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Metastasis and failure of present-day therapies represent the most common causes of mortality in patients with cutaneous melanoma. To identify the underlying genetic and transcriptomic landscapes, in this study we analyzed multi-organ metastases and tumor-adjacent tissues from 11 rapid autopsies after treatment with MAPK inhibitor (MAPKi) and/or immune checkpoint blockade (ICB) and death due to acquired resistance. Either treatment elicits shared genetic alterations that suggest immune-evasive, cross-therapy resistance mechanisms. Large, non-clustered deletions, inversions and inter-chromosomal translocations dominate rearrangements. Analyzing data from separate melanoma cohorts including 345 therapy-naive patients and 35 patients with patient-matched pre-treatment and post-acquired resistance tumor samples, we performed cross-cohort analyses to identify MAPKi and ICB as respective contributors to gene amplifications and deletions enriched in autopsy versus therapy-naive tumors. In the autopsy cohort, private/late mutations and structural variants display shifted mutational and rearrangement signatures, with MAPKi specifically selecting for signatures of defective homologous-recombination, mismatch and base-excision repair. Transcriptomic signatures and crosstalks with tumor-adjacent macroenvironments nominated organ-specific adaptive pathways. An immune-desert, CD8+-macrophage-biased archetype, T-cell exhaustion and type-2 immunity characterized the immune contexture. This multi-organ analysis of therapy-resistant melanoma presents preliminary insights with potential to improve therapeutic strategies.

Blocking cancer genomic instability may prevent tumor diversification and escape from therapies. We show that, after MAPK inhibitor (MAPKi) therapy in patients and mice bearing patient-derived xenografts (PDX), acquired resistant genomes of metastatic cutaneous melanoma specifically amplify resistance-driver, nonhomologous end-joining (NHEJ), and homologous recombination repair (HRR) genes via complex genomic rearrangements (CGR) and extrachromosomal DNAs (ecDNA). Almost all sensitive and acquired-resistant genomes harbor pervasive chromothriptic regions with disproportionately high mutational burdens and significant overlaps with ecDNA and CGR spans. Recurrently, somatic mutations within ecDNA and CGR amplicons enrich for HRR signatures, particularly within acquired resistant tumors. Regardless of sensitivity or resistance, breakpoint-junctional sequence analysis suggests NHEJ as critical to double-stranded DNA break repair underlying CGR and ecDNA formation. In human melanoma cell lines and PDXs, NHEJ targeting by a DNA-PKCS inhibitor prevents/delays acquired MAPKi resistance by reducing the size of ecDNAs and CGRs early on combination treatment. Thus, targeting the causes of genomic instability prevents acquired resistance.SignificanceAcquired resistance often results in heterogeneous, redundant survival mechanisms, which challenge strategies aimed at reversing resistance. Acquired-resistant melanomas recurrently evolve resistance-driving and resistance-specific amplicons via ecDNAs and CGRs, thereby nominating chromothripsis-ecDNA-CGR biogenesis as a resistance-preventive target. Specifically, targeting DNA-PKCS/NHEJ prevents resistance by suppressing ecDNA/CGR rearrangements in MAPKi-treated melanomas. This article is highlighted in the In This Issue feature, p. 799.

Neoadjuvant PD-1 blockade may be efficacious in some individuals with high-risk, resectable oral cavity head and neck cancer. To explore correlates of response patterns to neoadjuvant nivolumab treatment and post-surgical recurrences, we analyzed longitudinal tumor and blood samples in a cohort of 12 individuals displaying 33% responsiveness. Pretreatment tumor-based detection of FLT4 mutations and PTEN signature enrichment favors response, and high tumor mutational burden improves recurrence-free survival. In contrast, preexisting and/or acquired mutations (in CDKN2A, YAP1, or JAK2) correlate with innate resistance and/or tumor recurrence. Immunologically, tumor response after therapy entails T cell receptor repertoire diversification in peripheral blood and intratumoral expansion of preexisting T cell clones. A high ratio of regulatory T to T helper 17 cells in pretreatment blood predicts low T cell receptor repertoire diversity in pretreatment blood, a low cytolytic T cell signature in pretreatment tumors, and innate resistance. Our study provides a molecular framework to advance neoadjuvant anti-PD-1 therapy for individuals with resectable head and neck cancer.

