PyClone: statistical inference of clonal population structure in cancer

Evolution of genome and immunogenome in esophageal squamous cell carcinomas driven by neoadjuvant chemoradiotherapy

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is a standard treatment for locally advanced esophageal squamous cell carcinomas (ESCCs). However, the evolution of genome and immunogenome in ESCCs driven by NCRT remains incompletely elucidated. We performed whole-exome sequencing of 51 ESCC tumors collected before and after NCRT, 36 of which were subjected to transcriptome sequencing. Clonal analysis identified clonal extinction in 13 ESCC patients wherein all pre-NCRT clones disappeared after NCRT, and clonal persistence in 9 patients wherein clones endured following NCRT. The clone-persistent patients showed higher pre-NCRT genomic intratumoral heterogeneity and worse prognosis than the clone-extinct ones. In contrast to the clone-extinct patients, the clone-persistent patients demonstrated a high proportion of subclonal neoantigens within pre-treatment specimens. Transcriptome analysis revealed increased immune infiltrations and up-regulated immune-related pathways after NCRT, especially in the clone-extinct patients. The number of T cell receptor-neoantigen interactions was higher in the clone-extinct patients than in the clone-persistent ones. The decrease in T cell repertoire evenness positively correlated to the decreased number of clonal neoantigens after NCRT, especially in the clone-extinct patients. In conclusion, we identified two prognosis-related clonal dynamic modes driven by NCRT in ESCCs. This study extended our knowledge of the ESCC genome and immunogenome evolutions driven by NCRT.

Genetic Alterations in Chromatin Regulatory Genes in Upper Tract Urothelial Carcinoma and Urothelial Bladder Cancer

PURPOSE

Upper tract urothelial carcinoma (UTUC) and urothelial carcinoma of the bladder (UCB) share histomorphological and therapeutic features but distinct epidemiologic and clinicopathologic characteristics. We examined alterations of chromatin regulatory genes in molecular subtypes, clonal relatedness, and T-cell receptor (TCR) diversity in UTUC and UCB.

MATERIALS AND METHODS

Targeted next-generation sequencing or whole-exome DNA sequencing and TCR sequencing were conducted with 34 UTUC and 49 UCB specimens from 63 patients. Tumors were subtyped based on the expression of CK5 and GATA3. Results of tissue microarray of 78 muscle-invasive bladder cancer (MIBC) samples were used as prognostic factors of different subtypes of MIBC.

RESULTS

Chromatin regulatory genes were frequently mutated in both UTUC and UCB. Rapid relapse and progression of non-MIBC are correlated with alterations of KMT2C and EP300. Frequency of alterations in chromatin regulatory genes is higher in UTUC patients with SBS22 and SBS2 signatures and lower in UCB patients with SBS2 and SBS6 signatures. GATA3 and CK5 double-positive patients with higher frequencies of SMARCA4, ARID1A, and EP300 mutations have better prognoses than patients with basal subtypes. Although UTUC and UCB in the same patient can be either clonally related or developed independently, mutated genes in chromatin pathway were enriched in the related clones. Compared to UTUC, UCB had more deleterious mutations in DNA damage repair (DDR) genes, higher levels of tumor mutation burden (TMB) and copy number variations (CNVs), as well as higher TCR clonality and lower TCR diversity.

CONCLUSIONS

Since genetic alterations of the chromatin pathway genes are important in both UTUC and UCB, they could serve as potential biomarkers for predicting disease progression and therapeutic targets. Differences in mutation frequencies of DDR pathway, TMB, CNV, and TCR might be the contributing factors for the distinct responses to immune checkpoint inhibitor (ICI) between UTUC and UCB.

Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope

Spatial and genomic heterogeneity of tumors are crucial factors influencing cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on somatic point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their localization in close to single-cell resolution by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

The integrated molecular and histological analysis defines subtypes of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is highly heterogeneous. Our understanding of full molecular and immune landscape of ESCC remains limited, hindering the development of personalised therapeutic strategies. To address this, we perform genomic-transcriptomic characterizations and AI-aided histopathological image analysis of 120 Chinese ESCC patients. Here we show that ESCC can be categorized into differentiated, metabolic, immunogenic and stemness subtypes based on bulk and single-cell RNA-seq, each exhibiting specific molecular and histopathological features based on an amalgamated deep-learning model. The stemness subgroup with signature genes, such as WFDC2, SFRP1, LGR6 and VWA2, has the poorest prognosis and is associated with downregulated immune activities, a high frequency of EP300 mutation/activation, functional mutation enrichment in Wnt signalling and the highest level of intratumoural heterogeneity. The immune profiling by transcriptomics and immunohistochemistry reveals ESCC cells overexpress natural killer cell markers XCL1 and CD160 as immune evasion. Strikingly, XCL1 expression also affects the sensitivity of ESCC cells to common chemotherapy drugs. This study opens avenues for ESCC treatment and provides a valuable public resource to better understand ESCC.

Parameter estimation from single patient, single time-point sequencing data of recurrent tumors

In this study, we develop consistent estimators for key parameters that govern the dynamics of tumor cell populations when subjected to pharmacological treatments. While these treatments often lead to an initial reduction in the abundance of drug-sensitive cells, a population of drug-resistant cells frequently emerges over time, resulting in cancer recurrence. Samples from recurrent tumors present as an invaluable data source that can offer crucial insights into the ability of cancer cells to adapt and withstand treatment interventions. To effectively utilize the data obtained from recurrent tumors, we derive several large number limit theorems, specifically focusing on the metrics that quantify the clonal diversity of cancer cell populations at the time of cancer recurrence. These theorems then serve as the foundation for constructing our estimators. A distinguishing feature of our approach is that our estimators only require a single time-point sequencing data from a single tumor, thereby enhancing the practicality of our approach and enabling the understanding of cancer recurrence at the individual level.

Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens

Recent breakthroughs in cancer immunotherapies have emphasized the importance of harnessing the immune system for treating cancer. Vaccines, which have traditionally been used to promote protective immunity against pathogens, are now being explored as a method to target cancer neoantigens. Over the past few years, extensive preclinical research and more than a hundred clinical trials have been dedicated to investigating various approaches to neoantigen discovery and vaccine formulations, encouraging development of personalized medicine. Nucleic acids (DNA and mRNA) have become particularly promising platform for the development of these cancer immunotherapies. This shift towards nucleic acid-based personalized vaccines has been facilitated by advancements in molecular techniques for identifying neoantigens, antigen prediction methodologies, and the development of new vaccine platforms. Generating these personalized vaccines involves a comprehensive pipeline that includes sequencing of patient tumor samples, data analysis for antigen prediction, and tailored vaccine manufacturing. In this review, we will discuss the various shared and personalized antigens used for cancer vaccine development and introduce strategies for identifying neoantigens through the characterization of gene mutation, transcription, translation and post translational modifications associated with oncogenesis. In addition, we will focus on the most up-to-date nucleic acid vaccine platforms, discuss the limitations of cancer vaccines as well as provide potential solutions, and raise key clinical and technical considerations in vaccine development.

Molecular landscape of ERBB2 alterations in 3000 advanced NSCLC patients

ERBB2 (HER2) represents a newly recognized actionable oncogenic driver in non-small cell lung cancer (NSCLC), with approved targeted therapy available. Understanding the landscape of ERBB2 alterations and co-occurring mutations is essential for guiding treatment decisions. We conducted an analysis involving 3000 NSCLC patients with all types of ERBB2 alterations, drawn from two extensive retrospective cohorts: 1281 from Geneplus (Chinese) and 1719 from Guardant360 (the United States, US). The incidence of all types of ERBB2 alterations was found to be 5.6% in the Chinese group and 5.2% in the US group. In both cohorts, among oncogenic alterations of ERBB2, exon 20 insertion Y772_A775dupYVMA was the most frequent alteration (58% vs 41.6% in the Chinese vs the US), followed by G776delinsVC/LC/VV/IC (10.7% vs 9.7%), and S310X (10.5% vs 15.4%). EGFR ex20 insertions were identified in the A767-V774 region, whereas ERBB2 ex20 insertions were observed in the Y772-P780 region. Notably, EGFR ex20 insertions exhibited greater insertion diversity. Clinical characteristics of EGFR and ERBB2 ex20 NSCLC were similar, characterized by low tumor mutation burden (TMB), a predominant never-smoker population, and a majority of lung adenocarcinoma cases.

Longitudinal Comparative Analysis of Circulating Tumor DNA and Matched Tumor Tissue DNA in Patients with Metastatic Colorectal Cancer Receiving Palliative First-Line Systemic Anti-Cancer Therapy

PURPOSE

This study aimed to compare tumor tissue DNA (ttDNA) and circulating tumor DNA (ctDNA) to explore the clinical applicability of ctDNA and to better understand clonal evolution in patients with metastatic colorectal cancer undergoing palliative first-line systemic therapy.

MATERIALS AND METHODS

We performed targeted sequencing analysis of 88 cancer-associated genes using germline DNA, ctDNA at baseline (baseline-ctDNA), and ctDNA at progressive disease (PD-ctDNA). The results were compared with ttDNA data.

RESULTS

Among 208 consecutively enrolled patients, we selected 84 (41 males; median age, 59 years; range, 35 to 90 years) with all four sample types available. A total of 202 driver mutations were found in 34 genes. ttDNA exhibited the highest mutation frequency (n=232), followed by baseline-ctDNA (n=155) and PD-ctDNA (n=117). Sequencing ctDNA alongside ttDNA revealed additional mutations in 40 patients (47.6%). PD-ctDNA detected 13 novel mutations in 10 patients (11.9%) compared to ttDNA and baseline-ctDNA. Notably, seven mutations in five patients (6.0%) were missense or nonsense mutations in APC, TP53, SMAD4, and CDH1 genes. In baseline-ctDNA, higher maximal variant allele frequency (VAF) values (p=0.010) and higher VAF values of APC (p=0.012), TP53 (p=0.012), and KRAS (p=0.005) mutations were significantly associated with worse overall survival.

CONCLUSION

While ttDNA remains more sensitive than ctDNA, our ctDNA platform demonstrated validity and potential value when ttDNA was unavailable. Post-treatment analysis of PD-ctDNA unveiled new pathogenic mutations, signifying cancer's clonal evolution. Additionally, baseline-ctDNA's VAF values were prognostic after treatment.

Endocrine persistence in ER+ breast cancer is accompanied by metabolic vulnerability in oxidative phosphorylation

Despite adjuvant treatment with endocrine therapies, estrogen receptor-positive (ER+) breast cancers recur in a significant proportion of patients. Recurrences are attributable to clinically undetectable endocrine-tolerant persister cancer cells that retain tumor-forming potential. Therefore, strategies targeting such persister cells may prevent recurrent disease. Using CRISPR-Cas9 genome-wide knockout screening in ER+ breast cancer cells, we identified a survival mechanism involving metabolic reprogramming with reliance upon mitochondrial respiration in endocrine-tolerant persister cells. Quantitative proteomic profiling showed reduced levels of glycolytic proteins in persisters. Metabolic tracing of glucose revealed an energy-depleted state in persisters where oxidative phosphorylation was required to generate ATP. A phase II clinical trial was conducted to evaluate changes in mitochondrial markers in primary ER+/HER2-breast tumors induced by neoadjuvant endocrine therapy ( NCT04568616 ). In an analysis of tumor specimens from 32 patients, tumors exhibiting residual cell proliferation after aromatase inhibitor-induced estrogen deprivation with letrozole showed increased mitochondrial content. Genetic profiling and barcode lineage tracing showed that endocrine-tolerant persistence occurred stochastically without genetic predisposition. Mice bearing cell line- and patient-derived xenografts were used to measure the anti-tumor effects of mitochondrial complex I inhibition in the context of endocrine therapy. Pharmacological inhibition of complex I suppressed the tumor-forming potential of persisters and synergized with the anti-estrogen fulvestrant to induce regression of patient-derived xenografts. These findings indicate that mitochondrial metabolism is essential in endocrine-tolerant persister ER+ breast cancer cells and warrant the development of treatment strategies to leverage this vulnerability in the context of endocrine-sensitive disease.

Statement of Significance

Endocrine-tolerant persister cancer cells that survive endocrine therapy can cause recurrent disease. Persister cells exhibit increased energetic dependence upon mitochondria for survival and tumor re-growth potential.

