Intra-tumor heterogeneity: lessons from microbial evolution and clinical implications

Recombinant Human Adenovirus Type 5 (H101) Intra-Tumor Therapy in Patients with Persistent, Recurrent, or Metastatic Cervical Cancer: Genomic Profiling Relating to Clinical Efficacy

Objective

Genomic profiles relating to H101 treatment-induced alterations are yet to be achieved. Here, we evaluated the impact of H101 via exome-sequencing approaches aiming to probe for potential biomarkers that are actionable in the treatment of persistent/recurrent/metastatic (P/R/M) cervical cancer.

Methods

Whole exome sequencing (WES) was performd on paired pre- and post-H101 samples from 17 P/R/M cervical cancer patients who received serial intra-tumor injections of H101. Somatic mutations, including high-frequency mutations, microsatellite instability (MSI) status, tumor mutation burden (TMB), clonal evolution, and mutational signature were analyzed.

Results

The median follow-up time after the H101 treatment was 14 months. Complete response was achieved in 9 patients, 3 patients achieved partial response, and 2 patients had stable disease, resulting in an objective response rate (ORR) of 70.6% (95% CI: 46.4%-96.7%). WES analysis showed no difference in treatment-related mutation characteristics, including non-synonymous-SNVs and TMB status. Patients with lower TMB were correlated with improved H101 response rates (P=0.044), whereas the same was not evident in high MSI (MSI-H) versus non-MSI-H patients (P=0.528). We observed a few high-frequency mutation genes (TTN, KMT2D, ALDOA, DNAH7, ADAP1, PTPN23, and THEMIS2) that probably carry functional importance in response to H101 treatment, among which KMT2D and ADAP1 mutations were associated with inferior progression-free survival (PFS) and/or overall survival (OS) (P<0.05). Notably, H101 treatment-induced accumulating subclones or clusters in primary tumors and some (Signature 2) were associated with shorter PFS.

Conclusion

We conducted an unprecedented work via a WES-based approach and provided preliminary insights into H101 treatment-induced genetic aberrations in which some genes (TTN, KMT2D, ALDOA, DNAH7, ADAP1, PTPN23, and THEMIS2) could be considered potential therapeutic targets of H101-containing treatment in cervical carcinoma. Moreover, the therapy-associated characteristics such as clonal evolution and a mutational signature may warrant further evaluation of H101 in clinical settings for treating cervical carcinoma.

Pragmatic Expectancy on Microbiota and Non-Small Cell Lung Cancer: A Narrative Review

It is well known that lung cancer relies on a number of genes aberrantly expressed because of somatic lesions. Indeed, the lungs, based on their anatomical features, are organs at a high risk of development of extremely heterogeneous tumors due to the exposure to several environmental toxic agents. In this context, the microbiome identifies the whole assemblage of microorganisms present in the lungs, as well as in distant organs, together with their structural elements and metabolites, which actively interact with normal and transformed cells. A relevant amount of data suggest that the microbiota plays a role not only in cancer disease predisposition and risk but also in its initiation and progression, with an impact on patients’ prognosis. Here, we discuss the mechanistic insights of the complex interaction between lung cancer and microbiota as a relevant component of the microenvironment, mainly focusing on novel diagnostic and therapeutic objectives.

Can Environmental Manipulation Help Suppress Cancer? Non-Linear Competition Among Tumor Cells in Periodically Changing Conditions

It has been shown that the tumor population growth dynamics in a periodically varying environment can drastically differ from the one in a fixed environment. Thus, the environment of a tumor can potentially be manipulated to suppress cancer progression. Diverse evolutionary processes play vital roles in cancer progression and accordingly, understanding the interplay between these processes is essential in optimizing the treatment strategy. Somatic evolution and genetic instability result in intra-tumor cell heterogeneity. Various models have been developed to analyze the interactions between different types of tumor cells. Here, models of density-dependent interaction between different types of tumor cells under fast periodical environmental changes are examined. It is illustrated that tumor population densities, which vary on a slow time scale, are affected by fast environmental variations. Finally, the intriguing density-dependent interactions in metastatic castration-resistant prostate cancer (mCRPC) in which the different types of tumor cells are defined with respect to the production of and dependence on testosterone are discussed.

