The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer

Unraveling the molecular mechanisms of lymph node metastasis in ovarian cancer: focus on MEOX1

BACKGROUND

Lymph node metastasis (LNM) is a major factor contributing to the high mortality rate of ovarian cancer, making the treatment of this disease challenging. However, the molecular mechanism underlying LNM in ovarian cancer is still not well understood, posing a significant obstacle to overcome.

RESULTS

Through data mining from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we have identified MEOX1 as a specific gene associated with LNM in ovarian cancer. The expression of MEOX1 was found to be relatively high in serous ovarian adenocarcinoma, and its higher expression were associated with increased tumor grade and poorer clinical prognosis for ovarian cancer patients. Bioinformatics analysis revealed that MEOX1 exhibited the highest mRNA levels among all cancer types in ovarian cancer tissues and cell lines. Furthermore, gene set enrichment analysis (GSEA) and pathway analysis demonstrated that MEOX1 was involved in various LNM-related biological activities, such as lymphangiogenesis, lymphatic vessel formation during metastasis, epithelial-mesenchymal transition (EMT), G2/M checkpoint, degradation of extracellular matrix, and collagen formation. Additionally, the expression of MEOX1 was positively correlated with the expression of numerous prolymphangiogenic factors in ovarian cancer. To validate our findings, we conducted experiments using clinical tissue specimens and cell lines, which confirmed that MEOX1 was highly expressed in high-grade serous ovarian cancer (HGSOC) tissues and various ovarian cancer cell lines (A2780, SKOV3, HO8910, and OVCAR5) compared to normal ovarian tissues and normal ovarian epithelial cell line IOSE-80, respectively. Notably, we observed a higher protein level of MEOX1 in tumor tissues of LNM-positive HGSOC compared to LNM-negative HGSOC. Moreover, our fundamental experiments demonstrated that suppression of MEOX1 led to inhibitory effects on ovarian cancer cell proliferation and EMT, while overexpression of MEOX1 enhanced the proliferation and EMT capacities of ovarian cancer cells.

CONCLUSIONS

The results of our study indicate that MEOX1 plays a role in the lymph node metastasis of ovarian cancer by regulating multiple biological activities, including the proliferation and EMT of ovarian cancer, lymphangiogenesis, and ECM remodeling. Our findings suggest that MEOX1 could serve as a potential biomarker for the diagnosis and treatment of ovarian cancer with LNM.

Modeling the Effect of Spatial Structure on Solid Tumor Evolution and Circulating Tumor DNA Composition

Circulating tumor DNA (ctDNA) monitoring, while sufficiently advanced to reflect tumor evolution in real time and inform cancer diagnosis, treatment, and prognosis, mainly relies on DNA that originates from cell death via apoptosis or necrosis. In solid tumors, chemotherapy and immune infiltration can induce spatially variable rates of cell death, with the potential to bias and distort the clonal composition of ctDNA. Using a stochastic evolutionary model of boundary-driven growth, we study how elevated cell death on the edge of a tumor can simultaneously impact driver mutation accumulation and the representation of tumor clones and mutation detectability in ctDNA. We describe conditions in which invasive clones are over-represented in ctDNA, clonal diversity can appear elevated in the blood, and spatial bias in shedding can inflate subclonal variant allele frequencies (VAFs). Additionally, we find that tumors that are mostly quiescent can display similar biases but are far less detectable, and the extent of perceptible spatial bias strongly depends on sequence detection limits. Overall, we show that spatially structured shedding might cause liquid biopsies to provide highly biased profiles of tumor state. While this may enable more sensitive detection of expanding clones, it could also increase the risk of targeting a subclonal variant for treatment. Our results indicate that the effects and clinical consequences of spatially variable cell death on ctDNA composition present an important area for future work.

The dual-crosslinked prospective values of RAI14 for the diagnosis and chemosurveillance in triple negative breast cancer

OBJECTIVE

The exploration of non-invasive biomarkers for assessing tumor response is critical to optimize treatment decisions. In this study, we aimed at determining the potential role of RAI14 in the early diagnosis and evaluation of chemotherapy efficacy in triple-negative breast cancer (TNBC).

