Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine

Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

Data-independent acquisition for proteomic applications in early-stage endometrial cancer progression

AIM

Most endometrial cancer (EC) patients are diagnosed at an early-stage (FIGO stage I or II), with a favorable prognosis. However, some high-grade, early-stage EC patients have unexpected recurrences and undesirable results, the molecular alterations that underlie these tumors are far from being fully understood. Our goal was to use proteome analysis to examine dysregulated pathways in this specific subgroup of EC.

METHODS

We used data-independent acquisition (DIA) quantitative proteomics to analyze cancer and matched paracancerous tissues from 20 EC patients (10 high-grade and 10 low-grade). Immunohistochemistry (IHC) analysis was used to validate protein expression of six hub genes.

RESULTS

In total, 7107 proteins were quantified, while 225 downregulated and 366 upregulated proteins in high-grade cancer tissues, 130 downregulated and 413 upregulated proteins in high-grade paracancerous tissues. The pathway associated with oxidative phosphorylation (OXPHOS) was upregulated and have similar expression patterns in high-grade EC tissues and matched paracancerous tissues. OXPHOS-related protein, ATP5F1D showed the best classification and diagnostic ability in distinguishing high-grade from low-grade EC. In both cancer and paracancerous tissues, double-label immunofluorescence demonstrated ITGA4 and COL4A1 co-localized at the basal membrane.

CONCLUSIONS

Our present works elucidate that metabolism reprogramming is associated with high-grade, early-stage EC, particularly OXPHOS is upregulated. Noticeably, the paracancerous tissues have undergone molecular changes similar to cancer tissues, maybe they have signal exchange via secreted proteins (ITGA4 and COL4A1). The upregulation of OXPHOS-related proteins may be the potential biomarker for EC diagnosis, and targeting OXPHOS metabolism might be an effective treatment for high-grade, early-stage EC.

Improving the identification of high-risk non-metastatic castration-resistant prostate cancer patients in clinical practice

Non-metastatic castration-resistant prostate cancer (nmCRPC) represents a challenging disease state in prostate cancer care. nmCRPC patients with a high risk of progression to metastatic disease who are identified by a prostate-specific antigen doubling time (PSADT) ≤10 months are eligible for treatment with the novel androgen receptor inhibitors (ARIs), shown to delay disease progression and extend survival. However, nmCRPC is often unexploited in clinical practice due to a lack of standardization in the methodology and in the tools used for its identification. In this article, a group of Urology and Oncology specialists with acknowledged expertise in prostate cancer reviews the state of the art in the management of high-risk nmCRPC patients, identifies gaps and unmet needs, and proposes strategies to optimize the identification of this patient subgroup in the clinical practice and improve their health outcomes.

Discovery of (quinazolin-6-yl)benzamide derivatives containing a 6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline moiety as potent reversal agents against P-glycoprotein-mediated multidrug resistance

P-glycoprotein (P-gp) is an important factor leading to multidrug resistance (MDR) in cancer treatment. The co-administration of anticancer drugs and P-gp inhibitors has been a treatment strategy to overcome MDR. In recent years, tyrosine kinase inhibitor Lapatinib has been reported to reverse MDR through directly interacting with ABC transporters. In this work, a series of P-gp inhibitors (1-26) was designed and synthesized by integrating the quinazoline core of Lapatinib into the molecule framework of the third-generation P-gp inhibitor Tariquidar. Among them, compound 14 exhibited better MDR reversal activity than Tariquidar. The docking results showed compound 14 displayed the L-shaped molecular conformation. Importantly, compound 14 increased the accumulation of Adriamycin (ADM) and rhodamine 123 (Rh123) in MCF7/ADM cells. Besides, compound 14 significantly increased ADM-induced apoptosis and inhibited the proliferation, migration and invasion of MCF7/ADM cells. It was also demonstrated that compound 14 significantly inhibited the growth of MCF7/ADM xenograft tumors by increasing the sensitivity of ADM. In summary, compound 14 has the potential to overcome MDR caused by P-gp.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Genomic alterations driving precancerous to cancerous lesions in esophageal cancer development

Esophageal squamous cell carcinoma (ESCC) develops through a series of increasingly abnormal precancerous lesions. Previous studies have revealed the striking differences between normal esophageal epithelium and ESCC in copy number alterations (CNAs) and mutations in genes driving clonal expansion. However, due to limited data on early precancerous lesions, the timing of these transitions and which among them are prerequisites for malignant transformation remained unclear. Here, we analyze 1,275 micro-biopsies from normal esophagus, early and late precancerous lesions, and esophageal cancers to decipher the genomic alterations at each stage. We show that the frequency of TP53 biallelic inactivation increases dramatically in early precancerous lesion stage while CNAs and APOBEC mutagenesis substantially increase at late stages. TP53 biallelic loss is the prerequisite for the development of CNAs of genes in cell cycle, DNA repair, and apoptosis pathways, suggesting it might be one of the earliest steps initiating malignant transformation.

Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer

Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Subclonal accumulation of immune escape mechanisms in microsatellite instability-high colorectal cancers

BACKGROUND

Intratumor heterogeneity (ITH) in microsatellite instability-high (MSI-H) colorectal cancer (CRC) has been poorly studied. We aimed to clarify how the ITH of MSI-H CRCs is generated in cancer evolution and how immune selective pressure affects ITH.

