Statement of Retraction: Deciphering cell–cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing

Statement of Retraction We, the Editors and Publisher of the journal Bioengineered, have retracted the following article. Cao, Y. H., Ding, J., Tang, Q. H., Zhang, J., Huang, Z. Y., Tang, X. M., … Fu, D. (2022). Deciphering cell–cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing. Bioengineered, 13(7–12), 14974–14986. https://doi.org/10.1080/21655979.2023.2185434 Following publication, internal integrity audits revealed concerns around several authorship changes which were made between submission and acceptance of the article We have contacted the authors for an explanation, but have not received a satisfactory explanation regarding the contributions of authors added or removed at different stages and the rationale for these changes. As determining authorship and contributions is core to the integrity and accountability of published work, we are therefore retracting the article. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and COPE guidelines. The retracted article will remain online to maintain the scholarly record and will be digitally watermarked on each page as ‘Retracted’.

Background: At diagnosis, tumors often consist of multiple, genotypically distinct cell populations. Genome sequencing of single cells has opened new avenues of investigation, yet the application of this technology remains limited to fresh/frozen tissue samples. Here, we describe and validate a method to perform single-cell massively parallel sequencing using DNA extracted from individual nuclei from FFPE tumor samples. Methods: Tissue sections are deparaffinized and the area of interest microdissected, DNA is reverse-crosslinked and the extracellular matrix is digested. Extracted single nuclei are then FACS-sorted, lysed and the whole genome repaired using a broad-spectrum DNA-repair enzyme cocktail. Repaired DNA is whole genome amplified (WGA) using GenomePlex WGA4 (Sigma-Aldrich) with modifications for heavily damaged genomic templates. Illumina sequencing libraries are generated using standard approaches followed by multiplex sequencing on an Illumina HiSeq2000. To obtain copy number (CN) profiles, single-cell sequencing data are mapped to the reference genome with PCR duplicates removed. Uniquely mapped reads are allocated and counted in genomic intervals of variable length (bins) with CN states roughly proportional to the number of allocated sequencing reads. Bin counts are then normalized on the basis of GC-content, segmented and transformed to CN values to identify long contiguous regions of equivalent CN. Results: As a proof-of-principle we performed single-cell CN profiling on two aneuploid synchronous ductal carcinomas in situ (DCIS)/invasive ductal carcinomas (IDCs), where both FFPE and frozen material was available. Using our novel methodology, 24 single nuclei were multiplexed/lesion. An average of 3 million reads/cell (average coverage of 0.1X/cell) was obtained, providing sufficient data to infer CN profiles of single cells accurately. These data were successfully employed to identify non-neoplastic cells and distinct clonal lineages of neoplastic cells within each lesion. The data obtained from the analysis of FFPE samples were concordant with those obtained from the analysis of matched frozen samples. Conclusions: We developed a robust procedure to perform single-cell massively parallel sequencing of individual nuclei isolated from FFPE samples, providing the opportunity to unlock pathology archives for studies aiming to catalog and dissect the biological and clinical relevance of intra-tumor genetic heterogeneity. Citation Format: Luciano G. Martelotto, Rita A. Sakr, Timour Baslan, Linda Rodgers, Hilary Cox, Jude Kendall, Tari A. King, Britta Weigelt, James Hicks, Jorge S. Reis-Filho. Single-cell sequencing from formalin-fixed paraffin-embedded breast cancers: a powerful tool to address intratumor genetic heterogeneity. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4735. doi:10.1158/1538-7445.AM2015-4735

