Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence

Fewer than 20% of triple-negative breast cancer patients experience long-term responses to mainstay chemotherapy. Resistant tumor subpopulations use alternative metabolic pathways to escape therapy, survive, and eventually recur. Here, we show in vivo, longitudinal metabolic reprogramming in residual disease and recurrence of triple-negative breast cancer xenografts with varying sensitivities to the chemotherapeutic drug paclitaxel. Optical imaging coupled with metabolomics reported an increase in non-glucose-driven mitochondrial metabolism and an increase in intratumoral metabolic heterogeneity during regression and residual disease in resistant MDA-MB-231 tumors. Conversely, sensitive HCC-1806 tumors were primarily reliant on glucose uptake and minimal changes in metabolism or heterogeneity were observed over the tumors' therapeutic life cycles. Further, day-matched resistant HCC-1806 tumors revealed a higher reliance on mitochondrial metabolism and elevated metabolic heterogeneity compared to sensitive HCC-1806 tumors. Together, metabolic flexibility, increased reliance on mitochondrial metabolism, and increased metabolic heterogeneity are defining characteristics of persistent residual disease, features that will inform the appropriate type and timing of therapies.

Basal-epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis, and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviors remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis, we find that chemoresistant tumors may benefit from distinct inhibitors in treatment-naive versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.

Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data

Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intratumor heterogeneity and can influence tumor invasion, progression, and response to treatment. Here we report Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. To achieve so, LSGI scrutinizes each sliding window employing non-negative matrix factorization (NMF) combined with linear regression. With LSGI, we demonstrated the identification of spatially proximal yet opposite directed pathway gradients in a glioblastoma dataset. We further applied LSGI to 87 tumor ST datasets reported from nine published studies and identified both pan-cancer and tumor-type specific pathways with gradated expression patterns, such as epithelial mesenchymal transition, MHC complex, and hypoxia. The local gradients were further categorized according to their association to tumor-TME (tumor microenvironment) interface, highlighting the pathways related to spatial transcriptional intratumoral heterogeneity. We conclude that LSGI enables highly interpretable STG analysis which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.

Metabolic adaptation towards glycolysis supports resistance to neoadjuvant chemotherapy in early triple negative breast cancers

BACKGROUND

Neoadjuvant chemotherapy (NAC) is the standard of care for patients with early-stage triple negative breast cancers (TNBC). However, more than half of TNBC patients do not achieve a pathological complete response (pCR) after NAC, and residual cancer burden (RCB) is associated with dismal long-term prognosis. Understanding the mechanisms underlying differential treatment outcomes is therefore critical to limit RCB and improve NAC efficiency.

METHODS

Human TNBC cell lines and patient-derived organoids were used in combination with real-time metabolic assays to evaluate the effect of NAC (paclitaxel and epirubicin) on tumor cell metabolism, in particular glycolysis. Diagnostic biopsies (pre-NAC) from patients with early TNBC were analyzed by bulk RNA-sequencing to evaluate the predictive value of a glycolysis-related gene signature.

RESULTS

Paclitaxel induced a consistent metabolic switch to glycolysis, correlated with a reduced mitochondrial oxidative metabolism, in TNBC cells. In pre-NAC diagnostic biopsies from TNBC patients, glycolysis was found to be upregulated in non-responders. Furthermore, glycolysis inhibition greatly improved response to NAC in TNBC organoid models.

CONCLUSIONS

Our study pinpoints a metabolic adaptation to glycolysis as a mechanism driving resistance to NAC in TNBC. Our data pave the way for the use of glycolysis-related genes as predictive biomarkers for NAC response, as well as the development of inhibitors to overcome this glycolysis-driven resistance to NAC in human TNBC patients.

An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer

Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.

Reimbursement in the Context of Precision Oncology Approaches in Metastatic Breast Cancer: Challenges and Experiences

Background

Precision oncology programs using next-generation sequencing to detect predictive biomarkers are extending therapeutic options for patients with metastatic breast cancer (mBC). Regularly, based on the recommendations of the interdisciplinary molecular tumor board (iMTB), an inclusion in a clinical trial is not possible. In this case, the German health insurance system allows for the application of reimbursement for an off-label drug use. Here, we describe the current challenges and our experience with reimbursement of molecular therapies in mBC.

Methods

A total of 100 applications for reimbursement of off-label therapies recommended by an iMTB were filed for patients with mBC, of which 89 were evaluable for this analysis. The approval rate was correlated with the molecular level of evidence of the respective therapy according to the National Center for Tumor Diseases (NCT) and European Society for Medical Oncology Scale for Clinical Actionability of molecular Targets (ESCAT) classification as well as with pretreatment therapy lines.

Findings

Overall, 53.9% (48/89) of reimbursement applications were approved. Applications for therapies based on level of evidence m1 (NCT classification), tier I and II (ESCAT classification) had a significantly and clinically relevant increased chance of reimbursement, while a greater number of previous treatment lines had no significantly increased chance of approval, though a trend of approval toward higher treatment lines was detectable.

Interpretation

Currently, the German jurisdiction seems to aggravate the clinical implementation of clinically urgently needed molecular therapies.

Tumor heterogeneity: how could we use it to achieve better clinical outcomes?

Differences in tumors related to location, tissue type, and histological subtype have been well documented for decades. Tumors are also molecularly very diverse. In this short review we describe the current classification schemes for tumor heterogeneity. We enlist the various drivers of tumor heterogeneity generation and comment on their clinical significance. New molecular techniques promise to assess tumor heterogeneity at affordable cost, so that these techniques can soon enter the clinic. While tumor heterogeneity currently represents a major unfavorable barrier in the field of oncology, it may also be a key in revolutionizing cancer diagnosis and treatment. Information regarding tumor heterogeneity has the potential to provide more thorough prognostic information, guide more efficacious combination treatment regimens, and lead to the development of novel therapeutic strategies and identification of new targets. For these gains to be realized, assessment of tumor heterogeneity needs to be incorporated into current diagnostic protocols but standardized and reproducible assessment methods are required. Fortunately, when these advances are realized, tumor heterogeneity has the potential to improve clinical outcomes.