Genome landscapes of rectal cancer before and after preoperative chemoradiotherapy

Resistance to preoperative chemoradiotherapy (CRT) is a major obstacle to cancer treatment in patients with locally advanced rectal cancer. This study was to explore genome alterations in rectal cancer under CRT stress.

Methods: Whole-exome sequencing (WES) was performed on 28 paired tumors collected before and after CRT from the same patients who did not respond to CRT treatment. Somatic point mutations and copy number variations were detected by VarScan2 and Exome CNVs respectively using paired tumor and blood samples. Somatic alterations associated with CRT resistance were inferred considering differences in significantly mutated genes, mutation counts and cancer cell fraction between matched pre- and post-CRT tumors. We employed SignatureAnalyzer to infer mutation signatures and PyClone to decipher clonal evolution and examine intratumoral heterogeneity in tumors before and after CRT. The associations between intratumoral heterogeneity and patients' survival were analyzed using the log-rank test and the Cox regression model.

Results: (i) Recurrent mutations in CTDSP2, APC, KRAS, TP53 and NFKBIZ confer selective advantages on cancer cells and made them resistant to CRT treatment. (ii) CRT alters the genomic characteristics of tumors at both the somatic mutation and the copy number variation levels. (iii) CRT-resistant tumors exhibit either a branched or a linear evolution pattern. (iv) Different recurrent mutation signatures in pre-CRT and post-CRT patients implicate mutational processes underlying the evolution of CRT-resistant tumors. (v) High intratumoral heterogeneity in pre- or post-CRT is associated with poor patients' survival.

Conclusion: Our study reveals genome landscapes in rectal cancer before and after CRT and tumors evolution under CRT stress. The treatment-associated characteristics are useful for further investigations of CRT resistance in rectal cancer.

Deep learning with multimodal representation for pancancer prognosis prediction

MOTIVATION

Estimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type-using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs.

RESULTS

We used pancancer data to train these feature encodings and predict single cancer and pancancer overall survival, achieving a C-index of 0.78 overall. This work shows that it is possible to build a pancancer model for prognosis that also predicts prognosis in single cancer sites. Furthermore, our model handles multiple data modalities, efficiently analyzes WSIs and represents patient multimodal data flexibly into an unsupervised, informative representation. We thus present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients.

AVAILABILITY AND IMPLEMENTATION

https://github.com/gevaertlab/MultimodalPrognosis.

Predicting the evolution of Escherichia coli by a data-driven approach

A tantalizing question in evolutionary biology is whether evolution can be predicted from past experiences. To address this question, we created a coherent compendium of more than 15,000 mutation events for the bacterium Escherichia coli under 178 distinct environmental settings. Compendium analysis provides a comprehensive view of the explored environments, mutation hotspots and mutation co-occurrence. While the mutations shared across all replicates decrease with the number of replicates, our results argue that the pairwise overlapping ratio remains the same, regardless of the number of replicates. An ensemble of predictors trained on the mutation compendium and tested in forward validation over 35 evolution replicates achieves a 49.2 ± 5.8% (mean ± std) precision and 34.5 ± 5.7% recall in predicting mutation targets. This work demonstrates how integrated datasets can be harnessed to create predictive models of evolution at a gene level and elucidate the effect of evolutionary processes in well-defined environments.

How reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at a gene level.

Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

In experimental evolution, laboratory-controlled conditions select for the adaptation of species, which can be monitored in real time. Despite the current popularity of such experiments, nature's most pervasive biological force was long believed to be observable only on time scales that transcend a researcher's life-span, and studying evolution by natural selection was therefore carried out solely by comparative means. Eventually, microorganisms' propensity for fast evolutionary changes proved us wrong, displaying strong evolutionary adaptations over a limited time, nowadays massively exploited in laboratory evolution experiments. Here, we formulate a guide to experimental evolution with microorganisms, explaining experimental design and discussing evolutionary dynamics and outcomes and how it is used to assess ecoevolutionary theories, improve industrially important traits, and untangle complex phenotypes. Specifically, we give a comprehensive overview of the setups used in experimental evolution. Additionally, we address population dynamics and genetic or phenotypic diversity during evolution experiments and expand upon contributing factors, such as epistasis and the consequences of (a)sexual reproduction. Dynamics and outcomes of evolution are most profoundly affected by the spatiotemporal nature of the selective environment, where changing environments might lead to generalists and structured environments could foster diversity, aided by, for example, clonal interference and negative frequency-dependent selection. We conclude with future perspectives, with an emphasis on possibilities offered by fast-paced technological progress. This work is meant to serve as an introduction to those new to the field of experimental evolution, as a guide to the budding experimentalist, and as a reference work to the seasoned expert.

