NantOmics is a biotechnology company based in Culver City, California, which provides molecular diagnostic products for personalized cancer treatment. It is part of the NantWorks network, a corporation consisting of different startups in the medical and health industry.
Background The aromatase inhibitors (AI) letrozole and exemestane, are often used in sequence in targeting ER+ breast cancers. However resistance to AI poses a major barrier to sustained clinical benefit, while the biological mechanisms underlying the phenomenon remain largely unknown. In this study, we build on our clinical NeoLetExe trial, with the aim to investigate the molecular basis of resistance to AI, by analysing subclonal evolutionary dynamics during sequential treatment. Methods We use whole-exome sequencing (WES) data from 11 ER+ breast cancer patients and 3 timepoints of the Neoletexe trial to reconstruct cancer cell fraction–based subclonal composition. Single-cell DNA sequencing from matched tumour samples is used for validating identified clones and variants. Subclonal variants were annotated to genes by integrating evidence from public data and ExpectoSc. Pathway enrichment analysis using Human Base was conducted. Results Higher cancer cell fraction clone trajectories were significantly associated with reduced treatment response (p = 0.023). Clones reconstructed by WES were validated at 81% using single-cell DNA sequencing. Clones resistant to both letrozole and exemestane demonstrated PIK3CA/AKT/mTOR signaling activation, KRAS pathway dysregulation, hedgehog signaling, and androgen receptor pathways, alongside extensive immune activation and metabolic reprogramming. Drug-specific resistance patterns showed exemestane-resistant clones enriched for epigenetic control and miRNA-mediated silencing, while letrozole-resistant clones displayed metabolic dysregulation but notably lacked immune pathway activation. In contrast, treatment-sensitive clones maintained coordinated cell cycle control, preserved DNA damage responses, and retained immune signaling capacity. Analysis of FDA-approved breast cancer targets identified actionable alterations in PIK3CA (4 patients) and AKT1 (1 patient) that persisted through AI treatment, with RNA expression analysis revealing 48 additional therapeutic targets spanning PI3K/AKT/mTOR, CDK4/6, DNA repair (BRCA1/2, ATM), and immune checkpoint pathways. Conclusion WES-based cancer cell fraction analysis successfully captured subclonal evolutionary trajectories during AI treatment, revealing drug-specific mechanisms and identifying key molecular players in endocrine therapy resistance. This work establishes a framework for precision oncology approaches by providing actionable therapeutic targets and advancing our understanding of resistance mechanisms to improve clinical outcomes in sequential AI therapy. ### Competing Interest Statement The authors have declared no competing interest.
The consensus molecular subtype (CMS) classification of colorectal cancer is based on bulk transcriptomics. The underlying epithelial cell diversity remains unclear. We analyzed 373,058 single-cell transcriptomes from 63 patients, focusing on 49,155 epithelial cells. We identified a pervasive genetic and transcriptomic dichotomy of malignant cells, based on distinct gene expression, DNA copy number and gene regulatory network. We recapitulated these subtypes in bulk transcriptomes from 3,614 patients. The two intrinsic subtypes, iCMS2 and iCMS3, refine CMS. iCMS3 comprises microsatellite unstable (MSI-H) cancers and one-third of microsatellite-stable (MSS) tumors. iCMS3 MSS cancers are transcriptomically more similar to MSI-H cancers than to other MSS cancers. CMS4 cancers had either iCMS2 or iCMS3 epithelium; the latter had the worst prognosis. We defined the intrinsic epithelial axis of colorectal cancer and propose a refined ‘IMF’ classification with five subtypes, combining intrinsic epithelial subtype (I), microsatellite instability status (M) and fibrosis (F).
The clustering of mutations observed in cancer cells is reminiscent of the stress-induced mutagenesis (SIM) response in bacteria. Bacteria deploy SIM when faced with DNA double-strand breaks in the presence of conditions that elicit an SOS response. SIM employs DinB, the evolutionary precursor to human trans-lesion synthesis (TLS) error-prone polymerases, and results in mutations concentrated around DNA double-strand breaks with an abundance that decays with distance. We performed a quantitative study on single nucleotide variant calls for whole-genome sequencing data from 1950 tumors, non-inherited mutations from 129 normal samples, and acquired mutations in 3 cell line models of stress-induced adaptive mutation. We introduce statistical methods to identify mutational clusters, quantify their shapes and tease out the potential mechanism that produced them. Our results show that mutations in both normal and cancer samples are indeed clustered and have shapes indicative of SIM. Clusters in normal samples occur more often in the same genomic location across samples than in cancer suggesting loss of regulation over the mutational process during carcinogenesis. Additionally, the signatures of TLS contribute the most to mutational cluster formation in both patient samples as well as experimental models of SIM. Furthermore, a measure of cluster shape heterogeneity was associated with cancer patient survival with a hazard ratio of 5.744 (Cox Proportional Hazard Regression, 95% CI: 1.824–18.09). Our results support the conclusion that the ancient and evolutionary-conserved adaptive mutation response found in bacteria is a source of genomic instability in cancer. Biological adaptation through SIM might explain the ability of tumors to evolve in the face of strong selective pressures such as treatment and suggests that the conventional ‘hit it hard’ approaches to therapy could prove themselves counterproductive.
The current level of evidence for immunotherapy in previously untreated microsatellite unstable metastatic colorectal cancer is based on recent pieces of evidence of few studies that demonstrated durable response and clinical benefit, in terms of objective response rate, disease control rate, and progression-free survival in this subgroup of patients. On the basis of combinatorial immunotherapy with nivolumab plus ipilimumab, we report the exceptional case of a complete pathological response in a 21-year-old woman presenting a clinically aggressive stage IV colorectal cancer with massive nodal and liver involvement. Extensive molecular analyses based on whole genome next-generation DNA sequencing, RNA sequencing, fluorescent multiplex immunohistochemistry, and flow cytometry provided a detailed description of tumoral and immunological characteristics of this noteworthy clinical case.
Abstract Purpose: MET tyrosine kinase inhibitors (TKIs) can achieve modest clinical outcomes in MET exon 14–altered lung cancers, likely secondary to primary resistance. Mechanisms of primary resistance remain poorly characterized and comprehensive proteomic analyses have not previously been performed. Experimental Design: We performed hybrid capture-based DNA sequencing, targeted RNA sequencing, cell-free DNA sequencing, selected reaction monitoring mass spectrometry (SRM-MS), and immunohistochemistry on patient samples of MET exon 14–altered lung cancers treated with a MET TKI. Associations between overall response rate (ORR), progression-free survival (PFS), and putative genomic alterations and MET protein expression were evaluated. Results: Seventy-five of 168 MET exon 14–altered lung cancers received a MET TKI. Previously undescribed (zygosity, clonality, whole-genome duplication) and known (copy-number focality, tumor mutational burden, mutation region/type) genomic factors were not associated with ORR/PFS (P > 0.05). In contrast, MET expression was associated with MET TKI benefit. Only cases with detectable MET expression by SRM-MS (N = 15) or immunochemistry (N = 22) responded to MET TKI therapy, and cancers with H-score ≥ 200 had a higher PFS than cancers below this cutoff (10.4 vs. 5.5 months, respectively; HR, 3.87; P = 0.02). Conclusions: In MET exon 14–altered cancers treated with a MET TKI, a comprehensive analysis of previously unknown and known genomic factors did not identify a genomic mechanism of primary resistance. Instead, MET expression correlated with benefit, suggesting the potential role of interrogating the proteome in addition to the genome in confirmatory prospective trials.