Recently, two companion papers published in Nature by Timon Heide et al.1 and Jacob Househam et al.2 suggested that phenotypic characteristics can vary without heritable (epi)genetic alteration to drive gene expression change, namely phenotypic plastic, which could take part in intratumor heterogeneity in colorectal cancer (CRC) evolution. As in other cancer types, intratumor heterogeneity represents a challenge in the facets of tumorigenesis, evolution, and therapy response in CRC.3 However, the information on how genomes and epigenomes contribute to intratumor heterogeneity is limited. What's more, although consensus molecular subtypes (CMS) and CRC intrinsic subtypes (CRIS) are approaches used to classify CRC cases by gene expression patterns,4 Househam et al.2 found that very few tumors with sufficient samples could be homogeneously classified by these classifiers, indicating intratumor heterogeneity of molecular subtypes in CRC and the discrepancy between CMS and CRIS classifications. Therefore, the researchers collected a large number of samples from a multiregion of carcinoma, concomitant adenoma if present, and a distant region of the normal epithelium to integrate their spatially resolved mutiomics analysis with single gland profiling data set, and combine with computational modeling to understand the cancer cell biology and assess the functional impact of altered gene expression on the evolution of CRC, due to intratumor heterogeneity is a significant confounding factor in bulk-tumor profiling (Figure 1). As for spatially resolved multiomics analysis, it presents a new avenue to reveal tumors and microenvironments co-evolution, which could be used to clarify heterogeneity.5 In detail, it contains a strategy for the spatial sampling of tumor tissue to implement a series of new spatial genomic, transcriptomic, and proteomic technologies.5 Heide et al.1 first looked forward to measuring genome–epigenome co-evaluation in a quantitative manner and gained some evidence. First of all, it was confirmed that there were recurrent cancer driver mutation events in CRC. After examining somatic mutations in chromatin modifier genes and assessing the evolutionary selection by the ratio of nonsynonymous to synonymous substitutions (dN/dS), they identified clear evidence of clonal truncating mutations of chromatin modifiers genes, which indicated that somatic mutations affect the epigenome. Besides, somatic chromatin accessibility alterations (SCAAs), which were found in known driver genes without accompanying mutations, were a substitute pattern for driver gene (in)activation. Subsequently, Heide et al. found that most SCAAs occurred at the onset of the adenoma-carcinoma transition. And SCAAs were indeed changes that originated during tumorigenesis instead of the product of normal tissue aging. In addition, SCAAs might change the expression of associated genes. What's more, Heide et al. detected genome-wide differential chromatin accessibility alteration of transcription factor-binding sites (including the interferon-regulatory factor family, the CCCTC-binding factor, and the HOX, FOX, and SOX families) in CRC, which was reported that some of these binding sites were demethylated.1 And the alteration was stable and heritable. Taken together, the study utilized spatially resolved multiomics analysis to figure out the nongenetic determinants of cancer cell biology and clonal evolution in CRC. To explore whether the variability of gene expression resulted from genetic change, Househam et al. constructed phylogenetic trees for each tumor. However, only 61 out of 8368 genes recurrently reflected phylogenetic ancestry, and only the peroxisome proliferator-activated receptor signaling pathway was significantly shown with recurrent evidence of phylogenetic signal. Therefore, further study was on the influence of tumor microenvironment (TME) and discovered that TME could affect plastic gene expression programs irrespective of accrued genetic change, while Heide et al.1 demonstrated that the epigenome, in turn, contributes to the accumulation of DNA mutations. Given that phylogenetic signals do exist, Househam et al. then presented a linear regression framework to examine cis-associations between inter- and intratumor somatic genetic heterogeneity and gene expression and found 5927 genes ultimately, since gene expression could be modified by somatic mutations is a latent mechanistic interpretation. They measured that 1529 out of 5927 (25.8%) genes had gene expression associated with somatic genetic variations, which they termed expression