Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of BRAF-mutant melanoma

Over half of BRAF-mutant melanoma patients with initial response to targeted therapy will recur with resistant disease. It is thought that resistance can arise from the ability of cells to enter and exit a slow cycling persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. However, the expression states of persister and resistant cells, their spatial relationships in the tumor environment, and the evolutionary progression among these populations are not well understood. Using patient derived xenograft (PDX) models, we track melanoma evolution longitudinally from pre-treatment through maximum treatment response and tumor regrowth. We perform spatial transcriptomics (ST) to 1. define expression signatures within treatment-sensitive, persister, and re-emergent populations, 2. uncover fluctuating metabolic priorities as a hallmark of tumor evolution both across treatment time and geographically across tissue sections, and 3. identify specific genes and pathways within persister and resistant lineages as candidate resistance mechanisms, suggesting potential therapeutic targets. Our novel computational pipeline integrates ST data with deep learning imaging features derived from accompanying histopathological slides. We identify phenotypic states that correlate with the persister cell state, exploiting the juxtaposition of transcriptomic and histological features toward the goal of identifying clonal populations using imaging data alone. In summary, we provide insight into the dynamics of shifting expression state and lineage selection in melanoma during multiple stages of treatment with novel spatiotemporal resolution, defining an evolutionary roadmap to acquired resistance. Overall design: The two WISTAR PDX models, immunotherapy-treated WM4237-1 and treatment-naive WM4007, are BRAF V600E mutant metastatic melanomas of a subcutaneously implanted solid tumor on NSG mice. Twenty mice per model were used in the longitudinal experiment. When the tumors reached 300 mm3, measured biweekly, the continuous dose treatment of mice via chow with either a dual BRAF/MEK inhibitor or vehicle was initiated. One untreated mouse per model was sacrificed at time point T0, day 0. One vehicle-treated mouse per model was sacrificed at time point TC, days 42 and 38, WM4237-1 and WM4007, respectively. The tumors of therapy-treated sacrificed mice, one tumor tissue block per mouse per model per time point, were harvested at time points T1 (day 14), T2 (days 42 and 44), T3 (days 70 and 73), and T4 (days 94 and 133). We analyzed two near adjacent tissue sections from each tumor tissue block for each model and time point using 10x Visium Spatial Gene Expression.

约半数对靶向治疗产生初始应答的BRAF突变黑色素瘤患者，会出现耐药性复发。现有研究认为，耐药性的产生源于肿瘤细胞能够进入并退出缓慢循环的持久态（persister state），通过相对休眠逃避治疗，并在重新激活后重新填充肿瘤。然而，目前对于持久态与耐药细胞的表达谱特征、其在肿瘤微环境中的空间关联，以及这些细胞群间的进化进程仍不甚明晰。
本研究借助患者来源异种移植模型（patient derived xenograft, PDX），纵向追踪黑色素瘤从治疗前、达到最大治疗应答至肿瘤再生的完整进化过程。我们采用空间转录组学（spatial transcriptomics, ST）开展三项分析：1. 明确治疗敏感细胞、持久态细胞及再生细胞群的表达特征；2. 揭示随治疗进程及组织空间位置动态变化的代谢偏好，将其作为肿瘤进化的标志性特征；3. 鉴定持久态与耐药细胞谱系中的特定基因及通路，作为潜在耐药机制与治疗靶点。
本研究开发的新型计算流程将空间转录组学数据与配套组织病理切片的深度学习成像特征进行整合。通过结合转录组与组织学特征，我们鉴定出与持久态细胞相关的表型状态，旨在实现仅通过成像数据即可识别克隆细胞群的目标。
综上，本研究借助全新的时空分辨率技术，阐明了黑色素瘤在多阶段治疗过程中表达状态动态变化与谱系选择的机制，为获得性耐药的进化路径提供了全新认知。

实验设计：本研究使用两款WISTAR公司的患者来源异种移植模型：经免疫治疗的WM4237-1与未接受治疗的WM4007，二者均为BRAF V600E突变的转移性黑色素瘤，以皮下实体瘤形式接种于NSG小鼠体内。每个模型使用20只小鼠开展纵向实验。当肿瘤体积达到300 mm³（每两周测量一次）时，通过饲料持续给药，分别给予BRAF/MEK双靶点抑制剂或赋形剂对照。
每个模型在T0时间点（第0天）各处死1只未接受治疗的小鼠；在TC时间点分别处死1只接受赋形剂对照治疗的小鼠：WM4237-1为第42天，WM4007为第38天。接受治疗的小鼠在以下时间点处死后收集肿瘤：每个模型、每个时间点每只小鼠对应1块肿瘤组织。各时间点分别为T1（第14天）、T2（WM4237-1为第42天、WM4007为第44天）、T3（WM4237-1为第70天、WM4007为第73天）以及T4（WM4237-1为第94天、WM4007为第133天）。针对每个模型、每个时间点的每块肿瘤组织，我们取两份近乎相邻的组织切片，采用10x Visium空间基因表达技术进行分析。