Tumor Evolutionary Principles: How Intratumor Heterogeneity Influences Cancer Treatment and Outcome

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

Background

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

Methods

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

Findings

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

Interpretation

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

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

Genomic and evolutionary characteristics of metastatic gastric cancer by routes

BACKGROUND

In gastric cancer (GC) patients, metastatic progression through the lymphatic, hematogenous, peritoneal, and ovarian routes, is the ultimate cause of death. However, the genomic and evolutionary characteristics of metastatic GC have not been widely evaluated.

METHODS

Whole-exome sequencing data were analyzed for 99 primary and paired metastatic gastric cancers from 15 patients who underwent gastrectomy and metastasectomy.

RESULTS

Hematogenous metastatic tumors were associated with increased chromosomal instability and de novo gain/amplification in cancer driver genes, whereas peritoneal/ovarian metastasis was linked to sustained chromosomal stability and de novo somatic mutations in driver genes. The genomic distance of the hematogenous and peritoneal metastatic tumors was found to be closer to the primary tumors than lymph node (LN) metastasis, while ovarian metastasis was closer to LN and peritoneal metastasis than the primary tumor. Two migration patterns for metastatic GCs were identified; branched and diaspora. Both molecular subtypes of the metastatic tumors, rather than the primary tumor, and their migration patterns were related to patient survival.

CONCLUSIONS

Genomic characteristics of metastatic gastric cancer is distinctive by routes and associated with patients' prognosis along with genomic evolution pattenrs, indicating that both primary and metastatic gastric cancers require genomic evaluation.

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.

Optimization of antitumor radiotherapy fractionation via mathematical modeling with account of 4 R's of radiobiology

A spatially-distributed continuous mathematical model of solid tumor growth and treatment by fractionated radiotherapy is presented. The model explicitly accounts for the factors, widely referred to as 4 R's of radiobiology, which influence the efficacy of radiotherapy fractionation protocols: tumor cell repopulation, their redistribution in cell cycle, reoxygenation and repair of sublethal damage of both tumor and normal tissues. With the use of special algorithm the fractionation protocols that provide increased tumor control probability, compared to standard clinical protocol, are found for various physiologically-based values of model parameters under the constraints of fixed overall normal tissue damage and maximum admissible fractional dose. In particular, it is shown that significant gain in treatment efficacy can be achieved for tumors of low malignancy by the use of protracted hyperfractionated protocols. The optimized non-uniform protocols are characterized by gradual escalation of fractional doses in their last parts, which start after the levels of oxygen and nutrients significantly elevate throughout the tumor and accelerated tumor proliferation manifests itself, which is a well-known experimental phenomenon.

Unusual phylogenetic tree and circulating actionable ESR1 mutations in an aggressive luminal/HER2-low breast cancer: Case report

Under therapeutic pressure aggressive tumors evolve rapidly. Herein, a luminal B/HER2-low breast cancer was tracked for >3 years during a total of 6 largely unsuccessful therapy lines, from adjuvant to advanced settings. Targeted next generation sequencing (NGS) of the primary lesion, two metastases and 14 blood drawings suggested a striking, unprecedented coexistence of three evolution modes: punctuated, branched and convergent. Punctuated evolution of the trunk was supported by en bloc inheritance of a large set (19 distinct genes) of copy number alterations. Branched evolution was supported by the distribution of site-specific SNVs. Convergent evolution was characterized by a unique asynchronous expansion of three actionable (OncoKB level 3A) mutations at two consecutive ESR1 codons. Low or undetectable in all the sampled tumor tissues, ESR1 mutations expanded rapidly in blood during HER2/hormone double-blockade, and predicted life-threatening local progression at lung and liver metastatic foci. Dramatic clinical response to Fulvestrant (assigned off-label exclusively based on liquid biopsy) was associated with clearance of all 3 subclones and was in stark contrast to the poor therapeutic efficacy reported in large liquid biopsy-informed interventional trials. Altogether, deconvolution of the tumor phylogenetic tree, as shown herein, may help to customize treatment in breast cancers that rapidly develop refractoriness to multiple drugs.

Cervical cancer heterogeneity: a constant battle against viruses and drugs

Cervical cancer is the first identified human papillomavirus (HPV) associated cancer and the most promising malignancy to be eliminated. However, the ever-changing virus subtypes and acquired multiple drug resistance continue to induce failure of tumor prevention and treatment. The exploration of cervical cancer heterogeneity is the crucial way to achieve effective prevention and precise treatment. Tumor heterogeneity exists in various aspects including the immune clearance of viruses, tumorigenesis, neoplasm recurrence, metastasis and drug resistance. Tumor development and drug resistance are often driven by potential gene amplification and deletion, not only somatic genomic alterations, but also copy number amplifications, histone modification and DNA methylation. Genomic rearrangements may occur by selection effects from chemotherapy or radiotherapy which exhibits genetic intra-tumor heterogeneity in advanced cervical cancers. The combined application of cervical cancer therapeutic vaccine and immune checkpoint inhibitors has become an effective strategy to address the heterogeneity of treatment. In this review, we will integrate classic and recently updated epidemiological data on vaccination rates, screening rates, incidence and mortality of cervical cancer patients worldwide aiming to understand the current situation of disease prevention and control and identify the direction of urgent efforts. Additionally, we will focus on the tumor environment to summarize the conditions of immune clearance and gene integration after different HPV infections and to explore the genomic factors of tumor heterogeneity. Finally, we will make a thorough inquiry into completed and ongoing phase III clinical trials in cervical cancer and summarize molecular mechanisms of drug resistance among chemotherapy, radiotherapy, biotherapy, and immunotherapy.

Single-cell biology uncovers apoptotic cell death and its spatial organization as a potential modifier of tumor diversity in HCC

BACKGROUND AND AIMS

HCC is a highly aggressive and heterogeneous cancer type with limited treatment options. Identifying drivers of tumor heterogeneity may lead to better therapeutic options and favorable patient outcomes. We investigated whether apoptotic cell death and its spatial architecture is linked to tumor molecular heterogeneity using single-cell in situ hybridization analysis.

