Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy.

Cancer cells enter a reversible drug-tolerant persister (DTP) state to evade death from chemotherapy and targeted agents. It is increasingly appreciated that DTPs are important drivers of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer models to identify and characterize DTPs in response to chemotherapy. Barcode analysis revealed no loss of clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fit a mathematical model where all cancer cells, and not a small subpopulation, possess an equipotent capacity to become DTPs. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides insight into how cancer cells use a developmentally conserved mechanism to drive the DTP state, pointing to novel therapeutic opportunities to target DTPs.

An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.

Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment.

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers.

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.

Lung Cancer Heterogeneity and New Strategies for Drug Therapy.

Lung cancer heterogeneity plays an important role in the development of drug resistance. Comprehensive molecular characterizations of lung cancer can describe hereditary and somatic gene changes, mutation, and heterogeneity. We discuss heterogeneity specificity, characterization, and roles of PIK3CD, TP53, and KRAS, as well as target-driven therapies and strategies applied in clinical trials based on a proposed precise self-validation system. The system is a specifically selected strategy of treatment for patients with cancer gene mutations and heterogeneity based on gene sequencing, following validation of the strategies in the patient's own cancer cells or in patient-derived xenografts using their own cancer cells isolated during surgery or biopsies. These results will be more precise if the drugs used in the strategies are selected through protein structure-guided compound screening or a DNA-encoded chemical library before validation in the patient's own cancer cells. Thus, a deeper understanding of heterogeneity mechanisms and improved validation of the therapeutic strategy will result in more precise treatments for patients.

Mechanisms of acquired resistance to rapalogs in metastatic renal cell carcinoma.

The mechanistic target of rapamycin (mTOR) is an established therapeutic target in renal cell carcinoma (RCC). Mechanisms of secondary resistance to rapalog therapy in RCC have not been studied previously. We identified six patients with metastatic RCC who initially responded to mTOR inhibitor therapy and then progressed, and had pre-treatment and post-treatment tumor samples available for analysis. We performed deep whole exome sequencing on the paired tumor samples and a blood sample. Sequence data was analyzed using Mutect, CapSeg, Absolute, and Phylogic to identify mutations, copy number changes, and their changes over time. We also performed in vitro functional assays on PBRM1 in RCC cell lines. Five patients had clear cell and one had chromophobe RCC. 434 somatic mutations in 416 genes were identified in the 12 tumor samples. 201 (46%) of mutations were clonal in both samples while 129 (30%) were acquired in the post-treatment samples. Tumor heterogeneity or sampling issues are likely to account for some mutations that were acquired in the post-treatment samples. Three samples had mutations in TSC1; one in PTEN; and none in MTOR. PBRM1 was the only gene in which mutations were acquired in more than one post-treatment sample. We examined the effect of PBRM1 loss in multiple RCC cell lines, and could not identify any effect on rapalog sensitivity in in vitro culture assays. We conclude that mTOR pathway gene mutations did not contribute to rapalog resistance development in these six patients with advanced RCC. Furthermore, mechanisms of resistance to rapalogs in RCC remain unclear and our results suggest that PBRM1 loss may contribute to sensitivity through complex transcriptional effects.

The temporal dynamics of chromosome instability in ovarian cancer cell lines and primary patient samples.