Molecular features of NSCLC patients with liver metastasis

Background

Metastasis is the primary cause of lung cancer-related death. Primary cancer cells invade through the lymphatic or blood vessels to distant sites. Recently, it was proposed that lymphatic metastasis was more a hallmark of tumor aggressiveness or metastatic potential than a gateway to metastases. Therefore, the underlying molecular mechanism of metastasis is not entirely clear.

Objectives

This study aimed to explore the genetic mechanisms underlying liver metastases from lung cancer and to evaluate the efficacy of different therapies in these patients.

Design

We retrospectively analyzed the mutation spectrum of different biopsy samples including primary lung tumors, liver, lymph node metastasis, and circulating tumor DNA (ctDNA) from 1090 non-small-cell lung cancer (NSCLC) patients with liver metastasis between the years 2017 and 2022.

Methods

Demographic and disease characteristics were summarized using descriptive parameters. Time to treatment discontinuation was used to analyze the clinical outcome.

Results

More liquid biopsies were performed than tissue biopsies, especially in the treated advanced NSCLC patients. Liver metastasis before treatment was associated with poor response to immune checkpoint inhibitors and targeted therapy. Liver and lymph node metastasis had higher levels of single nucleotide variants and copy number variants than primary lung tumors. In paired lung and liver, lymph nodes, and simultaneous ctDNA, we found actionable mutations were always shared, while metastasis samples had multiple private mutations. Serial ctDNA analysis identifies potential resistant mutations and describes the evolution of tumor cells.

Conclusion

Liver and lymph node metastasis in NSCLC showed shared actionable mutations. Of note, the discrepancy of private mutations in liver and lymph node metastases indicated that liver metastases are mainly seeded by the primary tumor rather than the earlier colonized lymph node metastases.

Reconstructing tumor clonal heterogeneity and evolutionary relationships based on tumor DNA sequencing data

The heterogeneity of tumor clones drives the selection and evolution of distinct tumor cell populations, resulting in an intricate and dynamic tumor evolution process. While tumor bulk DNA sequencing helps elucidate intratumor heterogeneity, challenges such as the misidentification of mutation multiplicity due to copy number variations and uncertainties in the reconstruction process hinder the accurate inference of tumor evolution. In this study, we introduce a novel approach, REconstructing Tumor Clonal Heterogeneity and Evolutionary Relationships (RETCHER), which characterizes more realistic cancer cell fractions by accurately identifying mutation multiplicity while considering uncertainty during the reconstruction process and the credibility and reasonableness of subclone clustering. This method comprehensively and accurately infers multiple forms of tumor clonal heterogeneity and phylogenetic relationships. RETCHER outperforms existing methods on simulated data and infers clearer subclone structures and evolutionary relationships in real multisample sequencing data from five tumor types. By precisely analysing the complex clonal heterogeneity within tumors, RETCHER provides a new approach to tumor evolution research and offers scientific evidence for developing precise and personalized treatment strategies. This approach is expected to play a significant role in tumor evolution research, clinical diagnosis, and treatment. RETCHER is available for free at https://github.com/zlsys3/RETCHER.

Inferring cancer type-specific patterns of metastatic spread

The metastatic spread of a cancer can be reconstructed from DNA sequencing of primary and metastatic tumours, but doing so requires solving a challenging combinatorial optimization problem. This problem often has multiple solutions that cannot be distinguished based on current maximum parsimony principles alone. Current algorithms use ad hoc criteria to select among these solutions, and decide, a priori, what patterns of metastatic spread are more likely, which is itself a key question posed by studies of metastasis seeking to use these tools. Here we introduce Metient, a freely available open-source tool which proposes multiple possible hypotheses of metastatic spread in a cohort of patients and rescores these hypotheses using independent data on genetic distance of metastasizing clones and organotropism. Metient is more accurate and is up to 50x faster than current state-of-the-art. Given a cohort of patients, Metient can calibrate its parsimony criteria, thereby identifying shared patterns of metastatic dissemination in the cohort. Reanalyzing metastasis in 169 patients based on 490 tumors, Metient automatically identifies cancer type-specific trends of metastatic dissemination in melanoma, high-risk neuroblastoma and non-small cell lung cancer. Metient's reconstructions usually agree with semi-manual expert analysis, however, in many patients, Metient identifies more plausible migration histories than experts, and further finds that polyclonal seeding of metastases is more common than previously reported. By removing the need for hard constraints on what patterns of metastatic spread are most likely, Metient introduces a way to further our understanding of cancer type-specific metastatic spread.

Adoptive transfer of personalized neoantigen-reactive TCR-transduced T cells in metastatic colorectal cancer: phase 2 trial interim results

Adoptive cell transfer (ACT) with neoantigen-reactive T lymphocytes can mediate cancer regression. Here we isolated unique, personalized, neoantigen-reactive T cell receptors (TCRs) from tumor-infiltrating lymphocytes of patients with metastatic gastrointestinal cancers and incorporated the TCR α and β chains into gamma retroviral vectors. We transduced autologous peripheral blood lymphocytes and adoptively transferred these cells into patients after lymphodepleting chemotherapy. In a phase 2 single-arm study, we treated seven patients with metastatic, mismatch repair-proficient colorectal cancers who had progressive disease following multiple previous therapies. The primary end point of the study was the objective response rate as measured using RECIST 1.1, and the secondary end points were safety and tolerability. There was no prespecified interim analysis defined in this study. Three patients had objective clinical responses by RECIST criteria including regressions of metastases to the liver, lungs and lymph nodes lasting 4 to 7 months. All patients received T cell populations containing ≥50% TCR-transduced cells, and all T cell populations were polyfunctional in that they secreted IFNγ, GM-CSF, IL-2 and granzyme B specifically in response to mutant peptides compared with wild-type counterparts. TCR-transduced cells were detected in the peripheral blood of five patients, including the three responders, at levels ≥10% of CD3+ cells 1 month post-ACT. In one patient who responded to therapy, ~20% of CD3+ peripheral blood lymphocytes expressed transduced TCRs more than 2 years after treatment. This study provides early results suggesting that ACT with T cells genetically modified to express personalized neoantigen-reactive TCRs can be tolerated and can mediate tumor regression in patients with metastatic colorectal cancers. ClinicalTrials.gov registration: NCT03412877 .

