Subclonal diversification of primary breast cancer revealed by multiregion sequencing

Genomic Profiling Identifies Putative Pathogenic Alterations in NSCLC Brain Metastases

Introduction

Brain metastases (BM) severely affect the prognosis and quality of life of patients with NSCLC. Recently, molecularly targeted agents were found to have promising activity against BM in patients with NSCLC whose primary tumors carry "druggable" mutations. Nevertheless, it remains critical to identify specific pathogenic alterations that drive NSCLC-BM and that can provide novel and more effective therapeutic targets.

Methods

To identify potentially targetable pathogenic alterations in NSCLC-BM, we profiled somatic copy number alterations (SCNAs) in 51 matched pairs of primary NSCLC and BM samples from 33 patients with lung adenocarcinoma and 18 patients with lung squamous cell carcinoma. In addition, we performed multiregion copy number profiling on 15 BM samples and whole-exome sequencing on 40 of 51 NSCLC-BM pairs.

Results

BM consistently had a higher burden of SCNAs compared with the matched primary tumors, and SCNAs were typically homogeneously distributed within BM, suggesting BM do not undergo extensive evolution once formed. By comparing focal SCNAs in matched NSCLC-BM pairs, we identified putative BM-driving alterations affecting multiple cancer genes, including several potentially targetable alterations in genes such as CDK12, DDR2, ERBB2, and NTRK1, which we validated in an independent cohort of 84 BM samples. Finally, we identified putative pathogenic alterations in multiple cancer genes, including genes involved in epigenome editing and 3D genome organization, such as EP300, CTCF, and STAG2, which we validated by targeted sequencing of an independent cohort of 115 BM samples.

Conclusions

Our study represents the most comprehensive genomic characterization of NSCLC-BM available to date, paving the way to functional studies aimed at assessing the potential of the identified pathogenic alterations as clinical biomarkers and targets.

Spatial genomics maps the structure, nature and evolution of cancer clones

Genome sequencing of cancers often reveals mosaics of different subclones present in the same tumour1-3. Although these are believed to arise according to the principles of somatic evolution, the exact spatial growth patterns and underlying mechanisms remain elusive4,5. Here, to address this need, we developed a workflow that generates detailed quantitative maps of genetic subclone composition across whole-tumour sections. These provide the basis for studying clonal growth patterns, and the histological characteristics, microanatomy and microenvironmental composition of each clone. The approach rests on whole-genome sequencing, followed by highly multiplexed base-specific in situ sequencing, single-cell resolved transcriptomics and dedicated algorithms to link these layers. Applying the base-specific in situ sequencing workflow to eight tissue sections from two multifocal primary breast cancers revealed intricate subclonal growth patterns that were validated by microdissection. In a case of ductal carcinoma in situ, polyclonal neoplastic expansions occurred at the macroscopic scale but segregated within microanatomical structures. Across the stages of ductal carcinoma in situ, invasive cancer and lymph node metastasis, subclone territories are shown to exhibit distinct transcriptional and histological features and cellular microenvironments. These results provide examples of the benefits afforded by spatial genomics for deciphering the mechanisms underlying cancer evolution and microenvironmental ecology.

Mutational Signature 3 Detected from Clinical Panel Sequencing is Associated with Responses to Olaparib in Breast and Ovarian Cancers

PURPOSE

The identification of patients with homologous recombination deficiency (HRD) beyond BRCA1/2 mutations is an urgent task, as they may benefit from PARP inhibitors. We have previously developed a method to detect mutational signature 3 (Sig3), termed SigMA, associated with HRD from clinical panel sequencing data, that is able to reliably detect HRD from the limited sequencing data derived from gene-focused panel sequencing.

EXPERIMENTAL DESIGN

We apply this method to patients from two independent datasets: (i) high-grade serous ovarian cancer and triple-negative breast cancer (TNBC) from a phase Ib trial of the PARP inhibitor olaparib in combination with the PI3K inhibitor buparlisib (BKM120; NCT01623349), and (ii) TNBC patients who received neoadjuvant olaparib in the phase II PETREMAC trial (NCT02624973).

RESULTS

We find that Sig3 as detected by SigMA is positively associated with improved progression-free survival and objective responses. In addition, comparison of Sig3 detection in panel and exome-sequencing data from the same patient samples demonstrated highly concordant results and superior performance in comparison with the genomic instability score.

CONCLUSIONS

Our analyses demonstrate that HRD can be detected reliably from panel-sequencing data that are obtained as part of routine clinical care, and that this approach can identify patients beyond those with germline BRCA1/2mut who might benefit from PARP inhibitors. Prospective clinical utility testing is warranted.

Systems approaches to uncovering the contribution of environment-mediated drug resistance

Cancer drug response is heavily influenced by the extracellular matrix (ECM) environment. Despite a clear appreciation that the ECM influences cancer drug response and progression, a unified view of how, where, and when environment-mediated drug resistance contributes to cancer progression has not coalesced. Here, we survey some specific ways in which the ECM contributes to cancer resistance with a focus on how materials development can coincide with systems biology approaches to better understand and perturb this contribution. We argue that part of the reason that environment-mediated resistance remains a perplexing problem is our lack of a wholistic view of the entire range of environments and their impacts on cell behavior. We cover a series of recent experimental and computational tools that will aid exploration of ECM reactions space, and how they might be synergistically integrated.

Improving prediction of response to neoadjuvant treatment in patients with breast cancer by combining liquid biopsies with multiparametric MRI: protocol of the LIMA study - a multicentre prospective observational cohort study

INTRODUCTION

The response to neoadjuvant chemotherapy (NAC) in breast cancer has important prognostic implications. Dynamic prediction of tumour regression by NAC may allow for adaption of the treatment plan before completion, or even before the start of treatment. Such predictions may help prevent overtreatment and related toxicity and correct for undertreatment with ineffective regimens. Current imaging methods are not able to fully predict the efficacy of NAC. To successfully improve response prediction, tumour biology and heterogeneity as well as treatment-induced changes have to be considered. In the LIMA study, multiparametric MRI will be combined with liquid biopsies. In addition to conventional clinical and pathological information, these methods may give complementary information at multiple time points during treatment.