Tumor xenograft modeling identifies an association between TCF4 loss and breast cancer chemoresistance

Understanding the mechanisms of cancer therapeutic resistance is fundamental to improving cancer care. There is clear benefit from chemotherapy in different breast cancer settings; however, knowledge of the mutations and genes that mediate resistance is incomplete. In this study, by modeling chemoresistance in patient-derived xenografts (PDXs), we show that adaptation to therapy is genetically complex and identify that loss of transcription factor 4 (TCF4; also known as ITF2) is associated with this process. A triple-negative BRCA1-mutated PDX was used to study the genetics of chemoresistance. The PDX was treated in parallel with four chemotherapies for five iterative cycles. Exome sequencing identified few genes with de novo or enriched mutations in common among the different therapies, whereas many common depleted mutations/genes were observed. Analysis of somatic mutations from The Cancer Genome Atlas (TCGA) supported the prognostic relevance of the identified genes. A mutation in TCF4 was found de novo in all treatments, and analysis of drug sensitivity profiles across cancer cell lines supported the link to chemoresistance. Loss of TCF4 conferred chemoresistance in breast cancer cell models, possibly by altering cell cycle regulation. Targeted sequencing in chemoresistant tumors identified an intronic variant of TCF4 that may represent an expression quantitative trait locus associated with relapse outcome in TCGA. Immunohistochemical studies suggest a common loss of nuclear TCF4 expression post-chemotherapy. Together, these results from tumor xenograft modeling depict a link between altered TCF4 expression and breast cancer chemoresistance.

Summary: By modeling chemoresistance in patient-derived breast cancer xenografts, this study shows that adaptation to therapy is genetically complex and that loss of transcription factor 4 (TCF4) is associated with this process.

Simulating biological processes: stochastic physics from whole cells to colonies

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

Resource competition promotes tumour expansion in experimentally evolved cancer

BACKGROUND

Tumour progression involves a series of phenotypic changes to cancer cells, each of which presents therapeutic targets. Here, using techniques adapted from microbial experimental evolution, we investigate the evolution of tumour spreading - a precursor for metastasis and tissue invasion - in environments with varied resource supply. Evolutionary theory predicts that competition for resources within a population will select for individuals to move away from a natal site (i.e. disperse), facilitating the colonisation of unexploited resources and decreasing competition between kin.

RESULTS

After approximately 100 generations in environments with low resource supply, we find that MCF7 breast cancer spheroids (small in vitro tumours) show increased spreading. Conversely, spreading slows compared to the ancestor where resource supply is high. Common garden experiments confirm that the evolutionary responses differ between selection lines; with lines evolved under low resource supply showing phenotypic plasticity in spheroid spreading rate. These differences in spreading behaviour between selection lines are heritable (stable across multiple generations), and show that the divergently evolved lines differ in their response to resource supply.

CONCLUSIONS

We observe dispersal-like behaviour and an increased sensitivity to resource availability in our selection lines, which may be a response to selection, or alternatively may be due to epigenetic changes, provoked by prolonged resource limitation, that have persisted across many cell generations. Different clinical strategies may be needed depending on whether or not tumour progression is due to natural selection. This study highlights the effectiveness of experimental evolution approaches in cancer cell populations and demonstrates how simple model systems might enable us to observe and measure key selective drivers of clinically important traits.

Reconstructing cancer karyotypes from short read data: the half empty and half full glass

Background

During cancer progression genomes undergo point mutations as well as larger segmental changes. The latter include, among others, segmental deletions duplications, translocations and inversions.The result is a highly complex, patient-specific cancer karyotype. Using high-throughput technologies of deep sequencing and microarrays it is possible to interrogate a cancer genome and produce chromosomal copy number profiles and a list of breakpoints (“jumps”) relative to the normal genome. This information is very detailed but local, and does not give the overall picture of the cancer genome. One of the basic challenges in cancer genome research is to use such information to infer the cancer karyotype.