METHODS

We reanalyzed public whole-exome sequencing data on 246 MSI-H CRCs. In addition, we performed a multi-region analysis from 6 MSI-H CRCs. To verify the process of subclonal immune escape accumulation, a novel computational model of cancer evolution under immune pressure was developed.

RESULTS

Our analysis presented the enrichment of functional genomic alterations in antigen-presentation machinery (APM). Associative analysis of neoantigens indicated the generation of immune escape mechanisms via HLA alterations. Multiregion analysis revealed the clonal acquisition of driver mutations and subclonal accumulation of APM defects in MSI-H CRCs. Examination of variant allele frequencies demonstrated that subclonal mutations tend to be subjected to selective sweep. Computational simulations of tumour progression with the interaction of immune cells successfully verified the subclonal accumulation of immune escape mutations and suggested the efficacy of early initiation of an immune checkpoint inhibitor (ICI) -based treatment.

CONCLUSIONS

Our results demonstrate the heterogeneous acquisition of immune escape mechanisms in MSI-H CRCs by Darwinian selection, providing novel insights into ICI-based treatment strategies.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) in disease diagnosis: an overview

Tissue analysis, which is essential to histology and is considered the benchmark for the diagnosis and prognosis of many illnesses, including cancer, is significant. During surgery, the surgical margin of the tumor is assessed using the labor-intensive, challenging, and commonly subjective technique known as frozen section histopathology. In the biopsy section, large numbers of molecules can now be visualized at once (ion images) following recent developments in [MSI] mass spectrometry imaging under atmospheric conditions. This is vastly superior to and different from the single optical tissue image processing used in traditional histopathology. This review article will focus on the advancement of desorption electrospray ionization mass spectrometry imaging [DESI-MSI] technique, which is label-free and requires little to no sample preparation. Since the proportion of molecular species in normal and abnormal tissues is different, DESI-MSI can capture ion images of the distributions of lipids and metabolites on biopsy sections, which can provide rich diagnostic information. This is not a systematic review but a summary of well-known, cutting-edge and recent DESI-MSI applications in cancer research between 2018 and 2023.

Challenges and Opportunities in Developing Targeted Therapies for Triple Negative Breast Cancer

Triple negative breast cancer (TNBC) is a heterogeneous group of breast cancers characterized by their lack of estrogen receptors, progesterone receptors, and the HER2 receptor. They are more aggressive than other breast cancer subtypes, with a higher mean tumor size, higher tumor grade, the worst five-year overall survival, and the highest rates of recurrence and metastasis. Developing targeted therapies for TNBC has been a major challenge due to its heterogeneity, and its treatment still largely relies on surgery, radiation therapy, and chemotherapy. In this review article, we review the efforts in developing targeted therapies for TNBC, discuss insights gained from these efforts, and highlight potential opportunities going forward. Accumulating evidence supports TNBCs as multi-driver cancers, in which multiple oncogenic drivers promote cell proliferation and survival. In such multi-driver cancers, targeted therapies would require drug combinations that simultaneously block multiple oncogenic drivers. A strategy designed to generate mechanism-based combination targeted therapies for TNBC is discussed.

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity

Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, and this heterogeneity is a crucial determinant of malignant progression and response to treatments. In addition to genetic diversity and plasticity of cancer cells, the tumor microenvironment contributes to tumor heterogeneity shaping the physical and biological surroundings of the tumor. The activity of certain types of immune, endothelial or mesenchymal cells in the microenvironment can change the effectiveness of cancer therapies via a plethora of different mechanisms. Therefore, deciphering the interactions between the distinct cell types, their spatial organization and their specific contribution to tumor growth and drug sensitivity is still a major challenge. Dissecting intra-tumor heterogeneity is currently an urgent need to better define breast cancer biology and to develop therapeutic strategies targeting the microenvironment as helpful tools for combined and personalized treatment. In this review, we analyze the mechanisms by which the tumor microenvironment affects the characteristics of tumor heterogeneity that ultimately result in drug resistance, and we outline state of the art preclinical models and emerging technologies that will be instrumental in unraveling the impact of the tumor microenvironment on resistance to therapies.

Transcriptomic analysis reveals differential adaptation of colorectal cancer cells to low and acute doses of cisplatin

Over the years, the landscape of cisplatin-based cancer treatment options has undergone continuous transitions. Currently, there is much debate over the optimum dose of cisplatin to be administered to cancer patients. In clinical practice, it can extend from repeated low sub-toxic doses to a few cycles of acute high drug doses. Herein, the molecular understanding of the overall cellular response to such differential doses of cisplatin becomes crucial before any decision making; and it has been a grey area of research. In this study, colorectal cancer (CRC) cells were treated with either- a low sub-toxic dose (LD; 30 µM) or a ten times higher acute dose (HD; 300 µM) of cisplatin, and thereafter, the cellular response was mapped through RNA sequencing followed by transcriptomic analysis. Interestingly, we observed that the tumor cells' response to varying doses of cisplatin is distinctly different, and they activate unique transcriptional programs. The analysis of differentially regulated or uniquely expressed transcripts and corresponding pathways revealed a preferential enrichment of genes associated with chromatin organization, oxidative stress, senescence-associated signaling, and developmentally-active signaling pathways in HD; whereas, modulation of autophagy, protein homeostasis, or differential expression of ABC transporters was primarily enriched in LD. This study is the first of its kind to highlight cellular transcriptomic adaptations to different doses of cisplatin in CRC cells. Consequently, since, protein homeostasis was found to be deeply affected after cisplatin treatment, we further analyzed one of the primary cellular protein homeostatic mechanisms- autophagy. It was activated upon LD, but not HD, and served as a pro-survival strategy through the regulation of oxidative stress. Inhibition of autophagy improved sensitivity to LD. Overall, our study provides a holistic understanding of the distinct molecular signatures induced in CRC cells in response to differential cisplatin doses. These findings might facilitate the design of tailored therapy or appropriate drug dose for enhanced efficacy against CRCs.