AIM

To combine multiparametric MRI and liquid biopsies in patients with breast cancer to predict residual cancer burden (RCB) after NAC, in adjunct to standard clinico-pathological information. Predictions will be made before the start of NAC, approximately halfway during treatment and after completion of NAC.

METHODS

In this multicentre prospective observational study we aim to enrol 100 patients. Multiparametric MRI will be performed prior to NAC, approximately halfway and after completion of NAC. Liquid biopsies will be obtained immediately prior to every cycle of chemotherapy and after completion of NAC. The primary endpoint is RCB in the surgical resection specimen following NAC. Collected data will primarily be analysed using multivariable techniques such as penalised regression techniques.

ETHICS AND DISSEMINATION

Medical Research Ethics Committee Utrecht has approved this study (NL67308.041.19). Informed consent will be obtained from each participant. All data are anonymised before publication. The findings of this study will be submitted to international peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04223492.

Discovering significant evolutionary trajectories in cancer phylogenies

MOTIVATION

Tumors are the result of a somatic evolutionary process leading to substantial intra-tumor heterogeneity. Single-cell and multi-region sequencing enable the detailed characterization of the clonal architecture of tumors and have highlighted its extensive diversity across tumors. While several computational methods have been developed to characterize the clonal composition and the evolutionary history of tumors, the identification of significantly conserved evolutionary trajectories across tumors is still a major challenge.

RESULTS

We present a new algorithm, MAximal tumor treeS TRajectOries (MASTRO), to discover significantly conserved evolutionary trajectories in cancer. MASTRO discovers all conserved trajectories in a collection of phylogenetic trees describing the evolution of a cohort of tumors, allowing the discovery of conserved complex relations between alterations. MASTRO assesses the significance of the trajectories using a conditional statistical test that captures the coherence in the order in which alterations are observed in different tumors. We apply MASTRO to data from nonsmall-cell lung cancer bulk sequencing and to acute myeloid leukemia data from single-cell panel sequencing, and find significant evolutionary trajectories recapitulating and extending the results reported in the original studies.

AVAILABILITY AND IMPLEMENTATION

MASTRO is available at https://github.com/VandinLab/MASTRO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy

Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

Impact of cancer evolution on immune surveillance and checkpoint inhibitor response

Intratumour heterogeneity (ITH) is pervasive across all cancers studied and may provide the evolving tumour multiple routes to escape immune surveillance. Immune checkpoint inhibitors (CPIs) are rapidly becoming standard of care for many cancers. Here, we discuss recent work investigating the influence of ITH on patient response to immune checkpoint inhibitor (CPI) therapy. At its simplest, ITH may confound the diagnostic accuracy of predictive biomarkers used to stratify patients for CPI therapy. Furthermore, ITH is fuelled by mechanisms of genetic instability that can both engage immune surveillance and drive immune evasion. A greater appreciation of the interplay between ITH and the immune system may hold the key to increasing the proportion of patients experiencing durable responses from CPI therapy.

Clonal evolution in primary breast cancers under sequential epirubicin and docetaxel monotherapy

Background

Subclonal evolution during primary breast cancer treatment is largely unexplored. We aimed to assess the dynamic changes in subclonal composition of treatment-naïve breast cancers during neoadjuvant chemotherapy.

Methods

We performed whole exome sequencing of tumor biopsies collected before, at therapy switch, and after treatment with sequential epirubicin and docetaxel monotherapy in 51 out of 109 patients with primary breast cancer, who were included in a prospectively registered, neoadjuvant single-arm phase II trial.

Results

There was a profound and differential redistribution of subclones during epirubicin and docetaxel treatment, regardless of therapy response. While truncal mutations and main subclones persisted, smaller subclones frequently appeared or disappeared. Reassessment of raw data, beyond formal mutation calling, indicated that the majority of subclones seemingly appearing during treatment were in fact present in pretreatment breast cancers, below conventional detection limits. Likewise, subclones which seemingly disappeared were still present, below detection limits, in most cases where tumor tissue remained. Tumor mutational burden (TMB) dropped during neoadjuvant therapy, and copy number analysis demonstrated specific genomic regions to be systematically lost or gained for each of the two chemotherapeutics.

Conclusions

Sequential epirubicin and docetaxel monotherapy caused profound redistribution of smaller subclones in primary breast cancer, while early truncal mutations and major subclones generally persisted through treatment.

Trial registration

ClinicalTrials.gov, NCT00496795, registered on July 4, 2007.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01090-2.

Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning

Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)-stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87-0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77-0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications.

SIGNIFICANCE

This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity. See related commentary by Song et al., p. 2672.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Interrogating breast cancer heterogeneity using single and pooled circulating tumor cell analysis

Single cell technologies allow the interrogation of tumor heterogeneity, providing insights into tumor evolution and treatment resistance. To better understand whether circulating tumor cells (CTCs) could complement metastatic biopsies for tumor genomic profiling, we characterized 11 single CTCs and 10 pooled CTC samples at the mutational and copy number aberration (CNA) levels, and compared these results with matched synchronous tumor biopsies from 3 metastatic breast cancer patients with triple-negative (TNBC), HER2-positive and estrogen receptor-positive (ER+) tumors. Similar CNA profiles and the same patient-specific driver mutations were found in bulk tissue and CTCs for the HER2-positive and TNBC tumors, whereas different CNA profiles and driver mutations were identified for the ER+ tumor, which presented two distinct clones in CTCs defined by mutations in ESR1 Y537N and TP53, respectively. Furthermore, de novo mutational signatures derived from CTCs described patient-specific biological processes. These data suggest that tumor tissue and CTCs provide complementary clinically relevant information to map tumor heterogeneity and tumor evolution.

Clonal evolution and expansion associated with therapy resistance and relapse of colorectal cancer

Colorectal cancer (CRC) arises by a continuous process of genetic diversification and clonal evolution. Multiple genes and pathways have a role in tumor initiation and progression. The gradual accumulation of genetic and epigenetic processes leads to the establishment of adenoma and cancer. The important 'driver' mutations in tumor suppressor genes (such as TP53, APC, and SMAD4) and oncogenes (such as KRAS, NRAS, MET, and PIK3CA) confer selective growth advantages and cause CRC advancement. Clonal evolution induced by therapeutic pressure, as well as intra-tumoral heterogeneity, has been a great challenge in the treatment of metastatic CRC. Tumors often develop resistance to treatments as a result of intra-tumor heterogeneity, clonal evolution, and selection. Hence, the development of a multidrug personalized approach should be prioritized to pave the way for therapeutics repurposing and combination therapy to arrest tumor progression. This review summarizes how selective drug pressure can impact tumor evolution, resulting in the formation of polyclonal resistance mechanisms, ultimately promoting cancer progression. Current strategies for targeting clonal evolution are described. By understanding sources and consequences of tumor heterogeneity, customized and effective treatment plans to combat drug resistance may be devised.

