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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Mathematical analysis identifies the optimal treatment strategy for epidermal growth factor receptor-mutated non-small cell lung cancer

Introduction

In Asians, more than half of non-small cell lung cancers (NSCLC) are induced by epidermal growth factor receptor (EGFR) mutations. Although patients carrying EGFR driver mutations display a good initial response to EGFR-Tyrosine Kinase Inhibitors (EGFR-TKIs), additional mutations provoke drug resistance. Hence, predicting tumor dynamics before treatment initiation and formulating a reasonable treatment schedule is an urgent challenge.

Methods

To overcome this problem, we constructed a mathematical model based on clinical observations and investigated the optimal schedules for EGFR-TKI therapy.

Results

Based on published data on cell growth rates under different drugs, we found that using osimertinib that are efficient for secondary resistant cells as the first-line drug is beneficial in monotherapy, which is consistent with published clinical statistical data. Moreover, we identified the existence of a suitable drug-switching time; that is, changing drugs too early or too late was not helpful. Furthermore, we demonstrate that osimertinib combined with erlotinib or gefitinib as first-line treatment, has the potential for clinical application. Finally, we examined the relationship between the initial ratio of resistant cells and final cell number under different treatment conditions, and summarized it into a therapy suggestion map. By performing parameter sensitivity analysis, we identified the condition where　osimertinib-first therapy was recommended as the optimal treatment option.

Discussion

This study for the first time theoretically showed the optimal treatment strategies based on the known information in NSCLC. Our framework can be applied to other types of cancer in the future.

Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles

Introduction

Local and regional recurrence after surgical intervention is a significant problem in cancer management. The multistage theory of carcinogenesis precisely places the presence of histologically normal but mutated premalignant lesions surrounding the tumor - field cancerization, as a significant cause of cancer recurrence. The relationship between tissue dynamics, cancer initiation and cancer recurrence in multistage carcinogenesis is not well known.

Methods

This study constructs a computational model for cancer initiation and recurrence by combining the Moran and branching processes in which cells requires 3 or more mutations to become malignant. In addition, a spatial structure-setting is included in the model to account for positional relativity in cell turnover towards malignant transformation. The model consists of a population of normal cells with no mutation; several populations of premalignant cells with varying number of mutations and a population of malignant cells. The model computes a stage of cancer detection and surgery to eliminate malignant cells but spares premalignant cells and then estimates the time for malignant cells to re-emerge.

Results

We report the cellular conditions that give rise to different patterns of cancer initiation and the conditions favoring a shorter cancer recurrence by analyzing premalignant cell types at the time of surgery. In addition, the model is fitted to disease-free clinical data of 8,957 patients in 27 different cancer types; From this fitting, we estimate the turnover rate per month, relative fitness of premalignant cells, growth rate and death rate of cancer cells in each cancer type.

Discussion

Our study provides insights into how to identify patients who are likely to have a shorter recurrence and where to target the therapeutic intervention.

HLA Class I Analysis Provides Insight Into the Genetic and Epigenetic Background of Immune Evasion in Colorectal Cancer With High Microsatellite Instability

BACKGROUND & AIMS

A detailed understanding of antitumor immunity is essential for optimal cancer immune therapy. Although defective mutations in the B2M and HLA-ABC genes, which encode molecules essential for antigen presentation, have been reported in several studies, the effects of these defects on tumor immunity have not been quantitatively evaluated.

METHODS

Mutations in HLA-ABC genes were analyzed in 114 microsatellite instability-high colorectal cancers using a long-read sequencer. The data were further analyzed in combination with whole-exome sequencing, transcriptome sequencing, DNA methylation array, and immunohistochemistry data.

RESULTS

We detected 101 truncating mutations in 57 tumors (50%) and loss of 61 alleles in 21 tumors (18%). Based on the integrated analysis that enabled the immunologic subclassification of microsatellite instability-high colorectal cancers, we identified a subtype of tumors in which lymphocyte infiltration was reduced, partly due to reduced expression of HLA-ABC genes in the absence of apparent genetic alterations. Survival time of patients with such tumors was shorter than in patients with other tumor types. Paradoxically, tumor mutation burden was highest in the subtype, suggesting that the immunogenic effect of accumulating mutations was counterbalanced by mutations that weakened immunoreactivity. Various genetic and epigenetic alterations, including frameshift mutations in RFX5 and promoter methylation of PSMB8 and HLA-A, converged on reduced expression of HLA-ABC genes.

CONCLUSIONS

Our detailed immunogenomic analysis provides information that will facilitate the improvement and development of cancer immunotherapy.