The value of oral selective estrogen receptor degraders in patients with HR-positive, HER2-negative advanced breast cancer after progression on ≥ 1 line of endocrine therapy: systematic review and meta-analysis

BACKGROUND

Currently, the value of oral selective estrogen receptor degraders (SERDs) for hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (aBC) after progression on ≥ 1 line of endocrine therapy (ET) remains controversial. We conducted a meta-analysis to evaluate progression-free survival (PFS) and safety benefits in several clinical trials.

MATERIALS AND METHODS

Cochrane Library, Embase, PubMed, and conference proceedings (SABCS, ASCO, ESMO, and ESMO Breast) were searched systematically and comprehensively. Random effects models or fixed effects models were used to assess pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for treatment with oral SERDs versus standard of care.

RESULTS

A total of four studies involving 1,290 patients were included in our analysis. The hazard ratio (HR) of PFS showed that the oral SERD regimen was better than standard of care in patients with HR+/HER2- aBC after progression on ≥ 1 line of ET (HR: 0.75, 95% CI: 0.62-0.91, p = 0.004). In patients with ESR1 mutations, the oral SERD regimen provided better PFS than standard of care (HR: 0.58, 95% CI: 0.47-0.71, p < 0.00001). Regarding patients with disease progression following previous use of CDK4/6 inhibitors, PFS benefit was observed in oral SERD-treatment arms compared to standard of care (HR: 0.75, 95% CI: 0.64-0.87, p = 0.0002).

CONCLUSIONS

The oral SERD regimen provides a significant PFS benefit compared to standard-of-care ET in patients with HR+/HER2- aBC after progression on ≥ 1 line of ET. In particular, we recommend oral SERDs as a preferred choice for those patients with ESR1m, and it could be a potential replacement for fulvestrant. The oral SERD regimen is also beneficial after progression on CDK4/6 inhibitors combined with endocrine therapy.

Modeling early treatment response in AML from cell-free tumor DNA

Monitoring disease response after intensive chemotherapy for acute myeloid leukemia (AML) currently requires invasive bone marrow biopsies, imposing a significant burden on patients. In contrast, cell-free tumor DNA (ctDNA) in peripheral blood, carrying tumor-specific mutations, offers a less-invasive assessment of residual disease. However, the relationship between ctDNA levels and bone marrow blast kinetics remains unclear. We explored this in 10 AML patients with NPM1 and IDH2 mutations undergoing initial chemotherapy. Comparison of mathematical mixed-effect models showed that (1) inclusion of blast cell death in the bone marrow, (2) transition of ctDNA to peripheral blood, and (3) ctDNA decay in peripheral blood describes kinetics of blast cells and ctDNA best. The fitted model allows prediction of residual bone marrow blast content from ctDNA, and its scaling factor, representing clonal heterogeneity, correlates with relapse risk. Our study provides precise insights into blast and ctDNA kinetics, offering novel avenues for AML disease monitoring.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Dormancy in Breast Cancer

The pattern of delayed recurrence in a subset of breast cancer patients has long been explained by a model that incorporates a variable period of cellular or tumor mass dormancy prior to disease relapse. In this review, we critically evaluate existing data to develop a framework for inferring the existence of dormancy in clinical contexts of breast cancer. We integrate these clinical data with rapidly evolving mechanistic insights into breast cancer dormancy derived from a broad array of genetically engineered mouse models as well as experimental models of metastasis. Finally, we propose actionable interventions and discuss ongoing clinical trials that translate the wealth of knowledge gained in the laboratory to the long-term clinical management of patients at a high risk of developing recurrence.

Identifying the effectiveness of 3D culture systems to recapitulate breast tumor tissue in situ

PURPOSE

Breast cancer heterogeneity contributes to chemotherapy resistance and decreased patient survival. To improve patient outcomes it is essential to develop a technology that is able to rapidly select the most efficacious therapy that targets the diverse phenotypes present within the tumor. Breast cancer organoid technologies are proposed as an attractive approach for evaluating drug responses prior to patient therapy. However, there remain challenges in evaluating the effectiveness of organoid cultures to recapitulate the heterogeneity present in the patient tumor in situ.

METHOD

Organoids were generated from seven normal breast and nineteen breast cancer tissues diagnosed as estrogen receptor positive or triple negative. The Jensen-Shannon divergence index, a measure of the similarity between distributions, was used to compare and evaluate heterogeneity in starting tissue and their resultant organoids. Heterogeneity was analyzed using cytokeratin 8 and cytokeratin 14, which provided an easily scored readout.

RESULTS

In the in vitro culture system HER1 and FGFR were able to drive intra-tumor heterogeneity to generate divergent phenotypes that have different sensitivities to chemotherapies.

CONCLUSION

Our methodology, which focuses on quantifiable cellular phenotypes, provides a tractable system that complements omics approaches to provide an unprecedented view of heterogeneity and will enhance the identification of novel therapies and facilitate personalized medicine.

Unravelling the Complexity of Colorectal Cancer: Heterogeneity, Clonal Evolution, and Clinical Implications

Colorectal cancer (CRC) is a global health concern and a leading cause of death worldwide. The disease's course and response to treatment are significantly influenced by its heterogeneity, both within a single lesion and between primary and metastatic sites. Biomarkers, such as mutations in KRAS, NRAS, and BRAF, provide valuable guidance for treatment decisions in patients with metastatic CRC. While high concordance exists between mutational status in primary and metastatic lesions, some heterogeneity may be present. Circulating tumor DNA (ctDNA) analysis has proven invaluable in identifying genetic heterogeneity and predicting prognosis in RAS-mutated metastatic CRC patients. Tumor heterogeneity can arise from genetic and non-genetic factors, affecting tumor development and response to therapy. To comprehend and address clonal evolution and intratumoral heterogeneity, comprehensive genomic studies employing techniques such as next-generation sequencing and computational analysis are essential. Liquid biopsy, notably through analysis of ctDNA, enables real-time clonal evolution and treatment response monitoring. However, challenges remain in standardizing procedures and accurately characterizing tumor subpopulations. Various models elucidate the origin of CRC heterogeneity, highlighting the intricate molecular pathways involved. This review focuses on intrapatient cancer heterogeneity and genetic clonal evolution in metastatic CRC, with an emphasis on clinical applications.