Epithelial ovarian cancer (EOC) is the most prevalent form of ovarian cancer and has the highest mortality rate. Novel insight into EOC is required to minimize the morbidity and mortality rates caused by recurrent, drug resistant disease. Although numerous studies have evaluated genome instability in EOC, none have addressed the putative role chromosome instability (CIN) has in disease progression and drug resistance. CIN is defined as an increase in the rate at which whole chromosomes or large parts thereof are gained or lost, and can only be evaluated using approaches capable of characterizing genetic or chromosomal heterogeneity within populations of cells. Although CIN is associated with numerous cancer types, its prevalence and dynamics in EOC is unknown. In this study, we assessed CIN within serial samples collected from the ascites of five EOC patients, and in two well-established ovarian cancer cell models of drug resistance (PEO1/4 and A2780s/cp). We quantified and compared CIN (as measured by nuclear areas and CIN Score (CS) values) within and between serial samples to glean insight into the association and dynamics of CIN within EOC, with a particular focus on resistant and recurrent disease. Using quantitative, single cell analyses we determined that CIN is associated with every sample evaluated and further show that many EOC samples exhibit a large degree of nuclear size and CS value heterogeneity. We also show that CIN is dynamic and generally increases within resistant disease. Finally, we show that both drug resistance models (PEO1/4 and A2780s/cp) exhibit heterogeneity, albeit to a much lesser extent. Surprisingly, the two cell line models exhibit remarkably similar levels of CIN, as the nuclear areas and CS values are largely overlapping between the corresponding paired lines. Accordingly, these data suggest CIN may represent a novel biomarker capable of monitoring changes in EOC progression associated with drug resistance.

Tumor-stroma interactions differentially alter drug sensitivity based on the origin of stromal cells.

Due to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear, however, what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor microenvironment. Using a large-scale coculture assay optimized to measure drug-induced cell death, we identify tumor-stroma interactions that modulate drug sensitivity. Our data show that the chemo-insensitivity typically associated with aggressive subtypes of breast cancer is not observed if these cells are grown in 2D or 3D monoculture, but is manifested when these cells are cocultured with stromal cells, such as fibroblasts. Furthermore, we find that fibroblasts influence drug responses in two distinct and divergent manners, associated with the tissue from which the fibroblasts were harvested. These divergent phenotypes occur regardless of the drug tested and result from modulation of apoptotic priming within tumor cells. Our study highlights unexpected diversity in tumor-stroma interactions, and we reveal new principles that dictate how fibroblasts alter tumor drug responses.

Update on the Treatment of Early-Stage Triple-Negative Breast Cancer.

OPINION STATEMENT: Triple-negative breast cancer (TNBC) accounts for 15% of all breast cancers and is associated with poor long-term outcomes compared to other breast cancer subtypes. Currently, chemotherapy remains the main modality of treatment for early-stage TNBC, as there is no approved targeted therapy for this subtype. The biologic heterogeneity of TNBC has hindered the development and evaluation of novel agents, but recent advancements in subclassifying TNBC have paved the way for further investigation of more effective systemic therapies, including cytotoxic and targeted agents. TNBC is enriched for germline BRCA mutation and for somatic deficiencies in homologous recombination DNA repair, the so-called "BRCAness" phenotype. Together, germline BRCA mutations and BRCAness are promising biomarkers of susceptibility to DNA-damaging therapy. Various investigational approaches are consequently being investigated in early-stage TNBC, including immune checkpoint inhibitors, platinum compounds, PI3K pathway inhibitors, and androgen receptor inhibitors. Due to the biological diversity found within TNBC, patient selection based on molecular biomarkers could aid the design of early-phase clinical trials, ultimately accelerating the clinical application of effective new agents. TNBC is an aggressive breast cancer subtype, for which multiple targeted approaches will likely be required for patient outcomes to be substantially improved.

In vivo evidence for ribavirin-induced mutagenesis of the hepatitis E virus genome.

OBJECTIVE: Hepatitis E virus (HEV) infection can take chronic courses in immunocompromised patients potentially leading to liver cirrhosis and liver failure. Ribavirin (RBV) is currently the only treatment option for many patients, but treatment failure can occur which has been associated with the appearance of a distinct HEV polymerase mutant (G1634R). Here, we performed a detailed analysis of HEV viral intrahost evolution during chronic hepatitis E infections. DESIGN: Illumina deep sequencing was performed for the detection of intrahost variation in the HEV genome of chronically infected patients. Novel polymerase mutants were investigated in vitro using state-of-the-art HEV cell culture models. RESULTS: Together, these data revealed that (1) viral diversity differed markedly between patients but did not show major intraindividual short-term variations in untreated patients with chronic hepatitis E, (2) RBV therapy was associated with an increase in viral heterogeneity which was reversible when treatment was stopped, (3) the G1634R mutant was detectable as a minor population prior to therapy in patients who subsequently failed to achieve a sustained virological response to RBV therapy and (4) in addition to G1634R further dominant variants in the polymerase region emerged, impacting HEV replication efficiency in vitro. CONCLUSIONS: In summary, this first investigation of intrahost HEV population evolution indicates that RBV causes HEV mutagenesis in treated patients and that an emergence of distinct mutants within the viral population occurs during RBV therapy. We also suggest that next-generation sequencing could be useful to guide personalised antiviral strategies.