Mathematical Modeling of Locoregional Recurrence Caused by Premalignant Lesions Formed Before Initial Treatment

Locoregional recurrence after surgery is a major unresolved issue in cancer treatment. Premalignant lesions are considered a cause of cancer recurrence. A study showed that premalignant lesions surrounding the primary tumor drove a high local cancer recurrence rate after surgery in head and neck cancer. Based on the multistage theory of carcinogenesis, cells harboring an intermediate number of mutations are not cancer cells yet but have a higher risk of becoming cancer than normal cells. This study constructed a mathematical model for cancer initiation and recurrence by combining the Moran and branching processes in which cells require two specific mutations to become malignant. There are three populations in this model: (i) normal cells with no mutation, (ii) premalignant cells with one mutation, and (iii) cancer cells with two mutations. The total number of healthy tissue is kept constant to represent homeostasis, and there is a rare chance of mutation every time a cell divides. If a cancer cell with two mutations arises, the cancer population proliferates, violating the homeostatic balance of the tissue. Once the number of cancer cells reaches a certain size, we conduct computational resection and remove the cancer cell population, keeping the ratio of normal and premalignant cells in the tissue unchanged. After surgery, we considered tissue dynamics and eventually observed the second appearance of cancer cells as recurrence. Consequently, we computationally revealed the conditions where the time to recurrence became short by parameter sensitivity analysis. Particularly, when the premalignant cells’ fitness is higher than normal cells, the proportion of premalignant cells becomes large after the surgical resection. Moreover, the mathematical model was fitted to clinical data on disease-free survival of 1,087 patients in 23 cancer types from the TCGA database. Finally, parameter values of tissue dynamics are estimated for each cancer type, where the likelihood of recurrence can be elucidated. Thus, our approach provides insights into the concept to identify the patients likely to experience recurrence as early as possible.

Mathematical Modeling and Mutational Analysis Reveal Optimal Therapy to Prevent Malignant Transformation in Grade II IDH-Mutant Gliomas

Isocitrate dehydrogenase-mutant low-grade gliomas (IDHmut-LGG) grow slowly but frequently undergo malignant transformation, which eventually leads to premature death. Chemotherapy and radiotherapy treatments prolong survival, but can also induce genetic (or epigenetic) alterations involved in transformation. Here, we developed a mathematical model of tumor progression based on serial tumor volume data and treatment history of 276 IDHmut-LGGs classified by chromosome 1p/19q codeletion (IDH^mut/1p19q^codel and IDH^mut/1p19q^noncodel) and performed genome-wide mutational analyses, including targeted sequencing and longitudinal whole-exome sequencing data. These analyses showed that tumor mutational burden correlated positively with malignant transformation rate, and chemotherapy and radiotherapy significantly suppressed tumor growth but increased malignant transformation rate per cell by 1.8 to 2.8 times compared with before treatment. This model revealed that prompt adjuvant chemoradiotherapy prolonged malignant transformation-free survival in small IDHmut-LGGs (≤ 50 cm³). Furthermore, optimal treatment differed according to genetic alterations for large IDHmut-LGGs (> 50 cm³); adjuvant therapies delayed malignant transformation in IDH^mut/1p19q^noncodel but often accelerated it in IDH^mut/1p19q^codel. Notably, PI3K mutation was not associated with malignant transformation but increased net postoperative proliferation rate and decreased malignant transformation-free survival, prompting the need for adjuvant therapy in IDH^mut/1p19q^codel. Overall, this model uncovered therapeutic strategies that could prevent malignant transformation and, consequently, improve overall survival in patients with IDHmut-LGGs. SIGNIFICANCE: A mathematical model successfully estimates malignant transformation-free survival and reveals a link between genetic alterations and progression, identifying precision medicine approaches for optimal treatment of IDH-mutant low-grade gliomas.