METHODS

We recruited 116 patients newly diagnosed with breast cancer, 30 patients with benign breast disease and 30 healthy controls. In addition, 57 TNBC patients were collected in serum at different time points (C0, C2 and C4) for chemotherapy monitoring. The expression of serum RAI14 and CA15-3 were quantified by Elisa and electrochemiluminescence assay, respectively. Then we compared the performances of markers with the chemotherapy efficacy assessed by imaging.

RESULTS

RAI14 is significantly overexpressed in TNBC and is linked to adverse clinicopathological features such as tumor burden, CA15-3 levels and the ER, PR, and HER2 status of the patients. ROC curve analysis showed that RAI14 improves the diagnostic performance for CA15-3(AUCRAI14 = 0.934 vs. AUCCA15-3 = 0.836), especially embodied in early-stage breast cancer diagnosis and patients with CA15-3 negativity. Furthermore, RAI14 behaves well in reproducing treatment response which was consistent with clinical Imaging assessment.

CONCLUSIONS

Recent studies showed that RAI14 has a complementary effect to CA15-3 and a test combining the two parameters can improve the detection rate of early triple-negative breast cancer. At the same time, RAI14 plays a more important role in chemotherapy monitoring than CA15-3 as the change in its concentration is in line with the tumor volume variation. Taken together, RAI14 is a reliable novel marker in the early diagnosis and chemotherapy monitoring of triple-negative breast cancer.

Modeling the effect of spatial structure on solid tumor evolution and ctDNA composition

Circulating tumor DNA (ctDNA) monitoring, while sufficiently advanced to reflect tumor evolution in real time and inform on cancer diagnosis, treatment, and prognosis, mainly relies on DNA that originates from cell death via apoptosis or necrosis. In solid tumors, chemotherapy and immune infiltration can induce spatially variable rates of cell death, with the potential to bias and distort the clonal composition of ctDNA. Using a stochastic evolutionary model of boundary-driven growth, we study how elevated cell death on the edge of a tumor can simultaneously impact driver mutation accumulation and the representation of tumor clones and mutation detectability in ctDNA. We describe conditions in which invasive clones end up over-represented in ctDNA, clonal diversity can appear elevated in the blood, and spatial bias in shedding can inflate subclonal variant allele frequencies (VAFs). Additionally, we find that tumors that are mostly quiescent can display similar biases, but are far less detectable, and the extent of perceptible spatial bias strongly depends on sequence detection limits. Overall, we show that spatially structured shedding might cause liquid biopsies to provide highly biased profiles of tumor state. While this may enable more sensitive detection of expanding clones, it could also increase the risk of targeting a subclonal variant for treatment. Our results indicate that the effects and clinical consequences of spatially variable cell death on ctDNA composition present an important area for future work.

Tumor Characteristics Associated with Axillary Nodal Positivity in Triple Negative Breast Cancer

Larger-size primary tumors are correlated with axillary metastases and worse outcomes. We evaluated the relationships among tumor size, location, and distance to nipple relative to axillary node metastases in triple-negative breast cancer (TNBC) patients, as well as the predictive capacity of imaging. We conducted a single-institution, retrospective chart review of stage I-III TNBC patients diagnosed from 1998 to 2019 who underwent upfront surgery. Seventy-three patients had a mean tumor size of 20 mm (range 1-53 mm). All patients were clinically node negative. Thirty-two patients were sentinel lymph node positive, of whom 25 underwent axillary lymph node dissection. Larger tumor size was associated with positive nodes (p < 0.001): the mean tumor size was 14.30 mm in node negative patients and 27.31 mm in node positive patients. Tumor to nipple distance was shorter in node positive patients (51.0 mm) vs. node negative patients (73.3 mm) (p = 0.005). The presence of LVI was associated with nodal positivity (p < 0.001). Tumor quadrant was not associated with nodal metastasis. Ultrasound yielded the largest number of suspicious findings (21/49), with sensitivity of 0.25 and specificity of 0.40. On univariate analysis, age younger than 60 at diagnosis was also associated with nodal positivity (p < 0.002). Comparative analyses with other subtypes may identify biologic determinants.

