Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival

Spatial heterogeneity in tumor adhesion qualifies collective cell invasion

Collective cell invasion (CCI), a canon of most invasive solid tumors, is an emergent property of the interactions between cancer cells and their surrounding extracellular matrix (ECM). However, tumor populations invariably consist of cells expressing variable levels of adhesive proteins that mediate such interactions, disallowing an intuitive understanding of how tumor invasiveness at a multicellular scale is influenced by spatial heterogeneity of cell-cell and cell-ECM adhesion. Here, we have used a Cellular Potts model-based multiscale computational framework that is constructed on the histopathological principles of glandular cancers. In earlier efforts on homogenous cancer cell populations, this framework revealed the relative ranges of interactions, including cell-cell and cell-ECM adhesion that drove collective, dispersed, and mixed multimodal invasion. Here, we constitute a tumor core of two separate cell subsets showing distinct intra- and inter-subset cell-cell or cell-ECM adhesion strengths. These two subsets of cells are arranged to varying extents of spatial intermingling, which we call the heterogeneity index (HI). We observe that low and high inter-subset cell adhesion favors invasion of high-HI and low-HI intermingled populations with distinct intra-subset cell-cell adhesion strengths, respectively. In addition, for explored values of cell-ECM adhesion strengths, populations with high HI values collectively invade better than those with lower HI values. We then asked how spatial invasion is regulated by progressively intermingled cellular subsets that are epithelial, i.e., showed high cell-cell but poor cell-ECM adhesion, and mesenchymal, i.e., with reversed adhesion strengths to the former. Here too, inter-subset adhesion plays an important role in contextualizing the proportionate relationship between HI and invasion. An exception to this relationship is seen for cases of heterogeneous cell-ECM adhesion where sub-maximal HI patterns with higher outer localization of cells with stronger ECM adhesion collectively invade better than their relatively higher-HI counterparts. Our simulations also reveal how adhesion heterogeneity qualifies collective invasion, when either cell-cell or cell-ECM adhesion type is varied but results in an invasive dispersion when both adhesion types are simultaneously altered.

Subpopulation commensalism promotes Rac1-dependent invasion of single cells via laminin-332

Phenotypic heterogeneity poses a significant hurdle for cancer treatment but is under-characterized in the context of tumor invasion. Amidst the range of phenotypic heterogeneity across solid tumor types, collectively invading cells and single cells have been extensively characterized as independent modes of invasion, but their intercellular interactions have rarely been explored. Here, we isolated collectively invading cells and single cells from the heterogeneous 4T1 cell line and observed extensive transcriptional and epigenetic diversity across these subpopulations. By integrating these datasets, we identified laminin-332 as a protein complex exclusively secreted by collectively invading cells. Live-cell imaging revealed that laminin-332 derived from collectively invading cells increased the velocity and directionality of single cells. Despite collectively invading and single cells having similar expression of the integrin α6β4 dimer, single cells demonstrated higher Rac1 activation upon laminin-332 binding to integrin α6β4. This mechanism suggests a novel commensal relationship between collectively invading and single cells, wherein collectively invading cells promote the invasive potential of single cells through a laminin-332/Rac1 axis.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BACKGROUND

Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.

RESULTS

We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal .

CONCLUSIONS

Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy

Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.

Machine Learning Radiomics Liver Function Model for Prognostic Prediction After Radical Resection of Advanced Gastric Cancer: A Retrospective Study

PURPOSE

We aimed to establish a machine learning radiomics liver function model to explore how liver function affects the prognosis of patients with gastric cancer (GC).

METHODS

Patients with advanced GC were retrospectively enrolled in this study. Eight machine learning radiomic models were constructed by extracting radiomic features from portal-vein-phase contrast-enhanced computed tomography (CE-CT) images. Clinicopathological features were determined using univariate and multifactorial Cox regression analyses. These features were used to construct a GC survival nomogram.

RESULTS

A total of 510 patients with GC were split into training and test cohorts in an 8:2 ratio. Kaplan-Meier analysis showed that patients with type I liver function had a better prognosis. Fifteen significant features were retained to establish the machine learning model. LightBGM showed the best predictive performance in the training (area under the receiver operating characteristic curve [AUC] 0.978) and test cohorts (AUC 0.714). Multivariate analysis revealed that gender, age, liver function, Nutritional Risk Screening 2002 (NRS-2002) score, tumor-lymph node-metastasis stage, tumor size, and tumor differentiation were independent risk factors for GC prognosis. The survival nomogram based on machine learning radiomics, instead of liver biochemical indicators, still had high accuracy (C-index of 0.771 vs. 0.773).

CONCLUSION

The machine learning radiomics liver function model has high diagnostic value in predicting the influence of liver function on prognosis in patients with GC.

Distinct spatial landscapes in clear-cell renal cell carcinoma as revealed by whole transcriptome analysis

Background

Clear-cell renal cell carcinoma (ccRCC) is the most common and aggressive form of renal cancer and a paradigm of inter- and intratumor heterogeneity. We carried out an exploratory digital spatial profiling of the tumor interior and periphery of two ccRCC tumor specimens and mapped spatially the molecular and cellular composition of their tumor microenvironment and ecosystem.

Materials and methods

Digital spatial profiling of the whole transcriptome of 19 regions of interest (ROIs) was carried out from two selected highly immunogenic stage pT3a/grade 3 (G3) and stage pT3a/grade 4 (G4) ccRCC. A total of 9-10 ROIs were selected from distinct areas from each tumor, including tumor interior and tumor periphery, and differences in gene expression were analyzed by RNA sequencing, pathway enrichment analysis, and cell deconvolution.