APPROACH AND RESULTS

We analyzed 254 tumor samples from two HCC cohorts using tissue microarrays. We developed a mathematical model to quantify cellular diversity among HCC samples using two tumor markers, cyclin-dependent kinase inhibitor 3 and protein regulator of cytokinesis 1 as surrogates for heterogeneity and caspase 3 (CASP3) as an apoptotic cell death marker. We further explored the impact of potential dying-cell hubs on tumor cell diversity and patient outcome by density contour mapping and spatial proximity analysis. We also developed a selectively controlled in vitro model of cell death using CRISPR/CRISPR-associated 9 to determine therapy response and growth under hypoxic conditions. We found that increasing levels of CASP3⁺ tumor cells are associated with higher tumor diversity. Interestingly, we discovered regions of densely populated CASP3⁺ , which we refer to as CASP3⁺ cell islands, in which the nearby cellular heterogeneity was found to be the greatest compared to cells farther away from these islands and that this phenomenon was associated with survival. Additionally, cell culture experiments revealed that higher levels of cell death, accompanied by increased CASP3 expression, led to greater therapy resistance and growth under hypoxia.

CONCLUSIONS

These results are consistent with the hypothesis that increased apoptotic cell death may lead to greater tumor heterogeneity and thus worse patient outcomes.

Toward Targeted Therapies in Oesophageal Cancers: An Overview

Oesophageal cancer is one of the leading causes of cancer-related death worldwide. Oesophageal cancer occurs as squamous cell carcinoma (ESCC) or adenocarcinoma (EAC). Prognosis for patients with either ESCC or EAC is poor, with less than 20% of patients surviving more than 5 years after diagnosis. A major progress has been made in the development of biomarker-driven targeted therapies against breast and lung cancers, as well as melanoma. However, precision oncology for patients with oesophageal cancer is still virtually non-existent. In this review, we outline the recent advances in oesophageal cancer profiling and clinical trials based on targeted therapies in this disease.

A Century of Fractionated Radiotherapy: How Mathematical Oncology Can Break the Rules

Radiotherapy is involved in 50% of all cancer treatments and 40% of cancer cures. Most of these treatments are delivered in fractions of equal doses of radiation (Fractional Equivalent Dosing (FED)) in days to weeks. This treatment paradigm has remained unchanged in the past century and does not account for the development of radioresistance during treatment. Even if under-optimized, deviating from a century of successful therapy delivered in FED can be difficult. One way of exploring the infinite space of fraction size and scheduling to identify optimal fractionation schedules is through mathematical oncology simulations that allow for in silico evaluation. This review article explores the evidence that current fractionation promotes the development of radioresistance, summarizes mathematical solutions to account for radioresistance, both in the curative and non-curative setting, and reviews current clinical data investigating non-FED fractionated radiotherapy.

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.

A combination of intra-tumor genetic heterogeneity, estrogen receptor alpha and human papillomavirus status predicts outcomes in head and neck squamous cell carcinoma following chemoradiotherapy

BACKGROUND

Previous work indicates that mutant-allele tumor heterogeneity (MATH), estrogen receptor alpha (ERα) expression, and human papillomavirus (HPV) status provide prognostic utility in head and neck squamous cell carcinoma (HNSCC). We sought to assess whether the combination of these three objective biomarkers could provide better prognostication for patients who receive chemoradiotherapy (CRT).

METHODS

156 patients (75 oral cavity, 44 oropharyngeal and 37 laryngeal squamous cell carcinoma cancer patients) who received CRT as primary therapy or adjuvant to surgery were identified from The Cancer Genome Atlas (TCGA). MATH values were calculated from TCGA whole exome sequencing data, HPV status was determined by mapping RNA-seq reads, and ERα expression was determined from ESR1 mRNA expression data. Relationships among clinical characteristics were assessed by Fisher exact tests. Relationships of clinical characteristics and MATH, ERα and HPV to overall survival were evaluated with Cox proportional hazard analysis.

RESULTS

The combination of poor-prognosis values for all 3 biomarkers (high MATH, low ERα and HPV-negative status) has a predicted hazard ratio of 28.2 (95% CI: 5.4-148, p = 0.0001) versus the combination of their good-prognosis values (low MATH, high ERα and HPV-positive status). Addition of N classification to the combination of these three biomarkers added further prognostic value.

CONCLUSIONS

A combination of these three biomarkers, readily determined on pretreatment biopsy specimens, can stratify patients into prognostic groups. Their application potentially offers numerous opportunities to optimize treatment or explore de-intensification strategies in the clinical trial setting.

To portray clonal evolution in blood cancer, count your stem cells

Clonal evolution, the process of expansion and diversification of mutated cells, plays an important role in cancer development, resistance, and relapse. Although clonal evolution is most often conceived of as driven by natural selection, recent studies uncovered that neutral evolution shapes clonal evolution in a significant proportion of solid cancers. In hematological malignancies, the interplay between neutral evolution and natural selection is also disputed. Because natural selection selects cells with a greater fitness, providing a growth advantage to some cells relative to others, the architecture of clonal evolution serves as indirect evidence to distinguish natural selection from neutral evolution and has been associated with different prognoses for the patient. Linear architecture, when the new mutant clone grows within the previous one, is characteristic of hematological malignancies and is typically interpreted as being driven by natural selection. Here, we discuss the role of natural selection and neutral evolution in the production of linear clonal architectures in hematological malignancies. Although it is tempting to attribute linear evolution to natural selection, we argue that a lower number of contributing stem cells accompanied by genetic drift can also result in a linear pattern of evolution, as illustrated by simulations of clonal evolution in hematopoietic stem cells. The number of stem cells contributing to long-term clonal evolution is not known in the pathological context, and we advocate that estimating these numbers in the context of cancer and aging is crucial to parsing out neutral evolution from natural selection, 2 processes that require different therapeutic strategies.