A multi-targeted approach to suppress tumor-promoting inflammation.

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes.

Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.

BACKGROUND: Intratumor heterogeneity may foster tumor evolution and adaptation and hinder personalized-medicine strategies that depend on results from single tumor-biopsy samples. METHODS: To examine intratumor heterogeneity, we performed exome sequencing, chromosome aberration analysis, and ploidy profiling on multiple spatially separated samples obtained from primary renal carcinomas and associated metastatic sites. We characterized the consequences of intratumor heterogeneity using immunohistochemical analysis, mutation functional analysis, and profiling of messenger RNA expression. RESULTS: Phylogenetic reconstruction revealed branched evolutionary tumor growth, with 63 to 69% of all somatic mutations not detectable across every tumor region. Intratumor heterogeneity was observed for a mutation within an autoinhibitory domain of the mammalian target of rapamycin (mTOR) kinase, correlating with S6 and 4EBP phosphorylation in vivo and constitutive activation of mTOR kinase activity in vitro. Mutational intratumor heterogeneity was seen for multiple tumor-suppressor genes converging on loss of function; SETD2, PTEN, and KDM5C underwent multiple distinct and spatially separated inactivating mutations within a single tumor, suggesting convergent phenotypic evolution. Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor. Allelic composition and ploidy profiling analysis revealed extensive intratumor heterogeneity, with 26 of 30 tumor samples from four tumors harboring divergent allelic-imbalance profiles and with ploidy heterogeneity in two of four tumors. CONCLUSIONS: Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor-biopsy samples and may present major challenges to personalized-medicine and biomarker development. Intratumor heterogeneity, associated with heterogeneous protein function, may foster tumor adaptation and therapeutic failure through Darwinian selection. (Funded by the Medical Research Council and others.).

HVR-1 heterogeneity during treatment with telaprevir with or without pegylated interferon alfa-2a.

BACKGROUND: The extensive heterogeneity of the hypervariable region-1 (HVR-1) of hepatitis C virus (HCV) evidences the high genetic flexibility of HCV and was shown to be associated with virologic response to interferon-α-based therapies. However, the evolution of HVR-1 heterogeneity during treatment with directly acting antivirals has not been studied. METHODS: Clonal sequence analysis of HVR-1 quasispecies in the serum of patients who were treated with telaprevir (3 × 750 mg/day) alone, telaprevir plus pegylated interferon-α-2a (pegIFN-α-2a), or pegIFN-α-2a plus placebo for 14 days was performed. HVR-1 heterogeneity, expressed as Shannon complexity and Hamming distance, was analyzed with virologic response and with the emergence of variants associated with resistance to telaprevir. RESULTS: HVR-1 heterogeneity at baseline was not associated with response to telaprevir-based therapy (Shannon complexity 0.34 vs. 0.55, p = 0.38; Hamming distance 0.15 vs. 0.23, p = 0.51; for patients with or without viral breakthrough, respectively). No significant changes in HVR-1 complexity were observed from baseline to day 4 of therapy in patients in whom a continued decline in HCV RNA was observed (Shannon complexity = 0.55 vs. 0.51, p = 0.67; Hamming distance = 0.23 vs. 0.25, p = 0.81, respectively). This was similar in patients with viral breakthrough associated with telaprevir-resistant variants (Shannon complexity = 0.34 vs. 0.42, p = 0.68; Hamming distance = 0.15 vs. 0.2, p = 0.50, at baseline and day 4, respectively). CONCLUSIONS: Baseline and on-treatment HVR-1 heterogeneity are not associated with early viral response to telaprevir-based therapy.