Multiregion exome sequencing indicates a monoclonal origin of esophageal spindle-cell squamous cell carcinoma

Esophageal spindle-cell squamous cell carcinoma (ESS) is a rare biphasic neoplasm composed of a carcinomatous component (CaC) and a sarcomatous component (SaC). However, the genomic origin and gene signature of ESS remain unclear. Using whole-exome sequencing of laser-capture microdissection (LCM) tumor samples, we determined that CaC and SaC showed high mutational commonality, with the same top high-frequency mutant genes, mutation signatures, and tumor mutation burden; paired samples shared a median of 25.5% mutation sites. Focal gains were found on chromosomes 3q29, 5p15.33, and 11q13.3. Altered genes were mainly enriched in the RTK-RAS signaling pathway. Phylogenetic trees showed a monoclonal origin of ESS. The most frequently mutated oncogene in the trunk was TP53, followed by NFE2L2, KMT2D, and MUC16. Prognostic associations were found for CDC27, LRP2, APC, and SNAPC4. Our data highlight the monoclonal origin of ESS with TP53 as a potent driver oncogene, suggesting new targeted therapies and immunotherapies as treatment options. © 2024 The Pathological Society of Great Britain and Ireland.

RAS-ON inhibition overcomes clinical resistance to KRAS G12C-OFF covalent blockade

Selective KRASG12C inhibitors have been developed to covalently lock the oncogene in the inactive GDP-bound state. Two of these molecules, sotorasib and adagrasib, are approved for the treatment of adult patients with KRASG12C-mutated previously treated advanced non-small cell lung cancer. Drug treatment imposes selective pressures leading to the outgrowth of drug-resistant variants. Mass sequencing from patients' biopsies identified a number of acquired KRAS mutations -both in cis and in trans- in resistant tumors. We demonstrate here that disease progression in vivo can also occur due to adaptive mechanisms and increased KRAS-GTP loading. Using the preclinical tool tri-complex KRASG12C-selective covalent inhibitor, RMC-4998 (also known as RM-029), that targets the active GTP-bound (ON) state of the oncogene, we provide a proof-of-concept that the clinical stage KRASG12C(ON) inhibitor RMC-6291 alone or in combination with KRASG12C(OFF) drugs can be an alternative potential therapeutic strategy to circumvent resistance due to increased KRAS-GTP loading.

Multi-omics profiling of longitudinal samples reveals early genomic changes in follicular lymphoma

Follicular lymphoma (FL) is the most common indolent type of B-cell non-Hodgkin lymphoma. Advances in treatment have improved overall survival, but early relapse or transformation to aggressive disease is associated with inferior outcome. To identify early genetic events and track tumor clonal evolution, we performed multi-omics analysis of 94 longitudinal biopsies from 44 FL patients; 22 with transformation (tFL) and 22 with relapse without transformation (nFL). Deep whole-exome sequencing confirmed recurrent mutations in genes encoding epigenetic regulators (CREBBP, KMT2D, EZH2, EP300), with similar mutational landscape in nFL and tFL patients. Calculation of genomic distances between longitudinal samples revealed complex evolutionary patterns in both subgroups. CREBBP and KMT2D mutations were identified as genetic events that occur early in the disease course, and cases with CREBBP KAT domain mutations had low risk of transformation. Gains in chromosomes 12 and 18 (TCF4), and loss in 6q were identified as early and stable copy number alterations. Identification of such early and stable genetic events may provide opportunities for early disease detection and disease monitoring. Integrative analysis revealed that tumors with EZH2 mutations exhibited reduced gene expression of numerous histone genes, including histone linker genes. This might contribute to the epigenetic dysregulation in FL.

Comprehensive genomic profiling of infiltrative follicular variant of papillary thyroid carcinoma

BACKGROUND

Infiltrative follicular variant of papillary thyroid carcinoma (IFVPTC) exhibits nuclear characteristics typical of papillary thyroid carcinoma (PTC) but demonstrates a follicular growth pattern. The diagnosis of IFVPTC presenting with atypical nuclear features of PTC poses challenges for both preoperative cytopathology and postoperative histopathology. In such cases, molecular markers are needed to serve as diagnostic aids. Given the limited knowledge of IFVPTC's genomic features, this study aimed to characterize its genetic alterations and identify clinically relevant molecular markers.

METHODS

Whole-exome sequencing of 50 IFVPTC tumor-normal pairs identified single-nucleotide variants, somatic copy number alterations (sCNAs), and subclonal architecture. Key mutations were verified via polymerase chain reaction and Sanger sequencing, whereas valuable biomarkers were validated via immunohistochemistry (IHC).

RESULTS

This study found that endogenous processes rather than exogenous mutagens dominated the shaping of the genome of IFVPTC during tumorigenesis. BRAF V600E was the only common trunk mutation and significantly mutated gene in IFVPTC. Subcloning analysis found that most IFVPTC samples harbored two or more coexisting clones. sCNA analysis revealed that human leukocyte antigen C (HLA-C) and HLA-A were significantly amplified. Subsequent IHC investigations indicated that HLA-C shows promise in averting the misclassification of challenging-to-interpret IFVPTC and invasive encapsulated follicular variant of PTC (I-EFVPTC) as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Although there were several similarities between classic PTC and IFVPTC, they differed significantly in their sCNA patterns.

CONCLUSIONS

This study provides valuable insights into IFVPTC's genetic alterations and highlights the potential of HLA-C IHC to distinguish challenging-to-interpret IFVPTC and I-EFVPTC from NIFTP, which will enhance the understanding of its molecular features for improved diagnosis and management.

CLEMENT: genomic decomposition and reconstruction of non-tumor subclones

Genome-level clonal decomposition of a single specimen has been widely studied; however, it is mostly limited to cancer research. In this study, we developed a new algorithm CLEMENT, which conducts accurate decomposition and reconstruction of multiple subclones in genome sequencing of non-tumor (normal) samples. CLEMENT employs the Expectation-Maximization (EM) algorithm with optimization strategies specific to non-tumor subclones, including false variant call identification, non-disparate clone fuzzy clustering, and clonal allele fraction confinement. In the simulation and in vitro cell line mixture data, CLEMENT outperformed current cancer decomposition algorithms in estimating the number of clones (root-mean-square-error = 0.58-0.78 versus 1.43-3.34) and in the variant-clone membership agreement (∼85.5% versus 70.1-76.7%). Additional testing on human multi-clonal normal tissue sequencing confirmed the accurate identification of subclones that originated from different cell types. Clone-level analysis, including mutational burden and signatures, provided a new understanding of normal-tissue composition. We expect that CLEMENT will serve as a crucial tool in the currently emerging field of non-tumor genome analysis.