Magnetic Resonance Diagnosis of Early Triple-Negative Breast Cancer Based on the Ionic Covalent Organic Framework with High Relaxivity and Long Retention Time

Patients with triple-negative breast cancer (TNBC) have dismal prognoses due to the lack of therapeutic targets and susceptibility to lymph node (LN) metastasis. Therefore, it is essential to develop more effective approaches to identify early TNBC tissues and LNs. In this work, a magnetic resonance imaging (MRI) contrast agent (Mn-iCOF) was constructed based on the Mn(II)-chelated ionic covalent organic framework (iCOF). Because of the porous structure and hydrophilicity, the Mn-iCOF has a high longitudinal relaxivity (r₁) of 8.02 mM^-1 s^-1 at 3.0 T. For the tumor-bearing mice, a lower dose (0.02 mmol [Mn]/kg) of Mn-iCOF demonstrated a higher signal-to-noise ratio (SNR) value (1.8) and longer retention time (2 h) compared to a 10-fold dose of commercial Gd-DOTA (0.2 mmol [Gd]/kg). Moreover, the Mn-iCOF can provide continuous and significant MR contrast for the popliteal LNs within 24 h, allowing for accurate evaluation and dissection of LNs. These excellent MRI properties of the Mn-iCOF may open new avenues for designing more biocompatible MRI contrast agents with higher resolutions, particularly in the diagnosis of TNBC.

A phylogenetic approach to study the evolution of somatic mutational processes in cancer

Cancer cell genomes change continuously due to mutations, and mutational processes change over time in patients, leaving dynamic signatures in the accumulated genomic variation in tumors. Many computational methods detect the relative activities of known mutation signatures. However, these methods may produce erroneous signatures when applied to individual branches in cancer cell phylogenies. Here, we show that the inference of branch-specific mutational signatures can be improved through a joint analysis of the collections of mutations mapped on proximal branches of the cancer cell phylogeny. This approach reduces the false-positive discovery rate of branch-specific signatures and can sometimes detect faint signatures. An analysis of empirical data from 61 lung cancer patients supports trends based on computer-simulated datasets for which the correct signatures are known. In lung cancer somatic variation, we detect a decreasing trend of smoking-related mutational processes over time and an increasing influence of APOBEC mutational processes as the tumor evolution progresses. These analyses also reveal patterns of conservation and divergence of mutational processes in cell lineages within patients.

The Evolution of Clinically Aggressive Triple-Negative Breast Cancer Shows a Large Mutational Diversity and Early Metastasis to Lymph Nodes

In triple-negative breast cancer (TNBC), only 30% of patients treated with neoadjuvant chemotherapy achieve a pathological complete response after treatment and more than 90% die due to metastasis formation. The diverse clinical responses and metastatic developments are attributed to extensive intrapatient genetic heterogeneity and tumor evolution acting on this neoplasm. In this work, we aimed to evaluate genomic alterations and tumor evolution in TNBC patients with aggressive disease. We sequenced the whole exome of 16 lesions from four patients who did not respond to therapy, and took several follow-up samples, including samples from tumors before and after treatment, as well as from the lymph nodes and skin metastases. We found substantial intrapatient genetic heterogeneity, with a variable tumor mutational composition. Early truncal events were MCL1 amplifications. Metastatic lesions had deletions in RB1 and PTEN, along with TERT, AKT2, and CCNE1 amplifications. Mutational signatures 06 and 12 were mainly detected in skin metastases and lymph nodes. According to phylogenetic analysis, the lymph node metastases occurred at an early stage of TNBC development. Finally, each patient had three to eight candidate driving mutations for targeted treatments. This study delves into the genomic complexity and the phylogenetic and evolutionary development of aggressive TNBC, supporting early metastatic development, and identifies specific genetic alterations associated with a response to targeted therapies.