In the Right Place at the Right Time: Regulation of Cell Metabolism by IP3R-Mediated Inter-Organelle Ca2+ Fluxes

In the last few years, metabolism has been shown to be controlled by cross-organelle communication. The relationship between the endoplasmic reticulum and mitochondria/lysosomes is the most studied; here, inositol 1,4,5-triphosphate (IP3) receptor (IP3R)-mediated calcium (Ca²⁺) release plays a central role. Recent evidence suggests that IP3R isoforms participate in synthesis and degradation pathways. This minireview will summarize the current findings in this area, emphasizing the critical role of Ca²⁺ communication on organelle function as well as catabolism and anabolism, particularly in cancer.

Heterogeneity in 2,6-Linked Sialic Acids Potentiates Invasion of Breast Cancer Epithelia

Heterogeneity in phenotypes of malignantly transformed cells and aberrant glycan expression on their surface are two prominent hallmarks of cancers that have hitherto not been linked to each other. In this paper, we identify differential levels of a specific glycan linkage: α2,6-linked sialic acids within breast cancer cells in vivo and in culture. Upon sorting out two populations with moderate, and relatively higher, cell surface α2,6-linked sialic acid levels from the triple-negative breast cancer cell line MDA-MB-231, both populations (denoted as medium and high 2,6-Sial cells, respectively) stably retained their levels in early passages. Upon continuous culturing, medium 2,6-Sial cells recapitulated the heterogeneity of the unsorted line whereas high 2,6-Sial cells showed no such tendency. Compared with high 2,6-Sial cells, the medium 2,6-Sial counterparts showed greater adhesion to reconstituted extracellular matrices (ECMs) and invaded faster as single cells. The level of α2,6-linked sialic acids in the two sublines was found to be consistent with the expression of a specific glycosyl transferase, ST6GAL1. Stably knocking down ST6GAL1 in the high 2,6-Sial cells enhanced their invasiveness. When cultured together, medium 2,6-Sial cells differentially migrated to the edge of growing tumoroid-like cocultures, whereas high 2,6-Sial cells formed the central bulk. Multiscale simulations in a Cellular Potts model-based computational environment calibrated to our experimental findings suggest that differential levels of cell-ECM adhesion, likely regulated by α2,6-linked sialic acids, facilitate niches of highly invasive cells to efficiently migrate centrifugally as the invasive front of a malignant breast tumor.

Evolving insights: how DNA repair pathways impact cancer evolution

Viewing cancer as a large, evolving population of heterogeneous cells is a common perspective. Because genomic instability is one of the fundamental features of cancer, this intrinsic tendency of genomic variation leads to striking intratumor heterogeneity and functions during the process of cancer formation, development, metastasis, and relapse. With the increased mutation rate and abundant diversity of the gene pool, this heterogeneity leads to cancer evolution, which is the major obstacle in the clinical treatment of cancer. Cells rely on the integrity of DNA repair machineries to maintain genomic stability, but these machineries often do not function properly in cancer cells. The deficiency of DNA repair could contribute to the generation of cancer genomic instability, and ultimately promote cancer evolution. With the rapid advance of new technologies, such as single-cell sequencing in recent years, we have the opportunity to better understand the specific processes and mechanisms of cancer evolution, and its relationship with DNA repair. Here, we review recent findings on how DNA repair affects cancer evolution, and discuss how these mechanisms provide the basis for critical clinical challenges and therapeutic applications.

Cell-cell fusion of mesenchymal cells with distinct differentiations triggers genomic and transcriptomic remodelling toward tumour aggressiveness

Cell–cell fusion is a physiological process that is hijacked during oncogenesis and promotes tumour evolution. The main known impact of cell fusion is to promote the formation of metastatic hybrid cells following fusion between mobile leucocytes and proliferating tumour cells. We show here that cell fusion between immortalized myoblasts and transformed fibroblasts, through genomic instability and expression of a specific transcriptomic profile, leads to emergence of hybrid cells acquiring dissemination properties. This is associated with acquisition of clonogenic ability by fused cells. In addition, by inheriting parental properties, hybrid tumours were found to mimic the histological characteristics of a specific histotype of sarcomas: undifferentiated pleomorphic sarcomas with incomplete muscular differentiation. This finding suggests that cell fusion, as macroevolution event, favours specific sarcoma development according to the differentiation lineage of parent cells.

Cancer evolution: A means by which tumors evade treatment

Although various methods have been tried to study and treat cancer, the cancer remains a major challenge for human medicine today. One important reason for this is the presence of cancer evolution. Cancer evolution is a process in which tumor cells adapt to the external environment, which can suppress the human immune system's ability to recognize and attack tumors, and also reduce the reproducibility of cancer research. Among them, heterogeneity of the tumor provides intrinsic motivation for this process. Recently, with the development of related technologies such as liquid biopsy, more and more knowledge about cancer evolution has been gained and interest in this topic has also increased. Therefore, starting from the causes of tumorigenesis, this paper introduces several tumorigenesis processes and pathways, as well as treatment options for different targets.

The cellular origins of cancer with particular reference to the gastrointestinal tract

Stem cells or their closely related committed progenitor cells are the likely founder cells of most neoplasms. In the continually renewing and hierarchically organized epithelia of the oesophagus, stomach and intestine, homeostatic stem cells are located at the beginning of the cell flux, in the basal layer of the oesophagus, the isthmic region of gastric oxyntic glands and at the bottom of gastric pyloric-antral glands and colonic crypts. The introduction of mutant oncogenes such as Kras^G12D or loss of Tp53 or Apc to specific cell types expressing the likes of Lgr5 and Mist1 can be readily accomplished in genetically engineered mouse models to initiate tumorigenesis. Other origins of cancer are discussed including 'reserve' stem cells that may be activated by damage or through disruption of morphogen gradients along the crypt axis. In the liver and pancreas, with little cell turnover and no obvious stem cell markers, the importance of regenerative hyperplasia associated with chronic inflammation to tumour initiation is vividly apparent, though inflammatory conditions in the renewing populations are also permissive for tumour induction. In the liver, hepatocytes, biliary epithelial cells and hepatic progenitor cells are embryologically related, and all can give rise to hepatocellular carcinoma and cholangiocarcinoma. In the exocrine pancreas, both acinar and ductal cells can give rise to pancreatic ductal adenocarcinoma (PDAC), although the preceding preneoplastic states are quite different: acinar-ductal metaplasia gives rise to pancreatic intraepithelial neoplasia culminating in PDAC, while ducts give rise to PDAC via. mucinous cell metaplasia that may have a polyclonal origin.