Breast Cancer Genomics: Primary and Most Common Metastases

Specific genomic alterations have been found in primary breast cancer involving driver mutations that result in tumorigenesis. Metastatic breast cancer, which is uncommon at the time of disease onset, variably impacts patients throughout the course of their disease. Both the molecular profiles and diverse genomic pathways vary in the development and progression of metastatic breast cancer. From the most common metastatic site (bone), to the rare sites such as orbital, gynecologic, or pancreatic metastases, different levels of gene expression indicate the potential involvement of numerous genes in the development and spread of breast cancer. Knowledge of these alterations can, not only help predict future disease, but also lead to advancement in breast cancer treatments. This review discusses the somatic landscape of breast primary and metastatic tumors.

Molecular Assessment of HER2 to Identify Signatures Associated with Therapy Response in HER2-Positive Breast Cancer

Trastuzumab, the prototype HER2-directed therapy, has markedly improved survival for women with HER2-positive breast cancers. However, only 40-60% of women with HER2-positive breast cancers achieve a complete pathological response to chemotherapy combined with HER2-directed therapy. The current diagnostic assays have poor positive-predictive accuracy in identifying therapy-responsive breast cancers. Here, we deployed quantitative single molecule localization microscopy to assess the molecular features of HER2 in a therapy-responsive setting. Using fluorescently labeled trastuzumab as a probe, we first compared the molecular features of HER2 in trastuzumab-sensitive (BT-474 and SK-BR-3) and trastuzumab-resistant (BT-474R and JIMT-1) cultured cell lines. Trastuzumab-sensitive cells had significantly higher detected HER2 densities and clustering. We then evaluated HER2 in pre-treatment core biopsies from women with breast cancer undergoing neoadjuvant therapy. A complete pathological response was associated with a high detected HER2 density and significant HER2 clustering. These results established the nano-organization of HER2 as a potential signature of therapy-responsive disease.

Beyond Genetics: Metastasis as an Adaptive Response in Breast Cancer

Metastatic disease represents the primary cause of breast cancer (BC) mortality, yet it is still one of the most enigmatic processes in the biology of this tumor. Metastatic progression includes distinct phases: invasion, intravasation, hematogenous dissemination, extravasation and seeding at distant sites, micro-metastasis formation and metastatic outgrowth. Whole-genome sequencing analyses of primary BC and metastases revealed that BC metastatization is a non-genetically selected trait, rather the result of transcriptional and metabolic adaptation to the unfavorable microenvironmental conditions which cancer cells are exposed to (e.g., hypoxia, low nutrients, endoplasmic reticulum stress and chemotherapy administration). In this regard, the latest multi-omics analyses unveiled intra-tumor phenotypic heterogeneity, which determines the polyclonal nature of breast tumors and constitutes a challenge for clinicians, correlating with patient poor prognosis. The present work reviews BC classification and epidemiology, focusing on the impact of metastatic disease on patient prognosis and survival, while describing general principles and current in vitro/in vivo models of the BC metastatic cascade. The authors address here both genetic and phenotypic intrinsic heterogeneity of breast tumors, reporting the latest studies that support the role of the latter in metastatic spreading. Finally, the review illustrates the mechanisms underlying adaptive stress responses during BC metastatic progression.

A computational framework to unify orthogonal information in DNA methylation and copy number aberrations in cell-free DNA for early cancer detection

Cell-free DNA (cfDNA) provides a convenient diagnosis avenue for noninvasive cancer detection. The current methods are focused on identifying circulating tumor DNA (ctDNA)s genomic aberrations, e.g. mutations, copy number aberrations (CNAs) or methylation changes. In this study, we report a new computational method that unifies two orthogonal pieces of information, namely methylation and CNAs, derived from whole-genome bisulfite sequencing (WGBS) data to quantify low tumor content in cfDNA. It implements a Bayes model to enrich ctDNA from WGBS data based on hypomethylation haplotypes, and subsequently, models CNAs for cancer detection. We generated WGBS data in a total of 262 samples, including high-depth (>20×, deduped high mapping quality reads) data in 76 samples with matched triplets (tumor, adjacent normal and cfDNA) and low-depth (~2.5×, deduped high mapping quality reads) data in 186 samples. We identified a total of 54 Mb regions of hypomethylation haplotypes for model building, a vast majority of which are not covered in the HumanMethylation450 arrays. We showed that our model is able to substantially enrich ctDNA reads (tens of folds), with clearly elevated CNAs that faithfully match the CNAs in the paired tumor samples. In the 19 hepatocellular carcinoma cfDNA samples, the estimated enrichment is as high as 16 fold, and in the simulation data, it can achieve over 30-fold enrichment for a ctDNA level of 0.5% with a sequencing depth of 600×. We also found that these hypomethylation regions are also shared among many cancer types, thus demonstrating the potential of our framework for pancancer early detection.

Cancer evolution: special focus on the immune aspect of cancer

Cancer is an evolutionary disease. Intra-tumor heterogeneity (ITH), which describes the diversity within individual tumors, sets the foundation for evolution. The fitness of tumor cells is determined by their microenvironment, which exerts intense selection pressure that generally favors cells with survival and proliferation advantages. It has been revealed that host immunity dramatically influences the evolutionary trajectory of cancer. As technologies advance, a refined map of the immune system's involvement in cancer evolution has gradually come to our knowledge. Here we specifically view cancer through the lens of evolutionary immunological biology. We will cover the neoplastic evolution under immunosurveillance, including how the host immunity shapes the tumor evolutionary trajectory and how progressive tumors modulate the host immunity to survive. A comprehensive understanding of the interplay between cancer evolution and cancer immunity provides clues to combating cancer strategically.