Simvastatin induced ferroptosis for triple-negative breast cancer therapy

Triple-negative breast cancer (TNBC), a management of aggressive breast cancer, remains an unmet medical challenge. Although a wave of efforts had spurred to design novel therapeutic method of TNBC, unpredictable prognosis with lacking effective therapeutic targets along with the resistance to apoptosis seriously limited survival benefits. Ferroptosis is a non-apoptotic form of cell death that is induced by excessive lipid peroxidation, which provide an innovative way to combat cancer. Emerging evidence suggests that ferroptosis plays an important role in the treatment of TNBC cells. Herein, a novel ferroptosis nanomedicine was prepared by loading simvastatin (SIM), a ferroptosis drug, into zwitterionic polymer coated magnetic nanoparticles (Fe3O4@PCBMA) to improve the therapeutic effect of TNBC. The as-obtained Fe3O4@PCBMA-SIM nanoparticles demonstrated more cytotoxicity against MDA-MB-231 than MCF-7 due to the higher expression of 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR), which demonstrated that statins could effectively kill TNBC. Further experiments showed that SIM could inhibit the expression of HMGCR to downregulate the mevalonate (MVA) pathway and glutathione peroxidase 4 (GPX4), thereby inducing cancer cell ferroptosis. What's more, PCBMA endows Fe3O4@PCBMA longer blood circulation performance to enhance their accumulation at tumor sites. Given that Fe3O4 have proven for clinical applications by the U.S. Food and Drug Administration (FDA) and SIM could induce cancer cell ferroptosis, the developed Fe3O4@PCBMA-SIM nanosystem would have great potential in clinics for overcoming the drug resistance brought about by apoptotic drugs to cancer cells.

The role of single-cell sequencing in studying tumour evolution

Tumour evolution is a complex interplay between the acquisition of somatic (epi)genomic changes in tumour cells and the phenotypic consequences they cause, all in the context of a changing microenvironment. Single-cell sequencing offers a window into this dynamic process at the ultimate resolution of individual cells. In this review, we discuss the transformative insight offered by single-cell sequencing technologies for understanding tumour evolution.

Tracking Cancer Evolution through the Disease Course

During cancer evolution, constituent tumor cells compete under dynamic selection pressures. Phenotypic variation can be observed as intratumor heterogeneity, which is propagated by genome instability leading to mutations, somatic copy-number alterations, and epigenomic changes. TRACERx was set up in 2014 to observe the relationship between intratumor heterogeneity and patient outcome. By integrating multiregion sequencing of primary tumors with longitudinal sampling of a prospectively recruited patient cohort, cancer evolution can be tracked from early- to late-stage disease and through therapy. Here we review some of the key features of the studies and look to the future of the field. SIGNIFICANCE: Cancers evolve and adapt to environmental challenges such as immune surveillance and treatment pressures. The TRACERx studies track cancer evolution in a clinical setting, through primary disease to recurrence. Through multiregion and longitudinal sampling, evolutionary processes have been detailed in the tumor and the immune microenvironment in non-small cell lung cancer and clear-cell renal cell carcinoma. TRACERx has revealed the potential therapeutic utility of targeting clonal neoantigens and ctDNA detection in the adjuvant setting as a minimal residual disease detection tool primed for translation into clinical trials.

Genome-wide 5-hydroxymethylcytosine Profiling Analysis Identifies MAP7D1 as A Novel Regulator of Lymph Node Metastasis in Breast Cancer

Although DNA 5-hydroxymethylcytosine (5hmC) is recognized as an important epigenetic mark in cancer, its precise role in lymph node metastasis remains elusive. In this study, we investigated how 5hmC associates with lymph node metastasis in breast cancer. Accompanying with high expression of TET1 and TET2 proteins, large numbers of genes in the metastasis-positive primary tumors exhibit higher 5hmC levels than those in the metastasis-negative primary tumors. In contrast, the TET protein expression and DNA 5hmC decrease significantly within the metastatic lesions in the lymph nodes compared to those in their matched primary tumors. Through genome-wide analysis of 8 sets of primary tumors, we identified 100 high-confidence metastasis-associated 5hmC signatures, and it is found that increased levels of DNA 5hmC and gene expression of MAP7D1 associate with high risk of lymph node metastasis. Furthermore, we demonstrate that MAP7D1, regulated by TET1, promotes tumor growth and metastasis. In conclusion, the dynamic 5hmC profiles during lymph node metastasis suggest a link between DNA 5hmC and lymph node metastasis. Meanwhile, the role of MAP7D1 in breast cancer progression suggests that the metastasis-associated 5hmC signatures are potential biomarkers to predict the risk for lymph node metastasis, which may serve as diagnostic and therapeutic targets for metastatic breast cancer.