When, why and how tumour clonal diversity predicts survival

The utility of intratumour heterogeneity as a prognostic biomarker is the subject of ongoing clinical investigation. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here, we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate and progression-free survival. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. In cohorts of tumours with diverse evolutionary parameters, we find that clonal diversity is a reliable predictor of both growth rate and progression-free survival. We thus offer explanations-grounded in evolutionary theory-for empirical findings in various cancers, including survival analyses reported in the recent TRACERx Renal study of clear-cell renal cell carcinoma. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.

The leading role of pathology in assessing the somatic molecular alterations of cancer: Position Paper of the European Society of Pathology

Molecular pathology is an essential part of pathology complementing conventional morphological tools to obtain a correct integrated diagnosis with appropriate assessment of prognosis and prediction of response to therapy, particularly in cancer. There is a concern about the situation of molecular pathology in some areas of Europe, namely, regarding the central role of pathologists in assessing somatic genomic alterations in cancer. In some countries, there are attempts that other laboratory medicine specialists perform the molecular analysis of somatic alterations in cancer, particularly now when next generation sequencing (NGS) is incorporated into clinical practice. In this scenario, pathologists may play just the role of “tissue providers,” and other specialists may take the lead in molecular analysis. Geneticists and laboratory medicine specialists have all background and skills to perform genetic analysis of germline alterations in hereditary disorders, including familial forms of cancers. However, interpretation of somatic alterations of cancer belongs to the specific scientific domain of pathology. Pathologists are necessary to guarantee the quality of the results, for several reasons: (1) The identified molecular alterations should be interpreted in the appropriate morphologic context, since most of them are context-specific; (2) pre-analytical issues must be taken into consideration; (3) it is crucial to check the proportion of tumor cells in the sample subjected to analysis and presence of inflammatory infiltrate and necrosis should be monitored; and 4) the role of pathologists is crucial to select the most appropriate methods and to control the turnaround time in which the molecular results are delivered in the context of an integrated diagnosis. Obviously, there is the possibility of having core facilities for NGS in a hospital to perform the sequence analysis that are open to other specialties (microbiologists, geneticists), but also in this scenario, pathologists should have the lead in assessing somatic alterations of cancer. In this article, we emphasize the importance of interpreting somatic molecular alterations of the tumors in the context of morphology. In this Position Paper of the European Society of Pathology, we strongly support a central role of pathology departments in the process of analysis and interpretation of somatic molecular alterations in cancer.

Intratumor heterogeneity could inform the use and type of postoperative adjuvant therapy in patients with head and neck squamous cell carcinoma

BACKGROUND

After surgery for head and neck squamous cell carcinoma (HNSCC), decisions regarding adjuvant radiotherapy (RT) or chemoradiotherapy (CRT) are based on staging and the presence of high-risk pathology. Because higher mutant allele tumor heterogeneity (MATH; a measure of intratumor genetic heterogeneity) is associated with shorter overall survival (OS) in patients with HNSCC, the authors sought to determine whether MATH analysis might further inform these decisions.

METHODS

Adjuvant therapy-associated relationships between MATH and OS were analyzed for 389 patients with HNSCC who were treated surgically. Data were obtained from The Cancer Genome Atlas and analyzed with Cox proportional hazards multiple regression accounting for 7 other patient characteristics.

RESULTS

The relationship between MATH and OS differed with adjuvant therapy in a way that could inform therapy decisions. Adjuvant RT alone was found to provide substantial benefit for patients having high-MATH tumors (RT vs no adjuvant therapy: hazard ratio, 0.29 [95% CI, 0.17-0.51]) but no benefit for those having low-MATH tumors. In contrast, adjuvant CRT provided no benefit beyond that of adjuvant RT for patients with high-MATH tumors but substantially improved OS among patients with low-MATH tumors (CRT vs no adjuvant therapy: hazard ratio, 0.34 [95% CI, 0.15-0.78]).

CONCLUSIONS

The results of the current analysis suggested that patients with HNSCC with high-MATH tumors who underwent surgical treatment could benefit from adjuvant RT, even when current clinical guidelines indicate otherwise. The addition of adjuvant chemotherapy for patients with high-MATH tumors would not be indicated. Adding chemotherapy might be necessary to radiosensitize low-MATH tumors to adjuvant RT. This potential predictive role of tumor MATH analysis should be evaluated in prospective clinical trials.

Comparative Molecular Life History of Spontaneous Canine and Human Gliomas

Sporadic gliomas in companion dogs provide a window on the interaction between tumorigenic mechanisms and host environment. We compared the molecular profiles of canine gliomas with those of human pediatric and adult gliomas to characterize evolutionarily conserved mammalian mutational processes in gliomagenesis. Employing whole-genome, exome, transcriptome, and methylation sequencing of 83 canine gliomas, we found alterations shared between canine and human gliomas such as the receptor tyrosine kinases, TP53 and cell-cycle pathways, and IDH1 R132. Canine gliomas showed high similarity with human pediatric gliomas per robust aneuploidy, mutational rates, relative timing of mutations, and DNA-methylation patterns. Our cross-species comparative genomic analysis provides unique insights into glioma etiology and the chronology of glioma-causing somatic alterations.

Immunotherapeutic Transport Oncophysics: Space, Time, and Immune Activation in Cancer

Immuno-oncology has gained momentum thanks to the success of strategies aimed at enhancing immune-mediated antitumor response. The field of immunotherapeutic transport oncophysics investigates the physical processes that drive cancer immunotherapies. This review discusses three main aspects that determine the outcome of an immunotherapy-based treatment from a physical point of view; (i) space, the distribution of cancer and immune cells within tumor masses, (ii) time, the temporal dynamic of immune response against tumors, and (iii) activity, the ability of immune cell populations to suppress cancer. Upon introducing these topics with examples from the literature, we investigate in detail two cases where the interplay between space, time, and activation variables determines immune response: nanodendritic cell vaccines and immunosuppression in ovarian cancer.