Abstract 2048: Property analysis of chromosomal instability-adapted cells using multi-omics approaches

Background: Chromosomal instability (CIN), which includes polyploidy and aneuploidy, is characterized as the pillar of cancer hallmarks. CIN is observed in >90% solid tumors, being correlated with malignancy grades, poor prognosis, and drug resistance. However, it is still unclear yet how aneuploidy triggers cancer initiation/progression, or how aneuploidy gives negative impacts on cancer therapeutics. In this study, we established our original CIN cell line model, aiming to understand biological significance of cancer aneuploidy on the CIN-mediated stress adaptation.Method: A colorectal cancer cell, HCT116, was treated with an Aurora-B kinase inhibitor for 24 hours to transiently induce CIN in these cells, and then cultured in a drug-free medium for >10 days to establish “CIN-adapted cells”. Cell growth was evaluated by intracellular ATP concentrations. RNAs and DNAs extracted from the cells were subjected to RNA-seq, Exome-seq, and ATAC-seq by the NovaSeq sequencing systems. The cell lysates were subjected to metabolomics analyses by GC-MS system. Bioinformatics analyses were performed using these multiple-omics data.Results: In 10 days after aneuploid formation, 90% aneuploid cells were killed, while 10% aneuploid cells were proliferative with a high growth rate equivalent to that of the parental euploid cells, which appeared to overcome the CIN-mediated antiproliferative pressure. The CIN-adapted cells revealed to be highly sensitive to the low-glucose and hypoxic condition, suggesting that the metabolic pathways would be modulated to adjust the cells to the aneuploid-mediated stress. Transcriptome and metabolome analyses also revealed the gene expressions and the metabolites for the lipid or amino acids metabolisms were significantly changed in the CIN-adapted cells. These findings suggest that the CIN-adapted cells appear to restructure their signaling networks of the lipid and amino acid metabolic pathways, which is expected to play important roles for the aneuploid cells to survive under the CIN-mediated stress. We perform drug screening to identify essential factors in the process leading to CIN-adapted, aiming for a comprehensive understanding of the molecular mechanism and signal networks.Conclusions: We comprehensively characterized the CIN-adapted cancer cell model by the multi-omics approaches. These findings will help us to deeply understand the biological significance of CIN in cancer cells, providing a new insight to novel cancer therapeutics targeting CIN.

Citation Format: Tomoko Yamamori Morita, Hiroshi Haeno, Hideki Makinoshima, Ayako Suzuki, Susumu S. Kobayashi, Akihiro Ohashi. Property analysis of chromosomal instability-adapted cells using multi-omics approaches [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2048.

A CDC7 inhibitor sensitizes DNA-damaging chemotherapies by suppressing homologous recombination repair to delay DNA damage recovery

Cell division cycle 7 (CDC7), a serine/threonine kinase, plays important roles in DNA replication. We developed a highly specific CDC7 inhibitor, TAK-931, as a clinical cancer therapeutic agent. This study aimed to identify the potential combination partners of TAK-931 for guiding its clinical development strategies. Unbiased high-throughput chemical screening revealed that the highest synergistic antiproliferative effects observed were the combinations of DNA-damaging agents with TAK-931. Functional phosphoproteomic analysis demonstrated that TAK-931 suppressed homologous recombination repair activity, delayed recovery from double-strand breaks, and led to accumulation of DNA damages in the combination. Whole-genome small interfering RNA library screening identified sensitivity-modulating molecules, which propose the experimentally predicted target cancer types for the combination, including pancreatic, esophageal, ovarian, and breast cancers. The efficacy of combination therapy in these cancer types was preclinically confirmed in the corresponding primary-derived xenograft models. Thus, our findings would be helpful to guide the future clinical strategies for TAK-931.

Combination treatment with a PI3K/Akt/mTOR pathway inhibitor overcomes resistance to anti-HER2 therapy in PIK3CA-mutant HER2-positive breast cancer cells

Amplification and/or overexpression of human epidermal growth factor receptor 2 (HER2) are observed in 15–20% of breast cancers (HER2+ breast cancers), and anti-HER2 therapies have significantly improved prognosis of patients with HER2+ breast cancer. One resistance mechanism to anti-HER2 therapies is constitutive activation of the phosphoinositide 3-kinase (PI3K) pathway. Combination therapy with small-molecule inhibitors of AKT and HER2 was conducted in HER2+ breast cancer cell lines with or without PIK3CA mutations, which lead to constitutive activation of the PI3K pathway. PIK3CA mutations played important roles in resistance to single-agent anti-HER2 therapy in breast cancer cell lines. Combination therapy of a HER2 inhibitor and an AKT inhibitor, as well as other PI3K pathway inhibitors, could overcome the therapeutic limitations associated with single-agent anti-HER2 treatment in PIK3CA-mutant HER2+ breast cancer cell lines. Furthermore, expression of phosphorylated 4E-binding protein 1 (p4EBP1) following the treatment correlated with the antiproliferative activities of the combination, suggesting that p4EBP1 may have potential as a prognostic and/or efficacy-linking biomarkers for these combination therapies in patients with HER2+ breast cancer. These findings highlight potential clinical strategies using combination therapy to overcome the limitations associated with single-agent anti-HER2 therapies in patients with HER2+ breast cancer.