Clonal selection parallels between normal and cancer tissues

Clonal selection and drift drive both normal tissue and cancer development. However, the biological mechanisms and environmental conditions underpinning these processes remain to be elucidated. Clonal selection models are centered in Darwinian evolutionary theory, where some clones with the fittest features are selected and populate the tissue or tumor. We suggest that different subclasses of stem cells, each of which is responsible for a distinct feature of the selection process, share common features between normal and cancer conditions. While active stem cells populate the tissue, dormant cells account for tissue replenishment/regeneration in both normal and cancerous tissues. We also discuss potential mechanisms that drive clonal drift, their interactions with clonal selection, and their similarities during normal and cancer tissue development.

Microscopy-based phenotypic monitoring of MDA-MB-231 spheroids allows the evaluation of phenotype-directed therapy

Breast cancer (BC) is the most commonly diagnosed cancer among women. Prognosis has improved over the years, to a large extent, owing to personalized therapy informed by molecular profiling of hormone receptors. However, there is a need for new therapeutic approaches for a subgroup of BCs lacking molecular markers, the Triple Negative Breast Cancer (TNBC) subgroup. TNBC is the most aggressive type of BC, lacks an effective standard of care, shows high levels of resistance and relapse is often inevitable. High resistance to therapy has been hypothesized to be associated with high intratumoral phenotypic heterogeneity. To characterize and treat this phenotypic heterogeneity, we optimized a whole-mount staining and image analysis protocol for three-dimensions (3D) spheroids. Applying this protocol to TNBC spheroids located in the outer region of the spheroid the cells with selected phenotypes: dividing, migrating, and high mitochondrial mass phenotypes. To evaluate the relevance of phenotype-based targeting these cell populations were targeted with Paclitaxel, Trametinib, and Everolimus, respectively, in a dose-dependent manner. Single agents cannot specifically target all phenotypes at the same time. Therefore, we combined drugs that should target independent phenotype. With this rationale we observed that combining Trametinib and Everolimus achieves the highest cytotoxicity at lower doses from all the tested combinations. These findings suggest a rational approach to design treatments can be evaluated in spheroids prior to pre-clinical models and potentially reduce adverse effects.

Metabolically Active Zones Involving Fatty Acid Elongation Delineated by DESI-MSI Correlate with Pathological and Prognostic Features of Colorectal Cancer

Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by ‘gold standard’ histopathology. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive of pro-apoptotic mechanisms, was increased in LVI-negative compared to LVI-positive patients. This study provides evidence of the potential clinical utility of spatially-resolved DESI profiles to enhance the information available to clinicians for CRC diagnosis and prognosis.

Neuronal and tumourigenic boundaries of glioblastoma plasticity

Glioblastoma (GBM) remains the most lethal primary brain cancer largely due to recurrence of treatment-resistant disease. Current therapies are ultimately ineffective as GBM tumour cells adapt their identity to escape treatment. Recent advances in single-cell epigenetics and transcriptomics highlight heterogeneous cell populations in GBM tumours originating from unique cancerous genetic aberrations. However, they also suggest that tumour cells conserve molecular properties of parent neuronal cells, with their permissive epigenetic profiles enabling them to morph along a finite number of reprogramming routes to evade treatment. Here, we review the known tumourigenic, neurodevelopmental and brain-injury boundaries of GBM plasticity, and propose that effective treatment of GBM requires the addition of therapeutics that restrain GBM plasticity.

Cell Adaptive Fitness and Cancer Evolutionary Dynamics

Genome instability of cancer cells translates into increased entropy and lower information processing capacity, leading to metabolic reprograming toward higher energy states, presumed to be aligned with a cancer growth imperative. Dubbed as the cell adaptive fitness, the proposition postulates that the coupling between cell signaling and metabolism constrains cancer evolutionary dynamics along trajectories privileged by the maintenance of metabolic sufficiency for survival. In particular, the conjecture postulates that clonal expansion becomes restricted when genetic alterations induce a sufficiently high level of disorder, that is, high entropy, in the regulatory signaling network, abrogating as a result the ability of cancer cells to successfully replicate, leading to a stage of clonal stagnation. The proposition is analyzed in the context of an in-silico model of tumor evolutionary dynamics to illustrate how cell-inherent adaptive fitness may predictably constrain clonal evolution of tumors, which would have significant implications for the design of adaptive cancer therapies.