quantitative trait loci (eQTL) genes. Despite a large proportion of somatic mutations having little influence on cis gene expression, there remained 2.4% of subclonal genetic variants related with altered gene expression and they were enriched for phylogenetic signal. Secondly, Househam et al. focused on investigating the function of drivers that were considered to foster cancer evolution and their mutations. To accurately identify clone and subclone somatic variants and to call somatic copy number alterations, they used their extensive single-gland, multiregion whole genome sequencing (WGS) data, and low-pass WGS data. Most of the frequently mutated genes in CRC were clonal in their cases, except for two of them that had subclone KRAS or TP53 mutation. Thus, dN/dS was used to detect the selection of driver genes again. The ratio was greater than 1 for subclone variants in microsatellite stability, showing evidence of positive selection of a subset of putative subclonal CRC driver mutations in growing tumors, but not in microsatellite instability, which is similar to the result from Heide et al.1 Using DepMap data set for implementation of orthogonal assessment, seldom putative drivers demonstrated evidence of essentiality in CRC cell lines. Overall, based on the selection of subclones, even driver mutations played a slight role in phenotypic consequences. Subsequently, Househam et al. discovered balanced status in the majority of tumors, which indicates analogous branch lengths across samples and regions from the same tumor, when assessing the evolutionary dynamics via evaluation of the phylogenetic tree shape and the related clonal structure of the tumor. Given an “unbalanced” tree shown from tumor C539, BaseScope was used to visualize subclones and found the spatial segregation of subclones, which suggests that a subset of the blocks is heterogeneous. To identify the subclone variants previously selected by dN/dS, Househam et al. designed a spatial inference framework based on Approximate Bayesian Computation-Sequential Monte Carlo (ABC-SMC) to achieve computational modeling. To compare the model with authentic data, they simulated the sampling scheme on virtual tumors and reconstructed a phylogenetic tree, whose structures were generally consistent with the observed phylogenetic tree. Significant evidence of subclone selection was present in 7 out of 27 tumors; in addition, 4 of the 7 tumors presented a putative subclone mutation, and the variant was expressed in the RNA. Moreover, tumors were characterized by exponentially growing or growing more slowly at the periphery exclusively, and the growth rate of subclones that underwent selection was 20-fold higher than the background clone, and most of them originated in the early stages of tumor expansion. Finally, Househam et al. wondered about the reason for the evolution of the selected subclones. Examining matched transposase-accessible chromatin sequencing (ATAC-seq) and RNA-seq from selected subclones to generate epigenome and transcriptome. Dysregulation of focal adhesion pathways, upregulation of the epithelial-mesenchymal transition program that was confirmed by Heide et al.,1 and upregulation of MYC + E2F targets was found through enrichment analysis of differentially expressed genes between the subclone and background clone. Moreover, there were no proof that heritable variations in gene expression were able to suggest subclone selection, hinting that transcriptional variation remains occurring even in a selected clone. Herein, we have recognized the significance of epigenome for CRC evolution through these companion papers. Much work is still needed to better understand the critical role of epigenetic signatures in cancer initiation and progression, including further functional study and clarification of the mechanistic link. Nevertheless, these studies uncovered another factor, epigenetics, which universally influenced phenotype on cancer cells, and provided a perspective to better understand heterogeneity in CRC. Jia-qian Huang: Visualization (lead); writing—original draft (lead); writing—review & editing (equal). Hui-yan Luo: Conceptualization (lead); funding acquisition (lead); supervision (lead); writing—review & editing (equal). Both authors have read and approved the article. This research was supported by the National Key Research and Development Program of China (2021YFF1201300), the National Natural Science Foundation of China (82073112), and the Science and Technology Program of Guangzhou (202206080008). Not applicable. Not applicable. Not applicable.

Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide [1], generally arising on the background of chronic liver diseases such as chronic viral hepatitis, alcohol-induced liver injury, or fatty liver disease. So far, classification proposals for HCC based on molecular markers are not yet routinely applied in surgical pathology or clinical management of HCC patients. This stands in contrast to the classification of hepatocellular adenoma (HCA), which has been included in the latest WHO classification [2], and is the basis for a stratified management of HCA patients. Phenotypic intratumor heterogeneity in HCC with respect to morphology and differentiation grades within the same tumor is a well-known phenomenon to surgical pathologists. So far, genetic heterogeneity and the heterogeneity of biomarker expression in the surgical HCC specimens have not been systematically analyzed. However, to improve clinico-pathological classification systems and for the stratification of targeted therapies, it seems crucial to comprehensively characterize intratumor heterogeneity. In a systematic analysis of 23 treatment-naive unifocal HCC, we investigated individual tumors for morphologic, immunohistochemical and genetic intratumor heterogeneity as well as the association of these three features [3]. We found morphologic hetero-geneity in 87% of the tumors. Immunohistochemical heterogeneity with respect to five markers (CK7, CD44, AFP, EpCAM and glutamine synthethase) was present in 39% of cases and was always accompanied by morphologic heterogeneity. Clonal, i.e. genetic diversification was determined by sequencing the two most important HCC driver genes (TP53 and CTNNB1). Combining Sanger sequencing with deep sequencing techniques facilitated the discovery of low frequency mutations and mitigated the effect of wild-type contamination. A mean of 7 regions per tumor was sequenced (120 areas in total), and genetic intratumor heterogeneity was found in 22% of cases. Thus, already analysis of the two main HCC driver genes clearly revealed that mutations are not homogeneously present in all regions of an individual tumor. This was found especially for CTNNB1 mutations, but also for TP53 mutations. The thorough dissection of morphologic, immunohistochemical and genomic intratumor hetero-geneity in our study illustrates that the primary co-existence of different growth patterns can be associated with divers biomarker expression and TP53 or CTNNB1 gene mutations among wild type tumor cells. Somatic mutations of various other genes are described in liver cancer, e.g. AXIN1 (WNT signaling), ARID and MLL genes (epigenetic modifiers), CDKN2A and IRF2 (cell cycle regulators interrelated with TP53) or TERT promoter mutations [4]. Although not comprehensively analyzed so far, it can be expected that the picture of subclonality within single liver lesions is even more complex. Tracking the expansion of certain tumor clones might also elucidate the development of multifocal liver cancer or disease progression favoring intra- and extrahepatic metastasis. Intratumor heterogeneity has major implications for diagnosis and therapy of many solid cancers, indicating that a single tumor biopsy might not provide sufficient informative value regarding the molecular characteristics of the whole tumor. Primary renal cell cancer, as has been shown by Gerlinger et al. [5], displays common and private mutations throughout different, grossly demarcated tumor regions. Intratumor heterogeneity also limits the informative value of the widely used tissue microarray technique for studies on novel prognostic or predictive biomarkers. The histopathologic and molecular classification of a tumor, often determined by the mutational status of a certain target, has therapeutic implications, e.g. in colorectal cancer (EGFR) or gastrointestinal stroma tumors (c-KIT). In the era of targeted therapies, intratumor heterogeneity is a major challenge to successful cancer therapy since it may result in primary resistance or early evasion of treatment to chemotherapeutic or molecular targeted substances. In melanoma, resistant tumor clones tend to evade chemotherapies and overgrow due to a forced selection process as well as an adaption process during tumor evolution. Therapy-induced inflammatory processes result not only in phenotypic plasticity of the tumor and its genomic landscape, but also alter the composition of tumor-associated immune cells. This recomposition of the microenviroment can contribute to cancer evolution and therapy resistance [6]. In conclusion, determining the degree of intratumor heterogeneity might be seen as a biomarker by itself and have prognostic value for disease progression [7]. With the high-throughput techniques widely available now, we envision the systematic investigation of intratumor heterogeneity in different types of cancer in order to pinpoint its clinical relevance. We expect that intratumor tumor heterogeneity with clinical relevance for molecular targeted therapy approaches will be present probably not in all, but in many tumor entities. Figure 1 Implications of HCC intratumor heterogeneity for tumor classification and targeted therapy. A biopsy taken from tumor area 2 does not necessarily represent the whole tumor. This may result in a short falling tumor classification as type B, and potentially ...