Efficient Algorithms Unlock Understanding of Clonal Evolution in Cancer

In this issue of Blood Cancer Discovery, Wintersinger and colleagues present a new algorithm for quickly and accurately inferring clonal phylogenies from heterogeneous tumors sampled at many timepoints and/or many sites. When coupled with serial sequencing of tumors, this advance promises to increase our understanding of the clonal dynamics that shape tumor evolution and response to therapy. See related article by Wintersinger et al., p. 208 (9).

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Epigenome erosion and SOX10 drive neural crest phenotypic mimicry in triple-negative breast cancer

Intratumoral heterogeneity is caused by genomic instability and phenotypic plasticity, but how these features co-evolve remains unclear. SOX10 is a neural crest stem cell (NCSC) specifier and candidate mediator of phenotypic plasticity in cancer. We investigated its relevance in breast cancer by immunophenotyping 21 normal breast and 1860 tumour samples. Nuclear SOX10 was detected in normal mammary luminal progenitor cells, the histogenic origin of most TNBCs. In tumours, nuclear SOX10 was almost exclusive to TNBC, and predicted poorer outcome amongst cross-sectional (p = 0.0015, hazard ratio 2.02, n = 224) and metaplastic (p = 0.04, n = 66) cases. To understand SOX10’s influence over the transcriptome during the transition from normal to malignant states, we performed a systems-level analysis of co-expression data, de-noising the networks with an eigen-decomposition method. This identified a core module in SOX10’s normal mammary epithelial network that becomes rewired to NCSC genes in TNBC. Crucially, this reprogramming was proportional to genome-wide promoter methylation loss, particularly at lineage-specifying CpG-island shores. We propose that the progressive, genome-wide methylation loss in TNBC simulates more primitive epigenome architecture, making cells vulnerable to SOX10-driven reprogramming. This study demonstrates potential utility for SOX10 as a prognostic biomarker in TNBC and provides new insights about developmental phenotypic mimicry—a major contributor to intratumoral heterogeneity.

Serial Tumor Molecular Profiling of Newly Diagnosed HER2-Negative Breast Cancers During Chemotherapy in Combination with Angiogenesis Inhibitors

Background

Breast cancers are heterogeneous with variable clinical courses and treatment responses.

Objective

We sought to evaluate dynamic changes in the molecular landscape of HER2-negative tumors treated with chemotherapy and anti-angiogenic agents.

Patients and Methods

Newly diagnosed HER2-negative breast cancer patients received low-dose sunitinib or bevacizumab prior to four 2-weekly cycles of dose-dense doxorubicin and cyclophosphamide. Tumor biopsies were obtained at baseline, after 2 weeks and after 8 weeks of chemotherapy. Next-generation sequencing was performed to assess for single nucleotide variants (SNVs) and copy number alterations (CNAs) of 440 cancer-related genes (ACTOnco^®). Observed genomic changes were correlated with the Miller-Payne histological response to treatment.

Results

Thirty-four patients received sunitinib and 18 received bevacizumab. In total, 77% were hormone receptor positive (HER2−/HR+) and 23% were triple negative breast cancers (TNBC). New therapy-induced mutations were infrequent, occurring only in 13%, and appeared early after a single cycle of treatment. Seventy-two percent developed changes in the variant allele frequency (VAF) of pathogenic SNVs; the majority (51%) of these changes occurred early at 2 weeks and were sustained for 8 weeks. Changes in VAF of SNVs were most commonly seen in the PI3K/mTOR/AKT pathway; 13% developed changes in pathogenic mutations, which potentially confer sensitivity to PIK3CA inhibitors. Tumors with poor Miller-Payne response to treatment were less likely to experience changes in VAF of SNVs compared with those with good response (50% [7/14] vs 15% [4/24] had no changes observed at any timepoint, p = 0.029).

Conclusions

Serial molecular profiling identifies early therapy-induced genomic alterations, which may guide future selection of targeted therapies in breast cancer patients who progress after standard chemotherapy.

Clinical trial registration

ClinicalTrials.gov: NCT02790580 (first posted June 6, 2016).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-022-00886-x.

Copy-number intratumor heterogeneity increases the risk of relapse in chemotherapy-naive stage II colon cancer

Optimal selection of high-risk patients with stage II colon cancer is crucial to ensure clinical benefit of adjuvant chemotherapy. Here, we investigated the prognostic value of genomic intratumor heterogeneity and aneuploidy for disease recurrence. We combined targeted sequencing, SNP arrays, fluorescence in situ hybridization, and immunohistochemistry on a retrospective cohort of 84 untreated stage II colon cancer patients. We assessed the clonality of copy-number alterations (CNAs) and mutations, CD8⁺ lymphocyte infiltration, and their association with time to recurrence. Prognostic factors were included in machine learning analysis to evaluate their ability to predict individual relapse risk. Tumors from recurrent patients displayed a greater proportion of CNAs compared with non-recurrent (mean 31.3% versus 23%, respectively; p = 0.014). Furthermore, patients with elevated tumor CNA load exhibited a higher risk of recurrence compared with those with low levels [p = 0.038; hazard ratio (HR) 2.46], which was confirmed in an independent cohort (p = 0.004; HR 3.82). Candidate chromosome-specific aberrations frequently observed in recurrent cases included gain of the chromosome arm 13q (p = 0.02; HR 2.67) and loss of heterozygosity at 17q22-q24.3 (p = 0.05; HR 2.69). CNA load positively correlated with intratumor heterogeneity (R = 0.52; p < 0.0001). Consistently, incremental subclonal CNAs were associated with an elevated risk of relapse (p = 0.028; HR 2.20), which we did not observe for subclonal single-nucleotide variants and small insertions and deletions. The clinico-genomic model rated an area under the curve of 0.83, achieving a 10% incremental gain compared with clinicopathological markers (p = 0.047). In conclusion, tumor aneuploidy and copy-number intratumor heterogeneity were predictive of poor outcome and improved discriminative performance in early-stage colon cancer. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Heterogeneity of the tumor immune microenvironment and its clinical relevance

During the course of tumorigenesis and subsequent metastasis, malignant cells gradually diversify and become more heterogeneous. Consequently, the tumor mass might be infiltrated by diverse immune-related components, including the cytokine/chemokine environment, cytotoxic activity, or immunosuppressive elements. This immunological heterogeneity is universally presented spatially or varies temporally along with tumor evolution or therapeutic intervention across almost all solid tumors. The heterogeneity of anti-tumor immunity shows a profound association with the progression of disease and responsiveness to treatment, particularly in the realm of immunotherapy. Therefore, an accurate understanding of tumor immunological heterogeneity is essential for the development of effective therapies. Facilitated by multi-regional and -omics sequencing, single cell sequencing, and longitudinal liquid biopsy approaches, recent studies have demonstrated the potential to investigate the complexity of immunological heterogeneity of the tumors and its clinical relevance in immunotherapy. Here, we aimed to review the mechanism underlying the heterogeneity of the immune microenvironment. We also explored how clinical assessments of tumor heterogeneity might facilitate the development of more effective personalized therapies.

Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers

Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.

Saliva Based Liquid Biopsies in Head and Neck Cancer: How Far Are We From the Clinic?

Head and neck cancer (HNC) remains to be a major cause of mortality worldwide because of confounding factors such as late-stage tumor diagnosis, loco-regional aggressiveness and distant metastasis. The current standardized diagnostic regime for HNC is tissue biopsy which fails to determine the thorough tumor dynamics. Therefore, due to the ease of collection, recent studies have focused on the utility of saliva based liquid biopsy approach for serial sampling, early diagnosis, prognosis, longitudinal monitoring of disease progression and treatment response in HNC patients. Saliva collection is convenient, non-invasive, and pain-free and offers repetitive sampling along with real time monitoring of the disease. Moreover, the detection, isolation and analysis of tumor-derived components such as Circulating Tumor Nucleic Acids (CTNAs), Extracellular Vesicles (EVs), Circulating Tumor Cells (CTCs) and metabolites from saliva can be used for genomic and proteomic examination of HNC patients. Although, these circulatory biomarkers have a wide range of applications in clinical settings, no validated data has yet been established for their usage in clinical practice for HNC. Improvements in isolation and detection technologies and next-generation sequencing analysis have resolved many technological hurdles, allowing a wide range of saliva based liquid biopsy application in clinical backgrounds. Thus, in this review, we discussed the rationality of saliva as plausible biofluid and clinical sample for diagnosis, prognosis and therapeutics of HNC. We have described the molecular components of saliva that could mirror the disease status, recent outcomes of salivaomics associated with HNC and current technologies which have the potential to improve the clinical value of saliva in HNC.

Integrated DNA and RNA sequencing reveals early drivers involved in metastasis of gastric cancer

Gastric cancer (GC) is the second cause of cancer-related death and metastasis is an important cause of death. Considering difficulties in searching for metastatic driver mutations, we tried a novel strategy here. We conducted an integrative genomic analysis on GC and identified early drivers lead to metastasis. Whole-exome sequencing (WES), transcriptomes sequencing and targeted-exome sequencing (TES) were performed on tumors and matched normal tissues from 432 Chinese GC patients, especially the comparative analysis between higher metastatic-potential (HMP) group with T1 stage and lymph-node metastasis, and lower metastatic-potential (LMP) group without lymph-nodes or distant metastasis. HMP group presented higher mutation load and heterogeneity, enrichment in immunosuppressive signaling, more immune cell infiltration than LMP group. An integrated mRNA-lncRNA signature based on differentially expressed genes was constructed and its prognostic value was better than traditional TNM stage. We identified 176 candidate prometastatic mutations by WES and selected 8 genes for following TES. Mutated TP53 and MADCAM1 were significantly associated with poor metastasis-free survival. We further demonstrated that mutated MADCAM1 could not only directly promote cancer cells migration, but also could trigger tumor metastasis by establishing immunosuppressive microenvironment, including promoting PD-L1-mediated immune escape and reprogramming tumor-associated macrophages by regulating CCL2 through Akt/mTOR axis. In conclusion, GCs with different metastatic-potential are distinguishable at the genetic level and we revealed a number of potential metastatic driver mutations. Driver mutations in early-onset metastatic GC could promote metastasis by establishing an immunosuppressive microenvironment. This study provided possibility for future target therapy of GC.

Heterogeneity and tumor evolution reflected in liquid biopsy in metastatic breast cancer patients: a review

Breast cancer is a spatially and temporally dynamic disease in which differently evolving genetic clones are responsible for progression and clinical outcome. We review tumor heterogeneity and clonal evolution from studies comparing primary tumors and metastasis and discuss plasma circulating tumor DNA as a powerful real-time approach for monitoring the clonal landscape of breast cancer during treatment and recurrence. We found only a few early studies exploring clonal evolution and heterogeneity through analysis of multiregional tissue biopsies of different progression steps in comparison with circulating tumor DNA (ctDNA) from blood plasma. The model of linear progression seemed to be more often reported than the model of parallel progression. The results show complex routes to metastasis, however, and plasma most often reflected metastasis more than primary tumor. The described patterns of evolution and the polyclonal nature of breast cancer have clinical consequences and should be considered during patient diagnosis and treatment selection. Current studies focusing on the relevance of clonal evolution in the clinical setting illustrate the role of liquid biopsy as a noninvasive biomarker for monitoring clonal progression and response to treatment. In the clinical setting, circulating tumor DNA may be an ideal support for tumor biopsies to characterize the genetic landscape of the metastatic disease and to improve longitudinal monitoring of disease dynamics and treatment effectiveness through detection of residual tumor after resection, relapse, or metastasis within a particular patient.

Integrated molecular profiling of patient-derived ovarian cancer models identifies clinically relevant signatures and tumor vulnerabilities

High‐grade serous ovarian carcinoma (HGSOC) is a highly aggressive and intractable neoplasm, mainly because of its rapid dissemination into the abdominal cavity, a process that is favored by tumor‐associated peritoneal ascites. The precise molecular alterations involved in HGSOC onset and progression remain largely unknown due to the high biological and genetic heterogeneity of this tumor. We established a set of different tumor samples (termed the As11‐set) derived from a single HGSOC patient, consisting of peritoneal ascites, primary tumor cells, ovarian cancer stem cells (OCSC) and serially propagated tumor xenografts. The As11‐set was subjected to an integrated RNA‐seq and DNA‐seq analysis which unveiled molecular alterations that marked the different types of samples. Our profiling strategy yielded a panel of signatures relevant in HGSOC and in OCSC biology. When such signatures were used to interrogate the TCGA dataset from HGSOC patients, they exhibited prognostic and predictive power. The molecular alterations also identified potential vulnerabilities associated with OCSC, which were then tested functionally in stemness‐related assays. As a proof of concept, we defined PI3K signaling as a novel druggable target in OCSC.