We present here an algorithmic approach, based on graph theory and integer linear programming, that receives segmental copy number and breakpoint data as input and produces a cancer karyotype that is most concordant with them. We used simulations to evaluate the utility of our approach, and applied it to real data.

Results

By using a simulation model, we were able to estimate the correctness and robustness of the algorithm in a spectrum of scenarios. Under our base scenario, designed according to observations in real data, the algorithm correctly inferred 69% of the karyotypes. However, when using less stringent correctness metrics that account for incomplete and noisy data, 87% of the reconstructed karyotypes were correct. Furthermore, in scenarios where the data were very clean and complete, accuracy rose to 90%–100%. Some examples of analysis of real data, and the reconstructed karyotypes suggested by our algorithm, are also presented.

Conclusion

While reconstruction of complete, perfect karyotype based on short read data is very hard, a large fraction of the reconstruction will still be correct and can provide useful information.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1929-9) contains supplementary material, which is available to authorized users.

Non-cell-autonomous effects yield lower clonal diversity in expanding tumors

Recent cancer research has investigated the possibility that non-cell-autonomous (NCA) driving tumor growth can support clonal diversity (CD). Indeed, mutations can affect the phenotypes not only of their carriers (“cell-autonomous”, CA effects), but also sometimes of other cells (NCA effects). However, models that have investigated this phenomenon have only considered a restricted number of clones. Here, we designed an individual-based model of tumor evolution, where clones grow and mutate to yield new clones, among which a given frequency have NCA effects on other clones’ growth. Unlike previously observed for smaller assemblages, most of our simulations yield lower CD with high frequency of mutations with NCA effects. Owing to NCA effects increasing competition in the tumor, clones being already dominant are more likely to stay dominant, and emergent clones not to thrive. These results may help personalized medicine to predict intratumor heterogeneity across different cancer types for which frequency of NCA effects could be quantified.

Mutation patterns in small cell and non-small cell lung cancer patients suggest a different level of heterogeneity between primary and metastatic tumors

Several studies have shown heterogeneity in lung cancer, with parallel existence of multiple subclones characterized by their own specific mutational landscape. The extent to which minor clones become dominant in distinct metastasis is not clear. The aim of our study was to gain insight in the evolution pattern of lung cancer by investigating genomic heterogeneity between primary tumor and its distant metastases. Whole exome sequencing (WES) was performed on 24 tumor and five normal samples of two small cell lung carcinoma (SCLC) and three non-SCLC (NSCLC) patients. Validation of somatic variants in these 24 and screening of 33 additional samples was done by single primer enrichment technology. For each of the three NSCLC patients, about half of the mutations were shared between all tumor samples, whereas for SCLC patients, this percentage was around 95. Independent validation of the non-ubiquitous mutations confirmed the WES data for the vast majority of the variants. Phylogenetic trees indicated more distance between the tumor samples of the NSCLC patients as compared to the SCLC patients. Analysis of 30 independent DNA samples of 16 biopsies used for WES revealed a low degree of intra-tumor heterogeneity of the selected sets of mutations. In the primary tumors of all five patients, variable percentages (19-67%) of the seemingly metastases-specific mutations were present albeit at low read frequencies. Patients with advanced NSCLC have a high percentage of non-ubiquitous mutations indicative of branched evolution. In contrast, the low degree of heterogeneity in SCLC suggests a parallel and linear model of evolution.

Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations

Background

A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies.

Results

To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use.

Conclusions

Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the ‘maximum-tolerated-dose paradigm’, as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones.

Reviewers

This article was reviewed by Angela Pisco, Sébastien Benzekry and Heiko Enderling.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0143-4) contains supplementary material, which is available to authorized users.

Intratumor Heterogeneity of KRAS Mutation Status in Pancreatic Ductal Adenocarcinoma Is Associated With Smaller Lesions

OBJECTIVES

Recent studies have demonstrated intratumor heterogeneity (ITH) for genetic mutations in various tumors and suggest that ITH might have significant clinical impact. Because KRAS is the most commonly mutated oncogene in pancreatic ductal adenocarcinoma and has an important role in pancreatic carcinogenesis, the purpose of this study was to evaluate ITH for KRAS gene mutation and its clinical significance.

