020). Cluster analysis based on CNA profiles identified a triple-negative PABC-subgroup with a particularly poor prognosis, characterized by chromosome 8p copy number loss. Individual gene CNAs analysis revealed that FGFR1 copy number loss on chromosome 8p11.23 was an independent predictor of poor outcome in multivariate analysis (HR 3.59, p = 0.053) and predicted the development of distant metastases (p = 0.048).

Conclusion

This study provides novel insights into the biology of triple-negative PABC tumors suggesting that CNAs, particularly 8p loss and TOP2A loss, are involved in the development of breast cancer during pregnancy. FGFR1 loss and TOP2A loss seem to be promising new biomarkers that independently identify subgroups of PABC patients with poor prognosis. These genomic biomarkers may provide clues for personalized therapy.

BoBafit: a copy number clustering tool designed to refit and recalibrate the baseline region of tumors’ profiles

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Tools generating CN profiles derive the baseline region from samples’ median CN signal.

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This canonical approach might cause wrongly estimated CN profile in complex karyotypes.

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CNAs call is crucial for patients’ risk stratification aimed at personalized treatment.

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BoBafit computes the correct baseline region and the CN profile, taking into account tumor genomic complexity and samples-specific alterations.

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BoBafit should be implemented within CN analysis pipelines especially for clinical aims.

Human cancer arises from a population of cells that have acquired a wide range of genetic alterations, most of which are targets of therapeutic treatments or are used as prognostic factors for patient’s risk stratification. Among these, copy number alterations (CNAs) are quite frequent. Currently, several molecular biology technologies, such as microarrays, NGS and single-cell approaches are used to define the genomic profile of tumor samples. Output data need to be analyzed with bioinformatic approaches and particularly by employing computational algorithms.

Molecular biology tools estimate the baseline region by comparing either the mean probe signals, or the number of reads to the reference genome. However, when tumors display complex karyotypes, this type of approach could fail the baseline region estimation and consequently cause errors in the CNAs call. To overcome this issue, we designed an R-package, BoBafit, able to check and, eventually, to adjust the baseline region, according to both the tumor-specific alterations’ context and the sample-specific clustered genomic lesions.

Several databases have been chosen to set up and validate the designed package, thus demonstrating the potential of BoBafit to adjust copy number (CN) data from different tumors and analysis techniques.

Relevantly, the analysis highlighted that up to 25% of samples need a baseline region adjustment and a redefinition of CNAs calls, thus causing a change in the prognostic risk classification of the patients.

We support the implementation of BoBafit within CN analysis bioinformatics pipelines to ensure a correct patient’s stratification in risk categories, regardless of the tumor type.

Extensive protein dosage compensation in aneuploid human cancers

Aneuploidy is a hallmark of human cancers, but the effects of aneuploidy on protein expression remain poorly understood. To uncover how chromosome copy number changes influence the cancer proteome, we conducted an analysis of hundreds of human cancer cell lines and tumors with matched copy number, RNA expression, and protein expression data. We found that a majority of proteins show dosage compensation and fail to change by the degree expected based on chromosome copy number alone. We uncovered a variety of gene groups that were recurrently buffered upon both chromosome gain and loss, including protein complex subunits and cell cycle genes. Several genetic and biophysical factors were predictive of protein buffering, highlighting complex post-translational regulatory mechanisms that maintain appropriate gene product dosage. Finally, we established that chromosomal aneuploidy has a moderate effect on the expression of oncogenes and tumor suppressors, showing that these key cancer drivers can be subject to dosage compensation as well. In total, our comprehensive analysis of aneuploidy and dosage compensation across cancers will help identify the key driver genes encoded on altered chromosomes and will shed light on the overall consequences of aneuploidy during tumor development.

Widespread redundancy in -omics profiles of cancer mutation states

BACKGROUND

In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear.

RESULTS

We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type.

CONCLUSIONS

Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma

Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies. The source of dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas by spatially resolved transcriptomics, metabolomics, and proteomics. By deciphering regionally shared transcriptional programs across patients, we infer that glioblastoma is organized by spatial segregation of lineage states and adapts to inflammatory and/or metabolic stimuli, reminiscent of the reactive transformation in mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.

Analysis of the genomic landscapes of Barbadian and Nigerian women with triple negative breast cancer

PURPOSE

Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype that disproportionately affects women of African ancestry (WAA) and is often associated with poor survival. Although there is a high prevalence of TNBC across West Africa and in women of the African diaspora, there has been no comprehensive genomics study to investigate the mutational profile of ancestrally related women across the Caribbean and West Africa.

METHODS

This multisite cross-sectional study used 31 formalin-fixed paraffin-embedded (FFPE) samples from Barbadian and Nigerian TNBC participants. High-resolution whole exome sequencing (WES) was performed on the Barbadian and Nigerian TNBC samples to identify their mutational profiles and comparisons were made to African American, European American and Asian American sequencing data obtained from The Cancer Genome Atlas (TCGA). Whole exome sequencing was conducted on tumors with an average of 382 × coverage and 4335 × coverage for pooled germline non-tumor samples.

RESULTS

Variants detected at high frequency in our WAA cohorts were found in the following genes NBPF12, PLIN4, TP53 and BRCA1. In the TCGA TNBC cases, these genes had a lower mutation rate, except for TP53 (32% in our cohort; 63% in TCGA-African American; 67% in TCGA-European American; 63% in TCGA-Asian). For all altered genes, there were no differences in frequency of mutations between WAA TNBC groups including the TCGA-African American cohort. For copy number variants, high frequency alterations were observed in PIK3CA, TP53, FGFR2 and HIF1AN genes.