Intratumor genetic heterogeneity and clonal evolution to decode endometrial cancer progression

Analyzing different tumor regions by next generation sequencing allows the assessment of intratumor genetic heterogeneity (ITGH), a phenomenon that has been studied widely in some tumor types but has been less well explored in endometrial carcinoma (EC). In this study, we sought to characterize the spatial and temporal heterogeneity of 9 different ECs using whole-exome sequencing, and by performing targeted sequencing validation of the 42 primary tumor regions and 30 metastatic samples analyzed. In addition, copy number alterations of serous carcinomas were assessed by comparative genomic hybridization arrays. From the somatic mutations, identified by whole-exome sequencing, 532 were validated by targeted sequencing. Based on these data, the phylogenetic tree reconstructed for each case allowed us to establish the tumors’ evolution and correlate this to tumor progression, prognosis, and the presence of recurrent disease. Moreover, we studied the genetic landscape of an ambiguous EC and the molecular profile obtained was used to guide the selection of a potential personalized therapy for this patient, which was subsequently validated by preclinical testing in patient-derived xenograft models. Overall, our study reveals the impact of analyzing different tumor regions to decipher the ITGH in ECs, which could help make the best treatment decision.

Path to Clonal Theranostics in Luminal Breast Cancers

Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString^®) and CDx (Foundation One^®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.

Phenotyping clonal populations of glioma stem cell reveals a high degree of plasticity in response to changes of microenvironment

The phenotype of glioma-initiating cells (GIC) is modulated by cell-intrinsic and cell-extrinsic factors. Phenotypic heterogeneity and plasticity of GIC is an important limitation to therapeutic approaches targeting cancer stem cells. Plasticity also presents a challenge to the identification, isolation, and propagation of purified cancer stem cells. Here we use a barcode labelling approach of GIC to generate clonal populations over a number of passages, in combination with phenotyping using the established stem cell markers CD133, CD15, CD44, and A2B5. Using two cell lines derived from isocitrate dehydrogenase (IDH)-wildtype glioblastoma, we identify a remarkable heterogeneity of the phenotypes between the cell lines. During passaging, clonal expansion manifests as the emergence of a limited number of barcoded clones and a decrease in the overall number of clones. Dual-labelled GIC are capable of forming traceable clonal populations which emerge after as few as two passages from mixed cultures and through analyses of similarity of relative proportions of 16 surface markers we were able to pinpoint the fate of such populations. By generating tumour organoids we observed a remarkable persistence of dominant clones but also a significant plasticity of stemness marker expression. Our study presents an experimental approach to simultaneously barcode and phenotype glioma-initiating cells to assess their functional properties, for example to screen newly established GIC for tumour-specific therapeutic vulnerabilities.

Multifocal Organoid Capturing of Colon Cancer Reveals Pervasive Intratumoral Heterogenous Drug Responses

Intratumor heterogeneity (ITH) stands as one of the main difficulties in the treatment of colorectal cancer (CRC) as it causes the development of resistant clones and leads to heterogeneous drug responses. Here, 12 sets of patient-derived organoids (PDOs) and cell lines (PDCs) isolated from multiple regions of single tumors from 12 patients, capturing ITH by multiregion sampling of individual tumors, are presented. Whole-exome sequencing and RNA sequencing of the 12 sets are performed. The PDOs and PDCs of the 12 sets are also analyzed with a clinically relevant 24-compound library to assess their drug responses. The results reveal unexpectedly widespread subregional heterogeneity among PDOs and PDCs isolated from a single tumor, which is manifested by genetic and transcriptional heterogeneity and strong variance in drug responses, while each PDO still recapitulates the major histologic, genomic, and transcriptomic characteristics of the primary tumor. The data suggest an imminent drawback of single biopsy-originated PDO-based clinical diagnosis in evaluating CRC patient responses. Instead, the results indicate the importance of targeting common somatic driver mutations positioned in the trunk of all tumor subregional clones in parallel with a comprehensive understanding of the molecular ITH of each tumor.

Spatial structure governs the mode of tumour evolution

Characterizing the mode-the way, manner or pattern-of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. Sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour. Here we propose that tumour architecture is key to explaining the variety of observed genetic patterns. We examine this hypothesis using spatially explicit population genetics models and demonstrate that, within biologically relevant parameter ranges, different spatial structures can generate four tumour evolutionary modes: rapid clonal expansion, progressive diversification, branching evolution and effectively almost neutral evolution. Quantitative indices for describing and classifying these evolutionary modes are presented. Using these indices, we show that our model predictions are consistent with empirical observations for cancer types with corresponding spatial structures. The manner of cell dispersal and the range of cell-cell interactions are found to be essential factors in accurately characterizing, forecasting and controlling tumour evolution.

Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures

Breast cancer is characterized by some types of heterogeneity, high aggressive behaviour, and low immunotherapeutic efficiency. Detailed immune stratification is a prerequisite for interpreting resistance to treatment and escape from immune control. Hence, the immune landscape of breast cancer needs further understanding. We systematically clustered breast cancer into six immune subtypes based on the mRNA expression patterns of immune signatures and comprehensively depicted their characteristics. The immunotherapeutic benefit score (ITBscore) was validated to be a superior predictor of the response to immunotherapy in cohorts from various datasets. Six distinct immune subtypes related to divergences in biological functions, signatures of immune or stromal cells, extent of the adaptive immune response, genomic events, and clinical prognostication were identified. These six subtypes were characterized as immunologically quiet, chemokine dominant, lymphocyte depleted, wounding dominant, innate immune dominant, and IFN-γ dominant and exhibited features of the tumor microenvironment (TME). The high ITBscore subgroup, characterized by a high proportion of M1 macrophages:M2 macrophages, an activated inflammatory response, and increased mutational burden (such as mutations in TP53, CDH1 and CENPE), indicated better immunotherapeutic benefits. A low proportion of tumor-infiltrating lymphocytes (TILs) and an inadequate response to immune treatment were associated with the low ITBscore subgroup, which was also associated with poor survival. Analyses of four cohorts treated with immune checkpoint inhibitors (ICIs) suggested that patients with a high ITBscore received significant therapeutic advantages and clinical benefits. Our work may facilitate the understanding of immune phenotypes in shaping different TME landscapes and guide precision immuno-oncology and immunotherapy strategies.