METHODS

Deep sequencing of 47 pancreatic ductal adenocarcinoma cases was used to determine the fraction of KRAS mutant alleles. In addition, computerized morphometry was used to calculate the fraction of tumor cells. After analysis of both results, cases were classified as ITH or as having amplification of mutant KRAS. The presence of ITH was correlated with clinical and pathological factors.

RESULTS

KRAS mutation was found in 38 (81%) cases, of which 12 (32%) showed ITH and 9 (23%) were found to have KRAS mutant allele amplification. The presence of ITH was associated with smaller tumors, whereas amplification was associated with higher tumor diameter.

CONCLUSIONS

In pancreatic ductal adenocarcinoma, ITH for KRAS gene mutation was associated with smaller tumors. It is possible that, as the tumor progresses, more cells carry this mutation, which leads to more aggressive tumor features.

Promises and pitfalls for recombinant oligoclonal antibodies-based therapeutics in cancer and infectious disease

Monoclonal antibodies (mAbs) have revolutionized the diagnosis and treatment of many human diseases and the application of combinations of mAbs has demonstrated improved therapeutic activity in both preclinical and clinical testing. Combinations of antibodies have several advantages such as the capacities to target multiple and mutating antigens in complex pathogens and to engage varied epitopes on multiple disease-related antigens (e.g. receptors) to overcome heterogeneity and plasticity. Oligoclonal antibodies are an emerging therapeutic format in which a novel antibody combination is developed as a single drug product. Here, we will provide historical context on the use of oligoclonal antibodies in oncology and infectious diseases and will highlight practical considerations related to their preclinical and clinical development programs.

Determinants of Genetic Diversity of Spontaneous Drug Resistance in Bacteria

Any pathogen population sufficiently large is expected to harbor spontaneous drug-resistant mutants, often responsible for disease relapse after antibiotic therapy. It is seldom appreciated, however, that while larger populations harbor more mutants, the abundance distribution of these mutants is expected to be markedly uneven. This is because a larger population size allows early mutants to expand for longer, exacerbating their predominance in the final mutant subpopulation. Here, we investigate the extent to which this reduction in evenness can constrain the genetic diversity of spontaneous drug resistance in bacteria. Combining theory and experiments, we show that even small variations in growth rate between resistant mutants and the wild type result in orders-of-magnitude differences in genetic diversity. Indeed, only a slight fitness advantage for the mutant is enough to keep diversity low and independent of population size. These results have important clinical implications. Genetic diversity at antibiotic resistance loci can determine a population's capacity to cope with future challenges (i.e., second-line therapy). We thus revealed an unanticipated way in which the fitness effects of antibiotic resistance can affect the evolvability of pathogens surviving a drug-induced bottleneck. This insight will assist in the fight against multidrug-resistant microbes, as well as contribute to theories aimed at predicting cancer evolution.

A microRNA biomarker of hepatocellular carcinoma recurrence following liver transplantation accounting for within-patient heterogeneity

Background

Liver cancer, of which hepatocellular carcinoma (HCC) is by far the most common type, is the second most deadly cancer (746,000 deaths in 2012). Currently, the only curative treatment for HCC is surgery to remove the malignancy (resection) or to remove the entire diseased liver followed by transplantation of healthy liver tissue. Given the shortage of healthy livers, it is crucial to provide transplants to patients that have the best chance of long-term survival. Currently, transplantation is determined via the Milan criteria—patients within Milan (single tumor < 5 cm or 2–3 tumors < 3 cm with no extrahepatic spread nor intrahepatic vascular invasion) are typically eligible for transplantation. However, combining microRNA expression profiling with the Milan criteria can improve prediction of recurrence.

HCC often presents with multiple distinct tumor foci arising from local spread of a primary tumor or from the oncogenic predisposition of the diseased liver. Substantial genomic heterogeneity between tumor foci within a single patient has been reported; therefore, biomarker development must account for the possibility of highly heterogeneous genomic profiles from the same individual.

Methods

MicroRNA profiling was performed on 180 HCC tumor samples from 89 patients who underwent liver transplantation at the University of Rochester Medical Center. The primary outcome was recurrence-free survival time, and patients were observed for 3 years post-transplantation.