Nonclonal chromosomal alterations and poor survival in cytopenic patients without hematological malignancies

Background

Clonal chromosomal alterations (CCAs) reflect recurrent genetic changes derived from a single evolving clone, whereas nonclonal chromosomal alterations (NCCAs) comprise a single or nonrecurrent chromosomal abnormality. CCAs and NCCAs in hematopoietic cells have been partially investigated in cytopenic patients without hematological malignancies.

Methods

This single-center retrospective study included 253 consecutive patients who underwent bone marrow aspiration to determine the cause of cytopenia between 2012 and 2015. Patients with hematological malignancies were excluded. CCA was defined as a chromosomal aberration detected in more than two cells, and NCCA was defined as a chromosomal aberration detected in a single cell.

Results

The median age of the patients was 66 years. There were 135 patients without hematological malignancies (median age, 64 years; 69 females); of these, 27 patients (median age, 69 years; 8 females) harbored chromosomal abnormalities. CCAs were detected in 14 patients; the most common CCA was −Y in eight patients, followed by inv.(9) in three patients and mar1+, inv. (12), and t (19;21) in one patient each. NCCAs were detected in 13 patients; the most frequent NCCA was +Y in four patients, followed by del (20), + 8, inv. (2), − 8, and add (6) in one patient each. Moreover, nonclonal translocation abnormalities, including t (9;14), t (14;16), and t (13;21), were observed in three patients. One patient had a complex karyotype in a single cell. The remaining 106 patients with normal karyotypes comprised the control group (median age, 65 years; range, 1–92 years; 56 females). Further, follow-up analysis revealed that the overall survival of the NCCA group was worse than that of the CCA and the normal karyotype groups (P < 0.0001; log-rank test).

The survival of the NCCA-harboring cytopenic patients was worse than that of the CCA-harboring cytopenic patients without hematological malignancies, suggesting that follow-up should be considered for both CCA- and NCCA-harboring cytopenic patients.

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors

Background

Tumors exhibit extensive intra-tumor heterogeneity, the presence of groups of cellular populations with distinct sets of somatic mutations. This heterogeneity is the result of an evolutionary process, described by a phylogenetic tree. In addition to enabling clinicians to devise patient-specific treatment plans, phylogenetic trees of tumors enable researchers to decipher the mechanisms of tumorigenesis and metastasis. However, the problem of reconstructing a phylogenetic tree T given bulk sequencing data from a tumor is more complicated than the classic phylogeny inference problem. Rather than observing the leaves of T directly, we are given mutation frequencies that are the result of mixtures of the leaves of T. The majority of current tumor phylogeny inference methods employ the perfect phylogeny evolutionary model. The underlying Perfect Phylogeny Mixture (PPM) combinatorial problem typically has multiple solutions.

Results

We prove that determining the exact number of solutions to the PPM problem is #P-complete and hard to approximate within a constant factor. Moreover, we show that sampling solutions uniformly at random is hard as well. On the positive side, we provide a polynomial-time computable upper bound on the number of solutions and introduce a simple rejection-sampling based scheme that works well for small instances. Using simulated and real data, we identify factors that contribute to and counteract non-uniqueness of solutions. In addition, we study the sampling performance of current methods, identifying significant biases.

Conclusions

Awareness of non-uniqueness of solutions to the PPM problem is key to drawing accurate conclusions in downstream analyses based on tumor phylogenies. This work provides the theoretical foundations for non-uniqueness of solutions in tumor phylogeny inference from bulk DNA samples.

Intratumor heterogeneity index of breast carcinomas based on DNA methylation profiles

Background

Cancer cells evolve and constitute heterogeneous populations that fluctuate in space and time and are subjected to selection generating intratumor heterogeneity. This phenomenon is determined by the acquisition of genetic/epigenetic alterations and their selection over time which has clinical implications on drug resistance.

Methods

DNA extracted from different tumor cell populations (breast carcinomas, cancer cell lines and cellular clones) were analyzed by MS-MLPA. Methylation profiles were used to generate a heterogeneity index to quantify the magnitude of epigenetic heterogeneity in these populations. Cellular clones were obtained from single cells derived of MDA-MB 231 cancer cell lines applying serial limiting dilution method and morphology was analyzed by optical microscopy and flow cytometry. Clones characteristics were examined through cellular proliferation, migration capacity and apoptosis. Heterogeneity index was also calculated from beta values derived from methylation profiles of TCGA tumors.

Results

The study of methylation profiles of 23 fresh breast carcinomas revealed heterogeneous allele populations in these tumor pieces. With the purpose to measure the magnitude of epigenetic heterogeneity, we developed an heterogeneity index based on methylation information and observed that all tumors present their own heterogeneity level. Applying the index calculation in pure cancer cell populations such as cancer cell lines (MDA-MB 231, MCF-7, T47D, HeLa and K-562), we also observed epigenetic heterogeneity. In addition, we detected that clones obtained from the MDA-MB 231 cancer cell line generated their own new heterogeneity over time. Using TCGA tumors, we determined that the heterogeneity index correlated with prognostic and predictive factors like tumor size (p = 0.0088), number of affected axillary nodes (p = 0.007), estrogen receptor expression (p < 0.0001) and HER2 positivity (p = 0.0007). When we analyzed molecular subtypes we found that they presented different heterogeneity levels. Interestingly, we also observed that all mentioned tumor cell populations shared a similar Heterogeneity index (HI) mean.

Conclusions

Our results show that each tumor presents a unique epigenetic heterogeneity level, which is associated with prognostic and predictive factors. We also observe that breast tumor subtypes differ in terms of epigenetic heterogeneity, which could serve as a new contribution to understand the different prognosis of these groups.