Patient-derived models recapitulate heterogeneity of molecular signatures and drug response in pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is a major contributor to cancer-related death in children. In vitro and in vivo disease models reflecting the intimate connection between developmental context and pathogenesis of pHGG are essential to advance understanding and identify therapeutic vulnerabilities. Here we report establishment of 21 patient-derived pHGG orthotopic xenograft (PDOX) models and eight matched cell lines from diverse groups of pHGG. These models recapitulate histopathology, DNA methylation signatures, mutations and gene expression patterns of the patient tumors from which they were derived, and include rare subgroups not well-represented by existing models. We deploy 16 new and existing cell lines for high-throughput screening (HTS). In vitro HTS results predict variable in vivo response to PI3K/mTOR and MEK pathway inhibitors. These unique new models and an online interactive data portal for exploration of associated detailed molecular characterization and HTS chemical sensitivity data provide a rich resource for pediatric brain tumor research.

Targeting chromosomal instability and tumour heterogeneity in HER2-positive breast cancer.

Chromosomal instability (CIN) is a common cause of tumour heterogeneity and poor prognosis in solid tumours and describes cell-cell variation in chromosome structure or number across a tumour population. In this article we consider evidence suggesting that CIN may be targeted and may influence response to distinct chemotherapy regimens, using HER2-positive breast cancer as an example. Pre-clinical models have indicated a role for HER2 signalling in initiating CIN and defective cell-cycle control, and evidence suggests that HER2-targeting may attenuate this process. Anthracyclines and platinum agents may target tumours with distinct patterns of karyotypic complexity, whereas taxanes may have preferential activity in tumours with relative chromosomal stability. A greater understanding of karyotypic complexity and identification of methods to directly examine and target CIN may support novel strategies to improve outcome in cancer.

Ribosomal heterogeneity - A new inroad for pharmacological innovation.

The paradigm of ribosome usage in protein translation has shifted from a stance proposed as scientists began to unpick the genetic code that each mRNA was partnered by its own, unique ribosome to a rapid reversal of this view that ribosomes are completely interchangeable and simply recruited to mRNAs from a completely homogenous cellular pool. Evidence that the ribosomal proteome, ribosomal gene transcriptome and ribosome protein and RNA modifications differ between cells and tissues points to the fact that ribosomes are heterogeneous in their composition and have a degree of specialisation in their function. It has also been posited that the tissue-specificity of ribosome diseases provides an indication of functional ribosome heterogeneity, but there are substantial caveats to this interpretation. Only now have proteomic technologies developed to a level enabling accurate stoichiometric comparison of the abundance of specific ribosomal proteins in actively translating ribosomes and to measure protein in non-denatured ribosomes. This poises the field for the provocation that ribosome heterogeneity offers a novel and powerful inroad for the pharmacological targeting of disease. Such ribosome-targeted treatments may extend beyond specific ribosomopathies through strategies such as targeting features of ribosomes that are unique to diseased cells, particularly cancer cells, or to activated immune cells, as well as augmenting the action of other drugs through weakening the production of new proteins in target tissues. We may also be able to harness the potential power in ribosome diversity and specialism to better tune synthetic biology for the production of pharmaceutical proteins.

Impact of tumor heterogeneity and tissue sampling for genetic mutation testing: a systematic review and post hoc analysis.

OBJECTIVE: The objective of the study was to identify guidelines to assist systematic reviewers or clinical researchers in identifying sampling bias due to tumor heterogeneity (TH) in solid cancers assayed for somatic mutations. We also assessed current reporting standards to determine the impact of TH on sample bias. STUDY DESIGN AND SETTING: We conducted a systematic review searching 13 databases (to January 2019) to identify guidelines. A post hoc analysis was performed using 12 prostate tumor somatic mutation data sets from a previous systematic review to assess reporting on TH. RESULTS: Searches identified 2,085 records. No formal guidelines were identified. Forty publications contained incidental recommendations across five major themes: using multiple tumor samples (n = 29), sample purity thresholds (n = 14), using specific sequencing methods (n = 8), using liquid biopsies (n = 4), and microdissection (n = 4). In post hoc analyses, 50% (6 of 12) clearly reported pathology methods. Forty-two percent (5 of 12) did not report pathology results. Forty-two percent (5 of 12) confirmed the pathology of the sample by direct diagnosis rather than inference. Forty-two percent (5 of 12) used multiple samples per patient. Fifty-eight percent (7 of 12) reported on tumor purity (reported ranges 10% to 100%). CONCLUSIONS: As precision medicine progresses to the clinic, guidelines are required to help evidence-based decision makers understand how TH may impact sample bias. Authors need to clearly report pathology methods and results and tumor purity methods and results.