Proteomics of REPLICANT perfusate detects changes in the metastatic lymph node microenvironment

In breast cancer (BC), detecting low volumes of axillary lymph node (ALN) metastasis pre-operatively is difficult and novel biomarkers are needed. We recently showed that patient-derived ALNs can be sustained ex-vivo using normothermic perfusion. We now compare reactive (tumour-free; n = 5) and macrometastatic (containing tumour deposits >2 mm; n = 4) ALNs by combining whole section multiplex immunofluorescence with TMT-labelled LC-MS/MS of the circulating perfusate. Macrometastases contained significantly fewer B cells and T cells (CD4⁺/CD8⁺/regulatory) than reactive nodes (p = 0.02). Similarly, pathway analysis of the perfusate proteome (119/1453 proteins significantly differentially expressed) showed that immune function was diminished in macrometastases in favour of ‘extracellular matrix degradation’; only ‘neutrophil degranulation’ was preserved. Qualitative comparison of the perfusate proteome to that of node-positive pancreatic and prostatic adenocarcinoma also highlighted ‘neutrophil degranulation’ as a contributing factor to nodal metastasis. Thus, metastasis-induced changes in the REPLICANT perfusate proteome are detectable, and could facilitate biomarker discovery.

Molecular Heterogeneity and Evolution in Breast Cancer

Breast cancer comprises a heterogeneous group of tumor subtypes, whether defined by immunohistochemistry of key proteins, RNA expression profiles, or genetic alterations, and each of these subtypes may benefit from a distinct treatment approach. However, there can be striking heterogeneity within tumors, which may pose challenges to the development of personalized approaches to therapy. Intratumor heterogeneity can be divided into three main categories: genetic, phenotypic, and microenvironmental. Here, we review technologies to interrogate these three categories of heterogeneity in patient samples, as well as the current state of understanding of these categories in breast cancer, from cell to cell, across different regions of the same tumor mass, across treatment, and across metastasis. Efforts to characterize tumor heterogeneity longitudinally will be crucial to the development of personalized oncology for breast cancer.

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer

Background

Lymph node metastasis of triple-negative breast cancer (TNBC) is essential in treatment strategy formulation. This study aimed to build a nomogram that predicts lymph node metastasis in patients with TNBC.

Materials and Methods

A total of 28,966 TNBC patients diagnosed from 2010 to 2017 in the Surveillance, Epidemiology and End Results (SEER) database were enrolled, and randomized 1:1 into the training and validation sets, respectively. Univariate and multivariate logistic regression analysis were applied to identify the predictive factors, which composed the nomogram. The receiver operating characteristic curves showed the efficacy of the nomogram.

Result

Multivariate logistic regression analyses revealed that age, race, tumor size, tumor primary site, and pathological grade were independent predictive factors of lymph node status. Integrating these independent predictive factors, a nomogram was successfully developed for predicting lymph node status, and further validated in the validation set. The areas under the receiver operating characteristic curves of the nomogram in the training and validation sets were 0.684 and 0.689 respectively, showing a satisfactory performance.

Conclusion

We constructed a nomogram to predict the lymph node status in TNBC patients. After further validation in additional large cohorts, the nomogram developed here would do better in predicting, providing more information for staging and treatment, and enabling tailored treatment in TNBC patients.

Systems Biology and Experimental Model Systems of Cancer

Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

Prognostic factors in metastatic breast cancer: a prospective single-centre cohort study in a Finnish University Hospital

OBJECTIVES

Although novel early breast cancer prognostic factors are being continuously discovered, only rare factors predicting survival in metastatic breast cancer have been validated. The prognostic role of early breast cancer prognostic factors in metastatic disease also remains mostly unclear.