Spatial whole exome sequencing reveals the genetic features of highly-aggressive components in lung adenocarcinoma

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components. Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation. Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

Landscape of Concomitant Driver Alterations in Classical EGFR-Mutated Non-Small Cell Lung Cancer

PURPOSE

Next-generation sequencing (NGS) has enabled the detection of concomitant driver alterations in non-small cell lung cancer (NSCLC). However, the magnitude and clinical relevance of concomitant drivers remain to be explored.

METHODS

We profiled concomitant driver alterations of EGFR+ NSCLC by using targeted NGS. The associated genomic and clinical features were analyzed and validated in an independent The Cancer Genome Atlas cohort of patients with EGFR+ NSCLC.

RESULTS

Out of the total patient population, 334 patients had EGFR mutations along with concomitant driver mutations, comprising 3.09% of the entire cohort. The most frequent co-occurring mutations with sensitizing EGFR mutations include KRAS at 53.9%, followed by ERBB2 at 24.3%, MET at 16.5%, and BRAF at 3.3%. KRAS mutations in concomitant drivers were frequently hyperexchange mutations (25.6% v 8.2%, P < .001), compared with KRAS single drivers. EGFR/ERBB2 drivers exhibited a higher incidence of ERBB2 amplification (40.7% v 16.5%, P < .001) and p.S310F/Y mutations (44.4% v 4.3%, P < .001) compared with ERBB2 alone. EGFR/MET drivers had a higher frequency of MET amplification (71.4% v 43.3%) than MET single drivers. At the genomic level, the median number of additional concurrent mutations was four, with TSC2 (4%), CD274 (1%), and TP53 (63%) being the most frequently coaltered genes in concomitant driver tumors. Interestingly, clonality analysis indicated that EGFR mutations were more likely to occur as clonal events, whereas the codrivers were more often subclonal. Patients with concomitant drivers or with concomitant MET amplification exhibited worse prognosis.

CONCLUSION

These findings might aid in the selection of effective therapeutic regimens and facilitate the development of combination therapies.

Differential impact of intratumor heterogeneity (ITH) on survival outcomes in early-stage lung squamous and adenocarcinoma based on tumor mutational burden (TMB)

Background

Molecular biomarkers are reshaping patient stratification and treatment decisions, yet their precise use and best implementation remain uncertain. Intratumor heterogeneity (ITH), an area of increasing research interest with prognostic value across various conditions, lacks defined clinical relevance in certain non-small cell lung cancer (NSCLC) subtypes. Exploring the relationship between ITH and tumor mutational burden (TMB) is crucial, as their interplay might reveal distinct patient subgroups. This study evaluates how the ITH-TMB dynamic affects prognosis across the two main histological subtypes of NSCLC, squamous cell and adenocarcinoma, with a specific focus on early-stage cases to address their highly unmet clinical needs.

Methods

We stratify a cohort of 741 early-stage NSCLC patients from The Cancer Genome Atlas (TCGA) based on ITH and TMB and evaluate differences in clinical outcomes. Additionally, we compare driver mutations and the tumor microenvironment (TME) between high and low ITH groups.

Results

In lung squamous cell carcinoma (LUSC), high ITH predicts an extended progression-free survival (PFS) (median: 21 vs. 14 months, P=0.01), while in lung adenocarcinoma (LUAD), high ITH predicts a reduced PFS (median: 15 vs. 20 months, P=0.04). This relationship is driven by the low TMB subset of patients. Additionally, we found that CD8 T cells were enriched in better-performing subgroups, regardless of histologic subtype or ITH status.

Conclusions

There are significant differences in clinical outcomes, driver mutations, and the TME between high and low ITH groups among early-stage NSCLC patients. These differences may have treatment implications, necessitating further validation in other NSCLC datasets.

Genomic profiling of a multi-lineage and multi-passage patient-derived xenograft biobank reflects heterogeneity of ovarian cancer

Ovarian cancer (OC) manifests as a complex disease characterized by inter- and intra-patient heterogeneity. Despite enhanced biological and genetic insights, OC remains a recalcitrant malignancy with minimal survival improvement. Based on multi-site sampling and a multi-lineage patient-derived xenograft (PDX) establishment strategy, we present herein the establishment of a comprehensive PDX biobank from histologically and molecularly heterogeneous OC patients. Comprehensive profiling of matched PDX and patient samples demonstrates that PDXs closely recapitulate parental tumors. By leveraging multi-lineage models, we reveal that the previously reported genomic disparities of PDX could be mainly attributed to intra-patient spatial heterogeneity instead of substantial model-independent genomic evolution. Moreover, DNA damage response pathway inhibitor (DDRi) screening uncovers heterogeneous responses across models. Prolonged iterative drug exposure recapitulates acquired drug resistance in initially sensitive models. Meanwhile, interrogation of induced drug-resistant (IDR) models reveals that suppressed interferon (IFN) response and activated Wnt/β-catenin signaling contribute to acquired DDRi drug resistance.

Tumor-informed deep sequencing of ctDNA detects minimal residual disease and predicts relapse in osteosarcoma

Background

Current surveillance modalities of osteosarcoma relapse exhibit limited sensitivity and specificity. Although circulating tumor DNA (ctDNA) has been established as a biomarker of minimal residual disease (MRD) in many solid tumors, a sensitive ctDNA detection technique has not been thoroughly explored for longitudinal MRD detection in osteosarcoma.

Methods

From August 2019 to June 2023, 59 patients diagnosed with osteosarcoma at the First Affiliated Hospital of Sun Yat-sen University were evaluated in this study. Tumor-informed MRD panels were developed through whole exome sequencing (WES) of tumor tissues. Longitudinal blood samples were collected during treatment and subjected to multiplex PCR-based next-generation sequencing (NGS). Kaplan-Meier curves and Log-rank tests were used to compare outcomes, and Cox regression analysis was performed to identify prognostic factors.

