Biology subjects

Serasanambati, M.

Publications and source records attributed to Serasanambati, M..

4 recordsLinked to original sources

bioRxiv2026

Patient-Derived Melanoma Organoids Preserve Tumor-Immune Heterogeneity and Reveal Context-Dependent Responses to Immune Checkpoint Blockade

Abstract Background: Experimental models that retain endogenous tumor immune complexity are needed to investigate heterogeneous responses to immune checkpoint inhibitors (ICIs) in melanoma. We established patient-derived melanoma organoids (PDMOs) to examine retention of parental tumor cellular components and investigate patient-specific and context-dependent responses to checkpoint blockade. Methods: Fresh melanoma specimens (n=50), including primary tumors, neoadjuvant-treated tumors and tumor-infiltrating lymphocyte (TIL) associated samples were processed for Matrigel-embedded culture of patient-derived melanoma organoids (PDMOs). Characterization and functional studies were performed in subsets of established cultures. Immunofluorescence assessed tumor, stromal, immune, and checkpoint-marker expression in PDMOs and matched parental tissues. NGFR expression was evaluated in relation to organoid establishment and growth. Responses to anti PD1 alone or combined with anti-LAG3 or anti-CTLA4 therapies were evaluated using viability and morphological analyses, with selected models undergoing immune phenotyping and multiplex cytokine profiling. Available clinical outcomes were used for exploratory comparison. PDMOs and matched two-dimensional cultures were evaluated under 21% and 5% oxygen conditions. Results: PDMOs were established from 30 of 50 specimens (60%) and retained melanoma, stromal, lymphoid, and myeloid components identified in matched parental tissues during early culture, together with immune checkpoint expression. Higher NGFR (CD271) expression was associated with greater organoid-forming capacity in an exploratory subset. Responses to checkpoint blockade varied across patient-derived models and regimens, with combination blockade not uniformly producing lower viability than PD1 monotherapy. Ex vivo responses showed parallels with available clinical outcomes, with discordant cases also observed. In three selected PDMOs, divergent PD-1 responses were accompanied by differences in T-cell and myeloid representation, MHCII and AXL expression, and inflammatory versus immunoregulatory cytokine profiles. Under normoxic conditions, PDMOs and matched two-dimensional cultures exhibited divergent treatment sensitivities, including clinically responding cases in which sensitivity was retained in PDMOs but attenuated in monolayers. Changing oxygen tension further altered checkpoint sensitivity within individual PDMOs, with the direction and magnitude of the observed shifts varying by model and regimen. Conclusions: Early passage PDMOs retain key endogenous tumor, immune, and stromal components and support functional investigation of patient-derived melanoma biology. Their heterogenous responses across checkpoint regimens and oxygen conditions provide a platform for investigating how tumor-immune composition and environmental context influence treatment sensitivity.

immunology↗

bioRxiv2025

AXL tyrosine kinase inhibition rescues immune checkpoint blockade-resistant melanoma in a tumor microenvironment-dependent fashion