Multifocal origin of occupational cholangiocarcinoma revealed by comparison of multilesion mutational profiles

Recently identified occupational cholangiocarcinoma among printing workers is characterized by chronic bile duct injuries and precancerous or early cancerous lesions at multiple sites of the bile ducts. These observations suggested the potential multifocal carcinogenesis of the disease. We performed whole-exome analysis of multiple lesions, including the invasive carcinomas and precancerous lesions of four occupational cholangiocarcinoma cases. A much higher mutation burden was observed in both the invasive carcinomas (mean 76.3/Mb) and precancerous lesions (mean 71.8/Mb) than in non-occupational cholangiocarcinomas (mean 1.6/Mb). Most somatic mutations identified in 11 of 16 lesions did not overlap with each other. In contrast, a unique trinucleotide mutational signature of GpCpY to GpTpY was shared among the lesions. These results suggest that most of these lesions are multiclonal in origin and that common mutagenic processes, which may be induced by exposure to haloalkanes or their metabolites, generated somatic mutations at different sites of the bile ducts. A similarly high mutation rate had already been identified in the precancerous lesions, implying an increased potential for carcinogenesis throughout the biliary tree. These genomic features support the importance of ongoing close follow-up of the patients as a group at high risk of recurrence.

Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion

Despite recent progress in diagnostic and multimodal treatment approaches, most cancer deaths are still caused by metastatic spread and the subsequent growth of tumor cells in sites distant from the primary organ. So far, few quantitative studies are available that allow for the estimation of metastatic parameters and the evaluation of alternative treatment strategies. Most computational studies have focused on situations in which the tumor cell population expands exponentially over time; however, tumors may eventually be subject to resource and space limitations so that their growth patterns deviate from exponential growth to adhere to density-dependent growth models. In this study, we developed a stochastic evolutionary model of cancer progression that considers alterations in metastasis-related genes and intercellular growth competition leading to density effects described by logistic growth. Using this stochastic model, we derived analytical approximations for the time between the initiation of tumorigenesis and diagnosis, the expected number of metastatic sites, the total number of metastatic cells, the size of the primary tumor, and survival. Furthermore, we investigated the effects of drug administration and surgical resection on these quantities and predicted outcomes for different treatment regimens. Parameter values used in the analysis were estimated from data obtained from a pancreatic cancer rapid autopsy program. Our theoretical approach allows for flexible modeling of metastatic progression dynamics.

Liganded T3 receptor β2 inhibits the positive feedback autoregulation of the gene for GATA2, a transcription factor critical for thyrotropin production

The serum concentration of thyrotropin (thyroid stimulating hormone, TSH) is drastically reduced by small increase in the levels of thyroid hormones (T3 and its prohormone, T4); however, the mechanism underlying this relationship is unknown. TSH consists of the chorionic gonadotropin α (CGA) and the β chain (TSHβ). The expression of both peptides is induced by the transcription factor GATA2, a determinant of the thyrotroph and gonadotroph differentiation in the pituitary. We previously reported that the liganded T3 receptor (TR) inhibits transactivation activity of GATA2 via a tethering mechanism and proposed that this mechanism, but not binding of TR with a negative T3-responsive element, is the basis for the T3-dependent inhibition of the TSHβ and CGA genes. Multiple GATA-responsive elements (GATA-REs) also exist within the GATA2 gene itself and mediate the positive feedback autoregulation of this gene. To elucidate the effect of T3 on this non-linear regulation, we fused the GATA-REs at -3.9 kb or +9.5 kb of the GATA2 gene with the chloramphenicol acetyltransferase reporter gene harbored in its 1S-promoter. These constructs were co-transfected with the expression plasmids for GATA2 and the pituitary specific TR, TRβ2, into kidney-derived CV1 cells. We found that liganded TRβ2 represses the GATA2-induced transactivation of these reporter genes. Multi-dimensional input function theory revealed that liganded TRβ2 functions as a classical transcriptional repressor. Then, we investigated the effect of T3 on the endogenous expression of GATA2 protein and mRNA in the gonadotroph-derived LβT2 cells. In this cell line, T3 reduced GATA2 protein independently of the ubiquitin proteasome system. GATA2 mRNA was drastically suppressed by T3, the concentration of which corresponds to moderate hypothyroidism and euthyroidism. These results suggest that liganded TRβ2 inhibits the positive feedback autoregulation of the GATA2 gene; moreover this mechanism plays an important role in the potent reduction of TSH production by T3.