Single-cell profiling in multiple myeloma: insights, problems, and promises

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.

Intelligent recognition of CTCs from gallbladder cancer by ultrasensitive electrochemical cytosensor and diagnosis of chemotherapeutic resistance

Gallbladder carcinoma (GBC) is one of most aggressive and lethal malignancies. Early diagnosis of GBC is crucial for determining appropriate treatment and improving chances of cure. Chemotherapy represents the main therapeutic regimen for unresectable GBC patients to inhibit tumor growth & metastasis. But, chemoresistance is the major cause of GBC recurrence. Thus, there is an urgent need to explore potentially non-invasive and point-of-care approaches to screen GBC and monitor their chemoresistance. Herein, we established an electrochemical cytosensor to specifically detect circulating tumor cells (CTCs) and their chemoresistance. Trilayer of CdSe/ZnS quantum dots (QDs) were cladded upon SiO₂ nanoparticles (NPs), forming Tri-QDs/PEI@SiO₂ electrochemical probes. Upon conjugation of anti-ENPP1, the electrochemical probes were able to specifically label captured CTCs from GBC. The detection of CTCs and chemoresistance were realized by square wave anodic stripping voltammetric (SWASV) responses to anodic stripping current of Cd ²⁺ ion when cadmium in electrochemical probes was dissolved and eventually electrodeposited on bismuth film-modified glassy carbon electrode (BFE). Taking use of this cytosensor, one ensured the screening of GBC and limit of detection for CTCs approaches to ～10 cells/mL. Furthermore, by monitoring phenotypic changes of CTCs after drug treatment, the diagnosis of chemoresistance was achieved by our cytosensor.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Intratumoral heterogeneity affects tumor regression and Ki67 proliferation index in perioperatively treated gastric carcinoma

BACKGROUND

Intratumoral heterogeneity (ITH) is a major problem in gastric cancer (GC). We tested Ki67 and tumor regression for ITH after neoadjuvant/perioperative chemotherapy.

METHODS

429 paraffin blocks were obtained from 106 neoadjuvantly/perioperatively treated GCs (one to five blocks per case). Serial sections were stained with Masson's trichrome, antibodies directed against cytokeratin and Ki67, and finally digitalized. Tumor regression and three different Ki67 proliferation indices (PI), i.e., maximum PI (KiH), minimum PI (KiL), and the difference between KiH/KiL (KiD) were obtained per block. Statistics were performed in a block-wise (all blocks irrespective of their case-origin) and case-wise manner.

RESULTS

Ki67 and tumor regression showed extensive ITH in our series (maximum ITH within a case: 31% to 85% for KiH; 4.5% to 95.6% for tumor regression). In addition, Ki67 was significantly associated with tumor regression (p < 0.001). Responders (<10% residual tumor, p = 0.016) exhibited prolonged survival. However, there was no significant survival benefit after cut-off values were increased ≥20% residual tumor mass. Ki67 remained without prognostic value.

CONCLUSIONS

Digital image analysis in tumor regression evaluation might help overcome inter- and intraobserver variability and validate classification systems. Ki67 may serve as a sensitivity predictor for chemotherapy and an indicator of ITH.

JAK-STAT Signaling in Inflammatory Breast Cancer Enables Chemotherapy-Resistant Cell States

Inflammatory breast cancer (IBC) is a difficult-to-treat disease with poor clinical outcomes due to high risk of metastasis and resistance to treatment. In breast cancer, CD44+CD24- cells possess stem cell-like features and contribute to disease progression, and we previously described a CD44+CD24-pSTAT3+ breast cancer cell subpopulation that is dependent on JAK2/STAT3 signaling. Here we report that CD44+CD24- cells are the most frequent cell type in IBC and are commonly pSTAT3+. Combination of JAK2/STAT3 inhibition with paclitaxel decreased IBC xenograft growth more than either agent alone. IBC cell lines resistant to paclitaxel and doxorubicin were developed and characterized to mimic therapeutic resistance in patients. Multi-omic profiling of parental and resistant cells revealed enrichment of genes associated with lineage identity and inflammation in chemotherapy-resistant derivatives. Integrated pSTAT3 chromatin immunoprecipitation sequencing and RNA sequencing (RNA-seq) analyses showed pSTAT3 regulates genes related to inflammation and epithelial-to-mesenchymal transition (EMT) in resistant cells, as well as PDE4A, a cAMP-specific phosphodiesterase. Metabolomic characterization identified elevated cAMP signaling and CREB as a candidate therapeutic target in IBC. Investigation of cellular dynamics and heterogeneity at the single cell level during chemotherapy and acquired resistance by CyTOF and single cell RNA-seq identified mechanisms of resistance including a shift from luminal to basal/mesenchymal cell states through selection for rare preexisting subpopulations or an acquired change. Finally, combination treatment with paclitaxel and JAK2/STAT3 inhibition prevented the emergence of the mesenchymal chemo-resistant subpopulation. These results provide mechanistic rational for combination of chemotherapy with inhibition of JAK2/STAT3 signaling as a more effective therapeutic strategy in IBC.

SIGNIFICANCE

Chemotherapy resistance in inflammatory breast cancer is driven by the JAK2/STAT3 pathway, in part via cAMP/PKA signaling and a cell state switch, which can be overcome using paclitaxel combined with JAK2 inhibitors.