Metabolic activity grows in human cancers pushed by phenotypic variability

Different evolutionary processes push cancers to increasingly aggressive behaviors, energetically sustained by metabolic reprogramming. The collective signature emerging from this transition is macroscopically displayed by positron emission tomography (PET). In fact, the most readily PET measure, the maximum standardized uptake value (SUV_max), has been found to have prognostic value in different cancers. However, few works have linked the properties of this metabolic hotspot to cancer evolutionary dynamics. Here, by analyzing diagnostic PET images from 512 patients with cancer, we found that SUV_max scales superlinearly with the mean metabolic activity (SUV_mean), reflecting a dynamic preferential accumulation of activity on the hotspot. Additionally, SUV_max increased with metabolic tumor volume (MTV) following a power law. The behavior from the patients data was accurately captured by a mechanistic evolutionary dynamics model of tumor growth accounting for phenotypic transitions. This suggests that non-genetic changes may suffice to fuel the observed sustained increases in tumor metabolic activity.

Stochastic Fluctuations Drive Non-genetic Evolution of Proliferation in Clonal Cancer Cell Populations

Evolutionary dynamics allows us to understand many changes happening in a broad variety of biological systems, ranging from individuals to complete ecosystems. It is also behind a number of remarkable organizational changes that happen during the natural history of cancers. These reflect tumour heterogeneity, which is present at all cellular levels, including the genome, proteome and phenome, shaping its development and interrelation with its environment. An intriguing observation in different cohorts of oncological patients is that tumours exhibit an increased proliferation as the disease progresses, while the timescales involved are apparently too short for the fixation of sufficient driver mutations to promote explosive growth. Here, we discuss how phenotypic plasticity, emerging from a single genotype, may play a key role and provide a ground for a continuous acceleration of the proliferation rate of clonal populations with time. We address this question by combining the analysis of real-time growth of non-small-cell lung carcinoma cells (N-H460) together with stochastic and deterministic mathematical models that capture proliferation trait heterogeneity in clonal populations to elucidate the contribution of phenotypic transitions on tumour growth dynamics.

Citrullinated glucose-regulated protein 78 is a candidate target for melanoma immunotherapy

Introduction

Post translational modification of proteins plays a significant role in immune recognition. In particular the modification of arginine to citrulline which is mediated by PAD enzymes is increased during cellular stress (autophagy) which permits the presentation of modified epitopes upon MHC class II molecules for recognition by CD4 T cells. Citrullination also occurs in tumour cells as a result of continuous environmental stresses and increased autophagy. We have shown in animal models the efficient stimulation of citrullinated epitope specific CD4 T cells resulting in dramatic elimination/regression of tumours. The ER chaperone glucose-regulated protein 78 (GRP78) is known to also be required for stress-induced autophagy and is directly linked to autophagosome formation. GRP78 is known to be highly expressed by many tumour types. In this study we investigate the potential of targeting citrullinated GRP78 for cancer therapy.

Methods

A citrullinated GRP78 specific antibody was used to assess citrullinated GRP78 expression in murine and human tumour cells by flow cytometry. Five peptides were selected and used to vaccinate HLA transgenic mice and immune responses were characterised by ex vivo cytokine ELISpot assay. T cell repertoire in humans was assessed through proliferation assays and cytokine ELISpot assay. Citrullinated peptide was identified in murine B16 melanoma by mass spectrometry and the peptide vaccine was assessed for tumour therapy in a mouse melanoma model.

Results

We show the identification CD4 T cell responses to one citrullinated GRP78 epitope that are restricted through HLA DP*0401 and HLA-DR*0101 alleles. This peptide is detected by mass spectrometry in B16 melanoma grown in vivo and citrulline specific CD4 responses to two peptides spanning this epitope mediate efficient therapy of established B16 melanoma tumours in HHDII/DP4 (p<0.0001) transgenic mouse model. Finally, we demonstrate the existence of a repertoire of responses to the citrullinated GRP78 peptide in healthy individuals (p=0.0023) with 13/17 (76%) individuals showing a response to this peptide.

Conclusion

We propose that citrullinated GRP78 is a candidate tumour antigen and vaccination against citrullinated GRP78 may provide a promising tumour therapy approach.

Chromosomal Instability, Selection and Competition: Factors That Shape the Level of Karyotype Intra-Tumor Heterogeneity

Simple Summary

Each cancer consists of billions of cells. These cells are far from identical; hence, the population of cells that constitute a tumor is heterogeneous. A salient property that varies between cells in a tumor is their karyotype, the number and configuration of the chromosomes. The level of karyotype heterogeneity can be used to predict the survival of a patient. In this review, we describe the processes that shape the level of karyotype heterogeneity in a cancer.

Abstract

Intra-tumor heterogeneity (ITH) is a pan-cancer predictor of survival, with high ITH being correlated to a dismal prognosis. The level of ITH is, hence, a clinically relevant characteristic of a malignancy. ITH of karyotypes is driven by chromosomal instability (CIN). However, not all new karyotypes generated by CIN are viable or competitive, which limits the amount of ITH. Here, we review the cellular processes and ecological properties that determine karyotype ITH. We propose a framework to understand karyotype ITH, in which cells with new karyotypes emerge through CIN, are selected by cell intrinsic and cell extrinsic selective pressures, and propagate through a cancer in competition with other malignant cells. We further discuss how CIN modulates the cell phenotype and immune microenvironment, and the implications this has for the subsequent selection of karyotypes. Together, we aim to provide a comprehensive overview of the biological processes that shape the level of karyotype heterogeneity.