Findings

WES analysis of 83 patients revealed substantial mutational heterogeneity, with non-recurrent mutated genes accounting for 58.1%. Tumor-informed MRD panels were successfully obtained for 85.5% of patients (71/83). Among 59 patients with successful MRD panel customization and available blood samples, 13 patients exhibited positive ctDNA detection after surgery. Patients with negative post-operative ctDNA had better event-free survival (EFS) compared to those with positive ctDNA, at 1-6 months after surgery, after adjuvant chemotherapy, and more than 6 months after surgery (p < 0.05). In both univariate and multivariate Cox regression analysis, ctDNA results emerged as a significant predictor of EFS (p < 0.05). ctDNA detection preceded positive imaging in 5 patients, with an average lead time of 92.6 days. Thirty-nine patients remained disease-free, with ctDNA results consistently negative or turning negative during follow-up.

Interpretation

Our study underscores the applicability of tumor-informed deep sequencing of ctDNA in osteosarcoma MRD surveillance and, to our knowledge, represents the largest cohort to date. ctDNA detection is a significant prognostic factor, enabling the early identification of tumor relapse and progression compared to standard imaging, thus offering valuable insights in guiding osteosarcoma patient management.

Funding

The Grants of National Natural Science Foundation of China (No. 82072964, 82072965, 82203798, 82203026), the Natural Science Foundation of Guangdong (No. 2023A1515012659, 2023A1515010302), and the Regional Combination Project of Basic and Applied Basic Research Foundation of Guangdong (No. 2020A1515110010).

Identification of RNF213 as a Potential Suppressor of Local Invasion in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a lethal cancer with poor survival especially when it spreads. The histopathology of its rare intraductal papillary neoplasm of the bile duct type (IPNB) characteristically shows cancer cells originating within the confined bile duct space. These cells eventually invade and infiltrate the nearby liver tissues, making it a good model to study the mechanism of local invasion, which is the earliest step of metastasis. To discover potential suppressor genes of local invasion in ICC, we analyzed the somatic mutation profiles and performed clonal evolution analyses of the 11 pairs of macrodissected locally invasive IPNB tissues (LI-IPNB) and IPNB tissues without local invasion from the same patients. We identified a protein-truncating variant in an E3 ubiquitin ligase, RNF213 (c.6967C>T; p.Gln2323X; chr17: 78,319,102 [hg19], exon 29), as the most common protein-truncating variant event in LI-IPNB samples (4/11 patients). Knockdown of RNF213 in HuCCT1 and YSCCC cells showed increased migration and invasion, and reduced vasculogenic mimicry but maintained normal proliferation. Transcriptomic analysis of the RNF213-knockdown vs control cells was then performed in the HuCCT1, YSCCC, and KKU-100 cells. Gene ontology enrichment analysis of the common differentially expressed genes revealed significantly altered cytokine and oxidoreductase-oxidizing metal ion activities, as confirmed by Western blotting. Gene Set Enrichment Analysis identified the most enriched pathways being oxidative phosphorylation, fatty acid metabolism, reactive oxygen species, adipogenesis, and angiogenesis. In sum, loss-of-function mutation of RNF213 is a common genetic alteration in LI-IPNB tissues. RNF213 knockdown leads to increased migration and invasion of ICC cells, potentially through malfunctions of the pathways related to inflammation and energy metabolisms.

Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections

Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction

Summary

Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field.

Availability and implementation

All analysis done in the paper was based on publicly available data from the publication of each accessed tool.

Oncogene-Driven Non-Small Cell Lung Cancers in Patients with a History of Smoking Lack Smoking-Induced Mutations

Non-small cell lung cancers (NSCLC) in nonsmokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET and fusions involving ALK and RET. In addition to occurring in nonsmokers, alterations in these "nonsmoking-related oncogenes" (NSRO) also occur in smokers. To better understand the clonal architecture and genomic landscape of NSRO-driven tumors in smokers compared with typical-smoking NSCLCs, we investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 NSCLC patients. NSRO-driven NSCLCs in smokers and nonsmokers had similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs, which was confirmed in two large NSCLC data sets from other geographic regions. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to the regulation of the cell cycle. These findings suggest that, whereas the genomic landscape is similar between NSRO-driven NSCLC in smokers and nonsmokers, smoking still affects the tumor phenotype independently of genomic alterations.

SIGNIFICANCE

Non-small cell lung cancers driven by nonsmoking-related oncogenes do not harbor genomic scars caused by smoking regardless of smoking history, indicating that the impact of smoking on these tumors is mainly nongenomic.

Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma

In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor

Background

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies.

Methods

Here, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing.

Results

Direct comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses.

Conclusions

These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.

Phase I Trial of Viral Vector-Based Personalized Vaccination Elicits Robust Neoantigen-Specific Antitumor T-Cell Responses

PURPOSE

Personalized vaccines targeting multiple neoantigens (nAgs) are a promising strategy for eliciting a diversified antitumor T-cell response to overcome tumor heterogeneity. NOUS-PEV is a vector-based personalized vaccine, expressing 60 nAgs and consists of priming with a nonhuman Great Ape Adenoviral vector (GAd20) followed by boosts with Modified Vaccinia Ankara. Here, we report data of a phase Ib trial of NOUS-PEV in combination with pembrolizumab in treatment-naïve patients with metastatic melanoma (NCT04990479).

PATIENTS AND METHODS

The feasibility of this approach was demonstrated by producing, releasing, and administering to 6 patients 11 of 12 vaccines within 8 weeks from biopsy collection to GAd20 administration.

RESULTS

The regimen was safe, with no treatment-related serious adverse events observed and mild vaccine-related reactions. Vaccine immunogenicity was demonstrated in all evaluable patients receiving the prime/boost regimen, with detection of robust neoantigen-specific immune responses to multiple neoantigens comprising both CD4 and CD8 T cells. Expansion and diversification of vaccine-induced T-cell receptor (TCR) clonotypes was observed in the posttreatment biopsies of patients with clinical response, providing evidence of tumor infiltration by vaccine-induced neoantigen-specific T cells.