Inhibition of 6-Phosphogluconate Dehydrogenase Reverses Epirubicin Resistance Through Metabolic Reprograming in Triple-Negative Breast Cancer Cells

At present, chemotherapy is the most effective strategy for treating triple-negative breast cancer (TNBC), but its efficacy was limited by the development of chemo-resistance. The exact mechanism of chemoresistance still remains unclear. This study aims to examine whether 6-phosphogluconate dehydrogenase (6PGD), a key enzyme in the oxidative pentose phosphate pathway (PPP), could promote the resistance of TNBC cells to epirubicin. A TNBC epirubicin-resistant cell line was developed by increasing concentration and the effectiveness was tested. The expression and knockdown efficiency of 6PGD were further validated by performing quantitative real-time PCR (qPCR) and Western blot. The effects of 6PGD on parental and drug-resistant TNBC cell lines were verified based on proliferation and apoptosis experiments. Finally, nicotinamide adenine dinucleotide phosphate (NADPH) and lactate quantitative experiments were performed to examine the mechanism of 6PGD in promoting drug resistance. Epirubicin-resistant cancer cells exhibited a higher level of 6PGD in contrast to epirubicin-sensitive cells. In addition, 6PGD inhibited by genetic and pharmacological approaches significantly suppressed the growth and survival of both epirubicin-sensitive and epirubicin-resisteant TNBC cells. It should be noted that 6PGD inhibition sensitized epirubicin-resistant TNBC cells to epirubicin treatment. Moreover, it was also found that the levels of NADPH and lactate increased in epirubicin-resistant TNBC cells but decreased in response to 6PGD inhibition. The present results indicated that 6PGD inhibition disrupted metabolic reprogramming in epirubicin-resistant TNBC cells. Our work demonstrated that 6PGD inhibition reversed the resistance of TNBC cells to epirubicin, providing an alternative therapeutic choice to tackle the challenge of epirubicin resistance in TNBC treatment.

Evaluation of single-cell RNAseq labelling algorithms using cancer datasets

Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generally achieved higher performance and were faster compared to cluster-based methods. Cluster-based methods more successfully labelled non-malignant cell types, likely because of a lack of gene signatures for relevant malignant cell subpopulations. Larger cell numbers present in some cell types in training data positively impacted prediction scores for cell-based methods. Finally, we examined which methods performed favorably when trained and tested on separate patient cohorts in scenarios similar to clinical applications, and which were able to accurately label particularly small or under-represented cell populations in the given datasets. We conclude that scPred and SVM show the best overall performances with cancer-specific data and provide further suggestions for algorithm selection. Our analysis pipeline for assessing the performance of cell type labelling algorithms is available in https://github.com/shooshtarilab/scRNAseq-Automated-Cell-Type-Labelling.

Engineering chimeric antigen receptor T cells for solid tumour therapy

Cell-based immunotherapy, for example, chimeric antigen receptor T (CAR-T) cell immunotherapy, has revolutionized cancer treatment, particularly for blood cancers. However, factors such as insufficient T cell tracking, tumour heterogeneity, inhibitory tumour microenvironment (TME) and T cell exhaustion limit the broad application of CAR-based immunotherapy for solid tumours. In particular, the TME is a complex and evolving entity, which is composed of cells of different types (e.g., cancer cells, immune cells and stromal cells), vasculature, soluble factors and extracellular matrix (ECM), with each component playing a critical role in CAR-T immunotherapy. Thus, developing approaches to mitigate the inhibitory TME factors is critical for future success in applying CAR-T cells for solid tumour treatment. Accordingly, understanding the bilateral interaction of CAR-T cells with the TME is in pressing need to pave the way for more efficient therapeutics. In the following review, we will discuss TME-associated aspects with an emphasis on T cell trafficking, ECM barriers, abnormal vasculature, solid tumour heterogenicity and immune suppressive microenvironment. We will then summarize current engineering strategies to overcome the challenges posed by the TME-associated factors. Lastly, the future directions for engineering efficient CAR-T cells for solid tumour therapy will be discussed.

Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics

OBJECTIVE

This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers.

METHODS

Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity.

RESULTS

With an individually optimized dose (range = 12.11-15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17).

CONCLUSION

A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis.

SIGNIFICANCE

Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development

Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.

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.

Improved T-cell Immunity Following Neoadjuvant Chemotherapy in Ovarian Cancer

PURPOSE

Although local tissue-based immune responses are critical for elucidating direct tumor-immune cell interactions, peripheral immune responses are increasingly recognized as occupying an important role in anticancer immunity. We evaluated serial blood samples from patients with advanced epithelial ovarian cancer (EOC) undergoing standard-of-care neoadjuvant carboplatin and paclitaxel chemotherapy (including dexamethasone for prophylaxis of paclitaxel-associated hypersensitivity reactions) to characterize the evolution of the peripheral immune cell function and composition across the course of therapy.

EXPERIMENTAL DESIGN

Serial blood samples from 10 patients with advanced high-grade serous ovarian cancer treated with neoadjuvant chemotherapy (NACT) were collected before the initiation of chemotherapy, after the third and sixth cycles, and approximately 2 months after completion of chemotherapy. T-cell function was evaluated using ex vivo IFNγ ELISpot assays, and the dynamics of T-cell repertoire and immune cell composition were assessed using bulk and single-cell RNA sequencing (RNAseq).

RESULTS

T cells exhibited an improved response to viral antigens after NACT, which paralleled the decrease in CA125 levels. Single-cell analysis revealed increased numbers of memory T-cell receptor (TCR) clonotypes and increased central memory CD8+ and regulatory T cells throughout chemotherapy. Finally, administration of NACT was associated with increased monocyte frequency and expression of HLA class II and antigen presentation genes; single-cell RNAseq analyses showed that although driven largely by classical monocytes, increased class II gene expression was a feature observed across monocyte subpopulations after chemotherapy.

CONCLUSIONS

NACT may alleviate tumor-associated immunosuppression by reducing tumor burden and may enhance antigen processing and presentation. These findings have implications for the successful combinatorial applications of immune checkpoint blockade and therapeutic vaccine approaches in EOC.

Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation

Therapeutic resistance is a fundamental obstacle in cancer treatment. Tumors that initially respond to treatment may have a preexisting resistant subclone or acquire resistance during treatment, making relapse theoretically inevitable. Here, we investigate treatment strategies that may delay relapse using mathematical modeling. We find that for a single-drug therapy, pulse treatment-short, elevated doses followed by a complete break from treatment-delays relapse compared with continuous treatment with the same total dose over a length of time. For tumors treated with more than one drug, continuous combination treatment is only sometimes better than sequential treatment, while pulsed combination treatment or simply alternating between the two therapies at defined intervals delays relapse the longest. These results are independent of the fitness cost or benefit of resistance, and are robust to noise. Machine-learning analysis of simulations shows that the initial tumor response and heterogeneity at the start of treatment suffice to determine the benefit of pulsed or alternating treatment strategies over continuous treatment. Analysis of eight tumor burden trajectories of breast cancer patients treated at Memorial Sloan Kettering Cancer Center shows the model can predict time to resistance using initial responses to treatment and estimated preexisting resistant populations. The model calculated that pulse treatment would delay relapse in all eight cases. Overall, our results support that pulsed treatments optimized by mathematical models could delay therapeutic resistance.

TGF-β induced reprogramming and drug resistance in triple-negative breast cells

BACKGROUND

The development of drug resistance remains to be a major cause of therapeutic failure in breast cancer patients. How drug-sensitive cells first evade drug inhibition to proliferate remains to be fully investigated.

METHODS

Here we characterized the early transcriptional evolution in response to TGF-β in the human triple-negative breast cells through bioinformatical analysis using a published RNA-seq dataset, for which MCF10A cells were treated with 5 ng/ml TGF-β1 for 0 h, 24 h, 48 h and 72 h, and the RNA-seq were performed in biological duplicates. The protein-protein interaction networks of the differentially expressed genes were constructed. KEGG enrichment analysis, cis-regulatory sequence analysis and Kaplan-Meier analysis were also performed to analyze the cellular reprograming induced by TGF-β and its contribution to the survival probability decline of breast cancer patients.

RESULT

Transcriptomic analysis revealed that cell growth was severely suppressed by TGF-β in the first 24 h but this anti-proliferate impact attenuated between 48 h and 72 h. The oncogenic actions of TGF-β happened within the same time frame with its anti-proliferative effects. In addition, sustained high expression of several drug resistance markers was observed after TGF-β treatment. We also identified 17 TGF-β induced genes that were highly correlated with the survival probability decline of breast cancer patients.

CONCLUSION

Together, TGF-β plays an important role in tumorigenesis and the development of drug resistance, which implies potential therapeutic strategies targeting the early-stage TGF-β signaling activities.

HER2 changes to positive after neoadjuvant chemotherapy in breast cancer: A case report and literature review

BACKGROUND

As the most common cancer in women, breast cancer is the leading cause of death. Most patients are initially diagnosed as stage I-III. Among those without distant metastases, 64% are local tumors and 27% are regional tumors. Patients in stage IIA-IIIC and those who meet the breast-conserving criterion with the exception of tumor size can consider neoadjuvant chemotherapy (NACT). It is worth noting that the status of tumor cell biomarkers is not consistently static. Endocrine-related estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) encoded by erythroblastic leukemia viral oncogene homolog 2 gene can all alter from positive to negative or vice versa, especially in luminal B subtype after NACT. In addition, determination of HER2 status currently mainly relies on immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), but FISH is commonly used when the result of IHC is uncertain. HER2 is regarded as negative when the IHC result is 0/1+ without the addition of FISH. To the best of our knowledge, this is the first report of a case harboring HER2 status transformation and IHC1+ with positive amplification by FISH after NACT.

CASE SUMMARY

A 49-year-old woman discovered a mass in her right breast and underwent diagnostic workup. Biopsies of the right breast lesion and axillary lymph nodes were obtained. The results pointed to invasive ductal carcinoma with the IHC result for ER (80%), PR (60%), Ki-67 (20%) and ambiguous expression of HER2 (IHC 2+) with negative amplification by FISH (HER2/CEP17 ratio of 1.13). She underwent surgery after NACT. The pathological findings of the surgically resected sample supported invasive ductal carcinoma with the tumor measuring 1.1 cm × 0.8 cm × 0.5 cm and had spread to one of fifteen dissected lymph nodes. Retesting of the specimen showed that the tumor was positive for ER (2+, 85%) and PR (2+, 10%) but negative for HER2 by IHC (1+). Also Ki-67 had dropped to 2%. The patient was regularly monitored every 3 mo without evidence of recurrence.

CONCLUSION

Biomarker status should be reassessed after NACT especially in luminal subtypes.

The Cell-Isolation Capsules with Rod-Like Channels Ensure the Survival and Response of Cancer Cells to Their Microenvironment

Current macrocapsules with semipermeable but immunoprotective polymeric membranes are attractive devices to achieve the purpose of immunoisolation, however, their ability to allow diffusion of essential nutrients and oxygen is limited, which leads to a low survival rate of encapsulated cells. Here, a novel method is reported by taking advantage of thermotropic liquid crystals, sodium laurylsulfonate (SDS) liquid crystals (LCs), and rod-like crystal fragments (LCFs) to develop engineered alginate hydrogels with rod-like channels. This cell-isolation capsule with an engineered alginate hydrogel-wall allows small molecules, large molecules, and bacteria to diffuse out from the capsules freely but immobilizes the encapsulated cells inside and prevents cells in the microenvironment from moving in. The encapsulated cells show a high survival rate with isolation of host immune cells and long-term growth with adequate nutrients and oxygen supply. In addition, by sharing and responding to the normal molecular and vesicular microenvironment (NMV microenvironment), encapsulated cancer cells display a transition from tumorous phenotypes to ductal features of normal epithelial cells. Thus, this device will be potentially useful for clinical application in cell therapy by secreting molecules and for establishment of patient-derived xenograft (PDX) models that are often difficult to achieve for certain types of tumors, such as prostate cancer.

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.