Advances in single-cell sequencing technology in the field of hepatocellular carcinoma

Tumors are a class of diseases characterized by altered genetic information and uncontrolled growth. Sequencing technology provide researchers with a better way to explore specific tumor pathogenesis. In recent years, single-cell sequencing technology has shone in tumor research, especially in the study of liver cancer, revealing phenomena that were unexplored by previous studies. Single-cell sequencing (SCS) is a technique for sequencing the cellular genome, transcriptome, epigenome, proteomics, or metabolomics after dissociation of tissues into single cells. Compared with traditional bulk sequencing, single-cell sequencing can dissect human tumors at single-cell resolution, finely delineate different cell types, and reveal the heterogeneity of tumor cells. In view of the diverse pathological types and complex pathogenesis of hepatocellular carcinoma (HCC), the study of the heterogeneity among tumor cells can help improve its clinical diagnosis, treatment and prognostic judgment. On this basis, SCS has revolutionized our understanding of tumor heterogeneity, tumor immune microenvironment, and clonal evolution of tumor cells. This review summarizes the basic process and development of single-cell sequencing technology and its increasing role in the field of hepatocellular carcinoma.

The Value of Phenotypic Precision Medicine in Prostate Cancer

Prostate cancer is the most common cancer among men and the second leading cause of cancer-related death. For patients who develop metastatic disease, tissue-based and circulating-tumor-based molecular and genomic biomarkers have emerged as a means of improving outcomes through the application of precision medicine. However, the benefit is limited to a minority of patients. An additional approach to further characterize the biology of advanced prostate cancer is through the use of phenotypic precision medicine, or the identification and targeting of phenotypic features of an individual patient's cancer. In this review article, we will discuss the background, potential clinical benefits, and limitations of genomic and phenotypic precision medicine in prostate cancer. We will also highlight how the emergence of image-based phenotypic medicine may lead to greater characterization of advanced prostate cancer disease burden and more individualized treatment approaches in patients.

Discovering significant evolutionary trajectories in cancer phylogenies

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Spend less to achieve more: Economic analysis of intermittent versus continuous cetuximab in KRAS wild-type patients with metastatic colorectal cancer

BACKGROUND

In 2014, the COIN-B clinical trial demonstrated that intermittent cetuximab (IC) was a safe alternative to continuous cetuximab (CC), with less cytotoxic chemotherapy, in first-line treatment for KRAS wild-type metastatic colorectal cancer (mCRC). Cetuximab has been available for this indication in England since 2015, but treatment breaks beyond 6 weeks were prohibited, despite real-world evidence that therapy de-escalation maintains equivalent disease control, but with superior Quality-of-Life (QoL). We performed health economic analyses of IC versus CC and used this evidence to help underpin policy change and guide clinical practice through reduction in unnecessary treatment for mCRC patients.

METHODS

Employing cost-minimization analysis, we conducted partitioned survival modelling (PSM) and Markov Chain Monte-Carlo (MCMC) simulation to determine costs and quality-adjusted-life-years for IC versus CC.

RESULTS

IC reduced costs by £ 35,763 (PSM; p < 0.001) or £ 30,189 (MCMC) per patient annually, while preserving treatment efficacy and enhancing QoL. Extrapolating to all mCRC patients eligible for cetuximab therapy would have generated cost savings of ~£ 1.2 billion over this cohort's lifetime. These data helped underpin a request to NHS England to remove treatment break restrictions in first-line mCRC therapy, which has been adopted as an interim treatment option policy in colorectal cancer during the Covid-19 pandemic.

CONCLUSIONS

Our results highlight substantial cost savings achievable by treatment de-escalation, while also reinforcing the importance of therapy breaks to potentially increase tumour responsiveness and reduce treatment toxicity. Our study also highlights how health economic evidence can influence health policy, championing reduced treatment intensity approaches without compromising patient outcomes, which is of particular relevance when addressing the reduced capacity and treatment backlogs experienced during the pandemic.

Predicting cancer evolution for patient benefit: Renal cell carcinoma paradigm

Evolutionary features of cancer have important clinical implications, but their evaluation in the clinic is currently limited. Here, we review current approaches to reconstruct tumour subclonal structure and discuss tumour sampling method and experimental design influence. We describe clear-cell renal cell carcinoma (ccRCC) as an exemplar for understanding and predicting cancer evolutionary dynamics. Finally, we discuss how understanding cancer evolution can benefit patients.

Mitochondria dysfunction in circulating tumor cells

Circulating tumor cells (CTCs) represent a subset of heterogeneous cells, which, once released from a tumor site, have the potential to give rise to metastasis in secondary sites. Recent research focused on the attempt to detect and characterize these rare cells in the circulation, and advancements in defining their molecular profile have been reported in diverse tumor species, with potential implications for clinical applications. Of note, metabolic alterations, involving mitochondria, have been implicated in the metastatic process, as key determinants in the transition of tumor cells to a mesenchymal or stemness-like phenotype, in drug resistance, and in induction of apoptosis. This review aimed to briefly analyse the most recent knowledge relative to mitochondria dysfunction in CTCs, and to envision implications of altered mitochondria in CTCs for a potential utility in clinics.

Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning

Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of cancer, generating network features for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applied to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping to be biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer, and individualized treatment plan selection.

Transcriptomic Analysis of Canine Osteosarcoma from a Precision Medicine Perspective Reveals Limitations of Differential Gene Expression Studies

Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. The difficulty in managing OSA lies in its extreme genetic complexity, drug resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. The characterization of differentially expressed genes (DEGs) unique to the tumor provides insight into the phenotype and can be useful for informing appropriate therapies as well as the development of novel treatments. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics to the human disease and therefore serves as an excellent model. In this study, we use transcriptomic sequencing of RNA isolated from primary OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels across patients to identify DEGs at the individual level. These results can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals.