Background: Intra-tumor heterogeneity (ITH) plays a pivotal role in driving breast cancer progression and therapeutic resistance. Emerging evidence has indicated that the extent of genetic heterogeneity may serve as a clinically useful biomarker. While several studies have suggested the prognostic value of ITH in several cancer types, the clinical significance of genetic ITH and molecular portraits that correlated with different ITH levels were poorly understood in breast cancer. The establishment of algorithms estimating genetic ITH based on sequencing of bulk tumor DNA offered us an opportunity to explore the clinical implication of ITH in large breast cancer cohorts and, for the first time, to use integrative genomic analyses to reveal molecular portraits related to intra-tumor genetic heterogeneity. Methods: We assessed 916 female breast cancer patients from The Cancer Genome Atlas. Mutant-allele tumor heterogeneity (MATH) values were calculated from whole-exome sequencing data. We used integers nearest to the tertiles of the MATH values as cutoff points to divide the patients into three groups nearly equal in size. The association between MATH value and clinical characteristics was evaluated, followed by survival analyses in these different MATH groups. We then compared the rates of total non-silent somatic mutations among the different MATH groups, and further determined the mutations independently associated with high MATH by logistic regression adjusting for T classification and clinical subtypes. Similar methods, superadding somatic copy number alteration (SCNA) burden in logistic model, were used to evaluate SCNA events that were significantly associated with high MATH level. Gene enrichment between the high and rest MATH groups was analyzed using Gene Set Enrichment Analysis. Results: The patients were divided into low (MATH value lower than 33), intermediate (MATH between 33 and 46) and high (MATH higher than 46) MATH groups. High T stage, African American race, and triple-negative or basal-like subtype were associated with a higher MATH level (all P<0.001). In hormone receptor-positive and human epidermal growth factor receptor-negative patients, the high MATH group showed a tendency toward a worse overall survival (P=0.052); however, while in triple-negative breast cancer, both high and low MATH indicated a worse outcome (P=0.032). Furthermore, the TP53 mutation rate increased as MATH was elevated (P<0.001), whereas CDH1 mutations were correlated with a lower level of MATH (P=0.002). Several focal and arm-level SCNA events were more common in the high MATH group, including Chr8q24 with only the MYC gene in the “peak” region. Similarly, high MATH was associated with gene set enrichment related to the MYC pathway and proliferation. Conclusion: Our study extended the knowledge concerning the clinical role of ITH in breast cancer, especially the distinct pattern of prognostic values in different clinical subtypes, which may help promote the clinical utilization of genetic ITH. Our attempt at exploring the molecular features related to ITH might provide clues for the source and consequences of ITH, inspiring subsequent experiments investigating the laws underling tumor heterogeneity. Citation Format: Ma D, Jiang Y-Z, Liu X-Y, Liu Y-R, Yu K-D, Shao Z-M. Clinical and molecular relevance of intra-tumor genetic heterogeneity in breast cancer: Integrative analysis of data from The Cancer Genome Atlas [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-06-07.