CONCLUSIONS

These findings indicate the ability of NOUS-PEV to amplify and broaden the repertoire of tumor-reactive T cells to empower a diverse, potent, and durable antitumor immune response. Finally, a gene signature indicative of the reduced presence of activated T cells together with very poor expression of the antigen-processing machinery genes has been identified in pretreatment biopsies as a potential biomarker of resistance to the treatment.

Resolving tumor evolution: a phylogenetic approach

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Reconstructing phylogenetic trees from genome-wide somatic mutations in clonal samples

Phylogenetic trees are a powerful means to display the evolutionary history of species, pathogens and, more recently, individual cells of the human body. Whole-genome sequencing of laser capture microdissections or expanded stem cells has allowed the discovery of somatic mutations in clones, which can be used as natural barcodes to reconstruct the developmental history of individual cells. Here we describe Sequoia, our pipeline to reconstruct lineage trees from clones of normal cells. Candidate somatic mutations are called against the human reference genome and filtered to exclude germline mutations and artifactual variants. These filtered somatic mutations form the basis for phylogeny reconstruction using a maximum parsimony framework. Lastly, we use a maximum likelihood framework to explicitly map mutations to branches in the phylogenetic tree. The resulting phylogenies can then serve as a basis for many subsequent analyses, including investigating embryonic development, tissue dynamics in health and disease, and mutational signatures. Sequoia can be readily applied to any clonal somatic mutation dataset, including single-cell DNA sequencing datasets, using the commands and scripts provided. Moreover, Sequoia is highly flexible and can be easily customized. Typically, the runtime of the core script ranges from minutes to an hour for datasets with a moderate number (50,000-150,000) of variants. Competent bioinformatic skills, including in-depth knowledge of the R programming language, are required. A high-performance computing cluster (one that is capable of running mutation-calling algorithms and other aspects of the analysis at scale) is also required, especially if handling large datasets.

Whole-exome sequencing has revealed novel genetic characteristics in intracranial germ cell tumours in the Chinese

AIMS

Intracranial germ cell tumour (IGCT) is a type of rare central nervous system tumour that mainly occurs in children and adolescents, with great variation in its incidence rate and molecular characteristics in patients from different populations. The genetic alterations of IGCT in the Chinese population are still unknown.

METHODS AND RESULTS

In this study, 47 patients were enrolled and their tumour specimens were analysed by whole-exome sequencing (WES). We found that KIT was the most significantly mutated gene (15/47, 32%), which mainly occurred in the germinoma group (13/20, 65%), and less frequently in NGGCT (2/27, 7%). Copy number variations (CNVs) of FGF6 and TFE3 only appeared in NGGCT patients (P = 0.003 and 0.032, respectively), while CNVs of CXCR4, RAC2, PDGFA, and FEV only appeared in germinoma patients (P = 0.004 of CXCR4 and P = 0.027 for the last three genes). Compared with a previous Japanese cohort, the somatic mutation rates of RELN and SYNE1 were higher in the Chinese. Prognostic analysis showed that the NF1 mutation was associated with shorter overall survival and progression-free survival in IGCT patients. Clonal evolution analysis revealed an early branched evolutionary pattern in two IGCT patients who underwent changes in the histological subtype or degree of differentiation during disease surveillance.

CONCLUSION

This study indicated that Chinese IGCT patients may have distinct genetic characteristics and identified several possible genetic alterations that have the potential to become prognostic biomarkers of NGGCT patients.

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Genomic characterization reveals distinct mutational landscapes and therapeutic implications between different molecular subtypes of triple-negative breast cancer

Triple-negative breast cancer (TNBC) has high heterogeneity, poor prognosis, and limited treatment success. Recently, an immunohistochemistry-based surrogate classification for the "Fudan University Shanghai Cancer Center (FUSCC) subtyping" has been developed and is considered more suitable for clinical application. Seventy-one paraffin-embedded sections of surgically resected TNBC were classified into four molecular subtypes using the IHC-based surrogate classification. Genomic analysis was performed by targeted next-generation sequencing and the specificity of the subtypes was explored by bioinformatics, including survival analysis, multivariate Cox regression, pathway enrichment, Pyclone analysis, mutational signature analysis and PHIAL analysis. AKT1 and BRCA1 mutations were identified as independent prognostic factors in TNBC. TNBC molecular subtypes encompass distinct genomic landscapes that show specific heterogeneities. The luminal androgen receptor (LAR) subtype was associated with mutations in PIK3CA and PI3K pathways, which are potentially sensitive to PI3K pathway inhibitors. The basal-like immune-suppressed (BLIS) subtype was characterized by high genomic instability and the specific possession of signature 19 while patients in the immunomodulatory (IM) subtype belonged to the PD-L1 ≥ 1% subgroup with enrichment in Notch signaling, suggesting a possible benefit of immune checkpoint inhibitors and Notch inhibitors. Moreover, mesenchymal-like (MES) tumors displayed enrichment in the receptor tyrosine kinase (RTK)-RAS pathway and potential sensitivity to RTK pathway inhibitors. The findings suggest potential treatment targets and prognostic factors, indicating the possibility of TNBC stratified therapy in the future.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Scuphr: A probabilistic framework for cell lineage tree reconstruction

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.

Circulating tumor DNA: from discovery to clinical application in breast cancer

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

Integrated mutational landscape analysis of poorly differentiated high-grade neuroendocrine carcinoma of the uterine cervix