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning

Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks-substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.

High-dimensional role of AI and machine learning in cancer research

The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here named as 'Intelligent Systems' (IS), leverage unprecedented data volumes and aim to model their underlying heterogeneous influences and variables correlated with patients' outcomes. As functionality and performance of IS are associated with complex diagnosis and therapy decisions, a rich spectrum of patterns and features detected in high-dimensional data may be critical for inference purposes. Many challenges are also present in such discovery task. First, the generation of interpretable model results from a mix of structured and unstructured input information. Second, the design, and implementation of automated clinical decision processes for drawing disease trajectories and patient profiles. Ultimately, the clinical impacts depend on the data effectively subjected to steps such as harmonisation, integration, validation, etc. The aim of this work is to discuss the transformative value of IS applied to multimodal data acquired through various interrelated cancer domains (high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc.).

Analysis of Transcriptomic Data Generated from Drug-Treated Cancer Cell Line

Resistance to chemotherapy is one major obstacle in current cancer treatment. Therefore, understanding the molecular basis of the acquisition of resistance is vital for the design and development of appropriate cancer therapy. Importantly, acquisition of resistance is not a single-step process, and the molecular signature of cells dynamically changes during this process. With the advent of next-generation omic technologies, today one can precisely map the molecular alterations not only in a population of tumor cells but also at the single-cell level as they attain chemo-resistance. In this chapter, we describe a detailed transcriptomic pipeline following next-generation sequencing for mapping alteration in expression during the process of attainment of resistance. We provide comprehensive information on the process to (1) track the differential expression of transcripts, (2) understand the gene ontology functions, (3) filter out candidate key genes, (4) identify the pathways regulated by them, and (5) generate a map of their probable interactions. We assume that our analytical method will be useful for research in this direction.

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question "Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?" or, shortly, "What genotype comes next?". Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting method's use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method's results when key assumptions do not hold.

Shooting at Moving and Hidden Targets-Tumour Cell Plasticity and the Notch Signalling Pathway in Head and Neck Squamous Cell Carcinomas

Head and Neck Squamous Cell Carcinoma (HNSCC) is often aggressive, with poor response to current therapies in approximately 40-50% of the patients. Current therapies are restricted to operation and irradiation, often combined with a small number of standard-of-care chemotherapeutic drugs, preferentially for advanced tumour patients. Only very recently, newer targeted therapies have entered the clinics, including Cetuximab, which targets the EGF receptor (EGFR), and several immune checkpoint inhibitors targeting the immune receptor PD-1 and its ligand PD-L1. HNSCC tumour tissues are characterized by a high degree of intra-tumour heterogeneity (ITH), and non-genetic alterations that may affect both non-transformed cells, such as cancer-associated fibroblasts (CAFs), and transformed carcinoma cells. This very high degree of heterogeneity likely contributes to acquired drug resistance, tumour dormancy, relapse, and distant or lymph node metastasis. ITH, in turn, is likely promoted by pronounced tumour cell plasticity, which manifests in highly dynamic and reversible phenomena such as of partial or hybrid forms of epithelial-to-mesenchymal transition (EMT), and enhanced tumour stemness. Stemness and tumour cell plasticity are strongly promoted by Notch signalling, which remains poorly understood especially in HNSCC. Here, we aim to elucidate how Notch signal may act both as a tumour suppressor and proto-oncogenic, probably during different stages of tumour cell initiation and progression. Notch signalling also interacts with numerous other signalling pathways, that may also have a decisive impact on tumour cell plasticity, acquired radio/chemoresistance, and metastatic progression of HNSCC. We outline the current stage of research related to Notch signalling, and how this pathway may be intricately interconnected with other, druggable targets and signalling mechanisms in HNSCC.

CRISPR and KRAS: a match yet to be made

CRISPR (clustered regularly interspaced short palindromic repeats) systems are one of the most fascinating tools of the current era in molecular biotechnology. With the ease that they provide in genome editing, CRISPR systems generate broad opportunities for targeting mutations. Specifically in recent years, disease-causing mutations targeted by the CRISPR systems have been of main research interest; particularly for those diseases where there is no current cure, including cancer. KRAS mutations remain untargetable in cancer. Mutations in this oncogene are main drivers in common cancers, including lung, colorectal and pancreatic cancers, which are severe causes of public health burden and mortality worldwide, with no cure at hand. CRISPR systems provide an opportunity for targeting cancer causing mutations. In this review, we highlight the work published on CRISPR applications targeting KRAS mutations directly, as well as CRISPR applications targeting mutations in KRAS-related molecules. In specific, we focus on lung, colorectal and pancreatic cancers. To date, the limited literature on CRISPR applications targeting KRAS, reflect promising results. Namely, direct targeting of mutant KRAS variants using various CRISPR systems resulted in significant decrease in cell viability and proliferation in vitro, as well as tumor growth inhibition in vivo. In addition, the effect of mutant KRAS knockdown, via CRISPR, has been observed to exert regulatory effects on the downstream molecules including PI3K, ERK, Akt, Stat3, and c-myc. Molecules in the KRAS pathway have been subjected to CRISPR applications more often than KRAS itself. The aim of using CRISPR systems in these studies was mainly to analyze the therapeutic potential of possible downstream and upstream effectors of KRAS, as well as to discover further potential molecules. Although there have been molecules identified to have such potential in treatment of KRAS-driven cancers, a substantial amount of effort is still needed to establish treatment strategies based on these discoveries. We conclude that, at this point in time, despite being such a powerful directed genome editing tool, CRISPR remains to be underutilized for targeting KRAS mutations in cancer. Efforts channelled in this direction, might pave the way in solving the long-standing challenge of targeting the KRAS mutations in cancers.