The advent and development of single-cell whole-genome sequencing (scWGS) technology has shed lights on the genomic heterogeneities within biosamples at the single-cell resolution. The technology is particularly well-established in the recent decade and witnesses a variety of clinical applications, such as circulating tumor cell (CTC) detection and preimplantation genetic diagnosis/screening (PGD/PGS). In this review, we summarize the latest practical breakthroughs of scWGS in the field of biomedicine, with the hope of providing a guideline to apply single-cell genomic sequencing in clinical researches.

Single microbial cell genome sequencing is becoming a powerful tool for the discovery of the hidden genetic information valuable for many medical applications. One of the critical steps in single-cell genome sequencing is the physical isolation of individual cells from a highly diverse heterogeneous population. Amplifying the genome of a single microbial cell is another challenge due to the minute amount of DNA. Efforts have been directed in developing an optofluidic platform integrating advanced microscopy, optical tweezers and microfluidic technology for single cell isolation and genome amplification. Here, we investigate and evaluate the validity of this platform for single microbial cell genome amplification. The successful validation of this approach allows us to perform various single cell studies using this platform.

INTRODUCTION: Multigene tests (MGTs) have entered clinical practice and patient management. All MGTs are currently performed in reference laboratories using RNA isolated from a tumor section. The effect of intra-tumor heterogeneity on detection of genes within these MGTs, and the subsequent ability of to predict prognostic risk, has received little attention. To examine this directly, we used nCounter analysis to measure the expression of genes from five MGTs (OncotypeDx, Mammaprint, EndoPredict, PAM50, and Breast Cancer Index) in formalin fixed paraffin embedded tumor sections compared to cores taken from the tumor block. METHODS: We selected 71 estrogen receptor (ER) positive node-negative tumors, all of which had clinical OncotypeDx scoring performed at Genomic Health Inc, and for which ER, PR, HER2 and Ki67 clinical pathological measurements were available. For each patient we cut a 5uM section, and cut a 0.6mm core from a representative part of the tumor. If the tumor had an area of high focal Ki67 (n = 26), low PR (n = 13), or both (n = 5) cores were cut from these areas. In total we processed 181 samples. RNA was isolated using Qiagen RNAeasy kit, and 100ng used for nCounter analysis using a custom codeset incorporating five MGTs (n = 142 genes of which 12 are reference genes). MGTs were normalized to their respective reference genes, and nCounter OncotypeDx scores were scaled to the clinical OncotypeDx score for estimation of risk recurrence. RESULTS: Hierarchical clustering using all of the MGT genes combined (normalized to all reference genes) showed that the majority (61) of tumor samples clustered by patient, indicating greater inter than intra-tumor heterogeneity. However, when individual MGTs were examined alone, the intra-tumor heterogeneity increased. We found high correlation between Oncotype genes expression in the whole section versus representative tumor cores (r = 0.94). However, areas of low Ki67 and high PR staining showed low Ki67 and high PR gene expression, and consequently a slightly higher but not statistically significant recurrence score. For 17/75 (22.6%) patients, Oncotype Dx recurrence scores crossed the boundaries for low, intermediate and high risk. Conclusion: Inter-tumor heterogeneity is greater than intra-tumor heterogeneity for MGTs, and for the majority of patients a core of tumor gives gene expression measurements that are highly similar to a whole tumor section. This suggests that the tumor microenvironment (in our sample set mainly adipocytes) contribute little to the mRNA levels of MGTs. However, regions of high Ki67 or low PR within tumors have slightly higher OncotypeDx recurrence scores. The finding that recurrence scores differ by the tumor region that is sampled is consistent with other recent studies of intra-tumor genetic and transcriptomic heterogeneity, and indicates the potential clinical importance of these observations. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-11-10.

Copy number alterations assessed at the single-cell level revealed mono- and polyclonal seeding patterns of distant metastasis in a small-cell lung cancer patient

The presence of heritable genetic differences among cancer cells within a tumor, called intratumor genetic heterogeneity, has long been suspected of playing a role in the poor responses of some tumors to traditional therapies. Research over the past decade has now documented the existence of genetic heterogeneity within tumors of individual patients and acknowledged its potential clinical significance. The research methods for identifying intratumor genetic heterogeneity were not readily adaptable to widespread clinical application until the development of a quantitative measure (MATH—mutant allele tumor heterogeneity) based on whole-exome sequencing of individual tumor samples. MATH values could be readily derived from biopsy or surgical specimens prior to the decision to utilize adjuvant radiation or chemotherapy. MATH analysis has now been used in HNSCC genomic data sets, along with clinical outcome data, to document a relation of high intratumor genetic heterogeneity to shorter overall survival (OS) in a large, multi-institutional study. Recent analysis examining the impact of high and low heterogeneity as measured by MATH suggests that the relation of intratumor heterogeneity to OS likely depends on the specific type of adjuvant therapy used. Our results demonstrate that patients with high intratumor heterogeneity (high MATH scores) would benefit from adjuvant radiation even when NCCN clinical treatment guidelines suggest that it could be omitted. In contrast, patients with low MATH tumors should likely adhere to these guidelines. Our results also support that patients with high MATH tumors will likely not benefit from the addition of adjuvant chemotherapy to radiotherapy, and thus could be spared the additional morbidity of complications from combination therapy. Current HNSCC NCCN treatment guidelines are based on either clinical or pathologic tumor stage and in the postoperative setting the presence or absence of high-risk pathologic features such as positive or close margins and tumor extra nodal extension. Intratumor genetic heterogeneity as measured by MATH analysis has the potential to further inform stage- and pathology-based treatment decisions and consequently should be evaluated in controlled prospective trials that compare adjuvant radiation against chemoradiation following surgery for HNSCC. Citation Format: Edmund A. Mroz, James W. Rocco. Intratumor genetic heterogeneity as a predictive biomarker in head and neck squamous cell carcinoma [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr IA05.