Rationally sequencing and combining PD-1/L1-and MAPK-targeted therapies may overcome innate and acquired resistance. Since increased clinical benefit of MAPK inhibitors (MAPKi) is associated with previous immune checkpoint therapy, we compare the efficacies of sequential and/or combinatorial regimens in subcutaneous murine models of melanoma driven by BrafV600, Nras, or Nf1 mutations as well as colorectal and pancreatic carcinoma driven by KrasG12C. Anti-PD-1/L1 lead-in preceding MAPKi combination optimizes response durability by promoting pro-inflammatory polarization of macrophages and clonal expansion of interferon-γhi, and CD8+ cytotoxic and proliferative (versus CD4+ regulatory) T cells that highly express activation genes. Since therapeutic resistance of melanoma brain metastasis (MBM) limits patient survival, we demonstrate that sequencing anti-PD-1/L1 therapy before MAPKi combination suppresses MBM and improves mouse survival with robust T cell clonal expansion in both intracranial and extracranial metastatic sites. We propose clinically testing brief anti-PD-1/L1 (± anti-CTLA-4) dosing before MAPKi co-treatment to suppress therapeutic resistance.

MAPK targeting in cancer often fails due to MAPK reactivation. MEK inhibitor (MEKi) monotherapy provides limited clinical benefits but may serve as a foundation for combination therapies. Here, we showed that combining a type II RAF inhibitor (RAFi) with an allosteric MEKi durably prevents and overcomes acquired resistance among cancers with KRAS, NRAS, NF1, BRAF non-V600, and BRAF V600 mutations. Tumor cell-intrinsically, type II RAFi plus MEKi sequester MEK in RAF complexes, reduce MEK/MEK dimerization, and uncouple MEK from ERK in acquired-resistant tumor subpopulations. Immunologically, this combination expands memory and activated/exhausted CD8+ T cells, and durable tumor regression elicited by this combination requires CD8+ T cells, which can be reinvigorated by anti-PD-L1 therapy. Whereas MEKi reduces dominant intratumoral T-cell clones, type II RAFi cotreatment reverses this effect and promotes T-cell clonotypic expansion. These findings rationalize the clinical development of type II RAFi plus MEKi and their further combination with PD-1/L1-targeted therapy. SIGNIFICANCE: Type I RAFi + MEKi are indicated only in certain BRAF V600MUT cancers. In contrast, type II RAFi + MEKi are durably active against acquired MEKi resistance across broad cancer indications, which reveals exquisite MAPK addiction. Allosteric modulation of MAPK protein/protein interactions and temporal preservation of intratumoral CD8+ T cells are mechanisms that may be further exploited.This article is highlighted in the In This Issue feature, p. 521.

In order to improve the rationality of the planning and layout of electric vehicle (EV) charging stations and avoid the situation of high investment and low efficiency, a planning method of EV charging station that considered users' dynamic charging demand was proposed. Firstly, the starting and ending points of user were obtained by using the travel theory, start and end point (origin-destination, OD) matrix method; a dynamic traffic road network model with time-varying traffic congestion was constructed. The Dijkstra algorithm was improved to plan the EV travel path, considering the real-time changes of ambient temperature and vehicle speed. Based on the influence of mileage and power consumption, a charging station selection model considering the dynamic charging needs of users was established; then, the M/M/c queuing theory method was used to configure the capacity of charging stations. The cost of construction, operation and maintenance of charging stations and the economic losses of EV users (including the sum of time loss and power loss) was minimized as the objective function, and a charging station planning model was established. Finally, taking the actual road conditions in the main urban area of a city as the planning area, the model was solved by iterative arrangement optimization combined with particle swarm algorithm. The results showed that the locations of the six planned charging stations in the area were evenly distributed, which could reduce the cost of users' charging journeys. And the optimal configuration number of charging piles could ensure charging satisfaction while minimizing the total economic cost of charging stations. The proposed planning method was reasonable and effective. [ABSTRACT FROM AUTHOR]