CONCLUSION

This study provides novel insights into the underlying genomic alterations in WAA TNBC samples and shines light on the importance of inclusion of under-represented populations in cancer genomics and biomarker studies.

SWAN pathway-network identification of common aneuploidy-based oncogenic drivers

Haploinsufficiency drives Darwinian evolution. Siblings, while alike in many aspects, differ due to monoallelic differences inherited from each parent. In cancer, solid tumors exhibit aneuploid genetics resulting in hundreds to thousands of monoallelic gene-level copy-number alterations (CNAs) in each tumor. Aneuploidy patterns are heterogeneous, posing a challenge to identify drivers in this high-noise genetic environment. Here, we developed Shifted Weighted Annotation Network (SWAN) analysis to assess biology impacted by cumulative monoallelic changes. SWAN enables an integrated pathway-network analysis of CNAs, RNA expression, and mutations via a simple web platform. SWAN is optimized to best prioritize known and novel tumor suppressors and oncogenes, thereby identifying drivers and potential druggable vulnerabilities within cancer CNAs. Protein homeostasis, phospholipid dephosphorylation, and ion transport pathways are commonly suppressed. An atlas of CNA pathways altered in each cancer type is released. These CNA network shifts highlight new, attractive targets to exploit in solid tumors.

Genome-wide identification and analysis of prognostic features in human cancers

Clinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets. Instead, the strongest adverse biomarkers represent widely expressed cell-cycle and housekeeping genes, and, correspondingly, nearly all therapies directed against these features have failed in clinical trials. In total, our analysis establishes a rich resource for prognostic biomarker analysis and clarifies the use of patient survival data in preclinical cancer research and therapeutic development.

Early Breast Cancer Evolution by Autosomal Broad Copy Number Alterations

The availability of comprehensive genomic datasets across patient populations enables the application of novel methods for reconstructing tumor evolution within individual patients. To this end, we propose studying autosomal broad copy number alterations (CNAs) as a framework to better understand early tumor evolution. We compared the broad CNAs and somatic mutations of patients with 1 to 10 autosomal broad CNAs against the full set of patients, using data from The Cancer Genome Atlas breast cancer project. We reveal here that the frequency of a chromosome arm obtaining a broad CNA and a genome acquiring somatic mutations changes as autosomal broad CNAs accumulate. Therefore, we propose that the number of autosomal broad CNAs is an important characteristic of breast tumors that needs to be taken into consideration when studying breast tumors. To investigate this idea more in-depth, we next studied the frequency that specific chromosome arms acquire broad CNAs in patients with 1 to 10 broad CNAs. With this process, we identified the broad CNAs that exhibit the fastest rates of accumulation across all patients. This finding suggests a likely order of occurrence of these alterations in patients, which is apparent when we consider a subset of patients with few broad CNAs. Here, we lay the foundation for future studies to build upon our findings and use autosomal broad CNAs as a method to monitor breast tumor progression in vivo to further our understanding of how early tumor evolution unfolds.

PIK3CA co-occurring mutations and copy-number gain in hormone receptor positive and HER2 negative breast cancer

We aim to elucidate the prognostic value of PIK3CA mutations and copy number (CN) gain (PIK3CA-mut/gain) in hormone receptor-positive and HER2-negative (HR + /HER2−) breast cancer (BC). We analyzed primary HR + /HER2− BC from three publicly available datasets comprising over 2000 samples and assessed the associations with tumoral and clinical characteristics and outcome. Clinical benefit (CB) in alpelisib-treated patients from two studies including 46 patients was analyzed. About 8–10% of HR + /HER2− primary BC had PIK3CA-mut/gain. In two of the datasets analyzed, among patients with PIK3CA mutant tumors, those with mut/gain had significantly worse outcome compared to those with CN neutral (PIK3CA-mut/neut) and PIK3CA-mut/gain remained an independent prognostic factor. CB of alpelisib-treated patients with PIK3CA-mut/gain and PIK3CA-mut/neut tumors was comparable. PIK3CA CN might help clarifying the prognostic and predictive role of PIK3CA mutations. Further studies are warranted.

A gene signature consisting of ubiquitin ligases and deubiquitinating enzymes of SKP2 is associated with clinical outcome in breast cancer

The ubiquitination of SKP2, an oncoprotein, is controlled by its E3 ligases, including APC/C^FZR1 and deubiquitinases such as USP10. We identified a two-gene signature for the ubiquitination of SKP2, consisting of the copy number of FZR1 compared to the copy number of USP10. The signature reflects the level of SKP2 activity, stratifying BC patients into two groups with significantly different protein levels of SKP2 ubiquitination substrate p27 (t-test p < 0.01) and recapitulating the expression patterns of SKP2 between tumor and normal tissue (Spearman’s ρ = 0.39.) The signature is also highly associated with clinical outcome in luminal BC but not other subtypes, characterizing patients into two groups with significantly different overall survival times (log-rank p = 0.006). In addition, it is dramatically associated with tumor grade (Chi-squared p = 6.7 × 10⁻³), stage (Chi-squared p = 1.6 × 10⁻¹¹), and the number of positive lymph nodes (negative binomial regression coefficient p = 2.0 × 10⁻³). Our study provides a rationale for targeting the SKP2 ubiquitination pathway in luminal BC and for further investigation of the use of ubiquitinase/deubiquitinase genes as prognosis and treatment biomarkers.