TMOD-33. AN INTEGRATED APPROACH COMBINING MATHEMATICAL AND GENOMIC METHODS TO REVEAL THE OPTIMAL TIMING OF THERAPEUTIC INTERVENTION IN WHO GRADE II DIFFUSE GLIOMA

BACKGROUND

In WHO grade II diffuse gliomas (low-grade gliomas, hereafter called LGGs), chemotherapy and radiotherapy contribute to prolonged survival but could induce somatic mutations. The optimal timing of treatment in LGGs remain poorly understood. To delineate this, we designed a mathematical model for tumor growth and investigate the association among the treatment, malignant transformation (MT), and the accumulation of somatic mutations revealed by whole exome sequencing (WES) in LGGs.

METHODS

Totally, 290 patients with LGGs between 1990 and 2018 were analyzed. We assessed the statuses of IDH mutation and 1p19q co-deletion in all tumors. Among all, 114 patients (39%) underwent MT during follow-up periods (mean: 82.6 months). Tumor volume was evaluated with FLAIR and/or T2-weighted MRI. MT was evaluated with contrast-enhanced MRI and/or pathological diagnosis. To investigate the number of somatic mutations in a cohort of LGGs and their patient matched recurrence, WES was performed on 88 serial samples collected at least two time-points from 39 patients.

RESULTS

Oligodendroglioma, IDH-mutant and 1p/19q-codeleted (OD) showed longer transformation-free survival compared to other subtypes. An exponential model was chosen to estimate growth rate in LGGs, since the exponential model provided a better fit to our data as compared to a linear model. The growth rate significantly decreased in the middle of chemotherapy and after radiotherapy. By contrast, these treatments increased the number of somatic mutations identified by WES and the rate of MT in each subtype. The increasing number of mutations in recurrent tumors showed strong correlation with the rise in MT rate. Based on the growth rate and the risk of MT, optimal timing of treatments could be calculated for each genetic subtype.

CONCLUSIONS

The mathematical model and WES analysis delineates the optimal timing of treatments in each subtype, which will help to decide the treatment for LGGs.

Metastatic seeding of human colon cancer cell clusters expressing the hybrid epithelial/mesenchymal state

Emerging evidence supports the theory that tumor cell clusters efficiently metastasize to distant organs. However, the roles of epithelial-to-mesenchymal transition (EMT) in metastasizing tumor cell clusters have not yet been fully elucidated. To investigate this issue, tumor fragments were dissected from 40 colorectal cancer (CRC) patients and implanted subcutaneously into immunodeficient mice. We observed that tumors developed from the tumor fragments obtained from 28 of the 40 CRC patients. The tumors were then dissociated into cell suspensions to be orthotopically injected into secondary mice. The tumors from 13 of the 28 patients progressed. Furthermore, metastases formed spontaneously in the liver and lungs from the tumor fragments obtained from 8 of these 13 patients. Moreover, employing a mathematical analysis, we showed that tumor cell clusters seeded these metastases significantly more often than did single tumor cells. Membrane E-cadherin- and nuclear ZEB1-positive tumor cells indicating the hybrid epithelial/mesenchymal state were also detected in primary tumors of various CRC patients, and in the corresponding patient-derived xenografts (PDXs) and circulating tumor cell clusters in the bloodstreams of mice. In contrast, ZEB1 staining was barely detectable in the patient-matched liver metastases presumably developing through mesenchymal-to-epithelial transition. Inhibition of E-cadherin or ZEB1 expression by shRNA notably prevented the PDX-derived tumor organoids from colonizing the liver, when injected intrasplenically into mice, indicating E-cadherin and ZEB1 expressions to be required for their metastatic colonization. Taken together, these findings suggest that the epithelial/mesenchymal state mediates metastatic seeding of human CRC cell clusters into distant organs.

Computational modeling of pancreatic cancer patients receiving FOLFIRINOX and gemcitabine-based therapies identifies optimum intervention strategies