Results

MicroRNA expression profiles were used to develop a biomarker that distinguishes HCC patients at greater risk of recurrence post-transplantation. Unsupervised clustering uncovered two distinct subgroups with vast differences in standard transplantation selection criteria and recurrence-free survival times. These subgroups were subsequently used to identify microRNAs strongly associated with HCC recurrence. Our results show that reduced expression of five specific microRNAs is significantly associated with HCC recurrence post-transplantation.

Conclusions

MicroRNA profiling of distinct tumor foci, coupled with methods that address within-subject tumor heterogeneity, has the potential to significantly improve prediction of HCC recurrence post-transplantation. The development of a clinically applicable HCC biomarker would inform treatment options for patients and contribute to liver transplant selection criteria for practitioners.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0179-4) contains supplementary material, which is available to authorized users.

Early Events in the Molecular Pathogenesis of Lung Cancer

The majority of cancer-related deaths in the United States and worldwide are attributed to lung cancer. There are more than 90 million smokers in the United States who represent a significant population at elevated risk for lung malignancy. In other epithelial tumors, it has been shown that if neoplastic lesions can be detected and treated at their intraepithelial stage, patient prognosis is significantly improved. Thus, new strategies to detect and treat lung preinvasive lesions are urgently needed in order to decrease the overwhelming public health burden of lung cancer. Limiting these advances is a poor knowledge of the earliest events that underlie lung cancer development and that would constitute markers and targets for early detection and prevention. This review summarizes the state of knowledge of human lung cancer pathogenesis and the molecular pathology of premalignant lung lesions, with a focus on the molecular premalignant field that associates with lung cancer development. Lastly, we highlight new approaches and models to study genome-wide alterations in human lung premalignancy in order to facilitate the discovery of new markers for early detection and prevention of this fatal disease. Cancer Prev Res; 9(7); 518-27. ©2016 AACR.

Toward precision medicine of breast cancer

In this review, we report on breast cancer's molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.

Do cell-autonomous and non-cell-autonomous effects drive the structure of tumor ecosystems?

By definition, a driver mutation confers a growth advantage to the cancer cell in which it occurs, while a passenger mutation does not: the former is usually considered as the engine of cancer progression, while the latter is not. Actually, the effects of a given mutation depend on the genetic background of the cell in which it appears, thus can differ in the subclones that form a tumor. In addition to cell-autonomous effects generated by the mutations, non-cell-autonomous effects shape the phenotype of a cancer cell. Here, we review the evidence that a network of biological interactions between subclones drives cancer cell adaptation and amplifies intra-tumor heterogeneity. Integrating the role of mutations in tumor ecosystems generates innovative strategies targeting the tumor ecosystem's weaknesses to improve cancer treatment.

Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq

Single-cell sequencing, which is used to detect clinically important tumor subpopulations, is necessary for understanding tumor heterogeneity. Here, we analyzed transcriptomic data obtained from 34 single cells from human lung adenocarcinoma (LADC) patient-derived xenografts (PDXs). To focus on the intrinsic transcriptomic signatures of these tumors, we filtered out genes that displayed extensive expression changes following xenografting and cell culture. Then, we performed clustering analysis using co-regulated gene modules rather than individual genes to minimize read drop-out errors associated with single-cell sequencing. This combined approach revealed two distinct intra-tumoral subgroups that were primarily distinguished by the gene module G64. The G64 module was predominantly composed of cell-cycle genes. E2F1 was found to be the transcription factor that most likely mediates the expression of the G64 module in single LADC cells. Interestingly, the G64 module also indicated inter-tumoral heterogeneity based on its association with patient survival and other clinical variables such as smoking status and tumor stage. Taken together, these results demonstrate the feasibility of single-cell RNA sequencing and the strength of our analytical pipeline for the identification of tumor subpopulations.