Nonlinear adaptive control of competitive release and chemotherapeutic resistance

We use a three-component replicator system with healthy cells, sensitive cells, and resistant cells, with a prisoner's dilemma payoff matrix from evolutionary game theory, to model and control the nonlinear dynamical system governing the ecological mechanism of competitive release by which tumors develop chemotherapeutic resistance. The control method we describe is based on nonlinear trajectory design and energy transfer methods first introduced in the orbital mechanics literature for Hamiltonian systems. For continuous therapy, the basin boundaries of attraction associated with the chemo-sensitive population and the chemo-resistant population for increasing values of chemo-concentrations have an intertwined spiral structure with extreme sensitivity to changes in chemo-concentration level as well as sensitivity with respect to resistant mutations. For time-dependent therapies, we introduce an orbit transfer method to construct continuous families of periodic (closed) orbits by switching the chemo-dose at carefully chosen times and appropriate levels to design schedules that are superior to both maximum tolerated dose (MTD) schedules and low-dose metronomic (LDM) schedules, both of which ultimately lead to fixation of sensitive cells or resistant cells. By keeping the three subpopulations of cells in competition with each other indefinitely, we avoid fixation of the cancer cell population and regrowth of a resistant tumor. The method can be viewed as a way to dynamically shape the average population fitness landscape of a tumor to steer the chemotherapeutic response curve. We show that the method is remarkably insensitive to initial conditions and small changes in chemo-dosages, an important criterion for turning the method into an actionable strategy.

Cellular interactions constrain tumor growth

A tumor is made up of a heterogeneous collection of cell types, all competing on a fitness landscape mediated by microenvironmental conditions that dictate their interactions. Despite the fact that much is known about cell signaling, cellular cooperation, and the functional constraints that affect cellular behavior, the specifics of how these constraints (and the range over which they act) affect the macroscopic tumor growth laws that govern total volume, mass, and carrying capacity remain poorly understood. We develop a statistical mechanics approach that focuses on the total number of possible states each cell can occupy and show how different assumptions on correlations of these states give rise to the many different macroscopic tumor growth laws used in the literature. Although it is widely understood that molecular and cellular heterogeneity within a tumor is a driver of growth, here we emphasize that focusing on the functional coupling of states at the cellular level is what determines macroscopic growth characteristics.

Capitalizing on competition: An evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment

The development of chemotherapeutic resistance resulting in tumor relapse is largely the consequence of the mechanism of competitive release of pre-existing resistant tumor cells selected for regrowth after chemotherapeutic agents attack the previously dominant chemo-sensitive population. We introduce a prisoner's dilemma game theoretic mathematical model based on the replicator of three competing cell populations: healthy (cooperators), sensitive (defectors), and resistant (defectors) cells. The model is shown to recapitulate prostate-specific antigen measurement data from three clinical trials for metastatic castration-resistant prostate cancer patients treated with 1) prednisone, 2) mitoxantrone and prednisone and 3) docetaxel and prednisone. Continuous maximum tolerated dose schedules reduce the sensitive cell population, initially shrinking tumor burden, but subsequently "release" the resistant cells from competition to re-populate and re-grow the tumor in a resistant form. The evolutionary model allows us to quantify responses to conventional (continuous) therapeutic strategies as well as to design adaptive strategies.These novel adaptive strategies are robust to small perturbations in timing and extend simulated time to relapse from continuous therapy administration.

Phylogenetic Copy-Number Factorization of Multiple Tumor Samples

Cancer is an evolutionary process driven by somatic mutations. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the many types of mutations in cancer and the fact that nearly all cancer sequencing is of a bulk tumor, measuring a superposition of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy-number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy-number data from multiple samples of a tumor. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that either perform deconvolution/factorization of mixed tumor samples or build phylogenetic trees assuming homogeneous tumor samples. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher resolution view of copy-number evolution of this cancer than published analyses.

Cancer cells copy migratory behavior and exchange signaling networks via extracellular vesicles

Recent data showed that cancer cells from different tumor subtypes with distinct metastatic potential influence each other's metastatic behavior by exchanging biomolecules through extracellular vesicles (EVs). However, it is debated how small amounts of cargo can mediate this effect, especially in tumors where all cells are from one subtype, and only subtle molecular differences drive metastatic heterogeneity. To study this, we have characterized the content of EVs shed in vivo by two clones of melanoma (B16) tumors with distinct metastatic potential. Using the Cre‐LoxP system and intravital microscopy, we show that cells from these distinct clones phenocopy their migratory behavior through EV exchange. By tandem mass spectrometry and RNA sequencing, we show that EVs shed by these clones into the tumor microenvironment contain thousands of different proteins and RNAs, and many of these biomolecules are from interconnected signaling networks involved in cellular processes such as migration. Thus, EVs contain numerous proteins and RNAs and act on recipient cells by invoking a multi‐faceted biological response including cell migration.

Clonal evolution and heterogeneity in metastatic head and neck cancer-An analysis of the Austrian Study Group of Medical Tumour Therapy study group

BACKGROUND

Tumour heterogeneity and clonal evolution within a cancer patient are deemed responsible for relapse in malignancies and present challenges to the principles of targeted therapy, for which treatment modality is often decided based on the molecular pathology of the primary tumour. Nevertheless, the clonal architecture in distant relapse of head and neck cancer is fairly unknown.

PATIENTS AND METHODS

For this project, we analysed a cohort of 386 patients within the Austrian Registry of head and neck cancer. We identified 26 patients with material from the primary tumour, the distant metastasis after curative first-line treatment and a germline sample for analysis of clonal evolution. After pathological analyses, these samples were analysed using a targeted massively parallel sequencing (MPS) panel of 257 genes known to be recurrently mutated in head and neck cancer plus a genome-wide SNP-set.

RESULTS

Despite histological diagnosis of distant metastasis, no corresponding mutation in the supposed metastases was found in two of 23 (8.6%) evaluable patients suggesting a primary tumour of the lung instead of a distant metastasis of head and neck cancer. We observed a branched pattern of evolution in 31.6% of the analysed patients. This pattern was associated with a shorter time to distant metastasis, compared with a pattern of punctuated evolution. Structural genomic changes over time were also present in 7 of 12 (60%) evaluable patients with metachronous metastases.

CONCLUSION

Targeted MPS demonstrated substantial heterogeneity at the time of diagnosis and a complex pattern of evolution during disease progression in head and neck cancer. Copy number analyses revealed additional changes that were not detected by mutational analyses. Mutational and structural changes contribute to tumour heterogeneity at diagnosis and progression.