Pancreatic ductal adenocarcinoma (PDAC) exhibits a variety of phenotypes with regard to disease progression and treatment response. This variability complicates clinical decision-making despite the improvement of survival due to the recent introduction of FOLFIRINOX (FFX) and nab-paclitaxel. Questions remain as to the timing and sequence of therapies and the role of radiotherapy for unresectable PDAC. Here we developed a computational analysis platform to investigate the dynamics of growth, metastasis and treatment response to FFX, gemcitabine (GEM), and GEM+nab-paclitaxel. Our approach was informed using data of 1,089 patients treated at the Massachusetts General Hospital and validated using an independent cohort from Osaka Medical College. Our framework establishes a logistic growth pattern of PDAC and defines the Local Advancement Index (LAI), which determines the eventual primary tumor size and predicts the number of metastases. We found that a smaller LAI leads to a larger metastatic burden. Furthermore, our analyses ascertain that i) radiotherapy after induction chemotherapy improves survival in cases receiving induction FFX or with larger LAI, ii) neoadjuvant chemotherapy improves survival in cases with resectable PDAC, and iii) temporary cessations of chemotherapies do not impact overall survival, which supports the feasibility of treatment holidays for patients with FFX-associated adverse effects. Our findings inform clinical decision-making for PDAC patients and allow for the rational design of clinical strategies using FFX, GEM, GEM+nab-paclitaxel, neoadjuvant chemotherapy, and radiation.

Personalized Management of Pancreatic Ductal Adenocarcinoma Patients through Computational Modeling

Phenotypic diversity in pancreatic ductal adenocarcinoma (PDAC) results in a variety of treatment responses. Rapid autopsy studies have revealed a subgroup of PDAC patients with a lower propensity to develop metastatic disease, challenging the common perception that all patients die of widely metastatic disease, but questions remain about root causes of this difference and the potential impact on treatment strategies. In this study, we addressed these questions through the development of a mathematical model of PDAC progression that incorporates the major alteration status of specific genes with predictive utility. The model successfully reproduced clinical outcomes regarding metastatic patterns and the genetic alteration status of patients from two independent cohorts from the United States and Japan. Using this model, we defined a candidate predictive signature in patients with low metastatic propensity. If a primary tumor contained a small fraction of cells with KRAS and additional alterations to CDKN2A, TP53, or SMAD4 genes, the patient was likely to exhibit low metastatic propensity. By using this predictive signature, we computationally simulated a set of clinical trials to model whether this subgroup would benefit from locally intensive therapies such as surgery or radiation therapy. The largest overall survival benefit resulted from complete resection, followed by adjuvant chemoradiation therapy and salvage therapies for isolated recurrence. While requiring prospective validation in a clinical trial, our results suggest a new tool to help personalize care in PDAC patients in seeking the most effective therapeutic modality for each individual. Cancer Res; 77(12); 3325-35. ©2017 AACR.

Correction: Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer Evolution

[This corrects the article DOI: 10.1371/journal.pgen.1005778.].

Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99); and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85-90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84-87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer Evolution

Understanding intratumor heterogeneity is clinically important because it could cause therapeutic failure by fostering evolutionary adaptation. To this end, we profiled the genome and epigenome in multiple regions within each of nine colorectal tumors. Extensive intertumor heterogeneity is observed, from which we inferred the evolutionary history of the tumors. First, clonally shared alterations appeared, in which C>T transitions at CpG site and CpG island hypermethylation were relatively enriched. Correlation between mutation counts and patients’ ages suggests that the early-acquired alterations resulted from aging. In the late phase, a parental clone was branched into numerous subclones. Known driver alterations were observed frequently in the early-acquired alterations, but rarely in the late-acquired alterations. Consistently, our computational simulation of the branching evolution suggests that extensive intratumor heterogeneity could be generated by neutral evolution. Collectively, we propose a new model of colorectal cancer evolution, which is useful for understanding and confronting this heterogeneous disease.

Cancer is heterogeneous disease; each tumor in different patients has different cancer genomes. Furthermore, another level of heterogeneity exists: even a single tumor harbors multiple genetically distinct subclones. This intratumor heterogeneity is presumably one of causes of therapeutic difficulty, and its understanding is clinically necessary. In this study, we investigated intratumor heterogeneity in colorectal cancer by analyzing sample obtained from geographically separated regions of 9 colorectal tumors. Our integrated data analyses combined with computational simulation strongly suggest that, after clonally shared alterations were accumulated by aging, numerous subclones were generated by neutral evolution. Importantly, this view can explain the robustness and evolvability of cancer: therapeutic action inducing an environmental change would convert some of the numerous neutral mutations to driver genes that confer therapeutic resistance. We believe that this study not only provides insights into colorectal cancer pathogenesis, but also constitutes a new basis for designing therapeutic strategies.