Research advances and new challenges in overcoming triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a pathological term used to identify invasive breast cancers that lack expression of estrogen and progesterone receptors and do not have pathologic overexpression of the HER2 receptor or harbor ERBB2 gene amplification. TNBC includes a collection of multiple distinct disease entities based upon genomic, transcriptomic and phenotypic characterization. Despite improved clinical outcomes with the development of novel therapeutics, TNBC still yields the worst prognosis among all clinical subtypes of breast cancer. We will systematically review evidence of the genomic evolution of TNBC, as well as potential mechanisms of disease progression and treatment resistance, defined in part by advances in next-generation DNA sequencing technology (including single cell sequencing), providing a new perspective on treatment strategies, and promise to reveal new potential therapeutic targets. Moreover, we review novel therapies aimed at homologous recombination deficiency, PI3 kinase/AKT/PTEN pathway activation, androgen receptor blockade, immune checkpoint inhibition, as well as antibody-drug conjugates engaging novel cell surface targets, including recent progress in pre-clinical and clinical studies which further validate the role of targeted therapies in TNBC. Despite major advances in treatment for TNBC, including FDA approval of 2 PARP inhibitors for metastatic TNBC, the crossing of the superiority boundary in a phase 3, placebo-controlled study of adjuvant olaparib in early-stage patients with germline BRCA-mutated high-risk HER2-negative early breast cancer, the FDA approval of 2 PD-(L)1 checkpoint antibodies for metastatic TNBC, and the FDA approval of the first antibody drug conjugate for TNBC, significant challenges remain. For example, despite the dawn of immunotherapy in metastatic TNBC, durable responses are limited to a small subset of patients, definitive biomarkers for patient selection are lacking, and the Oncology Drug Advisory Committee to the FDA has recently voted against approval of an anti-PD-1 checkpoint antibody high risk early-stage TNBC in the neoadjuvant setting. Also, despite early positive randomized phase 2 studies of AKT inhibition in metastatic TNBC, a recent phase 3 registration trial failed to validate earlier phase 2 data. Finally, we note that level one evidence for clinical efficacy of androgen receptor blockade in TNBC is still lacking. To meet these and other challenges, we will catalogue the ongoing exponential increase in interest in basic, translational, and clinical research to develop new treatment paradigms for TNBC.

Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients

Breast cancer (BC) categorized as human epidermal growth factor receptor 2 (HER2) borderline [2+ by immunohistochemistry (IHC 2+)] presents challenges for the testing, frequently obscured by intratumoral heterogeneity (ITH). This leads to difficulties in therapy decisions. We aimed to establish prognostic models of overall survival (OS) of these patients, which take into account spatial aspects of ITH and tumor microenvironment by using hexagonal tiling analytics of digital image analysis (DIA). In particular, we assessed the prognostic value of Immunogradient indicators at the tumor–stroma interface zone (IZ) as a feature of antitumor immune response. Surgical excision samples stained for estrogen receptor (ER), progesterone receptor (PR), Ki67, HER2, and CD8 from 275 patients with HER2 IHC 2+ invasive ductal BC were used in the study. DIA outputs were subsampled by HexT for ITH quantification and tumor microenvironment extraction for Immunogradient indicators. Multiple Cox regression revealed HER2 membrane completeness (HER2 MC) (HR: 0.18, p = 0.0007), its spatial entropy (HR: 0.37, p = 0.0341), and ER contrast (HR: 0.21, p = 0.0449) as independent predictors of better OS, with worse OS predicted by pT status (HR: 6.04, p = 0.0014) in the HER2 non-amplified patients. In the HER2-amplified patients, HER2 MC contrast (HR: 0.35, p = 0.0367) and CEP17 copy number (HR: 0.19, p = 0.0035) were independent predictors of better OS along with worse OS predicted by pN status (HR: 4.75, p = 0.0018). In the non-amplified tumors, three Immunogradient indicators provided the independent prognostic value: CD8 density in the tumor aspect of the IZ and CD8 center of mass were associated with better OS (HR: 0.23, p = 0.0079 and 0.14, p = 0.0014, respectively), and CD8 density variance along the tumor edge predicted worse OS (HR: 9.45, p = 0.0002). Combining these three computational indicators of the CD8 cell spatial distribution within the tumor microenvironment augmented prognostic stratification of the patients. In the HER2-amplified group, CD8 cell density in the tumor aspect of the IZ was the only independent immune response feature to predict better OS (HR: 0.22, p = 0.0047). In conclusion, we present novel prognostic models, based on computational ITH and Immunogradient indicators of the IHC biomarkers, in HER2 IHC 2+ BC patients.

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

An Epigenetic Perspective on Intra-Tumour Heterogeneity: Novel Insights and New Challenges from Multiple Fields

Simple Summary

Although research on cancer biology in recent decades has unveiled the main genetic perturbations driving the onset of tumorigenesis, we are still far from properly treating this disease without the occurrence of drug resistance and metastatic burden. This achievement is hampered by the onset of intra-tumour heterogeneity (ITH), which increases cancer cell fitness and plasticity, thereby fostering cell adaptation to foreign environments and stimuli. In this review, we discuss the contribution of the epigenetic factors in sustaining ITH and their interplay with the tumour microenvironment. We also highlight the recent technological advancements that are contributing to defining the epigenetic mechanisms governing tumour heterogeneity at the single-cell level.

Abstract

Cancer is a group of heterogeneous diseases that results from the occurrence of genetic alterations combined with epigenetic changes and environmental stimuli that increase cancer cell plasticity. Indeed, multiple cancer cell populations coexist within the same tumour, favouring cancer progression and metastatic dissemination as well as drug resistance, thereby representing a major obstacle for treatment. Epigenetic changes contribute to the onset of intra-tumour heterogeneity (ITH) as they facilitate cell adaptation to perturbation of the tumour microenvironment. Despite being its central role, the intrinsic multi-layered and reversible epigenetic pattern limits the possibility to uniquely determine its contribution to ITH. In this review, we first describe the major epigenetic mechanisms involved in tumourigenesis and then discuss how single-cell-based approaches contribute to dissecting the key role of epigenetic changes in tumour heterogeneity. Furthermore, we highlight the importance of dissecting the interplay between genetics, epigenetics, and tumour microenvironments to decipher the molecular mechanisms governing tumour progression and drug resistance.