Extracellular Matrix Signals as Drivers of Mitochondrial Bioenergetics and Metabolic Plasticity of Cancer Cells During Metastasis

The role of metabolism in tumor growth and chemoresistance has received considerable attention, however, the contribution of mitochondrial bioenergetics in migration, invasion, and metastasis is recently being understood. Migrating cancer cells adapt their energy needs to fluctuating changes in the microenvironment, exhibiting high metabolic plasticity. This occurs due to dynamic changes in the contributions of metabolic pathways to promote localized ATP production in lamellipodia and control signaling mediated by mitochondrial reactive oxygen species. Recent evidence has shown that metabolic shifts toward a mitochondrial metabolism based on the reductive carboxylation, glutaminolysis, and phosphocreatine-creatine kinase pathways promote resistance to anoikis, migration, and invasion in cancer cells. The PGC1a-driven metabolic adaptations with increased electron transport chain activity and superoxide levels are essential for metastasis in several cancer models. Notably, these metabolic changes can be determined by the composition and density of the extracellular matrix (ECM). ECM stiffness, integrins, and small Rho GTPases promote mitochondrial fragmentation, mitochondrial localization in focal adhesion complexes, and metabolic plasticity, supporting enhanced migration and metastasis. Here, we discuss the role of ECM in regulating mitochondrial metabolism during migration and metastasis, highlighting the therapeutic potential of compounds affecting mitochondrial function and selectively block cancer cell migration.

Homologous Recombination Deficiency Assays in Epithelial Ovarian Cancer: Current Status and Future Direction

Epithelial ovarian cancer (EOC) patients are generally diagnosed at an advanced stage, usually relapse after initial treatments, which include debulking surgery and adjuvant platinum-based chemotherapy, and eventually have poor 5-year survival of less than 50%. In recent years, promising survival benefits from maintenance therapy with poly(ADP-ribose) polymerase (PARP) inhibitor (PARPi) has changed the management of EOC in newly diagnosed and recurrent disease. Identification of BRCA mutations and/or homologous recombination deficiency (HRD) is critical for selecting patients for PARPi treatment. However, the currently available HRD assays are not perfect predictors of the clinical response to PARPis in EOC patients. In this review, we introduce the concept of synthetic lethality, the rationale of using PARPi when HRD is present in tumor cells, the clinical trials of PARPi incorporating the HRD assays for EOC, the current HRD assays, and other HRD assays in development.

Characterizing Intra-Tumor Heterogeneity From Somatic Mutations Without Copy-Neutral Assumption

Bulk samples of the same patient are heterogeneous in nature, comprising of different subpopulations (subclones) of cancer cells. Cells in a tumor subclone are characterized by unique mutational genotype profile. Resolving tumor heterogeneity by estimating the genotypes, cellular proportions and the number of subclones present in the tumor can help in understanding cancer progression and treatment. We present a novel method, ChaClone2, to efficiently deconvolve the observed variant allele fractions (VAFs), with consideration for possible effects from copy number aberrations at the mutation loci. Our method describes a state-space formulation of the feature allocation model, deconvolving the observed VAFs from samples of the same patient into three matrices: subclonal total and variant copy numbers for mutated genes, and proportions of subclones in each sample. We describe an efficient sequential Monte Carlo (SMC) algorithm to estimate these matrices. Extensive simulation shows that the ChaClone2 yields better accuracy when compared with other state-of-the-art methods for addressing similar problem and it offers scalability to large datasets. Also, ChaClone2 features that the model parameter estimates can be refined whenever new mutation data of freshly sequenced genomic locations are available. MATLAB code and datasets are available to download at: https://github.com/moyanre/method2.

Clonal Evolution of Multiple Myeloma-Clinical and Diagnostic Implications

Plasma cell dyscrasias are a heterogeneous group of diseases characterized by the expansion of bone marrow plasma cells. Malignant transformation of plasma cells depends on the continuity of events resulting in a sequence of well-defined disease stages, from monoclonal gammopathy of undetermined significance (MGUS) through smoldering myeloma (SMM) to symptomatic multiple myeloma (MM). Evolution of a pre-malignant cell into a malignant cell, as well as further tumor progression, dissemination, and relapse, require development of multiple driver lesions conferring selective advantage of the dominant clone and allowing subsequent evolution under selective pressure of microenvironment and treatment. This process of natural selection facilitates tumor plasticity leading to the formation of genetically complex and heterogenous tumors that are notoriously difficult to treat. Better understanding of the mechanisms underlying tumor evolution in MM and identification of lesions driving the evolution from the premalignant clone is therefore a key to development of effective treatment and long-term disease control. Here, we review recent advances in clonal evolution patterns and genomic landscape dynamics of MM, focusing on their clinical implications.