Results

The distinct areas from the two locally advanced tumors displayed unique gene expression spatial patterns defining distinct biological pathways. Dimensional reduction analysis showed that the G3 ccRCC, compared to the G4 ccRCC, correlated with more variability between regions from the tumor interior and tumor periphery. Cell deconvolution analysis illustrated higher abundance of immune cells, including macrophages, myeloid dendritic cells, and CD4 T cells, and lower abundance of regulatory T cells in the tumor periphery compared to the tumor interior.

Conclusions

Transcriptome spatial profiling revealed high inter- and intratumor heterogeneity in the analyzed tumors and provided information with potential clinical utility. This included the finding of less intratumor heterogeneity and more tumor-infiltrated T cells in the ccRCC tumor specimen with a higher grade.

An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer

Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.

Assessment of postoperative circulating tumour DNA to predict early recurrence in patients with stage I-III right-sided colon cancer: prospective observational study

BACKGROUND

Right-sided colon cancer (RCC) differs in mutation profile and risk of recurrence compared to distal colon cancer. Circulating tumour DNA (ctDNA) present after surgery can identify patients with residual disease after curative surgery and predict risk of early recurrence.

METHODS

This is a prospective observational biomarker trial with exploration of ctDNA in 50 non-metastatic RCC patients for which oncological right-sided colectomy was performed. Blood samples were collected preoperatively, within 1 month post surgery, 3 months (not mandatory), 6 months and every 6 months thereafter. Plasma cell free DNA and/or tumour was investigated for cancer-related mutations by the next-generation sequencing (NGS) panel AVENIO surveillance specifically designed for ctDNA analysis. Detected mutations were quantified using digital droplet PCR (ddPCR) for follow-up. Recurrence-free survival was explored.

RESULTS

50 patients were recruited. Somatic cancer-related mutations were detected in 47/50 patients. ddPCR validated results from NGS for 27/34 (plasma) and 72/72 samples (tumour). Preoperative ctDNA was detected in 31/47 of the stage I/III patients and the majority of ctDNA positive patients showed reduction of ctDNA after surgery (27/31). ctDNA-positive patients at first postoperative sample had high recurrence risk compared to patients without measurable ctDNA (adjusted hazard ratio: 172.91; 95% c.i.: 8.70 to 3437.24; P: 0.001).

CONCLUSION

ctDNA was detectable in most patients with non-metastatic RCC before surgery. Positive postoperative ctDNA was strongly associated with early recurrence. Detectable postoperative ctDNA is a prognostic factor with high (100%) positive predictive value for recurrence in this cohort of non-metastatic RCC.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT03776591.

Loss of Human Leukocyte Antigen and Immune Escape in Head and Neck Cancer

OBJECTIVES/HYPOTHESIS

Cancer cells evade recognition by the immune system to survive. Head and neck squamous cell carcinoma (HNSCC) is characterized by high levels of immune infiltration and mutation-associated neoantigens; therefore, immune evasion is likely to be an important mechanism in HNSCC tumorigenesis and progression. A commonly employed mechanism of immune evasion is downregulation of human leukocyte antigen (HLA) or loss of heterozygosity (LOH) in tumor cells. The objective of this study was to integrate multi-dimensional genomic and transcriptomic data from HNSCC tumors to better understand the clinical and immunologic implications of HLA LOH.

STUDY TYPE/DESIGN

Cross-sectional integrated clinical and genomic analysis.

METHODS

Whole-exome sequencing and RNA-sequencing data from 522 tumors profiled in The Cancer Genome Atlas HNSCC cohort were analyzed and integrated with secondary analyses including immune cell deconvolution data. Associations were analyzed with categorical hypothesis testing and multivariable logistic and Cox regression.

RESULTS

HLA LOH was a prevalent event that was identified in 53% of HNSCC tumors; in many cases, more than one class I HLA gene was targeted for LOH. HLA LOH was more common in advanced-stage tumors. Tumors with somatic HLA LOH had tumor microenvironments defined by decreased lymphocyte and T cell infiltration.

CONCLUSIONS

HLA LOH is one of the most prevalent genetic alterations in HNSCC, and is associated with a cold immune microenvironment, suggesting that HLA LOH is a means of immune evasion. It may have value as a predictive biomarker or potential as a cancer cell-specific therapeutic target.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:160-165, 2024.

Early Evolution in Cancer: A Mathematical Support for Pathological and Genomic Evidence in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (CCRCC) is an aggressive form of cancer and a paradigmatic example of intratumor heterogeneity (ITH). The hawk-dove game is a mathematical tool designed to analyze competition in biological systems. Using this game, the study reported here analyzes the early phase of CCRCC development, comparing clonal fitness in homogeneous (linear evolutionary) and highly heterogeneous (branching evolutionary) models. Fitness in the analysis is a measure of tumor aggressiveness. The results show that the fittest clone in a heterogeneous environment is fitter than the clone in a homogeneous context in the early phases of tumor evolution. Early and late periods of tumor evolution in CCRCC are also compared. The study shows the convergence of mathematical, histological, and genomics studies with respect to clonal aggressiveness in different periods of the natural history of CCRCC. Such convergence highlights the importance of multidisciplinary approaches for obtaining a better understanding of the intricacies of cancer.

Whole genome sequencing refines stratification and therapy of patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing the most detailed somatic mutational landscape to date. We identify new driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for drug repurposing. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The twin observations that higher T-cell infiltration is associated with better outcome and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients.

Intratumor heterogeneity: models of malignancy emergence and evolution

Cancer is a complex and heterogeneous disease characterized by the accumulation of genetic alterations that drive uncontrolled cell growth and proliferation. Evolutionary dynamics plays a crucial role in the emergence and development of tumors, shaping the heterogeneity and adaptability of cancer cells. From the perspective of evolutionary theory, tumors are complex ecosystems that evolve through a process of microevolution influenced by genetic mutations, epigenetic changes, tumor microenvironment factors, and therapy-induced changes. This dynamic nature of tumors poses significant challenges for effective cancer treatment, and understanding it is essential for developing effective and personalized therapies. By uncovering the mechanisms that determine tumor heterogeneity, researchers can identify key genetic and epigenetic changes that contribute to tumor progression and resistance to treatment. This knowledge enables the development of innovative strategies for targeting specific tumor clones, minimizing the risk of recurrence and improving patient outcomes. To investigate the evolutionary dynamics of cancer, researchers employ a wide range of experimental and computational approaches. Traditional experimental methods involve genomic profiling techniques such as next-generation sequencing and fluorescence in situ hybridization. These techniques enable the identification of somatic mutations, copy number alterations, and structural rearrangements within cancer genomes. Furthermore, single-cell sequencing methods have emerged as powerful tools for dissecting intratumoral heterogeneity and tracing clonal evolution. In parallel, computational models and algorithms have been developed to simulate and analyze cancer evolution. These models integrate data from multiple sources to predict tumor growth patterns, identify driver mutations, and infer evolutionary trajectories. In this paper, we set out to describe the current approaches to address this evolutionary complexity and theories of its occurrence.

Adaptive therapy: a tumor therapy strategy based on Darwinian evolution theory

Cancer progression is a dynamic process of continuous evolution, in which genetic diversity and heterogeneity are generated by clonal and subclonal amplification based on random mutations. Traditional cancer treatment strategies have a great challenge, which often leads to treatment failure due to drug resistance. Integrating evolutionary dynamics into treatment regimens may be an effective way to overcome the problem of drug resistance. In particular, a potential treatment is adaptive therapy, which strategy advocates containment strategies that adjust the treatment cycles according to tumor evolution to control the growth of treatment-resistant cells. In this review, we first summarize the shortcomings of traditional tumor treatment methods in evolution and then introduce the theoretical basis and research status of adaptive therapy. By analyzing the limitations of adaptive therapy and exploring possible solutions, we can broaden people's understanding of adaptive therapy and provide new insights and strategies for tumor treatment.

Temporal genomic analysis of melanoma rejection identifies regulators of tumor immune evasion

Decreased intra-tumor heterogeneity (ITH) correlates with increased patient survival and immunotherapy response. However, even highly homogenous tumors may display variability in their aggressiveness, and how immunologic-factors impinge on their aggressiveness remains understudied. Here we studied the mechanisms responsible for the immune-escape of murine tumors with low ITH. We compared the temporal growth of homogeneous, genetically-similar single-cell clones that are rejected vs. those that are not-rejected after transplantation in-vivo using single-cell RNA sequencing and immunophenotyping. Non-rejected clones showed high infiltration of tumor-associated-macrophages (TAMs), lower T-cell infiltration, and increased T-cell exhaustion compared to rejected clones. Comparative analysis of rejection-associated gene expression programs, combined with in-vivo CRISPR knockout screens of candidate mediators, identified Mif (macrophage migration inhibitory factor) as a regulator of immune rejection. Mif knockout led to smaller tumors and reversed non-rejection-associated immune composition, particularly, leading to the reduction of immunosuppressive macrophage infiltration. Finally, we validated these results in melanoma patient data.

Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity

DNA mismatch repair deficiency (MMRd) is associated with a high tumor mutational burden (TMB) and sensitivity to immune checkpoint blockade (ICB) therapy. Nevertheless, most MMRd tumors do not durably respond to ICB and critical questions remain about immunosurveillance and TMB in these tumors. In the present study, we developed autochthonous mouse models of MMRd lung and colon cancer. Surprisingly, these models did not display increased T cell infiltration or ICB response, which we showed to be the result of substantial intratumor heterogeneity of mutations. Furthermore, we found that immunosurveillance shapes the clonal architecture but not the overall burden of neoantigens, and T cell responses against subclonal neoantigens are blunted. Finally, we showed that clonal, but not subclonal, neoantigen burden predicts ICB response in clinical trials of MMRd gastric and colorectal cancer. These results provide important context for understanding immune evasion in cancers with a high TMB and have major implications for therapies aimed at increasing TMB.

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Clonal cooperation through soluble metabolite exchange facilitates metastatic outgrowth by modulating Allee effect

Cancers feature substantial intratumoral heterogeneity of genetic and phenotypically distinct lineages. Although interactions between coexisting lineages are emerging as a potential contributor to tumor evolution, the extent and nature of these interactions remain largely unknown. We postulated that tumors develop ecological interactions that sustain diversity and facilitate metastasis. Using a combination of fluorescent barcoding, mathematical modeling, metabolic analysis, and in vivo models, we show that the Allee effect, i.e., growth dependency on population size, is a feature of tumor lineages and that cooperative ecological interactions between lineages alleviate the Allee barriers to growth in a model of triple-negative breast cancer. Soluble metabolite exchange formed the basis for these cooperative interactions and catalyzed the establishment of a polyclonal community that displayed enhanced metastatic dissemination and outgrowth in xenograft models. Our results highlight interclonal metabolite exchange as a key modulator of tumor ecology and a contributing factor to overcoming Allee effect-associated growth barriers to metastasis.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

BACKGROUND

Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.

METHODS

Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).

RESULTS

We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).

CONCLUSION

We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.

SIGNIFICANCE

Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

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

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

Unravelling the Complexity of Colorectal Cancer: Heterogeneity, Clonal Evolution, and Clinical Implications

Colorectal cancer (CRC) is a global health concern and a leading cause of death worldwide. The disease's course and response to treatment are significantly influenced by its heterogeneity, both within a single lesion and between primary and metastatic sites. Biomarkers, such as mutations in KRAS, NRAS, and BRAF, provide valuable guidance for treatment decisions in patients with metastatic CRC. While high concordance exists between mutational status in primary and metastatic lesions, some heterogeneity may be present. Circulating tumor DNA (ctDNA) analysis has proven invaluable in identifying genetic heterogeneity and predicting prognosis in RAS-mutated metastatic CRC patients. Tumor heterogeneity can arise from genetic and non-genetic factors, affecting tumor development and response to therapy. To comprehend and address clonal evolution and intratumoral heterogeneity, comprehensive genomic studies employing techniques such as next-generation sequencing and computational analysis are essential. Liquid biopsy, notably through analysis of ctDNA, enables real-time clonal evolution and treatment response monitoring. However, challenges remain in standardizing procedures and accurately characterizing tumor subpopulations. Various models elucidate the origin of CRC heterogeneity, highlighting the intricate molecular pathways involved. This review focuses on intrapatient cancer heterogeneity and genetic clonal evolution in metastatic CRC, with an emphasis on clinical applications.

Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography

AIM

The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR).

METHODS

Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR.

RESULTS

A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion.

CONCLUSION

A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.

Association between Preoperative 18-FDG PET-CT SUVmax and Next-Generation Sequencing Results in Postoperative Ovarian Malignant Tissue in Patients with Advanced Ovarian Cancer

This study investigated the association between maximum standardized uptake values (SUVmax) on preoperative 18-FDG PET-CT and next-generation sequencing (NGS) results in post-surgical ovarian malignant tissue in patients with advanced ovarian cancer. Twenty-five patients with stage IIIC or IV ovarian cancer who underwent both preoperative 18-FDG PET-CT and postoperative NGS for ovarian malignancies were retrospectively enrolled. Two patients had no detected variants, 21 of the 23 patients with any somatic variant had at least one single nucleotide variant (SNV) or insertion/deletion (indel), 10 patients showed copy number variation (CNV), and two patients had a fusion variant. SUVmax differed according to the presence of SNVs/indels, with an SUVmax of 13.06 for patients with ≥ 1 SNV/indel and 6.28 for patients without (p = 0.003). Seventeen of 20 patients with Tier 2 variants had TP53 variants, and there was a statistically significant association between SUVmax and the presence of TP53 variants (13.21 vs. 9.35, p = 0.041). Analysis of the correlation between the sum of the Tier 1 and Tier 2 numbers and SUVmax showed a statistically significant correlation (p = 0.002; Pearson’s r = 0.588). In conclusion, patients with advanced ovarian cancer with SNVs/indels on NGS, especially those with TP53 Tier 2 variants, showed a proportional association with tumor SUVmax on preoperative PET-CT.

Multi-omics analysis unravels the underlying mechanisms of poor prognosis and differential therapeutic responses of solid predominant lung adenocarcinoma

Background

Solid predominant adenocarcinoma (SPA) has been reported to be a subtype with poor prognosis and unsatisfactory response to chemotherapy and targeted therapy in lung adenocarcinoma (LUAD). However, the underlying mechanisms remain largely unknown and the suitability of immunotherapy for SPA has not been investigated.

Methods

We conducted a multi-omics analysis of 1078 untreated LUAD patients with clinicopathologic, genomic, transcriptomic, and proteomic data from both public and internal cohorts to determine the underlying mechanisms of poor prognosis and differential therapeutic responses of SPA and to investigate the potential of immunotherapy for SPA. The suitability of immunotherapy for SPA was further confirmed in a cohort of LUAD patients who received neoadjuvant immunotherapy in our center.

Results

Along with its aggressive clinicopathologic behaviors, SPA had significantly higher tumor mutation burden (TMB) and number of pathways altered, lower TTF-1 and Napsin-A expression, higher proliferation score and a more immunoresistant microenvironment than non-solid predominant adenocarcinoma (Non-SPA), accounting for its worse prognosis. Additionally, SPA had significantly lower frequency of therapeutically targetable driver mutations and higher frequency of EGFR/TP53 co-mutation which was related to resistance to EGFR tyrosine kinase inhibitors, indicating a lower potential for targeted therapy. Meanwhile, SPA was enriched for molecular features associated with poor response to chemotherapy (higher chemoresistence signature score, lower chemotherapy response signature score, hypoxic microenvironment, and higher frequency of TP53 mutation). Instead, muti-omics profiling revealed that SPA had stronger immunogenicity and was enriched for positive biomarkers for immunotherapy (higher TMB and T cell receptor diversity; higher PD-L1 expression and more immune cell infiltration; higher frequency of gene mutations predicting efficacious immunotherapy, and elevated expression of immunotherapy-related gene signatures). Furthermore, in the cohort of LUAD patients who received neoadjuvant immunotherapy, SPA had higher pathological regression rates than Non-SPA and patients with major pathological response were enriched in SPA, confirming that SPA was more prone to respond to immunotherapy.

Conclusions

Compared with Non-SPA, SPA was enriched for molecular features associated with poor prognosis, unsatisfactory response to chemotherapy and targeted therapy, and good response to immunotherapy, indicating more suitability for immunotherapy while less suitability for chemotherapy and targeted therapy.

Lipidomics-based tissue heterogeneity in specimens of luminal breast cancer revealed by clustering analysis of mass spectrometry imaging: A preliminary study

Cancer tissues reflect a greater number of pathological characteristics of cancer compared to cancer cells, so the evaluation of cancer tissues can be effective in determining cancer treatment strategies. Mass spectrometry imaging (MSI) can evaluate cancer tissues and even identify molecules while preserving spatial information. Cluster analysis of cancer tissues' MSI data is currently used to evaluate the phenotype heterogeneity of the tissues. Interestingly, it has been reported that phenotype heterogeneity does not always coincide with genotype heterogeneity in HER2-positive breast cancer. We thus investigated the phenotype heterogeneity of luminal breast cancer, which is generally known to have few gene mutations. As a result, we identified phenotype heterogeneity based on lipidomics in luminal breast cancer tissues. Clusters were composed of phosphatidylcholine (PC), triglycerides (TG), phosphatidylethanolamine, sphingomyelin, and ceramide. It was found that mainly the proportion of PC and TG correlated with the proportion of cancer and stroma on HE images. Furthermore, the number of carbons in these lipid class varied from cluster to cluster. This was consistent with the fact that enzymes that synthesize long-chain fatty acids are increased through cancer metabolism. It was then thought that clusters containing PCs with high carbon counts might reflect high malignancy. These results indicate that lipidomics-based phenotype heterogeneity could potentially be used to classify cancer for which genetic analysis alone is insufficient for classification.

Metabolic rearrangements and intratumoral heterogeneity for immune response in hepatocellular carcinoma

Liver cancer is one of the most common malignant tumors globally. Not only is it difficult to diagnose, but treatments are scarce and the prognosis is generally poor. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Aggressive cancer cells, such as those found in HCC, undergo extensive metabolic rewiring as tumorigenesis, the unique feature, ultimately causes adaptation to the neoplastic microenvironment. Intratumoral heterogeneity (ITH) is defined as the presence of distinct genetic features and different phenotypes in the same tumoral region. ITH, a property unique to malignant cancers, results in differences in many different features of tumors, including, but not limited to, tumor growth and resistance to chemotherapy, which in turn is partly responsible for metabolic reprogramming. Moreover, the different metabolic phenotypes might also activate the immune response to varying degrees and help tumor cells escape detection by the immune system. In this review, we summarize the reprogramming of glucose metabolism and tumoral heterogeneity and their associations that occur in HCC, to obtain a better understanding of the mechanisms of HCC oncogenesis.

Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development

Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.

Impact of cancer evolution on immune surveillance and checkpoint inhibitor response

Intratumour heterogeneity (ITH) is pervasive across all cancers studied and may provide the evolving tumour multiple routes to escape immune surveillance. Immune checkpoint inhibitors (CPIs) are rapidly becoming standard of care for many cancers. Here, we discuss recent work investigating the influence of ITH on patient response to immune checkpoint inhibitor (CPI) therapy. At its simplest, ITH may confound the diagnostic accuracy of predictive biomarkers used to stratify patients for CPI therapy. Furthermore, ITH is fuelled by mechanisms of genetic instability that can both engage immune surveillance and drive immune evasion. A greater appreciation of the interplay between ITH and the immune system may hold the key to increasing the proportion of patients experiencing durable responses from CPI therapy.

Prognostic Value of Molecular Intratumor Heterogeneity in Primary Oral Cancer and Its Lymph Node Metastases Assessed by Mass Spectrometry Imaging

Different aspects of intra-tumor heterogeneity (ITH), which are associated with the development of cancer and its response to treatment, have postulated prognostic value. Here we searched for potential association between phenotypic ITH analyzed by mass spectrometry imaging (MSI) and prognosis of head and neck cancer. The study involved tissue specimens resected from 77 patients with locally advanced oral squamous cell carcinoma, including 37 patients where matched samples of primary tumor and synchronous lymph node metastases were analyzed. A 3-year follow-up was available for all patients which enabled their separation into two groups: with no evidence of disease (NED, n = 41) and with progressive disease (PD, n = 36). After on-tissue trypsin digestion, peptide maps of all cancer regions were segmented using an unsupervised approach to reveal their intrinsic heterogeneity. We found that intra-tumor similarity of spectra was higher in the PD group and diversity of clusters identified during image segmentation was higher in the NED group, which indicated a higher level of ITH in patients with more favorable outcomes. Signature of molecular components that correlated with long-term outcomes could be associated with proteins involved in the immune functions. Furthermore, a positive correlation between ITH and histopathological lymphocytic host response was observed. Hence, we proposed that a higher level of ITH revealed by MSI in cancers with a better prognosis could reflect the presence of heterotypic components of tumor microenvironment such as infiltrating immune cells enhancing the response to the treatment.

Evolution of intra-tumoral heterogeneity across different pathological stages in papillary thyroid carcinoma

BACKGROUND

Intra-tumor heterogeneity (ITH) results from the continuous accumulation of mutations during disease progression, thus impacting patients' clinical outcome. How the ITH evolves across papillary thyroid carcinoma (PTC) different tumor stages is lacking.

METHODS

We used the whole-exome sequencing data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort to track the ITH and assessed its relationship with clinical features through different stages of the PTC progression. We further assayed the expression levels of the specific genes in papillary thyroid cancer cell lines compared to an immortalized normal thyroid epithelial cell line by qRT-PCR.

RESULTS

We revealed the timing of mutational processes and the dynamics of the temporal acquisition of somatic events during the lifetime of the PTC. ITH significantly influences the PTC patient's survival rate and, as genetic heterogeneity increases, the prognosis gets worse in advanced tumor stages. ITH also affects the mutational architecture of each clinical stage which is subject to periodic fluctuations. Different mutational processes may cooperate to shape a stage-specific mutational spectrum during the progression from early to advanced tumor stages. Moreover, different evolutionary paths characterize PTC progression across pathological stages due to both mutations recurrently occurring in all stages in hotspot positions and distinct codon changes dominating in different stages. A different expression level of specific genes also exists in different thyroid cancer cell lines.

CONCLUSIONS

Our findings suggest ITH as a potential unfavorable prognostic factor in PTC and highlight the dynamic changes in different clinical stages of PTC, providing some clues for the precision medicine and suggesting different diagnostic decisions depending on the clinical stages of patients. Finally, complete clear guidelines to define risk stratification of PTC patients are lacking; thus, this work could contribute to defining patients who need more aggressive treatments and, in turn, could reduce the social burden of this cancer.

Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

Untangling the web of intratumour heterogeneity

Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.

GEP-NET radiomics: a systematic review and radiomics quality score assessment

OBJECTIVE

The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence.

METHODS

PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data.

RESULTS

In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%.

CONCLUSION

Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce.

KEY POINTS

• The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.

Genomic landscapes of canine splenic angiosarcoma (hemangiosarcoma) contain extensive heterogeneity within and between patients

Cancer genomic heterogeneity presents significant challenges for understanding oncogenic processes and for cancer’s clinical management. Variation in driver mutation frequency between patients with the same tumor type as well as within an individual patients’ cancer can shape the use of mutations as diagnostic, prognostic, and predictive biomarkers. We have characterized genomic heterogeneity between and within canine splenic hemangiosarcoma (HSA), a common naturally occurring cancer in pet dogs that is similar to human angiosarcoma (AS). HSA is a clinically, physiologically, and genomically complex canine cancer that may serve as a valuable model for understanding the origin and clinical impact of cancer heterogeneity. We conducted a prospective collection of 52 splenic masses from 43 dogs (27 HSA, 15 benign masses, and 1 stromal sarcoma) presenting for emergency care with hemoperitoneum secondary to a ruptured splenic mass. Multi-platform genomic analysis included matched tumor/normal targeted sequencing panel and exome sequencing. We found candidate somatic cancer driver mutations in 14/27 (52%) HSAs. Among recurrent candidate driver mutations, TP53 was most commonly mutated (30%) followed by PIK3CA (15%), AKT1 (11%), and CDKN2AIP (11%). We also identified significant intratumoral genomic heterogeneity, consistent with a branched evolution model, through multi-region exome sequencing of three distinct tumor regions from selected primary splenic tumors. These data provide new perspectives on the genomic landscape of this veterinary cancer and suggest a cross-species value for using HSA in pet dogs as a naturally occurring model of intratumoral heterogeneity.

Using Circulating Tumor DNA in Colorectal Cancer: Current and Evolving Practices

There exists a tremendous opportunity in identifying and determining the appropriate predictive and prognostic biomarker(s) for risk stratification of patients with colorectal cancers (CRCs). Circulating tumor DNA (ctDNA) has emerged as a promising prognostic and possibly predictive biomarker in the personalized management of patients with CRCs. The disease is particularly suited to a liquid biopsy–based approach since there is a great deal of shedding of circulating tumor fragments (cells, DNA, methylation markers, etc). ctDNA has been shown to have several potential applications, including detecting minimal residual disease (MRD), monitoring for early recurrence, molecular profiling, and therapeutic response prediction. The utility of ctDNA has broadened from its initial use in the advanced/metastatic setting for molecular profiling and detection of acquired resistance mechanisms, toward identifying MRD, as well as early detection. Prospective studies such as CIRCULATE, COBRA, Dynamic II/III, and ACT3 are underway in the MRD setting to further understand how ctDNA may be used to inform clinical decision making using both tumor-informed and tumor-agnostic platforms. These prospective studies use ctDNA to guide management of patients with CRC and will be critical to help guide how and where ctDNA should or should not be used in clinical decision making. It is also important to understand that there are different types of ctDNA liquid biopsy platforms, each with advantages and disadvantages in different clinical indications. This review provides an overview of the current and evolving use of ctDNA in CRC.

The effect of genome graph expressiveness on the discrepancy between genome graph distance and string set distance

Motivation

Intra-sample heterogeneity describes the phenomenon where a genomic sample contains a diverse set of genomic sequences. In practice, the true string sets in a sample are often unknown due to limitations in sequencing technology. In order to compare heterogeneous samples, genome graphs can be used to represent such sets of strings. However, a genome graph is generally able to represent a string set universe that contains multiple sets of strings in addition to the true string set. This difference between genome graphs and string sets is not well characterized. As a result, a distance metric between genome graphs may not match the distance between true string sets.

Results

We extend a genome graph distance metric, Graph Traversal Edit Distance (GTED) proposed by Ebrahimpour Boroojeny et al., to FGTED to model the distance between heterogeneous string sets and show that GTED and FGTED always underestimate the Earth Mover’s Edit Distance (EMED) between string sets. We introduce the notion of string set universe diameter of a genome graph. Using the diameter, we are able to upper-bound the deviation of FGTED from EMED and to improve FGTED so that it reduces the average error in empirically estimating the similarity between true string sets. On simulated T-cell receptor sequences and actual Hepatitis B virus genomes, we show that the diameter-corrected FGTED reduces the average deviation of the estimated distance from the true string set distances by more than 250%.

Availability and implementation

Data and source code for reproducing the experiments are available at: https://github.com/Kingsford-Group/gtedemedtest/.

Supplementary information

Supplementary data are available at Bioinformatics online.

In Silico Investigations of Multi-Drug Adaptive Therapy Protocols

The standard of care for cancer patients aims to eradicate the tumor by killing the maximum number of cancer cells using the maximum tolerated dose (MTD) of a drug. MTD causes significant toxicity and selects for resistant cells, eventually making the tumor refractory to treatment. Adaptive therapy aims to maximize time to progression (TTP), by maintaining sensitive cells to compete with resistant cells. We explored both dose modulation (DM) protocols and fixed dose (FD) interspersed with drug holiday protocols. In contrast to previous single drug protocols, we explored the determinants of success of two-drug adaptive therapy protocols, using an agent-based model. In almost all cases, DM protocols (but not FD protocols) increased TTP relative to MTD. DM protocols worked well when there was more competition, with a higher cost of resistance, greater cell turnover, and when crowded proliferating cells could replace their neighbors. The amount that the drug dose was changed, mattered less. The more sensitive the protocol was to tumor burden changes, the better. In general, protocols that used as little drug as possible, worked best. Preclinical experiments should test these predictions, especially dose modulation protocols, with the goal of generating successful clinical trials for greater cancer control.

Emergence of hybrid states of stem-like cancer cells correlates with poor prognosis in oral cancer

Cancer cell state transitions emerged as powerful mechanisms responsible for drug tolerance and overall poor prognosis; however, evidences were largely missing in oral cancer. Here, by multiplexing phenotypic markers of stem-like cancer cells (SLCCs); CD44, CD24 and aldehyde dehydrogenase (ALDH), we characterized diversity among multiple oral tumor tissues and cell lines. Two distinct patterns of spontaneous transitions with stochastic bidirectional interconversions on ‘ALDH-axis’, and unidirectional non-interconvertible transitions on ‘CD24-axis’ were observed. Interestingly, plastic ‘ALDH-axis’ was harnessed by cells to adapt to a Cisplatin tolerant state. Furthermore, phenotype-specific RNA sequencing suggested the possible maintenance of intermediate hybrid cell states maintaining stemness within the differentiating subpopulations. Importantly, survival analysis with subpopulation-specific gene sets strongly suggested that cell-state transitions may drive non-genetic heterogeneity, resulting in poor prognosis. Therefore, we have described the phenotypic-composition of heterogeneous subpopulations critical for global tumor behavior in oral cancer; which may provide prerequisite knowledge for treatment strategies.

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Demonstrated population trajectory driven non-genetic heterogeneity in oral cancer

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Created transition maps for subpopulations using discrete time Markov chain model

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Demonstrated maintenance of stemness in cells undergoing differentiation

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Uniquely expressed genes of these subpopulations associated with disease prognosis

Case Report: Durable Complete Response After Combined Immunotherapy Following Resection of Primary Tumor in a Gallbladder Cancer Patient With Distant Metastatic Lymph Nodes of Favorable Immune-Microenvironment

Background

Metastatic gallbladder carcinoma (GBC) is one of the most aggressive malignancies. As GBC is usually diagnosed with distant metastases, only a few patients can receive R0 resection and the relapse rate remains high. Programmed cell death protein 1 (PD-1) blockade therapy has provided encouraging long-term outcomes in a subset of patients in many cancers. However, the data on efficacy of PD-1 blockade in GBC are very limited.

Case Presentation

We herein reported a stage IVB GBC patient with localized primary tumor and distant lymph node metastasis. Except for the unresectable multiple metastatic nodes including distant nodes, a complete resection of primary tumor en bloc with partial segment 4B+5 was performed. Tumor tissues of primary tumor and one metastatic lymph node were collected to perform whole-exome sequencing, RNA-seq, and immunohistochemistry. Low TMB (5.38 muts/Mb), low MSI (<20%), and negative PD-L1 expression (TC0) were observed in the primary tumor. Likewise, low TMB (5.44 muts/Mb), low MSI (<20%), and low PD-L1 expression (TC2) presented in the metastatic lymph node. Besides, low genetic intratumor heterogeneity exhibited between the primary and metastatic tumors in this patient. In contrast to the primary tumor, higher-level CD8⁺ T cell infiltration was revealed in the tumor microenvironment of the metastatic lymph node. Then, chemo-immunotherapy using S1 and anti-PD-1 antibody pembrolizumab was administrated as the first-line treatment for the residual metastatic nodes. Complete response was achieved after 7 courses and has lasted for 32 months up to present. Additionally, blood samples during treatment were further analyzed for immune repertoire sequencing, showing that several T cell receptor clones in metastatic lymph node were predominant in blood during the combined anti-PD-1 treatment.

Conclusions

Chemo-immunotherapy may provide a potential curative option for the lymph node metastases of gallbladder cancer. The low intratumor heterogeneity and high level of infiltrating CD8⁺ T-cells in metastatic node might be indispensable to the durable complete response in this patient.

Identification of Novel Prognostic Biomarkers Relevant to Immune Infiltration in Lung Adenocarcinoma

Background: Programmed death ligand-1 (PD-L1) is a biomarker for assessing the immune microenvironment, prognosis, and response to immune checkpoint inhibitors in the clinical treatment of lung adenocarcinoma (LUAD), but it does not work for all patients. This study aims to discover alternative biomarkers.

Methods: Public data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) were used to determine the gene modules relevant to tumor immunity. Protein–protein interaction (PPI) network and GO semantic similarity analyses were applied to identify the module hub genes with functional similarities to PD-L1, and we assessed their correlations with immune infiltration, patient prognosis, and immunotherapy response. Immunohistochemistry (IHC) and hematoxylin and eosin (H&E) staining were used to validate the outcome at the protein level.

Results: We identified an immune response–related module, and two hub genes (PSTPIP1 and PILRA) were selected as potential biomarkers with functional similarities to PD-L1. High expression levels of PSTPIP1 and PILRA were associated with longer overall survival and rich immune infiltration in LUAD patients, and both were significantly high in patients who responded to anti–PD-L1 treatment. Compared to PD-L1–negative LUAD tissues, the protein levels of PSTPIP1 and PILRA were relatively increased in the PD-L1–positive tissues, and the expression of PSTPIP1 and PILRA positively correlated with the tumor-infiltrating lymphocytes.

Conclusion: We identified PSTPIP1 and PILRA as prognostic biomarkers relevant to immune infiltration in LUAD, and both are associated with the response to anti–PD-L1 treatment.

Can aggressive cancers be identified by the "aggressiveness" of their chromatin?

Phenotypic plasticity is a crucial feature of aggressive cancer, providing the means for cancer progression. Stochastic changes in tumor cell transcriptional programs increase the chances of survival under any condition. I hypothesize that unstable chromatin permits stochastic transitions between transcriptional programs in aggressive cancers and supports non-genetic heterogeneity of tumor cells as a basis for their adaptability. I present a mechanistic model for unstable chromatin which includes destabilized nucleosomes, mobile chromatin fibers and random enhancer-promoter contacts, resulting in stochastic transcription. I suggest potential markers for "unsettled" chromatin in tumors associated with poor prognosis. Although many of the characteristics of unstable chromatin have been described, they were mostly used to explain changes in the transcription of individual genes. I discuss approaches to evaluate the role of unstable chromatin in non-genetic tumor cell heterogeneity and suggest using the degree of chromatin instability and transcriptional noise in tumor cells to predict cancer aggressiveness.

18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis

The best treatment strategy for oesophageal cancer patients achieving a complete clinical response after neoadjuvant chemoradiation is a burning topic. The available diagnostic tools, such as 18F-FDG PET/CT performed routinely, cannot accurately evaluate the presence or absence of the residual tumour. The emerging field of radiomics may encounter the critical challenge of personalised treatment. Radiomics is based on medical image analysis, executed by extracting information from many image features; it has been shown to provide valuable information for predicting treatment responses in oesophageal cancer. This systematic review with a meta-analysis aims to provide current evidence of 18F-FDG PET-based radiomics in predicting response treatments following neoadjuvant chemoradiotherapy in oesophageal cancer. A comprehensive literature review identified 1160 studies, of which five were finally included in the study. Our findings provided that pooled Area Under the Curve (AUC) of the five selected studies was relatively high at 0.821 (95% CI: 0.737–0.904) and not influenced by the sample size of the studies. Radiomics models exhibited a good performance in predicting pathological complete responses (pCRs). This review further strengthens the great potential of 18F-FDG PET-based radiomics to predict pCRs in oesophageal cancer patients who underwent neoadjuvant chemoradiotherapy. Additionally, our review imparts additional support to prospective studies on 18F-FDG PET radiomics for a tailored treatment strategy of oesophageal cancer patients.

Radiomics: A Primer on Processing Workflow and Analysis

Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

Simple Summary

Most malignant tumours are highly heterogeneous at molecular and phenotypic levels. Tumour variability poses challenges for the management of patients, as it arises between patients and even evolves in space and time within a single patient. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of tumour diversity in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate tumour heterogeneity.

Abstract

Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH.

Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma

PURPOSE

To develop and externally validate models incorporating a PET radiomics signature (R-signature) obtained by the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL).

METHODS

A total of 383 patients with DLBCL from two medical centres between 2011 and 2019 were included. The cross-combination method was used on three types of PET radiomics features from the training cohort to generate 49 feature selection-classification candidates based on 7 different machine learning models. The R-signature was then built by selecting the optimal candidates based on their progression-free survival (PFS) and overall survival (OS). Cox regression analysis was used to develop the survival prediction models. The calibration, discrimination, and clinical utility of the models were assessed and externally validated.

RESULTS

The R-signatures determined by 12 and 31 radiomics features were significantly associated with PFS and OS, respectively (P<0.05). The combined models that incorporated R-signatures, metabolic metrics, and clinical risk factors exhibited significant prognostic superiority over the clinical models, PET-based models, and the National Comprehensive Cancer Network International Prognostic Index in terms of both PFS (C-index: 0.801 vs. 0.732 vs. 0.785 vs. 0.720, respectively) and OS (C-index: 0.807 vs. 0.740 vs. 0.773 vs. 0.726, respectively). For external validation, the C-indices were 0.758 vs. 0.621 vs. 0.732 vs. 0.673 and 0.794 vs. 0.696 vs. 0.781 vs. 0.708 in the PFS and OS analyses, respectively. The calibration curves showed good consistency, and the decision curve analysis supported the clinical utility of the combined model.

CONCLUSION

The R-signature could be used as a survival predictor for DLBCL, and its combination with clinical factors may allow for accurate risk stratification.

Identification of Tumor Antigens and Immune Subtypes of Glioblastoma for mRNA Vaccine Development

The use of vaccines for cancer therapy is a promising immunotherapeutic strategy that has been shown to be effective against various cancers. Vaccines directly target tumors but their efficacy against glioblastoma multiforme (GBM) remains unclear. Immunotyping that classifies tumor samples is considered to be a biomarker for immunotherapy. This study aimed to identify potential GBM antigens suitable for vaccine development and develop a tool to predict the response of GBM patients to vaccination based on the immunotype. Gene Expression Profiling Interactive Analysis (GEPIA) was applied to evaluate the expression profile of GBM antigens and their influence on clinical prognosis, while the cBioPortal program was utilized to integrate and analyze genetic alterations. The correlation between antigens and antigen processing cells was assessed using TIMER. RNA-seq data of GBM samples and their corresponding clinical data were downloaded from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) for further clustering analysis. Six overexpressed and mutated tumor antigens (ARHGAP9, ARHGAP30, CLEC7A, MAN2B1, ARPC1B and PLB1) were highly correlated with the survival rate of GBM patients and the infiltration of antigen presenting cells in GBMs. With distinct cellular and molecular characteristics, three immune subtypes (IS1-IS3) of GBMs were identified and GBMs from IS3 subtype were more likely to benefit from vaccination. Through graph learning-based dimensional reduction, immune landscape was depicted and revealed the existence of heterogeneity among individual GBM patients. Finally, WGCNA can identify potential vaccination biomarkers by clustering immune related genes. In summary, the six tumor antigens are potential targets for developing anti-GBMs mRNA vaccine, and the immunotypes can be used for evaluating vaccination response.

Predictive biomarkers for response to immune checkpoint inhibition

Immune checkpoint inhibitors have transformed the prognosis and treatment paradigm of many cancer types, through the potential for durable responses. However, the majority of patients still do not benefit. Response to checkpoint inhibition is determined by dynamic host, tumour and tumour microenvironment factors that display spatial and temporal variability, but our understanding of these interactions is incomplete. Through investigating biomarkers of resistance and response, opportunities arise to discover new therapeutic targets and shape personalised treatment strategies. Here we review approved and emerging biomarkers of response to immune checkpoint inhibitors, in particular the recent rapid progress in host and tumour genomics. It is unlikely that a single biomarker will precisely predict response, but multivariate multiomic markers may provide a balanced assessment of these factors and more accurately identify patients who will benefit. Further efforts are required to translate these groundbreaking discoveries into novel therapeutics and biomarker driven clinical trials, to provide durable treatment response to a greater population of patients.