Single-cell RNA sequencing reveals a heterogeneous response to Glucocorticoids in breast cancer cells.

Steroid hormone receptors such as the Glucocorticoid Receptor (GR) mediate transcriptional responses to hormones and are frequently targeted in the treatment of human diseases. Experiments using bulk populations of cells have provided a detailed picture of the global transcriptional hormone response but are unable to interrogate cell-to-cell transcriptional heterogeneity. To examine the glucocorticoid response in individual cells, we performed single cell RNA sequencing (scRNAseq) in a human breast cancer cell line. The transcriptional response to hormone was robustly detected in individual cells and scRNAseq provided additional statistical power to identify over 100 GR-regulated genes that were not detected in bulk RNAseq. scRNAseq revealed striking cell-to-cell variability in the hormone response. On average, individual hormone-treated cells showed a response at only 30% of the total set of GR target genes. Understanding the basis of this heterogeneity will be critical for the development of more precise models of steroid hormone signaling.

Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer.

Clonal evolution of osimertinib-resistance mechanisms in EGFR mutant lung adenocarcinoma is poorly understood. Using multi-region whole-exome and RNA sequencing of prospectively collected pre- and post-osimertinib-resistant tumors, including at rapid autopsies, we identify a likely mechanism driving osimertinib resistance in all patients analyzed. The majority of patients acquire two or more resistance mechanisms either concurrently or in temporal sequence. Focal copy-number amplifications occur subclonally and are spatially and temporally separated from common resistance mutations such as EGFR C797S. MET amplification occurs in 66% (n = 6/9) of first-line osimertinib-treated patients, albeit spatially heterogeneous, often co-occurs with additional acquired focal copy-number amplifications and is associated with early progression. Noteworthy osimertinib-resistance mechanisms discovered include neuroendocrine differentiation without histologic transformation, PD-L1, KRAS amplification, and ESR1-AKAP12, MKRN1-BRAF fusions. The subclonal co-occurrence of acquired genomic alterations upon osimertinib resistance will likely require targeting multiple resistance mechanisms by combination therapies.

Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib response in CLL.

The Bruton tyrosine kinase (BTK) inhibitor ibrutinib provides effective treatment for patients with chronic lymphocytic leukemia (CLL), despite extensive heterogeneity in this disease. To define the underlining regulatory dynamics, we analyze high-resolution time courses of ibrutinib treatment in patients with CLL, combining immune-phenotyping, single-cell transcriptome profiling, and chromatin mapping. We identify a consistent regulatory program starting with a sharp decrease of NF-κB binding in CLL cells, which is followed by reduced activity of lineage-defining transcription factors, erosion of CLL cell identity, and acquisition of a quiescence-like gene signature. We observe patient-to-patient variation in the speed of execution of this program, which we exploit to predict patient-specific dynamics in the response to ibrutinib based on the pre-treatment patient samples. In aggregate, our study describes time-dependent cellular, molecular, and regulatory effects for therapeutic inhibition of B cell receptor signaling in CLL, and it establishes a broadly applicable method for epigenome/transcriptome-based treatment monitoring.

Understanding the Cause and Consequence of Tumor Heterogeneity.

Tumor heterogeneity is a large conundrum in cancer medicine, making most therapeutic interventions palliative rather than curative. Here we discuss the implications of how molecularly targeted therapies in solid malignancies that promote limited cancer cell death may in fact make tumors more heterogeneous, increase aggressive phenotypes, and thus worsen patient outcomes.