BackgroundImmune checkpoint blockade (ICB) achieves durable responses in approximately half of patients with advanced melanoma, but the mechanistic basis for resistance in the remaining patients remains incompletely defined. AXL tyrosine kinase has emerged as a candidate resistance mediator, yet clinical development of AXL inhibitors has yielded heterogeneous results in unselected patient populations, suggesting that cell-type and tumor microenvironment context may critically govern therapeutic response. MethodsWe characterized AXL expression across cell types in ICB-resistant melanoma using publicly available single-cell RNA sequencing datasets and The Cancer Genome Atlas (TCGA). In vivo efficacy was assessed in the YUMM1.7 PD-1-resistant syngeneic melanoma model using three pharmacologically distinct AXL inhibitors (warfarin, bemcentinib, and cabozantinib) as monotherapy and in combination with anti-PD-1 therapy. Tumor-associated macrophage (TAM) context-dependency was established using anti-CSF1R and anti-F4/80 depletion strategies. Functional PD-L1:PD-1 checkpoint interactions were quantified in tumor sections by immune Forster Resonance Energy Transfer (iFRET). TAM secretome reprogramming was characterized by 40-plex Luminex immunoassay in polarized RAW264.7 macrophages. ResultsAXL expression in ICB-resistant melanoma was predominantly localized to TAMs rather than tumor cells by single-cell analysis, with AXL+ TAMs distributed across both M1-like and M2-like phenotypic compartments. AXL inhibition significantly reduced tumor burden and synergized with anti-PD-1 therapy in vivo; however, efficacy was abolished by depletion of the monocyte-derived myeloid compartment (anti-CSF1R) and enhanced by depletion of tissue-resident TAMs (anti-F4/80), establishing TAM-context dependency. iFRET revealed a paradoxical gain of PD-L1:PD-1 interaction efficiency in anti-PD-1-treated resistant tumors, a functional resistance signature detectable by iFRET but not by PD-L1 expression that was reversed by AXL combination therapy. In vitro secretome profiling demonstrated that combination therapy reprograms macrophage secretome in a polarization-context-dependent manner, amplifying pro-inflammatory cytokines including IL-6 and GM-CSF in M2-like macrophages while selectively dampening T cell chemokine production. ConclusionsThese findings establish AXL as a TAM-resident immune target in ICB-resistant melanoma whose therapeutic relevance is governed by tumor-immune micronenvironment (TiME) macrophage composition rather than tumor cell AXL expression. TAM polarization contexture represents a candidate stratification axis for AXL inhibitor-based combination strategies, and iFRET-measured checkpoint interaction offers a functional complement to PD-L1 expression for monitoring resistance and response. Key MessagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIAXL tyrosine kinase has been studied predominantly as a tumor-intrinsic mesenchymal marker and therapeutic target in melanoma, with prior clinical development of AXL inhibitors predicated on tumor cell AXL expression as the primary biomarker; the contribution of TAM-expressed AXL to ICB resistance and the dependence of therapeutic response on TiME composition have not been defined. C_LI What this study addsO_LIAXL expression in ICB-resistant melanoma is predominantly localized to tumor-associated macrophages distributed across both M1-like and M2-like phenotypic compartments, reframing AXL as a TAM immune target whose therapeutic relevance is governed by TiME macrophage composition rather than tumor cell AXL expression. C_LIO_LICombination AXL inhibition and anti-PD-1 reprograms the macrophage secretome in a polarization-context-dependent manner and reverses a paradoxical gain of functional PD-L1:PD-1 checkpoint interaction in resistant tumors -- a resistance signature detectable by spatial iFRET but not by PD-L1 expression. C_LI How this study might affect research, practice or policyO_LIThe context-dependent effects of AXL inhibition (immunostimulatory in M2-heavy TiMEs, potentially counterproductive in M1-heavy TiMEs) indicate that TAM polarization profiling should be incorporated into clinical trial design for AXL inhibitor-based combinations, and that unselected enrollment may obscure meaningful efficacy signals in the subset most likely to benefit. C_LIO_LIiFRET-based quantification of functional checkpoint interaction represents a candidate dynamic biomarker for ICB resistance monitoring that is orthogonal to PD-L1 immunohistochemistry and may identify resistant patients who retain immunosuppressive checkpoint interactions that can be disrupted by AXL targeting. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=170 HEIGHT=200 SRC="FIGDIR/small/667666v2_ufig1.gif" ALT="Figure 1"> View larger version (90K): org.highwire.dtl.DTLVardef@521de1org.highwire.dtl.DTLVardef@127a933org.highwire.dtl.DTLVardef@d56132org.highwire.dtl.DTLVardef@e03ef9_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman Spectroscopy and AI