DESIGN AND SETTING

Prospective cohort study in a Finnish University Hospital.

PARTICIPANTS AND OUTCOMES

594 women with early breast cancer were originally followed. Sixty-one of these patients developed distant metastases during the follow-up, and their primary breast cancer properties, such as tumour size, nodal status, oestrogen receptor (ER) and progesterone receptor expression, grade, proliferation rate, histopathological subtype and breast cancer subtype were analysed as potential prognostic factors for metastatic disease.

RESULTS

In multivariate analysis, the presence of lymph node metastases at the time of early breast cancer surgery (HR, 2.17; 95% CI, 1.09-4.31; p=0.027) and ER status (negative vs positive, HR, 2.16; 95% CI, 1.14-4.10; p=0.018) were significant predictors of survival in metastatic disease.

CONCLUSIONS

These results confirm ER status as a primary prognostic factor in metastatic breast cancer. Furthermore, it also suggests that the presence of initial lymph node metastases could serve as a prognostic factor in recurrent breast cancer.

EBV-LMP1 induces APOBEC3s and mitochondrial DNA hypermutation in nasopharyngeal cancer

An Epstein-Barr virus (EBV)-encoded latent membrane protein 1 (LMP1) is a principal oncogene that plays a pivotal role in EBV-associated malignant tumors including nasopharyngeal cancer (NPC). Recent genomic landscape studies revealed that NPC also contained many genomic mutations, suggesting the role of LMP1 as a driver gene for the induction of these genomic mutations. Nonetheless, its exact mechanism has not been investigated. In this study, we report that LMP1 alters the expression profile of APOBEC3s(A3s), host deaminases that introduce consecutive C-to-U mutations (hypermutation). In vitro, LMP1 induces APOBEC3B (A3B) and 3F(A3F), in a nasopharyngeal cell line, AdAH. Overexpression of LMP1, A3B, or A3F induces mtDNA hypermutation, which is also detectable from NPC specimens. Expression of LMP1 and A3B in NPC was correlated with neck metastasis. These results provide evidence as to which LMP1 induces A3s and mtDNA hypermutation, and how LMP1 facilitates metastasis is also discussed.

Construction of an immune-related genes nomogram for the preoperative prediction of axillary lymph node metastasis in triple-negative breast cancer

Immune system disorder is associated with metastasis of triple-negative breast cancers (TNBCs). A robust, individualized immune-related genes (IRGs)-based classifier was aimed to develop and validate in our study to precisely estimate the axillary lymph node (ALN) status preoperatively in patients with early-stage TNBC. We first analyzed RNA sequencing profiles in TNBC patients from The Cancer Genome Atlas database by using bioinformatics approaches, and screened 23 differentially expressed IRGs. A 9-gene panel was generated with an area under the curve (AUC) of 0.77 [95% confidence interval (95% CI), 0.68-0.87]. We detected the 9 ALN-status-related IRGs in the training set (n = 133) and developed a reduced and optimized five-IRGs signature, which effectively distinguished TNBC patients with ALN metastasis (AUC, 0.80; 95% CI, 0.65-0.86), and was superior to preoperative ultrasound-based ALN status (AUC, 0.73; 95% CI, 0.53-0.93). Predictive efficiency (AUC, 0.77; 95% CI 0.61-0.93) of this five-IRGs signature was validated in the validation set (n = 81). Furthermore, IRGs nomogram incorporated IRGs signature with US-based ALN status showed higher ALN status prediction efficacy than US-based ALN status and five-IRGs signature alone in both training and validation sets. IRGs nomogram may aid in identifying patients who can be exempted from axillary surgery.Novelty and impact: An immune-related genes (IRGs) nomogram was first developed and externally validated in our study, which incorporated the IRGs signature with ultrasound (US)-based axillary lymph nodes (ALN) status. IRGs nomogram is superior to IRGs signature alone for preoperative estimation of ALN metastasis in patients with triple-negative breast cancer (TNBC). It is a favourable biomarker for preoperatively predicting ALN metastasis risk and may aid in clinical decision-making in early-stage TNBCs.