Cancer evolution: Darwin and beyond

Clinical and laboratory studies over recent decades have established branched evolution as a feature of cancer. However, while grounded in somatic selection, several lines of evidence suggest a Darwinian model alone is insufficient to fully explain cancer evolution. First, the role of macroevolutionary events in tumour initiation and progression contradicts Darwin's central thesis of gradualism. Whole-genome doubling, chromosomal chromoplexy and chromothripsis represent examples of single catastrophic events which can drive tumour evolution. Second, neutral evolution can play a role in some tumours, indicating that selection is not always driving evolution. Third, increasing appreciation of the role of the ageing soma has led to recent generalised theories of age-dependent carcinogenesis. Here, we review these concepts and others, which collectively argue for a model of cancer evolution which extends beyond Darwin. We also highlight clinical opportunities which can be grasped through targeting cancer vulnerabilities arising from non-Darwinian patterns of evolution.

Tumor-Specific Delivery of 5-Fluorouracil-Incorporated Epidermal Growth Factor Receptor-Targeted Aptamers as an Efficient Treatment in Pancreatic Ductal Adenocarcinoma Models

BACKGROUNDS & AIMS

Fluoropyrimidine c (5-fluorouracil [5FU]) increasingly represents the chemotherapeutic backbone for neoadjuvant, adjuvant, and palliative treatment of pancreatic ductal adenocarcinoma (PDAC). Even in combination with other agents, 5FU efficacy remains transient and limited. One explanation for the inadequate response is insufficient and nonspecific delivery of 5FU to the tumor.

METHODS

We designed, generated, and characterized 5FU-incorporated systematic evolution of ligands by exponential enrichment (SELEX)-selected epidermal growth factor receptor (EGFR)-targeted aptamers for tumor-specific delivery of 5FU to PDAC cells and tested their therapeutic efficacy in vitro and in vivo.

RESULTS

5FU-EGFR aptamers reduced proliferation in a concentration-dependent manner in mouse and human pancreatic cancer cell lines. Time-lapsed live imaging showed EGFR-specific uptake of aptamers via clathrin-dependent endocytosis. The 5FU-aptamer treatment was equally effective in 5FU-sensitive and 5FU-refractory PDAC cell lines. Biweekly treatment with 5FU-EGFR aptamers reduced tumor burden in a syngeneic orthotopic transplantation model of PDAC, in an autochthonously growing genetically engineered PDAC model (LSL-KrasG12D/+;LSL-Trp53flox/+;Ptf1a-Cre [KPC]), in an orthotopic cell line-derived xenograft model using human PDAC cells in athymic mice (CDX; Crl:NU-Foxn1nu), and in patient-derived organoids. Tumor growth was significantly attenuated during 5FU-EGFR aptamer treatment in the course of follow-up.

CONCLUSIONS

Tumor-specific targeted delivery of 5FU using EGFR aptamers as the carrier achieved high target specificity; overcame 5FU resistance; and proved to be effective in a syngeneic orthotopic transplantation model, in KPC mice, in a CDX model, and in patient-derived organoids and, therefore, represents a promising backbone for pancreatic cancer chemotherapy in patients. Furthermore, our approach has the potential to target virtually any cancer entity sensitive to 5FU treatment by incorporating 5FU into cancer cell-targeting aptamers as the delivery platform.

Untangling Triple-Negative Breast Cancer Molecular Peculiarity and Chemo-Resistance: Trailing Towards Marker-Based Targeted Therapies

Triple-negative breast cancer (TNBC), characterized by the absence of estrogen receptor, progesterone receptor, or human epidermal growth factor receptor-2, affects nearly 15% of women with breast cancer. To date, the mainstay of treatment remains chemotherapy, with all the associated consequences, such as the significant toxicity and the suboptimal effect on the five-year survival rates. RNA-expression profiling showed that TNBC is biologically a heterogeneous malignancy. Therefore, predictive biomarkers matched with the diverse subtypes of TNBC could classify patients that would most benefit from a certain targeted treatment. Three biomarker-driven therapies are currently available: poly-adenosine diphosphate (ADP) ribose polymerase inhibitors for patients with germline BReast CAncer gene (BRCA) mutations, atezolizumab combined with nab-paclitaxel for patients expressing programmed death-ligand 1 (PD-L1) on tumor-infiltrating immune cells, and sacituzumab govitecan, an antibody-drug conjugate targeting human trophoblast cell-surface antigen 2 (TROP-2). Identifying predictive biomarkers is crucial for the optimum generation and implementation of targeted agents for TNBC, while further relevant treatments are in the pipeline given the promising results in clinical trials. Finally, newly developed immunotherapies and other targeted agents should also be investigated in earlier stages of the disease, especially in the neoadjuvant setting, broadening the therapeutic application of such regimens.

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia

Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We, therefore, investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

Frameshift Mutations and Loss of Expression of CLCA4 Gene are Frequent in Colorectal Cancers With Microsatellite Instability

Chloride channel calcium-activated (CLCA) genes encode regulators for chloride transport across the cell membrane. As for cancer development, some CLCA genes are considered putative tumor suppressor genes. The aim of this study was to explore whether CLCA4 gene would have mutations in its nucleotide repeats in colorectal cancer (CRC). In a public database, we found that CLCA4 gene had mononucleotide repeats in the coding sequences that might be mutational targets in the cancers with microsatellite instability. For this, the current study studied 146 CRCs for mutation and expression analyses by single-strand conformation polymorphism analysis, DNA sequencing, and immunohistochemistry. Overall, we found CLCA4 frameshift mutations in 12/101 (11.8%) CRCs with high-microsatellite instability (MSI-H), but none in microsatellite stable CRCs (0/45) (P<0.01). In addition, we analyzed intratumoral heterogeneity of the CLCA4 frameshift mutations and found that 1 CRC harbored regional intratumoral heterogeneity of the CLCA4 frameshift mutation. Loss of CLCA4 protein expression was identified in 50% of CRCs. Also, cancers with MSI-H harboring CLCA4 frameshift mutations showed lower CLCA4 immunostaining than those with the wild-type. Our data indicate that the CLCA4 gene harbors alterations both in somatic mutation and expression, suggesting their roles in tumorigenesis of CRC with MSI-H.

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.

Applications of Topological Data Analysis in Oncology

The emergence of the information age in the last few decades brought with it an explosion of biomedical data. But with great power comes great responsibility: there is now a pressing need for new data analysis algorithms to be developed to make sense of the data and transform this information into knowledge which can be directly translated into the clinic. Topological data analysis (TDA) provides a promising path forward: using tools from the mathematical field of algebraic topology, TDA provides a framework to extract insights into the often high-dimensional, incomplete, and noisy nature of biomedical data. Nowhere is this more evident than in the field of oncology, where patient-specific data is routinely presented to clinicians in a variety of forms, from imaging to single cell genomic sequencing. In this review, we focus on applications involving persistent homology, one of the main tools of TDA. We describe some recent successes of TDA in oncology, specifically in predicting treatment responses and prognosis, tumor segmentation and computer-aided diagnosis, disease classification, and cellular architecture determination. We also provide suggestions on avenues for future research including utilizing TDA to analyze cancer time-series data such as gene expression changes during pathogenesis, investigation of the relation between angiogenic vessel structure and treatment efficacy from imaging data, and experimental confirmation that geometric and topological connectivity implies functional connectivity in the context of cancer.

Investigating Inter- and Intrasample Diversity of Single-Cell RNA Sequencing Datasets

Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for researchers to easily assay tumor heterogeneity at the transcriptomic level with high resolution. However, ongoing research focuses on different ways to analyze this big data and how to compare across multiple different samples. In this chapter, we provide a practical guide to calculate inter- and intrasample diversity metrics from single-cell RNA sequencing datasets. These measures of diversity are adapted from commonly used metrics in statistics and ecology to quantify and compare sample heterogeneity at single-cell resolution.

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.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity

DNA replication is a complex and remarkably robust process: despite its inherent uncertainty, manifested through stochastic replication timing at a single-cell level, multiple control mechanisms ensure its accurate and timely completion across a population. Disruptions in these mechanisms lead to DNA re-replication, closely connected to genomic instability and oncogenesis. Here, we present a stochastic hybrid model of DNA re-replication that accurately portrays the interplay between discrete dynamics, continuous dynamics and uncertainty. Using experimental data on the fission yeast genome, model simulations show how different regions respond to re-replication and permit insight into the key mechanisms affecting re-replication dynamics. Simulated and experimental population-level profiles exhibit a good correlation along the genome, robust to model parameters, validating our approach. At a single-cell level, copy numbers of individual loci are affected by intrinsic properties of each locus, in cis effects from adjoining loci and in trans effects from distant loci. In silico analysis and single-cell imaging reveal that cell-to-cell heterogeneity is inherent in re-replication and can lead to genome plasticity and a plethora of genotypic variations.

Impaired tumor immune response in metastatic tumors is a selective pressure for neutral evolution in CRC cases

A Darwinian evolutionary shift occurs early in the neutral evolution of advanced colorectal carcinoma (CRC), and copy number aberrations (CNA) are essential in the transition from adenoma to carcinoma. In light of this primary evolution, we investigated the evolutionary principles of the genome that foster postoperative recurrence of CRC. CNA and neoantigens (NAG) were compared between early primary tumors with recurrence (CRCR) and early primary tumors without recurrence (precancerous and early; PCRC). We compared CNA, single nucleotide variance (SNV), RNA sequences, and T-cell receptor (TCR) repertoire between 9 primary and 10 metastatic sites from 10 CRCR cases. We found that NAG in primary sites were fewer in CRCR than in PCRC, while the arm level CNA were significantly higher in primary sites in CRCR than in PCRC. Further, a comparison of genomic aberrations of primary and metastatic conditions revealed no significant differences in CNA. The driver mutations in recurrence were the trunk of the evolutionary phylogenic tree from primary sites to recurrence sites. Notably, PD-1 and TIM3, T cell exhaustion-related molecules of the tumor immune response, were abundantly expressed in metastatic sites compared to primary sites along with the increased number of CD8 expressing cells. The postoperative recurrence-free survival period was only significantly associated with the NAG levels and TCR repertoire diversity in metastatic sites. Therefore, CNA with diminished NAG and diverse TCR repertoire in pre-metastatic sites may determine postoperative recurrence of CRC.

We found that copy number aberrations (CNAs) may be the most important selective pressure promoting cancer evolution from early-to-advanced tumors in primary sites. The diminished neoantigens (NAG) in cancer cells and the diverse TCR repertoire in cytotoxic T cells were crucial for the onset of postoperative recurrence during genomic neutral evolution, along with clonal CNA and several driver SNVs from primary to recurrent sites. Therefore, cancer metastasis could be prevented by activating CTL at the premetastatic sites before priming of the metastatic process.

Methodological Advancements for Investigating Intra-tumoral Heterogeneity in Breast Cancer at the Bench and Bedside

There is a major need to overcome therapeutic resistance and metastasis that eventually arises in many breast cancer patients. Therapy resistant and metastatic tumors are increasingly recognized to possess intra-tumoral heterogeneity (ITH), a diversity of cells within an individual tumor. First hypothesized in the 1970s, the possibility that this complex ITH may endow tumors with adaptability and evolvability to metastasize and evade therapies is now supported by multiple lines of evidence. Our understanding of ITH has been driven by recent methodological advances including next-generation sequencing, computational modeling, lineage tracing, single-cell technologies, and multiplexed in situ approaches. These have been applied across a range of specimens, including patient tumor biopsies, liquid biopsies, cultured cell lines, and mouse models. In this review, we discuss these approaches and how they have deepened our understanding of the mechanistic origins of ITH amongst tumor cells, including stem cell-like differentiation hierarchies and Darwinian evolution, and the functional role for ITH in breast cancer progression. While ITH presents a challenge for combating tumor evolution, in-depth analyses of ITH in clinical biopsies and laboratory models hold promise to elucidate therapeutic strategies that should ultimately improve outcomes for breast cancer patients.