Toward a Liver Cell Atlas: Understanding Liver Biology in Health and Disease at Single-Cell Resolution

Single-cell technologies are revolutionizing our understanding of cellular heterogeneity and functional diversity in health and disease. Here, we review the current knowledge and advances in liver biology using single-cell approaches. We focus on the landscape of the composition and the function of cells in a healthy liver in the context of its spatial organization. We also highlight the alterations of the molecular landscape in chronic liver disease and liver cancer, which includes the identification of disease-related cell types, altered cellular functions, dynamic cell-cell interactions, the plasticity of malignant cells, the collective behavior of a cell community, and microenvironmental reprogramming. We anticipate that the uncovered liver cell atlas will help deciphering the molecular and cellular mechanisms driving a healthy liver into a disease state. It also offers insight into the detection of new therapeutic targets and paves the way for effective disease interventions.

APOBEC and Cancer Viroimmunotherapy: Thinking the Unthinkable

The apolipoprotein B mRNA editing enzyme catalytic polypeptide (APOBEC) family protects against infection by degrading incoming viral genomes through cytosine deamination. Here, we review how the potential to unleash these potent DNA mutagens comes at a price as APOBEC DNA mutagenesis can contribute to development of multiple types of cancer. In addition, because viral infection induces its expression, APOBEC is seen as the enemy of oncolytic virotherapy through mutation of the viral genome and by generating virotherapy-resistant tumors. Therefore, overall APOBEC in cancer has received very poor press. However, we also speculate how there may be silver linings to the storm clouds (kataegis) associated with APOBEC activity. Thus, although mutagenic genomic chaos promotes emergence of ever more aggressive subclones, it also provides significant opportunity for cytotoxic and immune therapies. In particular, the superpower of cancer immunotherapy derives in part from mutation, wherein generation of tumor neoantigens-neoantigenesis-exposes tumor cells to functional T-cell repertoires, and susceptibility to immune checkpoint blockade. Moreover, APOBECs may be able to induce suprathreshold levels of cellular mutation leading to mitotic catastrophe and direct tumor cell killing. Finally, we discuss the possibility that linking predictable APOBEC-induced mutation with escape from specific frontline therapies could identify mutated molecules/pathways that can be targeted with small molecules and/or immunotherapies in a Trap and Ambush strategy. Together, these considerations lead to the counterintuitive hypothesis that, instead of attempting to expunge and excoriate APOBEC activity in cancer therapy, it might be exploited-and even, counterintuitively, encouraged.

Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations

The effect of scheduling of targeted therapy combinations on drug resistance is underexplored in triple-negative breast cancer (TNBC). TNBC constitutes heterogeneous cancer cell populations the composition of which can change dynamically during treatment resulting in the selection of resistant clones with a fitness advantage. We evaluated crizotinib (ALK/MET inhibitor) and navitoclax (ABT-263; Bcl-2/Bcl-xL inhibitor) combinations in a large design consisting of 696 two-cycle sequential and concomitant treatment regimens with varying treatment dose, duration, and drug holiday length over a 26-day period in MDA-MB-231 TNBC cells and found that patterns of resistance depend on the schedule and sequence in which the drugs are given. Further, we tracked the clonal dynamics and mechanisms of resistance using DNA-integrated barcodes and single-cell RNA sequencing. Our study suggests that longer formats of treatment schedules in vitro screening assays are required to understand the effects of resistance and guide more realistically in vivo and clinical studies.

Mutational signatures impact the evolution of anti-EGFR antibody resistance in colorectal cancer

Anti-EGFR antibodies such as cetuximab are active against KRAS/NRAS wild-type colorectal cancers (CRC) but acquired resistance invariably evolves. Which mutational mechanisms enable resistance evolution and whether adaptive mutagenesis, a transient cetuximab-induced increase in mutation generation, contributes in patients is unknown. Here, we investigate this in exome sequencing data of 42 baseline and progression biopsies from cetuximab treated CRCs. Mutation loads did not increase from baseline to progression and evidence for a contribution of adaptive mutagenesis was limited. However, the chemotherapy-induced mutational signature SBS17b was the main contributor of specific KRAS/NRAS and EGFR driver mutations that are enriched at acquired resistance. Detectable SBS17b activity before treatment predicted for shorter progression free survival and for the evolution of these specific mutations during subsequent cetuximab treatment. This suggests that chemotherapy mutagenesis can accelerate resistance evolution. Mutational signatures may be a new class of cancer evolution predictor.

Adaptive Evolution: How Bacteria and Cancer Cells Survive Stressful Conditions and Drug Treatment

Cancer is characterized by loss of the regulatory mechanisms that preserve homeostasis in multicellular organisms, such as controlled proliferation, cell-cell adhesion, and tissue differentiation. The breakdown of multicellularity rules is accompanied by activation of "selfish," unicellular-like life features, which are linked to the increased adaptability to environmental changes displayed by cancer cells. Mechanisms of stress response, resembling those observed in unicellular organisms, are actively exploited by mammalian cancer cells to boost genetic diversity and increase chances of survival under unfavorable conditions, such as lack of oxygen/nutrients or exposure to drugs. Unicellular organisms under stressful conditions (e.g., antibiotic treatment) stop replicating or slowly divide and transiently increase their mutation rates to foster diversity, a process known as adaptive mutability. Analogously, tumor cells exposed to drugs enter a persister phenotype and can reduce DNA replication fidelity, which in turn fosters genetic diversity. The implications of adaptive evolution are of relevance to understand resistance to anticancer therapies.