Epidermal growth factor receptor immunohistochemistry: new opportunities in metastatic colorectal cancer

The treatment of cancer is becoming more precise, targeting specific oncogenic drivers with targeted molecular therapies. The epidermal growth factor receptor has been found to be over-expressed in a multitude of solid tumours. Immunohistochemistry is widely used in the fields of diagnostic and personalised medicine to localise and visualise disease specific proteins. To date the clinical utility of epidermal growth factor receptor immunohistochemistry in determining monoclonal antibody efficacy has remained somewhat inconclusive. The lack of an agreed reproducible scoring criteria for epidermal growth factor receptor immunohistochemistry has, in various clinical trials yielded conflicting results as to the use of epidermal growth factor receptor immunohistochemistry assay as a companion diagnostic. This has resulted in this test being removed from the licence for the drug panitumumab and not performed in clinical practice for cetuximab. In this review we explore the reasons behind this with a particular emphasis on colorectal cancer, and to suggest a way of resolving the situation through improving the precision of epidermal growth factor receptor immunohistochemistry with quantitative image analysis of digitised images complemented with companion molecular morphological techniques such as in situ hybridisation and section based gene mutation analysis.

Target irradiation induced bystander effects between stem-like and non stem-like cancer cells

Tumors are heterogeneous in nature and consist of multiple cell types. Among them, cancer stem-like cells (CSCs) are suggested to be the principal cause of tumor metastasis, resistance and recurrence. Therefore, understanding the behavior of CSCs in direct and indirect irradiations is crucial for clinical radiotherapy. Here, the CSCs and their counterpart non stem-like cancer cells (NSCCs) in human HT1080 fibrosarcoma cell line were sorted and labeled, then the two cell subtypes were mixed together and chosen separately to be irradiated via a proton microbeam. The radiation-induced bystander effect (RIBE) between the CSCs and NSCCs was measured by imaging 53BP1 foci, a widely used indicator for DNA double strand break (DSB). CSCs were found to be less active than NSCCs in both the generation and the response of bystander signals. Moreover, the nitric oxide (NO) scavenger c-PTIO can effectively alleviate the bystander effect in bystander NSCCs but not in bystander CSCs, indicating a difference of the two cell subtypes in NO signal response. To our knowledge, this is the first report shedding light on the RIBE between CSCs and NSCCs, which might contribute to a further understanding of the out-of-field effect in cancer radiotherapy.

Cancer evolution: mathematical models and computational inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Imaging tumour heterogeneity of the consequences of a PKCα-substrate interaction in breast cancer patients

Breast cancer heterogeneity demands that prognostic models must be biologically driven and recent clinical evidence indicates that future prognostic signatures need evaluation in the context of early compared with late metastatic risk prediction. In pre-clinical studies, we and others have shown that various protein-protein interactions, pertaining to the actin microfilament-associated proteins, ezrin and cofilin, mediate breast cancer cell migration, a prerequisite for cancer metastasis. Moreover, as a direct substrate for protein kinase Cα, ezrin has been shown to be a determinant of cancer metastasis for a variety of tumour types, besides breast cancer; and has been described as a pivotal regulator of metastasis by linking the plasma membrane to the actin cytoskeleton. In the present article, we demonstrate that our tissue imaging-derived parameters that pertain to or are a consequence of the PKC-ezrin interaction can be used for breast cancer prognostication, with inter-cohort reproducibility. The application of fluorescence lifetime imaging microscopy (FLIM) in formalin-fixed paraffin-embedded patient samples to probe protein proximity within the typically <10 nm range to address the oncological challenge of tumour heterogeneity, is discussed.

Cancer: evolution within a lifetime

Subclonal cancer populations change spatially and temporally during the disease course. Studies are revealing branched evolutionary cancer growth with low-frequency driver events present in subpopulations of cells, providing escape mechanisms for targeted therapeutic approaches. Despite such complexity, evidence is emerging for parallel evolution of subclones, mediated through distinct somatic events converging on the same gene, signal transduction pathway, or protein complex in different subclones within the same tumor. Tumors may follow gradualist paths (microevolution) as well as major shifts in evolutionary trajectories (macroevolution). Although macroevolution has been subject to considerable controversy in post-Darwinian evolutionary theory, we review evidence that such nongradual, saltatory leaps, driven through chromosomal rearrangements or genome doubling, may be particularly relevant to tumor evolution. Adapting cancer care to the challenges imposed by tumor micro- and macroevolution and developing deeper insight into parallel evolutionary events may prove central to improving outcome and reducing drug development costs.

Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine

The presence of multiple subclones within tumors mandates understanding of longitudinal and spatial subclonal dynamics. Resolving the spatial and temporal heterogeneity of subclones with cancer driver events may offer insight into therapy response, tumor evolutionary histories and clinical trial design.