High-grade neuroendocrine cervical cancers (NETc) are exceedingly rare, highly aggressive tumors. We analyzed 64 NETc tumor samples by whole-exome sequencing (WES). Human papillomavirus DNA was detected in 65.6% (42/64) of the tumors. Recurrent mutations were identified in PIK3CA, KMT2D/MLL2, K-RAS, ARID1A, NOTCH2, and RPL10. The top mutated genes included RB1, ARID1A, PTEN, KMT2D/MLL2, and WDFY3, a gene not yet implicated in NETc. Somatic CNV analysis identified two copy number gains (3q27.1 and 19q13.12) and five copy number losses (1p36.21/5q31.3/6p22.2/9q21.11/11p15.5). Also, gene fusions affecting the ACLY-CRHR1 and PVT1-MYC genes were identified in one of the eight samples subjected to RNA sequencing. To resolve evolutionary history, multiregion WES in NETc admixed with adenocarcinoma cells was performed (i.e., mixed-NETc). Phylogenetic analysis of mixed-NETc demonstrated that adenocarcinoma and neuroendocrine elements derive from a common precursor with mutations typical of adenocarcinomas. Over one-third (22/64) of NETc demonstrated a mutator phenotype of C > T at CpG consistent with deficiencies in MBD4, a member of the base excision repair (BER) pathway. Mutations in the PI3K/AMPK pathways were identified in 49/64 samples. We used two patient-derived-xenografts (PDX) (i.e., NET19 and NET21) to evaluate the activity of pan-HER (afatinib), PIK3CA (copanlisib), and ATR (elimusertib) inhibitors, alone and in combination. PDXs harboring alterations in the ERBB2/PI3K/AKT/mTOR/ATR pathway were sensitive to afatinib, copanlisib, and elimusertib (P < 0.001 vs. controls). However, combinations of copanlisib/afatinib and copanlisib/elimusertib were significantly more effective in controlling NETc tumor growth. These findings define the genetic landscape of NETc and suggest that a large subset of these highly lethal malignancies might benefit from existing targeted therapies.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

ASCL1 Drives Tolerance to Osimertinib in EGFR Mutant Lung Cancer in Permissive Cellular Contexts

The majority of EGFR mutant lung adenocarcinomas respond well to EGFR tyrosine kinase inhibitors (TKI). However, most of these responses are partial, with drug-tolerant residual disease remaining even at the time of maximal response. This residual disease can ultimately lead to relapses, which eventually develop in most patients. To investigate the cellular and molecular properties of residual tumor cells in vivo, we leveraged patient-derived xenograft (PDX) models of EGFR mutant lung cancer. Subcutaneous EGFR mutant PDXs were treated with the third-generation TKI osimertinib until maximal tumor regression. Residual tissue inevitably harbored tumor cells that were transcriptionally distinct from bulk pretreatment tumor. Single-cell transcriptional profiling provided evidence of cells matching the profiles of drug-tolerant cells present in the pretreatment tumor. In one of the PDXs analyzed, osimertinib treatment caused dramatic transcriptomic changes that featured upregulation of the neuroendocrine lineage transcription factor ASCL1. Mechanistically, ASCL1 conferred drug tolerance by initiating an epithelial-to-mesenchymal gene-expression program in permissive cellular contexts. This study reveals fundamental insights into the biology of drug tolerance, the plasticity of cells through TKI treatment, and why specific phenotypes are observed only in certain tumors.

SIGNIFICANCE

Analysis of residual disease following tyrosine kinase inhibitor treatment identified heterogeneous and context-specific mechanisms of drug tolerance in lung cancer that could lead to the development of strategies to forestall drug resistance. See related commentary by Rumde and Burns, p. 1188.

Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial

PURPOSE

Identifying molecular and immune features to guide immune checkpoint inhibitor (ICI)-based regimens remains an unmet clinical need.

EXPERIMENTAL DESIGN

Tissue and longitudinal blood specimens from phase III trial S1400I in patients with metastatic squamous non-small cell carcinoma (SqNSCLC) treated with nivolumab monotherapy (nivo) or nivolumab plus ipilimumab (nivo+ipi) were subjected to multi-omics analyses including multiplex immunofluorescence (mIF), nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink.

RESULTS

Higher immune scores from immune gene expression profiling or immune cell infiltration by mIF were associated with response to ICIs and improved survival, except regulatory T cells, which were associated with worse overall survival (OS) for patients receiving nivo+ipi. Immune cell density and closer proximity of CD8+GZB+ T cells to malignant cells were associated with superior progression-free survival and OS. The cold immune landscape of NSCLC was associated with a higher level of chromosomal copy-number variation (CNV) burden. Patients with LRP1B-mutant tumors had a shorter survival than patients with LRP1B-wild-type tumors. Olink assays revealed soluble proteins such as LAMP3 increased in responders while IL6 and CXCL13 increased in nonresponders. Upregulation of serum CXCL13, MMP12, CSF-1, and IL8 were associated with worse survival before radiologic progression.

CONCLUSIONS

The frequency, distribution, and clustering of immune cells relative to malignant ones can impact ICI efficacy in patients with SqNSCLC. High CNV burden may contribute to the cold immune microenvironment. Soluble inflammation/immune-related proteins in the blood have the potential to monitor therapeutic benefit from ICI treatment in patients with SqNSCLC.

Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma

Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a "spatially competing distribution" (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Understanding the malignant potential of gastric metaplasia of the oesophagus and its relevance to Barrett's oesophagus surveillance: individual-level data analysis

OBJECTIVE

Whether gastric metaplasia (GM) of the oesophagus should be considered as Barrett's oesophagus (BO) is controversial. Given concern intestinal metaplasia (IM) may be missed due to sampling, the UK guidelines include GM as a type of BO. Here, we investigated whether the risk of misdiagnosis and the malignant potential of GM warrant its place in the UK surveillance.

DESIGN

We performed a thorough pathology and endoscopy review to follow clinical outcomes in a novel UK cohort of 244 patients, covering 1854 person years of follow-up. We complemented this with a comparative genomic analysis of 160 GM and IM specimens, focused on early molecular hallmarks of BO and oesophageal adenocarcinoma (OAC).

RESULTS

We found that 58 of 77 short-segment (<3 cm) GM (SS-GM) cases (75%) continued to be observed as GM-only across a median of 4.4 years of follow-up. We observed that disease progression in GM-only cases and GM+IM cases (cases with reported GM on some occasions, IM on others) was significantly lower than in the IM-only cases (Kaplan-Meier, p=0.03). Genomic analysis revealed that the mutation burden in GM is significantly lower than in IM (p<0.01). Moreover, GM does not bear the mutational hallmarks of OAC, with an absence of associated signatures and driver gene mutations. Finally, we established that GM found adjacent to OAC is evolutionarily distant from cancer.

CONCLUSION

SS-GM is a distinct entity from SS-IM and the malignant potential of GM is lower than IM. It is questionable whether SS-GM warrants inclusion in BO surveillance.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Background

Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.

Method

To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.

Results

We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.

Conclusion

Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.

Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections

Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.