PURPOSEIdentifying reliable predictors of immunotherapeutic response in melanoma remains an outstanding challenge. Existing transcriptomic and proteomic profiling methods for the tumor-immune microenvironment (TIME) are costly and may not faithfully capture modifications actively impacting tumor behavior. Here, we present a non-destructive, single-cell approach combining Raman spectroscopy and machine learning (ML) that enables rapid cell profiling and therapeutic response prediction. METHODSWe analyzed single-cell Raman spectra of mouse and human melanoma cell lines alongside nine melanoma patient-derived samples with known resistance profiles to targeted and immunotherapeutic inhibitors bemcentinib, cabozantinib, dabrafenib, nivolumab, and a combination of nivolumab and relatlimab. We assessed cell phenotyping classification and treatment resistance using random forests and feature importance analysis. For patient samples, we constructed a two-stage evaluation workflow to determine clinical drug resistance through aggregated single-cell predictions and identified corresponding highly variant spectral signatures using computational methods adapted from single-cell RNA sequencing methods. RESULTSIn cell lines, our approach achieved >96% differentiation accuracy across tumor microenvironment cell types and induced functional phenotypes. Persistent (drug-resistant) cells formed subclusters based on genetic mutations rather than sample origin, with Raman signatures reflecting biochemical changes relevant to therapeutic pathways. For patient samples, our workflow correctly inferred resistance likelihoods for 30 of 33 clinically-relevant patient-drug combinations (91% accuracy). CONCLUSIONSingle-cell Raman spectroscopy combined with machine learning offers a scalable, prognostic platform to predict therapeutic resistance likelihood, with further potential to advance clinical, multi-omic biomarker efforts for melanoma. Our approach may improve first-and second-line therapy selection assessments for precision medicine by providing rapid, non-destructive prediction of therapeutic response based on cellular spectral profiles. Context SummaryO_ST_ABSKey objectiveC_ST_ABSCan label-free, single-cell Raman spectroscopy and machine learning approach accurately profile melanoma cell states and therapeutic resistance likelihood to targeted and immunotherapeutic agents? Knowledge generatedRaman spectroscopy with machine learning differentiated tumor microenvironment cell types and functional phenotypes with >96% accuracy in cell lines. When applied to patient-derived metastatic melanoma samples, the approach correctly inferred patient response to a panel of targeted and immunotherapeutic inhibitors with 91% accuracy (30 of 33 cases). High-likelihood persistent and sensitive cells across diverse patients exhibited recurrent spectral features. RelevanceSingle-cell Raman-based profiling supports functional-diagnostic assessment or resistance likelihood and may contribute to improved therapeutic selection and precision oncology strategies for melanoma patients.

cancer biology↗

bioRxiv2021

Spatial drivers and pre-cancer populations collaborate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is a lethal disease with limited treatment options and poor survival. We studied 73 samples from 21 patients (7 treatment-naive and 14 treated with neoadjuvant regimens), analyzing distinct spatial units and performing bulk proteogenomics, single cell sequencing, and cellular imaging. Spatial drivers, including mutant KRAS, SMAD4, and GNAQ, were associated with differential phosphosignaling and metabolic responses compared to wild type. Single cell subtyping discovered 12 of 21 tumors with mixed basal and classical features. Trefoil factor family members were upregulated in classical populations, while the basal populations showed enhanced expression of mesenchymal genes, including VIM and IGTB1. Acinar-ductal metaplasia (ADM) populations, present in 95% of patients, with 46% reduction of driver mutation fractions compared to tumor populations, exhibited suppressive and oncogenic features linked to morphologic states. We identified coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin receptor expression in tumor cells. Higher expression of angiogenic and stress response genes in dendritic cells compared to tumor cells suggests they have a pro-tumorigenic role in remodeling the microenvironment. Treated samples contain a three-fold enrichment of inflammatory CAFs when compared to untreated samples, while other CAF subtypes remain similar. A subset of tumor and/or ADM-specific biomarkers showed differential expression between treatment groups, and several known drug targets displayed potential cross-cell type reactivities. This resolution that spatially defined single cell omics provides reveals the diversity of tumor and microenvironment populations in PDAC. Such understanding may lead to more optimal treatment regimens for patients with this devastating disease. HIGHLIGHTSO_LIAcinar-ductal metaplasia (ADM) cells represent a genetic and morphologic transition state between acinar and tumor cells. C_LIO_LIInflammatory cancer associated fibroblasts (iCAFs) are a major component of the PDAC TME and are significantly higher in treated samples C_LIO_LIReceptor-ligand analysis reveals tumor cell-TME interactions through NECTIN4-TIGIT C_LIO_LITumor and ADM cell proteogenomics differ between treated and untreated samples, with unique and shared potential drug targets C_LI

cancer biology↗