Integrated molecular analysis demonstrated that colorectal cancer can be classified into four molecular subtypes (proliferative, immunomodulatory, immunosuppressed, and immune-excluded subtypes), providing valuable insight into the intricate relationship between tumor microenvironment heterogeneity and various clinical phenotypes.

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse.

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse.

Single-Cell Analysis of Clonal Dynamics in Childhood ALL Reveals a Key Role for Transcriptional Intratumor Heterogeneity in Driving Resistance to Chemotherapy

Background: Genetic evolution of clear cell renal cell carcinoma (ccRCC) follows distinct trajectories, with varying levels of intratumor heterogeneity (ITH) and chromosomal complexity (WGII). While these patterns associate with clinical outcomes, it remains unknown whether they fully reconcile tumor behavior and how genetic and transcriptional features co-evolve in relation to the tumor microenvironment (TME). Methods: To analyze the patterns of transcriptional and TME heterogeneity, we performed bulk whole-transcriptome sequencing on 244 samples, including 22 metastatic and 12 tumor-adjacent normal samples, from 79 ccRCC patients recruited to the TRACERx Renal study. We integrated transcriptional data with previously published genetic, phylogenetic, spatial and clinical information. Results: Transcriptional distances between paired samples from the same primary tumor mirrored but were not fully determined by genetic distance (p-value < 0.001); and increased from primary-primary to primary-metastasis and primary-normal pairs. Within primary-metastasis pairs, metastasis-seeding primary tumor regions were transcriptionally closest to their matched metastasis (p-value < 0.001), suggesting that an important fraction of metastatic transcriptional traits were acquired in the primary tumor. Regarding the tumor clonal structure, transcriptional evolution followed a conserved path through increasing cell proliferation and oxidative phosphorylation and downregulating DNA repair from earlier to later clones. Further, within tumors with increasing WGII we observed upregulation and downregulation of repressors and downstream effectors, respectively, of the canonical cGAS-STING pathway. Combining the presence of this transcriptional pattern with WGII predicted shorter PFS in TRACERx Renal (p-value < 0.001) and in TCGA-KIRC (p-value < 0.001). Clonal evolution was also linked to changes in TME, with each of the previously defined genetic evolutionary trajectories associated to a specific TME (p-value < 0.001). For example, ccRCCs on a PBRM1-SETD2 trajectory demonstrated increased infiltration of cytotoxic immune cells. TME ITH was pervasive and associated with shorter PFS (p-value = 0.03). A recurrent trend from earlier to later clones was progressive T cell depletion (p-value < 0.001). The evolution of the TCR repertoire mirrored the tumor clonal structure (p-value = 0.002), suggesting the thus far elusive antigenic source in ccRCC is heritable. Accordingly, the TCR repertoire in metastasis-seeding primary tumor regions resembled the closest the TCR repertoire of matched metastasis (p-value = 0.06). Conclusion: Integrated analysis of genetic and transcriptional data in TRACERx Renal showed i) transcriptional and TME ITH not fully recapitulated by genetic ITH, ii) conserved paths of transcriptional and TME evolution and iii) a heritable nature of part of the ccRCC antigen source. Citation Format: Ángel Fernández Sanromán, Lewis Au, Benjy Jek Yang Tan, Charlotte Spencer, Anne-Laure Catin, Irene Lobon, Husayn Pallikonda, Kevin Litchfield, Fiona Byrne, James Larkin, Annika Fendler, Samra Turajlic. Integrated analysis of genetic, transcriptional and TME evolution of ccRCC: TRACERx Renal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1621.