Bacterial c-di-GMP affects hematopoietic stem/progenitors and their niches through STING

Upon systemic bacterial infection, hematopoietic stem and progenitor cells (HSPCs) migrate to the periphery in order to supply a sufficient number of immune cells. Although pathogen-associated molecular patterns reportedly mediate HSPC activation, how HSPCs detect pathogen invasion in vivo remains elusive. Bacteria use the second messenger bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) for a variety of activities. Here, we report that c-di-GMP comprehensively regulated both HSPCs and their niche cells through an innate immune sensor, STING, thereby inducing entry into the cell cycle and mobilization of HSPCs while decreasing the number and repopulation capacity of long-term hematopoietic stem cells. Furthermore, we show that type I interferon acted as a downstream target of c-di-GMP to inhibit HSPC expansion in the spleen, while transforming growth factor-β was required for c-di-GMP-dependent splenic HSPC expansion. Our results define machinery underlying the dynamic regulation of HSPCs and their niches during bacterial infection through c-di-GMP/STING signaling.

Abstract 3961: Computational modeling of pancreatic cancer reveals growth and dissemination kinetics and suggests optimum treatment strategies

Pancreatic cancer is a leading cause of cancer-related death, largely due to metastatic dissemination. We investigated pancreatic cancer progression by utilizing a mathematical framework of metastasis formation together with comprehensive data of 228 patients, 101 of whom had autopsies. We found that pancreatic cancer growth is initially exponential. After estimating the rates of pancreatic cancer growth and dissemination, we determined that patients likely harbor metastases at diagnosis and predicted the number and size distribution of metastases as well as patient survival. These findings were validated in an independent database. Finally, we analyzed the effects of different treatment modalities, finding that therapies which efficiently reduce the growth rate of cells earlier in the course of treatment appear to be superior to upfront tumor resection. These predictions can be validated in the clinic. Our interdisciplinary approach provides insights into the dynamics of pancreatic cancer metastasis and identifies optimum therapeutic interventions.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3961. doi:1538-7445.AM2012-3961

Computational modeling of pancreatic cancer reveals kinetics of metastasis suggesting optimum treatment strategies

Pancreatic cancer is a leading cause of cancer-related death, largely due to metastatic dissemination. We investigated pancreatic cancer progression by utilizing a mathematical framework of metastasis formation together with comprehensive data of 228 patients, 101 of whom had autopsies. We found that pancreatic cancer growth is initially exponential. After estimating the rates of pancreatic cancer growth and dissemination, we determined that patients likely harbor metastases at diagnosis and predicted the number and size distribution of metastases as well as patient survival. These findings were validated in an independent database. Finally, we analyzed the effects of different treatment modalities, finding that therapies that efficiently reduce the growth rate of cells earlier in the course of treatment appear to be superior to upfront tumor resection. These predictions can be validated in the clinic. Our interdisciplinary approach provides insights into the dynamics of pancreatic cancer metastasis and identifies optimum therapeutic interventions.

Computational modeling of pancreatic cancer reveals kinetics of metastasis suggesting optimum treatment strategies

Pancreatic cancer is a leading cause of cancer-related death, largely due to metastatic dissemination. We investigated pancreatic cancer progression by utilizing a mathematical framework of metastasis formation together with comprehensive data of 228 patients, 101 of whom had autopsies. We found that pancreatic cancer growth is initially exponential. After estimating the rates of pancreatic cancer growth and dissemination, we determined that patients likely harbor metastases at diagnosis and predicted the number and size distribution of metastases as well as patient survival. These findings were validated in an independent database. Finally, we analyzed the effects of different treatment modalities, finding that therapies that efficiently reduce the growth rate of cells earlier in the course of treatment appear to be superior to upfront tumor resection. These predictions can be validated in the clinic. Our interdisciplinary approach provides insights into the dynamics of pancreatic cancer metastasis and identifies optimum therapeutic interventions.

A race between tumor immunoescape and genome maintenance selects for optimum levels of (epi)genetic instability

The human immune system functions to provide continuous body-wide surveillance to detect and eliminate foreign agents such as bacteria and viruses as well as the body's own cells that undergo malignant transformation. To counteract this surveillance, tumor cells evolve mechanisms to evade elimination by the immune system; this tumor immunoescape leads to continuous tumor expansion, albeit potentially with a different composition of the tumor cell population ("immunoediting"). Tumor immunoescape and immunoediting are products of an evolutionary process and are hence driven by mutation and selection. Higher mutation rates allow cells to more rapidly acquire new phenotypes that help evade the immune system, but also harbor the risk of an inability to maintain essential genome structure and functions, thereby leading to an error catastrophe. In this paper, we designed a novel mathematical framework, based upon the quasispecies model, to study the effects of tumor immunoediting and the evolution of (epi)genetic instability on the abundance of tumor and immune system cells. We found that there exists an optimum number of tumor variants and an optimum magnitude of mutation rates that maximize tumor progression despite an active immune response. Our findings provide insights into the dynamics of tumorigenesis during immune system attacks and help guide the choice of treatment strategies that best inhibit diverse tumor cell populations.