Pathogenesis of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) encompasses a heterogeneous group of fundamentally different diseases with different histologic, genomic, and immunologic profiles, which are aggregated under this term because of their lack of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expression. Massively parallel sequencing and other omics technologies have demonstrated the level of heterogeneity in TNBCs and shed light into the pathogenesis of this therapeutically challenging entity in breast cancer. In this review, we discuss the histologic and molecular classifications of TNBC, the genomic alterations these different tumor types harbor, and the potential impact of these alterations on the pathogenesis of these tumors. We also explore the role of the tumor microenvironment in the biology of TNBCs and its potential impact on therapeutic response. Dissecting the biology and understanding the therapeutic dependencies of each TNBC subtype will be essential to delivering on the promise of precision medicine for patients with triple-negative disease.

The initial hormone receptor/HER2 subtype is the main determinator of subtype discordance in advanced breast cancer: a study of the SONABRE registry

Purpose

The hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) are the main parameters in guiding systemic treatment choices in breast cancer, but can change during the disease course. This study aims to evaluate the biopsy rate and receptor subtype discordance rate in patients diagnosed with advanced breast cancer (ABC).

Methods

Patients diagnosed with ABC in seven hospitals in 2007–2018 were selected from the SOutheast Netherlands Advanced BREast cancer (SONABRE) registry. Multivariable logistic regression analyses were performed to identify factors influencing biopsy and discordance rates.

Results

Overall, 60% of 2854 patients had a biopsy of a metastatic site at diagnosis. One of the factors associated with a reduced biopsy rate was the HR + /HER2 + primary tumor subtype (versus HR + /HER2- subtype: OR = 0.68; 95% CI: 0.51–0.90). Among the 748 patients with a biopsy of the primary tumor and a metastatic site, the overall receptor discordance rate was 18%. This was the highest for the HR + /HER2 + primary tumor subtype, with 55%. In 624 patients with metachronous metastases, the HR + /HER2 + subtype remained the only predictor significantly related to a higher discordance rate, irrespective of prior (neo-)adjuvant therapies (OR = 7.49; 95% CI: 3.69–15.20).

Conclusion

The HR + /HER2 + subtype has the highest discordance rate, but the lowest biopsy rate of all four receptor subtypes. Prior systemic therapy was not independently related to subtype discordance. This study highlights the importance of obtaining a biopsy of metastatic disease, especially in the HR + /HER2 + subtype to determine the most optimal treatment strategy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06472-5.

Aiming at a Tailored Cure for ERBB2-Positive Metastatic Breast Cancer: A Review

Importance

Metastatic breast cancer (MBC) has traditionally been considered incurable. Accordingly, current treatment algorithms are aimed at maintaining quality of life and improving overall survival, rather than at complete eradication of the disease. Attempts to achieve cure with high-dose chemotherapy were conducted in the 1990s, with no observed long-term benefit compared with conventional chemotherapy. Nonetheless, Erb-B2 receptor tyrosine kinase 2 (ERBB2, formerly HER2)-targeted biologic treatments, developed in the past 2 decades, are currently challenging this paradigm. Indeed, a fraction of patients with ERBB2-positive MBC achieve long-lasting responses to chemotherapy and ERBB2-blockade, resembling a cure. In this setting, the challenge of identifying the optimal curable population has emerged, including identifying populations in whom treatment escalation strategies may be beneficial, while avoiding overtreatment in patients with incurable disease.

Observations

A number of clinical and pathologic features allow physicians to identify patients with ERBB2-positive MBC who are more likely to experience a long-lasting response to chemotherapy and ERBB2-blockade. Long-term responders tend to be de novo metastatic, have a reduced disease burden, and tend to show deep responses to systemic treatment. In pathologic terms, features associated with long-term response are high ERBB2 expression, lack of detrimental genomic aberrations, and antitumor immune activation. This population of patients may potentially derive benefit from a tailored escalation of frontline treatment with novel anti-ERBB2 drugs, such as trastuzumab deruxtecan, tucatinib, or margetuximab. Additional recent therapeutic and diagnostic advancements could further aid in the path toward a cure for ERBB2-positive MBC.

Conclusions and Relevance

Careful implementation of novel diagnostic and treatment tools could potentially expand the population of patients with ERBB2-positive MBC experiencing long-lasting disease response. Trials are in preparation to confirm this paradigm, and hopefully lead to a new era of precision therapy for breast cancer.

C/EBPB-dependent adaptation to palmitic acid promotes tumor formation in hormone receptor negative breast cancer

Epidemiological studies have established a positive association between obesity and the incidence of postmenopausal breast cancer. Moreover, it is known that obesity promotes stem cell-like properties of breast cancer cells. However, the cancer cell-autonomous mechanisms underlying this correlation are not well defined. Here we demonstrate that obesity-associated tumor formation is driven by cellular adaptation rather than expansion of pre-existing clones within the cancer cell population. While there is no correlation with specific mutations, cellular adaptation to obesity is governed by palmitic acid (PA) and leads to enhanced tumor formation capacity of breast cancer cells. This process is governed epigenetically through increased chromatin occupancy of the transcription factor CCAAT/enhancer-binding protein beta (C/EBPB). Obesity-induced epigenetic activation of C/EBPB regulates cancer stem-like properties by modulating the expression of key downstream regulators including CLDN1 and LCN2. Collectively, our findings demonstrate that obesity drives cellular adaptation to PA drives tumor initiation in the obese setting through activation of a C/EBPB dependent transcriptional network.

Obesity is linked to cancer risk in post-menopausal breast cancer. At the molecular level this is governed by cellular adaption to palmitic acid through epigenetic activation of a C/EBPB-dependent transcriptional network that drives tumor formation.

Mutational landscape of paired primary and synchronous metastatic lymph node in chemotherapy naive gallbladder cancer

BACKGROUND

Comprehensive genomic analysis of paired primary tumors and their metastatic lesions may provide new insights into the biology of metastatic processes and therefore guide the development of novel strategies for intervention. To date, our knowledge of the genetic divergence and phylogenetic relationships in gallbladder cancer (GBC) is limited.