Effect of neoadjuvant therapy on breast cancer biomarker profile

BACKGROUND

Breast cancer clinical management requires the assessment of hormone receptors (estrogen (ER) and progesterone receptor (PR)), human epidermal growth factor receptor 2 (HER2) and cellular proliferation index Ki67, by immunohistochemistry (IHC), in order to choose and guide therapy according to tumor biology. Many studies have reported contradictory results regarding changes in the biomarker profile after neoadjuvant therapy (NAT). Given its clinical implications for the disease management, we aimed to analyze changes in ER, PR, HER2, and Ki67 expression in paired core-needle biopsies and surgical samples in breast cancer patients that had either been treated or not with NAT.

METHODS

We included 139 patients with confirmed diagnosis of invasive ductal breast carcinoma from the Colombian National Cancer Institute. Variation in biomarker profile were assessed according to NAT administration (NAT and no-NAT treated cases) and NAT scheme (hormonal, cytotoxic, cytotoxic + trastuzumab, combined). Chi-squared and Wilcoxon signed-rank test were used to identify changes in biomarker status and percentage expression, respectively, in the corresponding groups.

RESULTS

We did not find any significant variations in biomarker status or expression values in the no-NAT group. In cases previously treated with NAT, we did find a statistically significant decrease in Ki67 (p < 0.001) and PR (p = 0.02605) expression. When changes were evaluated according to NAT scheme, we found a significant decrease in both Ki67 status (p = 0.02977) and its expression values (p < 0.001) in cases that received the cytotoxic treatment.

CONCLUSIONS

Our results suggest that PR and Ki67 expression can be altered by NAT administration, whereas cases not previously treated with NAT do not present IHC biomarker profile variations. The re-evaluation of these two biomarkers after NAT could provide valuable information regarding treatment response and prognosis for breast cancer patients.

Pathology of triple negative breast cancer

Triple negative breast cancer (TNBC) is a subtype of breast tumor lacking hormone receptors expression and HER2 gene amplification and represents 24 % of newly diagnosed breast neoplasms. In this review, pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the seminal studies that defined this subtype of breast cancer by a molecular point of view. This paper also focuses on practical issues raised in clinical routine by the introduction of genetic expression breast cancer profiling and the innovative prognostic and predictive impact on triple-negative breast cancer pathology. Moreover, histopathological aspects of triple-negative neoplasms are also mentioned, underlying the importance of histologic diagnosis of particular cancer subtypes with decisive impact on clinical outcome. Importantly, focus on new therapeutic frontier represented by immunotherapy is illustrated, with particular mention of immune checkpoint inhibitors introduction in TNBC therapy and their impact on future treatments.

Mapping the breast cancer metastatic cascade onto ctDNA using genetic and epigenetic clonal tracking

Circulating tumour DNA (ctDNA) allows tracking of the evolution of human cancers at high resolution, overcoming many limitations of tissue biopsies. However, exploiting ctDNA to determine how a patient's cancer is evolving in order to aid clinical decisions remains difficult. This is because ctDNA is a mix of fragmented alleles, and the contribution of different cancer deposits to ctDNA is largely unknown. Profiling ctDNA almost invariably requires prior knowledge of what genomic alterations to track. Here, we leverage on a rapid autopsy programme to demonstrate that unbiased genomic characterisation of several metastatic sites and concomitant ctDNA profiling at whole-genome resolution reveals the extent to which ctDNA is representative of widespread disease. We also present a methylation profiling method that allows tracking evolutionary changes in ctDNA at single-molecule resolution without prior knowledge. These results have critical implications for the use of liquid biopsies to monitor cancer evolution in humans and guide treatment.

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BACKGROUND

Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).

METHODS

To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.

RESULTS

Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.

CONCLUSIONS

AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .

Measuring Clonal Evolution in Cancer with Genomics

Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics.

Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy

Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance.