An analysis of genetic heterogeneity in untreated cancers

Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression

Cancer initiation is driven by the cooperation between genetic and epigenetic aberrations that disrupt gene regulatory programs critical to maintaining specialized cellular functions. After initiation, cells acquire additional genetic and epigenetic alterations influenced by tumor-intrinsic and -extrinsic mechanisms, which increase intratumoral heterogeneity, reshape the cell's underlying gene regulatory networks and promote cancer evolution. Furthermore, environmental or therapeutic insults drive the selection of heterogeneous cell states, with implications for cancer initiation, maintenance, and drug resistance. The advancement of single-cell genomics has begun to uncover the full repertoire of chromatin and gene expression states (cell states) that exist within individual tumors. These single-cell analyses suggest that cells diversify in their regulatory states upon transformation by co-opting damage-induced and nonlineage regulatory programs that can lead to epigenomic plasticity. Here, we review these recent studies related to regulatory state changes in cancer progression and highlight the growing single-cell epigenomics toolkit poised to address unresolved questions in the field.

A clinician’s handbook for using ctDNA throughout the patient journey

Background

The promise of precision cancer medicine presently centers around the genomic sequence of a patient’s tumor being translated into timely, actionable information to inform clinical care. The analysis of cell-free DNA from liquid biopsy, which contains circulating tumor DNA (ctDNA) in patients with cancer, has proven to be amenable to various settings in oncology. However, open questions surrounding the clinical validity and utility of plasma-based analyses have hindered widespread clinical adoption.

Main body

Owing to the rapid evolution of the field, studies supporting the use of ctDNA as a biomarker throughout a patient’s journey with cancer have accumulated in the last few years, warranting a review of the latest status for clinicians who may employ ctDNA in their precision oncology programs. In this work, we take a step back from the intricate coverage of detection approaches described extensively elsewhere and cover basic concepts around the practical implementation of next generation sequencing (NGS)-guided liquid biopsy. We compare relevant targeted and untargeted approaches to plasma DNA analysis, describe the latest evidence for clinical validity and utility, and highlight the value of genome-wide ctDNA analysis, particularly as it relates to early detection strategies and discovery applications harnessing the non-coding genome.

Conclusions

The maturation of liquid biopsy for clinical application will require interdisciplinary efforts to address current challenges. However, patients and clinicians alike may greatly benefit in the future from its incorporation into routine oncology care.

The biology of pancreatic cancer morphology

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal of all human malignancies. PDAC precursor lesions, invasive primary PDAC, and metastatic PDAC each display distinct morphologies that reflect unique biology. This 'biomorphology' is determined by a complex neoplastic history of clonal phylogenetic relationships, geographic locations, external environmental exposures, intrinsic metabolic demands, and tissue migration patterns. Understanding the biomorphological evolution of PDAC progression is not only of academic interest but also of great practical value. Applying this knowledge to surgical pathology practice facilitates the correct diagnosis on routine H&E stains without additional ancillary studies in most cases. Here I provide a concise overview of the entire biomorphological spectrum of PDAC progression beginning with initial neoplastic transformation and ending in terminal distant metastasis. Most biopsy and resection specimens are currently obtained prior to treatment. As such, our understanding of untreated PDAC biomorphology is mature. The biomorphology of treated PDAC is less defined but will assume greater importance as the frequency of neoadjuvant therapy increases. Although this overview is slanted towards pathology, it is written so that pathologists, clinicians, and scientists alike might find it instructive for their respective disciplines.

Intratumor genetic heterogeneity and clonal evolution to decode endometrial cancer progression

Analyzing different tumor regions by next generation sequencing allows the assessment of intratumor genetic heterogeneity (ITGH), a phenomenon that has been studied widely in some tumor types but has been less well explored in endometrial carcinoma (EC). In this study, we sought to characterize the spatial and temporal heterogeneity of 9 different ECs using whole-exome sequencing, and by performing targeted sequencing validation of the 42 primary tumor regions and 30 metastatic samples analyzed. In addition, copy number alterations of serous carcinomas were assessed by comparative genomic hybridization arrays. From the somatic mutations, identified by whole-exome sequencing, 532 were validated by targeted sequencing. Based on these data, the phylogenetic tree reconstructed for each case allowed us to establish the tumors’ evolution and correlate this to tumor progression, prognosis, and the presence of recurrent disease. Moreover, we studied the genetic landscape of an ambiguous EC and the molecular profile obtained was used to guide the selection of a potential personalized therapy for this patient, which was subsequently validated by preclinical testing in patient-derived xenograft models. Overall, our study reveals the impact of analyzing different tumor regions to decipher the ITGH in ECs, which could help make the best treatment decision.

Towards an integrative understanding of cancer mechanobiology: calcium, YAP, and microRNA under biophysical forces

An increasing number of studies have demonstrated the significant roles of the interplay between microenvironmental mechanics in tissues and biochemical-genetic activities in resident tumor cells at different stages of tumor progression. Mediated by molecular mechano-sensors or -transducers, biomechanical cues in tissue microenvironments are transmitted into the tumor cells and regulate biochemical responses and gene expression through mechanotransduction processes. However, the molecular interplay between the mechanotransduction processes and intracellular biochemical signaling pathways remains elusive. This paper reviews the recent advances in understanding the crosstalk between biomechanical cues and three critical biochemical effectors during tumor progression: calcium ions (Ca²⁺), yes-associated protein (YAP), and microRNAs (miRNAs). We address the molecular mechanisms underpinning the interplay between the mechanotransduction pathways and each of the three effectors. Furthermore, we discuss the functional interactions among the three effectors in the context of soft matter and mechanobiology. We conclude by proposing future directions on studying the tumor mechanobiology that can employ Ca²⁺, YAP, and miRNAs as novel strategies for cancer mechanotheraputics. This framework has the potential to bring insights into the development of novel next-generation cancer therapies to suppress and treat tumors.

Molecular Mechanisms Related with Oligometastatic Prostate Cancer-Is It Just a Matter of Numbers?

During the last decade, the body of knowledge regarding the oligometastatic state has increased exponentially. Several molecular frameworks have been established, aiding our understanding of metastatic spread caused by genetically unstable cells that adapt to a tissue environment which is distant from the primary tumor. In the current narrative review, we provide an overview of the current treatment landscape of oligometastatic cancer, focusing on the current biomarkers used in the identification of true oligometastatic disease and highlighting the impact of molecular imaging on stage shift in different scenarios. Finally, we address current and future directions regarding the use of genetic and epigenetic targeting treatments in oligometastatic prostate cancer.

Metal-free bioorthogonal click chemistry in cancer theranostics

Bioorthogonal chemistry is a powerful tool to site-specifically activate drugs in living systems. Bioorthogonal reactions between a pair of biologically reactive groups can rapidly and specifically take place in a mild physiological milieu without perturbing inherent biochemical processes. Attributed to their high selectivity and efficiency, bioorthogonal reactions can significantly decrease background signals in bioimaging. Compared with metal-catalyzed bioorthogonal click reactions, metal-free click reactions are more biocompatible without the metal catalyst-induced cytotoxicity. Although a great number of bioorthogonal chemistry-based strategies have been reported for cancer theranostics, a comprehensive review is scarce to highlight the advantages of these strategies. In this review, recent progress in cancer theranostics guided by metal-free bioorthogonal click chemistry will be depicted in detail. The elaborate design as well as the advantages of bioorthogonal chemistry in tumor theranostics are summarized and future prospects in this emerging field are emphasized.

Beyond Sequencing: Prioritizing and Delivering Neoantigens for Cancer Vaccines

Neoantigens are tumor-specific proteins and peptides that can be highly immunogenic. Immune-mediated tumor rejection is strongly associated with cytotoxic responses to neoantigen-derived peptides in noncovalent association with self-HLA molecules. Neoantigen-based therapies, such as adoptive T cell transfer, have shown the potential to induce remission of treatment-resistant metastatic disease in select patients. Cancer vaccines are similarly designed to elicit or amplify antigen-specific T cell populations and stimulate directed antitumor immunity, but the selection and prioritization of the neoantigens remains a challenge. Bioinformatic algorithms can predict tumor neoantigens from somatic mutations, insertion-deletions, and other aberrant peptide products, but this often leads to hundreds of potential neoepitopes, all unique for that tumor. Selecting neoantigens for cancer vaccines is complicated by the technical challenges of neoepitope discovery, the diversity of HLA molecules, and intratumoral heterogeneity of passenger mutations leading to immune escape. Despite strong preclinical evidence, few neoantigen cancer vaccines tested in vivo have generated epitope-specific T cell populations, suggesting suboptimal immune system activation. In this chapter, we review factors affecting the prioritization and delivery of candidate neoantigens in the design of therapeutic and preventive cancer vaccines and consider synergism with standard chemotherapies.

Single-Cell RNA Sequencing Reveals CXCLs Enriched Fibroblasts Within Odontogenic Keratocysts

Purpose

We aimed to define cell subpopulations of odontogenic keratocyst (OKC), particularly relating to angiogenesis and explored the potential regulation mechanism for angiogenesis.

Materials and Methods

Single-cell RNA sequencing (scRNA-seq) analysis was investigated on 14,072 cells from 3 donors with OKC. The differential expressed genes, cell trajectory and intercellular communications were evaluated by bioinformatic analysis. Hydrostatic pressure (80 mmHg, 6h) was applied to the primary fibroblasts of OKC and the supernatant was collected for cytokines detection by cytokine antibody array. The chemokine (C-X-C motif) ligand 12 (CXCL12) and CD31 expressions were explored by immunohistochemistry in tissue microarray of OKC.

Results

Five different cell types were identified in the epithelium of OKC and 3 different cell types in the OKC fibroblasts were characterized, indicating high intra-lesional heterogeneity. CXCLs were highly enriched in the subset of fibroblasts and showed close interactions with endothelial cells. Hydrostatic pressure (80mmHg) significantly increased CXCL12 secretions in OKC fibroblasts. Stromal CXCL12 expressions were closely related to CD31 expressions of tissue microarray of OKC.

Conclusion

CXCLs enriched fibroblasts are crucial for angiogenesis of OKCs which could be partially regulated by hydrostatic pressure.

Mutational landscape of paired primary and synchronous metastatic lymph node in chemotherapy naive gallbladder cancer

BACKGROUND

Comprehensive genomic analysis of paired primary tumors and their metastatic lesions may provide new insights into the biology of metastatic processes and therefore guide the development of novel strategies for intervention. To date, our knowledge of the genetic divergence and phylogenetic relationships in gallbladder cancer (GBC) is limited.

METHODS

We performed whole exome sequencing for 5 patients with primary tumor, metastatic lymph node (LNM) and corresponding normal tissue. Mutations, mutation signatures and copy number variations were analyzed with state-of-art bioinformatics methods. Phylogenetic tree was also generated to infer metastatic pattern.

RESULTS

Five driver mutations were detected in these patients. Among which, TP53 was the only shared mutation between primary tumor and LNM. Although tumor mutational burden was comparable between primary tumor and LNM, higher mutation burden was observed in LNM of one patient. Copy number variations (CNVs) burden was higher in LNM than their primary tumor. Phylogenetic analysis indicated both linear and parallel progression of metastasis exist in these patients. TP53 mutation and CNVs were homogenously between primary tumor and LNM.

CONCLUSIONS

High consistence of genetic landscape were shown between primary tumor and LNM in GBC. However, heterogenicity still exist between primary tumor and LNM in particular patients in term of driver mutation, TMB and CNV burden. Phylogenetic analysis indicated both Linear and parallel progression of metastasis were exist among these patients.

Mesenchymal stem cell-derived exosomes for gastrointestinal cancer

Gastrointestinal (GI) malignancies, a series of malignant conditions originating from the digestive system, include gastric cancer, hepatocellular carcinoma, pancreatic cancer, and colorectal cancer. GI cancers have been regarded as the leading cancer-related cause of death in recent years. Therefore, it is essential to develop effective treatment strategies for GI malignancies. Mesenchymal stem cells (MSCs), a type of distinct non-hematopoietic stem cells and an important component of the tumor microenvironment, play important roles in regulating GI cancer development and progression through multiple mechanisms, such as secreting cytokines and direct interactions. Currently, studies are focusing on the anti-cancer effect of MSCs on GI malignancies. However, the effects and functional mechanisms of MSC-derived exosomes on GI cancer are less studied. MSC-derived exosomes can regulate GI tumor growth, drug response, metastasis, and invasion through transplanting proteins and miRNA to tumor cells to activate the specific signal pathway. Besides, the MSC-derived exosomes are also seen as an important drug delivery system and have shown potential in anti-cancer treatment. This study aims to summarize the effect and biological functions of MSC-derived exosomes on the development of GI cancers and discuss their possible clinical applications for the treatment of GI malignancies.

Anticancer drug resistance: An update and perspective

Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.

ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways

Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.

Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine

BACKGROUND

Cancer is a somatic evolutionary disease and adenocarcinomas of the stomach and gastroesophageal junction (GC) may serve as a two-dimensional model of cancer expansion, in which tumor subclones are not evenly mixed during tumor progression but rather spatially separated and diversified. We hypothesize that precision medicine efforts are compromised when clinical decisions are based on a single-sample analysis, which ignores the mechanisms of cancer evolution and resulting intratumoral heterogeneity. Using multiregional whole-exome sequencing, we investigated the effect of somatic evolution on intratumoral heterogeneity aiming to shed light on the evolutionary biology of GC.

METHODS

The study comprised a prospective discovery cohort of 9 and a validation cohort of 463 GCs. Multiregional whole-exome sequencing was performed using samples form 45 primary tumors and 3 lymph node metastases (range 3-10 tumor samples/patient) of the discovery cohort.

RESULTS

In total, the discovery cohort harbored 16,537 non-synonymous mutations. Intratumoral heterogeneity of somatic mutations and copy number variants were present in all tumors of the discovery cohort. Of the non-synonymous mutations, 53-91% were not present in each patient's sample; 399 genes harbored 2-4 different non-synonymous mutations in the same patient; 175 genes showed copy number variations, the majority being heterogeneous, including CD274 (PD-L1). Multi-sample tree-based analyses provided evidence for branched evolution being most complex in a microsatellite instable GC. The analysis of the mode of evolution showed a high degree of heterogeneity in deviation from neutrality within each tumor. We found evidence of parallel evolution and evolutionary trajectories: different mutations of SMAD4 aligned with different subclones and were found only in TP53 mutant GCs.

CONCLUSIONS

Neutral and non-neutral somatic evolution shape the mutational landscape in GC along its lateral expansions. It leads to complex spatial intratumoral heterogeneity, where lymph node metastases may stem from different areas of the primary tumor, synchronously. Our findings may have profound effects on future patient management. They illustrate the risk of mis-interpreting tumor genetics based on single-sample analysis and open new avenues for an evolutionary classification of GC, i.e., the discovery of distinct evolutionary trajectories which can be utilized for precision medicine.

Analysis of KRAS Mutation Subtype in Tissue DNA and Cell-Free DNA Using Droplet Digital PCR and the Function of Cell-Free DNA as a Recurrence Predictive Marker in Pancreatic Cancer

KRAS mutation is a major regulator in the tumor progression of pancreatic cancer. Here, we compared the frequency and mutation burden of KRAS mutation subtypes with paired tumor tissue and blood in patients and examined their clinical significance. DNA from tumor tissues and cell-free DNA (cfDNA) from preoperative blood were obtained from 70 patients with pancreatic cancer. Subtypes and mutation burdens of KRAS G12D and G12V mutations were evaluated using droplet digital PCR. Comparing the presence of mutations in tissue, accumulative and simultaneous mutations of G12D or G12V were identified of 67 (95.7%), and 48 patients (68.6%). Conversely, in blood, they were only identified in 18 (25.7%) and four (5.7%) patients; respectively. Next, comparing the mutation burden in tissue, the mutation burden varied from less than 0.1 to more than five, whereas that of cfDNA in blood was mostly between one and five, as cases with a mutation burden lower than 0.1 and higher than five were rare. Finally, the presence of the G12V mutation alone in cfDNA and the combination of the G12V mutation with elevated CA 19-9 levels were associated with poor recurrence-free survival. These fundamental data on the KRAS mutation subtypes and their clinical significance could support their potential as predictive markers for postoperative recurrence.

Meta-Analysis of Joint Test of SNP and SNP-Environment Interaction with Heterogeneity

Many complex diseases are caused by single nucleotide polymorphisms (SNPs), environmental factors, and the interaction between SNPs and environment. Joint tests of the SNP and SNP-environment interaction effects (JMA) and meta-regression (MR) are commonly used to evaluate these SNP-environment interactions. However, these two methods do not consider genetic heterogeneity. We previously presented a random-effect MR, which provided higher power than the MR in datasets with high heterogeneity. However, this method requires group-level data, which sometimes are not available. Given this, we designed this study to evaluate the introduction of the random effects of SNP and SNP-environment interaction into the JMA, and then extended this to the random effect model. Likelihood ratio statistic is applied to test the JMA and the new method we proposed in this paper. We evaluated the null distributions of these tests, and the powers for this method. This method was verified by simulation and was shown to provide similar powers to the random effect meta-regression method (RMR). However, this method only requires study-level data which relaxed the condition of the RMR. Our study suggests that this method is more suitable for finding the association between SNP and diseases in the absence of group-level data.

Multiplex gene quantification as digital markers for extremely rapid evaluation of chemo-drug sensitivity

Current administrations for precision drug uses are limited in evaluation speed. Here, we propose the use of multiplex gene-based digital markers for the extremely rapid personalized prediction of individual sensitivity to cancer drugs. We first screen the transcriptional profiles by applying two to three gene filters and scoring genes by their impact on drug sensitivity and finalize the gene lists by K-nearest neighbors cross-validation. The digital markers are cancer type dependent, are composed of tens to hundreds of gene expressions, and are rapidly quantified by reverse transcription quantitative real-time PCR (qRT-PCR) within 1–3 h after tumor sampling. The area under the receiver operating characteristic curve reached 0.88 when testing the performance of digital markers on organoids derived from colorectal cancer patient tumors. The algorithm and corresponding graphic user interface were developed to demonstrate the promise of digital markers for extremely rapid drug recommendation.

•

Non-targeted multiplex genes are screened as digital markers for drug sensitivity

•

Transcription level cohort of 10s to 100s genes predicts drug sensitivity

•

Digital markers are quantified using qRT-PCR within 1–3 h

•

Digital markers guide extremely rapid chemo-drug uses after patient hospitalization

In clinical cancer medicine, many patients require immediate chemotherapy after hospitalization. Current administrations for precision drug uses are limited in evaluation speed, including genomic sequencing and tumor organoid evaluation. An extremely rapid evaluation protocol is in high demand to realize drug recommendation within a few hours after tumor sampling. In this work, we have proposed an approach for extremely rapid and personalized drug recommendation.

Genetic evolution to tyrosine kinase inhibitory therapy in patients with EGFR-mutated non-small-cell lung cancer

BACKGROUND

Tumour heterogeneity impacts the efficacy of metastatic cancer treatment even if actionable mutations are identified. Clinicians need to understand if assessing one lesion provides reliable information to drive a therapeutic decision in non-small-cell lung cancer (NSCLC) patients.

METHODS

We analysed inter-tumour heterogeneity from five autopsied individuals with NSCLC-harbouring mutations in the epidermal growth factor receptor (EGFR), treated with EGFR tyrosine kinase inhibitors (TKIs). Through a comprehensive next-generation sequencing (NGS) oncopanel, and an EGFR panel for digital droplet PCR (ddPCR), we compared metastases within individuals, longitudinal biopsies from the same lesions and, whenever possible, the primary naive tumour.

RESULTS

Analysis of 22 necropsies from five patients revealed homogeneity in pathogenic mutations and TKI-resistance mechanisms within each patient in four of them. In-depth analysis by whole-exome sequencing from patient 1 confirmed homogeneity in clonal mutations, but heterogeneity in passenger subclonal alterations. Different resistance mechanisms were detected depending on the patient and line of treatment. Three patients treated with a c-MET inhibitor in combination with TKI lost MET amplification upon progression.

CONCLUSION

At a given point and under selective TKI pressure, a single metastasis biopsy in disseminated tumours from EGFR-mutated NSCLC patients could provide a reasonable assessment of actionable alterations useful for therapeutic decisions.

Cancer evolution: Darwin and beyond

Clinical and laboratory studies over recent decades have established branched evolution as a feature of cancer. However, while grounded in somatic selection, several lines of evidence suggest a Darwinian model alone is insufficient to fully explain cancer evolution. First, the role of macroevolutionary events in tumour initiation and progression contradicts Darwin's central thesis of gradualism. Whole-genome doubling, chromosomal chromoplexy and chromothripsis represent examples of single catastrophic events which can drive tumour evolution. Second, neutral evolution can play a role in some tumours, indicating that selection is not always driving evolution. Third, increasing appreciation of the role of the ageing soma has led to recent generalised theories of age-dependent carcinogenesis. Here, we review these concepts and others, which collectively argue for a model of cancer evolution which extends beyond Darwin. We also highlight clinical opportunities which can be grasped through targeting cancer vulnerabilities arising from non-Darwinian patterns of evolution.

Impact of intratumoural CD73 expression on prognosis and therapeutic response in patients with gastric cancer

AIM

CD73 overexpression has been reported in several malignancies and is considered to be a novel immune checkpoint. However, the role and significance of CD73 in gastric cancer (GC) still remain obscure. We aim to investigate the role of CD73 expression in predicting prognosis, shaping immune contexture and guiding therapeutic strategy in GC.

METHODS

The study enrolled four independent cohorts with a total of 902 patients with GC. CD73 expression and immune contexture were examined by immunohistochemistry, single-sample gene set enrichment analysis and flow cytometry. Clinical outcomes of patient subgroups were evaluated using the Kaplan-Meier curves and Cox proportional hazard analysis. All statistical tests were two-sided.

RESULTS

CD73 was identified as an independent adverse prognostic factor for survival in GC. CD73^high tumours showed a specific microenvironment with more CD8+ T cell infiltration, but these CD8+ T cells displayed a dysfunctional phenotype. Furthermore, the CD73 (NT5E) mRNA level was associated with the Cancer Genome Atlas molecular subtypes, and NT5E high tumours showed significant fibroblast growth factor receptor 2 activation and vascular endothelial growth factor and receptor enrichment. In addition, CD73^high tumours indicated better chemotherapeutic responsiveness to fluorouracil yet a worse objective response rate to pembrolizumab in GC.

CONCLUSIONS

High CD73 expression indicated an immunoevasive contexture with CD8+ T cell dysfunction and represented an independent predictor for adverse clinical outcomes. As a potential immunotherapeutic target, CD73 could potentially be a novel biomarker for adjuvant chemotherapy, targeted therapies and immunotherapy. The crucial role of CD73 in the therapeutic landscape of GC needs further validation retrospectively and prospectively.

Genomic Heterogeneity of Pancreatic Ductal Adenocarcinoma and Its Clinical Impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death due to limited advances in recent years in early diagnosis and personalized therapy capable of overcoming tumor resistance to chemotherapy. In the last decades, significant advances have been achieved in the identification of recurrent genetic and molecular alterations of PDAC including those involving the KRAS, CDKN2A, SMAD4, and TP53 driver genes. Despite these common genetic traits, PDAC are highly heterogeneous tumors at both the inter- and intra-tumoral genomic level, which might contribute to distinct tumor behavior and response to therapy, with variable patient outcomes. Despite this, genetic and genomic data on PDAC has had a limited impact on the clinical management of patients. Integration of genomic data for classification of PDAC into clinically defined entities-i.e., classical vs. squamous subtypes of PDAC-leading to different treatment approaches has the potential for significantly improving patient outcomes. In this review, we summarize current knowledge about the most relevant genomic subtypes of PDAC including the impact of distinct patterns of intra-tumoral genomic heterogeneity on the classification and clinical and therapeutic management of PDAC.

The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

Genetic regressive trajectories in colorectal cancer: A new hallmark of oligo-metastatic disease?

Colorectal cancer (CRC) originates as consequence of multiple genetic alterations. Some of the involved genes have been extensively studied (APC, TP53, KRAS, SMAD4, PIK3CA, MMR genes) in highly heterogeneous and poly-metastatic cohorts. However, about 10% of metastatic CRC patients presents with an indolent oligo-metastatic disease differently from other patients with poly-metastatic and aggressive clinical course. Which are the genetic dynamics underlying the differences between oligo- and poly-metastatic CRC? The understanding of the genetic trajectories (primary→metastatic) of CRC, in patients selected to represent homogenous clinical models, is crucial to make genotype/phenotype correlations and to identify the molecular events pushing the disease towards an increasing malignant phenotype. This information is crucial to plan innovative therapeutic strategies aimed to reverse or inhibit these phenomena. In the present study, we review the genetic evolution of CRC with the intent to give a developmental perspective on the border line between oligo- and poly-metastatic diseases.

Co-occurrence of cohesin complex and Ras signaling mutations during progression from myelodysplastic syndromes to secondary acute myeloid leukemia

Myelodysplastic syndromes (MDS) are hematological disorders at high risk of progression to secondary acute myeloid leukemia (sAML). However, the mutational dynamics and clonal evolution underlying disease progression are poorly understood at present. To elucidate the mutational dynamics of pathways and genes occurring during the evolution to sAML, next generation sequencing was performed on 84 serially paired samples of MDS patients who developed sAML (discovery cohort) and 14 paired samples from MDS patients who did not progress to sAML during follow-up (control cohort). Results were validated in an independent series of 388 MDS patients (validation cohort). We used an integrative analysis to identify how mutations, alone or in combination, contribute to leukemic transformation. The study showed that MDS progression to sAML is characterized by greater genomic instability and the presence of several types of mutational dynamics, highlighting increasing (STAG2) and newly-acquired (NRAS and FLT3) mutations. Moreover, we observed cooperation between genes involved in the cohesin and Ras pathways in 15-20% of MDS patients who evolved to sAML, as well as a high proportion of newly acquired or increasing mutations in the chromatin-modifier genes in MDS patients receiving a disease-modifying therapy before their progression to sAML.

The pancreatic cancer genome revisited

Pancreatic cancer is a genetic disease, and the recurrent genetic alterations characteristic of pancreatic cancer indicate the cellular processes that are targeted for malignant transformation. In addition to somatic alterations in the most common driver genes (KRAS, CDKN2A, TP53 and SMAD4), large-scale studies have revealed major roles for genetic alterations of the SWI/SNF and COMPASS complexes, copy number alterations in GATA6 and MYC that partially define phenotypes of pancreatic cancer, and the role(s) of polyploidy and chromothripsis as factors contributing to pancreatic cancer biology and progression. Germline variants that increase the risk of pancreatic cancer continue to be discovered along with a greater appreciation of the features of pancreatic cancers with mismatch repair deficiencies and homologous recombination deficiencies that confer sensitivity to therapeutic targeting. Wild-type KRAS pancreatic cancers, some of which are driven by alternative oncogenic events affecting NRG1 or NTRK1 - for which targeted therapies exist - further underscore that pancreatic cancer is formally entering the era of precision medicine. Given the vast developments within this field, here we review the wide-ranging and most current information related to pancreatic cancer genomics with the goal of integrating this information into a unifying description of the life history of pancreatic cancer.

Starvation and Climate Change—How to Constrain Cancer Cell Epigenetic Diversity and Adaptability to Enhance Treatment Efficacy

Advanced metastatic cancer is currently not curable and the major barrier to eliminating the disease in patients is the resistance of subpopulations of tumor cells to drug treatments. These resistant subpopulations can arise stochastically among the billions of tumor cells in a patient or emerge over time during therapy due to adaptive mechanisms and the selective pressures of drug therapies. Epigenetic mechanisms play important roles in tumor cell diversity and adaptability, and are regulated by metabolic pathways. Here, I discuss knowledge from ecology, evolution, infectious disease, species extinction, metabolism and epigenetics to synthesize a roadmap to a clinically feasible approach to help homogenize tumor cells and, in combination with drug treatments, drive their extinction. Specifically, cycles of starvation and hyperthermia could help synchronize tumor cells and constrain epigenetic diversity and adaptability by limiting substrates and impairing the activity of chromatin modifying enzymes. Hyperthermia could also help prevent cancer cells from entering dangerous hibernation-like states. I propose steps to a treatment paradigm to help drive cancer extinction that builds on the successes of fasting, hyperthermia and immunotherapy and is achievable in patients. Finally, I highlight the many unknowns, opportunities for discovery and that stochastic gene and allele level epigenetic mechanisms pose a major barrier to cancer extinction that warrants deeper investigation.

Targeting the genetic landscape of oral potentially malignant disorders has the potential as a preventative strategy in oral cancer

This study reviews the molecular landscape of oral potentially malignant disorders (OPMD). We examine the impact of tumour heterogeneity, the spectrum of driver mutations (TP53, CDKN2A, TERT, NOTCH1, AJUBA, PIK3CA, CASP8) and gene transcription on tumour progression. We comment on how some of these mutations impact cellular senescence, field cancerization and cancer stem cells. We propose that OPMD can be monitored more closely and more dynamically through the use of liquid biopsies using an appropriate biomarker of transformation. We describe new gene interactions through the use of a systems biology approach and we highlight some of the first studies to identify functional genes using CRISPR-Cas9 technology. We believe that this information has translational implications for the use of re-purposed existing drugs and/or new drug development. Further, we argue that the use of digital technology encompassing clinical and laboratory-based data will create relevant datasets for machine learning/artificial intelligence. We believe that therapeutic intervention at an early molecular premalignant stage should be an important preventative strategy to inhibit the development of oral squamous cell carcinoma and that this approach is applicable to other aerodigestive tract cancers.

Pan-cancer Network Disorders Revealed by Overall and Local Signaling Entropy

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics, which is driven by intracellular rewiring of signaling network. The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors. Here, we proposed signaling entropy (SR) to assess the degree of tumor network disorder. We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcriptomic and proteomic data. The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell stemness, cancer type, tumor grade, and metastasis. We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation. Overall, SR could reveal cancer network disorders related to tumor malignant potency, clinical prognosis, and drug response.

Genetic and non-genetic clonal diversity in cancer evolution

The observation and analysis of intra-tumour heterogeneity (ITH), particularly in genomic studies, has advanced our understanding of the evolutionary forces that shape cancer growth and development. However, only a subset of the variation observed in a single tumour will have an impact on cancer evolution, highlighting the need to distinguish between functional and non-functional ITH. Emerging studies highlight a role for the cancer epigenome, transcriptome and immune microenvironment in functional ITH. Here, we consider the importance of both genetic and non-genetic ITH and their role in tumour evolution, and present the rationale for a broad research focus beyond the cancer genome. Systems-biology analytical approaches will be necessary to outline the scale and importance of functional ITH. By allowing a deeper understanding of tumour evolution this will, in time, encourage development of novel therapies and improve outcomes for patients.

Chromosomal copy number heterogeneity predicts survival rates across cancers

Survival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing. Here, we introduce a scalable measure of chromosomal copy number heterogeneity (CNH) that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data and live cell imaging we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 37 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.

Genetic diversity through social heterosis can increase virulence in RNA viral infections and cancer progression

In viral infections and cancer tumours, negative health outcomes often correlate with increasing genetic diversity. Possible evolutionary processes for such relationships include mutant lineages escaping host control or diversity, per se, creating too many immune system targets. Another possibility is social heterosis where mutations and replicative errors create clonal lineages varying in intrinsic capability for successful dispersal; improved environmental buffering; resource extraction or effective defence against immune systems. Rather than these capabilities existing in one genome, social heterosis proposes complementary synergies occur across lineages in close proximity. Diverse groups overcome host defences as interacting 'social genomes' with group genetic tool kits exceeding limited individual plasticity. To assess the possibility of social heterosis in viral infections and cancer progression, we conducted extensive literature searches for examples consistent with general and specific predictions from the social heterosis hypothesis. Numerous studies found supportive patterns in cancers across multiple tissues and in several families of RNA viruses. In viruses, social heterosis mechanisms probably result from long coevolutionary histories of competition between pathogen and host. Conversely, in cancers, social heterosis is a by-product of recent mutations. Investigating how social genomes arise and function in viral quasi-species swarms and cancer tumours may lead to new therapeutic approaches.

Targeting public neoantigens for cancer immunotherapy

Several current immunotherapy approaches target private neoantigens derived from mutations that are unique to individual patients' tumors. However, immunotherapeutic agents can also be developed against public neoantigens derived from recurrent mutations in cancer driver genes. The latter approaches target proteins that are indispensable for tumor growth, and each therapeutic agent can be applied to numerous patients. Here we review the opportunities and challenges involved in the identification of suitable public neoantigen targets and the development of therapeutic agents targeting them.

Genomic Correlates of DNA Damage in Breast Cancer Subtypes

Simple Summary

Breast cancer (BC) is the most common invasive tumor in women and the second leading cause of cancer-related death. Therefore, identification of druggable targets to improve current therapies and overcome resistance is a major goal. In this work, we performed an in silico analysis of transcriptomic datasets in breast cancer, and focused on those involved in DNA damage, as were clearly upregulated using gene set enrichment analyses (GSEA), particular the following pathways: ATM/ATR, BARD1 and Fanconi Anemia. BHLHE40, RFWD2, BRIP1, PRKDC, NBN, RNF8, FANCD2, RAD1, BLM, DCLRE1C, UBE2T, CSTF1, MCM7, RFC4, YWHAB, YWHAZ, CDC6, CCNE1, and FANCI genes were amplified/overexpressed in BC, and correlated with detrimental prognosis. Finally, we selected the best transcriptomic signature of genes within this function that associated with clinical outcome to identify functional genomic correlates of outcome.

Abstract

Among the described druggable vulnerabilities, acting on the DNA repair mechanism has gained momentum, with the approval of PARP inhibitors in several indications, including breast cancer. However, beyond the mere presence of BRCA1/BRCA2 mutations, the identification of additional biomarkers that would help to select tumors with an extreme dependence on DNA repair machinery would help to stratify therapeutic decisions. Gene set enrichment analyses (GSEA) using public datasets evaluating expression values between normal breast tissue and breast cancer identified a set of upregulated genes. Genes included in different pathways, such as ATM/ATR, BARD1, and Fanconi Anemia, which are involved in the DNA damage response, were selected and confirmed using molecular alterations data contained at cBioportal. Nineteen genes from these gene sets were identified to be amplified and upregulated in breast cancer but only five of them NBN, PRKDC, RFWD2, UBE2T, and YWHAZ meet criteria in all breast cancer molecular subtypes. Correlation of the selected genes with prognosis (relapse free survival, RFS, and overall survival, OS) was performed using the KM Plotter Online Tool. In last place, we selected the best signature of genes within this process whose upregulation can be indicative of a more aggressive phenotype and linked with worse outcome. In summary, we identify genomic correlates within DNA damage pathway associated with prognosis in breast cancer.

Karyotype Aberrations in Action: The Evolution of Cancer Genomes and the Tumor Microenvironment

Cancer is a disease of cellular evolution. For this cellular evolution to take place, a population of cells must contain functional heterogeneity and an assessment of this heterogeneity in the form of natural selection. Cancer cells from advanced malignancies are genomically and functionally very different compared to the healthy cells from which they evolved. Genomic alterations include aneuploidy (numerical and structural changes in chromosome content) and polyploidy (e.g., whole genome doubling), which can have considerable effects on cell physiology and phenotype. Likewise, conditions in the tumor microenvironment are spatially heterogeneous and vastly different than in healthy tissues, resulting in a number of environmental niches that play important roles in driving the evolution of tumor cells. While a number of studies have documented abnormal conditions of the tumor microenvironment and the cellular consequences of aneuploidy and polyploidy, a thorough overview of the interplay between karyotypically abnormal cells and the tissue and tumor microenvironments is not available. Here, we examine the evidence for how this interaction may unfold during tumor evolution. We describe a bidirectional interplay in which aneuploid and polyploid cells alter and shape the microenvironment in which they and their progeny reside; in turn, this microenvironment modulates the rate of genesis for new karyotype aberrations and selects for cells that are most fit under a given condition. We conclude by discussing the importance of this interaction for tumor evolution and the possibility of leveraging our understanding of this interplay for cancer therapy.

Male Breast Cancer-Immunohistochemical Patterns and Clinical Relevance of FASN, ATF3, and Collagen IV

Background

Male breast carcinoma (male BC) is an uncommon neoplasia without individualized strategies for diagnosis and therapeutics. Low overall survival (OS) rates have been reported, mostly associated with patients' advanced stage and older age. Intratumoral heterogeneity versus homogeneity of malignant epithelial cells seems to be an important factor to consider for the development of combination therapies with curative intention.

Objective

In this preliminary study, we aim to provide valuable insight into the distinct clinicopathologic features of male BC.

Material and methods

In a series of 40 male BC patients, we evaluated by immunohistochemistry androgen receptor; activating transcription factor 3 (ATF3); p16; cyclin D1; fatty acid synthase (FASN); fatty acid transport protein 1 (FATP1); β1, β3, β4, and β6 integrins; collagen I and collagen IV; and their interactions. Kaplan-Meier survival curves and log-rank tests were assessed for statistical analysis.

Results

Homogeneous epithelial staining of p16, ATF3, β6 integrin, FASN, and FATP1 was found to be significantly intercorrelated, and associated with high Ki67. These markers also stained tumor stromal fibroblasts. The prognostic analysis showed statistically significant associations of FASN with disease-free survival (DFS) and OS, as well as of ATF3 with OS and collagen IV with DFS.

Conclusions

This study highlights, as a novel finding, the relevance of FASN, ATF3, and collagen IV immunophenotypes, which may have innovative application in the clinical management of male BC.

Metastasis-Initiating Cells and Ecosystems

Metastasis is initiated and sustained through therapy by cancer cells with stem-like and immune-evasive properties, termed metastasis-initiating cells (MIC). Recent progress suggests that MICs result from the adoption of a normal regenerative progenitor phenotype by malignant cells, a phenotype with intrinsic programs to survive the stresses of the metastatic process, undergo epithelial-mesenchymal transitions, enter slow-cycling states for dormancy, evade immune surveillance, establish supportive interactions with organ-specific niches, and co-opt systemic factors for growth and recurrence after therapy. Mechanistic understanding of the molecular mediators of MIC phenotypes and host tissue ecosystems could yield cancer therapeutics to improve patient outcomes. SIGNIFICANCE: Understanding the origins, traits, and vulnerabilities of progenitor cancer cells with the capacity to initiate metastasis in distant organs, and the host microenvironments that support the ability of these cells to evade immune surveillance and regenerate the tumor, is critical for developing strategies to improve the prevention and treatment of advanced cancer. Leveraging recent progress in our understanding of the metastatic process, here we review the nature of MICs and their ecosystems and offer a perspective on how this knowledge is informing innovative treatments of metastatic cancers.

Artificial intelligence in cancer research: learning at different levels of data granularity

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

Exploring epigenetic biomarkers of universal specificities and commonalities among pan-cancer cohorts in The Cancer Genome Atlas

Aim: To explore the mechanism of cancer by employing a comprehensive analysis of DNA methylation patterns and variations among pan-cancer cohorts. Materials & methods: This research focused on the discovery of universally specific or common biomarkers by mathematical statistics and machine learning methods in The Cancer Genome Atlas. Results: We found 138 differently methylated CpGs (DMCs) with a common methylation trend and eight common differently methylated regions in different cancer cohorts. Additionally, we found 99 DMCs to distinguish 32 different cancer cohorts in random forest analysis because of the specificity mechanism, but each DMC still had high instability. Conclusion: Our results could facilitate the development of biomarkers that are universally specific and common features across pan-cancer cohorts.

MiR-6838-5p facilitates the proliferation and invasion of renal cell carcinoma cells through inhibiting the DMTF1/ARF-p53 axis

Renal cell carcinoma (RCC) is one of the most common renal malignancies in the urinary system. Numerous studies have demonstrated that miRNAs can regulate tumorigenesis and progression. This study aims to investigate the role and regulatory mechanism of miR-6838-5p in RCC. Our study confirmed that miR-6838-5p was upregulated in human RCC tissues (30/42, 77.43%, P < 0.01) and RCC cell lines (P < 0.05) compared to adjacent non-neoplastic tissues and normal renal epithelial cells. In vitro, overexpression of miR-6838-5p enhanced cell proliferation and invasion in human RCC cell lines (ACHN and 786-O), which were detected by CCK-8, Transwell and Colony formation assays (P < 0.05), and knockdown of miR-6838-5p suppressed cell proliferation and invasion (P < 0.05). Results of Bioinformatics analysis combined with Dual-luciferase reporter gene assay demonstrated that miR-6838-5p could bind to Cyclin D binding myb-like transcription factor 1 (DMTF1). In addition, RT-qPCR and Western blotting confirmed that DMTF1 was downregulated in RCC tissues and cell lines. Meanwhile, it was demonstrated that overexpression of miR-6838-5p inhibited DMTF1 level in ACHN cells. Next, we confirmed that DMTF1 overexpression reversed the inhibitory effects of overexpression of miR-6838-5p on phosphatase and tensin homolog (PTEN), tumor protein 53(p53), murine double minute 2 (MDM2) and alternative reading frame (ARF) protein levels in the ARF-p53 signaling pathway. In conclusion, our research showed that miR-6838-5p enhanced the proliferation and invasion of RCC cells by inhibiting the DMTF1/ARF-p53 axis.

Inhibition of Mitochondrial ATP Synthesis and Regulation of Oxidative Stress Based on {SbW8 O30 } Determined by Single-Cell Proteomics Analysis

The 10-nuclear heteroatom cluster modified {SbW₈ O₃₀ } was successfully synthesized and exhibited inhibitory activity (IC₅₀ =0.29 μM). Based on proteomics analysis, Na₄ Ni₂ Sb₂ W₂ -SbW₈ inhibited ATP production by affecting the expression of 16 related proteins, hindering metabolic functions in vivo and cell proliferation due to reactive oxygen species (ROS) stress. In particular, the low expression of FAD/FMN-binding redox enzymes (relative expression ratio of the experimental group to the control=0.43843) could be attributed to the redox mechanism of Na₄ Ni₂ Sb₂ W₂ -SbW₈ , which was consistent with the effect of polyoxometalates (POMs) and FMN-binding proteins on ATP formation. An electrochemical study showed that Na₄ Ni₂ Sb₂ W₂ -SbW₈ combined with FMN to form Na₄ Ni₂ Sb₂ W₂ -SbW₈ -2FMN complex through a one-electron process of the W atoms. Na₄ Ni₂ Sb₂ W₂ -SbW₈ acted as catalase and glutathione peroxidase to protect the cell from ROS stress, and the inhibition rates were 63.3 % at 1.77 μM of NADPH and 86.06 % at 10.62 μM of 2-hydroxyterephthalic acid. Overall, our results showed that POMs can be specific oxidative/antioxidant regulatory agents.

Tumor-intrinsic determinants of immunogenic cell death modalities

The immune system can recognize tumor cells to mount antigen-specific T cell response. Central to the establishment of T cell-mediated adaptive immunity are the inflammatory events that facilitate antigen presentation by stimulating the expression of MHC and costimulatory molecules and the secretion of pro-inflammatory cytokines. Such inflammatory events can be triggered upon cytotoxic treatments that induce immunogenic cancer cell death modalities. However, cancers have acquired a plethora of mechanisms to subvert, or to hide from, host-encoded immunosurveillance. Here, we discuss how tumor intrinsic oncogenic factors subvert desirable intratumoral inflammation by suppressing immunogenic cell death.

Profiling of circulating tumor DNA and tumor tissue for treatment selection in patients with advanced and refractory carcinoma: a prospective, two-stage phase II Individualized Cancer Treatment trial

BACKGROUND

Molecular profiling (MP) represents an opportunity to match patients to a targeted therapy and when tumor tissue is unavailable, circulating tumor deoxyribonucleic acid (ctDNA) can be harnessed as a non-invasive analyte for this purpose. We evaluated the success of a targeted therapy selected by profiling of ctDNA and tissue in patients with advanced and refractory carcinoma.

PATIENTS AND METHODS

A blood draw as well as an optional tissue biopsy were obtained for MP. Whole-genome sequencing and a cancer hotspot panel were performed, and publicly available databases were used to match the molecular profile to targeted treatments. The primary endpoint was the progression-free survival (PFS) ratio (PFS on MP-guided therapy/PFS on the last evidence-based therapy), whereas the success of the targeted therapy was defined as a PFS ratio ⩾1.2. To test the impact of molecular profile-treatment matching strategies, we retrospectively analyzed selected cases via the CureMatch PreciGENE™ decision support algorithm.

RESULTS

Interim analysis of 24 patients yielded informative results from 20 patients (83%). A potential tumor-specific drug could be matched in 11 patients (46%) and eight (33%) received a matched treatment. Median PFS in the matched treatment group was 61.5 days [interquartile range (IQR) 49.8-71.0] compared with 81.5 days (IQR 68.5-117.8) for the last evidence-based treatment, resulting in a median PFS ratio of 0.7 (IQR 0.6-0.9). Hence, as no patient experienced a PFS ratio ⩾1.2, the study was terminated. Except for one case, the CureMatch analysis identified either a two-drug or three-drug combination option.

CONCLUSIONS

Our study employed a histotype-agnostic approach to harness molecular profiling data from both ctDNA and metastatic tumor tissue. The outcome results indicate that more innovative approaches to study design and matching algorithms are necessary to achieve improved patient outcomes.EU Clinical Trials Registry (https://www.clinicaltrialsregister.eu): EudraCT: 2014-005341-44.

Genomic and phenotypic heterogeneity in prostate cancer

From a clinical, morphological and molecular perspective, prostate cancer is a heterogeneous disease. Primary prostate cancers are often multifocal, having topographically and morphologically distinct tumour foci. Sequencing studies have revealed that individual tumour foci can arise as clonally distinct lesions with no shared driver gene alterations. This finding demonstrates that multiple genomically and phenotypically distinct primary prostate cancers can be present in an individual patient. Lethal metastatic prostate cancer seems to arise from a single clone in the primary tumour but can exhibit subclonal heterogeneity at the genomic, epigenetic and phenotypic levels. Collectively, this complex heterogeneous constellation of molecular alterations poses obstacles for the diagnosis and treatment of prostate cancer. However, advances in our understanding of intra-tumoural heterogeneity and the development of novel technologies will allow us to navigate these challenges, refine approaches for translational research and ultimately improve patient care.

Liquid biopsy enters the clinic - implementation issues and future challenges

Historically, studies of disseminated tumour cells in bone marrow and circulating tumour cells in peripheral blood have provided crucial insights into cancer biology and the metastatic process. More recently, advances in the detection and characterization of circulating tumour DNA (ctDNA) have finally enabled the introduction of liquid biopsy assays into clinical practice. The FDA has already approved several single-gene assays and, more recently, multigene assays to detect genetic alterations in plasma cell-free DNA (cfDNA) for use as companion diagnostics matched to specific molecularly targeted therapies for cancer. These approvals mark a tipping point for the widespread use of liquid biopsy in the clinic, and mostly in patients with advanced-stage cancer. The next frontier for the clinical application of liquid biopsy is likely to be the systemic treatment of patients with 'ctDNA relapse', a term we introduce for ctDNA detection prior to imaging-detected relapse after curative-intent therapy for early stage disease. Cancer screening and diagnosis are other potential future applications. In this Perspective, we discuss key issues and gaps in technology, clinical trial methodologies and logistics for the eventual integration of liquid biopsy into the clinical workflow.

Targeting metastatic cancer

Despite recent therapeutic advances in cancer treatment, metastasis remains the principal cause of cancer death. Recent work has uncovered the unique biology of metastasis-initiating cells that results in tumor growth in distant organs, evasion of immune surveillance and co-option of metastatic microenvironments. Here we review recent progress that is enabling therapeutic advances in treating both micro- and macrometastases. Such insights were gained from cancer sequencing, mechanistic studies and clinical trials, including of immunotherapy. These studies reveal both the origins and nature of metastases and identify new opportunities for developing more effective strategies to target metastatic relapse and improve patient outcomes.

Metabolic regulation of prostate cancer heterogeneity and plasticity

Metabolic reprogramming is one of the main hallmarks of cancer cells. It refers to the metabolic adaptations of tumor cells in response to nutrient deficiency, microenvironmental insults, and anti-cancer therapies. Metabolic transformation during tumor development plays a critical role in the continued tumor growth and progression and is driven by a complex interplay between the tumor mutational landscape, epigenetic modifications, and microenvironmental influences. Understanding the tumor metabolic vulnerabilities might open novel diagnostic and therapeutic approaches with the potential to improve the efficacy of current tumor treatments. Prostate cancer is a highly heterogeneous disease harboring different mutations and tumor cell phenotypes. While the increase of intra-tumor genetic and epigenetic heterogeneity is associated with tumor progression, less is known about metabolic regulation of prostate cancer cell heterogeneity and plasticity. This review summarizes the central metabolic adaptations in prostate tumors, state-of-the-art technologies for metabolic analysis, and the perspectives for metabolic targeting and diagnostic implications.

Targeted Sequencing of Pancreatic Adenocarcinomas from Patients with Metachronous Pulmonary Metastases

Mutation spectra of 250 cancer driver, druggable, and actionable genes were analyzed in surgically resected pancreatic ductal adenocarcinoma (PDAC) patients who developed metachronous pulmonary metastases. Targeted sequencing was performed in DNA from blood and archival samples of 15 primary tumors and three paired metastases. Results were complemented with the determination of G12V mutation in KRAS by droplet digital PCR. The median number of protein-changing mutations was 52 per patient. KRAS and TP53 were significantly enriched in fractions of mutations in hotspots. Individual gene mutation frequencies or mutational loads accounting separately for drivers, druggable, or clinically actionable genes, did not significantly associate with patients' survival. LRP1B was markedly mutated in primaries of patients who generalized (71%) compared to those developing solitary pulmonary metastases (0%). FLG2 was mutated exclusively in primary tumors compared to paired metastases. In conclusion, signatures of prognostically differing subgroups of PDAC patients were generated for further utilization in precision medicine.