METHODS

We performed whole exome sequencing for 5 patients with primary tumor, metastatic lymph node (LNM) and corresponding normal tissue. Mutations, mutation signatures and copy number variations were analyzed with state-of-art bioinformatics methods. Phylogenetic tree was also generated to infer metastatic pattern.

RESULTS

Five driver mutations were detected in these patients. Among which, TP53 was the only shared mutation between primary tumor and LNM. Although tumor mutational burden was comparable between primary tumor and LNM, higher mutation burden was observed in LNM of one patient. Copy number variations (CNVs) burden was higher in LNM than their primary tumor. Phylogenetic analysis indicated both linear and parallel progression of metastasis exist in these patients. TP53 mutation and CNVs were homogenously between primary tumor and LNM.

CONCLUSIONS

High consistence of genetic landscape were shown between primary tumor and LNM in GBC. However, heterogenicity still exist between primary tumor and LNM in particular patients in term of driver mutation, TMB and CNV burden. Phylogenetic analysis indicated both Linear and parallel progression of metastasis were exist among these patients.

Spatial patterns of tumour growth impact clonal diversification in a computational model and the TRACERx Renal study

Genetic intra-tumour heterogeneity fuels clonal evolution, but our understanding of clinically relevant clonal dynamics remain limited. We investigated spatial and temporal features of clonal diversification in clear cell renal cell carcinoma through a combination of modelling and real tumour analysis. We observe that the mode of tumour growth, surface or volume, impacts the extent of subclonal diversification, enabling interpretation of clonal diversity in patient tumours. Specific patterns of proliferation and necrosis explain clonal expansion and emergence of parallel evolution and microdiversity in tumours. In silico time-course studies reveal the appearance of budding structures before detectable subclonal diversification. Intriguingly, we observe radiological evidence of budding structures in early-stage clear cell renal cell carcinoma, indicating that future clonal evolution may be predictable from imaging. Our findings offer a window into the temporal and spatial features of clinically relevant clonal evolution.

Clonal populations of a human TNBC model display significant functional heterogeneity and divergent growth dynamics in distinct contexts

Intratumoral heterogeneity has been described for various tumor types and models of human cancer, and can have profound effects on tumor progression and drug resistance. This study describes an in-depth analysis of molecular and functional heterogeneity among subclonal populations (SCPs) derived from a single triple-negative breast cancer cell line, including copy number analysis, whole-exome and RNA sequencing, proteome analysis, and barcode analysis of clonal dynamics, as well as functional assays. The SCPs were found to have multiple unique genetic alterations and displayed significant variation in anchorage independent growth and tumor forming ability. Analyses of clonal dynamics in SCP mixtures using DNA barcode technology revealed selection for distinct clonal populations in different in vitro and in vivo environmental contexts, demonstrating that in vitro propagation of cancer cell lines using different culture conditions can contribute to the establishment of unique strains. These analyses also revealed strong enrichment of a single SCP during the development of xenograft tumors in immune-compromised mice. This SCP displayed attenuated interferon signaling in vivo and reduced sensitivity to the antiproliferative effects of type I interferons. Reduction in interferon signaling was found to provide a selective advantage within the xenograft microenvironment specifically. In concordance with the previously described role of interferon signaling as tumor suppressor, these findings suggest that similar selective pressures may be operative in human cancer and patient-derived xenograft models.

Recent advances of nanotechnology in the diagnosis and therapy of triple-negative breast cancer (TNBC)

BACKGROUND

The development of advanced treatment of triple-negative breast cancer (TNBC) is the utmost need of an era. TNBC is recognized as the most aggressive, metastatic cancer and the leading cause of mortality in females worldwide. The lack of expression of triple receptors namely, estrogen, progesterone, and human epidermal receptor2 defined TNBC.

OBJECTIVE

The current review introduced the novel biomarkers such as miRNA and family, PD1, EGFR, VEGF, TILs, P53, AR and PI3K, etc. contributed significantly to the prognosis and diagnosis of TNBC. Once diagnosed the utilization advanced approaches available for TNBC because of the limitations of chemotherapy. Novel approaches include lipid-based (liposomes, SLN, NLC, and SNEDDS), polymer-based (micelle, nanoparticles, dendrimers, and quantum dots), advanced nanocarriers such as (exosomes, antibody and peptide-drug conjugates), carbon-based nanocarriers (Carbon nanotubes, and graphene oxide). Lipid-based delivery is used for excellent carriers for hydrophobic drugs, biocompatibility, and lesser systemic toxicities than chemotherapeutic agents. Polymer-based approaches are preferred over lipids for providing longer circulation time, nanosize, high loading efficiency, high linking; avoiding the expulsion of drugs, targeted action, diagnostic and biosensing abilities. Advanced approaches like exosomes, conjugated moieties are preferred over polymeric for possessing potency, high penetrability, biomarkers, and avoiding the toxicity of tissues. Carbon-based gained wide applicability for their unique properties like a versatile carrier, prognostic, diagnostic, sensing, photodynamic, and photothermal characteristics.

CONCLUSION

The survival rate can be increased by utilizing several kinds of biomarkers. The advanced approaches can also be significantly useful in the prognosis and theranostic of triple-negative breast cancer. One of the biggest successes in treating with nanotechnology-based approaches is the marked reduction of systemic toxicity with high therapeutic effectiveness compared with chemotherapy, surgery, etc. The requirements such as prompt diagnosis, longer circulation time, high efficiency, and high potency, can be fulfilled with these nanocarriers.

Subclonal heterogeneity and evolution in breast cancer

Subclonal heterogeneity and evolution are characteristics of breast cancer that play a fundamental role in tumour development, progression and resistance to current therapies. In this review, we focus on the recent advances in understanding the epigenetic and transcriptomic changes that occur within breast cancer and their importance in terms of cancer development, progression and therapy resistance with a particular focus on alterations at the single-cell level. Furthermore, we highlight the utility of using single-cell tracing and molecular barcoding methodologies in preclinical models to assess disease evolution and response to therapy. We discuss how the integration of single-cell profiling from patient samples can be used in conjunction with results from preclinical models to untangle the complexities of this disease and identify biomarkers of disease progression, including measures of intra-tumour heterogeneity themselves, and how enhancing this understanding has the potential to uncover new targetable vulnerabilities in breast cancer.