Prognostic implications of HER2 heterogeneity in gastric cancer

The prognostic implications of human epidermal growth receptor 2 (HER2) heterogeneity in gastric cancer (GC) are not well established. Therefore, the aim of the present study was to determine to the effect of HER2 status on the prognosis of GC patients. We retrieved data on 248 pathologically-confirmed, consecutive patients with primary adenocarcinoma of the stomach or gastro-esophageal junction who underwent surgical resection at Kurume University Medical Center between July 2000 and December 2012. HER2 status was classified as HER2 positive or negative and HER2 heterogeneity or homogeneity. The endpoint was overall survival (OS), which was compared using the generalized Wilcoxon test. HER2 status was positive in 36 patients (14.5%) and negative in 212 patients (85.5%). Among the 36 HER2 positive patients, 25 patients (69.4%) had HER2 heterogeneity and the remaining 11 patients (30.6%) had HER2 homogeneity. Among the 141 patients with stage III or IV disease, the prognosis of the HER2 homogeneity group was significantly worse than that of the HER2 heterogeneity group (p = 0.019; median OS 193 and 831 days, respectively). The prognosis was not significantly different between the HER2 positive group and the HER2 negative group (p = 0.84; median OS 552 and 556 days, respectively). The present study was conducted with small samples, however, the results of the study suggest that HER2 homogeneity but not HER2 positivity may represent a prognostic indicator in GC.

Comparative Genomic Profiling of Matched Primary and Metastatic Tumors in Renal Cell Carcinoma

BACKGROUND

Next-generation sequencing (NGS) studies of matched pairs of primary and metastatic tumors in renal cell carcinoma (RCC) have been limited to small cohorts.

OBJECTIVE

To evaluate the discordance in somatic mutations between matched primary and metastatic RCC tumors.

DESIGN, SETTING, AND PARTICIPANTS

Primary tumor (P), metastasis (M), and germline DNA from 60 patients with RCC was subjected to NGS with a targeted exon capture-based assay of 341 cancer-associated genes. Somatic mutations were called using a validated pipeline.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Mutations were classified as shared (S) or private (Pr) in relation to each other within individual P-M pairs. The concordance score was calculated as (S-Pr)/(S+Pr). To calculate enrichment of Pr/S mutations for a particular gene, we calculated a two-sided p value from a binomial model for each gene with at least ten somatic mutation events, and also implemented a separate permutation test procedure. We adjusted p values for multiple hypothesis testing using the Benjamini-Hochberg procedure. The mutation discordance was calculated using Mann-Whitney U tests according to gene mutations or metastatic sites.

RESULTS AND LIMITATIONS

Twenty-one pairs (35%) showed Pr mutations in both P and M samples. Of the remaining 39 pairs (65%), 14 (23%) had Pr mutations specific to P samples, 12 (20%) had Pr mutations to M samples, and 13 (22%) had identical somatic mutations. No individual gene mutation was preferentially enriched in either P or M samples. P-M pairs with SETD2 mutations demonstrated higher discordance than pairs with wild-type SETD2. We observed that patients who received therapy before sampling of the P or M tissue had higher concordance of mutations for P-M pairs than patients who did not (Mann-Whitney p=0.088).

CONCLUSIONS

Our data show mutation discordance within matched P-M RCC tumor pairs. As most contemporary precision medicine trials do not differentiate mutations detected in P and M tumors, the prognostic and predictive value of mutations in P versus M tumors warrants further investigation.

PATIENT SUMMARY

In this study we evaluated the concordance of mutations between matched primary and metastatic tumors for 60 kidney cancer patients using a panel of 341 cancer genes. Forty-seven patients carried nonidentical cancer gene mutations within their matched primary-metastatic pair. The mutation profile of the primary tumor alone could compromise precision in selecting effective targeted therapies and result in suboptimal clinical outcomes.

Homeostasis Back and Forth: An Ecoevolutionary Perspective of Cancer

The role of genetic mutations in cancer is indisputable: They are a key source of tumor heterogeneity and drive its evolution to malignancy. But, the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here, we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, because they perturb the normal regulatory dynamics of these tissues, often coopting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor-centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This ecoevolutionary view of cancer requires mechanistic mathematical models to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.

Computational Methods for Characterizing Cancer Mutational Heterogeneity

Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.

phyC: Clustering cancer evolutionary trees

Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC.

Elucidating the differences between cancer evolutionary patterns among patients is valuable in personalized medicine, since therapeutic response mostly depends on cancer evolution process. Recently, computational methods have been extensively studied to reconstruct a cancer evolutionary pattern within a patient, which is visualized as a so-called “cancer evolutionary tree” constructed from multi-regional sequencing data. However, there have been few studies on comparisons of a set of cancer evolutionary trees to better understand the relationship between a set of cancer evolutionary patterns and patient phenotypes. Given a set of tree objects for multiple patients, we propose an unsupervised learning approach to identify subgroups of patients through clustering the respective cancer evolutionary trees. Using this approach, we effectively identified the patterns of different evolutionary modes in a simulation analysis, and also successfully detected the phenotype-related and cancer type-related subgroups to characterize tree structures within subgroups using actual datasets. We believe that the value and impact of our work will grow as more and more datasets for the cancer evolution of patients become available.

Overcome tumor heterogeneity-imposed therapeutic barriers through convergent genomic biomarker discovery: A braided cancer river model of kidney cancer

Tumor heterogeneity, encompassing genetic, epigenetic, and microenvironmental variables, is extremely complex and presents challenges to cancer diagnosis and therapy. Genomic efforts on genetic intratumor heterogeneity (G-ITH) confirm branched evolution, support the trunk-branch cancer model, and present a seemingly insurmountable obstacle to conquering cancers. G-ITH is conspicuous in clear cell renal cell carcinoma (ccRCC), where its presence complicates identification and validation of biomarkers and thwarts efforts in advancing precision cancer therapeutics. However, long-term clinical benefits on targeted therapy are not uncommon in metastatic ccRCC patients, implicating that there are underlying constraints during ccRCC evolution, which in turn force a nonrandom sequence of parallel gene/pathway/function/phenotype convergence within individual tumors. Accordingly, we proposed a "braided cancer river model" depicting ccRCC evolution, which deduces cancer development based on multiregion tumor genomics of exceptional mTOR inhibitor (mTORi) responders. Furthermore, we employ an outlier case to explore the river model and highlight the importance of "Five NGS Matters: Number, Frequency, Position, Site and Time" in assessing cancer genomics for precision medicine. This mutable cancer river model may capture clinically significant phenotype-convergent events, predict vulnerability/resistance mechanisms, and guide effective therapeutic strategies. Our model originates from studying exceptional responders in ccRCC, which warrants further refinement and future validation concerning its applicability to other cancer types. The goal of this review is employing kidney cancer as an example to illustrate critical issues concerning tumor heterogeneity.