A novel tumour-suppressor function for the Notch pathway in myeloid leukaemia

Notch signaling is a central regulator of differentiation in a variety of organisms and tissue types¹. Its activity is controlled by the multi-subunit γ–secretase complex (γSE) complex². Although Notch signaling can play both oncogenic and tumor suppressor roles in solid tumors, in the hematopoietic system, it is exclusively oncogenic, notably in T cell acute lymphoblastic leukemia (T-ALL), a disease characterized by Notch1 activating mutations³. Here we identify novel somatic inactivating Notch pathway mutations in a fraction of chronic myelomonocytic leukemia (CMML) patients. Inactivation of Notch signaling in mouse hematopoietic stem cells (HSC) resulted in an aberrant accumulation of granulocyte/monocyte progenitors (GMP), extramedullary hematopoieisis and the induction of CMML-like disease. Transcriptome analysis revealed that Notch signaling regulates an extensive myelomonocytic-specific gene signature, through the direct suppression of gene transcription by the Notch target Hes1. Our studies identify a novel role for Notch signaling during early hematopoietic stem cell differentiation and suggest that the Notch pathway can play both tumor-promoting and suppressive roles within the same tissue.

The evolution of tumor metastases during clonal expansion

Cancer is a leading cause of morbidity and mortality in many countries. Solid tumors generally initiate at one particular site called the primary tumor, but eventually disseminate and form new colonies in other organs. The development of such metastases greatly diminishes the potential for a cure of patients and is thought to represent the final stage of the multi-stage progression of human cancer. The concept of early metastatic dissemination, however, postulates that cancer cell spread might arise early during the development of a tumor. It is important to know whether metastases are present at diagnosis since this determines treatment strategies and outcome. In this paper, we design a stochastic mathematical model of the evolution of tumor metastases in an expanding cancer cell population. We calculate the probability of metastasis at a given time during tumor evolution, the expected number of metastatic sites, and the total number of cancer cells as well as metastasized cells. Furthermore, we investigate the effect of drug administration and tumor resection on these quantities and predict the survival time of cancer patients. The model presented in this paper allows us to determine the probability and number of metastases at diagnosis and to identify the optimum treatment strategy to maximally prolong survival of cancer patients.

The evolution of two mutations during clonal expansion

Knudson's two-hit hypothesis proposes that two genetic changes in the RB1 gene are the rate-limiting steps of retinoblastoma. In the inherited form of this childhood eye cancer, only one mutation emerges during somatic cell divisions while in sporadic cases, both alleles of RB1 are inactivated in the growing retina. Sporadic retinoblastoma serves as an example of a situation in which two mutations are accumulated during clonal expansion of a cell population. Other examples include evolution of resistance against anticancer combination therapy and inactivation of both alleles of a metastasis-suppressor gene during tumor growth. In this article, we consider an exponentially growing population of cells that must evolve two mutations to (i) evade treatment, (ii) make a step toward (invasive) cancer, or (iii) display a disease phenotype. We calculate the probability that the population has evolved both mutations before it reaches a certain size. This probability depends on the rates at which the two mutations arise; the growth and death rates of cells carrying none, one, or both mutations; and the size the cell population reaches. Further, we develop a formula for the expected number of cells carrying both mutations when the final population size is reached. Our theory establishes an understanding of the dynamics of two mutations during clonal expansion.

Probability of resistance evolution for exponentially growing virus in the host

Chemotherapy for tumor and pathogenic virus often faces an emergence of resistant mutants, which may lead to medication failure. Here we study the risk of resistance to evolve in a virus population which grows exponentially. We assume that infected cells experience a "proliferation event" of virus at a random time and that the number of newly infected cells from an infected cell follows a Poisson distribution. Virus starts from a single infected cell and the virus infection is detected when the number of infected cells reaches a detection size. Initially virus is sensitive to a drug but later acquires resistance by mutations. We ask the probability that one or more cells infected with drug-resistant virus exist at the time of detection. We derive a formula for the probability of resistance and confirm its accuracy by direct computer simulations. The probability of resistance increases with detection size and mutation rate but decreases with the population growth rate of sensitive virus. The risk of resistance is smaller when more cells are newly infected by viral particles from a single infected cell if the viral growth rate is the same.