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

Renal cell carcinoma

Renal cell carcinoma (RCC) denotes cancer originated from the renal epithelium and accounts for >90% of cancers in the kidney. The disease encompasses >10 histological and molecular subtypes, of which clear cell RCC (ccRCC) is most common and accounts for most cancer-related deaths. Although somatic VHL mutations have been described for some time, more-recent cancer genomic studies have identified mutations in epigenetic regulatory genes and demonstrated marked intra-tumour heterogeneity, which could have prognostic, predictive and therapeutic relevance. Localized RCC can be successfully managed with surgery, whereas metastatic RCC is refractory to conventional chemotherapy. However, over the past decade, marked advances in the treatment of metastatic RCC have been made, with targeted agents including sorafenib, sunitinib, bevacizumab, pazopanib and axitinib, which inhibit vascular endothelial growth factor (VEGF) and its receptor (VEGFR), and everolimus and temsirolimus, which inhibit mechanistic target of rapamycin complex 1 (mTORC1), being approved. Since 2015, agents with additional targets aside from VEGFR have been approved, such as cabozantinib and lenvatinib; immunotherapies, such as nivolumab, have also been added to the armamentarium for metastatic RCC. Here, we provide an overview of the biology of RCC, with a focus on ccRCC, as well as updates to complement the current clinical guidelines and an outline of potential future directions for RCC research and therapy.

Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature

Background:

Tumours are diverse ecosystems with persistent heterogeneity in various cancer hallmarks like self-sufficiency of growth factor production for angiogenesis and reprogramming of energy metabolism for aerobic glycolysis. This heterogeneity has consequences for diagnosis, treatment and disease progression.

Methods:

We introduce the double goods game to study the dynamics of these traits using evolutionary game theory. We model glycolytic acid production as a public good for all tumour cells and oxygen from vascularisation via vascular endothelial growth factor production as a club good benefiting non-glycolytic tumour cells. This results in three viable phenotypic strategies: glycolytic, angiogenic and aerobic non-angiogenic.

Results:

We classify the dynamics into three qualitatively distinct regimes: (1) fully glycolytic; (2) fully angiogenic; or (3) polyclonal in all three cell types. The third regime allows for dynamic heterogeneity even with linear goods, something that was not possible in prior public good models that considered glycolysis or growth factor production in isolation.

Conclusions:

The cyclic dynamics of the polyclonal regime stress the importance of timing for anti-glycolysis treatments like lonidamine. The existence of qualitatively different dynamic regimes highlights the order effects of treatments. In particular, we consider the potential of vascular normalisation as a neoadjuvant therapy before follow-up with interventions like buffer therapy.

Tumor evolution: Linear, branching, neutral or punctuated?

Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

The challenges of tumor genetic diversity

The authors review and discuss the implications of genomic analyses documenting the diversity of tumors, both among patients and within individual tumors. Genetic diversity among solid tumors limits targeted therapies, because few mutations that drive tumors are both targetable and at high prevalence. Many more driver mutations and how they affect cellular signaling pathways must be identified if targeted therapy is to become widely useful. Genetic diversity within a tumor-intratumor genetic heterogeneity-makes the tumor a collection of subclones: related yet distinct cancers. Selection for pre-existing, resistant subclones by conventional or targeted therapies may explain many treatment failures. Immune therapy faces the same fundamental challenges. Nevertheless, the processes that generate and maintain heterogeneity might provide novel therapeutic targets. Addressing both types of diversity requires genomic tumor analyses linked to detailed clinical data. The trend toward sequencing restricted cancer gene panels, however, limits the ability to discover new driver mutations and assess intratumor heterogeneity. Clinical data currently collected with genomic analyses often lack critical information, substantially limiting their use in understanding tumor diversity. Now that diversity among and within tumors can no longer be ignored, research and clinical practice must adapt to take diversity into account. Cancer 2017;123:917-27. © 2016 American Cancer Society.

Multi-site tumor sampling (MSTS) improves the performance of histological detection of intratumor heterogeneity in clear cell renal cell carcinoma (CCRCC)

Current standard-of-care tumor sampling protocols for CCRCC (and other cancers) are not efficient at detecting intratumoural heterogeneity (ITH). We have demonstrated in silico that an alternative protocol, multi-site tumor sampling (MSTS) based upon the divide and conquer (DAC) algorithm, can significantly increase the efficiency of ITH detection without extra costs. Now we test this protocol on routine hematoxylin-eosin (HE) sections in a series of 38 CCRCC cases. MSTS was found to outperform traditional sampling when detecting either high grade (p=0.0136) or granular/eosinophilic cells (p=0.0114). We therefore propose that MSTS should be used in routine clinical practice.

Multi-site tumor sampling (MSTS) improves the performance of histological detection of intratumor heterogeneity in clear cell renal cell carcinoma (CCRCC)

Current standard-of-care tumor sampling protocols for CCRCC (and other cancers) are not efficient at detecting intratumoural heterogeneity (ITH). We have demonstrated in silico that an alternative protocol, multi-site tumor sampling (MSTS) based upon the divide and conquer (DAC) algorithm, can significantly increase the efficiency of ITH detection without extra costs. Now we test this protocol on routine hematoxylin-eosin (HE) sections in a series of 38 CCRCC cases. MSTS was found to outperform traditional sampling when detecting either high grade (p=0.0136) or granular/eosinophilic cells (p=0.0114). We therefore propose that MSTS should be used in routine clinical practice.